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Sterzhang/PVIT-3M

Sterzhang

--- configs: - config_name: PVIT-3M data_files: - split: all_data path: PVIT-3M.json language: - en task_categories: - visual-question-answering - image-text-to-text tags: - multi-modal - personalized license: apache-2.0 pretty_name: personalized visual instruction tuning size_categories: - 1M<n<10M --- # PVIT-3M The paper titled "[**Personalized Visual Instruction Tuning**](https://arxiv.org/abs/2410.07113)" introduces a novel dataset called PVIT-3M. This dataset is specifically designed for tuning MLLMs in the context of personalized visual instruction tasks. The dataset consists of 3 million image-text pairs that aim to improve MLLMs' abilities to generate responses based on personalized visual inputs, making them more tailored and adaptable to individual user needs and preferences. Here’s the PVIT-3M statistics: <img width="900" alt="image" src="https://cdn-uploads.huggingface.co/production/uploads/65d8b0f0661492b25c6623de/BLF2Uo72nqpvjPv_yplbt.png"> ## Directory The images in the PVIT-3M dataset have been organized into 40 separate folders, each named pvit-3m_0 to pvit-3m_39. After downloading, you can combine all the subfolders for a unified dataset structure. ``` Sterzhang/PVIT-3M └─ image └─ pvit-3m_0 └─ aug-face └─ coco └─ concat-person └─ crop-face └─ flickr30k └─ object365 └─ vg └─ pvit-3m_1 └─ ... └─ ... └─ PVIT-3M.json └─ README.md ``` ## Json Structure The PVIT-3M.json file contains structured data for each conversation instance in the PVIT-3M dataset. Below is an outline of the key fields: - `image`: A list of image file paths related to the conversation. - `conversations`: A list of dictionaries, each representing a turn in the conversation. - `from`: `human` or `gpt`. - `value`: The content of the conversation. - `type`: A string indicating the type. ## Example Here is an example from the PVIT-3M: ```json { "image": [ "crop-face/object365/objects365_v2_01169026/2.jpg", "object365/objects365_v2_01169026.jpg" ], "conversations": [ { "from": "human", "value": "<image>\nThis is a photo of Erin.\n<image>\nHow does Erin look in this photo?" }, { "from": "gpt", "value": "Erin is a woman with short, brown hair. She is wearing a dark blue dress with short sleeves and a ruffled design. She has a pearl necklace and a watch on her left wrist. She is also wearing a ring on her left hand." } ], "type": "crop description" } ``` "crop-face/object365/objects365_v2_01169026/2.jpg": <img width="100" alt="image" src="https://cdn-uploads.huggingface.co/production/uploads/65d8b0f0661492b25c6623de/lJi0aDYE44wyGP2QMZ13W.png"> "object365/objects365_v2_01169026.jpg": <img width="400" alt="image" src="https://cdn-uploads.huggingface.co/production/uploads/65d8b0f0661492b25c6623de/RY_80A5rSOO1vv6A6CuJy.png"> ## Script The script processes conversation data in the **PVIT-3M** dataset by adding personalized wrapper tokens (`<person_s>` and `<person_e>`) around specific segments. This helps the model correctly associate personalized text and images with each individual, reducing ambiguity in multimodal training. ```python import json def process_image_description(text): segments = text.split('<image>\n') processed_segments = [] for i, segment in enumerate(segments): if i == 0: processed_segments.append(segment) elif i == len(segments) - 1: continue else: last_newline_index = segment.rfind('\n') if last_newline_index != -1: segment = segment[:last_newline_index] + '<person_e>' + segment[last_newline_index:] else: segment += '<person_e>' processed_segments.append(f'<person_s><image>\n{segment}') processed_segments.append(f"<image>\n{segments[-1]}") return ''.join(processed_segments) def process_conversation_data(input_path, output_path): with open(input_path, 'r', encoding='utf-8') as f: data = json.load(f) for item in data: conversation_value = item["conversations"][0]["value"] item["conversations"][0]["value"] = process_image_description(conversation_value) with open(output_path, 'w', encoding='utf-8') as f: json.dump(data, f, ensure_ascii=False, indent=4) input_file = "" output_file = "" process_conversation_data(input_file, output_file) ``` # Code Our code will be released in [PVIT](https://github.com/sterzhang/PVIT), containing scripts for generating PVIT dataset as well as our code for training. # Case Study <img width="1000" alt="image" src="https://github.com/user-attachments/assets/d50fa03f-fdb6-41ff-ab25-806578d29f3e"> # Citation Our paper is now available at: [https://arxiv.org/abs/2410.07113](https://arxiv.org/abs/2410.07113) ```bibtex @misc{pi2024personalizedvisualinstructiontuning, title={Personalized Visual Instruction Tuning}, author={Renjie Pi and Jianshu Zhang and Tianyang Han and Jipeng Zhang and Rui Pan and Tong Zhang}, year={2024}, eprint={2410.07113}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2410.07113}, }

AVS-Net/knee_fast_mri

AVS-Net

--- license: afl-3.0 tags: - medical size_categories: - 100M<n<1B --- # Dataset for AVS-Net Pre-training The dataset utilized in the pre-training of the AVS-Net: Attention-based Variable Splitting Network for P-MRI Acceleration model, developed by Y Zhang, J Li, Z Wang, J Duan, and J Li, incorporates data from five distinct protocol sequences. These are: - (coronal_pd)Coronal Spin Density-weighted without Fat Suppression - (coronal_pd_fs)Coronal Spin Density-weighted with Fat Suppression - (sagittal_pd)Sagittal Spin Density-weighted - (sagittal_t2)Sagittal T2-weighted with Fat Suppression - (axial_t2)Axial T2-weighted with Fat Suppression The dataset is structured on a slice-by-slice basis, with each slice containing 20 cases. Each case is comprised of two files: rawdata*.mat and espirit*.mat. The dataset's structure can be outlined as follows: ## Dataset architecture: - name: /rds/projects/d/duanj-ai-in-medical-imaging/knee_fast_mri - Protocol: [coronal_pd, coronal_pd_fs, sagittal_pd, sagittal_t2, axial_t2] Approximately 40 slices per protocol, each slice containing 15 channels, with a height and width (HW) of (640, 368) ``` knee_nyu - axial_t2 coronal_pd(X) coronal_pd_fs sagittal_pd sagittal_t2 | | | | | - [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], [11, 12, 13, 14, 15, 16, 17, 18, 19, 20] masks | | - [train] [val] | | - espirit*.mat(1-40), rawdata*.mat(1-40) *_masks.mat ``` In this structure, each protocol has approximately 40 slices, each consisting of 15 channels. The dimensions of the data are 640x368 (height x width). For each protocol, the slices are further divided into two groups: the training set ([train]) and the validation set ([val]). The training set includes the espirit*.mat and rawdata*.mat files for each slice, while the validation set contains *_masks.mat files. ## Dataset Usage > For a standalone knee dataset download, use `git lfs`(<https://git-lfs.com/>) to download from the `huggingface` datasets(<https://huggingface.co/datasets/AVS-Net/knee_fast_mri>): ```bash # Make sure you have git-lfs installed (https://git-lfs.com) git lfs install git clone -j8 [email protected]:datasets/AVS-Net/knee_fast_mri ``` ## Known Issues and Resolutions - 1. Network Connection Issue For enhanced network connection quality, it is recommended to employ the `ssh` protocol instead of `https`. ```bash # Rather than utilizing `https://huggingface.co/datasets/AVS-Net/knee_fast_mri` # Clone the repository using `[email protected]:datasets/AVS-Net/knee_fast_mri` # As an example: git clone -j8 [email protected]:datasets/AVS-Net/knee_fast_mri ``` - 2. Interruptions During Download Certain error messages may appear during the download process due to interruptions. These errors can include: ``` error: ... : cannot add to the index - missing --add option? batch response: Post ... : read: connection reset by peer error: failed to fetch some objects from 'https://hf.co/datasets/AVS-Net/knee_fast_mri.git/info/lfs' ``` Following the instructions below allows for the handling of these interruptions. ```bash # Navigate (`cd`) to the directory containing the `lfs` folder # Intead of using `git pull`, # Use `git lfs pull` to resume the download progress for `lfs` projects git lfs pull ``` Please note that this process will resume the download from where it was interrupted, thereby ensuring the integrity of your downloaded data.

Matthijs/cmu-arctic-xvectors

Matthijs

--- pretty_name: CMU ARCTIC X-Vectors task_categories: - text-to-speech - audio-to-audio license: mit --- # Speaker embeddings extracted from CMU ARCTIC There is one `.npy` file for each utterance in the dataset, 7931 files in total. The speaker embeddings are 512-element X-vectors. The [CMU ARCTIC](http://www.festvox.org/cmu_arctic/) dataset divides the utterances among the following speakers: - bdl (US male) - slt (US female) - jmk (Canadian male) - awb (Scottish male) - rms (US male) - clb (US female) - ksp (Indian male) The X-vectors were extracted using [this script](https://huggingface.co/mechanicalsea/speecht5-vc/blob/main/manifest/utils/prep_cmu_arctic_spkemb.py), which uses the `speechbrain/spkrec-xvect-voxceleb` model. Usage: ```python from datasets import load_dataset embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation") speaker_embeddings = embeddings_dataset[7306]["xvector"] speaker_embeddings = torch.tensor(speaker_embeddings).unsqueeze(0) ```

asahi417/seamless-align-enA-zhA.speaker-embedding.xlsr-2b

asahi417

--- dataset_info: - config_name: subset_1 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: zhA.id dtype: string - name: zhA.laser_score dtype: float64 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 - name: zhA.audio.speaker_embedding sequence: float32 - name: zhA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 14209259131 num_examples: 1962 download_size: 14256120203 dataset_size: 14209259131 - config_name: subset_10 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: zhA.id dtype: string - name: zhA.laser_score dtype: float64 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 - name: zhA.audio.speaker_embedding sequence: float32 - name: zhA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 13574781625 num_examples: 2031 download_size: 13621966757 dataset_size: 13574781625 - config_name: subset_100 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: zhA.id dtype: string - name: zhA.laser_score dtype: float64 - name: zhA.audio.speaker_embedding sequence: float32 - name: zhA.audio.speaker_embedding.full sequence: sequence: float32 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 13223964877 num_examples: 1891 download_size: 13269307182 dataset_size: 13223964877 - config_name: subset_101 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: zhA.id dtype: string - name: zhA.laser_score dtype: float64 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 - name: zhA.audio.speaker_embedding sequence: float32 - name: zhA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 13038203739 num_examples: 1885 download_size: 13083404216 dataset_size: 13038203739 - config_name: subset_102 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: zhA.id dtype: string - name: zhA.laser_score dtype: float64 - name: zhA.audio.speaker_embedding sequence: float32 - name: zhA.audio.speaker_embedding.full sequence: sequence: float32 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 12731679458 num_examples: 1863 download_size: 12775688644 dataset_size: 12731679458 - config_name: subset_103 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: zhA.id dtype: string - name: zhA.laser_score dtype: float64 - name: zhA.audio.speaker_embedding sequence: float32 - name: zhA.audio.speaker_embedding.full sequence: sequence: float32 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 12967209285 num_examples: 1861 download_size: 13011071076 dataset_size: 12967209285 - config_name: subset_104 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: zhA.id dtype: string - name: zhA.laser_score dtype: float64 - name: zhA.audio.speaker_embedding sequence: float32 - name: zhA.audio.speaker_embedding.full sequence: sequence: float32 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 12798692606 num_examples: 1875 download_size: 12842795816 dataset_size: 12798692606 - config_name: subset_105 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: zhA.id dtype: string - name: zhA.laser_score dtype: float64 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 - name: zhA.audio.speaker_embedding sequence: float32 - name: zhA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 13127114114 num_examples: 1871 download_size: 13172271401 dataset_size: 13127114114 - config_name: subset_106 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: zhA.id dtype: string - name: zhA.laser_score dtype: float64 - name: zhA.audio.speaker_embedding sequence: float32 - name: zhA.audio.speaker_embedding.full sequence: sequence: float32 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 12426801586 num_examples: 1865 download_size: 12469421998 dataset_size: 12426801586 - config_name: subset_107 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: zhA.id dtype: string - name: zhA.laser_score dtype: float64 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 - name: zhA.audio.speaker_embedding sequence: float32 - name: zhA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 12484775174 num_examples: 1838 download_size: 12527398592 dataset_size: 12484775174 - config_name: subset_108 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: zhA.id dtype: string - name: zhA.laser_score dtype: float64 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 - name: zhA.audio.speaker_embedding sequence: float32 - name: zhA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 13018346253 num_examples: 1860 download_size: 13063301347 dataset_size: 13018346253 - config_name: subset_109 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: zhA.id dtype: string - name: zhA.laser_score dtype: float64 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 - name: zhA.audio.speaker_embedding sequence: float32 - name: zhA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 12986696298 num_examples: 1866 download_size: 13030608940 dataset_size: 12986696298 - config_name: subset_11 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: zhA.id dtype: string - name: zhA.laser_score dtype: float64 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 - name: zhA.audio.speaker_embedding sequence: float32 - name: zhA.audio.speaker_embedding.full sequence: sequence: float32 splits: - 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config_name: subset_119 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: zhA.id dtype: string - name: zhA.laser_score dtype: float64 - name: zhA.audio.speaker_embedding sequence: float32 - name: zhA.audio.speaker_embedding.full sequence: sequence: float32 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 12663849536 num_examples: 1823 download_size: 12706307005 dataset_size: 12663849536 - config_name: subset_12 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: zhA.id dtype: string - name: zhA.laser_score dtype: float64 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 - name: zhA.audio.speaker_embedding sequence: float32 - name: zhA.audio.speaker_embedding.full sequence: sequence: float32 splits: - 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name: enA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 12554140420 num_examples: 1860 download_size: 12598098176 dataset_size: 12554140420 - config_name: subset_99 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: zhA.id dtype: string - name: zhA.laser_score dtype: float64 - name: zhA.audio.speaker_embedding sequence: float32 - name: zhA.audio.speaker_embedding.full sequence: sequence: float32 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 13502104593 num_examples: 1915 download_size: 13548188642 dataset_size: 13502104593 configs: - config_name: subset_1 data_files: - split: train path: subset_1/train-* - config_name: subset_10 data_files: - split: train path: subset_10/train-* - config_name: subset_100 data_files: - split: train path: subset_100/train-* - config_name: subset_101 data_files: - 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split: train path: subset_95/train-* - config_name: subset_96 data_files: - split: train path: subset_96/train-* - config_name: subset_97 data_files: - split: train path: subset_97/train-* - config_name: subset_98 data_files: - split: train path: subset_98/train-* - config_name: subset_99 data_files: - split: train path: subset_99/train-* ---

japanese-asr/whisper_transcriptions.mls

japanese-asr

--- dataset_info: - config_name: subset_0 features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription dtype: string - name: transcription/ja_gpt3.5 dtype: string - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 splits: - name: train num_bytes: 4620566948.406 num_examples: 69119 download_size: 4539342285 dataset_size: 4620566948.406 - config_name: subset_1 features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription dtype: string - name: transcription/ja_gpt3.5 dtype: string - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 splits: - name: train num_bytes: 4615371441.665 num_examples: 69119 download_size: 4534685370 dataset_size: 4615371441.665 - config_name: subset_2 features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription dtype: string - name: transcription/ja_gpt3.5 dtype: string - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 splits: - name: train num_bytes: 4618927963.262 num_examples: 69119 download_size: 4538188311 dataset_size: 4618927963.262 - config_name: subset_3 features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription dtype: string - name: transcription/ja_gpt3.5 dtype: string - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 splits: - name: train num_bytes: 4619422742.072 num_examples: 69119 download_size: 4538693362 dataset_size: 4619422742.072 - config_name: subset_4 features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription dtype: string - name: transcription/ja_gpt3.5 dtype: string - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 splits: - name: train num_bytes: 4621146964.69 num_examples: 69119 download_size: 4539941481 dataset_size: 4621146964.69 - config_name: subset_5 features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription dtype: string - name: transcription/ja_gpt3.5 dtype: string - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 splits: - name: train num_bytes: 4613770963.88 num_examples: 69119 download_size: 4532835277 dataset_size: 4613770963.88 - config_name: subset_6 features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription dtype: string - name: transcription/ja_gpt3.5 dtype: string - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 splits: - name: train num_bytes: 4621350054.763 num_examples: 69119 download_size: 4539905454 dataset_size: 4621350054.763 - config_name: subset_7 features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription dtype: string - name: transcription/ja_gpt3.5 dtype: string - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 splits: - name: train num_bytes: 62389.0 num_examples: 1 download_size: 67468 dataset_size: 62389.0 - config_name: subset_8 features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription dtype: string - name: transcription/ja_gpt3.5 dtype: string - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 splits: - name: train num_bytes: 4619270537.072 num_examples: 69119 download_size: 4538494120 dataset_size: 4619270537.072 - config_name: subset_9 features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription dtype: string - name: transcription/ja_gpt3.5 dtype: string - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 splits: - name: train num_bytes: 4626844255.859 num_examples: 69119 download_size: 4545739510 dataset_size: 4626844255.859 - config_name: subset_10 features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription dtype: string - name: transcription/ja_gpt3.5 dtype: string - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 splits: - name: train num_bytes: 4627867441.571 num_examples: 69119 download_size: 4546685210 dataset_size: 4627867441.571 - config_name: subset_11 features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription dtype: string - name: transcription/ja_gpt3.5 dtype: string - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 splits: - name: train num_bytes: 4621426380.882 num_examples: 69119 download_size: 4540022795 dataset_size: 4621426380.882 - config_name: subset_12 features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription dtype: string - name: transcription/ja_gpt3.5 dtype: string - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 splits: - name: train num_bytes: 4616601770.406 num_examples: 69119 download_size: 4535459814 dataset_size: 4616601770.406 - config_name: subset_13 features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription dtype: string - 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name: train num_bytes: 4619690973.82 num_examples: 69118 download_size: 4538372407 dataset_size: 4619690973.82 configs: - config_name: subset_0 data_files: - split: train path: subset_0.0/train-* - config_name: subset_1 data_files: - split: train path: subset_0.1/train-* - config_name: subset_2 data_files: - split: train path: subset_0.10/train-* - config_name: subset_3 data_files: - split: train path: subset_0.11/train-* - config_name: subset_4 data_files: - split: train path: subset_0.12/train-* - config_name: subset_5 data_files: - split: train path: subset_0.13/train-* - config_name: subset_6 data_files: - split: train path: subset_0.14/train-* - config_name: subset_7 data_files: - split: train path: subset_0.15/train-* - config_name: subset_8 data_files: - split: train path: subset_0.2/train-* - config_name: subset_9 data_files: - split: train path: subset_0.3/train-* - config_name: subset_10 data_files: - split: train path: subset_0.4/train-* - config_name: subset_11 data_files: - 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split: train path: subset_9.13/train-* - config_name: subset_129 data_files: - split: train path: subset_9.14/train-* - config_name: subset_130 data_files: - split: train path: subset_9.15/train-* - config_name: subset_131 data_files: - split: train path: subset_9.2/train-* - config_name: subset_132 data_files: - split: train path: subset_9.3/train-* - config_name: subset_133 data_files: - split: train path: subset_9.4/train-* - config_name: subset_134 data_files: - split: train path: subset_9.5/train-* - config_name: subset_135 data_files: - split: train path: subset_9.6/train-* - config_name: subset_136 data_files: - split: train path: subset_9.7/train-* - config_name: subset_137 data_files: - split: train path: subset_9.8/train-* - config_name: subset_138 data_files: - split: train path: subset_9.9/train-* ---

HuggingFaceTB/finemath

HuggingFaceTB

--- license: odc-by dataset_info: - config_name: finemath-3plus features: - name: url dtype: string - name: fetch_time dtype: int64 - name: content_mime_type dtype: string - name: warc_filename dtype: string - name: warc_record_offset dtype: int32 - name: warc_record_length dtype: int32 - name: text dtype: string - name: token_count dtype: int32 - name: char_count dtype: int32 - name: metadata dtype: string - name: score dtype: float64 - name: int_score dtype: int64 - name: crawl dtype: string - name: snapshot_type dtype: string - name: language dtype: string - name: language_score dtype: float64 splits: - name: train num_bytes: 137764105388.93857 num_examples: 21405610 download_size: 65039196945 dataset_size: 137764105388.93857 - config_name: finemath-4plus features: - name: url dtype: string - name: fetch_time dtype: int64 - name: content_mime_type dtype: string - name: warc_filename dtype: string - name: warc_record_offset dtype: int32 - name: warc_record_length dtype: int32 - name: text dtype: string - name: token_count dtype: int32 - name: char_count dtype: int32 - name: metadata dtype: string - name: score dtype: float64 - name: int_score dtype: int64 - name: crawl dtype: string - name: snapshot_type dtype: string - name: language dtype: string - name: language_score dtype: float64 splits: - name: train num_bytes: 39101488149.09091 num_examples: 6699493 download_size: 18365184633 dataset_size: 39101488149.09091 - config_name: infiwebmath-3plus features: - name: url dtype: string - name: metadata dtype: string - name: score dtype: float64 - name: int_score dtype: int64 - name: token_count dtype: int64 - name: char_count dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 96485696853.10182 num_examples: 13882669 download_size: 46808660851 dataset_size: 96485696853.10182 - config_name: infiwebmath-4plus features: - name: url dtype: string - name: metadata dtype: string - name: score dtype: float64 - name: int_score dtype: int64 - name: token_count dtype: int64 - name: char_count dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 40002719500.1551 num_examples: 6296212 download_size: 19234328998 dataset_size: 40002719500.1551 configs: - config_name: finemath-3plus data_files: - split: train path: finemath-3plus/train-* - config_name: finemath-4plus data_files: - split: train path: finemath-4plus/train-* - config_name: infiwebmath-3plus data_files: - split: train path: infiwebmath-3plus/train-* - config_name: infiwebmath-4plus data_files: - split: train path: infiwebmath-4plus/train-* --- # 📐 FineMath  ## What is it? 📐 FineMath consists of **34B tokens** (FineMath-3+) and **54B tokens** (FineMath-3+ with InfiMM-WebMath-3+) of mathematical educational content filtered from CommonCrawl. To curate this dataset, we trained a mathematical content [classifier](https://huggingface.co/HuggingFaceTB/finemath-classifier) using annotations generated by LLama-3.1-70B-Instruct. We used the classifier to retain only the most educational mathematics content, focusing on clear explanations and step-by-step problem solving rather than advanced academic papers. The [Dataset Curation](#dataset-curation) section details the process for creating the dataset. More details in our paper: https://arxiv.org/abs/2502.02737v1. <img src="assets/train_curves.png" width="800"/> ## What is being released? The dataset is released in two versions: - **FineMath-3+**: 34B tokens, 21.4M documents containing mathematical reasoning and problem solving, formatted with Markdown and LaTeX. - **FineMath-4+** (a subset of FineMath-3+): 9.6B tokens, 6.7M documents of higher quality with detailed explanations. Models trained on this dataset perform better on GSM8k and MATH. <!-- (the image looks kinda meh) <img src="assets/stats.png" width="512"/> --> We also release a filtered English text-only portion of the **[InfiMM-WebMath-40B](https://huggingface.co/datasets/Infi-MM/InfiMM-WebMath-40B)** dataset, classified using the same approach as FineMath: - **InfiMM-WebMath-3+**: 20.5B tokens, 13.9M documents. - **InfiMM-WebMath-4+** (a subset of InfiMM-WebMath-3+): 8.5B tokens, 6.3M documents. ## How to load the dataset Use one of the available configs: `finemath-3plus`, `finemath-4plus`, `infiwebmath-3plus`, or `infiwebmath-4plus`. ```python from datasets import load_dataset # Load the high-quality subset data = load_dataset("HuggingFaceTB/finemath", "finemath-4plus", split="train", num_proc=8) # Or load the larger subset data = load_dataset("HuggingFaceTB/finemath", "finemath-3plus", split="train", num_proc=8) ``` ## Dataset curation Recent language models like DeepSeekMath and MathStral have demonstrated strong mathematical capabilities, trained on specialized datasets that aren't publicly available. We developed a pipeline to identify and extract high-quality mathematical content from CommonCrawl, with several iterations of refinement to improve quality. ### Phase 1: Initial content extraction and classification We began by re-extracting pages from CommonCrawl WARCs using URLs from the FineWeb dataset, collecting both the latest and largest versions of each page to capture the evolution of pages across the years. Unlike FineWeb which uses Trafilatura, we employed Resiliparse for text extraction as it better preserves forum discussions and QA answers that often contain crucial reasoning steps and solutions. For initial quality assessment, we used [Llama-3.1-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct) to generate annotations on a 3-point scale: 1. Contains general mathematical content 2. Shows logical reasoning in mathematical context 3. Contains clear step-by-step solutions at appropriate level A `multilingual-e5-small`-based classifier finetuned on these annotations was used to score the initial corpus. However, this first version performed below the OpenWebMath baseline, leading to several important refinements. ### Phase 2: Recalling more candidate pages Analysis revealed that FineWeb's C4 filter removes pages containing '{' characters, inadvertently filtering out content with LaTeX notation. To address this and expand coverage, we: 1. Identified promising website domains by selecting those where at least 10% of pages received a classifier score ≥ 2 2. Added URLs from OpenWebMath and InfiMM-WebMath datasets 3. Recovered URLs of pages filtered by FineWeb's '{' rule from its rejection logs 4. Re-extracted all content from scratch using the [OpenWebMath pipeline](https://github.com/keirp/OpenWebMath), which properly handles mathematical notation across various HTML markup formats and standardizes them to LaTeX ### Phase 3: Refined quality assessment The expanded corpus underwent a more fine-grained quality evaluation: Once again, we used LLama-3.1-70B-Instruct to score a sample of newly extracted pages on a 5-point scale (full prompt available in [here](assets/prompt.txt)): We finetuned a new [classifier](https://huggingface.co/HuggingFaceTB/finemath-classifier) on these annotations and scored the entire corpus. After leaving only pages with a score of 3 or higher, and deduplicating the samples using simple single-band MinHash-LSH, we obtained FineMath-3+ with 34B tokens. The same classifier was applied to InfiMM-WebMath's text content, focusing more on reasoning rather than advanced mathematics. Both datasets were additionally filtered using FineWeb's language classification pipeline to remove non-English content. ### Decontamination Following Qwen2.5-Math's approach, we removed samples with 13-gram overlaps against test sets from GSM8k, MATH, MMLU and ARC. Decontamination logs are available at [HuggingFaceTB/finemath_contamination_report](https://huggingface.co/datasets/HuggingFaceTB/finemath_contamination_report). ## Results and Performance <img src="assets/eval_bar.png" width="600"/> Our evaluations show several key findings: 1. FineMath-3+ outperforms the base InfiWebMath on GSM8k and MATH benchmarks 2. FineMath-4+ demonstrates superior performance compared to both FineMath-3+ and InfiWebMath-4+ on GSM8k and MATH 3. Combining the datasets (50% FineMath-3+ with 50% InfiWebMath-3+) yields approximately 50B tokens while matching the performance of FineMath-3+ 4. Deduplicating the pages repeated between FineMath and InfiWebMath reduces performance compared to a non-deduplicated combination ## Dataset Schema ```python { 'url': string, # Source page URL 'fetch_time': int64, # Crawler timestamp 'content_mime_type': string, # MIME type 'warc_filename': string, # Common Crawl WARC source file 'warc_record_offset': int32, # WARC record offset, in bytes 'warc_record_length': int32, # WARC record size, in bytes 'text': string, # Page content 'token_count': int32, # Number of Llama tokens 'char_count': int32, # Character count 'metadata': string, # Additional OpenWebMath metadata 'score': float64, # Raw quality score 'int_score': int64, # Integer quality score 'crawl': string, # Common Crawl crawl identifier 'snapshot_type': string, # Whether the page is the latest or the largest for this URL 'language': string, # Document language 'language_score': float64 # LangID probability } ``` ## Considerations for Using the Data ### Social Impact of Dataset With the release of this dataset, we aim to make high-quality mathematical educational content more accessible to the machine learning community. While multiple language models have demonstrated strong mathematical capabilities, the datasets used to train these capabilities are often not publicly available. By releasing FineMath, we hope to: - Make the dataset creation process more transparent - Reduce the barrier to entry for training models with strong mathematical capabilities - Provide a benchmark for mathematical content quality filtering ### Discussion of Biases The dataset may have certain inherent biases: - Focus on English language content - Emphasis on popular educational approaches to mathematics - Bias towards certain types of mathematical notation and formatting ### Other Known Limitations - The dataset is limited to English language content - The filtering criteria may not capture advanced mathematical content (e.g. advanced research subjects) - Some mathematical notation (e.g. image-based) may not be preserved - Long-form content may have varying quality even within high-scoring documents ## Licensing Information The dataset is released under the **Open Data Commons Attribution License (ODC-By) v1.0** [license](https://opendatacommons.org/licenses/by/1-0/). The use of this dataset is also subject to [CommonCrawl's Terms of Use](https://commoncrawl.org/terms-of-use). ## Future work There are several avenues for future work: - Expand language coverage beyond English - Improve mathematical notation extraction and preservation - Develop more sophisticated quality metrics - Create specialized subsets for different educational levels ### Citation Information ``` @misc{allal2025smollm2smolgoesbig, title={SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model}, author={Loubna Ben Allal and Anton Lozhkov and Elie Bakouch and Gabriel Martín Blázquez and Guilherme Penedo and Lewis Tunstall and Andrés Marafioti and Hynek Kydlíček and Agustín Piqueres Lajarín and Vaibhav Srivastav and Joshua Lochner and Caleb Fahlgren and Xuan-Son Nguyen and Clémentine Fourrier and Ben Burtenshaw and Hugo Larcher and Haojun Zhao and Cyril Zakka and Mathieu Morlon and Colin Raffel and Leandro von Werra and Thomas Wolf}, year={2025}, eprint={2502.02737}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2502.02737}, } ```

lukaemon/bbh

lukaemon

--- dataset_info: - config_name: boolean_expressions features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 11790 num_examples: 250 download_size: 17172 dataset_size: 11790 - config_name: causal_judgement features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 198021 num_examples: 187 download_size: 202943 dataset_size: 198021 - config_name: date_understanding features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 54666 num_examples: 250 download_size: 61760 dataset_size: 54666 - config_name: disambiguation_qa features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 78620 num_examples: 250 download_size: 85255 dataset_size: 78620 - config_name: dyck_languages features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 38432 num_examples: 250 download_size: 43814 dataset_size: 38432 - config_name: formal_fallacies features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 138224 num_examples: 250 download_size: 145562 dataset_size: 138224 - config_name: geometric_shapes features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 68560 num_examples: 250 download_size: 77242 dataset_size: 68560 - config_name: hyperbaton features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 38574 num_examples: 250 download_size: 44706 dataset_size: 38574 - config_name: logical_deduction_five_objects features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 148595 num_examples: 250 download_size: 155477 dataset_size: 148595 - config_name: logical_deduction_seven_objects features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 191022 num_examples: 250 download_size: 198404 dataset_size: 191022 - config_name: logical_deduction_three_objects features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 105831 num_examples: 250 download_size: 112213 dataset_size: 105831 - config_name: movie_recommendation features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 50985 num_examples: 250 download_size: 57684 dataset_size: 50985 - config_name: multistep_arithmetic_two features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 12943 num_examples: 250 download_size: 18325 dataset_size: 12943 - config_name: navigate features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 49031 num_examples: 250 download_size: 55163 dataset_size: 49031 - config_name: object_counting features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 30508 num_examples: 250 download_size: 35890 dataset_size: 30508 - config_name: penguins_in_a_table features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 70062 num_examples: 146 download_size: 74516 dataset_size: 70062 - config_name: reasoning_about_colored_objects features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 89579 num_examples: 250 download_size: 98694 dataset_size: 89579 - config_name: ruin_names features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 46537 num_examples: 250 download_size: 53178 dataset_size: 46537 - config_name: salient_translation_error_detection features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 277110 num_examples: 250 download_size: 286443 dataset_size: 277110 - config_name: snarks features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 38223 num_examples: 178 download_size: 42646 dataset_size: 38223 - config_name: sports_understanding features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 22723 num_examples: 250 download_size: 28617 dataset_size: 22723 - config_name: temporal_sequences features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 139546 num_examples: 250 download_size: 148176 dataset_size: 139546 - config_name: tracking_shuffled_objects_five_objects features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 162590 num_examples: 250 download_size: 169722 dataset_size: 162590 - config_name: tracking_shuffled_objects_seven_objects features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 207274 num_examples: 250 download_size: 214906 dataset_size: 207274 - config_name: tracking_shuffled_objects_three_objects features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 122104 num_examples: 250 download_size: 128736 dataset_size: 122104 - config_name: web_of_lies features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 47582 num_examples: 250 download_size: 52964 dataset_size: 47582 - config_name: word_sorting features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 60918 num_examples: 250 download_size: 66300 dataset_size: 60918 --- # BIG-bench Hard dataset homepage: https://github.com/suzgunmirac/BIG-Bench-Hard ``` @article{suzgun2022challenging, title={Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them}, author={Suzgun, Mirac and Scales, Nathan and Sch{\"a}rli, Nathanael and Gehrmann, Sebastian and Tay, Yi and Chung, Hyung Won and Chowdhery, Aakanksha and Le, Quoc V and Chi, Ed H and Zhou, Denny and and Wei, Jason}, journal={arXiv preprint arXiv:2210.09261}, year={2022} } ```

fixie-ai/librispeech_asr

fixie-ai

--- language: - en dataset_info: - config_name: clean features: - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: text dtype: string - name: speaker_id dtype: int64 - name: chapter_id dtype: int64 - name: id dtype: string - name: continuation dtype: string splits: - name: test num_bytes: 623948478.48 num_examples: 2620 - name: validation num_bytes: 622190064.956 num_examples: 2703 - name: train.360 num_bytes: 41953890926.124 num_examples: 104014 - name: train.100 num_bytes: 11606313661.774 num_examples: 28539 download_size: 53886816833 dataset_size: 54806343131.334 - config_name: other features: - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: text dtype: string - name: speaker_id dtype: int64 - name: chapter_id dtype: int64 - name: id dtype: string - name: continuation dtype: string splits: - name: train.500 num_bytes: 57330687390.808 num_examples: 148688 - name: validation num_bytes: 591511495.496 num_examples: 2864 - name: test num_bytes: 616939198.113 num_examples: 2939 download_size: 57019309170 dataset_size: 58539138084.417 configs: - config_name: clean data_files: - split: test path: clean/test-* - split: validation path: clean/validation-* - split: train.360 path: clean/train.360-* - split: train.100 path: clean/train.100-* - config_name: other data_files: - split: train.500 path: other/train.500-* - split: validation path: other/validation-* - split: test path: other/test-* ---

aklein4/OpenHermes-Llama-3.2-Instruct-Shuffled

aklein4

--- dataset_info: features: - name: __key__ dtype: string - name: __url__ dtype: string - name: gen_mask.npy sequence: bool - name: input_ids.npy sequence: uint32 - name: pad_mask.npy sequence: bool - name: segment_ids.npy sequence: uint32 - name: text.txt dtype: string splits: - name: train num_bytes: 4970493095.0 num_examples: 374215 download_size: 1516295098 dataset_size: 4970493095.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

princeton-nlp/SWE-bench

princeton-nlp

--- dataset_info: features: - name: repo dtype: string - name: instance_id dtype: string - name: base_commit dtype: string - name: patch dtype: string - name: test_patch dtype: string - name: problem_statement dtype: string - name: hints_text dtype: string - name: created_at dtype: string - name: version dtype: string - name: FAIL_TO_PASS dtype: string - name: PASS_TO_PASS dtype: string - name: environment_setup_commit dtype: string splits: - name: dev num_bytes: 4783179 num_examples: 225 - name: test num_bytes: 44127008 num_examples: 2294 - name: train num_bytes: 367610377 num_examples: 19008 download_size: 120089218 dataset_size: 416520564 configs: - config_name: default data_files: - split: dev path: data/dev-* - split: test path: data/test-* - split: train path: data/train-* --- ### Dataset Summary SWE-bench is a dataset that tests systems’ ability to solve GitHub issues automatically. The dataset collects 2,294 Issue-Pull Request pairs from 12 popular Python repositories. Evaluation is performed by unit test verification using post-PR behavior as the reference solution. The dataset was released as part of [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://arxiv.org/abs/2310.06770) ## Want to run inference now? This dataset only contains the `problem_statement` (i.e. issue text) and the `base_commit` which can represents the state of the codebase before the issue has been resolved. If you want to run inference using the "Oracle" or BM25 retrieval settings mentioned in the paper, consider the following datasets. [princeton-nlp/SWE-bench_oracle](https://huggingface.co/datasets/princeton-nlp/SWE-bench_oracle) [princeton-nlp/SWE-bench_bm25_13K](https://huggingface.co/datasets/princeton-nlp/SWE-bench_bm25_13K) [princeton-nlp/SWE-bench_bm25_27K](https://huggingface.co/datasets/princeton-nlp/SWE-bench_bm25_27K) [princeton-nlp/SWE-bench_bm25_40K](https://huggingface.co/datasets/princeton-nlp/SWE-bench_bm25_40K) [princeton-nlp/SWE-bench_bm25_50k_llama](https://huggingface.co/datasets/princeton-nlp/SWE-bench_bm25_50k_llama) ### Supported Tasks and Leaderboards SWE-bench proposes a new task: issue resolution provided a full repository and GitHub issue. The leaderboard can be found at www.swebench.com ### Languages The text of the dataset is primarily English, but we make no effort to filter or otherwise clean based on language type. ## Dataset Structure ### Data Instances An example of a SWE-bench datum is as follows: ``` instance_id: (str) - A formatted instance identifier, usually as repo_owner__repo_name-PR-number. patch: (str) - The gold patch, the patch generated by the PR (minus test-related code), that resolved the issue. repo: (str) - The repository owner/name identifier from GitHub. base_commit: (str) - The commit hash of the repository representing the HEAD of the repository before the solution PR is applied. hints_text: (str) - Comments made on the issue prior to the creation of the solution PR’s first commit creation date. created_at: (str) - The creation date of the pull request. test_patch: (str) - A test-file patch that was contributed by the solution PR. problem_statement: (str) - The issue title and body. version: (str) - Installation version to use for running evaluation. environment_setup_commit: (str) - commit hash to use for environment setup and installation. FAIL_TO_PASS: (str) - A json list of strings that represent the set of tests resolved by the PR and tied to the issue resolution. PASS_TO_PASS: (str) - A json list of strings that represent tests that should pass before and after the PR application. ``` [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

google/fleurs

google

--- annotations_creators: - expert-generated - crowdsourced - machine-generated language_creators: - crowdsourced - expert-generated language: - afr - amh - ara - asm - ast - azj - bel - ben - bos - cat - ceb - cmn - ces - cym - dan - deu - ell - eng - spa - est - fas - ful - fin - tgl - fra - gle - glg - guj - hau - heb - hin - hrv - hun - hye - ind - ibo - isl - ita - jpn - jav - kat - kam - kea - kaz - khm - kan - kor - ckb - kir - ltz - lug - lin - lao - lit - luo - lav - mri - mkd - mal - mon - mar - msa - mlt - mya - nob - npi - nld - nso - nya - oci - orm - ory - pan - pol - pus - por - ron - rus - bul - snd - slk - slv - sna - som - srp - swe - swh - tam - tel - tgk - tha - tur - ukr - umb - urd - uzb - vie - wol - xho - yor - yue - zul license: - cc-by-4.0 multilinguality: - multilingual size_categories: - 10K<n<100K task_categories: - automatic-speech-recognition task_ids: [] pretty_name: 'The Cross-lingual TRansfer Evaluation of Multilingual Encoders for Speech (XTREME-S) benchmark is a benchmark designed to evaluate speech representations across languages, tasks, domains and data regimes. It covers 102 languages from 10+ language families, 3 different domains and 4 task families: speech recognition, translation, classification and retrieval.' tags: - speech-recognition --- # FLEURS ## Dataset Description - **Fine-Tuning script:** [pytorch/speech-recognition](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition) - **Paper:** [FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech](https://arxiv.org/abs/2205.12446) - **Total amount of disk used:** ca. 350 GB Fleurs is the speech version of the [FLoRes machine translation benchmark](https://arxiv.org/abs/2106.03193). We use 2009 n-way parallel sentences from the FLoRes dev and devtest publicly available sets, in 102 languages. Training sets have around 10 hours of supervision. Speakers of the train sets are different than speakers from the dev/test sets. Multilingual fine-tuning is used and ”unit error rate” (characters, signs) of all languages is averaged. Languages and results are also grouped into seven geographical areas: - **Western Europe**: *Asturian, Bosnian, Catalan, Croatian, Danish, Dutch, English, Finnish, French, Galician, German, Greek, Hungarian, Icelandic, Irish, Italian, Kabuverdianu, Luxembourgish, Maltese, Norwegian, Occitan, Portuguese, Spanish, Swedish, Welsh* - **Eastern Europe**: *Armenian, Belarusian, Bulgarian, Czech, Estonian, Georgian, Latvian, Lithuanian, Macedonian, Polish, Romanian, Russian, Serbian, Slovak, Slovenian, Ukrainian* - **Central-Asia/Middle-East/North-Africa**: *Arabic, Azerbaijani, Hebrew, Kazakh, Kyrgyz, Mongolian, Pashto, Persian, Sorani-Kurdish, Tajik, Turkish, Uzbek* - **Sub-Saharan Africa**: *Afrikaans, Amharic, Fula, Ganda, Hausa, Igbo, Kamba, Lingala, Luo, Northern-Sotho, Nyanja, Oromo, Shona, Somali, Swahili, Umbundu, Wolof, Xhosa, Yoruba, Zulu* - **South-Asia**: *Assamese, Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Nepali, Oriya, Punjabi, Sindhi, Tamil, Telugu, Urdu* - **South-East Asia**: *Burmese, Cebuano, Filipino, Indonesian, Javanese, Khmer, Lao, Malay, Maori, Thai, Vietnamese* - **CJK languages**: *Cantonese and Mandarin Chinese, Japanese, Korean* ## How to use & Supported Tasks ### How to use The `datasets` library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the `load_dataset` function. For example, to download the Hindi config, simply specify the corresponding language config name (i.e., "hi_in" for Hindi): ```python from datasets import load_dataset fleurs = load_dataset("google/fleurs", "hi_in", split="train") ``` Using the datasets library, you can also stream the dataset on-the-fly by adding a `streaming=True` argument to the `load_dataset` function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk. ```python from datasets import load_dataset fleurs = load_dataset("google/fleurs", "hi_in", split="train", streaming=True) print(next(iter(fleurs))) ``` *Bonus*: create a [PyTorch dataloader](https://huggingface.co/docs/datasets/use_with_pytorch) directly with your own datasets (local/streamed). Local: ```python from datasets import load_dataset from torch.utils.data.sampler import BatchSampler, RandomSampler fleurs = load_dataset("google/fleurs", "hi_in", split="train") batch_sampler = BatchSampler(RandomSampler(fleurs), batch_size=32, drop_last=False) dataloader = DataLoader(fleurs, batch_sampler=batch_sampler) ``` Streaming: ```python from datasets import load_dataset from torch.utils.data import DataLoader fleurs = load_dataset("google/fleurs", "hi_in", split="train") dataloader = DataLoader(fleurs, batch_size=32) ``` To find out more about loading and preparing audio datasets, head over to [hf.co/blog/audio-datasets](https://huggingface.co/blog/audio-datasets). ### Example scripts Train your own CTC or Seq2Seq Automatic Speech Recognition models on FLEURS with `transformers` - [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition). Fine-tune your own Language Identification models on FLEURS with `transformers` - [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/audio-classification) ### 1. Speech Recognition (ASR) ```py from datasets import load_dataset fleurs_asr = load_dataset("google/fleurs", "af_za") # for Afrikaans # to download all data for multi-lingual fine-tuning uncomment following line # fleurs_asr = load_dataset("google/fleurs", "all") # see structure print(fleurs_asr) # load audio sample on the fly audio_input = fleurs_asr["train"][0]["audio"] # first decoded audio sample transcription = fleurs_asr["train"][0]["transcription"] # first transcription # use `audio_input` and `transcription` to fine-tune your model for ASR # for analyses see language groups all_language_groups = fleurs_asr["train"].features["lang_group_id"].names lang_group_id = fleurs_asr["train"][0]["lang_group_id"] all_language_groups[lang_group_id] ``` ### 2. Language Identification LangID can often be a domain classification, but in the case of FLEURS-LangID, recordings are done in a similar setting across languages and the utterances correspond to n-way parallel sentences, in the exact same domain, making this task particularly relevant for evaluating LangID. The setting is simple, FLEURS-LangID is splitted in train/valid/test for each language. We simply create a single train/valid/test for LangID by merging all. ```py from datasets import load_dataset fleurs_langID = load_dataset("google/fleurs", "all") # to download all data # see structure print(fleurs_langID) # load audio sample on the fly audio_input = fleurs_langID["train"][0]["audio"] # first decoded audio sample language_class = fleurs_langID["train"][0]["lang_id"] # first id class language = fleurs_langID["train"].features["lang_id"].names[language_class] # use audio_input and language_class to fine-tune your model for audio classification ``` ### 3. Retrieval Retrieval provides n-way parallel speech and text data. Similar to how XTREME for text leverages Tatoeba to evaluate bitext mining a.k.a sentence translation retrieval, we use Retrieval to evaluate the quality of fixed-size representations of speech utterances. Our goal is to incentivize the creation of fixed-size speech encoder for speech retrieval. The system has to retrieve the English "key" utterance corresponding to the speech translation of "queries" in 15 languages. Results have to be reported on the test sets of Retrieval whose utterances are used as queries (and keys for English). We augment the English keys with a large number of utterances to make the task more difficult. ```py from datasets import load_dataset fleurs_retrieval = load_dataset("google/fleurs", "af_za") # for Afrikaans # to download all data for multi-lingual fine-tuning uncomment following line # fleurs_retrieval = load_dataset("google/fleurs", "all") # see structure print(fleurs_retrieval) # load audio sample on the fly audio_input = fleurs_retrieval["train"][0]["audio"] # decoded audio sample text_sample_pos = fleurs_retrieval["train"][0]["transcription"] # positive text sample text_sample_neg = fleurs_retrieval["train"][1:20]["transcription"] # negative text samples # use `audio_input`, `text_sample_pos`, and `text_sample_neg` to fine-tune your model for retrieval ``` Users can leverage the training (and dev) sets of FLEURS-Retrieval with a ranking loss to build better cross-lingual fixed-size representations of speech. ## Dataset Structure We show detailed information the example configurations `af_za` of the dataset. All other configurations have the same structure. ### Data Instances **af_za** - Size of downloaded dataset files: 1.47 GB - Size of the generated dataset: 1 MB - Total amount of disk used: 1.47 GB An example of a data instance of the config `af_za` looks as follows: ``` {'id': 91, 'num_samples': 385920, 'path': '/home/patrick/.cache/huggingface/datasets/downloads/extracted/310a663d52322700b3d3473cbc5af429bd92a23f9bc683594e70bc31232db39e/home/vaxelrod/FLEURS/oss2_obfuscated/af_za/audio/train/17797742076841560615.wav', 'audio': {'path': '/home/patrick/.cache/huggingface/datasets/downloads/extracted/310a663d52322700b3d3473cbc5af429bd92a23f9bc683594e70bc31232db39e/home/vaxelrod/FLEURS/oss2_obfuscated/af_za/audio/train/17797742076841560615.wav', 'array': array([ 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., -1.1205673e-04, -8.4638596e-05, -1.2731552e-04], dtype=float32), 'sampling_rate': 16000}, 'raw_transcription': 'Dit is nog nie huidiglik bekend watter aantygings gemaak sal word of wat owerhede na die seun gelei het nie maar jeugmisdaad-verrigtinge het in die federale hof begin', 'transcription': 'dit is nog nie huidiglik bekend watter aantygings gemaak sal word of wat owerhede na die seun gelei het nie maar jeugmisdaad-verrigtinge het in die federale hof begin', 'gender': 0, 'lang_id': 0, 'language': 'Afrikaans', 'lang_group_id': 3} ``` ### Data Fields The data fields are the same among all splits. - **id** (int): ID of audio sample - **num_samples** (int): Number of float values - **path** (str): Path to the audio file - **audio** (dict): Audio object including loaded audio array, sampling rate and path ot audio - **raw_transcription** (str): The non-normalized transcription of the audio file - **transcription** (str): Transcription of the audio file - **gender** (int): Class id of gender - **lang_id** (int): Class id of language - **lang_group_id** (int): Class id of language group ### Data Splits Every config only has the `"train"` split containing of *ca.* 1000 examples, and a `"validation"` and `"test"` split each containing of *ca.* 400 examples. ## Dataset Creation We collect between one and three recordings for each sentence (2.3 on average), and buildnew train-dev-test splits with 1509, 150 and 350 sentences for train, dev and test respectively. ## Considerations for Using the Data ### Social Impact of Dataset This dataset is meant to encourage the development of speech technology in a lot more languages of the world. One of the goal is to give equal access to technologies like speech recognition or speech translation to everyone, meaning better dubbing or better access to content from the internet (like podcasts, streaming or videos). ### Discussion of Biases Most datasets have a fair distribution of gender utterances (e.g. the newly introduced FLEURS dataset). While many languages are covered from various regions of the world, the benchmark misses many languages that are all equally important. We believe technology built through FLEURS should generalize to all languages. ### Other Known Limitations The dataset has a particular focus on read-speech because common evaluation benchmarks like CoVoST-2 or LibriSpeech evaluate on this type of speech. There is sometimes a known mismatch between performance obtained in a read-speech setting and a more noisy setting (in production for instance). Given the big progress that remains to be made on many languages, we believe better performance on FLEURS should still correlate well with actual progress made for speech understanding. ## Additional Information All datasets are licensed under the [Creative Commons license (CC-BY)](https://creativecommons.org/licenses/). ### Citation Information You can access the FLEURS paper at https://arxiv.org/abs/2205.12446. Please cite the paper when referencing the FLEURS corpus as: ``` @article{fleurs2022arxiv, title = {FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech}, author = {Conneau, Alexis and Ma, Min and Khanuja, Simran and Zhang, Yu and Axelrod, Vera and Dalmia, Siddharth and Riesa, Jason and Rivera, Clara and Bapna, Ankur}, journal={arXiv preprint arXiv:2205.12446}, url = {https://arxiv.org/abs/2205.12446}, year = {2022}, ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten) and [@aconneau](https://github.com/aconneau) for adding this dataset.

fixie-ai/covost2

fixie-ai

--- dataset_info: - config_name: ar_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 61607709.192 num_examples: 2283 - name: validation num_bytes: 56223234.024 num_examples: 1758 - name: test num_bytes: 54650910.41 num_examples: 1695 download_size: 160468333 dataset_size: 172481853.626 - config_name: ca_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 4397026262.322 num_examples: 95854 - name: validation num_bytes: 544108371.96 num_examples: 12730 - name: test num_bytes: 604755238.63 num_examples: 12730 download_size: 4957773433 dataset_size: 5545889872.912 - config_name: cy_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 51478765.087 num_examples: 1241 - name: validation num_bytes: 26992697.0 num_examples: 690 - name: test num_bytes: 28772216.0 num_examples: 690 download_size: 102604972 dataset_size: 107243678.087 - config_name: de_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 5680326209.222 num_examples: 127834 - name: validation num_bytes: 631442490.202 num_examples: 13511 - name: test num_bytes: 637042944.685 num_examples: 13511 download_size: 6490850158 dataset_size: 6948811644.108999 - config_name: en_ar features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 14105902817.18 num_examples: 289430 - name: validation num_bytes: 718527564.808 num_examples: 15531 - name: test num_bytes: 729114452.301 num_examples: 15531 download_size: 13815709729 dataset_size: 15553544834.289001 - config_name: en_ca features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 14099092976.18 num_examples: 289430 - name: validation num_bytes: 718171719.808 num_examples: 15531 - name: test num_bytes: 728790610.301 num_examples: 15531 download_size: 13814365593 dataset_size: 15546055306.289001 - config_name: en_cy features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 14098487703.18 num_examples: 289430 - name: validation num_bytes: 718141953.808 num_examples: 15531 - name: test num_bytes: 728793811.301 num_examples: 15531 download_size: 13813953593 dataset_size: 15545423468.289001 - config_name: en_de features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 14099886814.18 num_examples: 289430 - name: validation num_bytes: 718219105.808 num_examples: 15531 - name: test num_bytes: 728857067.301 num_examples: 15531 download_size: 13815103686 dataset_size: 15546962987.289001 - config_name: en_et features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 14096877545.18 num_examples: 289430 - name: validation num_bytes: 718057559.808 num_examples: 15531 - name: test num_bytes: 728710692.301 num_examples: 15531 download_size: 13813410823 dataset_size: 15543645797.289001 - config_name: en_fa features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 14108661241.18 num_examples: 289430 - name: validation num_bytes: 718670909.808 num_examples: 15531 - name: test num_bytes: 729271000.301 num_examples: 15531 download_size: 13816798013 dataset_size: 15556603151.289001 - config_name: en_id features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 14098627451.18 num_examples: 289430 - name: validation num_bytes: 718144327.808 num_examples: 15531 - name: test num_bytes: 728802322.301 num_examples: 15531 download_size: 13813201260 dataset_size: 15545574101.289001 - config_name: en_ja features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 14103911774.18 num_examples: 289430 - name: validation num_bytes: 718409304.808 num_examples: 15531 - name: test num_bytes: 729050991.301 num_examples: 15531 download_size: 13815875328 dataset_size: 15551372070.289001 - config_name: en_lv features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 14098703097.18 num_examples: 289430 - name: validation num_bytes: 718152571.808 num_examples: 15531 - name: test num_bytes: 728792572.301 num_examples: 15531 download_size: 13814849886 dataset_size: 15545648241.289001 - config_name: en_mn features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 14113120657.18 num_examples: 289430 - name: validation num_bytes: 718940418.808 num_examples: 15531 - name: test num_bytes: 729461016.301 num_examples: 15531 download_size: 13819427515 dataset_size: 15561522092.289001 - config_name: en_sl features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 14097158381.18 num_examples: 289430 - name: validation num_bytes: 718085673.808 num_examples: 15531 - name: test num_bytes: 728705188.301 num_examples: 15531 download_size: 13813603812 dataset_size: 15543949243.289001 - config_name: en_sv-SE features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 14097728051.18 num_examples: 289430 - name: validation num_bytes: 718093292.808 num_examples: 15531 - name: test num_bytes: 728747422.301 num_examples: 15531 download_size: 13813332908 dataset_size: 15544568766.289001 - config_name: en_ta features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 14135489205.18 num_examples: 289430 - name: validation num_bytes: 720191394.808 num_examples: 15531 - name: test num_bytes: 730578783.301 num_examples: 15531 download_size: 13825121271 dataset_size: 15586259383.289001 - config_name: en_tr features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 14098644786.18 num_examples: 289430 - name: validation num_bytes: 718161996.808 num_examples: 15531 - name: test num_bytes: 728786654.301 num_examples: 15531 download_size: 13814279798 dataset_size: 15545593437.289001 - config_name: en_zh-CN features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 14095661460.18 num_examples: 289430 - name: validation num_bytes: 717982705.808 num_examples: 15531 - name: test num_bytes: 728655191.301 num_examples: 15531 download_size: 13812699892 dataset_size: 15542299357.289001 - config_name: es_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: validation num_bytes: 630615357.241 num_examples: 13221 - name: test num_bytes: 666447063.067 num_examples: 13221 - name: train num_bytes: 3769457359.8 num_examples: 79015 download_size: 4531969416 dataset_size: 5066519780.108 - config_name: et_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 97124727.544 num_examples: 1782 - name: validation num_bytes: 80290798.168 num_examples: 1576 - name: test num_bytes: 81970364.51 num_examples: 1571 download_size: 257604448 dataset_size: 259385890.222 - config_name: fa_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 1872724297.149 num_examples: 53949 - name: validation num_bytes: 140067911.23 num_examples: 3445 - name: test num_bytes: 149319550.35 num_examples: 3445 download_size: 1679853440 dataset_size: 2162111758.729 - config_name: fr_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: validation num_bytes: 632191608.84 num_examples: 14760 - name: test num_bytes: 698178059.08 num_examples: 14760 - name: train num_bytes: 8128016830.77 num_examples: 207374 download_size: 8900934523 dataset_size: 9458386498.69 - config_name: id_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 36136135.768 num_examples: 1243 - name: validation num_bytes: 25058845.0 num_examples: 792 - name: test num_bytes: 26577467.0 num_examples: 844 download_size: 86110062 dataset_size: 87772447.768 - config_name: it_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 1517510665.568 num_examples: 31698 - name: validation num_bytes: 422409218.1 num_examples: 8940 - name: test num_bytes: 454569171.595 num_examples: 8951 download_size: 2125529183 dataset_size: 2394489055.2630005 - config_name: ja_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 38181610.624 num_examples: 1119 - name: validation num_bytes: 24623052.0 num_examples: 635 - name: test num_bytes: 25558787.0 num_examples: 684 download_size: 88228548 dataset_size: 88363449.624 - config_name: lv_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 66152116.328 num_examples: 2337 - name: validation num_bytes: 32655276.0 num_examples: 1125 - name: test num_bytes: 50997551.638 num_examples: 1629 download_size: 137700207 dataset_size: 149804943.96600002 - config_name: mn_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 87891433.547 num_examples: 2067 - name: validation num_bytes: 77519039.943 num_examples: 1761 - name: test num_bytes: 83667460.167 num_examples: 1759 download_size: 242638800 dataset_size: 249077933.657 - config_name: nl_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 216102081.4 num_examples: 7108 - name: validation num_bytes: 55386349.319 num_examples: 1699 - name: test num_bytes: 60219179.711 num_examples: 1699 download_size: 320267264 dataset_size: 331707610.43 - config_name: pt_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 274723273.528 num_examples: 9158 - name: validation num_bytes: 118345891.704 num_examples: 3318 - name: test num_bytes: 166247624.001 num_examples: 4023 download_size: 540891735 dataset_size: 559316789.233 - config_name: ru_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 557219472.672 num_examples: 12112 - name: validation num_bytes: 290218427.6 num_examples: 6110 - name: test num_bytes: 312622838.0 num_examples: 6300 download_size: 1112848246 dataset_size: 1160060738.272 - config_name: sl_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 55992153.0 num_examples: 1843 - name: validation num_bytes: 15074155.0 num_examples: 509 - name: test num_bytes: 10209711.0 num_examples: 360 download_size: 83863293 dataset_size: 81276019.0 - config_name: sv-SE_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 48298330.64 num_examples: 2160 - name: validation num_bytes: 32544646.416 num_examples: 1349 - name: test num_bytes: 46894324.615 num_examples: 1595 download_size: 121860373 dataset_size: 127737301.671 - config_name: ta_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 47757197.616 num_examples: 1358 - name: validation num_bytes: 13670695.0 num_examples: 384 - name: test num_bytes: 29891516.0 num_examples: 786 download_size: 87791516 dataset_size: 91319408.616 - config_name: tr_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 119299427.798 num_examples: 3966 - name: validation num_bytes: 52552534.232 num_examples: 1624 - name: test num_bytes: 59106253.862 num_examples: 1629 download_size: 224018260 dataset_size: 230958215.89200002 - config_name: zh-CN_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: validation num_bytes: 231018998.33 num_examples: 4843 - name: test num_bytes: 243850956.45 num_examples: 4898 - name: train num_bytes: 341425113.6 num_examples: 7085 download_size: 766660661 dataset_size: 816295068.38 configs: - config_name: ar_en data_files: - split: train path: ar_en/train-* - split: validation path: ar_en/validation-* - split: test path: ar_en/test-* - config_name: ca_en data_files: - split: train path: ca_en/train-* - split: validation path: ca_en/validation-* - split: test path: ca_en/test-* - config_name: cy_en data_files: - split: train path: cy_en/train-* - split: validation path: cy_en/validation-* - split: test path: cy_en/test-* - config_name: de_en data_files: - split: train path: de_en/train-* - split: validation path: de_en/validation-* - split: test path: de_en/test-* - config_name: en_ar data_files: - split: train path: en_ar/train-* - split: validation path: en_ar/validation-* - split: test path: en_ar/test-* - config_name: en_ca data_files: - split: train path: en_ca/train-* - split: validation path: en_ca/validation-* - split: test path: en_ca/test-* - config_name: en_cy data_files: - split: train path: en_cy/train-* - split: validation path: en_cy/validation-* - split: test path: en_cy/test-* - config_name: en_de data_files: - split: train path: en_de/train-* - split: validation path: en_de/validation-* - split: test path: en_de/test-* - config_name: en_et data_files: - split: train path: en_et/train-* - split: validation path: en_et/validation-* - split: test path: en_et/test-* - config_name: en_fa data_files: - split: train path: en_fa/train-* - split: validation path: en_fa/validation-* - split: test path: en_fa/test-* - config_name: en_id data_files: - split: train path: en_id/train-* - split: validation path: en_id/validation-* - split: test path: en_id/test-* - config_name: en_ja data_files: - split: train path: en_ja/train-* - split: validation path: en_ja/validation-* - split: test path: en_ja/test-* - config_name: en_lv data_files: - split: train path: en_lv/train-* - split: validation path: en_lv/validation-* - split: test path: en_lv/test-* - config_name: en_mn data_files: - split: train path: en_mn/train-* - split: validation path: en_mn/validation-* - split: test path: en_mn/test-* - config_name: en_sl data_files: - split: train path: en_sl/train-* - split: validation path: en_sl/validation-* - split: test path: en_sl/test-* - config_name: en_sv-SE data_files: - split: train path: en_sv-SE/train-* - split: validation path: en_sv-SE/validation-* - split: test path: en_sv-SE/test-* - config_name: en_ta data_files: - split: train path: en_ta/train-* - split: validation path: en_ta/validation-* - split: test path: en_ta/test-* - config_name: en_tr data_files: - split: train path: en_tr/train-* - split: validation path: en_tr/validation-* - split: test path: en_tr/test-* - config_name: en_zh-CN data_files: - split: train path: en_zh-CN/train-* - split: validation path: en_zh-CN/validation-* - split: test path: en_zh-CN/test-* - config_name: es_en data_files: - split: validation path: es_en/validation-* - split: test path: es_en/test-* - split: train path: es_en/train-* - config_name: et_en data_files: - split: train path: et_en/train-* - split: validation path: et_en/validation-* - split: test path: et_en/test-* - config_name: fa_en data_files: - split: train path: fa_en/train-* - split: validation path: fa_en/validation-* - split: test path: fa_en/test-* - config_name: fr_en data_files: - split: validation path: fr_en/validation-* - split: test path: fr_en/test-* - split: train path: fr_en/train-* - config_name: id_en data_files: - split: train path: id_en/train-* - split: validation path: id_en/validation-* - split: test path: id_en/test-* - config_name: it_en data_files: - split: train path: it_en/train-* - split: validation path: it_en/validation-* - split: test path: it_en/test-* - config_name: ja_en data_files: - split: train path: ja_en/train-* - split: validation path: ja_en/validation-* - split: test path: ja_en/test-* - config_name: lv_en data_files: - split: train path: lv_en/train-* - split: validation path: lv_en/validation-* - split: test path: lv_en/test-* - config_name: mn_en data_files: - split: train path: mn_en/train-* - split: validation path: mn_en/validation-* - split: test path: mn_en/test-* - config_name: nl_en data_files: - split: train path: nl_en/train-* - split: validation path: nl_en/validation-* - split: test path: nl_en/test-* - config_name: pt_en data_files: - split: train path: pt_en/train-* - split: validation path: pt_en/validation-* - split: test path: pt_en/test-* - config_name: ru_en data_files: - split: train path: ru_en/train-* - split: validation path: ru_en/validation-* - split: test path: ru_en/test-* - config_name: sl_en data_files: - split: train path: sl_en/train-* - split: validation path: sl_en/validation-* - split: test path: sl_en/test-* - config_name: sv-SE_en data_files: - split: train path: sv-SE_en/train-* - split: validation path: sv-SE_en/validation-* - split: test path: sv-SE_en/test-* - config_name: ta_en data_files: - split: train path: ta_en/train-* - split: validation path: ta_en/validation-* - split: test path: ta_en/test-* - config_name: tr_en data_files: - split: train path: tr_en/train-* - split: validation path: tr_en/validation-* - split: test path: tr_en/test-* - config_name: zh-CN_en data_files: - split: validation path: zh-CN_en/validation-* - split: test path: zh-CN_en/test-* - split: train path: zh-CN_en/train-* --- This is a partial copy of [CoVoST2](https://huggingface.co/datasets/facebook/covost2) dataset. The main difference is that the audio data is included in the dataset, which makes usage easier and allows browsing the samples using HF Dataset Viewer. The limitation of this method is that all audio samples of the `EN_XX` subsets are duplicated, as such the size of the dataset is larger. As such, not all the data is included: Only the `validation` and `test` subsets are available. From the `XX_EN` subsets, only `fr`, `es`, and `zh-CN` are included.

locuslab/TOFU

locuslab

--- annotations_creators: - machine-generated language: - en language_creators: - machine-generated license: mit multilinguality: - monolingual pretty_name: TOFU size_categories: - 1K<n<10K source_datasets: - original tags: - unlearning - question answering - TOFU - NLP - LLM task_categories: - question-answering task_ids: - closed-domain-qa configs: - config_name: full data_files: full.json default: true - config_name: forget01 data_files: forget01.json - config_name: forget05 data_files: forget05.json - config_name: forget10 data_files: forget10.json - config_name: retain90 data_files: retain90.json - config_name: retain95 data_files: retain95.json - config_name: retain99 data_files: retain99.json - config_name: world_facts data_files: world_facts.json - config_name: real_authors data_files: real_authors.json - config_name: forget01_perturbed data_files: forget01_perturbed.json - config_name: forget05_perturbed data_files: forget05_perturbed.json - config_name: forget10_perturbed data_files: forget10_perturbed.json - config_name: retain_perturbed data_files: retain_perturbed.json - config_name: world_facts_perturbed data_files: world_facts_perturbed.json - config_name: real_authors_perturbed data_files: real_authors_perturbed.json --- # TOFU: Task of Fictitious Unlearning 🍢 The TOFU dataset serves as a benchmark for evaluating unlearning performance of large language models on realistic tasks. The dataset comprises question-answer pairs based on autobiographies of 200 different authors that do not exist and are completely fictitiously generated by the GPT-4 model. The goal of the task is to unlearn a fine-tuned model on various fractions of the forget set. ## Quick Links - [**Website**](https://locuslab.github.io/tofu): The landing page for TOFU - [**arXiv Paper**](http://arxiv.org/abs/2401.06121): Detailed information about the TOFU dataset and its significance in unlearning tasks. - [**GitHub Repository**](https://github.com/locuslab/tofu): Access the source code, fine-tuning scripts, and additional resources for the TOFU dataset. - [**Dataset on Hugging Face**](https://huggingface.co/datasets/locuslab/TOFU): Direct link to download the TOFU dataset. - [**Leaderboard on Hugging Face Spaces**](https://huggingface.co/spaces/locuslab/tofu_leaderboard): Current rankings and submissions for the TOFU dataset challenges. - [**Summary on Twitter**](https://x.com/_akhaliq/status/1745643293839327268): A concise summary and key takeaways from the project. ## Applicability 🚀 The dataset is in QA format, making it ideal for use with popular chat models such as Llama2, Mistral, or Qwen. However, it also works for any other large language model. The corresponding code base is written for the Llama2 chat, and Phi-1.5 models, but can be easily adapted to other models. ## Loading the Dataset To load the dataset, use the following code: ```python from datasets import load_dataset dataset = load_dataset("locuslab/TOFU", "full") ``` ### Available forget sets are: - `forget01`: Forgetting 1% of the original dataset, all entries correspond to a single author. - `forget05`: Forgetting 5% of the original dataset, all entries correspond to a single author. - `forget10`: Forgetting 10% of the original dataset, all entries correspond to a single author. Retain sets corresponding to each forget set are also available, which can be used to train an Oracle model. ## Codebase The code for training the models and the availability of all fine-tuned models can be found at our [GitHub repository](https://github.com/locuslab/tofu). ## Citing Our Work If you find our codebase and dataset beneficial, please cite our work: ``` @misc{tofu2024, title={TOFU: A Task of Fictitious Unlearning for LLMs}, author={Pratyush Maini and Zhili Feng and Avi Schwarzschild and Zachary C. Lipton and J. Zico Kolter}, year={2024}, archivePrefix={arXiv}, primaryClass={cs.LG} } ```

liwu/MNBVC

liwu

--- annotations_creators: - other language: - zh language_creators: - other license: - mit multilinguality: - monolingual pretty_name: MNBVC size_categories: - unknown source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling --- # Dataset Card for MNBVC ## Table of Contents - [Dataset Card for MNBVC](#dataset-card-for-mnbvc) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [数据集介绍](#数据集介绍) - [数据子集](#数据子集) - [数据格式](#数据格式) - [文本数据](#文本数据) - [问答数据](#问答数据) - [Contributions](#contributions) ## Dataset Description - **Homepage:** http://mnbvc.253874.net/ - **Repository:** https://github.com/esbatmop/MNBVC - **Paper:** N/A - **Leaderboard:** N/A - **Point of Contact:** N/A ### 数据集介绍 中文互联网上最古老最神秘(没有之一)的里屋社区于2023.1.1庄重宣布: 在英明神武的里屋管子带领下，决心发挥社区所长(哪都长)，帮助开源社区长期更新一份最大的中文互联网语料集。 Huggingface上的MNBVC数据集在逐渐更新中，请到[https://github.com/esbatmop/MNBVC](https://github.com/esbatmop/MNBVC) 获取未完成清洗的更多数据。 可以使用如下脚本加载： ```python from datasets import load_dataset dataset = load_dataset("liwu/MNBVC", 'law_judgement', split='train', streaming=True) next(iter(dataset)) # get the first line ``` ## 数据子集 MNBVC数据集包含数个子集： - `law_judgement`: 来自法律文书的文本。 - `gov_xuexiqiangguo`: 来自学习强国的文本。 - `gov_report`: 来自政府工作报告的文本。 - `co_ann_report`: 企业年报文本。 - `code_metadata`: 代码元数据。 - `qa_zhihu`: 来自[知乎](https://huggingface.co/datasets/wangrui6/Zhihu-KOL)的问答数据。 - `qa_wikihow`: 来自wikihow的问答数据。 - `qa_mfa`: 外交部问答数据。 - `news_peoples_daily`: 来自人民日报的文本数据。 - `wikipedia`: 来自维基百科的文本数据。 - `qa_stackexchange`: 来自StackExchange的问答数据。 - `qa_chatgpt`: 使用ChatGPT构造的问答语料，感谢[genggui001](https://github.com/genggui001)贡献语料。 - `math`: - `math_qa `: 和数学领域有关的问答数据。 - `emath` :中国数学爱好者论坛语料数据 - `math_chat`: 和数学领域有关的对话数据数据，可以提升模型Chain of Thought的能力。 - `crawler_oscar`: 从CommonCrawl中清洗出来的通用文本数据。 - `game` : 一些游戏的平行语料数据。 - `Hogwarts_legacy` : 霍格沃茨指遗 - `The_Wither_3` : 巫师三 ## 数据格式 目前MNBVC数据集包含如下几类数据： - 通用文本 - 问答语料 - 代码语料 - 多轮对话 - 论坛语料 - 平行语料 可以在[MNBVC的wiki页面](https://wiki.mnbvc.org/doku.php/%E7%8E%B0%E6%9C%89%E8%AF%AD%E6%96%99%E6%A0%BC%E5%BC%8F)上查看这几类数据的具体格式。 项目早期所上传的数据使用如下格式，以后这一格式会被废弃，相应数据也会重新上传： ```json { "text": datasets.Value("string"), "meta": datasets.Value("string") } ``` ### Contributions Thanks to the [Liwu community](http://mnbvc.253874.net/) for constructing this dataset. Thanks to [silver](https://github.com/silverriver) and [jiaming](https://huggingface.co/Yjiaming) for adding and uploading this dataset to Huggingface. ### Citation Please cite the repo if you use the data or code in this repo. ``` @misc{mnbvc, author = {{MOP-LIWU Community} and {MNBVC Team}}, title = {MNBVC: Massive Never-ending BT Vast Chinese corpus}, year = {2023}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/esbatmop/MNBVC}}, } ```

ceval/ceval-exam

ceval

--- license: cc-by-nc-sa-4.0 task_categories: - text-classification - multiple-choice - question-answering language: - zh pretty_name: C-Eval size_categories: - 10K<n<100K --- C-Eval is a comprehensive Chinese evaluation suite for foundation models. It consists of 13948 multi-choice questions spanning 52 diverse disciplines and four difficulty levels. Please visit our [website](https://cevalbenchmark.com/) and [GitHub](https://github.com/SJTU-LIT/ceval/tree/main) or check our [paper](https://arxiv.org/abs/2305.08322) for more details. Each subject consists of three splits: dev, val, and test. The dev set per subject consists of five exemplars with explanations for few-shot evaluation. The val set is intended to be used for hyperparameter tuning. And the test set is for model evaluation. Labels on the test split are not released, users are required to submit their results to automatically obtain test accuracy. [How to submit?](https://github.com/SJTU-LIT/ceval/tree/main#how-to-submit) ### Load the data ```python from datasets import load_dataset dataset=load_dataset(r"ceval/ceval-exam",name="computer_network") print(dataset['val'][0]) # {'id': 0, 'question': '使用位填充方法，以01111110为位首flag，数据为011011111111111111110010，求问传送时要添加几个0____', 'A': '1', 'B': '2', 'C': '3', 'D': '4', 'answer': 'C', 'explanation': ''} ``` More details on loading and using the data are at our [github page](https://github.com/SJTU-LIT/ceval#data). Please cite our paper if you use our dataset. ``` @article{huang2023ceval, title={C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models}, author={Huang, Yuzhen and Bai, Yuzhuo and Zhu, Zhihao and Zhang, Junlei and Zhang, Jinghan and Su, Tangjun and Liu, Junteng and Lv, Chuancheng and Zhang, Yikai and Lei, Jiayi and Fu, Yao and Sun, Maosong and He, Junxian}, journal={arXiv preprint arXiv:2305.08322}, year={2023} } ```

CropNet/CropNet

CropNet

--- license: cc-by-4.0 language: - en tags: - agriculture - climate size_categories: - n>1T --- # An Open and Large-Scale Dataset for Multi-Modal Climate Change-aware Crop Yield Predictions  The CropNet dataset is an open, large-scale, and deep learning-ready dataset, specifically targeting climate change-aware crop yield predictions for the contiguous United States (U.S.) continent at the county level. It is composed of three modalities of data, i.e., Sentinel-2 Imagery, WRF-HRRR Computed Dataset, and USDA Crop Dataset, aligned in both the spatial and temporal domains, for over 2200 U.S. counties spanning 6 years (2017-2022). It is expected to facilitate researchers in developing deep learning models for timely and precisely predicting crop yields at the county level, by accounting for the effects of both short-term growing season weather variations and long-term climate change on crop yields. Although our initial goal of crafting the CropNet dataset is for precise crop yield prediction, we believe its future applicability is broad and can benefit the deep learning, agriculture, and meteorology communities, for exploring more interesting, critical, and climate change-related applications, by using one or more modalities of data. ## Citation If you use our dataset, please cite [our paper](https://dl.acm.org/doi/10.1145/3637528.3671536): ``` @inproceedings{fudong:kdd24:crop_net, author = {Fudong Lin and Kaleb Guillot and Summer Crawford and Yihe Zhang and Xu Yuan and Nian{-}Feng Tzeng}, title = {An Open and Large-Scale Dataset for Multi-Modal Climate Change-aware Crop Yield Predictions}, booktitle = {Proceedings of the 30th {ACM} {SIGKDD} Conference on Knowledge Discovery and Data Mining (KDD)}, pages = {5375--5386}, year = {2024} } ``` [Our MMST-ViT model](https://openaccess.thecvf.com/content/ICCV2023/papers/Lin_MMST-ViT_Climate_Change-aware_Crop_Yield_Prediction_via_Multi-Modal_Spatial-Temporal_Vision_ICCV_2023_paper.pdf) demonstrates how to develop deep-learning models for climate change-aware crop yield predictions. If you use MMST-ViT in your research, please cite our paper: ``` @inproceedings{fudong:iccv23:mmst_vit, title={MMST-ViT: Climate Change-aware Crop Yield Prediction via Multi-Modal Spatial-Temporal Vision Transformer}, author={Lin, Fudong and Crawford, Summer and Guillot, Kaleb and Zhang, Yihe and Chen, Yan and Yuan, Xu and others}, booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision}, pages={5774--5784}, year={2023} } ``` ## Contributions #### The `CropNet` dataset - The first *terabyte-sized*, publicly available, and multi-modal dataset for climate change-aware crop yield predictions #### The `CropNet` package - A *deep learning-ready* Python package for facilitating researchers in downloading the CropNet data on the fly over the time and region of interest, and developing deep neural networks (DNNs) for climate change-aware crop yield predictions - The `CropNet` package is available at [Python Package Index (PyPI)](https://pypi.org/project/cropnet/) ## Tutorials The tutorials for the CropNet dataset are available at Google Colab, with their links listed below - [Sentinel-2 Imagery Tutorial](https://colab.research.google.com/drive/1Tj69JdhO7aX8ks-4UWYvHrFm9GB1PNCd?usp=sharing) - [WRF-HRRR Computed Dataset Tutorial](https://colab.research.google.com/drive/14l-JSNHtelawNu3kVG_ukTd2WUJpaZEc?usp=sharing) - [USDA Crop Dataset Tutorial](https://colab.research.google.com/drive/1U-vFoRyLSb2l2Q67LeGbkUKTeRaHDkkK?usp=sharing) ## The CropNet Dataset 0ur CropNet dataset is composed of three modalities of data, i.e., Sentinel-2 Imagery, WRF-HRRR Computed Dataset, and USDA Crop Dataset, spanning from 2017 to 2022 (i.e., 6 years) across 2291 U.S. counties, with its geographic distribution illustrated below. We also include the number of counties corresponding to each crop type in the USDA Crop Dataset (see the rightmost bar chart in the figure) since crop planting is highly geography-dependent.  ### Sentinel-2 Imagery The Sentinel-2 Imagery, obtained from the Sentinel-2 mission, provides high-resolution satellite images for monitoring crop growth on the ground. It contains two types of 224x224 RGB satellite images, agriculture imagery (AG) and normalized difference vegetation index (NDVI), both with a spatial resolution of 9x9 km, and a revisit frequency of 14 days. Examples of AG and NDVI images are depicted as follows. - **Agriculture Imagery (AG)**  - **Normalized Difference Vegetation Index (NDVI)**  ### WRF-HRRR Computed Dataset The WRF-HRRR Computed Dataset, sourced from the WRF-HRRR model, contains daily and monthly meteorological parameters, with the former and the latter designed for capturing direct effects of short-term growing season weather variations on crop growth, and for learning indirect impacts of long-term climate change on crop yields, respectively. It contains 9 meteorological parameters gridded at 9 km in a one-day (and one-month) interval. The figures show the temperature in the spring, the summer, the fall, and the winter, respectively.  ### USDA Crop Dataset The USDA Crop Dataset, collected from the USDA Quick Statistic website, offers valuable information, such as production, yield, etc., for crops grown at each available county. It offers crop information for four types of crops, i.e., corn, cotton, soybeans, and winter wheat, at a county-level basis, with a temporal resolution of one year. The figure illustrates the 2022 Corn Yield across the United States.  ### The CropNet Package Beyond the contribution of our CropNet dataset, we also release the CropNet package in the Python Package Index (PyPI) for facilitating researchers in downloading the CropNet data based on the time and region of interest, and flexibly building their deep learning models for accurate crop yield predictions. In particular, the CropNet package includes three types of APIs, listed as follows: - **DataDownloader**: This API allows users to download the CropNet data over the time/region of interest on the fly. - **DataRetriever**: With this API, users can conveniently obtain the CropNet data stored in the local machine (e.g., if you have downloaded our curated CropNet from Google Drive) over the time/region of interest. - **DataLoader**: This API is designed to facilitate researchers in developing their DNNs for accurate crop yield predictions. Specifically, the code in this API ( 1) combines all three modalities of data to create $(\mathbf{x}, \mathbf{y_{s}}, \mathbf{y_{l}}, \mathbf{z})$ tuples, with $\mathbf{x}, \mathbf{y_{s}}, \mathbf{y_{l}}, \text{and}~ \mathbf{z}$, respectively representing satellite images, short-term daily whether parameters, long-term monthly meteorological parameters, and ground-truth crop yield (or production) information, and then (2) exposes those tuples via a `Dataset` object after appropriate data pre-processing techniques. ### Installation Researchers and practitioners can install the latest version of CropNet with the following commands: ```python # Create and activate a conda environment conda create -n cropnet_api python=3.10 conda activate cropnet_api # Install the latest version of CropNet pip install cropnet # Slove the ecCodes library dependency issue pip install ecmwflibs ``` ### CropNet API Examples - **Example 1: A DataDownloader Example for Downloading the Up-to-date CropNet Data** Given the time and region (i.e., the FIPS codes for two U.S. counties) of interest, the following code presents how to utilize the **DataDownloader** to download the up-to-date CropNet data: ```python from cropnet.data_downloader import DataDownloader # Use the "target_dir" to specify where the data should be downloaded to downloader = DataDownloader(target_dir="./data") # Download 2022 USDA Soybean data # Note that most of the 2023 USDA data are not yet available downloader.download_USDA("Soybean", fips_codes=["10003", "22007"], years=["2022"]) # Download the 2023 (the 1st and 2nd quarters) Sentinel-2 Imagery downloader.download_Sentinel2(fips_codes=["10003", "22007"], years=["2023"], image_type="AG") downloader.download_Sentinel2(fips_codes=["10003", "22007"], years=["2023"], image_type="NDVI") # Download the 2023 (January to July) WRF-HRRR data downloader.download_HRRR(fips_codes=["10003", "22007"], years=["2023"]) ``` - **Example 2: A DataRetriever Example for Obtaining Our Curated CropNet Data** Given the time and region of interest, the following code shows how to use the **DataRetriever** to obtain the CropNet data stored in the local machine in a user-friendly format: ```python # Use the "base_fir" to specify where the CropNet data is stored retriever = DataRetriever(base_dir="/mnt/data/CropNet") # Retrieve the 2022 USDA Soybean data usda_data = retriever.retrieve_USDA(crop_type="Soybean", fips_codes=["10003", "22007"], years=["2022"]) # Retrieve the 2022 Sentinel-2 Imagery data sentinel2_data = retriever.retrieve_Sentinel2(fips_codes=["10003", "22007"], years=["2022"], image_type="AG") sentinel2_data = retriever.retrieve_Sentinel2(fips_codes=["10003", "22007"], years=["2022"], image_type="NDVI") # Retrieve the 2022 WRF-HRRR data hrrr_data = retriever.retrieve_HRRR(fips_codes=["10003","22007"], years=["2022"]) ``` - **Example 3: A PyTorch Example for Using the DataLoader API for Training DNNs** The following code presents a PyTorch example of training a deep learning model (i.e., MMST-ViT) for climate change-aware crop yield predictions, by utilizing the DataLoader APIs: ```python import torch from torch.utils.data import DataLoader from models_mmst_vit import MMST_ViT from cropnet.dataset.hrrr_computed_dataset import HRRRComputedDataset from cropnet.dataset.sentinel2_imagery import Sentinel2Imagery from cropnet.dataset.usda_crop_dataset import USDACropDataset # The base directory for the CropNet dataset base_dir = "/mnt/data/CropNet" # The JSON configuration file config_file = "data/soybeans_train.json" # The dataloaders for each modality of data sentinel2_loader = DataLoader(Sentinel2Imagery(base_dir, config_file), batch_size=1) hrrr_loader = DataLoader(HRRRComputedDataset(base_dir, config_file), batch_size=1) usda_loader = DataLoader(USDACropDataset(base_dir, config_file), batch_size=1) # The model, the optimizer, and the loss function model = MMST_ViT() optimizer = torch.optim.AdamW(model.parameters(), lr=1e-3, betas=(0.9, 0.999)) criterion = torch.nn.MSELoss() # Traning the model for one epoch for s, h, u in zip(sentinel2_loader, hrrr_loader, usda_loader): # x: satellite images # ys (or yl): short-term daily (or long-term monthly) weather parameters # z: ground-truth crop yield (or production) information x, ys, yl, z, = s[0], h[0], h[1], u[0] optimizer.zero_grad() z_hat = model(x, ys, yl) loss = criterion(z, z_hat) loss.backward() optimizer.step() ``` ## License CropNet has a [Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/) license. ## Dataset Terms of Use This dataset is available for research purposes only. By downloading, you agree to these terms. We are aware that unauthorized copies of our dataset have been republished on HuggingFace. Please note that any republication or distribution of this dataset without permission is prohibited and constitutes copyright infringement.

roneneldan/TinyStories

roneneldan

--- license: cdla-sharing-1.0 task_categories: - text-generation language: - en --- Dataset containing synthetically generated (by GPT-3.5 and GPT-4) short stories that only use a small vocabulary. Described in the following paper: https://arxiv.org/abs/2305.07759. The models referred to in the paper were trained on TinyStories-train.txt (the file tinystories-valid.txt can be used for validation loss). These models can be found on Huggingface, at roneneldan/TinyStories-1M/3M/8M/28M/33M/1Layer-21M. Additional resources: tinystories_all_data.tar.gz - contains a superset of the stories together with metadata and the prompt that was used to create each story. TinyStoriesV2-GPT4-train.txt - Is a new version of the dataset that is based on generations by GPT-4 only (the original dataset also has generations by GPT-3.5 which are of lesser quality). It contains all the examples in TinyStories.txt which were GPT-4 generated as a subset (but is significantly larger). Evaluation_prompts.yaml: List of prompts used to evaluate our models (see paper)

bigscience/xP3mt

bigscience

--- annotations_creators: - expert-generated - crowdsourced language: - ak - ar - as - bm - bn - ca - code - en - es - eu - fon - fr - gu - hi - id - ig - ki - kn - lg - ln - ml - mr - ne - nso - ny - or - pa - pt - rn - rw - sn - st - sw - ta - te - tn - ts - tum - tw - ur - vi - wo - xh - yo - zh - zu programming_language: - C - C++ - C# - Go - Java - JavaScript - Lua - PHP - Python - Ruby - Rust - Scala - TypeScript license: - apache-2.0 multilinguality: - multilingual pretty_name: xP3 size_categories: - 100M<n<1B task_categories: - other --- # Dataset Card for xP3 ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Additional Information](#additional-information) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** https://github.com/bigscience-workshop/xmtf - **Paper:** [Crosslingual Generalization through Multitask Finetuning](https://arxiv.org/abs/2211.01786) - **Point of Contact:** [Niklas Muennighoff](mailto:[email protected]) ### Dataset Summary > xP3 (Crosslingual Public Pool of Prompts) is a collection of prompts & datasets across 46 of languages & 16 NLP tasks. It is used for the training of BLOOMZ and mT0, multilingual language models capable of following human instructions in dozens of languages zero-shot. - **Creation:** The dataset can be recreated using instructions available [here](https://github.com/bigscience-workshop/xmtf#create-xp3). We provide this version to save processing time and ease reproducibility. - **Languages:** 46 (Can be extended by [recreating with more splits](https://github.com/bigscience-workshop/xmtf#create-xp3)) - **xP3 Dataset Family:** <table> <tr> <th>Name</th> <th>Explanation</th> <th>Example models</th> </tr> <tr> <td><a href=https://huggingface.co/datasets/Muennighoff/xP3x>xP3x</a></t> <td>Mixture of 17 tasks in 277 languages with English prompts</td> <td>WIP - Join us at Project Aya @<a href=https://cohere.for.ai/>C4AI</a> to help!</td> </tr> <tr> <td><a href=https://huggingface.co/datasets/bigscience/xP3>xP3</a></t> <td>Mixture of 13 training tasks in 46 languages with English prompts</td> <td><a href=https://huggingface.co/bigscience/bloomz>bloomz</a> & <a href=https://huggingface.co/bigscience/mt0-xxl>mt0-xxl</a></td> </tr> <tr> <td><a href=https://huggingface.co/datasets/bigscience/xP3mt>xP3mt</a></t> <td>Mixture of 13 training tasks in 46 languages with prompts in 20 languages (machine-translated from English)</td> <td><a href=https://huggingface.co/bigscience/bloomz-mt>bloomz-mt</a> & <a href=https://huggingface.co/bigscience/mt0-xxl-mt>mt0-xxl-mt</a></td> </tr> <tr> <td><a href=https://huggingface.co/datasets/bigscience/xP3all>xP3all</a></t> <td>xP3 + evaluation datasets adding an additional 3 tasks for a total of 16 tasks in 46 languages with English prompts</td> <td></td> </tr> <tr> <td><a href=https://huggingface.co/datasets/bigscience/xP3megds>xP3megds</a></t> <td><a href=https://github.com/bigscience-workshop/Megatron-DeepSpeed>Megatron-DeepSpeed</a> processed version of xP3</td> <td><a href=https://huggingface.co/bigscience/bloomz>bloomz</a></td> </tr> <tr> <td><a href=https://huggingface.co/datasets/Muennighoff/P3>P3</a></t> <td>Repreprocessed version of the English-only <a href=https://huggingface.co/datasets/bigscience/P3>P3</a> with 8 training tasks</td> <td><a href=https://huggingface.co/bigscience/bloomz-p3>bloomz-p3</a> & <a href=https://huggingface.co/bigscience/mt0-xxl-p3>mt0-xxl-p3</a></td> </tr> </table> ## Dataset Structure ### Data Instances An example of "train" looks as follows: ```json { "inputs": "Oración 1: Fue académico en literatura metafísica, teología y ciencias clásicas.\Oración 2: Fue académico en literatura metafísica, teología y ciencia clásica.\nPregunta: ¿La oración 1 parafrasea la oración 2? ¿Si o no?", "targets": "Sí" } ``` ### Data Fields The data fields are the same among all splits: - `inputs`: the natural language input fed to the model - `targets`: the natural language target that the model has to generate ### Data Splits The below table summarizes sizes per language (computed from the `merged_{lang}.jsonl` files). Due to languages like `tw` only being single sentence translation samples from Flores, their byte percentage is significantly lower than their sample percentage. We machine-translated prompts for monolingual datasets, thus languages with only crosslingual datasets (e.g. Translation) do not have non-English prompts. Languages without non-English prompts are equivalent to [xP3](https://huggingface.co/datasets/bigscience/xP3). |Language|Kilobytes|%|Samples|%|Non-English prompts| |--------|------:|-:|---:|-:|-:| |tw|106288|0.11|265071|0.33| | |bm|107056|0.11|265180|0.33| | |ak|108096|0.11|265071|0.33| | |ca|110608|0.11|271191|0.34| | |eu|113008|0.12|281199|0.35| | |fon|113072|0.12|265063|0.33| | |st|114080|0.12|265063|0.33| | |ki|115040|0.12|265180|0.33| | |tum|116032|0.12|265063|0.33| | |wo|122560|0.13|365063|0.46| | |ln|126304|0.13|365060|0.46| | |as|156256|0.16|265063|0.33| | |or|161472|0.17|265063|0.33| | |kn|165456|0.17|265063|0.33| | |ml|175040|0.18|265864|0.33| | |rn|192992|0.2|318189|0.4| | |nso|229712|0.24|915051|1.14| | |tn|235536|0.24|915054|1.14| | |lg|235936|0.24|915021|1.14| | |rw|249360|0.26|915043|1.14| | |ts|250256|0.26|915044|1.14| | |sn|252496|0.26|865056|1.08| | |xh|254672|0.26|915058|1.14| | |zu|263712|0.27|915061|1.14| | |ny|272128|0.28|915063|1.14| | |ig|325440|0.33|950097|1.19|✅| |yo|339664|0.35|913021|1.14|✅| |ne|398144|0.41|315754|0.39|✅| |pa|529632|0.55|339210|0.42|✅| |sw|561392|0.58|1114439|1.39|✅| |gu|566576|0.58|347499|0.43|✅| |mr|674000|0.69|417269|0.52|✅| |bn|854864|0.88|428725|0.54|✅| |ta|943440|0.97|410633|0.51|✅| |te|1384016|1.42|573354|0.72|✅| |ur|1944416|2.0|855756|1.07|✅| |vi|3113184|3.2|1667306|2.08|✅| |code|4330752|4.46|2707724|3.38| | |hi|4469712|4.6|1543441|1.93|✅| |id|4538768|4.67|2582272|3.22|✅| |zh|4604112|4.74|3571636|4.46|✅| |ar|4703968|4.84|2148970|2.68|✅| |fr|5558912|5.72|5055942|6.31|✅| |pt|6130016|6.31|3562772|4.45|✅| |es|7579424|7.8|5151349|6.43|✅| |en|39252528|40.4|32740750|40.87| | |total|97150128|100.0|80100816|100.0|✅| ## Dataset Creation ### Source Data #### Training datasets - Code Miscellaneous - [CodeComplex](https://huggingface.co/datasets/codeparrot/codecomplex) - [Docstring Corpus](https://huggingface.co/datasets/teven/code_docstring_corpus) - [GreatCode](https://huggingface.co/datasets/great_code) - [State Changes](https://huggingface.co/datasets/Fraser/python-state-changes) - Closed-book QA - [Hotpot QA](https://huggingface.co/datasets/hotpot_qa) - [Trivia QA](https://huggingface.co/datasets/trivia_qa) - [Web Questions](https://huggingface.co/datasets/web_questions) - [Wiki QA](https://huggingface.co/datasets/wiki_qa) - Extractive QA - [Adversarial QA](https://huggingface.co/datasets/adversarial_qa) - [CMRC2018](https://huggingface.co/datasets/cmrc2018) - [DRCD](https://huggingface.co/datasets/clue) - [DuoRC](https://huggingface.co/datasets/duorc) - [MLQA](https://huggingface.co/datasets/mlqa) - [Quoref](https://huggingface.co/datasets/quoref) - [ReCoRD](https://huggingface.co/datasets/super_glue) - [ROPES](https://huggingface.co/datasets/ropes) - [SQuAD v2](https://huggingface.co/datasets/squad_v2) - [xQuAD](https://huggingface.co/datasets/xquad) - TyDI QA - [Primary](https://huggingface.co/datasets/khalidalt/tydiqa-primary) - [Goldp](https://huggingface.co/datasets/khalidalt/tydiqa-goldp) - Multiple-Choice QA - [ARC](https://huggingface.co/datasets/ai2_arc) - [C3](https://huggingface.co/datasets/c3) - [CoS-E](https://huggingface.co/datasets/cos_e) - [Cosmos](https://huggingface.co/datasets/cosmos) - [DREAM](https://huggingface.co/datasets/dream) - [MultiRC](https://huggingface.co/datasets/super_glue) - [OpenBookQA](https://huggingface.co/datasets/openbookqa) - [PiQA](https://huggingface.co/datasets/piqa) - [QUAIL](https://huggingface.co/datasets/quail) - [QuaRel](https://huggingface.co/datasets/quarel) - [QuaRTz](https://huggingface.co/datasets/quartz) - [QASC](https://huggingface.co/datasets/qasc) - [RACE](https://huggingface.co/datasets/race) - [SciQ](https://huggingface.co/datasets/sciq) - [Social IQA](https://huggingface.co/datasets/social_i_qa) - [Wiki Hop](https://huggingface.co/datasets/wiki_hop) - [WiQA](https://huggingface.co/datasets/wiqa) - Paraphrase Identification - [MRPC](https://huggingface.co/datasets/super_glue) - [PAWS](https://huggingface.co/datasets/paws) - [PAWS-X](https://huggingface.co/datasets/paws-x) - [QQP](https://huggingface.co/datasets/qqp) - Program Synthesis - [APPS](https://huggingface.co/datasets/codeparrot/apps) - [CodeContests](https://huggingface.co/datasets/teven/code_contests) - [JupyterCodePairs](https://huggingface.co/datasets/codeparrot/github-jupyter-text-code-pairs) - [MBPP](https://huggingface.co/datasets/Muennighoff/mbpp) - [NeuralCodeSearch](https://huggingface.co/datasets/neural_code_search) - [XLCoST](https://huggingface.co/datasets/codeparrot/xlcost-text-to-code) - Structure-to-text - [Common Gen](https://huggingface.co/datasets/common_gen) - [Wiki Bio](https://huggingface.co/datasets/wiki_bio) - Sentiment - [Amazon](https://huggingface.co/datasets/amazon_polarity) - [App Reviews](https://huggingface.co/datasets/app_reviews) - [IMDB](https://huggingface.co/datasets/imdb) - [Rotten Tomatoes](https://huggingface.co/datasets/rotten_tomatoes) - [Yelp](https://huggingface.co/datasets/yelp_review_full) - Simplification - [BiSECT](https://huggingface.co/datasets/GEM/BiSECT) - Summarization - [CNN Daily Mail](https://huggingface.co/datasets/cnn_dailymail) - [Gigaword](https://huggingface.co/datasets/gigaword) - [MultiNews](https://huggingface.co/datasets/multi_news) - [SamSum](https://huggingface.co/datasets/samsum) - [Wiki-Lingua](https://huggingface.co/datasets/GEM/wiki_lingua) - [XLSum](https://huggingface.co/datasets/GEM/xlsum) - [XSum](https://huggingface.co/datasets/xsum) - Topic Classification - [AG News](https://huggingface.co/datasets/ag_news) - [DBPedia](https://huggingface.co/datasets/dbpedia_14) - [TNEWS](https://huggingface.co/datasets/clue) - [TREC](https://huggingface.co/datasets/trec) - [CSL](https://huggingface.co/datasets/clue) - Translation - [Flores-200](https://huggingface.co/datasets/Muennighoff/flores200) - [Tatoeba](https://huggingface.co/datasets/Helsinki-NLP/tatoeba_mt) - Word Sense disambiguation - [WiC](https://huggingface.co/datasets/super_glue) - [XL-WiC](https://huggingface.co/datasets/pasinit/xlwic) #### Evaluation datasets (included in [xP3all](https://huggingface.co/datasets/bigscience/xP3all) except for NLI & HumanEval) - Natural Language Inference (NLI) - [ANLI](https://huggingface.co/datasets/anli) - [CB](https://huggingface.co/datasets/super_glue) - [RTE](https://huggingface.co/datasets/super_glue) - [XNLI](https://huggingface.co/datasets/xnli) - Coreference Resolution - [Winogrande](https://huggingface.co/datasets/winogrande) - [XWinograd](https://huggingface.co/datasets/Muennighoff/xwinograd) - Program Synthesis - [HumanEval](https://huggingface.co/datasets/openai_humaneval) - Sentence Completion - [COPA](https://huggingface.co/datasets/super_glue) - [Story Cloze](https://huggingface.co/datasets/story_cloze) - [XCOPA](https://huggingface.co/datasets/xcopa) - [XStoryCloze](https://huggingface.co/datasets/Muennighoff/xstory_cloze) ## Additional Information ### Licensing Information The dataset is released under Apache 2.0. ### Citation Information ```bibtex @misc{muennighoff2022crosslingual, title={Crosslingual Generalization through Multitask Finetuning}, author={Niklas Muennighoff and Thomas Wang and Lintang Sutawika and Adam Roberts and Stella Biderman and Teven Le Scao and M Saiful Bari and Sheng Shen and Zheng-Xin Yong and Hailey Schoelkopf and Xiangru Tang and Dragomir Radev and Alham Fikri Aji and Khalid Almubarak and Samuel Albanie and Zaid Alyafeai and Albert Webson and Edward Raff and Colin Raffel}, year={2022}, eprint={2211.01786}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to the contributors of [promptsource](https://github.com/bigscience-workshop/promptsource/graphs/contributors) for adding many prompts used in this dataset.

pico-lm/pretokenized-dolma

pico-lm

--- license: apache-2.0 language: - en pretty_name: 'Pico Dataset: Pre-tokenized, Pre-shuffled Dolma' size_categories: - 100B<n<1T --- ## The Pico Dataset A pre-tokenized, pre-shuffled version of [Dolma](https://huggingface.co/datasets/allenai/dolma), the high-quality text corpus from AI2. ### Overview The Pico dataset simplifies training by providing: - Pre-tokenized text in chunks of 2048 tokens, using the [OLMo Tokenizer](https://huggingface.co/allenai/OLMo-7B-0724-hf/blob/main/tokenizer_config.json) - Pre-shuffled data for consistent training - Streaming-friendly format - 420B tokens total (perfect for 200K steps at batch size 1024) ### Benefits - **Storage Efficient**: No need to download the full 10TB Dolma dataset - **Memory Efficient**: Stream data directly with `load_dataset(..., streaming=True)` - **Reproducible**: All models see identical data in identical order - **Fast**: Skip tokenization during training - **Simple**: Minimal boilerplate code needed ### Usage ``` from datasets import load_dataset dataset = load_dataset("pico-lm/pretokenized-dolma", streaming=True) ```

nguha/legalbench

nguha

--- language: - en license: other size_categories: - 10K<n<100K task_categories: - text-classification - question-answering - text-generation tags: - legal - law - finance dataset_info: - config_name: abercrombie features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 307 num_examples: 5 - name: test num_bytes: 6240 num_examples: 95 download_size: 19558988 dataset_size: 6547 - config_name: canada_tax_court_outcomes features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 2975 num_examples: 6 - name: test num_bytes: 157411 num_examples: 244 download_size: 19558988 dataset_size: 160386 - config_name: citation_prediction_classification features: - name: answer dtype: string - name: citation dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 660 num_examples: 2 - name: test num_bytes: 26112 num_examples: 108 download_size: 19558988 dataset_size: 26772 - config_name: citation_prediction_open features: - name: answer dtype: string - name: circuit dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 555 num_examples: 2 - name: test num_bytes: 13460 num_examples: 53 download_size: 19558988 dataset_size: 14015 - config_name: consumer_contracts_qa features: - name: answer dtype: string - name: contract dtype: string - name: index dtype: string - name: question dtype: string splits: - name: train num_bytes: 9941 num_examples: 4 - name: test num_bytes: 1221320 num_examples: 396 download_size: 19558988 dataset_size: 1231261 - config_name: contract_nli_confidentiality_of_agreement features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 4070 num_examples: 8 - name: test num_bytes: 43818 num_examples: 82 download_size: 19558988 dataset_size: 47888 - config_name: contract_nli_explicit_identification features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 3615 num_examples: 8 - name: test num_bytes: 62133 num_examples: 109 download_size: 19558988 dataset_size: 65748 - config_name: contract_nli_inclusion_of_verbally_conveyed_information features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 3817 num_examples: 8 - name: test num_bytes: 81933 num_examples: 139 download_size: 19558988 dataset_size: 85750 - config_name: contract_nli_limited_use features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 4855 num_examples: 8 - name: test num_bytes: 98534 num_examples: 208 download_size: 19558988 dataset_size: 103389 - config_name: contract_nli_no_licensing features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2591 num_examples: 8 - name: test num_bytes: 78173 num_examples: 162 download_size: 19558988 dataset_size: 80764 - config_name: contract_nli_notice_on_compelled_disclosure features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 3907 num_examples: 8 - name: test num_bytes: 80470 num_examples: 142 download_size: 19558988 dataset_size: 84377 - config_name: contract_nli_permissible_acquirement_of_similar_information features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2736 num_examples: 8 - name: test num_bytes: 87469 num_examples: 178 download_size: 19558988 dataset_size: 90205 - config_name: contract_nli_permissible_copy features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 3480 num_examples: 8 - name: test num_bytes: 39015 num_examples: 87 download_size: 19558988 dataset_size: 42495 - config_name: contract_nli_permissible_development_of_similar_information features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 3921 num_examples: 8 - name: test num_bytes: 62603 num_examples: 136 download_size: 19558988 dataset_size: 66524 - config_name: contract_nli_permissible_post-agreement_possession features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 4608 num_examples: 8 - name: test num_bytes: 65932 num_examples: 111 download_size: 19558988 dataset_size: 70540 - config_name: contract_nli_return_of_confidential_information features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 3499 num_examples: 8 - name: test num_bytes: 35672 num_examples: 66 download_size: 19558988 dataset_size: 39171 - config_name: contract_nli_sharing_with_employees features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 3173 num_examples: 8 - name: test num_bytes: 104240 num_examples: 170 download_size: 19558988 dataset_size: 107413 - config_name: contract_nli_sharing_with_third-parties features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 3249 num_examples: 8 - name: test num_bytes: 104822 num_examples: 180 download_size: 19558988 dataset_size: 108071 - config_name: contract_nli_survival_of_obligations features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2272 num_examples: 8 - name: test num_bytes: 75450 num_examples: 157 download_size: 19558988 dataset_size: 77722 - config_name: contract_qa features: - name: answer dtype: string - name: index dtype: string - name: question dtype: string - name: text dtype: string splits: - name: train num_bytes: 2408 num_examples: 8 - name: test num_bytes: 26370 num_examples: 80 download_size: 19558988 dataset_size: 28778 - config_name: corporate_lobbying features: - name: answer dtype: string - name: bill_summary dtype: string - name: bill_title dtype: string - name: company_description dtype: string - name: company_name dtype: string - name: index dtype: string splits: - name: train num_bytes: 54334 num_examples: 10 - name: test num_bytes: 2974813 num_examples: 490 download_size: 19558988 dataset_size: 3029147 - config_name: cuad_affiliate_license-licensee features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 4067 num_examples: 6 - name: test num_bytes: 115798 num_examples: 198 download_size: 19558988 dataset_size: 119865 - config_name: cuad_affiliate_license-licensor features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 4247 num_examples: 6 - name: test num_bytes: 64931 num_examples: 88 download_size: 19558988 dataset_size: 69178 - config_name: cuad_anti-assignment features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2070 num_examples: 6 - name: test num_bytes: 513026 num_examples: 1172 download_size: 19558988 dataset_size: 515096 - config_name: cuad_audit_rights features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2555 num_examples: 6 - name: test num_bytes: 526977 num_examples: 1216 download_size: 19558988 dataset_size: 529532 - config_name: cuad_cap_on_liability features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2621 num_examples: 6 - name: test num_bytes: 587220 num_examples: 1246 download_size: 19558988 dataset_size: 589841 - config_name: cuad_change_of_control features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2231 num_examples: 6 - name: test num_bytes: 203823 num_examples: 416 download_size: 19558988 dataset_size: 206054 - config_name: cuad_competitive_restriction_exception features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2774 num_examples: 6 - name: test num_bytes: 115844 num_examples: 220 download_size: 19558988 dataset_size: 118618 - config_name: cuad_covenant_not_to_sue features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2581 num_examples: 6 - name: test num_bytes: 153799 num_examples: 308 download_size: 19558988 dataset_size: 156380 - config_name: cuad_effective_date features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2080 num_examples: 6 - name: test num_bytes: 87802 num_examples: 236 download_size: 19558988 dataset_size: 89882 - config_name: cuad_exclusivity features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 1897 num_examples: 6 - name: test num_bytes: 355097 num_examples: 762 download_size: 19558988 dataset_size: 356994 - config_name: cuad_expiration_date features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 1638 num_examples: 6 - name: test num_bytes: 354232 num_examples: 876 download_size: 19558988 dataset_size: 355870 - config_name: cuad_governing_law features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2420 num_examples: 6 - name: test num_bytes: 337322 num_examples: 876 download_size: 19558988 dataset_size: 339742 - config_name: cuad_insurance features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2537 num_examples: 6 - name: test num_bytes: 475827 num_examples: 1030 download_size: 19558988 dataset_size: 478364 - config_name: cuad_ip_ownership_assignment features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 4756 num_examples: 6 - name: test num_bytes: 294749 num_examples: 576 download_size: 19558988 dataset_size: 299505 - config_name: cuad_irrevocable_or_perpetual_license features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 5328 num_examples: 6 - name: test num_bytes: 160279 num_examples: 280 download_size: 19558988 dataset_size: 165607 - config_name: cuad_joint_ip_ownership features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 5011 num_examples: 6 - name: test num_bytes: 90592 num_examples: 192 download_size: 19558988 dataset_size: 95603 - config_name: cuad_license_grant features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 3690 num_examples: 6 - name: test num_bytes: 709331 num_examples: 1396 download_size: 19558988 dataset_size: 713021 - config_name: cuad_liquidated_damages features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 3579 num_examples: 6 - name: test num_bytes: 97839 num_examples: 220 download_size: 19558988 dataset_size: 101418 - config_name: cuad_minimum_commitment features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2874 num_examples: 6 - name: test num_bytes: 354078 num_examples: 772 download_size: 19558988 dataset_size: 356952 - config_name: cuad_most_favored_nation features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2103 num_examples: 6 - name: test num_bytes: 32800 num_examples: 64 download_size: 19558988 dataset_size: 34903 - config_name: cuad_no-solicit_of_customers features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 3310 num_examples: 6 - name: test num_bytes: 40828 num_examples: 84 download_size: 19558988 dataset_size: 44138 - config_name: cuad_no-solicit_of_employees features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 3619 num_examples: 6 - name: test num_bytes: 72661 num_examples: 142 download_size: 19558988 dataset_size: 76280 - config_name: cuad_non-compete features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 3675 num_examples: 6 - name: test num_bytes: 211272 num_examples: 442 download_size: 19558988 dataset_size: 214947 - config_name: cuad_non-disparagement features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2168 num_examples: 6 - name: test num_bytes: 49850 num_examples: 100 download_size: 19558988 dataset_size: 52018 - config_name: cuad_non-transferable_license features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 3643 num_examples: 6 - name: test num_bytes: 269505 num_examples: 542 download_size: 19558988 dataset_size: 273148 - config_name: cuad_notice_period_to_terminate_renewal features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 4166 num_examples: 6 - name: test num_bytes: 100014 num_examples: 222 download_size: 19558988 dataset_size: 104180 - config_name: cuad_post-termination_services features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 3349 num_examples: 6 - name: test num_bytes: 419477 num_examples: 808 download_size: 19558988 dataset_size: 422826 - config_name: cuad_price_restrictions features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2945 num_examples: 6 - name: test num_bytes: 19430 num_examples: 46 download_size: 19558988 dataset_size: 22375 - config_name: cuad_renewal_term features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2163 num_examples: 6 - name: test num_bytes: 168528 num_examples: 386 download_size: 19558988 dataset_size: 170691 - config_name: cuad_revenue-profit_sharing features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2581 num_examples: 6 - name: test num_bytes: 363594 num_examples: 774 download_size: 19558988 dataset_size: 366175 - config_name: cuad_rofr-rofo-rofn features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2817 num_examples: 6 - name: test num_bytes: 338243 num_examples: 690 download_size: 19558988 dataset_size: 341060 - config_name: cuad_source_code_escrow features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2696 num_examples: 6 - name: test num_bytes: 58125 num_examples: 118 download_size: 19558988 dataset_size: 60821 - config_name: cuad_termination_for_convenience features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 1506 num_examples: 6 - name: test num_bytes: 181164 num_examples: 430 download_size: 19558988 dataset_size: 182670 - config_name: cuad_third_party_beneficiary features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2378 num_examples: 6 - name: test num_bytes: 24106 num_examples: 68 download_size: 19558988 dataset_size: 26484 - config_name: cuad_uncapped_liability features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2570 num_examples: 6 - name: test num_bytes: 158009 num_examples: 294 download_size: 19558988 dataset_size: 160579 - config_name: cuad_unlimited-all-you-can-eat-license features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2414 num_examples: 6 - name: test num_bytes: 22347 num_examples: 48 download_size: 19558988 dataset_size: 24761 - config_name: cuad_volume_restriction features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 1397 num_examples: 6 - name: test num_bytes: 129456 num_examples: 322 download_size: 19558988 dataset_size: 130853 - config_name: cuad_warranty_duration features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 1815 num_examples: 6 - name: test num_bytes: 142580 num_examples: 320 download_size: 19558988 dataset_size: 144395 - config_name: definition_classification features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 1826 num_examples: 8 - name: test num_bytes: 371743 num_examples: 1337 download_size: 19558988 dataset_size: 373569 - config_name: definition_extraction features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 2731 num_examples: 8 - name: test num_bytes: 254689 num_examples: 687 download_size: 19558988 dataset_size: 257420 - config_name: diversity_1 features: - name: aic_is_met dtype: string - name: answer dtype: string - name: index dtype: string - name: parties_are_diverse dtype: string - name: text dtype: string splits: - name: train num_bytes: 803 num_examples: 6 - name: test num_bytes: 41135 num_examples: 300 download_size: 19558988 dataset_size: 41938 - config_name: diversity_2 features: - name: aic_is_met dtype: string - name: answer dtype: string - name: index dtype: string - name: parties_are_diverse dtype: string - name: text dtype: string splits: - name: train num_bytes: 1041 num_examples: 6 - name: test num_bytes: 53537 num_examples: 300 download_size: 19558988 dataset_size: 54578 - config_name: diversity_3 features: - name: aic_is_met dtype: string - name: answer dtype: string - name: index dtype: string - name: parties_are_diverse dtype: string - name: text dtype: string splits: - name: train num_bytes: 992 num_examples: 6 - name: test num_bytes: 50744 num_examples: 300 download_size: 19558988 dataset_size: 51736 - config_name: diversity_4 features: - name: aic_is_met dtype: string - name: answer dtype: string - name: index dtype: string - name: parties_are_diverse dtype: string - name: text dtype: string splits: - name: train num_bytes: 1070 num_examples: 6 - name: test num_bytes: 53464 num_examples: 300 download_size: 19558988 dataset_size: 54534 - config_name: diversity_5 features: - name: aic_is_met dtype: string - name: answer dtype: string - name: index dtype: string - name: parties_are_diverse dtype: string - name: text dtype: string splits: - name: train num_bytes: 1232 num_examples: 6 - name: test num_bytes: 62550 num_examples: 300 download_size: 19558988 dataset_size: 63782 - config_name: diversity_6 features: - name: aic_is_met dtype: string - name: answer dtype: string - name: index dtype: string - name: parties_are_diverse dtype: string - name: text dtype: string splits: - name: train num_bytes: 2016 num_examples: 6 - name: test num_bytes: 100411 num_examples: 300 download_size: 19558988 dataset_size: 102427 - config_name: function_of_decision_section features: - name: Citation dtype: string - name: Paragraph dtype: string - name: answer dtype: string - name: index dtype: string splits: - name: train num_bytes: 1547 num_examples: 7 - name: test num_bytes: 210419 num_examples: 367 download_size: 19558988 dataset_size: 211966 - config_name: hearsay features: - name: answer dtype: string - name: index dtype: string - name: slice dtype: string - name: text dtype: string splits: - name: train num_bytes: 788 num_examples: 5 - name: test num_bytes: 17150 num_examples: 94 download_size: 19558988 dataset_size: 17938 - config_name: insurance_policy_interpretation features: - name: answer dtype: string - name: claim dtype: string - name: index dtype: string - name: policy dtype: string splits: - name: train num_bytes: 3119 num_examples: 5 - name: test num_bytes: 70764 num_examples: 133 download_size: 19558988 dataset_size: 73883 - config_name: international_citizenship_questions features: - name: answer dtype: string - name: index dtype: string - name: question dtype: string splits: - name: train num_bytes: 832 num_examples: 4 - name: test num_bytes: 2089107 num_examples: 9306 download_size: 19558988 dataset_size: 2089939 - config_name: jcrew_blocker features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 7352 num_examples: 6 - name: test num_bytes: 59879 num_examples: 54 download_size: 19558988 dataset_size: 67231 - config_name: learned_hands_benefits features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 8267 num_examples: 6 - name: test num_bytes: 87512 num_examples: 66 download_size: 19558988 dataset_size: 95779 - config_name: learned_hands_business features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 6075 num_examples: 6 - name: test num_bytes: 202116 num_examples: 174 download_size: 19558988 dataset_size: 208191 - config_name: learned_hands_consumer features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 6355 num_examples: 6 - name: test num_bytes: 795463 num_examples: 614 download_size: 19558988 dataset_size: 801818 - config_name: learned_hands_courts features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 10693 num_examples: 6 - name: test num_bytes: 228204 num_examples: 192 download_size: 19558988 dataset_size: 238897 - config_name: learned_hands_crime features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 7322 num_examples: 6 - name: test num_bytes: 846597 num_examples: 688 download_size: 19558988 dataset_size: 853919 - config_name: learned_hands_divorce features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 10651 num_examples: 6 - name: test num_bytes: 189279 num_examples: 150 download_size: 19558988 dataset_size: 199930 - config_name: learned_hands_domestic_violence features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 11170 num_examples: 6 - name: test num_bytes: 239797 num_examples: 174 download_size: 19558988 dataset_size: 250967 - config_name: learned_hands_education features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 6992 num_examples: 6 - name: test num_bytes: 79184 num_examples: 56 download_size: 19558988 dataset_size: 86176 - config_name: learned_hands_employment features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 11223 num_examples: 6 - name: test num_bytes: 909220 num_examples: 710 download_size: 19558988 dataset_size: 920443 - config_name: learned_hands_estates features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 5970 num_examples: 6 - name: test num_bytes: 216836 num_examples: 178 download_size: 19558988 dataset_size: 222806 - config_name: learned_hands_family features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 8714 num_examples: 6 - name: test num_bytes: 3073508 num_examples: 2265 download_size: 19558988 dataset_size: 3082222 - config_name: learned_hands_health features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 6155 num_examples: 6 - name: test num_bytes: 336934 num_examples: 226 download_size: 19558988 dataset_size: 343089 - config_name: learned_hands_housing features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 9726 num_examples: 6 - name: test num_bytes: 6028612 num_examples: 4494 download_size: 19558988 dataset_size: 6038338 - config_name: learned_hands_immigration features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 3955 num_examples: 6 - name: test num_bytes: 165352 num_examples: 134 download_size: 19558988 dataset_size: 169307 - config_name: learned_hands_torts features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 4484 num_examples: 6 - name: test num_bytes: 615649 num_examples: 432 download_size: 19558988 dataset_size: 620133 - config_name: learned_hands_traffic features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 6250 num_examples: 6 - name: test num_bytes: 667539 num_examples: 556 download_size: 19558988 dataset_size: 673789 - config_name: legal_reasoning_causality features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 4688 num_examples: 4 - name: test num_bytes: 87007 num_examples: 55 download_size: 19558988 dataset_size: 91695 - config_name: maud_ability_to_consummate_concept_is_subject_to_mae_carveouts features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 5322 num_examples: 1 - name: test num_bytes: 304051 num_examples: 69 download_size: 19558988 dataset_size: 309373 - config_name: maud_accuracy_of_fundamental_target_rws_bringdown_standard features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 271 num_examples: 1 - name: test num_bytes: 148869 num_examples: 175 download_size: 19558988 dataset_size: 149140 - config_name: maud_accuracy_of_target_capitalization_rw_(outstanding_shares)_bringdown_standard_answer features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 1493 num_examples: 1 - name: test num_bytes: 152224 num_examples: 181 download_size: 19558988 dataset_size: 153717 - config_name: maud_accuracy_of_target_general_rw_bringdown_timing_answer features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 1000 num_examples: 1 - name: test num_bytes: 152717 num_examples: 181 download_size: 19558988 dataset_size: 153717 - config_name: maud_additional_matching_rights_period_for_modifications_(cor) features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 2170 num_examples: 1 - name: test num_bytes: 312632 num_examples: 158 download_size: 19558988 dataset_size: 314802 - config_name: maud_application_of_buyer_consent_requirement_(negative_interim_covenant) features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 558 num_examples: 1 - name: test num_bytes: 96990 num_examples: 180 download_size: 19558988 dataset_size: 97548 - config_name: maud_buyer_consent_requirement_(ordinary_course) features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 2620 num_examples: 1 - name: test num_bytes: 138668 num_examples: 181 download_size: 19558988 dataset_size: 141288 - config_name: maud_change_in_law__subject_to_disproportionate_impact_modifier features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 6000 num_examples: 1 - name: test num_bytes: 448666 num_examples: 99 download_size: 19558988 dataset_size: 454666 - config_name: maud_changes_in_gaap_or_other_accounting_principles__subject_to_disproportionate_impact_modifier features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 5998 num_examples: 1 - name: test num_bytes: 444442 num_examples: 98 download_size: 19558988 dataset_size: 450440 - config_name: maud_cor_permitted_in_response_to_intervening_event features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 2631 num_examples: 1 - name: test num_bytes: 195447 num_examples: 100 download_size: 19558988 dataset_size: 198078 - config_name: maud_cor_permitted_with_board_fiduciary_determination_only features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 3970 num_examples: 1 - name: test num_bytes: 194108 num_examples: 100 download_size: 19558988 dataset_size: 198078 - config_name: maud_cor_standard_(intervening_event) features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 727 num_examples: 1 - name: test num_bytes: 175140 num_examples: 84 download_size: 19558988 dataset_size: 175867 - config_name: maud_cor_standard_(superior_offer) features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 1173 num_examples: 1 - name: test num_bytes: 196905 num_examples: 100 download_size: 19558988 dataset_size: 198078 - config_name: maud_definition_contains_knowledge_requirement_-_answer features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 1899 num_examples: 1 - name: test num_bytes: 231405 num_examples: 147 download_size: 19558988 dataset_size: 233304 - config_name: maud_definition_includes_asset_deals features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 614 num_examples: 1 - name: test num_bytes: 289644 num_examples: 146 download_size: 19558988 dataset_size: 290258 - config_name: maud_definition_includes_stock_deals features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 683 num_examples: 1 - name: test num_bytes: 292466 num_examples: 148 download_size: 19558988 dataset_size: 293149 - config_name: maud_fiduciary_exception__board_determination_standard features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 1594 num_examples: 1 - name: test num_bytes: 288180 num_examples: 179 download_size: 19558988 dataset_size: 289774 - config_name: maud_fiduciary_exception_board_determination_trigger_(no_shop) features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 3538 num_examples: 1 - name: test num_bytes: 286236 num_examples: 179 download_size: 19558988 dataset_size: 289774 - config_name: maud_financial_point_of_view_is_the_sole_consideration features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 3290 num_examples: 1 - name: test num_bytes: 217048 num_examples: 112 download_size: 19558988 dataset_size: 220338 - config_name: maud_fls_(mae)_standard features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 4669 num_examples: 1 - name: test num_bytes: 349856 num_examples: 77 download_size: 19558988 dataset_size: 354525 - config_name: maud_general_economic_and_financial_conditions_subject_to_disproportionate_impact_modifier features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 5998 num_examples: 1 - name: test num_bytes: 445306 num_examples: 98 download_size: 19558988 dataset_size: 451304 - config_name: maud_includes_consistent_with_past_practice features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 1127 num_examples: 1 - name: test num_bytes: 140161 num_examples: 181 download_size: 19558988 dataset_size: 141288 - config_name: maud_initial_matching_rights_period_(cor) features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 3041 num_examples: 1 - name: test num_bytes: 311761 num_examples: 158 download_size: 19558988 dataset_size: 314802 - config_name: maud_initial_matching_rights_period_(ftr) features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 1850 num_examples: 1 - name: test num_bytes: 279202 num_examples: 132 download_size: 19558988 dataset_size: 281052 - config_name: maud_intervening_event_-_required_to_occur_after_signing_-_answer features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 3055 num_examples: 1 - name: test num_bytes: 230249 num_examples: 147 download_size: 19558988 dataset_size: 233304 - config_name: maud_knowledge_definition features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 240 num_examples: 1 - name: test num_bytes: 359730 num_examples: 167 download_size: 19558988 dataset_size: 359970 - config_name: maud_liability_standard_for_no-shop_breach_by_target_non-do_representatives features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 154 num_examples: 1 - name: test num_bytes: 40946 num_examples: 156 download_size: 19558988 dataset_size: 41100 - config_name: maud_ordinary_course_efforts_standard features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 1037 num_examples: 1 - name: test num_bytes: 140251 num_examples: 181 download_size: 19558988 dataset_size: 141288 - config_name: maud_pandemic_or_other_public_health_event__subject_to_disproportionate_impact_modifier features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 3728 num_examples: 1 - name: test num_bytes: 447053 num_examples: 98 download_size: 19558988 dataset_size: 450781 - config_name: maud_pandemic_or_other_public_health_event_specific_reference_to_pandemic-related_governmental_responses_or_measures features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 3728 num_examples: 1 - name: test num_bytes: 447053 num_examples: 98 download_size: 19558988 dataset_size: 450781 - config_name: maud_relational_language_(mae)_applies_to features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 4948 num_examples: 1 - name: test num_bytes: 409477 num_examples: 90 download_size: 19558988 dataset_size: 414425 - config_name: maud_specific_performance features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 771 num_examples: 1 - name: test num_bytes: 107392 num_examples: 178 download_size: 19558988 dataset_size: 108163 - config_name: maud_tail_period_length features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 406 num_examples: 1 - name: test num_bytes: 108632 num_examples: 179 download_size: 19558988 dataset_size: 109038 - config_name: maud_type_of_consideration features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 258 num_examples: 1 - name: test num_bytes: 139270 num_examples: 172 download_size: 19558988 dataset_size: 139528 - config_name: nys_judicial_ethics features: - name: answer dtype: string - name: index dtype: string - name: question dtype: string - name: year dtype: string splits: - name: train num_bytes: 1697 num_examples: 8 - name: test num_bytes: 53974 num_examples: 292 download_size: 19558988 dataset_size: 55671 - config_name: opp115_data_retention features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 1791 num_examples: 8 - name: test num_bytes: 18620 num_examples: 88 download_size: 19558988 dataset_size: 20411 - config_name: opp115_data_security features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 2123 num_examples: 8 - name: test num_bytes: 352667 num_examples: 1334 download_size: 19558988 dataset_size: 354790 - config_name: opp115_do_not_track features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 2507 num_examples: 8 - name: test num_bytes: 26363 num_examples: 110 download_size: 19558988 dataset_size: 28870 - config_name: opp115_first_party_collection_use features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 2227 num_examples: 8 - name: test num_bytes: 463566 num_examples: 2086 download_size: 19558988 dataset_size: 465793 - config_name: opp115_international_and_specific_audiences features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 1643 num_examples: 8 - name: test num_bytes: 338196 num_examples: 980 download_size: 19558988 dataset_size: 339839 - config_name: opp115_policy_change features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 1201 num_examples: 8 - name: test num_bytes: 94060 num_examples: 431 download_size: 19558988 dataset_size: 95261 - config_name: opp115_third_party_sharing_collection features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 1217 num_examples: 8 - name: test num_bytes: 383909 num_examples: 1590 download_size: 19558988 dataset_size: 385126 - config_name: opp115_user_access,_edit_and_deletion features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 1251 num_examples: 8 - name: test num_bytes: 108969 num_examples: 462 download_size: 19558988 dataset_size: 110220 - config_name: opp115_user_choice_control features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 1695 num_examples: 8 - name: test num_bytes: 353113 num_examples: 1546 download_size: 19558988 dataset_size: 354808 - config_name: oral_argument_question_purpose features: - name: Docket No. dtype: string - name: answer dtype: string - name: index dtype: string - name: question dtype: string splits: - name: train num_bytes: 2415 num_examples: 7 - name: test num_bytes: 95262 num_examples: 312 download_size: 19558988 dataset_size: 97677 - config_name: overruling features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 629 num_examples: 6 - name: test num_bytes: 443484 num_examples: 2394 download_size: 19558988 dataset_size: 444113 - config_name: personal_jurisdiction features: - name: answer dtype: string - name: index dtype: string - name: slice dtype: string - name: text dtype: string splits: - name: train num_bytes: 1660 num_examples: 4 - name: test num_bytes: 21089 num_examples: 50 download_size: 19558988 dataset_size: 22749 - config_name: privacy_policy_entailment features: - name: answer dtype: string - name: description dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 6282 num_examples: 8 - name: test num_bytes: 3174950 num_examples: 4335 download_size: 19558988 dataset_size: 3181232 - config_name: privacy_policy_qa features: - name: answer dtype: string - name: index dtype: string - name: question dtype: string - name: text dtype: string splits: - name: train num_bytes: 2231 num_examples: 8 - name: test num_bytes: 2817986 num_examples: 10923 download_size: 19558988 dataset_size: 2820217 - config_name: proa features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 1057 num_examples: 5 - name: test num_bytes: 25475 num_examples: 95 download_size: 19558988 dataset_size: 26532 - config_name: rule_qa features: - name: answer dtype: string - name: doctrine dtype: string - name: index dtype: string - name: text dtype: string splits: - name: test num_bytes: 12665 num_examples: 50 download_size: 19558988 dataset_size: 12665 - config_name: sara_entailment features: - name: answer dtype: string - name: case id dtype: string - name: description dtype: string - name: index dtype: string - name: question dtype: string - name: statute dtype: string - name: text dtype: string splits: - name: train num_bytes: 2528 num_examples: 4 - name: test num_bytes: 225560 num_examples: 272 download_size: 19558988 dataset_size: 228088 - config_name: sara_numeric features: - name: answer dtype: string - name: case id dtype: string - name: description dtype: string - name: index dtype: string - name: question dtype: string - name: statute dtype: string - name: text dtype: string splits: - name: train num_bytes: 238363 num_examples: 4 - name: test num_bytes: 5725392 num_examples: 96 download_size: 19558988 dataset_size: 5963755 - config_name: scalr features: - name: answer dtype: string - name: choice_0 dtype: string - name: choice_1 dtype: string - name: choice_2 dtype: string - name: choice_3 dtype: string - name: choice_4 dtype: string - name: index dtype: string - name: question dtype: string splits: - name: test num_bytes: 1026740 num_examples: 571 download_size: 19558988 dataset_size: 1026740 - config_name: ssla_company_defendants features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 5847 num_examples: 3 - name: test num_bytes: 2313039 num_examples: 1228 download_size: 19558988 dataset_size: 2318886 - config_name: ssla_individual_defendants features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 5962 num_examples: 3 - name: test num_bytes: 2002620 num_examples: 1012 download_size: 19558988 dataset_size: 2008582 - config_name: ssla_plaintiff features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 5831 num_examples: 3 - name: test num_bytes: 1926518 num_examples: 1033 download_size: 19558988 dataset_size: 1932349 - config_name: successor_liability features: - name: answer dtype: string - name: index dtype: string - name: issue dtype: string - name: text dtype: string splits: - name: train num_bytes: 1734 num_examples: 3 - name: test num_bytes: 26490 num_examples: 47 download_size: 19558988 dataset_size: 28224 - config_name: supply_chain_disclosure_best_practice_accountability features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 18987 num_examples: 8 - name: test num_bytes: 1347025 num_examples: 379 download_size: 19558988 dataset_size: 1366012 - config_name: supply_chain_disclosure_best_practice_audits features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 23879 num_examples: 8 - name: test num_bytes: 1342065 num_examples: 379 download_size: 19558988 dataset_size: 1365944 - config_name: supply_chain_disclosure_best_practice_certification features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 22058 num_examples: 8 - name: test num_bytes: 1338516 num_examples: 378 download_size: 19558988 dataset_size: 1360574 - config_name: supply_chain_disclosure_best_practice_training features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 24071 num_examples: 8 - name: test num_bytes: 1341885 num_examples: 379 download_size: 19558988 dataset_size: 1365956 - config_name: supply_chain_disclosure_best_practice_verification features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 27158 num_examples: 8 - name: test num_bytes: 1338739 num_examples: 379 download_size: 19558988 dataset_size: 1365897 - config_name: supply_chain_disclosure_disclosed_accountability features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 18902 num_examples: 8 - name: test num_bytes: 1344444 num_examples: 378 download_size: 19558988 dataset_size: 1363346 - config_name: supply_chain_disclosure_disclosed_audits features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 24404 num_examples: 8 - name: test num_bytes: 1341624 num_examples: 379 download_size: 19558988 dataset_size: 1366028 - config_name: supply_chain_disclosure_disclosed_certification features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 17987 num_examples: 8 - name: test num_bytes: 1342646 num_examples: 378 download_size: 19558988 dataset_size: 1360633 - config_name: supply_chain_disclosure_disclosed_training features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 27093 num_examples: 8 - name: test num_bytes: 1338919 num_examples: 379 download_size: 19558988 dataset_size: 1366012 - config_name: supply_chain_disclosure_disclosed_verification features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 25387 num_examples: 8 - name: test num_bytes: 1340578 num_examples: 379 download_size: 19558988 dataset_size: 1365965 - config_name: telemarketing_sales_rule features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 1230 num_examples: 4 - name: test num_bytes: 17140 num_examples: 47 download_size: 19558988 dataset_size: 18370 - config_name: textualism_tool_dictionaries features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 4842 num_examples: 4 - name: test num_bytes: 102644 num_examples: 107 download_size: 19558988 dataset_size: 107486 - config_name: textualism_tool_plain features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 3338 num_examples: 4 - name: test num_bytes: 167428 num_examples: 165 download_size: 19558988 dataset_size: 170766 - config_name: ucc_v_common_law features: - name: answer dtype: string - name: contract dtype: string - name: index dtype: string splits: - name: train num_bytes: 904 num_examples: 6 - name: test num_bytes: 12694 num_examples: 94 download_size: 19558988 dataset_size: 13598 - config_name: unfair_tos features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 3308 num_examples: 9 - name: test num_bytes: 787108 num_examples: 3813 download_size: 19558988 dataset_size: 790416 --- # Dataset Card for Dataset Name - **Homepage: https://hazyresearch.stanford.edu/legalbench/** - **Repository: https://github.com/HazyResearch/legalbench/** - **Paper: https://arxiv.org/abs/2308.11462** ## Dataset Description ### Dataset Summary The LegalBench project is an ongoing open science effort to collaboratively curate tasks for evaluating legal reasoning in English large language models (LLMs). The benchmark currently consists of 162 tasks gathered from 40 contributors. Note: Because LegalBench is intended to test zero and few-shot reasoning, the available "train" splits are small. However, if you are interested in finetuning models or studying model performance in a more traditional train/test regime, you can combine and re-partition train and test data. If you have questions about the project or would like to get involved, please see the website for more information. ### Supported Tasks and Leaderboards LegalBench tasks span multiple types (binary classification, multi-class classification, extraction, generation, entailment), multiple types of text (statutes, judicial opinions, contracts, etc.), and multiple areas of law (evidence, contracts, civil procedure, etc.). For more information on tasks, we recommend visiting the website, where you can search through task descriptions, or the Github repository, which contains more granular task descriptions. We also recommend reading the paper, which provides more background on task significance and construction process. ### Languages All LegalBench tasks are in English. ## Dataset Structure ### Data Instances Detailed descriptions of the instances for each task can be found on the Github. An example of an instance, for the `abercrombie` task, is provided below: ``` { "text": "The mark "Ivory" for a product made of elephant tusks.", "label": "generic" "idx": 0 } ``` A substantial number of LegalBench tasks are binary classification tasks, which require the LLM to determine if a piece of text has some legal attribute. Because these are framed as Yes/No questions, the label space is "Yes" or "No". ### Data Fields Detailed descriptions of the instances for each task can be found on the Github. ### Data Splits Each task (except for `rule_qa` and `scalr`) has both a training and evaluation split. Following [RAFT](https://huggingface.co/datasets/ought/raft), train splits only consists of a few-labeled instances, reflecting the few-shot nature of most LLMs. ## Dataset Creation ### Curation Rationale LegalBench was created to enable researchers to better benchmark the legal reasoning capabilities of LLMs. ### Source Data #### Initial Data Collection and Normalization Broadly, LegalBench tasks are drawn from three sources. The first source of tasks are existing available datasets and corpora. Most of these were originally released for non-LLM evaluation settings. In creating tasks for LegalBench from these sources, we often significantly reformatted data and restructured the prediction objective. For instance, the original [CUAD dataset](https://github.com/TheAtticusProject/cuad) contains annotations on long-documents and is intended for evaluating extraction with span-prediction models. We restructure this corpora to generate a binary classification task for each type of contractual clause. While the original corpus emphasized the long-document aspects of contracts, our restructured tasks emphasize whether LLMs can identify the distinguishing features of different types of clauses. The second source of tasks are datasets that were previously constructed by legal professionals but never released. This primarily includes datasets hand-coded by legal scholars as part of prior empirical legal projects. The last category of tasks are those that were developed specifically for \name, by the authors of this paper. Overall, tasks are drawn from 36 distinct corpora. Please see the Appendix of the paper for more details. #### Who are the source language producers? LegalBench data was created by humans. Demographic information for these individuals is not available. ### Annotations #### Annotation process Please see the paper for more information on the annotation process used in the creation of each task. #### Who are the annotators? Please see the paper for more information on the identity of annotators for each task. ### Personal and Sensitive Information Data in this benchmark has either been synthetically generated, or derived from an already public source (e.g., contracts from the EDGAR database). Several tasks have been derived from the LearnedHands corpus, which consists of public posts on /r/LegalAdvice. Some posts may discuss sensitive issues. ## Considerations for Using the Data ### Social Impact of Dataset Please see the original paper for a discussion of social impact. ### Discussion of Biases Please see the original paper for a discussion of social impact. ### Other Known Limitations LegalBench primarily contains tasks corresponding to American law. ## Additional Information ### Dataset Curators Please see the website for a full list of participants in the LegalBench project. ### Licensing Information LegalBench tasks are subject to different licenses. Please see the paper for a description of the licenses. ### Citation Information If you intend to reference LegalBench broadly, please use the citation below. If you are working with a particular task, please use the citation below in addition to the task specific citation (which can be found on the task page on the website or Github). ``` @misc{guha2023legalbench, title={LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models}, author={Neel Guha and Julian Nyarko and Daniel E. Ho and Christopher Ré and Adam Chilton and Aditya Narayana and Alex Chohlas-Wood and Austin Peters and Brandon Waldon and Daniel N. Rockmore and Diego Zambrano and Dmitry Talisman and Enam Hoque and Faiz Surani and Frank Fagan and Galit Sarfaty and Gregory M. Dickinson and Haggai Porat and Jason Hegland and Jessica Wu and Joe Nudell and Joel Niklaus and John Nay and Jonathan H. Choi and Kevin Tobia and Margaret Hagan and Megan Ma and Michael Livermore and Nikon Rasumov-Rahe and Nils Holzenberger and Noam Kolt and Peter Henderson and Sean Rehaag and Sharad Goel and Shang Gao and Spencer Williams and Sunny Gandhi and Tom Zur and Varun Iyer and Zehua Li}, year={2023}, eprint={2308.11462}, archivePrefix={arXiv}, primaryClass={cs.CL} } @article{koreeda2021contractnli, title={ContractNLI: A dataset for document-level natural language inference for contracts}, author={Koreeda, Yuta and Manning, Christopher D}, journal={arXiv preprint arXiv:2110.01799}, year={2021} } @article{hendrycks2021cuad, title={Cuad: An expert-annotated nlp dataset for legal contract review}, author={Hendrycks, Dan and Burns, Collin and Chen, Anya and Ball, Spencer}, journal={arXiv preprint arXiv:2103.06268}, year={2021} } @article{wang2023maud, title={MAUD: An Expert-Annotated Legal NLP Dataset for Merger Agreement Understanding}, author={Wang, Steven H and Scardigli, Antoine and Tang, Leonard and Chen, Wei and Levkin, Dimitry and Chen, Anya and Ball, Spencer and Woodside, Thomas and Zhang, Oliver and Hendrycks, Dan}, journal={arXiv preprint arXiv:2301.00876}, year={2023} } @inproceedings{wilson2016creation, title={The creation and analysis of a website privacy policy corpus}, author={Wilson, Shomir and Schaub, Florian and Dara, Aswarth Abhilash and Liu, Frederick and Cherivirala, Sushain and Leon, Pedro Giovanni and Andersen, Mads Schaarup and Zimmeck, Sebastian and Sathyendra, Kanthashree Mysore and Russell, N Cameron and others}, booktitle={Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}, pages={1330--1340}, year={2016} } @inproceedings{zheng2021does, title={When does pretraining help? assessing self-supervised learning for law and the casehold dataset of 53,000+ legal holdings}, author={Zheng, Lucia and Guha, Neel and Anderson, Brandon R and Henderson, Peter and Ho, Daniel E}, booktitle={Proceedings of the eighteenth international conference on artificial intelligence and law}, pages={159--168}, year={2021} } @article{zimmeck2019maps, title={Maps: Scaling privacy compliance analysis to a million apps}, author={Zimmeck, Sebastian and Story, Peter and Smullen, Daniel and Ravichander, Abhilasha and Wang, Ziqi and Reidenberg, Joel R and Russell, N Cameron and Sadeh, Norman}, journal={Proc. Priv. Enhancing Tech.}, volume={2019}, pages={66}, year={2019} } @article{ravichander2019question, title={Question answering for privacy policies: Combining computational and legal perspectives}, author={Ravichander, Abhilasha and Black, Alan W and Wilson, Shomir and Norton, Thomas and Sadeh, Norman}, journal={arXiv preprint arXiv:1911.00841}, year={2019} } @article{holzenberger2021factoring, title={Factoring statutory reasoning as language understanding challenges}, author={Holzenberger, Nils and Van Durme, Benjamin}, journal={arXiv preprint arXiv:2105.07903}, year={2021} } @article{lippi2019claudette, title={CLAUDETTE: an automated detector of potentially unfair clauses in online terms of service}, author={Lippi, Marco and Pa{\l}ka, Przemys{\l}aw and Contissa, Giuseppe and Lagioia, Francesca and Micklitz, Hans-Wolfgang and Sartor, Giovanni and Torroni, Paolo}, journal={Artificial Intelligence and Law}, volume={27}, pages={117--139}, year={2019}, publisher={Springer} } ```

alvations/c4p0

alvations

--- dataset_info: features: - name: source dtype: string - name: target dtype: string - name: target_backto_source dtype: string - name: raw_target list: - name: generated_text dtype: string - name: raw_target_backto_source list: - name: generated_text dtype: string - name: prompt dtype: string - name: reverse_prompt dtype: string - name: source_langid dtype: string - name: target_langid dtype: string - name: target_backto_source_langid dtype: string - name: doc_id dtype: int64 - name: sent_id dtype: int64 - name: timestamp dtype: string - name: url dtype: string - name: doc_hash dtype: string splits: - name: train num_bytes: 4134 num_examples: 3 download_size: 19374 dataset_size: 4134 configs: - config_name: default data_files: - split: train path: f2527aa0a4051632/train-* ---

Forceless/Zenodo10K

Forceless

--- dataset_info: features: - name: filename dtype: string - name: size dtype: int64 - name: url dtype: string - name: license dtype: string - name: title dtype: string - name: created dtype: string - name: updated dtype: string - name: doi dtype: string - name: checksum dtype: string splits: - name: pptx num_bytes: 3925161 num_examples: 10448 download_size: 2028492 dataset_size: 3925161 configs: - config_name: default data_files: - split: pptx path: data/pptx-* --- # PPTAgent/Zenodo10K This is the dataset used in [PPTAgent](https://arxiv.org/abs/2501.03936), crawled from [zenodo](http://zenodo.org). To the best of our knowledge, it is the **largest presentation dataset** currently available, comprising over 10,000 **PowerPoint (.pptx)** files, all distributed under a clear and compliant license. For more information, please visit our [github repo](https://github.com/icip-cas/PPTAgent). ```python dirname = f"zenodo-pptx/pptx/{task['license']}/{task['created'][:4]}/" basename = f"{task['checksum'][4:]}-{task['filename']}" filepath = dirname + basename try: open('/tmp/'+basename,'wb').close() except: filepath = dirname + basename[:240] + ".pptx" ``` ## Citation If you find this project helpful, please use the following to cite it: ```bibtex @misc{zheng2025pptagentgeneratingevaluatingpresentations, title={PPTAgent: Generating and Evaluating Presentations Beyond Text-to-Slides}, author={Hao Zheng and Xinyan Guan and Hao Kong and Jia Zheng and Hongyu Lin and Yaojie Lu and Ben He and Xianpei Han and Le Sun}, year={2025}, eprint={2501.03936}, archivePrefix={arXiv}, primaryClass={cs.AI}, url={https://arxiv.org/abs/2501.03936}, } ```

google-research-datasets/nq_open

google-research-datasets

--- annotations_creators: - expert-generated language_creators: - other language: - en license: - cc-by-sa-3.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - extended|natural_questions task_categories: - question-answering task_ids: - open-domain-qa pretty_name: NQ-Open dataset_info: config_name: nq_open features: - name: question dtype: string - name: answer sequence: string splits: - name: train num_bytes: 6651236 num_examples: 87925 - name: validation num_bytes: 313829 num_examples: 3610 download_size: 4678245 dataset_size: 6965065 configs: - config_name: nq_open data_files: - split: train path: nq_open/train-* - split: validation path: nq_open/validation-* default: true --- # Dataset Card for nq_open ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://efficientqa.github.io/ - **Repository:** https://github.com/google-research-datasets/natural-questions/tree/master/nq_open - **Paper:** https://www.aclweb.org/anthology/P19-1612.pdf - **Leaderboard:** https://ai.google.com/research/NaturalQuestions/efficientqa - **Point of Contact:** [Mailing List]([email protected]) ### Dataset Summary The NQ-Open task, introduced by Lee et.al. 2019, is an open domain question answering benchmark that is derived from Natural Questions. The goal is to predict an English answer string for an input English question. All questions can be answered using the contents of English Wikipedia. ### Supported Tasks and Leaderboards Open Domain Question-Answering, EfficientQA Leaderboard: https://ai.google.com/research/NaturalQuestions/efficientqa ### Languages English (`en`) ## Dataset Structure ### Data Instances ``` { "question": "names of the metropolitan municipalities in south africa", "answer": [ "Mangaung Metropolitan Municipality", "Nelson Mandela Bay Metropolitan Municipality", "eThekwini Metropolitan Municipality", "City of Tshwane Metropolitan Municipality", "City of Johannesburg Metropolitan Municipality", "Buffalo City Metropolitan Municipality", "City of Ekurhuleni Metropolitan Municipality" ] } ``` ### Data Fields - `question` - Input open domain question. - `answer` - List of possible answers to the question ### Data Splits - Train : 87925 - validation : 3610 ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization Natural Questions contains question from aggregated queries to Google Search (Kwiatkowski et al., 2019). To gather an open version of this dataset, we only keep questions with short answers and discard the given evidence document. Answers with many tokens often resemble extractive snippets rather than canonical answers, so we discard answers with more than 5 tokens. #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases Evaluating on this diverse set of question-answer pairs is crucial, because all existing datasets have inherent biases that are problematic for open domain QA systems with learned retrieval. In the Natural Questions dataset the question askers do not already know the answer. This accurately reflects a distribution of genuine information-seeking questions. However, annotators must separately find correct answers, which requires assistance from automatic tools and can introduce a moderate bias towards results from the tool. ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information All of the Natural Questions data is released under the [CC BY-SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/) license. ### Citation Information ``` @article{doi:10.1162/tacl\_a\_00276, author = {Kwiatkowski, Tom and Palomaki, Jennimaria and Redfield, Olivia and Collins, Michael and Parikh, Ankur and Alberti, Chris and Epstein, Danielle and Polosukhin, Illia and Devlin, Jacob and Lee, Kenton and Toutanova, Kristina and Jones, Llion and Kelcey, Matthew and Chang, Ming-Wei and Dai, Andrew M. and Uszkoreit, Jakob and Le, Quoc and Petrov, Slav}, title = {Natural Questions: A Benchmark for Question Answering Research}, journal = {Transactions of the Association for Computational Linguistics}, volume = {7}, number = {}, pages = {453-466}, year = {2019}, doi = {10.1162/tacl\_a\_00276}, URL = { https://doi.org/10.1162/tacl_a_00276 }, eprint = { https://doi.org/10.1162/tacl_a_00276 }, abstract = { We present the Natural Questions corpus, a question answering data set. Questions consist of real anonymized, aggregated queries issued to the Google search engine. An annotator is presented with a question along with a Wikipedia page from the top 5 search results, and annotates a long answer (typically a paragraph) and a short answer (one or more entities) if present on the page, or marks null if no long/short answer is present. The public release consists of 307,373 training examples with single annotations; 7,830 examples with 5-way annotations for development data; and a further 7,842 examples with 5-way annotated sequestered as test data. We present experiments validating quality of the data. We also describe analysis of 25-way annotations on 302 examples, giving insights into human variability on the annotation task. We introduce robust metrics for the purposes of evaluating question answering systems; demonstrate high human upper bounds on these metrics; and establish baseline results using competitive methods drawn from related literature. } } @inproceedings{lee-etal-2019-latent, title = "Latent Retrieval for Weakly Supervised Open Domain Question Answering", author = "Lee, Kenton and Chang, Ming-Wei and Toutanova, Kristina", booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2019", address = "Florence, Italy", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/P19-1612", doi = "10.18653/v1/P19-1612", pages = "6086--6096", abstract = "Recent work on open domain question answering (QA) assumes strong supervision of the supporting evidence and/or assumes a blackbox information retrieval (IR) system to retrieve evidence candidates. We argue that both are suboptimal, since gold evidence is not always available, and QA is fundamentally different from IR. We show for the first time that it is possible to jointly learn the retriever and reader from question-answer string pairs and without any IR system. In this setting, evidence retrieval from all of Wikipedia is treated as a latent variable. Since this is impractical to learn from scratch, we pre-train the retriever with an Inverse Cloze Task. We evaluate on open versions of five QA datasets. On datasets where the questioner already knows the answer, a traditional IR system such as BM25 is sufficient. On datasets where a user is genuinely seeking an answer, we show that learned retrieval is crucial, outperforming BM25 by up to 19 points in exact match.", } ``` ### Contributions Thanks to [@Nilanshrajput](https://github.com/Nilanshrajput) for adding this dataset.

japanese-asr/whisper_transcriptions.mls.wer_10.0

japanese-asr

--- dataset_info: - config_name: subset_0 features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription sequence: int64 - name: transcription/ja_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 - name: input_length dtype: int64 splits: - name: train num_bytes: 29741913577.241814 num_examples: 62101 download_size: 28406057868 dataset_size: 29741913577.241814 - config_name: subset_1 features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription sequence: int64 - name: transcription/ja_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 - name: input_length dtype: int64 splits: - name: train num_bytes: 29815585138.73427 num_examples: 62323 download_size: 28488972470 dataset_size: 29815585138.73427 - config_name: subset_10 features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription sequence: int64 - 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split: train path: subset_58/train-* - config_name: subset_59 data_files: - split: train path: subset_59/train-* - config_name: subset_6 data_files: - split: train path: subset_6/train-* - config_name: subset_60 data_files: - split: train path: subset_60/train-* - config_name: subset_61 data_files: - split: train path: subset_61/train-* - config_name: subset_62 data_files: - split: train path: subset_62/train-* - config_name: subset_63 data_files: - split: train path: subset_63/train-* - config_name: subset_64 data_files: - split: train path: subset_64/train-* - config_name: subset_65 data_files: - split: train path: subset_65/train-* - config_name: subset_66 data_files: - split: train path: subset_66/train-* - config_name: subset_67 data_files: - split: train path: subset_67/train-* - config_name: subset_68 data_files: - split: train path: subset_68/train-* - config_name: subset_69 data_files: - split: train path: subset_69/train-* - config_name: subset_7 data_files: - split: train path: subset_7/train-* - config_name: subset_70 data_files: - split: train path: subset_70/train-* - config_name: subset_71 data_files: - split: train path: subset_71/train-* - config_name: subset_72 data_files: - split: train path: subset_72/train-* - config_name: subset_73 data_files: - split: train path: subset_73/train-* - config_name: subset_74 data_files: - split: train path: subset_74/train-* - config_name: subset_75 data_files: - split: train path: subset_75/train-* - config_name: subset_76 data_files: - split: train path: subset_76/train-* - config_name: subset_77 data_files: - split: train path: subset_77/train-* - config_name: subset_78 data_files: - split: train path: subset_78/train-* - config_name: subset_79 data_files: - split: train path: subset_79/train-* - config_name: subset_8 data_files: - split: train path: subset_8/train-* - config_name: subset_80 data_files: - split: train path: subset_80/train-* - config_name: subset_81 data_files: - split: train path: subset_81/train-* - config_name: subset_82 data_files: - split: train path: subset_82/train-* - config_name: subset_83 data_files: - split: train path: subset_83/train-* - config_name: subset_84 data_files: - split: train path: subset_84/train-* - config_name: subset_85 data_files: - split: train path: subset_85/train-* - config_name: subset_86 data_files: - split: train path: subset_86/train-* - config_name: subset_87 data_files: - split: train path: subset_87/train-* - config_name: subset_88 data_files: - split: train path: subset_88/train-* - config_name: subset_89 data_files: - split: train path: subset_89/train-* - config_name: subset_9 data_files: - split: train path: subset_9/train-* - config_name: subset_90 data_files: - split: train path: subset_90/train-* - config_name: subset_91 data_files: - split: train path: subset_91/train-* - config_name: subset_92 data_files: - split: train path: subset_92/train-* - config_name: subset_93 data_files: - split: train path: subset_93/train-* - config_name: subset_94 data_files: - split: train path: subset_94/train-* - config_name: subset_95 data_files: - split: train path: subset_95/train-* - config_name: subset_96 data_files: - split: train path: subset_96/train-* - config_name: subset_97 data_files: - split: train path: subset_97/train-* - config_name: subset_98 data_files: - split: train path: subset_98/train-* - config_name: subset_99 data_files: - split: train path: subset_99/train-* ---

fixie-ai/peoples_speech

fixie-ai

--- dataset_info: - config_name: clean features: - name: id dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: duration_ms dtype: int32 - name: text dtype: string - name: continuation dtype: string splits: - name: validation num_bytes: 2511523987.692 num_examples: 18622 - name: test num_bytes: 4259695510.794 num_examples: 34898 - name: train num_bytes: 401646320552.671 num_examples: 1501271 download_size: 398922548670 dataset_size: 408417540051 - config_name: dirty_sa features: - name: id dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: duration_ms dtype: int32 - name: text dtype: string - name: continuation dtype: string splits: - name: train num_bytes: 144432442623.054 num_examples: 548014 - name: validation num_bytes: 2511524241.692 num_examples: 18622 - name: test num_bytes: 4259695588.794 num_examples: 34898 download_size: 149491764186 dataset_size: 151203662453.53998 configs: - config_name: clean data_files: - split: validation path: clean/validation-* - split: test path: clean/test-* - split: train path: data/train-* - config_name: dirty_sa data_files: - split: train path: dirty_sa/train-* - split: validation path: dirty_sa/validation-* - split: test path: dirty_sa/test-* ---

ilsp/mmlu_greek

ilsp

--- dataset_info: - config_name: abstract_algebra features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 58157 num_examples: 100 - name: validation num_bytes: 6010 num_examples: 11 - name: dev num_bytes: 2497 num_examples: 5 download_size: 0 dataset_size: 66664 - config_name: all features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 20041347 num_examples: 14042 - name: validation num_bytes: 2196992 num_examples: 1531 - name: dev num_bytes: 360807 num_examples: 285 download_size: 10333898 dataset_size: 22599146 - config_name: anatomy features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 97333 num_examples: 135 - name: validation num_bytes: 9131 num_examples: 14 - name: dev num_bytes: 2731 num_examples: 5 download_size: 67694 dataset_size: 109195 - config_name: astronomy features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 141580 num_examples: 152 - name: validation num_bytes: 15462 num_examples: 16 - name: dev num_bytes: 6380 num_examples: 5 download_size: 95251 dataset_size: 163422 - config_name: business_ethics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 101936 num_examples: 100 - name: validation num_bytes: 9096 num_examples: 11 - name: dev num_bytes: 6368 num_examples: 5 download_size: 77394 dataset_size: 117400 - config_name: clinical_knowledge features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 193539 num_examples: 265 - name: validation num_bytes: 20500 num_examples: 29 - name: dev num_bytes: 3720 num_examples: 5 download_size: 126056 dataset_size: 217759 - config_name: college_biology features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 152394 num_examples: 144 - name: validation num_bytes: 14995 num_examples: 16 - name: dev num_bytes: 4638 num_examples: 5 download_size: 105576 dataset_size: 172027 - config_name: college_chemistry features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 72251 num_examples: 100 - name: validation num_bytes: 6677 num_examples: 8 - name: dev num_bytes: 3862 num_examples: 5 download_size: 61210 dataset_size: 82790 - config_name: college_computer_science features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 135321 num_examples: 100 - name: validation num_bytes: 15037 num_examples: 11 - name: dev num_bytes: 8606 num_examples: 5 download_size: 101342 dataset_size: 158964 - config_name: college_mathematics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 74448 num_examples: 100 - name: validation num_bytes: 8274 num_examples: 11 - name: dev num_bytes: 4276 num_examples: 5 download_size: 63556 dataset_size: 86998 - config_name: college_medicine features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 251805 num_examples: 173 - name: validation num_bytes: 24431 num_examples: 22 - name: dev num_bytes: 5031 num_examples: 5 download_size: 144635 dataset_size: 281267 - config_name: college_physics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 90708 num_examples: 102 - name: validation num_bytes: 10367 num_examples: 11 - name: dev num_bytes: 4139 num_examples: 5 download_size: 68341 dataset_size: 105214 - config_name: computer_security features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 86922 num_examples: 100 - name: validation num_bytes: 14003 num_examples: 11 - name: dev num_bytes: 3445 num_examples: 5 download_size: 75244 dataset_size: 104370 - config_name: conceptual_physics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 127706 num_examples: 235 - name: validation num_bytes: 14286 num_examples: 26 - name: dev num_bytes: 2978 num_examples: 5 download_size: 82813 dataset_size: 144970 - config_name: econometrics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 136916 num_examples: 114 - name: validation num_bytes: 14730 num_examples: 12 - name: dev num_bytes: 4794 num_examples: 5 download_size: 86025 dataset_size: 156440 - config_name: electrical_engineering features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 80296 num_examples: 145 - name: validation num_bytes: 9138 num_examples: 16 - name: dev num_bytes: 2824 num_examples: 5 download_size: 62008 dataset_size: 92258 - config_name: elementary_mathematics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 211831 num_examples: 378 - name: validation num_bytes: 27305 num_examples: 41 - name: dev num_bytes: 4252 num_examples: 5 download_size: 131272 dataset_size: 243388 - config_name: formal_logic features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 146101 num_examples: 126 - name: validation num_bytes: 18160 num_examples: 14 - name: dev num_bytes: 4917 num_examples: 5 download_size: 77094 dataset_size: 169178 - config_name: global_facts features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 55953 num_examples: 100 - name: validation num_bytes: 5672 num_examples: 10 - name: dev num_bytes: 3547 num_examples: 5 download_size: 0 dataset_size: 65172 - config_name: high_school_biology features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 338155 num_examples: 310 - name: validation num_bytes: 33555 num_examples: 32 - name: dev num_bytes: 4992 num_examples: 5 download_size: 200936 dataset_size: 376702 - config_name: high_school_chemistry features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 170771 num_examples: 203 - name: validation num_bytes: 20157 num_examples: 22 - name: dev num_bytes: 3387 num_examples: 5 download_size: 108321 dataset_size: 194315 - config_name: high_school_computer_science features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 139128 num_examples: 100 - name: validation num_bytes: 10800 num_examples: 9 - name: dev num_bytes: 9269 num_examples: 5 download_size: 99359 dataset_size: 159197 - config_name: high_school_european_history features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 799080 num_examples: 165 - name: validation num_bytes: 88740 num_examples: 18 - name: dev num_bytes: 34585 num_examples: 5 download_size: 503439 dataset_size: 922405 - config_name: high_school_geography features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 132655 num_examples: 198 - name: validation num_bytes: 13612 num_examples: 22 - name: dev num_bytes: 4597 num_examples: 5 download_size: 90939 dataset_size: 150864 - config_name: high_school_government_and_politics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 215224 num_examples: 193 - name: validation num_bytes: 22888 num_examples: 21 - name: dev num_bytes: 5640 num_examples: 5 download_size: 132695 dataset_size: 243752 - config_name: high_school_macroeconomics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 374553 num_examples: 390 - name: validation num_bytes: 41817 num_examples: 43 - name: dev num_bytes: 4310 num_examples: 5 download_size: 177813 dataset_size: 420680 - config_name: high_school_mathematics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 161023 num_examples: 270 - name: validation num_bytes: 17224 num_examples: 29 - name: dev num_bytes: 3682 num_examples: 5 download_size: 105683 dataset_size: 181929 - config_name: high_school_microeconomics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 241816 num_examples: 238 - name: validation num_bytes: 24317 num_examples: 26 - name: dev num_bytes: 4029 num_examples: 5 download_size: 125789 dataset_size: 270162 - config_name: high_school_physics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 175856 num_examples: 151 - name: validation num_bytes: 19899 num_examples: 17 - name: dev num_bytes: 4348 num_examples: 5 download_size: 109639 dataset_size: 200103 - config_name: high_school_psychology features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 494955 num_examples: 545 - name: validation num_bytes: 53743 num_examples: 60 - name: dev num_bytes: 5900 num_examples: 5 download_size: 285730 dataset_size: 554598 - config_name: high_school_statistics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 333736 num_examples: 216 - name: validation num_bytes: 30252 num_examples: 23 - name: dev num_bytes: 7320 num_examples: 5 download_size: 191017 dataset_size: 371308 - config_name: high_school_us_history features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 883614 num_examples: 204 - name: validation num_bytes: 93694 num_examples: 22 - name: dev num_bytes: 26282 num_examples: 5 download_size: 533320 dataset_size: 1003590 - config_name: high_school_world_history features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 1126143 num_examples: 237 - name: validation num_bytes: 135245 num_examples: 26 - name: dev num_bytes: 14589 num_examples: 5 download_size: 662773 dataset_size: 1275977 - config_name: human_aging features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 145275 num_examples: 223 - name: validation num_bytes: 15038 num_examples: 23 - name: dev num_bytes: 3062 num_examples: 5 download_size: 99856 dataset_size: 163375 - config_name: human_sexuality features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 100379 num_examples: 131 - name: validation num_bytes: 7585 num_examples: 12 - name: dev num_bytes: 3504 num_examples: 5 download_size: 74540 dataset_size: 111468 - config_name: international_law features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 162013 num_examples: 121 - name: validation num_bytes: 18937 num_examples: 13 - name: dev num_bytes: 7290 num_examples: 5 download_size: 0 dataset_size: 188240 - config_name: jurisprudence features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 102393 num_examples: 108 - name: validation num_bytes: 11049 num_examples: 11 - name: dev num_bytes: 3754 num_examples: 5 download_size: 21545 dataset_size: 117196 - config_name: logical_fallacies features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 153973 num_examples: 163 - name: validation num_bytes: 15857 num_examples: 18 - name: dev num_bytes: 4919 num_examples: 5 download_size: 82298 dataset_size: 174749 - config_name: machine_learning features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 102745 num_examples: 112 - name: validation num_bytes: 9797 num_examples: 11 - name: dev num_bytes: 7448 num_examples: 5 download_size: 70870 dataset_size: 119990 - config_name: management features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 63772 num_examples: 103 - name: validation num_bytes: 5671 num_examples: 11 - name: dev num_bytes: 2677 num_examples: 5 download_size: 52323 dataset_size: 72120 - config_name: marketing features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 191635 num_examples: 234 - name: validation num_bytes: 22377 num_examples: 25 - name: dev num_bytes: 4734 num_examples: 5 download_size: 122877 dataset_size: 218746 - config_name: medical_genetics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 64177 num_examples: 100 - name: validation num_bytes: 9298 num_examples: 11 - name: dev num_bytes: 3405 num_examples: 5 download_size: 58337 dataset_size: 76880 - config_name: miscellaneous features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 443155 num_examples: 783 - name: validation num_bytes: 42990 num_examples: 86 - name: dev num_bytes: 1877 num_examples: 5 download_size: 283087 dataset_size: 488022 - config_name: moral_disputes features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 332269 num_examples: 346 - name: validation num_bytes: 38501 num_examples: 38 - name: dev num_bytes: 5222 num_examples: 5 download_size: 193075 dataset_size: 375992 - config_name: moral_scenarios features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 1061634 num_examples: 895 - name: validation num_bytes: 120664 num_examples: 100 - name: dev num_bytes: 5816 num_examples: 5 download_size: 283716 dataset_size: 1188114 - config_name: nutrition features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 281680 num_examples: 306 - name: validation num_bytes: 25350 num_examples: 33 - name: dev num_bytes: 6423 num_examples: 5 download_size: 168790 dataset_size: 313453 - config_name: philosophy features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 240333 num_examples: 311 - name: validation num_bytes: 27480 num_examples: 34 - name: dev num_bytes: 2986 num_examples: 5 download_size: 153970 dataset_size: 270799 - config_name: prehistory features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 267644 num_examples: 324 - name: validation num_bytes: 30414 num_examples: 35 - name: dev num_bytes: 5577 num_examples: 5 download_size: 172053 dataset_size: 303635 - config_name: professional_accounting features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 377751 num_examples: 282 - name: validation num_bytes: 42879 num_examples: 31 - name: dev num_bytes: 6331 num_examples: 5 download_size: 228950 dataset_size: 426961 - config_name: professional_law features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 5612166 num_examples: 1534 - name: validation num_bytes: 604980 num_examples: 170 - name: dev num_bytes: 19825 num_examples: 5 download_size: 3065337 dataset_size: 6236971 - config_name: professional_medicine features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 639421 num_examples: 272 - name: validation num_bytes: 70186 num_examples: 31 - name: dev num_bytes: 11017 num_examples: 5 download_size: 391893 dataset_size: 720624 - config_name: professional_psychology features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 687869 num_examples: 612 - name: validation num_bytes: 87912 num_examples: 69 - name: dev num_bytes: 6693 num_examples: 5 download_size: 405705 dataset_size: 782474 - config_name: public_relations features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 89435 num_examples: 110 - name: validation num_bytes: 14174 num_examples: 12 - name: dev num_bytes: 4718 num_examples: 5 download_size: 0 dataset_size: 108327 - config_name: security_studies features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 632255 num_examples: 245 - name: validation num_bytes: 69100 num_examples: 27 - name: dev num_bytes: 16171 num_examples: 5 download_size: 0 dataset_size: 717526 - config_name: sociology features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 204018 num_examples: 201 - name: validation num_bytes: 22531 num_examples: 22 - name: dev num_bytes: 5054 num_examples: 5 download_size: 9676 dataset_size: 231603 - config_name: us_foreign_policy features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 89965 num_examples: 100 - name: validation num_bytes: 10270 num_examples: 11 - name: dev num_bytes: 5111 num_examples: 5 download_size: 68974 dataset_size: 105346 - config_name: virology features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 116211 num_examples: 166 - name: validation num_bytes: 16273 num_examples: 18 - name: dev num_bytes: 3185 num_examples: 5 download_size: 96586 dataset_size: 135669 - config_name: world_religions features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: orig_question dtype: string - name: orig_subject dtype: string - name: orig_choices sequence: string splits: - name: test num_bytes: 77273 num_examples: 171 - name: validation num_bytes: 8462 num_examples: 19 - name: dev num_bytes: 2073 num_examples: 5 download_size: 61169 dataset_size: 87808 configs: - config_name: abstract_algebra data_files: - split: test path: abstract_algebra/test-* - split: validation path: abstract_algebra/validation-* - split: dev path: abstract_algebra/dev-* - config_name: all data_files: - split: test path: all/test-* - split: validation path: all/validation-* - split: dev path: all/dev-* - config_name: anatomy data_files: - split: test path: anatomy/test-* - split: validation path: anatomy/validation-* - split: dev path: anatomy/dev-* - config_name: astronomy data_files: - split: test path: astronomy/test-* - split: validation path: astronomy/validation-* - split: dev path: astronomy/dev-* - config_name: business_ethics data_files: - split: test path: business_ethics/test-* - split: validation path: business_ethics/validation-* - split: dev path: business_ethics/dev-* - config_name: clinical_knowledge data_files: - split: test path: clinical_knowledge/test-* - split: validation path: clinical_knowledge/validation-* - split: dev path: clinical_knowledge/dev-* - config_name: college_biology data_files: - split: test path: college_biology/test-* - split: validation path: college_biology/validation-* - split: dev path: college_biology/dev-* - config_name: college_chemistry data_files: - split: test path: college_chemistry/test-* - split: validation path: college_chemistry/validation-* - split: dev path: college_chemistry/dev-* - config_name: college_computer_science data_files: - split: test path: college_computer_science/test-* - split: validation path: college_computer_science/validation-* - split: dev path: college_computer_science/dev-* - config_name: college_mathematics data_files: - split: test path: college_mathematics/test-* - split: validation path: college_mathematics/validation-* - split: dev path: college_mathematics/dev-* - config_name: college_medicine data_files: - split: test path: college_medicine/test-* - split: validation path: college_medicine/validation-* - split: dev path: college_medicine/dev-* - config_name: college_physics data_files: - split: test path: college_physics/test-* - split: validation path: college_physics/validation-* - split: dev path: college_physics/dev-* - config_name: computer_security data_files: - split: test path: computer_security/test-* - split: validation path: computer_security/validation-* - split: dev path: computer_security/dev-* - config_name: conceptual_physics data_files: - split: test path: conceptual_physics/test-* - split: validation path: conceptual_physics/validation-* - split: dev path: conceptual_physics/dev-* - config_name: econometrics data_files: - split: test path: econometrics/test-* - split: validation path: econometrics/validation-* - split: dev path: econometrics/dev-* - config_name: electrical_engineering data_files: - split: test path: electrical_engineering/test-* - split: validation path: electrical_engineering/validation-* - split: dev path: electrical_engineering/dev-* - config_name: elementary_mathematics data_files: - split: test path: elementary_mathematics/test-* - split: validation path: elementary_mathematics/validation-* - split: dev path: elementary_mathematics/dev-* - config_name: formal_logic data_files: - split: test path: formal_logic/test-* - split: validation path: formal_logic/validation-* - split: dev path: formal_logic/dev-* - config_name: global_facts data_files: - split: test path: global_facts/test-* - split: validation path: global_facts/validation-* - split: dev path: global_facts/dev-* - config_name: high_school_biology data_files: - split: test path: high_school_biology/test-* - split: validation path: high_school_biology/validation-* - split: dev path: high_school_biology/dev-* - config_name: high_school_chemistry data_files: - split: test path: high_school_chemistry/test-* - split: validation path: high_school_chemistry/validation-* - split: dev path: high_school_chemistry/dev-* - config_name: high_school_computer_science data_files: - split: test path: high_school_computer_science/test-* - split: validation path: high_school_computer_science/validation-* - split: dev path: high_school_computer_science/dev-* - config_name: high_school_european_history data_files: - split: test path: high_school_european_history/test-* - split: validation path: high_school_european_history/validation-* - split: dev path: high_school_european_history/dev-* - config_name: high_school_geography data_files: - split: test path: high_school_geography/test-* - split: validation path: high_school_geography/validation-* - split: dev path: high_school_geography/dev-* - config_name: high_school_government_and_politics data_files: - split: test path: high_school_government_and_politics/test-* - split: validation path: high_school_government_and_politics/validation-* - split: dev path: high_school_government_and_politics/dev-* - config_name: high_school_macroeconomics data_files: - split: test path: high_school_macroeconomics/test-* - split: validation path: high_school_macroeconomics/validation-* - split: dev path: high_school_macroeconomics/dev-* - config_name: high_school_mathematics data_files: - split: test path: high_school_mathematics/test-* - split: validation path: high_school_mathematics/validation-* - split: dev path: high_school_mathematics/dev-* - config_name: high_school_microeconomics data_files: - split: test path: high_school_microeconomics/test-* - split: validation path: high_school_microeconomics/validation-* - split: dev path: high_school_microeconomics/dev-* - config_name: high_school_physics data_files: - split: test path: high_school_physics/test-* - split: validation path: high_school_physics/validation-* - split: dev path: high_school_physics/dev-* - config_name: high_school_psychology data_files: - split: test path: high_school_psychology/test-* - split: validation path: high_school_psychology/validation-* - split: dev path: high_school_psychology/dev-* - config_name: high_school_statistics data_files: - split: test path: high_school_statistics/test-* - split: validation path: high_school_statistics/validation-* - split: dev path: high_school_statistics/dev-* - config_name: high_school_us_history data_files: - split: test path: high_school_us_history/test-* - split: validation path: high_school_us_history/validation-* - split: dev path: high_school_us_history/dev-* - config_name: high_school_world_history data_files: - split: test path: high_school_world_history/test-* - split: validation path: high_school_world_history/validation-* - split: dev path: high_school_world_history/dev-* - config_name: human_aging data_files: - split: test path: human_aging/test-* - split: validation path: human_aging/validation-* - split: dev path: human_aging/dev-* - config_name: human_sexuality data_files: - split: test path: human_sexuality/test-* - split: validation path: human_sexuality/validation-* - split: dev path: human_sexuality/dev-* - config_name: international_law data_files: - split: test path: international_law/test-* - split: validation path: international_law/validation-* - split: dev path: international_law/dev-* - config_name: jurisprudence data_files: - split: test path: jurisprudence/test-* - split: validation path: jurisprudence/validation-* - split: dev path: jurisprudence/dev-* - config_name: logical_fallacies data_files: - split: test path: logical_fallacies/test-* - split: validation path: logical_fallacies/validation-* - split: dev path: logical_fallacies/dev-* - config_name: machine_learning data_files: - split: test path: machine_learning/test-* - split: validation path: machine_learning/validation-* - split: dev path: machine_learning/dev-* - config_name: management data_files: - split: test path: management/test-* - split: validation path: management/validation-* - split: dev path: management/dev-* - config_name: marketing data_files: - split: test path: marketing/test-* - split: validation path: marketing/validation-* - split: dev path: marketing/dev-* - config_name: medical_genetics data_files: - split: test path: medical_genetics/test-* - split: validation path: medical_genetics/validation-* - split: dev path: medical_genetics/dev-* - config_name: miscellaneous data_files: - split: test path: miscellaneous/test-* - split: validation path: miscellaneous/validation-* - split: dev path: miscellaneous/dev-* - config_name: moral_disputes data_files: - split: test path: moral_disputes/test-* - split: validation path: moral_disputes/validation-* - split: dev path: moral_disputes/dev-* - config_name: moral_scenarios data_files: - split: test path: moral_scenarios/test-* - split: validation path: moral_scenarios/validation-* - split: dev path: moral_scenarios/dev-* - config_name: nutrition data_files: - split: test path: nutrition/test-* - split: validation path: nutrition/validation-* - split: dev path: nutrition/dev-* - config_name: philosophy data_files: - split: test path: philosophy/test-* - split: validation path: philosophy/validation-* - split: dev path: philosophy/dev-* - config_name: prehistory data_files: - split: test path: prehistory/test-* - split: validation path: prehistory/validation-* - split: dev path: prehistory/dev-* - config_name: professional_accounting data_files: - split: test path: professional_accounting/test-* - split: validation path: professional_accounting/validation-* - split: dev path: professional_accounting/dev-* - config_name: professional_law data_files: - split: test path: professional_law/test-* - split: validation path: professional_law/validation-* - split: dev path: professional_law/dev-* - config_name: professional_medicine data_files: - split: test path: professional_medicine/test-* - split: validation path: professional_medicine/validation-* - split: dev path: professional_medicine/dev-* - config_name: professional_psychology data_files: - split: test path: professional_psychology/test-* - split: validation path: professional_psychology/validation-* - split: dev path: professional_psychology/dev-* - config_name: public_relations data_files: - split: test path: public_relations/test-* - split: validation path: public_relations/validation-* - split: dev path: public_relations/dev-* - config_name: security_studies data_files: - split: test path: security_studies/test-* - split: validation path: security_studies/validation-* - split: dev path: security_studies/dev-* - config_name: sociology data_files: - split: test path: sociology/test-* - split: validation path: sociology/validation-* - split: dev path: sociology/dev-* - config_name: us_foreign_policy data_files: - split: test path: us_foreign_policy/test-* - split: validation path: us_foreign_policy/validation-* - split: dev path: us_foreign_policy/dev-* - config_name: virology data_files: - split: test path: virology/test-* - split: validation path: virology/validation-* - split: dev path: virology/dev-* - config_name: world_religions data_files: - split: test path: world_religions/test-* - split: validation path: world_religions/validation-* - split: dev path: world_religions/dev-* --- # Dataset Card for MMLU Greek The MMLU Greek dataset is a set of 15858 examples from the MMLU dataset [available from here and here], machine-translated into Greek. The original dataset consists of multiple-choice questions from 57 tasks including elementary mathematics, US history, computer science, law, etc. ## Dataset Details ### Dataset Description - **Curated by:** ILSP/Athena RC - **Language(s) (NLP):** el - **License:** cc-by-nc-sa-4.0 ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> This dataset is the result of machine translation. ## Dataset Card Contact https://www.athenarc.gr/en/ilsp

Helsinki-NLP/news_commentary

Helsinki-NLP

--- annotations_creators: - found language_creators: - found language: - ar - cs - de - en - es - fr - it - ja - nl - pt - ru - zh license: - unknown multilinguality: - multilingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - translation task_ids: [] pretty_name: News-Commentary dataset_info: - config_name: ar-cs features: - name: id dtype: string - name: translation dtype: translation: languages: - ar - cs splits: - name: train num_bytes: 51546388 num_examples: 52128 download_size: 28342257 dataset_size: 51546388 - config_name: ar-de features: - name: id dtype: string - name: translation dtype: translation: languages: - ar - de splits: - name: train num_bytes: 69681335 num_examples: 68916 download_size: 37202855 dataset_size: 69681335 - config_name: ar-en features: - name: id dtype: string - name: translation dtype: translation: languages: - ar - en splits: - name: train num_bytes: 80655165 num_examples: 83187 download_size: 42807620 dataset_size: 80655165 - config_name: ar-es features: - name: id dtype: string - name: translation dtype: translation: languages: - ar - es splits: - name: train num_bytes: 79255889 num_examples: 78074 download_size: 42005622 dataset_size: 79255889 - config_name: ar-fr features: - name: id dtype: string - name: translation dtype: translation: languages: - ar - fr splits: - name: train num_bytes: 71034977 num_examples: 69157 download_size: 37543169 dataset_size: 71034977 - config_name: ar-it features: - name: id dtype: string - name: translation dtype: translation: languages: - ar - it splits: - name: train num_bytes: 17413426 num_examples: 17227 download_size: 9186088 dataset_size: 17413426 - config_name: ar-ja features: - name: id dtype: string - name: translation dtype: translation: languages: - ar - ja splits: - name: train num_bytes: 661980 num_examples: 569 download_size: 354690 dataset_size: 661980 - config_name: ar-nl features: - name: id dtype: string - name: translation dtype: translation: languages: - ar - nl splits: - name: train num_bytes: 9054122 num_examples: 9047 download_size: 4808380 dataset_size: 9054122 - config_name: ar-pt features: - name: id dtype: string - name: translation dtype: translation: languages: - ar - pt splits: - name: train num_bytes: 11340050 num_examples: 11433 download_size: 6098489 dataset_size: 11340050 - config_name: ar-ru features: - name: id dtype: string - name: translation dtype: translation: languages: - ar - ru splits: - name: train num_bytes: 105804195 num_examples: 84455 download_size: 52467607 dataset_size: 105804195 - config_name: ar-zh features: - name: id dtype: string - name: translation dtype: translation: languages: - ar - zh splits: - name: train num_bytes: 65483120 num_examples: 66021 download_size: 36527030 dataset_size: 65483120 - config_name: cs-de features: - name: id dtype: string - name: translation dtype: translation: languages: - cs - de splits: - name: train num_bytes: 57470583 num_examples: 172706 download_size: 37013107 dataset_size: 57470583 - config_name: cs-en features: - name: id dtype: string - name: translation dtype: translation: languages: - cs - en splits: - name: train num_bytes: 54487658 num_examples: 177278 download_size: 35385370 dataset_size: 54487658 - config_name: cs-es features: - name: id dtype: string - name: translation dtype: translation: languages: - cs - es splits: - name: train num_bytes: 56794609 num_examples: 170489 download_size: 36325813 dataset_size: 56794609 - config_name: cs-fr features: - name: id dtype: string - name: translation dtype: translation: languages: - cs - fr splits: - name: train num_bytes: 50364657 num_examples: 148578 download_size: 31970167 dataset_size: 50364657 - config_name: cs-it features: - name: id dtype: string - name: translation dtype: translation: languages: - cs - it splits: - name: train num_bytes: 10441797 num_examples: 30547 download_size: 6651753 dataset_size: 10441797 - config_name: cs-ja features: - name: id dtype: string - name: translation dtype: translation: languages: - cs - ja splits: - name: train num_bytes: 487890 num_examples: 622 download_size: 304917 dataset_size: 487890 - config_name: cs-nl features: - name: id dtype: string - name: translation dtype: translation: languages: - cs - nl splits: - name: train num_bytes: 5860952 num_examples: 17358 download_size: 3727739 dataset_size: 5860952 - config_name: cs-pt features: - name: id dtype: string - name: translation dtype: translation: languages: - cs - pt splits: - name: train num_bytes: 6183701 num_examples: 18356 download_size: 3984228 dataset_size: 6183701 - config_name: cs-ru features: - name: id dtype: string - name: translation dtype: translation: languages: - cs - ru splits: - name: train num_bytes: 71185491 num_examples: 161133 download_size: 40217853 dataset_size: 71185491 - config_name: cs-zh features: - name: id dtype: string - name: translation dtype: translation: languages: - cs - zh splits: - name: train num_bytes: 29971132 num_examples: 45424 download_size: 20270691 dataset_size: 29971132 - config_name: de-en features: - name: id dtype: string - name: translation dtype: translation: languages: - de - en splits: - name: train num_bytes: 73085175 num_examples: 223153 download_size: 45240694 dataset_size: 73085175 - config_name: de-es features: - name: id dtype: string - name: translation dtype: translation: languages: - de - es splits: - name: train num_bytes: 74708488 num_examples: 209839 download_size: 45574007 dataset_size: 74708488 - config_name: de-fr features: - name: id dtype: string - name: translation dtype: translation: languages: - de - fr splits: - name: train num_bytes: 67083671 num_examples: 185442 download_size: 40685965 dataset_size: 67083671 - config_name: de-it features: - name: id dtype: string - name: translation dtype: translation: languages: - de - it splits: - name: train num_bytes: 13993406 num_examples: 38961 download_size: 8509324 dataset_size: 13993406 - config_name: de-ja features: - name: id dtype: string - name: translation dtype: translation: languages: - de - ja splits: - name: train num_bytes: 465563 num_examples: 582 download_size: 281101 dataset_size: 465563 - config_name: de-nl features: - name: id dtype: string - name: translation dtype: translation: languages: - de - nl splits: - name: train num_bytes: 7645529 num_examples: 21439 download_size: 4664824 dataset_size: 7645529 - config_name: de-pt features: - name: id dtype: string - name: translation dtype: translation: languages: - de - pt splits: - name: train num_bytes: 7699047 num_examples: 21884 download_size: 4755247 dataset_size: 7699047 - config_name: de-ru features: - name: id dtype: string - name: translation dtype: translation: languages: - de - ru splits: - name: train num_bytes: 81811798 num_examples: 175905 download_size: 44732705 dataset_size: 81811798 - config_name: de-zh features: - name: id dtype: string - name: translation dtype: translation: languages: - de - zh splits: - name: train num_bytes: 39044632 num_examples: 59020 download_size: 25362199 dataset_size: 39044632 - config_name: en-es features: - name: id dtype: string - name: translation dtype: translation: languages: - en - es splits: - name: train num_bytes: 78600501 num_examples: 238872 download_size: 48099801 dataset_size: 78600501 - config_name: en-fr features: - name: id dtype: string - name: translation dtype: translation: languages: - en - fr splits: - name: train num_bytes: 70339762 num_examples: 209479 download_size: 42791798 dataset_size: 70339762 - config_name: en-it features: - name: id dtype: string - name: translation dtype: translation: languages: - en - it splits: - name: train num_bytes: 14213912 num_examples: 40009 download_size: 8519809 dataset_size: 14213912 - config_name: en-ja features: - name: id dtype: string - name: translation dtype: translation: languages: - en - ja splits: - name: train num_bytes: 485472 num_examples: 637 download_size: 292084 dataset_size: 485472 - config_name: en-nl features: - name: id dtype: string - name: translation dtype: translation: languages: - en - nl splits: - name: train num_bytes: 7316575 num_examples: 19399 download_size: 4313377 dataset_size: 7316575 - config_name: en-pt features: - name: id dtype: string - name: translation dtype: translation: languages: - en - pt splits: - name: train num_bytes: 9238783 num_examples: 25929 download_size: 5612678 dataset_size: 9238783 - config_name: en-ru features: - name: id dtype: string - name: translation dtype: translation: languages: - en - ru splits: - name: train num_bytes: 83282240 num_examples: 190104 download_size: 45349681 dataset_size: 83282240 - config_name: en-zh features: - name: id dtype: string - name: translation dtype: translation: languages: - en - zh splits: - name: train num_bytes: 44596003 num_examples: 69206 download_size: 28997427 dataset_size: 44596003 - config_name: es-fr features: - name: id dtype: string - name: translation dtype: translation: languages: - es - fr splits: - name: train num_bytes: 71025693 num_examples: 195241 download_size: 42650193 dataset_size: 71025693 - config_name: es-it features: - name: id dtype: string - name: translation dtype: translation: languages: - es - it splits: - name: train num_bytes: 15139576 num_examples: 41497 download_size: 9097532 dataset_size: 15139576 - config_name: es-ja features: - name: id dtype: string - name: translation dtype: translation: languages: - es - ja splits: - name: train num_bytes: 484451 num_examples: 602 download_size: 289298 dataset_size: 484451 - config_name: es-nl features: - name: id dtype: string - name: translation dtype: translation: languages: - es - nl splits: - name: train num_bytes: 7560087 num_examples: 21012 download_size: 4572049 dataset_size: 7560087 - config_name: es-pt features: - name: id dtype: string - name: translation dtype: translation: languages: - es - pt splits: - name: train num_bytes: 9195649 num_examples: 25551 download_size: 5633226 dataset_size: 9195649 - config_name: es-ru features: - name: id dtype: string - name: translation dtype: translation: languages: - es - ru splits: - name: train num_bytes: 84345622 num_examples: 180217 download_size: 45710609 dataset_size: 84345622 - config_name: es-zh features: - name: id dtype: string - name: translation dtype: translation: languages: - es - zh splits: - name: train num_bytes: 43939929 num_examples: 65424 download_size: 28264415 dataset_size: 43939929 - config_name: fr-it features: - name: id dtype: string - name: translation dtype: translation: languages: - fr - it splits: - name: train num_bytes: 14216031 num_examples: 38485 download_size: 8499047 dataset_size: 14216031 - config_name: fr-ja features: - name: id dtype: string - name: translation dtype: translation: languages: - fr - ja splits: - name: train num_bytes: 418176 num_examples: 519 download_size: 251240 dataset_size: 418176 - config_name: fr-nl features: - name: id dtype: string - name: translation dtype: translation: languages: - fr - nl splits: - name: train num_bytes: 7603467 num_examples: 20898 download_size: 4553502 dataset_size: 7603467 - config_name: fr-pt features: - name: id dtype: string - name: translation dtype: translation: languages: - fr - pt splits: - name: train num_bytes: 9261133 num_examples: 25642 download_size: 5614816 dataset_size: 9261133 - config_name: fr-ru features: - name: id dtype: string - name: translation dtype: translation: languages: - fr - ru splits: - name: train num_bytes: 75967049 num_examples: 160740 download_size: 41078195 dataset_size: 75967049 - config_name: fr-zh features: - name: id dtype: string - name: translation dtype: translation: languages: - fr - zh splits: - name: train num_bytes: 40143999 num_examples: 59060 download_size: 25753128 dataset_size: 40143999 - config_name: it-nl features: - name: id dtype: string - name: translation dtype: translation: languages: - it - nl splits: - name: train num_bytes: 5380888 num_examples: 15428 download_size: 3279009 dataset_size: 5380888 - config_name: it-pt features: - name: id dtype: string - name: translation dtype: translation: languages: - it - pt splits: - name: train num_bytes: 3988546 num_examples: 11407 download_size: 2432377 dataset_size: 3988546 - config_name: it-ru features: - name: id dtype: string - name: translation dtype: translation: languages: - it - ru splits: - name: train num_bytes: 12915037 num_examples: 27267 download_size: 7009784 dataset_size: 12915037 - config_name: it-zh features: - name: id dtype: string - name: translation dtype: translation: languages: - it - zh splits: - name: train num_bytes: 9676732 num_examples: 14652 download_size: 6219158 dataset_size: 9676732 - config_name: ja-ru features: - name: id dtype: string - name: translation dtype: translation: languages: - ja - ru splits: - name: train num_bytes: 596154 num_examples: 586 download_size: 324916 dataset_size: 596154 - config_name: ja-zh features: - name: id dtype: string - name: translation dtype: translation: languages: - ja - zh splits: - name: train num_bytes: 462673 num_examples: 570 download_size: 290801 dataset_size: 462673 - config_name: nl-pt features: - name: id dtype: string - name: translation dtype: translation: languages: - nl - pt splits: - name: train num_bytes: 3612315 num_examples: 10598 download_size: 2204974 dataset_size: 3612315 - config_name: nl-ru features: - name: id dtype: string - name: translation dtype: translation: languages: - nl - ru splits: - name: train num_bytes: 8933781 num_examples: 19112 download_size: 4857132 dataset_size: 8933781 - config_name: nl-zh features: - name: id dtype: string - name: translation dtype: translation: languages: - nl - zh splits: - name: train num_bytes: 5509058 num_examples: 8433 download_size: 3573395 dataset_size: 5509058 - config_name: pt-ru features: - name: id dtype: string - name: translation dtype: translation: languages: - pt - ru splits: - name: train num_bytes: 8645451 num_examples: 18458 download_size: 4739066 dataset_size: 8645451 - config_name: pt-zh features: - name: id dtype: string - name: translation dtype: translation: languages: - pt - zh splits: - name: train num_bytes: 7152750 num_examples: 10873 download_size: 4668616 dataset_size: 7152750 - config_name: ru-zh features: - name: id dtype: string - name: translation dtype: translation: languages: - ru - zh splits: - name: train num_bytes: 43112764 num_examples: 47687 download_size: 24587160 dataset_size: 43112764 configs: - config_name: ar-cs data_files: - split: train path: ar-cs/train-* - config_name: ar-de data_files: - split: train path: ar-de/train-* - config_name: ar-en data_files: - split: train path: ar-en/train-* - config_name: ar-es data_files: - split: train path: ar-es/train-* - config_name: ar-fr data_files: - split: train path: ar-fr/train-* - config_name: ar-it data_files: - split: train path: ar-it/train-* - config_name: ar-ja data_files: - split: train path: ar-ja/train-* - config_name: ar-nl data_files: - split: train path: ar-nl/train-* - config_name: ar-pt data_files: - split: train path: ar-pt/train-* - config_name: ar-ru data_files: - split: train path: ar-ru/train-* - config_name: ar-zh data_files: - split: train path: ar-zh/train-* - config_name: cs-de data_files: - split: train path: cs-de/train-* - config_name: cs-en data_files: - split: train path: cs-en/train-* - config_name: cs-es data_files: - split: train path: cs-es/train-* - config_name: cs-fr data_files: - split: train path: cs-fr/train-* - config_name: cs-it data_files: - split: train path: cs-it/train-* - config_name: cs-ja data_files: - split: train path: cs-ja/train-* - config_name: cs-nl data_files: - split: train path: cs-nl/train-* - config_name: cs-pt data_files: - split: train path: cs-pt/train-* - config_name: cs-ru data_files: - split: train path: cs-ru/train-* - config_name: cs-zh data_files: - split: train path: cs-zh/train-* - config_name: de-en data_files: - split: train path: de-en/train-* - config_name: de-es data_files: - split: train path: de-es/train-* - config_name: de-fr data_files: - split: train path: de-fr/train-* - config_name: de-it data_files: - split: train path: de-it/train-* - config_name: de-ja data_files: - split: train path: de-ja/train-* - config_name: de-nl data_files: - split: train path: de-nl/train-* - config_name: de-pt data_files: - split: train path: de-pt/train-* - config_name: de-ru data_files: - split: train path: de-ru/train-* - config_name: de-zh data_files: - split: train path: de-zh/train-* - config_name: en-es data_files: - split: train path: en-es/train-* - config_name: en-fr data_files: - split: train path: en-fr/train-* - config_name: en-it data_files: - split: train path: en-it/train-* - config_name: en-ja data_files: - split: train path: en-ja/train-* - config_name: en-nl data_files: - split: train path: en-nl/train-* - config_name: en-pt data_files: - split: train path: en-pt/train-* - config_name: en-ru data_files: - split: train path: en-ru/train-* - config_name: en-zh data_files: - split: train path: en-zh/train-* - config_name: es-fr data_files: - split: train path: es-fr/train-* - config_name: es-it data_files: - split: train path: es-it/train-* - config_name: es-ja data_files: - split: train path: es-ja/train-* - config_name: es-nl data_files: - split: train path: es-nl/train-* - config_name: es-pt data_files: - split: train path: es-pt/train-* - config_name: es-ru data_files: - split: train path: es-ru/train-* - config_name: es-zh data_files: - split: train path: es-zh/train-* - config_name: fr-it data_files: - split: train path: fr-it/train-* - config_name: fr-ja data_files: - split: train path: fr-ja/train-* - config_name: fr-nl data_files: - split: train path: fr-nl/train-* - config_name: fr-pt data_files: - split: train path: fr-pt/train-* - config_name: fr-ru data_files: - split: train path: fr-ru/train-* - config_name: fr-zh data_files: - split: train path: fr-zh/train-* - config_name: it-nl data_files: - split: train path: it-nl/train-* - config_name: it-pt data_files: - split: train path: it-pt/train-* - config_name: it-ru data_files: - split: train path: it-ru/train-* - config_name: it-zh data_files: - split: train path: it-zh/train-* - config_name: ja-ru data_files: - split: train path: ja-ru/train-* - config_name: ja-zh data_files: - split: train path: ja-zh/train-* - config_name: nl-pt data_files: - split: train path: nl-pt/train-* - config_name: nl-ru data_files: - split: train path: nl-ru/train-* - config_name: nl-zh data_files: - split: train path: nl-zh/train-* - config_name: pt-ru data_files: - split: train path: pt-ru/train-* - config_name: pt-zh data_files: - split: train path: pt-zh/train-* - config_name: ru-zh data_files: - split: train path: ru-zh/train-* --- # Dataset Card for OPUS News-Commentary ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://opus.nlpl.eu/News-Commentary/corpus/version/News-Commentary - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** https://aclanthology.org/L12-1246/ - **Leaderboard:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information Please cite the following article if you use any part of the OPUS corpus in your own work: ```bibtex @inproceedings{tiedemann-2012-parallel, title = "Parallel Data, Tools and Interfaces in {OPUS}", author = {Tiedemann, J{\"o}rg}, editor = "Calzolari, Nicoletta and Choukri, Khalid and Declerck, Thierry and Do{\u{g}}an, Mehmet U{\u{g}}ur and Maegaard, Bente and Mariani, Joseph and Moreno, Asuncion and Odijk, Jan and Piperidis, Stelios", booktitle = "Proceedings of the Eighth International Conference on Language Resources and Evaluation ({LREC}'12)", month = may, year = "2012", address = "Istanbul, Turkey", publisher = "European Language Resources Association (ELRA)", url = "http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf", pages = "2214--2218", } ``` ### Contributions Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.

japanese-asr/whisper_transcriptions.mls.wer_10.0.vectorized

japanese-asr

--- dataset_info: - config_name: subset_0 features: - name: transcription sequence: int64 - name: transcription/ja_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 95550219596 num_examples: 62101 download_size: 43092578892 dataset_size: 95550219596 - config_name: subset_1 features: - name: transcription sequence: int64 - name: transcription/ja_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 95892233884 num_examples: 62323 download_size: 43217224829 dataset_size: 95892233884 - config_name: subset_10 features: - name: transcription sequence: int64 - name: transcription/ja_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 95659534424 num_examples: 62172 download_size: 43197712726 dataset_size: 95659534424 - config_name: subset_100 features: - name: transcription sequence: int64 - name: transcription/ja_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 95570234896 num_examples: 62114 download_size: 43084233453 dataset_size: 95570234896 - config_name: subset_101 features: - name: transcription sequence: int64 - name: transcription/ja_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 95741000524 num_examples: 62225 download_size: 43183665345 dataset_size: 95741000524 - config_name: subset_102 features: - name: transcription sequence: int64 - name: transcription/ja_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 95732487892 num_examples: 62219 download_size: 43229537725 dataset_size: 95732487892 - config_name: subset_103 features: - name: transcription sequence: int64 - name: transcription/ja_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 95776664816 num_examples: 62248 download_size: 43187441638 dataset_size: 95776664816 - config_name: subset_104 features: - name: transcription sequence: int64 - name: transcription/ja_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 95930500816 num_examples: 62348 download_size: 43294625977 dataset_size: 95930500816 - config_name: subset_105 features: - name: transcription sequence: int64 - name: transcription/ja_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 95836868972 num_examples: 62287 download_size: 43251807028 dataset_size: 95836868972 - config_name: subset_106 features: - name: transcription sequence: int64 - name: transcription/ja_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 95940716900 num_examples: 62355 download_size: 43289304103 dataset_size: 95940716900 - config_name: subset_107 features: - name: transcription sequence: int64 - name: transcription/ja_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 95782476488 num_examples: 62252 download_size: 43209137820 dataset_size: 95782476488 - config_name: subset_108 features: - name: transcription sequence: int64 - name: transcription/ja_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 96007104792 num_examples: 62398 download_size: 43221018658 dataset_size: 96007104792 - config_name: subset_109 features: - name: transcription sequence: int64 - name: transcription/ja_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 95764776944 num_examples: 62240 download_size: 43162176171 dataset_size: 95764776944 - config_name: subset_11 features: - name: transcription sequence: int64 - name: transcription/ja_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 95749669360 num_examples: 62230 download_size: 43193067430 dataset_size: 95749669360 - config_name: subset_110 features: - name: transcription sequence: int64 - name: transcription/ja_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 95826353540 num_examples: 62281 download_size: 43217482451 dataset_size: 95826353540 - config_name: subset_111 features: - name: transcription sequence: int64 - name: transcription/ja_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 95910493660 num_examples: 62335 download_size: 43268379463 dataset_size: 95910493660 - config_name: subset_112 features: - name: transcription sequence: int64 - name: transcription/ja_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 95782539616 num_examples: 62252 download_size: 43198507530 dataset_size: 95782539616 - config_name: subset_113 features: - name: transcription sequence: int64 - name: transcription/ja_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 95496693376 num_examples: 62066 download_size: 43106662052 dataset_size: 95496693376 - config_name: subset_114 features: - name: transcription sequence: int64 - name: transcription/ja_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 16920876 num_examples: 11 download_size: 7573002 dataset_size: 16920876 - config_name: subset_115 features: - name: transcription sequence: int64 - name: transcription/ja_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 95639177564 num_examples: 62159 download_size: 43180784518 dataset_size: 95639177564 - config_name: subset_116 features: - name: transcription sequence: int64 - name: transcription/ja_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 95625267448 num_examples: 62150 download_size: 43124129761 dataset_size: 95625267448 - config_name: subset_117 features: - name: transcription sequence: int64 - name: transcription/ja_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 95545048296 num_examples: 62098 download_size: 43082968259 dataset_size: 95545048296 - config_name: subset_118 features: - name: transcription sequence: int64 - name: transcription/ja_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 95866827908 num_examples: 62307 download_size: 43167164098 dataset_size: 95866827908 - config_name: subset_119 features: - name: transcription sequence: int64 - name: transcription/ja_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 96068332252 num_examples: 62437 download_size: 43339136980 dataset_size: 96068332252 - config_name: subset_12 features: - name: transcription sequence: int64 - name: transcription/ja_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 95728691164 num_examples: 62217 download_size: 43198747627 dataset_size: 95728691164 - config_name: subset_120 features: - name: transcription sequence: int64 - name: transcription/ja_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 95722632700 num_examples: 62213 download_size: 43167373358 dataset_size: 95722632700 - config_name: subset_121 features: - name: transcription sequence: int64 - name: transcription/ja_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 95732191100 num_examples: 62219 download_size: 43221505796 dataset_size: 95732191100 - config_name: subset_122 features: - name: transcription sequence: int64 - name: transcription/ja_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 95699024432 num_examples: 62198 download_size: 43219580053 dataset_size: 95699024432 - config_name: subset_123 features: - name: transcription sequence: int64 - name: transcription/ja_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 95713523564 num_examples: 62207 download_size: 43177149081 dataset_size: 95713523564 - config_name: subset_124 features: - name: transcription sequence: int64 - 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name: train num_bytes: 95636401316 num_examples: 62157 download_size: 43147436863 dataset_size: 95636401316 configs: - config_name: subset_0 data_files: - split: train path: subset_0/train-* - config_name: subset_1 data_files: - split: train path: subset_1/train-* - config_name: subset_10 data_files: - split: train path: subset_10/train-* - config_name: subset_100 data_files: - split: train path: subset_100/train-* - config_name: subset_101 data_files: - split: train path: subset_101/train-* - config_name: subset_102 data_files: - split: train path: subset_102/train-* - config_name: subset_103 data_files: - split: train path: subset_103/train-* - config_name: subset_104 data_files: - split: train path: subset_104/train-* - config_name: subset_105 data_files: - split: train path: subset_105/train-* - config_name: subset_106 data_files: - split: train path: subset_106/train-* - config_name: subset_107 data_files: - split: train path: subset_107/train-* - config_name: subset_108 data_files: - 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config_name: subset_56 data_files: - split: train path: subset_56/train-* - config_name: subset_57 data_files: - split: train path: subset_57/train-* - config_name: subset_58 data_files: - split: train path: subset_58/train-* - config_name: subset_59 data_files: - split: train path: subset_59/train-* - config_name: subset_6 data_files: - split: train path: subset_6/train-* - config_name: subset_60 data_files: - split: train path: subset_60/train-* - config_name: subset_61 data_files: - split: train path: subset_61/train-* - config_name: subset_62 data_files: - split: train path: subset_62/train-* - config_name: subset_63 data_files: - split: train path: subset_63/train-* - config_name: subset_64 data_files: - split: train path: subset_64/train-* - config_name: subset_65 data_files: - split: train path: subset_65/train-* - config_name: subset_66 data_files: - split: train path: subset_66/train-* - config_name: subset_67 data_files: - split: train path: subset_67/train-* - config_name: subset_68 data_files: - split: train path: subset_68/train-* - config_name: subset_69 data_files: - split: train path: subset_69/train-* - config_name: subset_7 data_files: - split: train path: subset_7/train-* - config_name: subset_70 data_files: - split: train path: subset_70/train-* - config_name: subset_71 data_files: - split: train path: subset_71/train-* - config_name: subset_72 data_files: - split: train path: subset_72/train-* - config_name: subset_73 data_files: - split: train path: subset_73/train-* - config_name: subset_74 data_files: - split: train path: subset_74/train-* - config_name: subset_75 data_files: - split: train path: subset_75/train-* - config_name: subset_76 data_files: - split: train path: subset_76/train-* - config_name: subset_77 data_files: - split: train path: subset_77/train-* - config_name: subset_78 data_files: - split: train path: subset_78/train-* - config_name: subset_79 data_files: - split: train path: subset_79/train-* - config_name: subset_8 data_files: - split: train path: subset_8/train-* - config_name: subset_80 data_files: - split: train path: subset_80/train-* - config_name: subset_81 data_files: - split: train path: subset_81/train-* - config_name: subset_82 data_files: - split: train path: subset_82/train-* - config_name: subset_83 data_files: - split: train path: subset_83/train-* - config_name: subset_84 data_files: - split: train path: subset_84/train-* - config_name: subset_85 data_files: - split: train path: subset_85/train-* - config_name: subset_86 data_files: - split: train path: subset_86/train-* - config_name: subset_87 data_files: - split: train path: subset_87/train-* - config_name: subset_88 data_files: - split: train path: subset_88/train-* - config_name: subset_89 data_files: - split: train path: subset_89/train-* - config_name: subset_9 data_files: - split: train path: subset_9/train-* - config_name: subset_90 data_files: - split: train path: subset_90/train-* - config_name: subset_91 data_files: - split: train path: subset_91/train-* - config_name: subset_92 data_files: - split: train path: subset_92/train-* - config_name: subset_93 data_files: - split: train path: subset_93/train-* - config_name: subset_94 data_files: - split: train path: subset_94/train-* - config_name: subset_95 data_files: - split: train path: subset_95/train-* - config_name: subset_96 data_files: - split: train path: subset_96/train-* - config_name: subset_97 data_files: - split: train path: subset_97/train-* - config_name: subset_98 data_files: - split: train path: subset_98/train-* - config_name: subset_99 data_files: - split: train path: subset_99/train-* ---

eriktks/conll2003

eriktks

--- annotations_creators: - crowdsourced language_creators: - found language: - en license: - other multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - extended|other-reuters-corpus task_categories: - token-classification task_ids: - named-entity-recognition - part-of-speech paperswithcode_id: conll-2003 pretty_name: CoNLL-2003 dataset_info: features: - name: id dtype: string - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': '"' '1': '''''' '2': '#' '3': $ '4': ( '5': ) '6': ',' '7': . '8': ':' '9': '``' '10': CC '11': CD '12': DT '13': EX '14': FW '15': IN '16': JJ '17': JJR '18': JJS '19': LS '20': MD '21': NN '22': NNP '23': NNPS '24': NNS '25': NN|SYM '26': PDT '27': POS '28': PRP '29': PRP$ '30': RB '31': RBR '32': RBS '33': RP '34': SYM '35': TO '36': UH '37': VB '38': VBD '39': VBG '40': VBN '41': VBP '42': VBZ '43': WDT '44': WP '45': WP$ '46': WRB - name: chunk_tags sequence: class_label: names: '0': O '1': B-ADJP '2': I-ADJP '3': B-ADVP '4': I-ADVP '5': B-CONJP '6': I-CONJP '7': B-INTJ '8': I-INTJ '9': B-LST '10': I-LST '11': B-NP '12': I-NP '13': B-PP '14': I-PP '15': B-PRT '16': I-PRT '17': B-SBAR '18': I-SBAR '19': B-UCP '20': I-UCP '21': B-VP '22': I-VP - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC '7': B-MISC '8': I-MISC config_name: conll2003 splits: - name: train num_bytes: 6931345 num_examples: 14041 - name: validation num_bytes: 1739223 num_examples: 3250 - name: test num_bytes: 1582054 num_examples: 3453 download_size: 982975 dataset_size: 10252622 train-eval-index: - config: conll2003 task: token-classification task_id: entity_extraction splits: train_split: train eval_split: test col_mapping: tokens: tokens ner_tags: tags metrics: - type: seqeval name: seqeval --- # Dataset Card for "conll2003" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://www.aclweb.org/anthology/W03-0419/](https://www.aclweb.org/anthology/W03-0419/) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 4.85 MB - **Size of the generated dataset:** 10.26 MB - **Total amount of disk used:** 15.11 MB ### Dataset Summary The shared task of CoNLL-2003 concerns language-independent named entity recognition. We will concentrate on four types of named entities: persons, locations, organizations and names of miscellaneous entities that do not belong to the previous three groups. The CoNLL-2003 shared task data files contain four columns separated by a single space. Each word has been put on a separate line and there is an empty line after each sentence. The first item on each line is a word, the second a part-of-speech (POS) tag, the third a syntactic chunk tag and the fourth the named entity tag. The chunk tags and the named entity tags have the format I-TYPE which means that the word is inside a phrase of type TYPE. Only if two phrases of the same type immediately follow each other, the first word of the second phrase will have tag B-TYPE to show that it starts a new phrase. A word with tag O is not part of a phrase. Note the dataset uses IOB2 tagging scheme, whereas the original dataset uses IOB1. For more details see https://www.clips.uantwerpen.be/conll2003/ner/ and https://www.aclweb.org/anthology/W03-0419 ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### conll2003 - **Size of downloaded dataset files:** 4.85 MB - **Size of the generated dataset:** 10.26 MB - **Total amount of disk used:** 15.11 MB An example of 'train' looks as follows. ``` { "chunk_tags": [11, 12, 12, 21, 13, 11, 11, 21, 13, 11, 12, 13, 11, 21, 22, 11, 12, 17, 11, 21, 17, 11, 12, 12, 21, 22, 22, 13, 11, 0], "id": "0", "ner_tags": [0, 3, 4, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], "pos_tags": [12, 22, 22, 38, 15, 22, 28, 38, 15, 16, 21, 35, 24, 35, 37, 16, 21, 15, 24, 41, 15, 16, 21, 21, 20, 37, 40, 35, 21, 7], "tokens": ["The", "European", "Commission", "said", "on", "Thursday", "it", "disagreed", "with", "German", "advice", "to", "consumers", "to", "shun", "British", "lamb", "until", "scientists", "determine", "whether", "mad", "cow", "disease", "can", "be", "transmitted", "to", "sheep", "."] } ``` The original data files have `-DOCSTART-` lines used to separate documents, but these lines are removed here. Indeed `-DOCSTART-` is a special line that acts as a boundary between two different documents, and it is filtered out in this implementation. ### Data Fields The data fields are the same among all splits. #### conll2003 - `id`: a `string` feature. - `tokens`: a `list` of `string` features. - `pos_tags`: a `list` of classification labels (`int`). Full tagset with indices: ```python {'"': 0, "''": 1, '#': 2, '$': 3, '(': 4, ')': 5, ',': 6, '.': 7, ':': 8, '``': 9, 'CC': 10, 'CD': 11, 'DT': 12, 'EX': 13, 'FW': 14, 'IN': 15, 'JJ': 16, 'JJR': 17, 'JJS': 18, 'LS': 19, 'MD': 20, 'NN': 21, 'NNP': 22, 'NNPS': 23, 'NNS': 24, 'NN|SYM': 25, 'PDT': 26, 'POS': 27, 'PRP': 28, 'PRP$': 29, 'RB': 30, 'RBR': 31, 'RBS': 32, 'RP': 33, 'SYM': 34, 'TO': 35, 'UH': 36, 'VB': 37, 'VBD': 38, 'VBG': 39, 'VBN': 40, 'VBP': 41, 'VBZ': 42, 'WDT': 43, 'WP': 44, 'WP$': 45, 'WRB': 46} ``` - `chunk_tags`: a `list` of classification labels (`int`). Full tagset with indices: ```python {'O': 0, 'B-ADJP': 1, 'I-ADJP': 2, 'B-ADVP': 3, 'I-ADVP': 4, 'B-CONJP': 5, 'I-CONJP': 6, 'B-INTJ': 7, 'I-INTJ': 8, 'B-LST': 9, 'I-LST': 10, 'B-NP': 11, 'I-NP': 12, 'B-PP': 13, 'I-PP': 14, 'B-PRT': 15, 'I-PRT': 16, 'B-SBAR': 17, 'I-SBAR': 18, 'B-UCP': 19, 'I-UCP': 20, 'B-VP': 21, 'I-VP': 22} ``` - `ner_tags`: a `list` of classification labels (`int`). Full tagset with indices: ```python {'O': 0, 'B-PER': 1, 'I-PER': 2, 'B-ORG': 3, 'I-ORG': 4, 'B-LOC': 5, 'I-LOC': 6, 'B-MISC': 7, 'I-MISC': 8} ``` ### Data Splits | name |train|validation|test| |---------|----:|---------:|---:| |conll2003|14041| 3250|3453| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information From the [CoNLL2003 shared task](https://www.clips.uantwerpen.be/conll2003/ner/) page: > The English data is a collection of news wire articles from the Reuters Corpus. The annotation has been done by people of the University of Antwerp. Because of copyright reasons we only make available the annotations. In order to build the complete data sets you will need access to the Reuters Corpus. It can be obtained for research purposes without any charge from NIST. The copyrights are defined below, from the [Reuters Corpus page](https://trec.nist.gov/data/reuters/reuters.html): > The stories in the Reuters Corpus are under the copyright of Reuters Ltd and/or Thomson Reuters, and their use is governed by the following agreements: > > [Organizational agreement](https://trec.nist.gov/data/reuters/org_appl_reuters_v4.html) > > This agreement must be signed by the person responsible for the data at your organization, and sent to NIST. > > [Individual agreement](https://trec.nist.gov/data/reuters/ind_appl_reuters_v4.html) > > This agreement must be signed by all researchers using the Reuters Corpus at your organization, and kept on file at your organization. ### Citation Information ``` @inproceedings{tjong-kim-sang-de-meulder-2003-introduction, title = "Introduction to the {C}o{NLL}-2003 Shared Task: Language-Independent Named Entity Recognition", author = "Tjong Kim Sang, Erik F. and De Meulder, Fien", booktitle = "Proceedings of the Seventh Conference on Natural Language Learning at {HLT}-{NAACL} 2003", year = "2003", url = "https://www.aclweb.org/anthology/W03-0419", pages = "142--147", } ``` ### Contributions Thanks to [@jplu](https://github.com/jplu), [@vblagoje](https://github.com/vblagoje), [@lhoestq](https://github.com/lhoestq) for adding this dataset.

Pendrokar/open_tts_tracker

Pendrokar

--- configs: - config_name: train data_files: - split: train path: open_tts_tracker.tsv sep: "\t" - config_name: train_data data_files: - split: ttsds path: data.csv sep: ',' license: mit --- _above models sorted by the amount of capabilities_; [#legend](#legend) Cloned the GitHub repo for easier viewing and embedding the above table as once requested by @reach-vb: https://github.com/Vaibhavs10/open-tts-tracker/issues/30#issuecomment-1946367525 --- # 🗣️ Open TTS Tracker A one stop shop to track all open-access/ source Text-To-Speech (TTS) models as they come out. Feel free to make a PR for all those that aren't linked here. This is aimed as a resource to increase awareness for these models and to make it easier for researchers, developers, and enthusiasts to stay informed about the latest advancements in the field. > [!NOTE] > This repo will only track open source/access codebase TTS models. More motivation for everyone to open-source! 🤗 Some of the models are also being battle tested at TTS arenas hosted on HuggingFace: * 🏆 [TTS Arena](https://huggingface.co/spaces/TTS-AGI/TTS-Arena) - _Battle_ tab allows to choose 2 candidates and compare them * 🤗🏆 [TTS Spaces Arena](https://huggingface.co/spaces/Pendrokar/TTS-Spaces-Arena) - Uses online HuggingFace Spaces, which have the Gradio API enabled And in automated benchmarks: * 🏅 [TTSDS](https://huggingface.co/spaces/ttsds/benchmark) | Name | GitHub | Weights | License | Fine-tune | Languages | Paper | Demo | Issues | |---|---|---|---|---|---|---|---|---| | AI4Bharat | [Repo](https://github.com/AI4Bharat/Indic-TTS) | [Hub](https://huggingface.co/ai4bharat) | [MIT](https://github.com/AI4Bharat/Indic-TTS/blob/master/LICENSE.txt) | [Yes](https://github.com/AI4Bharat/Indic-TTS?tab=readme-ov-file#training-steps) | Indic | [Paper](https://arxiv.org/abs/2211.09536) | [Demo](https://models.ai4bharat.org/#/tts) | | Amphion | [Repo](https://github.com/open-mmlab/Amphion) | [Hub](https://huggingface.co/amphion) | [MIT](https://github.com/open-mmlab/Amphion/blob/main/LICENSE) | No | Multilingual | [Paper](https://arxiv.org/abs/2312.09911) | [🤗 Space](https://huggingface.co/amphion) | | | Bark | [Repo](https://github.com/huggingface/transformers/tree/main/src/transformers/models/bark) | [Hub](https://huggingface.co/suno/bark) | [MIT](https://github.com/suno-ai/bark/blob/main/LICENSE) | No | Multilingual | [Paper](https://arxiv.org/abs/2209.03143) | [🤗 Space](https://huggingface.co/spaces/suno/bark) | | | EmotiVoice | [Repo](https://github.com/netease-youdao/EmotiVoice) | [GDrive](https://drive.google.com/drive/folders/1y6Xwj_GG9ulsAonca_unSGbJ4lxbNymM) | [Apache 2.0](https://github.com/netease-youdao/EmotiVoice/blob/main/LICENSE) | [Yes](https://github.com/netease-youdao/EmotiVoice/wiki/Voice-Cloning-with-your-personal-data) | ZH + EN | Not Available | Not Available | Separate [GUI agreement](https://github.com/netease-youdao/EmotiVoice/blob/main/EmotiVoice_UserAgreement_%E6%98%93%E9%AD%94%E5%A3%B0%E7%94%A8%E6%88%B7%E5%8D%8F%E8%AE%AE.pdf) | | F5-TTS | [Repo](https://github.com/SWivid/F5-TTS) | [Hub](https://huggingface.co/SWivid/F5-TTS) | [MIT](https://github.com/SWivid/F5-TTS/blob/master/LICENSE) | Yes | ZH + EN | [Paper](https://arxiv.org/abs/2410.06885) | [🤗 Space](https://huggingface.co/spaces/mrfakename/E2-F5-TTS) | | | Fish Speech | [Repo](https://github.com/fishaudio/fish-speech) | [Hub](https://huggingface.co/fishaudio) | [CC-BY-NC-SA 4.0](https://github.com/fishaudio/fish-speech/blob/master/LICENSE) | Yes | Multilingual | Not Available | [🤗 Space](https://huggingface.co/spaces/fishaudio/fish-speech-1) | | | Glow-TTS | [Repo](https://github.com/jaywalnut310/glow-tts) | [GDrive](https://drive.google.com/file/d/1JiCMBVTG4BMREK8cT3MYck1MgYvwASL0/view) | [MIT](https://github.com/jaywalnut310/glow-tts/blob/master/LICENSE) | [Yes](https://github.com/jaywalnut310/glow-tts?tab=readme-ov-file#2-pre-requisites) | English | [Paper](https://arxiv.org/abs/2005.11129) | [GH Pages](https://jaywalnut310.github.io/glow-tts-demo/index.html) | | | GPT-SoVITS | [Repo](https://github.com/RVC-Boss/GPT-SoVITS) | [Hub](https://huggingface.co/lj1995/GPT-SoVITS) | [MIT](https://github.com/RVC-Boss/GPT-SoVITS/blob/main/LICENSE) | [Yes](https://github.com/RVC-Boss/GPT-SoVITS?tab=readme-ov-file#pretrained-models) | Multilingual | Not Available | Not Available | | | HierSpeech++ | [Repo](https://github.com/sh-lee-prml/HierSpeechpp) | [GDrive](https://drive.google.com/drive/folders/1-L_90BlCkbPyKWWHTUjt5Fsu3kz0du0w) | [MIT](https://github.com/sh-lee-prml/HierSpeechpp/blob/main/LICENSE) | No | KR + EN | [Paper](https://arxiv.org/abs/2311.12454) | [🤗 Space](https://huggingface.co/spaces/LeeSangHoon/HierSpeech_TTS) | | | IMS-Toucan | [Repo](https://github.com/DigitalPhonetics/IMS-Toucan) | [GH release](https://github.com/DigitalPhonetics/IMS-Toucan/tags) | [Apache 2.0](https://github.com/DigitalPhonetics/IMS-Toucan/blob/ToucanTTS/LICENSE) | [Yes](https://github.com/DigitalPhonetics/IMS-Toucan#build-a-toucantts-pipeline) | ALL\* | [Paper](https://arxiv.org/abs/2206.12229) | [🤗 Space](https://huggingface.co/spaces/Flux9665/IMS-Toucan), [🤗 Space](https://huggingface.co/spaces/Flux9665/MassivelyMultilingualTTS)\* | | | Kokoro | [Repo](https://github.com/hexgrad/kokoro) | [Hub](https://huggingface.co/hexgrad/Kokoro-82M) | [Apache 2.0](https://github.com/hexgrad/kokoro/blob/main/LICENSE) | No | Multilingual | [Paper](https://arxiv.org/abs/2306.07691) | [🤗 Space](https://huggingface.co/spaces/hexgrad/Kokoro-TTS) | GPL-licensed phonemizer | | MahaTTS | [Repo](https://github.com/dubverse-ai/MahaTTS) | [Hub](https://huggingface.co/Dubverse/MahaTTS) | [Apache 2.0](https://github.com/dubverse-ai/MahaTTS/blob/main/LICENSE) | No | English + Indic | Not Available | [Recordings](https://github.com/dubverse-ai/MahaTTS/blob/main/README.md#sample-outputs), [Colab](https://colab.research.google.com/drive/1qkZz2km-PX75P0f6mUb2y5e-uzub27NW?usp=sharing) | | | MaskGCT (Amphion) | [Repo](https://github.com/open-mmlab/Amphion) | [Hub](https://huggingface.co/amphion/MaskGCT) | [CC-BY-NC 4.0](https://huggingface.co/amphion/MaskGCT) | No | Multilingual | [Paper](https://arxiv.org/abs/2409.00750) | [🤗 Space](https://huggingface.co/spaces/amphion/maskgct) | | | Matcha-TTS | [Repo](https://github.com/shivammehta25/Matcha-TTS) | [GDrive](https://drive.google.com/drive/folders/17C_gYgEHOxI5ZypcfE_k1piKCtyR0isJ) | [MIT](https://github.com/shivammehta25/Matcha-TTS/blob/main/LICENSE) | [Yes](https://github.com/shivammehta25/Matcha-TTS/tree/main#train-with-your-own-dataset) | English | [Paper](https://arxiv.org/abs/2309.03199) | [🤗 Space](https://huggingface.co/spaces/shivammehta25/Matcha-TTS) | GPL-licensed phonemizer | | MeloTTS | [Repo](https://github.com/myshell-ai/MeloTTS) | [Hub](https://huggingface.co/myshell-ai) | [MIT](https://github.com/myshell-ai/MeloTTS/blob/main/LICENSE) | Yes | Multilingual | Not Available | [🤗 Space](https://huggingface.co/spaces/mrfakename/MeloTTS) | | | MetaVoice-1B | [Repo](https://github.com/metavoiceio/metavoice-src) | [Hub](https://huggingface.co/metavoiceio/metavoice-1B-v0.1/tree/main) | [Apache 2.0](https://github.com/metavoiceio/metavoice-src/blob/main/LICENSE) | [Yes](https://github.com/metavoiceio/metavoice-src?tab=readme-ov-file) | Multilingual | Not Available | [🤗 Space](https://huggingface.co/spaces/mrfakename/MetaVoice-1B-v0.1) | | | Neural-HMM TTS | [Repo](https://github.com/shivammehta25/Neural-HMM) | [GitHub](https://github.com/shivammehta25/Neural-HMM/releases) | [MIT](https://github.com/shivammehta25/Neural-HMM/blob/main/LICENSE) | [Yes](https://github.com/shivammehta25/Neural-HMM?tab=readme-ov-file#setup-and-training-using-lj-speech) | English | [Paper](https://arxiv.org/abs/2108.13320) | [GH Pages](https://shivammehta25.github.io/Neural-HMM/) | | | OpenVoice | [Repo](https://github.com/myshell-ai/OpenVoice) | [Hub](https://huggingface.co/myshell-ai/OpenVoice) | [MIT](https://github.com/myshell-ai/OpenVoice/blob/main/LICENSE) | No | Multilingual | [Paper](https://arxiv.org/abs/2312.01479) | [🤗 Space](https://huggingface.co/spaces/myshell-ai/OpenVoice) | | | OverFlow TTS | [Repo](https://github.com/shivammehta25/OverFlow) | [GitHub](https://github.com/shivammehta25/OverFlow/releases) | [MIT](https://github.com/shivammehta25/OverFlow/blob/main/LICENSE) | [Yes](https://github.com/shivammehta25/OverFlow/tree/main?tab=readme-ov-file#setup-and-training-using-lj-speech) | English | [Paper](https://arxiv.org/abs/2211.06892) | [GH Pages](https://shivammehta25.github.io/OverFlow/) | | | Parler TTS | [Repo](https://github.com/huggingface/parler-tts) | [Hub](https://huggingface.co/parler-tts/parler_tts_mini_v0.1) | [Apache 2.0](https://github.com/huggingface/parler-tts/blob/main/LICENSE) | [Yes](https://github.com/huggingface/parler-tts/tree/main/training) | English | Not Available | [🤗 Space](https://huggingface.co/spaces/parler-tts/parler_tts) | | | pflowTTS | [Unofficial Repo](https://github.com/p0p4k/pflowtts_pytorch) | [GDrive](https://drive.google.com/drive/folders/1x-A2Ezmmiz01YqittO_GLYhngJXazaF0) | [MIT](https://github.com/p0p4k/pflowtts_pytorch/blob/master/LICENSE) | [Yes](https://github.com/p0p4k/pflowtts_pytorch#instructions-to-run) | English | [Paper](https://openreview.net/pdf?id=zNA7u7wtIN) | Not Available | GPL-licensed phonemizer | | Pheme | [Repo](https://github.com/PolyAI-LDN/pheme) | [Hub](https://huggingface.co/PolyAI/pheme) | [CC-BY](https://github.com/PolyAI-LDN/pheme/blob/main/LICENSE) | [Yes](https://github.com/PolyAI-LDN/pheme#training) | English | [Paper](https://arxiv.org/abs/2401.02839) | [🤗 Space](https://huggingface.co/spaces/PolyAI/pheme) | | | Piper | [Repo](https://github.com/rhasspy/piper) | [Hub](https://huggingface.co/datasets/rhasspy/piper-checkpoints/) | [MIT](https://github.com/rhasspy/piper/blob/master/LICENSE.md) | [Yes](https://github.com/rhasspy/piper/blob/master/TRAINING.md) | Multilingual | Not Available | [🤗 Space](https://huggingface.co/spaces/Gregniuki/Pipertts) | [GPL-licensed phonemizer](https://github.com/rhasspy/piper/issues/93) | | RAD-MMM | [Repo](https://github.com/NVIDIA/RAD-MMM) | [GDrive](https://drive.google.com/file/d/1p8SEVHRlyLQpQnVP2Dc66RlqJVVRDCsJ/view) | [MIT](https://github.com/NVIDIA/RAD-MMM/blob/main/LICENSE) | [Yes](https://github.com/NVIDIA/RAD-MMM?tab=readme-ov-file#training) | Multilingual | [Paper](https://arxiv.org/pdf/2301.10335.pdf) | [Jupyter Notebook](https://github.com/NVIDIA/RAD-MMM/blob/main/inference.ipynb), [Webpage](https://research.nvidia.com/labs/adlr/projects/radmmm/) | | | RAD-TTS | [Repo](https://github.com/NVIDIA/radtts) | [GDrive](https://drive.google.com/file/d/1Rb2VMUwQahGrnpFSlAhCPh7OpDN3xgOr/view?usp=sharing) | [MIT](https://github.com/NVIDIA/radtts/blob/main/LICENSE) | [Yes](https://github.com/NVIDIA/radtts#training-radtts-without-pitch-and-energy-conditioning) | English | [Paper](https://openreview.net/pdf?id=0NQwnnwAORi) | [GH Pages](https://nv-adlr.github.io/RADTTS) | | | Silero | [Repo](https://github.com/snakers4/silero-models) | [GH links](https://github.com/snakers4/silero-models/blob/master/models.yml) | [CC BY-NC-SA](https://github.com/snakers4/silero-models/blob/master/LICENSE) | [No](https://github.com/snakers4/silero-models/discussions/78) | Multilingual | Not Available | Not Available | [Non Commercial](https://github.com/snakers4/silero-models/wiki/Licensing-and-Tiers) | | StyleTTS 2 | [Repo](https://github.com/yl4579/StyleTTS2) | [Hub](https://huggingface.co/yl4579/StyleTTS2-LibriTTS/tree/main) | [MIT](https://github.com/yl4579/StyleTTS2/blob/main/LICENSE) | [Yes](https://github.com/yl4579/StyleTTS2#finetuning) | English | [Paper](https://arxiv.org/abs/2306.07691) | [🤗 Space](https://huggingface.co/spaces/styletts2/styletts2) | GPL-licensed phonemizer | | Tacotron 2 | [Unofficial Repo](https://github.com/NVIDIA/tacotron2) | [GDrive](https://drive.google.com/file/d/1c5ZTuT7J08wLUoVZ2KkUs_VdZuJ86ZqA/view) | [BSD-3](https://github.com/NVIDIA/tacotron2/blob/master/LICENSE) | [Yes](https://github.com/NVIDIA/tacotron2/tree/master?tab=readme-ov-file#training) | English | [Paper](https://arxiv.org/abs/1712.05884) | [Webpage](https://google.github.io/tacotron/publications/tacotron2/) | | | TorToiSe TTS | [Repo](https://github.com/neonbjb/tortoise-tts) | [Hub](https://huggingface.co/jbetker/tortoise-tts-v2) | [Apache 2.0](https://github.com/neonbjb/tortoise-tts/blob/main/LICENSE) | [Yes](https://git.ecker.tech/mrq/tortoise-tts) | English | [Technical report](https://arxiv.org/abs/2305.07243) | [🤗 Space](https://huggingface.co/spaces/Manmay/tortoise-tts) | | | TTTS | [Repo](https://github.com/adelacvg/ttts) | [Hub](https://huggingface.co/adelacvg/TTTS) | [MPL 2.0](https://github.com/adelacvg/ttts/blob/master/LICENSE) | No | Multilingual | Not Available | [Colab](https://colab.research.google.com/github/adelacvg/ttts/blob/master/demo.ipynb), [🤗 Space](https://huggingface.co/spaces/mrfakename/TTTS) | | | VALL-E | [Unofficial Repo](https://github.com/enhuiz/vall-e) | Not Available | [MIT](https://github.com/enhuiz/vall-e/blob/main/LICENSE) | [Yes](https://github.com/enhuiz/vall-e#get-started) | NA | [Paper](https://arxiv.org/abs/2301.02111) | Not Available | | | VITS/ MMS-TTS | [Repo](https://github.com/huggingface/transformers/tree/7142bdfa90a3526cfbed7483ede3afbef7b63939/src/transformers/models/vits) | [Hub](https://huggingface.co/kakao-enterprise) / [MMS](https://huggingface.co/models?search=mms-tts) | [Apache 2.0](https://github.com/huggingface/transformers/blob/main/LICENSE) | [Yes](https://github.com/ylacombe/finetune-hf-vits) | English | [Paper](https://arxiv.org/abs/2106.06103) | [🤗 Space](https://huggingface.co/spaces/kakao-enterprise/vits) | GPL-licensed phonemizer | | WhisperSpeech | [Repo](https://github.com/collabora/WhisperSpeech) | [Hub](https://huggingface.co/collabora/whisperspeech) | [MIT](https://github.com/collabora/WhisperSpeech/blob/main/LICENSE) | No | Multilingual | Not Available | [🤗 Space](https://huggingface.co/spaces/collabora/WhisperSpeech), [Recordings](https://github.com/collabora/WhisperSpeech/blob/main/README.md), [Colab](https://colab.research.google.com/github/collabora/WhisperSpeech/blob/8168a30f26627fcd15076d10c85d9e33c52204cf/Inference%20example.ipynb) | | | XTTS | [Repo](https://github.com/coqui-ai/TTS) | [Hub](https://huggingface.co/coqui/XTTS-v2) | [CPML](https://coqui.ai/cpml) | [Yes](https://docs.coqui.ai/en/latest/models/xtts.html#training) | Multilingual | [Paper](https://arxiv.org/abs/2406.04904) | [🤗 Space](https://huggingface.co/spaces/coqui/xtts) | Non Commercial | | xVASynth | [Repo](https://github.com/DanRuta/xVA-Synth) | [Hub](https://huggingface.co/Pendrokar/xvapitch) | [GPL-3.0](https://github.com/DanRuta/xVA-Synth/blob/master/LICENSE.md) | [Yes](https://github.com/DanRuta/xva-trainer) | Multilingual | Not Available | [🤗 Space](https://huggingface.co/spaces/Pendrokar/xVASynth) | Base model trained on non-permissive datasets | * *Multilingual* - Amount of supported languages is ever changing, check the Space and Hub for which specific languages are supported * *ALL* - Claims to support all natural languages; this may not include artificial/contructed languages Also to find a model for a specific language, filter out the TTS models hosted on HuggingFace: <https://huggingface.co/models?pipeline_tag=text-to-speech&language=en&sort=trending> --- ## Legend For the [#above](#) TTS capability table. Open the [viewer](../../viewer/) in another window or even another monitor to keep both it and the legend in view. * Processor ⚡ - Inference done by * CPU (CPU**s** = multithreaded) - All models can be run on CPU, so real-time factor should be below 2.0 to qualify for CPU tag, though some more leeway can be given if it supports audio streaming * CUDA by *NVIDIA*™ * ROCm by *AMD*™, also see [ONNX Runtime HF guide](https://huggingface.co/docs/optimum/en/onnxruntime/usage_guides/amdgpu) * Phonetic alphabet 🔤 - Phonetic transcription that allows to control pronunciation of words before inference * [IPA](https://en.wikipedia.org/wiki/International_Phonetic_Alphabet) - International Phonetic Alphabet * [ARPAbet](https://en.wikipedia.org/wiki/ARPABET) - American English focused phonetics * Insta-clone 👥 - Zero-shot model for quick voice cloning * Emotion control 🎭 - Able to force an emotional state of speaker * 🎭 <# emotions> ( 😡 anger; 😃 happiness; 😭 sadness; 😯 surprise; 🤫 whispering; 😊 friendlyness ) * 🎭👥 strict insta-clone switch - cloned on sample with specific emotion; may sound different than normal speaking voice; no ability to go in-between states * 🎭📖 strict control through prompt - prompt input parameter * Prompting 📖 - Also a side effect of narrator based datasets and a way to affect the emotional state * 📖 - Prompt as a separate input parameter * 🗣📖 - The prompt itself is also spoken by TTS; [ElevenLabs docs](https://elevenlabs.io/docs/speech-synthesis/prompting#emotion) * Streaming support 🌊 - Can playback audio while it is still being generated * Speech control 🎚 - Ability to change the pitch, duration, etc. for the whole and/or per-phoneme of the generated speech * Voice conversion / Speech-To-Speech 🦜 - Streaming support implies real-time S2S; S2T=>T2S does not count * Longform synthesis 📜 - Able to synthesize whole paragraphs, as some TTS models tend to break down after a certain audio length limit Example if the proprietary ElevenLabs were to be added to the capabilities table: | Name | Processor<br>⚡ | Phonetic alphabet<br>🔤 | Insta-clone<br>👥 | Emotional control<br>🎭 | Prompting<br>📖 | Speech control<br>🎚 | Streaming support<br>🌊 | Voice conversion<br>🦜 | Longform synthesis<br>📜 | |---|---|---|---|---|---|---|---|---| --- | |ElevenLabs|CUDA|IPA, ARPAbet|👥|🎭📖|🗣📖|🎚 stability, voice similarity|🌊|🦜|📜 Projects| More info on how the capabilities table came about can be found within the [GitHub Issue](https://github.com/Vaibhavs10/open-tts-tracker/issues/14). ## train_data Legend Legend for the separate TTSDS Datasets ([_train_data_ viewer](https://huggingface.co/datasets/Pendrokar/open_tts_tracker/viewer/train_data) [GitHub](https://github.com/ttsds/ttsds_systems)) - 🌐 Multilingual - The ISO codes of languages the model is capable off. ❌ if English only. - 📚 Training Amount (k hours) - The number of hours the model was trained on - 🧠 Num. Parameters (M) - How many parameters the model has, excluding vocoder and text-only components - 🎯 Target Repr. - Which output representations the model uses, for example audio codecs or mel spectrograms - 📖 LibriVox Only - If the model was trained on librivox-like (audiobook) data alone - 🔄 NAR - If the model has a significant non-autoregressive component - 🔁 AR - If the model has a significant autoregressive component - 🔡 G2P - If the model uses G2P (phone inputs) - 🧩 Language Model - If an LM-like approach is used (next token prediction) - 🎵 Prosody Prediction - If prosodic correlates such as pitch or energy are predicted - 🌊 Diffusion - If diffusion is used (outside vocoder) - ⏱️ Delay Pattern - If a delay pattern is used for audio codes (see [Lyth & King, 2024](https://arxiv.org/abs/2402.01912)) _Please create pull requests to update the info on the models!_ ---

jacobbieker/eumetsat-cloudmask-rss

jacobbieker

--- license: mit ---

open-llm-leaderboard/contents

open-llm-leaderboard

--- dataset_info: features: - name: eval_name dtype: string - name: Precision dtype: string - name: Type dtype: string - name: T dtype: string - name: Weight type dtype: string - name: Architecture dtype: string - name: Model dtype: string - name: fullname dtype: string - name: Model sha dtype: string - name: Average ⬆️ dtype: float64 - name: Hub License dtype: string - name: Hub ❤️ dtype: int64 - name: '#Params (B)' dtype: float64 - name: Available on the hub dtype: bool - name: MoE dtype: bool - name: Flagged dtype: bool - name: Chat Template dtype: bool - name: CO₂ cost (kg) dtype: float64 - name: IFEval Raw dtype: float64 - name: IFEval dtype: float64 - name: BBH Raw dtype: float64 - name: BBH dtype: float64 - name: MATH Lvl 5 Raw dtype: float64 - name: MATH Lvl 5 dtype: float64 - name: GPQA Raw dtype: float64 - name: GPQA dtype: float64 - name: MUSR Raw dtype: float64 - name: MUSR dtype: float64 - name: MMLU-PRO Raw dtype: float64 - name: MMLU-PRO dtype: float64 - name: Merged dtype: bool - name: Official Providers dtype: bool - name: Upload To Hub Date dtype: string - name: Submission Date dtype: string - name: Generation dtype: int64 - name: Base Model dtype: string splits: - name: train num_bytes: 3314527 num_examples: 3788 download_size: 918513 dataset_size: 3314527 configs: - config_name: default data_files: - split: train path: data/train-* ---

asahi417/seamless-align-enA-esA.speaker-embedding.hubert-xl

asahi417

--- dataset_info: - config_name: subset_1 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: esA.id dtype: string - name: esA.laser_score dtype: float64 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 - name: esA.audio.speaker_embedding sequence: float32 - name: esA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 11746773654 num_examples: 2178 download_size: 11781544893 dataset_size: 11746773654 - config_name: subset_10 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: esA.id dtype: string - name: esA.laser_score dtype: float64 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 - name: esA.audio.speaker_embedding sequence: float32 - name: esA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 11307338436 num_examples: 2228 download_size: 11341290257 dataset_size: 11307338436 - config_name: subset_100 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: esA.id dtype: string - name: esA.laser_score dtype: float64 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 - name: esA.audio.speaker_embedding sequence: float32 - name: esA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 8904860486 num_examples: 2123 download_size: 8933746388 dataset_size: 8904860486 - config_name: subset_101 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: esA.id dtype: string - name: esA.laser_score dtype: float64 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 - name: esA.audio.speaker_embedding sequence: float32 - name: esA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 8895621885 num_examples: 2123 download_size: 8924456769 dataset_size: 8895621885 - config_name: subset_102 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: esA.id dtype: string - name: esA.laser_score dtype: float64 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 - name: esA.audio.speaker_embedding sequence: float32 - name: esA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 8640163870 num_examples: 2048 download_size: 8668862607 dataset_size: 8640163870 - config_name: subset_103 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: esA.id dtype: string - name: esA.laser_score dtype: float64 - name: esA.audio.speaker_embedding sequence: float32 - name: esA.audio.speaker_embedding.full sequence: sequence: float32 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 8961420966 num_examples: 2082 download_size: 8990061075 dataset_size: 8961420966 - config_name: subset_104 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: esA.id dtype: string - name: esA.laser_score dtype: float64 - name: esA.audio.speaker_embedding sequence: float32 - name: esA.audio.speaker_embedding.full sequence: sequence: float32 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 8976813498 num_examples: 2145 download_size: 9005760839 dataset_size: 8976813498 - config_name: subset_105 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: esA.id dtype: string - name: esA.laser_score dtype: float64 - name: esA.audio.speaker_embedding sequence: float32 - name: esA.audio.speaker_embedding.full sequence: sequence: float32 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 8940344650 num_examples: 2129 download_size: 8969237447 dataset_size: 8940344650 - config_name: subset_106 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: esA.id dtype: string - name: esA.laser_score dtype: float64 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 - name: esA.audio.speaker_embedding sequence: float32 - name: esA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 8799046787 num_examples: 2085 download_size: 8827709482 dataset_size: 8799046787 - config_name: subset_107 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - 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config_name: subset_136 data_files: - split: train path: subset_136/train-* - config_name: subset_137 data_files: - split: train path: subset_137/train-* - config_name: subset_138 data_files: - split: train path: subset_138/train-* - config_name: subset_139 data_files: - split: train path: subset_139/train-* - config_name: subset_14 data_files: - split: train path: subset_14/train-* - config_name: subset_140 data_files: - split: train path: subset_140/train-* - config_name: subset_141 data_files: - split: train path: subset_141/train-* - config_name: subset_142 data_files: - split: train path: subset_142/train-* - config_name: subset_143 data_files: - split: train path: subset_143/train-* - config_name: subset_144 data_files: - split: train path: subset_144/train-* - config_name: subset_145 data_files: - split: train path: subset_145/train-* - config_name: subset_146 data_files: - split: train path: subset_146/train-* - config_name: subset_147 data_files: - split: train path: subset_147/train-* - config_name: subset_148 data_files: - split: train path: subset_148/train-* - config_name: subset_149 data_files: - split: train path: subset_149/train-* - config_name: subset_15 data_files: - split: train path: subset_15/train-* - config_name: subset_150 data_files: - split: train path: subset_150/train-* - config_name: subset_151 data_files: - split: train path: subset_151/train-* - config_name: subset_152 data_files: - split: train path: subset_152/train-* - config_name: subset_153 data_files: - split: train path: subset_153/train-* - config_name: subset_154 data_files: - split: train path: subset_154/train-* - config_name: subset_155 data_files: - split: train path: subset_155/train-* - config_name: subset_156 data_files: - split: train path: subset_156/train-* - config_name: subset_157 data_files: - split: train path: subset_157/train-* - config_name: subset_158 data_files: - split: train path: subset_158/train-* - config_name: subset_159 data_files: - split: train path: subset_159/train-* - config_name: subset_16 data_files: - split: train path: subset_16/train-* - config_name: subset_160 data_files: - split: train path: subset_160/train-* - config_name: subset_161 data_files: - split: train path: subset_161/train-* - config_name: subset_162 data_files: - split: train path: subset_162/train-* - config_name: subset_163 data_files: - split: train path: subset_163/train-* - config_name: subset_164 data_files: - split: train path: subset_164/train-* - config_name: subset_165 data_files: - split: train path: subset_165/train-* - config_name: subset_166 data_files: - split: train path: subset_166/train-* - config_name: subset_167 data_files: - split: train path: subset_167/train-* - config_name: subset_168 data_files: - split: train path: subset_168/train-* - config_name: subset_169 data_files: - split: train path: subset_169/train-* - config_name: subset_17 data_files: - split: train path: subset_17/train-* - config_name: subset_170 data_files: - split: train path: subset_170/train-* - config_name: subset_171 data_files: - split: train path: subset_171/train-* - config_name: subset_172 data_files: - split: train path: subset_172/train-* - config_name: subset_173 data_files: - split: train path: subset_173/train-* - config_name: subset_174 data_files: - split: train path: subset_174/train-* - config_name: subset_175 data_files: - split: train path: subset_175/train-* - config_name: subset_176 data_files: - split: train path: subset_176/train-* - config_name: subset_177 data_files: - split: train path: subset_177/train-* - config_name: subset_178 data_files: - split: train path: subset_178/train-* - config_name: subset_179 data_files: - split: train path: subset_179/train-* - config_name: subset_18 data_files: - split: train path: subset_18/train-* - config_name: subset_180 data_files: - split: train path: subset_180/train-* - config_name: subset_181 data_files: - split: train path: subset_181/train-* - config_name: subset_182 data_files: - split: train path: subset_182/train-* - config_name: subset_183 data_files: - split: train path: subset_183/train-* - config_name: subset_184 data_files: - split: train path: subset_184/train-* - config_name: subset_185 data_files: - split: train path: subset_185/train-* - config_name: subset_186 data_files: - split: train path: subset_186/train-* - config_name: subset_187 data_files: - split: train path: subset_187/train-* - config_name: subset_188 data_files: - split: train path: subset_188/train-* - config_name: subset_189 data_files: - split: train path: subset_189/train-* - config_name: subset_19 data_files: - split: train path: subset_19/train-* - config_name: subset_190 data_files: - split: train path: subset_190/train-* - config_name: subset_191 data_files: - split: train path: subset_191/train-* - config_name: subset_192 data_files: - split: train path: subset_192/train-* - config_name: subset_193 data_files: - split: train path: subset_193/train-* - config_name: subset_194 data_files: - split: train path: subset_194/train-* - config_name: subset_195 data_files: - split: train path: subset_195/train-* - config_name: subset_196 data_files: - split: train path: subset_196/train-* - config_name: subset_197 data_files: - split: train path: subset_197/train-* - config_name: subset_198 data_files: - split: train path: subset_198/train-* - config_name: subset_199 data_files: - split: train path: subset_199/train-* - config_name: subset_2 data_files: - split: train path: subset_2/train-* - config_name: subset_20 data_files: - split: train path: subset_20/train-* - config_name: subset_200 data_files: - split: train path: subset_200/train-* - config_name: subset_201 data_files: - split: train path: subset_201/train-* - config_name: subset_202 data_files: - split: train path: subset_202/train-* - config_name: subset_203 data_files: - split: train path: subset_203/train-* - config_name: subset_204 data_files: - split: train path: subset_204/train-* - config_name: subset_205 data_files: - split: train path: subset_205/train-* - config_name: subset_206 data_files: - split: train path: subset_206/train-* - config_name: subset_207 data_files: - split: train path: subset_207/train-* - config_name: subset_208 data_files: - split: train path: subset_208/train-* - config_name: subset_209 data_files: - split: train path: subset_209/train-* - config_name: subset_21 data_files: - split: train path: subset_21/train-* - config_name: subset_210 data_files: - split: train path: subset_210/train-* - config_name: subset_211 data_files: - split: train path: subset_211/train-* - config_name: subset_212 data_files: - split: train path: subset_212/train-* - config_name: subset_213 data_files: - split: train path: subset_213/train-* - config_name: subset_214 data_files: - split: train path: subset_214/train-* - config_name: subset_215 data_files: - split: train path: subset_215/train-* - config_name: subset_216 data_files: - split: train path: subset_216/train-* - config_name: subset_217 data_files: - split: train path: subset_217/train-* - config_name: subset_218 data_files: - split: train path: subset_218/train-* - config_name: subset_219 data_files: - split: train path: subset_219/train-* - config_name: subset_22 data_files: - split: train path: subset_22/train-* - config_name: subset_220 data_files: - split: train path: subset_220/train-* - config_name: subset_221 data_files: - split: train path: subset_221/train-* - config_name: subset_222 data_files: - split: train path: subset_222/train-* - config_name: subset_223 data_files: - split: train path: subset_223/train-* - config_name: subset_224 data_files: - split: train path: subset_224/train-* - config_name: subset_225 data_files: - split: train path: subset_225/train-* - config_name: subset_226 data_files: - split: train path: subset_226/train-* - config_name: subset_227 data_files: - split: train path: subset_227/train-* - config_name: subset_228 data_files: - split: train path: subset_228/train-* - config_name: subset_229 data_files: - split: train path: subset_229/train-* - config_name: subset_23 data_files: - split: train path: subset_23/train-* - config_name: subset_230 data_files: - split: train path: subset_230/train-* - config_name: subset_231 data_files: - split: train path: subset_231/train-* - config_name: subset_232 data_files: - split: train path: subset_232/train-* - config_name: subset_233 data_files: - split: train path: subset_233/train-* - config_name: subset_234 data_files: - split: train path: subset_234/train-* - config_name: subset_235 data_files: - split: train path: subset_235/train-* - config_name: subset_236 data_files: - split: train path: subset_236/train-* - config_name: subset_237 data_files: - split: train path: subset_237/train-* - config_name: subset_238 data_files: - split: train path: subset_238/train-* - config_name: subset_239 data_files: - split: train path: subset_239/train-* - config_name: subset_24 data_files: - split: train path: subset_24/train-* - config_name: subset_240 data_files: - split: train path: subset_240/train-* - config_name: subset_241 data_files: - split: train path: subset_241/train-* - config_name: subset_242 data_files: - split: train path: subset_242/train-* - config_name: subset_243 data_files: - split: train path: subset_243/train-* - config_name: subset_244 data_files: - split: train path: subset_244/train-* - config_name: subset_245 data_files: - split: train path: subset_245/train-* - config_name: subset_246 data_files: - split: train path: subset_246/train-* - config_name: subset_247 data_files: - split: train path: subset_247/train-* - config_name: subset_248 data_files: - split: train path: subset_248/train-* - config_name: subset_249 data_files: - split: train path: subset_249/train-* - config_name: subset_25 data_files: - split: train path: subset_25/train-* - config_name: subset_250 data_files: - split: train path: subset_250/train-* - config_name: subset_251 data_files: - split: train path: subset_251/train-* - config_name: subset_252 data_files: - split: train path: subset_252/train-* - config_name: subset_253 data_files: - split: train path: subset_253/train-* - config_name: subset_254 data_files: - split: train path: subset_254/train-* - config_name: subset_255 data_files: - split: train path: subset_255/train-* - config_name: subset_256 data_files: - split: train path: subset_256/train-* - config_name: subset_257 data_files: - split: train path: subset_257/train-* - config_name: subset_258 data_files: - split: train path: subset_258/train-* - config_name: subset_259 data_files: - split: train path: subset_259/train-* - config_name: subset_26 data_files: - split: train path: subset_26/train-* - config_name: subset_260 data_files: - split: train path: subset_260/train-* - config_name: subset_261 data_files: - split: train path: subset_261/train-* - config_name: subset_262 data_files: - split: train path: subset_262/train-* - config_name: subset_263 data_files: - split: train path: subset_263/train-* - config_name: subset_264 data_files: - split: train path: subset_264/train-* - config_name: subset_265 data_files: - split: train path: subset_265/train-* - config_name: subset_266 data_files: - split: train path: subset_266/train-* - config_name: subset_267 data_files: - split: train path: subset_267/train-* - config_name: subset_268 data_files: - split: train path: subset_268/train-* - config_name: subset_269 data_files: - split: train path: subset_269/train-* - config_name: subset_27 data_files: - split: train path: subset_27/train-* - config_name: subset_270 data_files: - split: train path: subset_270/train-* - config_name: subset_271 data_files: - split: train path: subset_271/train-* - config_name: subset_272 data_files: - split: train path: subset_272/train-* - config_name: subset_273 data_files: - split: train path: subset_273/train-* - config_name: subset_274 data_files: - split: train path: subset_274/train-* - config_name: subset_275 data_files: - split: train path: subset_275/train-* - config_name: subset_276 data_files: - split: train path: subset_276/train-* - config_name: subset_277 data_files: - split: train path: subset_277/train-* - config_name: subset_278 data_files: - split: train path: subset_278/train-* - config_name: subset_279 data_files: - split: train path: subset_279/train-* - config_name: subset_28 data_files: - split: train path: subset_28/train-* - config_name: subset_280 data_files: - split: train path: subset_280/train-* - config_name: subset_281 data_files: - split: train path: subset_281/train-* - config_name: subset_282 data_files: - split: train path: subset_282/train-* - config_name: subset_283 data_files: - split: train path: subset_283/train-* - config_name: subset_284 data_files: - split: train path: subset_284/train-* - config_name: subset_285 data_files: - split: train path: subset_285/train-* - config_name: subset_286 data_files: - split: train path: subset_286/train-* - config_name: subset_287 data_files: - split: train path: subset_287/train-* - config_name: subset_288 data_files: - split: train path: subset_288/train-* - config_name: subset_289 data_files: - split: train path: subset_289/train-* - config_name: subset_29 data_files: - split: train path: subset_29/train-* - config_name: subset_290 data_files: - split: train path: subset_290/train-* - config_name: subset_291 data_files: - split: train path: subset_291/train-* - config_name: subset_292 data_files: - split: train path: subset_292/train-* - config_name: subset_293 data_files: - split: train path: subset_293/train-* - config_name: subset_294 data_files: - split: train path: subset_294/train-* - config_name: subset_295 data_files: - split: train path: subset_295/train-* - config_name: subset_296 data_files: - split: train path: subset_296/train-* - config_name: subset_297 data_files: - split: train path: subset_297/train-* - config_name: subset_298 data_files: - split: train path: subset_298/train-* - config_name: subset_299 data_files: - split: train path: subset_299/train-* - config_name: subset_3 data_files: - split: train path: subset_3/train-* - config_name: subset_30 data_files: - split: train path: subset_30/train-* - config_name: subset_300 data_files: - split: train path: subset_300/train-* - config_name: subset_301 data_files: - split: train path: subset_301/train-* - config_name: subset_302 data_files: - split: train path: subset_302/train-* - config_name: subset_303 data_files: - split: train path: subset_303/train-* - config_name: subset_304 data_files: - split: train path: subset_304/train-* - config_name: subset_305 data_files: - split: train path: subset_305/train-* - config_name: subset_306 data_files: - split: train path: subset_306/train-* - config_name: subset_307 data_files: - split: train path: subset_307/train-* - config_name: subset_308 data_files: - split: train path: subset_308/train-* - config_name: subset_309 data_files: - split: train path: subset_309/train-* - config_name: subset_31 data_files: - split: train path: subset_31/train-* - config_name: subset_310 data_files: - split: train path: subset_310/train-* - config_name: subset_311 data_files: - split: train path: subset_311/train-* - config_name: subset_312 data_files: - split: train path: subset_312/train-* - config_name: subset_313 data_files: - split: train path: subset_313/train-* - config_name: subset_314 data_files: - split: train path: subset_314/train-* - config_name: subset_315 data_files: - split: train path: subset_315/train-* - config_name: subset_316 data_files: - split: train path: subset_316/train-* - config_name: subset_317 data_files: - split: train path: subset_317/train-* - config_name: subset_318 data_files: - split: train path: subset_318/train-* - config_name: subset_319 data_files: - split: train path: subset_319/train-* - config_name: subset_32 data_files: - split: train path: subset_32/train-* - config_name: subset_320 data_files: - split: train path: subset_320/train-* - config_name: subset_321 data_files: - split: train path: subset_321/train-* - config_name: subset_322 data_files: - split: train path: subset_322/train-* - config_name: subset_323 data_files: - split: train path: subset_323/train-* - config_name: subset_324 data_files: - split: train path: subset_324/train-* - config_name: subset_325 data_files: - split: train path: subset_325/train-* - config_name: subset_326 data_files: - split: train path: subset_326/train-* - config_name: subset_327 data_files: - split: train path: subset_327/train-* - config_name: subset_328 data_files: - split: train path: subset_328/train-* - config_name: subset_329 data_files: - split: train path: subset_329/train-* - config_name: subset_33 data_files: - split: train path: subset_33/train-* - config_name: subset_330 data_files: - split: train path: subset_330/train-* - config_name: subset_331 data_files: - split: train path: subset_331/train-* - config_name: subset_332 data_files: - split: train path: subset_332/train-* - config_name: subset_333 data_files: - split: train path: subset_333/train-* - config_name: subset_334 data_files: - split: train path: subset_334/train-* - config_name: subset_335 data_files: - split: train path: subset_335/train-* - config_name: subset_336 data_files: - split: train path: subset_336/train-* - config_name: subset_337 data_files: - split: train path: subset_337/train-* - config_name: subset_338 data_files: - split: train path: subset_338/train-* - config_name: subset_339 data_files: - split: train path: subset_339/train-* - config_name: subset_34 data_files: - split: train path: subset_34/train-* - config_name: subset_340 data_files: - split: train path: subset_340/train-* - config_name: subset_341 data_files: - split: train path: subset_341/train-* - config_name: subset_342 data_files: - split: train path: subset_342/train-* - config_name: subset_343 data_files: - split: train path: subset_343/train-* - config_name: subset_344 data_files: - split: train path: subset_344/train-* - config_name: subset_345 data_files: - split: train path: subset_345/train-* - config_name: subset_346 data_files: - split: train path: subset_346/train-* - config_name: subset_347 data_files: - split: train path: subset_347/train-* - config_name: subset_348 data_files: - split: train path: subset_348/train-* - config_name: subset_349 data_files: - split: train path: subset_349/train-* - config_name: subset_35 data_files: - split: train path: subset_35/train-* - config_name: subset_350 data_files: - split: train path: subset_350/train-* - config_name: subset_351 data_files: - split: train path: subset_351/train-* - config_name: subset_352 data_files: - split: train path: subset_352/train-* - config_name: subset_353 data_files: - split: train path: subset_353/train-* - config_name: subset_354 data_files: - split: train path: subset_354/train-* - config_name: subset_355 data_files: - split: train path: subset_355/train-* - config_name: subset_356 data_files: - split: train path: subset_356/train-* - config_name: subset_357 data_files: - split: train path: subset_357/train-* - config_name: subset_358 data_files: - split: train path: subset_358/train-* - config_name: subset_359 data_files: - split: train path: subset_359/train-* - config_name: subset_36 data_files: - split: train path: subset_36/train-* - config_name: subset_360 data_files: - split: train path: subset_360/train-* - config_name: subset_361 data_files: - split: train path: subset_361/train-* - config_name: subset_362 data_files: - split: train path: subset_362/train-* - config_name: subset_363 data_files: - split: train path: subset_363/train-* - config_name: subset_364 data_files: - split: train path: subset_364/train-* - config_name: subset_365 data_files: - split: train path: subset_365/train-* - config_name: subset_366 data_files: - split: train path: subset_366/train-* - config_name: subset_367 data_files: - split: train path: subset_367/train-* - config_name: subset_368 data_files: - split: train path: subset_368/train-* - config_name: subset_369 data_files: - split: train path: subset_369/train-* - config_name: subset_37 data_files: - split: train path: subset_37/train-* - config_name: subset_370 data_files: - split: train path: subset_370/train-* - config_name: subset_371 data_files: - split: train path: subset_371/train-* - config_name: subset_372 data_files: - split: train path: subset_372/train-* - config_name: subset_373 data_files: - split: train path: subset_373/train-* - config_name: subset_374 data_files: - split: train path: subset_374/train-* - config_name: subset_375 data_files: - split: train path: subset_375/train-* - config_name: subset_376 data_files: - split: train path: subset_376/train-* - config_name: subset_377 data_files: - split: train path: subset_377/train-* - config_name: subset_378 data_files: - split: train path: subset_378/train-* - config_name: subset_379 data_files: - split: train path: subset_379/train-* - config_name: subset_38 data_files: - split: train path: subset_38/train-* - config_name: subset_380 data_files: - split: train path: subset_380/train-* - config_name: subset_381 data_files: - split: train path: subset_381/train-* - config_name: subset_382 data_files: - split: train path: subset_382/train-* - config_name: subset_383 data_files: - split: train path: subset_383/train-* - config_name: subset_384 data_files: - split: train path: subset_384/train-* - config_name: subset_385 data_files: - split: train path: subset_385/train-* - config_name: subset_386 data_files: - split: train path: subset_386/train-* - config_name: subset_387 data_files: - split: train path: subset_387/train-* - config_name: subset_388 data_files: - split: train path: subset_388/train-* - config_name: subset_389 data_files: - split: train path: subset_389/train-* - config_name: subset_39 data_files: - split: train path: subset_39/train-* - config_name: subset_390 data_files: - split: train path: subset_390/train-* - config_name: subset_391 data_files: - split: train path: subset_391/train-* - config_name: subset_392 data_files: - split: train path: subset_392/train-* - config_name: subset_393 data_files: - split: train path: subset_393/train-* - config_name: subset_394 data_files: - split: train path: subset_394/train-* - config_name: subset_395 data_files: - split: train path: subset_395/train-* - config_name: subset_396 data_files: - split: train path: subset_396/train-* - config_name: subset_397 data_files: - split: train path: subset_397/train-* - config_name: subset_398 data_files: - split: train path: subset_398/train-* - config_name: subset_399 data_files: - split: train path: subset_399/train-* - config_name: subset_4 data_files: - split: train path: subset_4/train-* - config_name: subset_40 data_files: - split: train path: subset_40/train-* - config_name: subset_400 data_files: - split: train path: subset_400/train-* - config_name: subset_401 data_files: - split: train path: subset_401/train-* - config_name: subset_402 data_files: - split: train path: subset_402/train-* - config_name: subset_403 data_files: - split: train path: subset_403/train-* - config_name: subset_404 data_files: - split: train path: subset_404/train-* - config_name: subset_405 data_files: - split: train path: subset_405/train-* - config_name: subset_406 data_files: - split: train path: subset_406/train-* - config_name: subset_407 data_files: - split: train path: subset_407/train-* - config_name: subset_408 data_files: - split: train path: subset_408/train-* - config_name: subset_409 data_files: - split: train path: subset_409/train-* - config_name: subset_41 data_files: - split: train path: subset_41/train-* - config_name: subset_410 data_files: - split: train path: subset_410/train-* - config_name: subset_411 data_files: - split: train path: subset_411/train-* - config_name: subset_412 data_files: - split: train path: subset_412/train-* - config_name: subset_413 data_files: - split: train path: subset_413/train-* - config_name: subset_414 data_files: - split: train path: subset_414/train-* - config_name: subset_415 data_files: - split: train path: subset_415/train-* - config_name: subset_416 data_files: - split: train path: subset_416/train-* - config_name: subset_417 data_files: - split: train path: subset_417/train-* - config_name: subset_418 data_files: - split: train path: subset_418/train-* - config_name: subset_419 data_files: - split: train path: subset_419/train-* - config_name: subset_42 data_files: - split: train path: subset_42/train-* - config_name: subset_420 data_files: - split: train path: subset_420/train-* - config_name: subset_421 data_files: - split: train path: subset_421/train-* - config_name: subset_422 data_files: - split: train path: subset_422/train-* - config_name: subset_423 data_files: - split: train path: subset_423/train-* - config_name: subset_424 data_files: - split: train path: subset_424/train-* - config_name: subset_425 data_files: - split: train path: subset_425/train-* - config_name: subset_426 data_files: - split: train path: subset_426/train-* - config_name: subset_427 data_files: - split: train path: subset_427/train-* - config_name: subset_428 data_files: - split: train path: subset_428/train-* - config_name: subset_429 data_files: - split: train path: subset_429/train-* - config_name: subset_43 data_files: - split: train path: subset_43/train-* - config_name: subset_430 data_files: - split: train path: subset_430/train-* - config_name: subset_431 data_files: - split: train path: subset_431/train-* - config_name: subset_432 data_files: - split: train path: subset_432/train-* - config_name: subset_433 data_files: - split: train path: subset_433/train-* - config_name: subset_434 data_files: - split: train path: subset_434/train-* - config_name: subset_435 data_files: - split: train path: subset_435/train-* - config_name: subset_436 data_files: - split: train path: subset_436/train-* - config_name: subset_437 data_files: - split: train path: subset_437/train-* - config_name: subset_438 data_files: - split: train path: subset_438/train-* - config_name: subset_439 data_files: - split: train path: subset_439/train-* - config_name: subset_44 data_files: - split: train path: subset_44/train-* - 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split: train path: subset_91/train-* - config_name: subset_92 data_files: - split: train path: subset_92/train-* - config_name: subset_93 data_files: - split: train path: subset_93/train-* - config_name: subset_94 data_files: - split: train path: subset_94/train-* - config_name: subset_95 data_files: - split: train path: subset_95/train-* - config_name: subset_96 data_files: - split: train path: subset_96/train-* - config_name: subset_97 data_files: - split: train path: subset_97/train-* - config_name: subset_98 data_files: - split: train path: subset_98/train-* - config_name: subset_99 data_files: - split: train path: subset_99/train-* ---

andstor/the_pile_github

andstor

--- annotations_creators: - no-annotation language: - en language_creators: - found license: - other multilinguality: - monolingual pretty_name: The Pile GitHub size_categories: [] source_datasets: - original tags: [] task_categories: - text-generation - fill-mask - text-classification task_ids: [] --- # Dataset Card for The Pile GitHub ## Table of Contents - [Dataset Card for Smart Contracts](#dataset-card-for-the-pile-github) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Additional Information](#additional-information) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [ElutherAI](https://pile.eleuther.ai) - **Repository:** [GitHub](https://github.com/andstor/the-pile-github) - **Paper:** [arXiv](https://arxiv.org/abs/2101.00027) - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Needs More Information] ### Dataset Summary This is the GitHub subset of EleutherAi/The Pile dataset and contains GitHub repositories. The programming languages are identified using the [guesslang library](https://github.com/yoeo/guesslang). A total of 54 programming languages are included in the dataset. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The following languages are covered by the dataset: ``` 'Assembly', 'Batchfile', 'C', 'C#', 'C++', 'CMake', 'COBOL', 'CSS', 'CSV', 'Clojure', 'CoffeeScript', 'DM', 'Dart', 'Dockerfile', 'Elixir', 'Erlang', 'Fortran', 'Go', 'Groovy', 'HTML', 'Haskell', 'INI', 'JSON', 'Java', 'JavaScript', 'Julia', 'Kotlin', 'Lisp', 'Lua', 'Makefile', 'Markdown', 'Matlab', 'None', 'OCaml', 'Objective-C', 'PHP', 'Pascal', 'Perl', 'PowerShell', 'Prolog', 'Python', 'R', 'Ruby', 'Rust', 'SQL', 'Scala', 'Shell', 'Swift', 'TOML', 'TeX', 'TypeScript', 'Verilog', 'Visual Basic', 'XML', 'YAML' ``` The [guesslang library](https://github.com/yoeo/guesslang) is used to identify the programming languages. It has a guessing accuracy of above 90%. Hence, there will be some misclassifications in the language identification. ## Dataset Structure ### Data Instances [More Information Needed] ``` { 'text': ..., 'meta': {'language': ...} } ``` ### Data Fields - `text` (`string`): the source code. - `meta` (`dict`): the metadata of the source code. - `language` (`string`): the programming language of the source code. ### Data Splits [More Information Needed] | | train | validation | test | |-------------------------|------:|-----------:|-----:| | Input Sentences | | | | | Average Sentence Length | | | | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data The data is purely a subset of the [EleutherAI/The Pile dataset](https://huggingface.co/datasets/the_pile). See the original [dataset](https://arxiv.org/abs/2201.07311) for more details. ## Additional Information ### Licensing Information The Pile dataset was released on January 1st, 2021. It is licensed under the MIT License. See the [dataset](https://arxiv.org/abs/2201.07311) for more details. ### Citation Information Provide the [BibTex](http://www.bibtex.org/)-formatted reference for the dataset. For example: ``` @article{pile, title={The {P}ile: An 800GB Dataset of Diverse Text for Language Modeling}, author={Gao, Leo and Biderman, Stella and Black, Sid and Golding, Laurence and Hoppe, Travis and Foster, Charles and Phang, Jason and He, Horace and Thite, Anish and Nabeshima, Noa and Presser, Shawn and Leahy, Connor}, journal={arXiv preprint arXiv:2101.00027}, year={2020} } ``` ### Contributions Thanks to [@andstor](https://github.com/andstor) for adding this dataset.

Open-Orca/FLAN

Open-Orca

--- license: cc-by-4.0 language: - en library_name: transformers pipeline_tag: text-generation datasets: - Open-Orca/OpenOrca size_categories: - 1B<n<10B --- <p><h1>🍮 The WHOLE FLAN Collection! 🍮</h1></p>  # Overview This repository includes the full dataset from the [FLAN Collection](https://ai.googleblog.com/2023/02/the-flan-collection-advancing-open.html), totalling ~300GB as parquets. Generated using the official seqio templating from the [Google FLAN Collection GitHub repo](https://github.com/google-research/FLAN/tree/main/flan/v2). The data is subject to all the same licensing of the component datasets. To keep up with our continued work on OpenOrca and other exciting research, find our Discord here: https://AlignmentLab.ai # Motivation This work was done as part of the requirements for the OpenOrca project. There was not a large enough subset of FLAN Collection generated publicly to subsample from to complete the work. So, we opted to process the entire collection ourselves. Generating this requires an understanding of seqio and a Linux server with 512GB of CPU ram, as well as fast drives and custom limits for many parameters beyond what is default on Linux server distributions (e.g., requiring up to 45,000 threads running at once). It takes downloading over 400GB of datasets, working around tfds bugs, and then processing the datasets over the course of several days. We provide this repo as a resource to other ML researchers, as it saves these time consuming and laborious steps to getting the data into a more accessible format for further consumption. # Data ## Organization * JSON files at top level are used for subsampling in OpenOrca * Parquets in subdirectories contain the entire FLAN collection in Dask-sharded folders by submix fractions ## Zero-Shot vs Few-Shot and Options vs No-Options The core sub-collections of FLAN are `CoT`, `Dialog`, `NIv2`, `T0`, and `flan2021`. Within those sub-collections are four "remixes" of the data that are templated differently: * `Zero-Shot` and `Few-Shot` * `Zero-Shot` provides a prompt, question, or challenge without any exemplaries prior * `Few-Shot` provides exemplaries first * `Options` and `No-Options` * `Options` provides a question or challenge with multiple-choice (e.g. A/B/C/D) answer options provided to select from * `No-Options` requires a free-form answer For every sub-collection, only some of the "remixes" may officially be provided. All available have been generated in full without any redaction or sub-sampling. An example: `t0_fsopt_data` folder contains the sub-collection `T0`'s Few-Shot (FS), Options (OPT) remix set. Notably, this is the largest "remix" and the one that necessitates 512GB CPU ram to generate. The raw json output is nearly 200GB. ## Parquet Sizes Each sub-collection's individual remixes are provided as [Parquet](https://huggingface.co/docs/datasets/loading#parquet) files which have been sharded by [Dask](https://huggingface.co/docs/datasets/main/en/filesystems#dask) into ~160MB chunks (starting from 256MB blocks of the source jsonl files). The folder structure along with size sums is provided below. ``` $ du -h --max-depth=1 ./ 9.1G ./niv2_fsopt_data 2.4G ./niv2_zsopt_data 59G ./flan_fsopt_data 984M ./dialog_zsopt_data 11G ./flan_zsopt_data 8.6G ./dialog_fsopt_data 16G ./t0_zsnoopt_data 149M ./cot_fsopt_data 20M ./cot_zsopt_data 17G ./t0_zsopt_data 11G ./flan_zsnoopt_data 101G ./t0_fsopt_data 25G ./flan_fsnoopt_data 39G ./t0_fsnoopt_data 296G ./ ``` # Citations ```bibtex @misc{goodson2023huggyflan title={Fine FLAN: Seqio to Parquet So You Don't Have To}, author={Bleys Goodson}, year={2023}, publisher = {HuggingFace}, journal = {HuggingFace repository}, howpublished = {\url{https://https://huggingface.co/datasets/Open-Orca/FLAN}, } ``` ```bibtex @misc{longpre2023flan, title={The Flan Collection: Designing Data and Methods for Effective Instruction Tuning}, author={Shayne Longpre and Le Hou and Tu Vu and Albert Webson and Hyung Won Chung and Yi Tay and Denny Zhou and Quoc V. Le and Barret Zoph and Jason Wei and Adam Roberts}, year={2023}, eprint={2301.13688}, archivePrefix={arXiv}, primaryClass={cs.AI} } ``` ```bibtex @misc{wei2022finetuned, title={Finetuned Language Models Are Zero-Shot Learners}, author={Jason Wei and Maarten Bosma and Vincent Y. Zhao and Kelvin Guu and Adams Wei Yu and Brian Lester and Nan Du and Andrew M. Dai and Quoc V. Le}, year={2022}, eprint={2109.01652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ```bibtex @misc{sanh2022multitask, title={Multitask Prompted Training Enables Zero-Shot Task Generalization}, author={Victor Sanh and Albert Webson and Colin Raffel and Stephen H. Bach and Lintang Sutawika and Zaid Alyafeai and Antoine Chaffin and Arnaud Stiegler and Teven Le Scao and Arun Raja and Manan Dey and M Saiful Bari and Canwen Xu and Urmish Thakker and Shanya Sharma Sharma and Eliza Szczechla and Taewoon Kim and Gunjan Chhablani and Nihal Nayak and Debajyoti Datta and Jonathan Chang and Mike Tian-Jian Jiang and Han Wang and Matteo Manica and Sheng Shen and Zheng Xin Yong and Harshit Pandey and Rachel Bawden and Thomas Wang and Trishala Neeraj and Jos Rozen and Abheesht Sharma and Andrea Santilli and Thibault Fevry and Jason Alan Fries and Ryan Teehan and Tali Bers and Stella Biderman and Leo Gao and Thomas Wolf and Alexander M. Rush}, year={2022}, eprint={2110.08207}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` ```bibtex @misc{wang2022supernaturalinstructions, title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks}, author={Yizhong Wang and Swaroop Mishra and Pegah Alipoormolabashi and Yeganeh Kordi and Amirreza Mirzaei and Anjana Arunkumar and Arjun Ashok and Arut Selvan Dhanasekaran and Atharva Naik and David Stap and Eshaan Pathak and Giannis Karamanolakis and Haizhi Gary Lai and Ishan Purohit and Ishani Mondal and Jacob Anderson and Kirby Kuznia and Krima Doshi and Maitreya Patel and Kuntal Kumar Pal and Mehrad Moradshahi and Mihir Parmar and Mirali Purohit and Neeraj Varshney and Phani Rohitha Kaza and Pulkit Verma and Ravsehaj Singh Puri and Rushang Karia and Shailaja Keyur Sampat and Savan Doshi and Siddhartha Mishra and Sujan Reddy and Sumanta Patro and Tanay Dixit and Xudong Shen and Chitta Baral and Yejin Choi and Noah A. Smith and Hannaneh Hajishirzi and Daniel Khashabi}, year={2022}, eprint={2204.07705}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

bigscience/P3

bigscience

--- annotations_creators: - crowdsourced - expert-generated language: - en license: - apache-2.0 multilinguality: - monolingual size_categories: - 100M<n<1B task_categories: - other pretty_name: P3 dataset_info: - config_name: adversarial_qa_dbert_answer_the_following_q features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 18313753 num_examples: 10000 - name: validation num_bytes: 1791034 num_examples: 1000 download_size: 6288641 dataset_size: 20104787 - config_name: adversarial_qa_dbert_based_on features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 17580553 num_examples: 10000 - name: validation num_bytes: 1717566 num_examples: 1000 download_size: 6206744 dataset_size: 19298119 - config_name: adversarial_qa_dbert_generate_question features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 18552810 num_examples: 10000 - name: validation num_bytes: 1824231 num_examples: 1000 - name: test num_bytes: 1954952 num_examples: 1000 download_size: 5882604 dataset_size: 22331993 - config_name: adversarial_qa_dbert_question_context_answer features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 16859685 num_examples: 10000 - name: validation num_bytes: 1646118 num_examples: 1000 download_size: 6180363 dataset_size: 18505803 - config_name: adversarial_qa_dbert_tell_what_it_is features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 17793277 num_examples: 10000 - name: validation num_bytes: 1739418 num_examples: 1000 download_size: 6276720 dataset_size: 19532695 - config_name: adversarial_qa_dbidaf_answer_the_following_q features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 18273217 num_examples: 10000 - name: validation num_bytes: 1797789 num_examples: 1000 download_size: 6321670 dataset_size: 20071006 - config_name: adversarial_qa_dbidaf_based_on features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 17539777 num_examples: 10000 - name: validation num_bytes: 1724577 num_examples: 1000 download_size: 6247591 dataset_size: 19264354 - config_name: adversarial_qa_dbidaf_generate_question features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 18508967 num_examples: 10000 - name: validation num_bytes: 1830585 num_examples: 1000 - name: test num_bytes: 1925723 num_examples: 1000 download_size: 5983857 dataset_size: 22265275 - config_name: adversarial_qa_dbidaf_question_context_answer features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 16821505 num_examples: 10000 - name: validation num_bytes: 1652425 num_examples: 1000 download_size: 6292806 dataset_size: 18473930 - config_name: adversarial_qa_dbidaf_tell_what_it_is features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 17755161 num_examples: 10000 - name: validation num_bytes: 1745717 num_examples: 1000 download_size: 6250903 dataset_size: 19500878 - config_name: adversarial_qa_droberta_answer_the_following_q features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 18084393 num_examples: 10000 - name: validation num_bytes: 1798375 num_examples: 1000 download_size: 6223439 dataset_size: 19882768 - config_name: adversarial_qa_droberta_based_on features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 17352073 num_examples: 10000 - name: validation num_bytes: 1725151 num_examples: 1000 download_size: 6202901 dataset_size: 19077224 - config_name: adversarial_qa_droberta_generate_question features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 18257414 num_examples: 10000 - name: validation num_bytes: 1828966 num_examples: 1000 - name: test num_bytes: 1997556 num_examples: 1000 download_size: 5928633 dataset_size: 22083936 - config_name: adversarial_qa_droberta_question_context_answer features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 16638393 num_examples: 10000 - name: validation num_bytes: 1653815 num_examples: 1000 download_size: 6193786 dataset_size: 18292208 - config_name: adversarial_qa_droberta_tell_what_it_is features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 17571837 num_examples: 10000 - name: validation num_bytes: 1747043 num_examples: 1000 download_size: 6152157 dataset_size: 19318880 - config_name: ag_news_classify features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 79459523 num_examples: 120000 - name: test num_bytes: 5007082 num_examples: 7600 download_size: 37504540 dataset_size: 84466605 - config_name: ag_news_classify_question_first features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 79339523 num_examples: 120000 - name: test num_bytes: 4999482 num_examples: 7600 download_size: 37311664 dataset_size: 84339005 - config_name: ag_news_classify_with_choices features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 91699523 num_examples: 120000 - name: test num_bytes: 5782282 num_examples: 7600 download_size: 38377186 dataset_size: 97481805 - config_name: ag_news_classify_with_choices_question_first features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 91699523 num_examples: 120000 - name: test num_bytes: 5782282 num_examples: 7600 download_size: 38318638 dataset_size: 97481805 - config_name: ag_news_recommend features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 94039523 num_examples: 120000 - name: test num_bytes: 5930482 num_examples: 7600 download_size: 38368116 dataset_size: 99970005 - config_name: ag_news_which_section features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 83899523 num_examples: 120000 - name: test num_bytes: 5288282 num_examples: 7600 download_size: 37893964 dataset_size: 89187805 - config_name: ag_news_which_section_choices features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 100099523 num_examples: 120000 - name: test num_bytes: 6314282 num_examples: 7600 download_size: 39167925 dataset_size: 106413805 - config_name: ai2_arc_ARC_Challenge_heres_a_problem features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 870695 num_examples: 1119 - name: validation num_bytes: 237526 num_examples: 299 - name: test num_bytes: 929144 num_examples: 1172 download_size: 796298 dataset_size: 2037365 - config_name: ai2_arc_ARC_Challenge_i_am_hesitating features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 1063080 num_examples: 1119 - name: validation num_bytes: 290313 num_examples: 299 - name: test num_bytes: 1135794 num_examples: 1172 download_size: 1087298 dataset_size: 2489187 - config_name: ai2_arc_ARC_Challenge_multiple_choice features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 1079865 num_examples: 1119 - name: validation num_bytes: 294798 num_examples: 299 - name: test num_bytes: 1153374 num_examples: 1172 download_size: 1096748 dataset_size: 2528037 - config_name: ai2_arc_ARC_Challenge_pick_false_options features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 965402 num_examples: 1119 - name: validation num_bytes: 263171 num_examples: 299 - name: test num_bytes: 1032956 num_examples: 1172 download_size: 1043688 dataset_size: 2261529 - config_name: ai2_arc_ARC_Challenge_pick_the_most_correct_option features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 812508 num_examples: 1119 - name: validation num_bytes: 221981 num_examples: 299 - name: test num_bytes: 868204 num_examples: 1172 download_size: 791475 dataset_size: 1902693 - config_name: ai2_arc_ARC_Challenge_qa_options features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 815781 num_examples: 1119 - name: validation num_bytes: 224234 num_examples: 299 - name: test num_bytes: 876782 num_examples: 1172 download_size: 1044349 dataset_size: 1916797 - config_name: ai2_arc_ARC_Easy_heres_a_problem features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 1585434 num_examples: 2251 - name: validation num_bytes: 402833 num_examples: 570 - name: test num_bytes: 1680740 num_examples: 2376 download_size: 1372031 dataset_size: 3669007 - config_name: ai2_arc_ARC_Easy_i_am_hesitating features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 1893561 num_examples: 2251 - name: validation num_bytes: 479155 num_examples: 570 - name: test num_bytes: 2003593 num_examples: 2376 download_size: 1829256 dataset_size: 4376309 - config_name: ai2_arc_ARC_Easy_multiple_choice features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 1927326 num_examples: 2251 - name: validation num_bytes: 487705 num_examples: 570 - name: test num_bytes: 2039233 num_examples: 2376 download_size: 1833872 dataset_size: 4454264 - config_name: ai2_arc_ARC_Easy_pick_false_options features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 1702829 num_examples: 2251 - name: validation num_bytes: 431949 num_examples: 570 - name: test num_bytes: 1803223 num_examples: 2376 download_size: 1773690 dataset_size: 3938001 - config_name: ai2_arc_ARC_Easy_pick_the_most_correct_option features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 1468388 num_examples: 2251 - name: validation num_bytes: 373194 num_examples: 570 - name: test num_bytes: 1557195 num_examples: 2376 download_size: 1359858 dataset_size: 3398777 - config_name: ai2_arc_ARC_Easy_qa_options features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 1396090 num_examples: 2251 - name: validation num_bytes: 353185 num_examples: 570 - name: test num_bytes: 1478497 num_examples: 2376 download_size: 1744673 dataset_size: 3227772 - config_name: amazon_polarity_Is_this_product_review_positive features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 3657525221 num_examples: 3600000 - name: test num_bytes: 406170885 num_examples: 400000 download_size: 2087209082 dataset_size: 4063696106 - config_name: amazon_polarity_Is_this_review features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 3691725225 num_examples: 3600000 - name: test num_bytes: 409970885 num_examples: 400000 download_size: 2092135054 dataset_size: 4101696110 - config_name: amazon_polarity_Is_this_review_negative features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 3596325225 num_examples: 3600000 - name: test num_bytes: 399370885 num_examples: 400000 download_size: 2088926047 dataset_size: 3995696110 - config_name: amazon_polarity_User_recommend_this_product features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 3647231922 num_examples: 3600000 - name: test num_bytes: 405019064 num_examples: 400000 download_size: 1970470915 dataset_size: 4052250986 - config_name: amazon_polarity_convey_negative_or_positive_sentiment features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 3853725225 num_examples: 3600000 - name: test num_bytes: 427970885 num_examples: 400000 download_size: 2107131644 dataset_size: 4281696110 - config_name: amazon_polarity_flattering_or_not features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 4156125225 num_examples: 3600000 - name: test num_bytes: 461570885 num_examples: 400000 download_size: 2121811218 dataset_size: 4617696110 - config_name: amazon_polarity_negative_or_positive_tone features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 3983325221 num_examples: 3600000 - name: test num_bytes: 442370885 num_examples: 400000 download_size: 2105973069 dataset_size: 4425696106 - config_name: amazon_polarity_user_satisfied features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 4269525221 num_examples: 3600000 - name: test num_bytes: 474170885 num_examples: 400000 download_size: 2112525496 dataset_size: 4743696106 - config_name: amazon_polarity_would_you_buy features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 4541325221 num_examples: 3600000 - name: test num_bytes: 504370885 num_examples: 400000 download_size: 2145762328 dataset_size: 5045696106 - config_name: anli_GPT_3_style_r1 features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 15891829 num_examples: 16946 - name: validation num_bytes: 939241 num_examples: 1000 - name: test num_bytes: 937388 num_examples: 1000 download_size: 6820413 dataset_size: 17768458 - config_name: anli_GPT_3_style_r1_score_eval features: - name: idx sequence: int32 - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: is_correct dtype: bool - name: targets sequence: int32 - name: targets_pretokenized dtype: string - name: weight dtype: float32 splits: - name: train num_bytes: 46818519 num_examples: 50838 - name: validation num_bytes: 2767114 num_examples: 3000 - name: test num_bytes: 2761555 num_examples: 3000 download_size: 9095632 dataset_size: 52347188 - config_name: anli_GPT_3_style_r2 features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 42010764 num_examples: 45460 - name: validation num_bytes: 926684 num_examples: 1000 - name: test num_bytes: 932575 num_examples: 1000 download_size: 13987598 dataset_size: 43870023 - config_name: anli_GPT_3_style_r2_score_eval features: - name: idx sequence: int32 - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: is_correct dtype: bool - name: targets sequence: int32 - name: targets_pretokenized dtype: string - name: weight dtype: float32 splits: - name: train num_bytes: 123746670 num_examples: 136380 - name: validation num_bytes: 2729443 num_examples: 3000 - name: test num_bytes: 2747116 num_examples: 3000 download_size: 17660861 dataset_size: 129223229 - config_name: anli_GPT_3_style_r3 features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 88846603 num_examples: 100459 - name: validation num_bytes: 1075843 num_examples: 1200 - name: test num_bytes: 1071704 num_examples: 1200 download_size: 28572176 dataset_size: 90994150 - config_name: anli_GPT_3_style_r3_score_eval features: - name: idx sequence: int32 - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: is_correct dtype: bool - name: targets sequence: int32 - name: targets_pretokenized dtype: string - name: weight dtype: float32 splits: - name: train num_bytes: 261465576 num_examples: 301377 - name: validation num_bytes: 3166845 num_examples: 3600 - name: test num_bytes: 3154428 num_examples: 3600 download_size: 36725759 dataset_size: 267786849 - config_name: anli_MNLI_crowdsource_r1 features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 18848410 num_examples: 16946 - name: validation num_bytes: 1112388 num_examples: 1000 - name: test num_bytes: 1110687 num_examples: 1000 download_size: 7035294 dataset_size: 21071485 - config_name: anli_MNLI_crowdsource_r1_score_eval features: - name: idx sequence: int32 - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - 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name: validation num_bytes: 4386790 num_examples: 2985 - name: test num_bytes: 10260599 num_examples: 6963 download_size: 18455761 dataset_size: 49315834 - config_name: cosmos_qa_description_context_question_answer_text features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 40046420 num_examples: 25262 - name: validation num_bytes: 5170736 num_examples: 2985 - name: test num_bytes: 12050974 num_examples: 6963 download_size: 22574952 dataset_size: 57268130 - config_name: cosmos_qa_description_context_question_text features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 30105735 num_examples: 25262 - name: validation num_bytes: 3812735 num_examples: 2985 - name: test num_bytes: 8896748 num_examples: 6963 download_size: 17392729 dataset_size: 42815218 - config_name: cosmos_qa_no_prompt_id features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 29843403 num_examples: 25262 - name: validation num_bytes: 3816655 num_examples: 2985 - name: test num_bytes: 8930666 num_examples: 6963 download_size: 17856956 dataset_size: 42590724 - config_name: cosmos_qa_no_prompt_text features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 35221378 num_examples: 25262 - name: validation num_bytes: 4600601 num_examples: 2985 - name: test num_bytes: 10721041 num_examples: 6963 download_size: 21950786 dataset_size: 50543020 - config_name: cosmos_qa_only_question_answer features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 9307051 num_examples: 25262 - name: validation num_bytes: 1265511 num_examples: 2985 - name: test num_bytes: 2916821 num_examples: 6963 download_size: 6171348 dataset_size: 13489383 - config_name: dbpedia_14_given_a_choice_of_categories_ features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 719436519 num_examples: 560000 - name: test num_bytes: 89954668 num_examples: 70000 download_size: 231812702 dataset_size: 809391187 - config_name: dbpedia_14_given_a_list_of_category_what_does_the_title_belong_to features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 409923864 num_examples: 560000 - name: test num_bytes: 51249097 num_examples: 70000 download_size: 38870531 dataset_size: 461172961 - config_name: dbpedia_14_given_list_what_category_does_the_paragraph_belong_to features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 698518491 num_examples: 560000 - name: test num_bytes: 87332355 num_examples: 70000 download_size: 219363263 dataset_size: 785850846 - config_name: dbpedia_14_pick_one_category_for_the_following_text features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 717756507 num_examples: 560000 - name: test num_bytes: 89744668 num_examples: 70000 download_size: 230680647 dataset_size: 807501175 - config_name: dream_answer_to_dialogue features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 9167493 num_examples: 6116 - name: validation num_bytes: 3008442 num_examples: 2040 - name: test num_bytes: 3008242 num_examples: 2041 download_size: 3571012 dataset_size: 15184177 - config_name: dream_baseline features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 10027147 num_examples: 6116 - name: validation num_bytes: 3280100 num_examples: 2040 - name: test num_bytes: 3289529 num_examples: 2041 download_size: 6311330 dataset_size: 16596776 - config_name: dream_generate_first_utterance features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 7880062 num_examples: 6116 - name: validation num_bytes: 2580535 num_examples: 2040 - name: test num_bytes: 2584957 num_examples: 2041 download_size: 2989013 dataset_size: 13045554 - config_name: dream_generate_last_utterance features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 8125880 num_examples: 6116 - name: validation num_bytes: 2659720 num_examples: 2040 - name: test num_bytes: 2660169 num_examples: 2041 download_size: 3018904 dataset_size: 13445769 - config_name: dream_read_the_following_conversation_and_answer_the_question features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - 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name: test num_bytes: 57135051 num_examples: 13449 download_size: 71643871 dataset_size: 362121445 - config_name: duorc_ParaphraseRC_decide_worth_it features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 314845789 num_examples: 69524 - name: validation num_bytes: 70331271 num_examples: 15591 - name: test num_bytes: 72204115 num_examples: 15857 download_size: 100794562 dataset_size: 457381175 - config_name: duorc_ParaphraseRC_extract_answer features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 308636910 num_examples: 69524 - name: validation num_bytes: 68940369 num_examples: 15591 - name: test num_bytes: 70789828 num_examples: 15857 download_size: 99839398 dataset_size: 448367107 - config_name: duorc_ParaphraseRC_generate_question features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 289153644 num_examples: 69524 - name: validation num_bytes: 64571759 num_examples: 15591 - name: test num_bytes: 66337503 num_examples: 15857 download_size: 74472346 dataset_size: 420062906 - config_name: duorc_ParaphraseRC_generate_question_by_answer features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 254613731 num_examples: 58752 - name: validation num_bytes: 56695982 num_examples: 13111 - name: test num_bytes: 58319337 num_examples: 13449 download_size: 85228208 dataset_size: 369629050 - config_name: duorc_ParaphraseRC_movie_director features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - 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config_name: duorc_SelfRC_answer_question features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 263617804 num_examples: 60721 - name: validation num_bytes: 56257282 num_examples: 12961 - name: test num_bytes: 54002992 num_examples: 12559 download_size: 81555005 dataset_size: 373878078 - config_name: duorc_SelfRC_build_story_around_qa features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 245194648 num_examples: 60094 - name: validation num_bytes: 52411094 num_examples: 12845 - name: test num_bytes: 50178336 num_examples: 12415 download_size: 64377895 dataset_size: 347784078 - config_name: duorc_SelfRC_decide_worth_it features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - 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name: test num_bytes: 50703125 num_examples: 12559 download_size: 60820233 dataset_size: 351170378 - config_name: duorc_SelfRC_generate_question_by_answer features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 250482850 num_examples: 60094 - name: validation num_bytes: 53541352 num_examples: 12845 - name: test num_bytes: 51271129 num_examples: 12415 download_size: 76508439 dataset_size: 355295331 - config_name: duorc_SelfRC_movie_director features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 268967019 num_examples: 60721 - name: validation num_bytes: 57398891 num_examples: 12961 - name: test num_bytes: 55109435 num_examples: 12559 download_size: 80004661 dataset_size: 381475345 - config_name: duorc_SelfRC_question_answering features: - 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name: validation num_bytes: 102511962 num_examples: 189651 - name: test num_bytes: 1022016 num_examples: 1951 download_size: 1034760505 dataset_size: 2154438464 - config_name: gigaword_first_sentence_title features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 2214474621 num_examples: 3803957 - name: validation num_bytes: 110666955 num_examples: 189651 - name: test num_bytes: 1105909 num_examples: 1951 download_size: 1045083572 dataset_size: 2326247485 - config_name: gigaword_generate_summary_for_this features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 2282945863 num_examples: 3803957 - name: validation num_bytes: 114080673 num_examples: 189651 - name: test num_bytes: 1141027 num_examples: 1951 download_size: 1047958875 dataset_size: 2398167563 - 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name: test num_bytes: 150346271 num_examples: 390965 download_size: 125586835 dataset_size: 303843504 - config_name: hellaswag_Appropriate_continuation_Yes_or_No features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 36636395 num_examples: 39905 - name: validation num_bytes: 9457712 num_examples: 10042 - name: test num_bytes: 9207968 num_examples: 10003 download_size: 22929700 dataset_size: 55302075 - config_name: hellaswag_Open_ended_completion features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 53208771 num_examples: 39905 - name: validation num_bytes: 13804081 num_examples: 10042 - name: test num_bytes: 13323189 num_examples: 10003 download_size: 44228748 dataset_size: 80336041 - config_name: hellaswag_Open_ended_start features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 31586178 num_examples: 39905 - name: validation num_bytes: 8175505 num_examples: 10042 - name: test num_bytes: 7918171 num_examples: 10003 download_size: 23750142 dataset_size: 47679854 - config_name: hellaswag_Predict_ending_with_hint features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 103772125 num_examples: 39905 - name: validation num_bytes: 26953584 num_examples: 10042 - name: test num_bytes: 26056289 num_examples: 10003 download_size: 79049479 dataset_size: 156781998 - config_name: hellaswag_Predict_ending_with_hint_score_eval features: - name: idx sequence: int32 - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: is_correct dtype: bool - name: targets sequence: int32 - name: targets_pretokenized dtype: string - name: weight dtype: float32 splits: - name: train num_bytes: 327006481 num_examples: 159620 - name: validation num_bytes: 84933063 num_examples: 40168 - name: test num_bytes: 82304557 num_examples: 40012 download_size: 132747083 dataset_size: 494244101 - config_name: hellaswag_Randomized_prompts_template features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 101707929 num_examples: 39905 - name: validation num_bytes: 26424150 num_examples: 10042 - name: test num_bytes: 25517504 num_examples: 10003 download_size: 78615384 dataset_size: 153649583 - config_name: hellaswag_Randomized_prompts_template_score_eval features: - name: idx sequence: int32 - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: is_correct dtype: bool - name: targets sequence: int32 - name: targets_pretokenized dtype: string - name: weight dtype: float32 splits: - name: train num_bytes: 318749697 num_examples: 159620 - name: validation num_bytes: 82815327 num_examples: 40168 - name: test num_bytes: 80149417 num_examples: 40012 download_size: 133148565 dataset_size: 481714441 - config_name: hellaswag_Reversed_appropriate_continuation_Yes_or_No features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 37685857 num_examples: 39905 - name: validation num_bytes: 9718940 num_examples: 10042 - name: test num_bytes: 9484298 num_examples: 10003 download_size: 23013938 dataset_size: 56889095 - config_name: hellaswag_Topic_of_the_context features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - 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name: validation num_bytes: 26660776 num_examples: 10042 - name: test num_bytes: 25754067 num_examples: 10003 download_size: 78228282 dataset_size: 155083558 - config_name: hellaswag_complete_first_then_score_eval features: - name: idx sequence: int32 - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: is_correct dtype: bool - name: targets sequence: int32 - name: targets_pretokenized dtype: string - name: weight dtype: float32 splits: - name: train num_bytes: 322592841 num_examples: 159620 - name: validation num_bytes: 83761831 num_examples: 40168 - name: test num_bytes: 81095669 num_examples: 40012 download_size: 132338669 dataset_size: 487450341 - config_name: hellaswag_how_ends features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 71330813 num_examples: 39905 - name: validation num_bytes: 18491297 num_examples: 10042 - name: test num_bytes: 17929217 num_examples: 10003 download_size: 47966583 dataset_size: 107751327 - config_name: hellaswag_if_begins_how_continues features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 74842453 num_examples: 39905 - name: validation num_bytes: 19374993 num_examples: 10042 - name: test num_bytes: 18809481 num_examples: 10003 download_size: 48306373 dataset_size: 113026927 - config_name: hellaswag_if_begins_how_continues_score_eval features: - name: idx sequence: int32 - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: is_correct dtype: bool - name: targets sequence: int32 - name: targets_pretokenized dtype: string - name: weight dtype: float32 splits: - name: train num_bytes: 293643445 num_examples: 159620 - name: validation num_bytes: 76058945 num_examples: 40168 - name: test num_bytes: 73802494 num_examples: 40012 download_size: 94001678 dataset_size: 443504884 - config_name: imdb_Movie_Expressed_Sentiment features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 62032706 num_examples: 25000 - name: test num_bytes: 61156510 num_examples: 25000 - name: unsupervised num_bytes: 124406157 num_examples: 50000 download_size: 128577979 dataset_size: 247595373 - config_name: imdb_Movie_Expressed_Sentiment_2 features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 62632706 num_examples: 25000 - name: test num_bytes: 61756510 num_examples: 25000 - name: unsupervised num_bytes: 125606157 num_examples: 50000 download_size: 128508345 dataset_size: 249995373 - config_name: imdb_Negation_template_for_positive_and_negative features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 61932706 num_examples: 25000 - name: test num_bytes: 61056510 num_examples: 25000 - name: unsupervised num_bytes: 123606157 num_examples: 50000 download_size: 128322307 dataset_size: 246595373 - config_name: imdb_Reviewer_Enjoyment features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 63445206 num_examples: 25000 - name: test num_bytes: 62569010 num_examples: 25000 - name: unsupervised num_bytes: 126656157 num_examples: 50000 download_size: 128649514 dataset_size: 252670373 - config_name: imdb_Reviewer_Enjoyment_Yes_No features: - 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name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 62220206 num_examples: 25000 - name: test num_bytes: 61344010 num_examples: 25000 - name: unsupervised num_bytes: 124806157 num_examples: 50000 download_size: 128595877 dataset_size: 248370373 - config_name: imdb_Reviewer_Sentiment_Feeling features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 62257706 num_examples: 25000 - name: test num_bytes: 61381510 num_examples: 25000 - name: unsupervised num_bytes: 124856157 num_examples: 50000 download_size: 128516819 dataset_size: 248495373 - config_name: imdb_Sentiment_with_choices_ features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 62082706 num_examples: 25000 - name: test num_bytes: 61206510 num_examples: 25000 - name: unsupervised num_bytes: 124506157 num_examples: 50000 download_size: 128468742 dataset_size: 247795373 - config_name: imdb_Text_Expressed_Sentiment features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 62357706 num_examples: 25000 - name: test num_bytes: 61481510 num_examples: 25000 - name: unsupervised num_bytes: 125056157 num_examples: 50000 download_size: 128646772 dataset_size: 248895373 - config_name: imdb_Writer_Expressed_Sentiment features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 62657706 num_examples: 25000 - name: test num_bytes: 61781510 num_examples: 25000 - name: unsupervised num_bytes: 125656157 num_examples: 50000 download_size: 128736120 dataset_size: 250095373 - config_name: kilt_tasks_hotpotqa_combining_facts features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 28006020 num_examples: 88869 - name: validation num_bytes: 1631261 num_examples: 5600 download_size: 16337892 dataset_size: 29637281 - config_name: kilt_tasks_hotpotqa_complex_question features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 38936907 num_examples: 88869 - name: validation num_bytes: 2320061 num_examples: 5600 download_size: 17061376 dataset_size: 41256968 - config_name: kilt_tasks_hotpotqa_final_exam features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 28094889 num_examples: 88869 - name: validation num_bytes: 1636861 num_examples: 5600 download_size: 16329789 dataset_size: 29731750 - config_name: kilt_tasks_hotpotqa_formulate features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 30938697 num_examples: 88869 - name: validation num_bytes: 1816061 num_examples: 5600 download_size: 16488556 dataset_size: 32754758 - config_name: kilt_tasks_hotpotqa_straighforward_qa features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 23118225 num_examples: 88869 - name: validation num_bytes: 1323261 num_examples: 5600 download_size: 15949825 dataset_size: 24441486 - config_name: multi_news_distill features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 526482331 num_examples: 44972 - name: validation num_bytes: 64826209 num_examples: 5622 - name: test num_bytes: 65237355 num_examples: 5622 download_size: 357690260 dataset_size: 656545895 - config_name: multi_news_expand_reverse_task_ features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 267362109 num_examples: 44972 - name: validation num_bytes: 33300262 num_examples: 5622 - name: test num_bytes: 33227745 num_examples: 5622 download_size: 189087861 dataset_size: 333890116 - config_name: multi_news_summarize features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 525663317 num_examples: 44972 - name: validation num_bytes: 64723513 num_examples: 5622 - name: test num_bytes: 65134796 num_examples: 5622 download_size: 357146250 dataset_size: 655521626 - config_name: multi_news_summary_scenario features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 527516687 num_examples: 44972 - name: validation num_bytes: 64955515 num_examples: 5622 - name: test num_bytes: 65366661 num_examples: 5622 download_size: 357925759 dataset_size: 657838863 - config_name: multi_news_synthesize features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 525154825 num_examples: 44972 - name: validation num_bytes: 64662427 num_examples: 5622 - name: test num_bytes: 65072614 num_examples: 5622 download_size: 357282630 dataset_size: 654889866 - config_name: multi_news_what_are_the_key_points features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 526122555 num_examples: 44972 - name: validation num_bytes: 64781233 num_examples: 5622 - name: test num_bytes: 65192379 num_examples: 5622 download_size: 357472016 dataset_size: 656096167 - config_name: openbookqa_main_choices features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 2153221 num_examples: 4957 - name: validation num_bytes: 236646 num_examples: 500 - name: test num_bytes: 224988 num_examples: 500 download_size: 1525965 dataset_size: 2614855 - config_name: openbookqa_main_choose_an_answer_with_options features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 2351501 num_examples: 4957 - name: validation num_bytes: 256646 num_examples: 500 - name: test num_bytes: 244988 num_examples: 500 download_size: 1540999 dataset_size: 2853135 - config_name: openbookqa_main_only_options features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 2044167 num_examples: 4957 - name: validation num_bytes: 225646 num_examples: 500 - name: test num_bytes: 213988 num_examples: 500 download_size: 1510736 dataset_size: 2483801 - config_name: openbookqa_main_pick_answer_with_options features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 2391157 num_examples: 4957 - name: validation num_bytes: 260646 num_examples: 500 - name: test num_bytes: 248988 num_examples: 500 download_size: 1543503 dataset_size: 2900791 - config_name: openbookqa_main_pick_using_id features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 2231304 num_examples: 4957 - name: validation num_bytes: 235175 num_examples: 500 - name: test num_bytes: 228627 num_examples: 500 download_size: 1091533 dataset_size: 2695106 - config_name: openbookqa_main_which_correct features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 2311845 num_examples: 4957 - name: validation num_bytes: 252646 num_examples: 500 - name: test num_bytes: 240988 num_examples: 500 download_size: 1539423 dataset_size: 2805479 - config_name: openbookqa_main_which_correct_inverse features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 2311845 num_examples: 4957 - name: validation num_bytes: 252646 num_examples: 500 - name: test num_bytes: 240988 num_examples: 500 download_size: 1557407 dataset_size: 2805479 - config_name: paws_labeled_final_Concatenation features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 35504031 num_examples: 49401 - name: validation num_bytes: 5747157 num_examples: 8000 - name: test num_bytes: 5751626 num_examples: 8000 download_size: 16144636 dataset_size: 47002814 - config_name: paws_labeled_final_Concatenation_no_label features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 34170204 num_examples: 49401 - name: validation num_bytes: 5531157 num_examples: 8000 - name: test num_bytes: 5535626 num_examples: 8000 download_size: 16107402 dataset_size: 45236987 - config_name: paws_labeled_final_Meaning features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 36887259 num_examples: 49401 - name: validation num_bytes: 5971157 num_examples: 8000 - name: test num_bytes: 5975626 num_examples: 8000 download_size: 16398207 dataset_size: 48834042 - config_name: paws_labeled_final_Meaning_no_label features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 35553432 num_examples: 49401 - name: validation num_bytes: 5755157 num_examples: 8000 - name: test num_bytes: 5759626 num_examples: 8000 download_size: 16275164 dataset_size: 47068215 - config_name: paws_labeled_final_PAWS_ANLI_GPT3 features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 29160017 num_examples: 49401 - name: validation num_bytes: 4719767 num_examples: 8000 - name: test num_bytes: 4724266 num_examples: 8000 download_size: 15896734 dataset_size: 38604050 - config_name: paws_labeled_final_PAWS_ANLI_GPT3_no_label features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 28587891 num_examples: 49401 - name: validation num_bytes: 4627157 num_examples: 8000 - name: test num_bytes: 4631626 num_examples: 8000 download_size: 15859385 dataset_size: 37846674 - config_name: paws_labeled_final_Rewrite features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 36195645 num_examples: 49401 - name: validation num_bytes: 5859157 num_examples: 8000 - name: test num_bytes: 5863626 num_examples: 8000 download_size: 16218433 dataset_size: 47918428 - config_name: paws_labeled_final_Rewrite_no_label features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - 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name: test num_bytes: 382021 num_examples: 552 download_size: 762421 dataset_size: 1905984 - config_name: quarel_heres_a_story features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 1308176 num_examples: 1941 - name: validation num_bytes: 189143 num_examples: 278 - name: test num_bytes: 375385 num_examples: 552 download_size: 755827 dataset_size: 1872704 - config_name: quarel_logic_test features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 1226662 num_examples: 1941 - name: validation num_bytes: 177475 num_examples: 278 - name: test num_bytes: 352213 num_examples: 552 download_size: 750383 dataset_size: 1756350 - config_name: quarel_testing_students features: - 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name: validation num_bytes: 289568 num_examples: 384 - name: test num_bytes: 576980 num_examples: 784 download_size: 899987 dataset_size: 2838504 - config_name: quartz_paragraph_question_plain_concat features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 1350435 num_examples: 2696 - name: validation num_bytes: 200100 num_examples: 384 - name: test num_bytes: 396345 num_examples: 784 download_size: 819662 dataset_size: 1946880 - config_name: quartz_read_passage_below_choose features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 1939604 num_examples: 2696 - name: validation num_bytes: 284960 num_examples: 384 - name: test num_bytes: 567572 num_examples: 784 download_size: 900803 dataset_size: 2792136 - 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name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 77399413 num_examples: 19399 - name: validation num_bytes: 9525595 num_examples: 2418 download_size: 21172797 dataset_size: 86925008 - config_name: quoref_Answer_Question_Given_Context features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 75906482 num_examples: 19399 - name: validation num_bytes: 9339515 num_examples: 2418 download_size: 21085034 dataset_size: 85245997 - config_name: quoref_Answer_Test features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 77478073 num_examples: 19399 - name: validation num_bytes: 9535373 num_examples: 2418 download_size: 20833370 dataset_size: 87013446 - config_name: quoref_Context_Contains_Answer features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 76410209 num_examples: 19399 - name: validation num_bytes: 9402213 num_examples: 2418 download_size: 20984076 dataset_size: 85812422 - config_name: quoref_Find_Answer features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 76972842 num_examples: 19399 - name: validation num_bytes: 9472336 num_examples: 2418 download_size: 21102482 dataset_size: 86445178 - config_name: quoref_Found_Context_Online features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 76216636 num_examples: 19399 - name: validation num_bytes: 9378034 num_examples: 2418 download_size: 21073714 dataset_size: 85594670 - config_name: quoref_Given_Context_Answer_Question features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 75847706 num_examples: 19399 - name: validation num_bytes: 9331924 num_examples: 2418 download_size: 20955369 dataset_size: 85179630 - config_name: quoref_Guess_Answer features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 76701159 num_examples: 19399 - name: validation num_bytes: 9438300 num_examples: 2418 download_size: 20961433 dataset_size: 86139459 - config_name: quoref_Guess_Title_For_Context features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 73151029 num_examples: 19399 - name: validation num_bytes: 9007516 num_examples: 2418 download_size: 15926200 dataset_size: 82158545 - config_name: quoref_Read_And_Extract_ features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 76216632 num_examples: 19399 - name: validation num_bytes: 9378203 num_examples: 2418 download_size: 21186451 dataset_size: 85594835 - config_name: quoref_What_Is_The_Answer features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 76274484 num_examples: 19399 - name: validation num_bytes: 9385073 num_examples: 2418 download_size: 20988976 dataset_size: 85659557 - config_name: race_high_Is_this_the_right_answer features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - 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name: train num_bytes: 241414491 num_examples: 62445 - name: validation num_bytes: 13240279 num_examples: 3451 - name: test num_bytes: 13378074 num_examples: 3498 download_size: 88927188 dataset_size: 268032844 - config_name: race_high_Select_the_best_answer_generate_span_ features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 253585983 num_examples: 62445 - name: validation num_bytes: 13907799 num_examples: 3451 - name: test num_bytes: 14065912 num_examples: 3498 download_size: 98442058 dataset_size: 281559694 - config_name: race_high_Select_the_best_answer_no_instructions_ features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 233109306 num_examples: 62445 - name: validation num_bytes: 12781296 num_examples: 3451 - name: test num_bytes: 12912840 num_examples: 3498 download_size: 88914316 dataset_size: 258803442 - config_name: race_high_Taking_a_test features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 247096986 num_examples: 62445 - name: validation num_bytes: 13554320 num_examples: 3451 - name: test num_bytes: 13696392 num_examples: 3498 download_size: 88119386 dataset_size: 274347698 - config_name: race_high_Write_a_multi_choice_question_for_the_following_article features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 241476936 num_examples: 62445 - name: validation num_bytes: 13243730 num_examples: 3451 - name: test num_bytes: 13381572 num_examples: 3498 download_size: 82830693 dataset_size: 268102238 - config_name: race_high_Write_a_multi_choice_question_options_given_ features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 249780949 num_examples: 62445 - name: validation num_bytes: 13701386 num_examples: 3451 - name: test num_bytes: 13849582 num_examples: 3498 download_size: 90227530 dataset_size: 277331917 - config_name: race_middle_Is_this_the_right_answer features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 59522502 num_examples: 25421 - name: validation num_bytes: 3374951 num_examples: 1436 - name: test num_bytes: 3426265 num_examples: 1436 download_size: 20970954 dataset_size: 66323718 - config_name: race_middle_Read_the_article_and_answer_the_question_no_option_ features: - 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name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 68147373 num_examples: 25421 - name: validation num_bytes: 3865611 num_examples: 1436 - name: test num_bytes: 3920536 num_examples: 1436 download_size: 26118277 dataset_size: 75933520 - config_name: race_middle_Select_the_best_answer_no_instructions_ features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 61583726 num_examples: 25421 - name: validation num_bytes: 3492957 num_examples: 1436 - name: test num_bytes: 3545486 num_examples: 1436 download_size: 23049312 dataset_size: 68622169 - config_name: race_middle_Taking_a_test features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - 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name: validation num_bytes: 3847385 num_examples: 1436 - name: test num_bytes: 3900558 num_examples: 1436 download_size: 24079756 dataset_size: 75590573 - config_name: ropes_background_new_situation_answer features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 24148867 num_examples: 10924 - name: validation num_bytes: 3456292 num_examples: 1688 download_size: 3693602 dataset_size: 27605159 - config_name: ropes_background_situation_middle features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 24028703 num_examples: 10924 - name: validation num_bytes: 3437724 num_examples: 1688 download_size: 3632205 dataset_size: 27466427 - config_name: ropes_given_background_situation features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 23700983 num_examples: 10924 - name: validation num_bytes: 3387084 num_examples: 1688 download_size: 3700990 dataset_size: 27088067 - config_name: ropes_new_situation_background_answer features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 24312727 num_examples: 10924 - name: validation num_bytes: 3481612 num_examples: 1688 download_size: 3650421 dataset_size: 27794339 - config_name: ropes_plain_background_situation features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 22357331 num_examples: 10924 - name: validation num_bytes: 3179460 num_examples: 1688 download_size: 3644216 dataset_size: 25536791 - config_name: ropes_plain_bottom_hint features: - 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config_name: ropes_prompt_bottom_hint_beginning features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 24170715 num_examples: 10924 - name: validation num_bytes: 3459668 num_examples: 1688 download_size: 3722200 dataset_size: 27630383 - config_name: ropes_prompt_bottom_no_hint features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 8691807 num_examples: 10924 - name: validation num_bytes: 1664512 num_examples: 1688 download_size: 1734881 dataset_size: 10356319 - config_name: ropes_prompt_mix features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 23919463 num_examples: 10924 - name: validation num_bytes: 3420844 num_examples: 1688 download_size: 3642481 dataset_size: 27340307 - config_name: ropes_read_background_situation features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 26606767 num_examples: 10924 - name: validation num_bytes: 3836092 num_examples: 1688 download_size: 3774488 dataset_size: 30442859 - config_name: rotten_tomatoes_Movie_Expressed_Sentiment features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 3167752 num_examples: 8530 - name: validation num_bytes: 396113 num_examples: 1066 - name: test num_bytes: 398890 num_examples: 1066 download_size: 1715193 dataset_size: 3962755 - config_name: rotten_tomatoes_Movie_Expressed_Sentiment_2 features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 3372472 num_examples: 8530 - name: validation num_bytes: 421697 num_examples: 1066 - name: test num_bytes: 424474 num_examples: 1066 download_size: 1718990 dataset_size: 4218643 - config_name: rotten_tomatoes_Reviewer_Enjoyment features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 3619842 num_examples: 8530 - name: validation num_bytes: 452611 num_examples: 1066 - name: test num_bytes: 455388 num_examples: 1066 download_size: 1724405 dataset_size: 4527841 - config_name: rotten_tomatoes_Reviewer_Enjoyment_Yes_No features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 3001417 num_examples: 8530 - name: validation num_bytes: 375326 num_examples: 1066 - name: test num_bytes: 378103 num_examples: 1066 download_size: 1712605 dataset_size: 3754846 - config_name: rotten_tomatoes_Reviewer_Expressed_Sentiment features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 3560132 num_examples: 8530 - name: validation num_bytes: 445149 num_examples: 1066 - name: test num_bytes: 447926 num_examples: 1066 download_size: 1752369 dataset_size: 4453207 - config_name: rotten_tomatoes_Reviewer_Opinion_bad_good_choices features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 3231727 num_examples: 8530 - name: validation num_bytes: 404108 num_examples: 1066 - name: test num_bytes: 406885 num_examples: 1066 download_size: 1722171 dataset_size: 4042720 - config_name: rotten_tomatoes_Reviewer_Sentiment_Feeling features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 3244522 num_examples: 8530 - name: validation num_bytes: 405707 num_examples: 1066 - name: test num_bytes: 408484 num_examples: 1066 download_size: 1719424 dataset_size: 4058713 - config_name: rotten_tomatoes_Sentiment_with_choices_ features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 3184812 num_examples: 8530 - name: validation num_bytes: 398245 num_examples: 1066 - name: test num_bytes: 401022 num_examples: 1066 download_size: 1716500 dataset_size: 3984079 - config_name: rotten_tomatoes_Text_Expressed_Sentiment features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 3278642 num_examples: 8530 - name: validation num_bytes: 409971 num_examples: 1066 - name: test num_bytes: 412748 num_examples: 1066 download_size: 1721990 dataset_size: 4101361 - config_name: rotten_tomatoes_Writer_Expressed_Sentiment features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 3381002 num_examples: 8530 - name: validation num_bytes: 422763 num_examples: 1066 - name: test num_bytes: 425540 num_examples: 1066 download_size: 1726264 dataset_size: 4229305 - config_name: samsum_Generate_a_summary_for_this_dialogue features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 20847939 num_examples: 14732 - name: validation num_bytes: 1132408 num_examples: 818 - name: test num_bytes: 1178375 num_examples: 819 download_size: 12231176 dataset_size: 23158722 - config_name: samsum_Given_the_above_dialogue_write_a_summary features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 20995259 num_examples: 14732 - name: validation num_bytes: 1140588 num_examples: 818 - name: test num_bytes: 1186565 num_examples: 819 download_size: 12287796 dataset_size: 23322412 - config_name: samsum_Sum_up_the_following_dialogue features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 20582763 num_examples: 14732 - name: validation num_bytes: 1117684 num_examples: 818 - name: test num_bytes: 1163633 num_examples: 819 download_size: 12224086 dataset_size: 22864080 - config_name: samsum_Summarize_ features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 20155535 num_examples: 14732 - name: validation num_bytes: 1093962 num_examples: 818 - name: test num_bytes: 1139882 num_examples: 819 download_size: 12178625 dataset_size: 22389379 - config_name: samsum_Summarize_this_dialogue_ features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 20494371 num_examples: 14732 - name: validation num_bytes: 1112776 num_examples: 818 - name: test num_bytes: 1158719 num_examples: 819 download_size: 12217491 dataset_size: 22765866 - config_name: samsum_To_sum_up_this_dialog features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 20450175 num_examples: 14732 - name: validation num_bytes: 1110322 num_examples: 818 - name: test num_bytes: 1156262 num_examples: 819 download_size: 12250518 dataset_size: 22716759 - config_name: samsum_Write_a_dialogue_that_match_this_summary features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 20951063 num_examples: 14732 - name: validation num_bytes: 1138134 num_examples: 818 - name: test num_bytes: 1184108 num_examples: 819 download_size: 12142707 dataset_size: 23273305 - config_name: sciq_Direct_Question features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 13620270 num_examples: 11679 - name: validation num_bytes: 1155436 num_examples: 1000 - name: test num_bytes: 1179499 num_examples: 1000 download_size: 7728424 dataset_size: 15955205 - config_name: sciq_Direct_Question_Closed_Book_ features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 3203761 num_examples: 11679 - name: validation num_bytes: 278888 num_examples: 1000 - name: test num_bytes: 272132 num_examples: 1000 download_size: 2012231 dataset_size: 3754781 - config_name: sciq_Multiple_Choice features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 15429508 num_examples: 11679 - name: validation num_bytes: 1311751 num_examples: 1000 - name: test num_bytes: 1331575 num_examples: 1000 download_size: 8635433 dataset_size: 18072834 - config_name: sciq_Multiple_Choice_Closed_Book_ features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 5012999 num_examples: 11679 - name: validation num_bytes: 435203 num_examples: 1000 - name: test num_bytes: 424208 num_examples: 1000 download_size: 2927347 dataset_size: 5872410 - config_name: sciq_Multiple_Choice_Question_First features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 15943384 num_examples: 11679 - name: validation num_bytes: 1355751 num_examples: 1000 - name: test num_bytes: 1375575 num_examples: 1000 download_size: 8754807 dataset_size: 18674710 - config_name: social_i_qa_Check_if_a_random_answer_is_valid_or_not features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 13459148 num_examples: 33410 - name: validation num_bytes: 789738 num_examples: 1954 download_size: 4919461 dataset_size: 14248886 - config_name: social_i_qa_Generate_answer features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 12738672 num_examples: 33410 - name: validation num_bytes: 748953 num_examples: 1954 download_size: 6421176 dataset_size: 13487625 - config_name: social_i_qa_Generate_the_question_from_the_answer features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 13496939 num_examples: 33410 - name: validation num_bytes: 790867 num_examples: 1954 download_size: 4698667 dataset_size: 14287806 - config_name: social_i_qa_I_was_wondering features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 13607332 num_examples: 33410 - name: validation num_bytes: 799757 num_examples: 1954 download_size: 6486811 dataset_size: 14407089 - config_name: social_i_qa_Show_choices_and_generate_answer features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 17810931 num_examples: 33410 - name: validation num_bytes: 1050997 num_examples: 1954 download_size: 8848333 dataset_size: 18861928 - 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name: validation num_bytes: 52937 num_examples: 56 - name: test num_bytes: 235065 num_examples: 250 download_size: 236740 dataset_size: 502849 - config_name: super_glue_cb_should_assume_score_eval features: - name: idx sequence: int32 - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: is_correct dtype: bool - name: targets sequence: int32 - name: targets_pretokenized dtype: string - name: weight dtype: float32 splits: - name: train num_bytes: 637646 num_examples: 750 - name: validation num_bytes: 157262 num_examples: 168 - name: test num_bytes: 674789 num_examples: 750 download_size: 297354 dataset_size: 1469697 - config_name: super_glue_cb_take_the_following_as_truth features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 237389 num_examples: 250 - name: validation num_bytes: 58031 num_examples: 56 - name: test num_bytes: 255815 num_examples: 250 download_size: 238453 dataset_size: 551235 - config_name: super_glue_cb_take_the_following_as_truth_score_eval features: - name: idx sequence: int32 - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: is_correct dtype: bool - name: targets sequence: int32 - name: targets_pretokenized dtype: string - name: weight dtype: float32 splits: - name: train num_bytes: 700396 num_examples: 750 - name: validation num_bytes: 171318 num_examples: 168 - name: test num_bytes: 737539 num_examples: 750 download_size: 301514 dataset_size: 1609253 - config_name: super_glue_copa_C1_or_C2_premise_so_because_ features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 145012 num_examples: 400 - name: validation num_bytes: 36931 num_examples: 100 - name: test num_bytes: 168625 num_examples: 500 download_size: 196088 dataset_size: 350568 - 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config_name: super_glue_copa_exercise features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 179021 num_examples: 400 - name: validation num_bytes: 45427 num_examples: 100 - name: test num_bytes: 211083 num_examples: 500 download_size: 200024 dataset_size: 435531 - config_name: super_glue_copa_exercise_score_eval features: - name: idx sequence: int32 - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: is_correct dtype: bool - name: targets sequence: int32 - name: targets_pretokenized dtype: string - name: weight dtype: float32 splits: - name: train num_bytes: 317459 num_examples: 800 - name: validation num_bytes: 80417 num_examples: 200 - name: test num_bytes: 389994 num_examples: 1000 download_size: 253031 dataset_size: 787870 - config_name: super_glue_copa_i_am_hesitating features: - 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name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 195627 num_examples: 400 - name: validation num_bytes: 49571 num_examples: 100 - name: test num_bytes: 231833 num_examples: 500 download_size: 205679 dataset_size: 477031 - config_name: super_glue_copa_more_likely_score_eval features: - name: idx sequence: int32 - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: is_correct dtype: bool - name: targets sequence: int32 - name: targets_pretokenized dtype: string - name: weight dtype: float32 splits: - name: train num_bytes: 350671 num_examples: 800 - name: validation num_bytes: 88705 num_examples: 200 - name: test num_bytes: 431494 num_examples: 1000 download_size: 260606 dataset_size: 870870 - config_name: super_glue_copa_plausible_alternatives features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 184629 num_examples: 400 - name: validation num_bytes: 46819 num_examples: 100 - name: test num_bytes: 218083 num_examples: 500 download_size: 201203 dataset_size: 449531 - config_name: super_glue_copa_plausible_alternatives_score_eval features: - name: idx sequence: int32 - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: is_correct dtype: bool - name: targets sequence: int32 - name: targets_pretokenized dtype: string - name: weight dtype: float32 splits: - name: train num_bytes: 328675 num_examples: 800 - name: validation num_bytes: 83201 num_examples: 200 - name: test num_bytes: 403994 num_examples: 1000 download_size: 254263 dataset_size: 815870 - config_name: super_glue_multirc_I_was_going_to_say_ features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 87327367 num_examples: 27243 - name: validation num_bytes: 15270172 num_examples: 4848 - name: test num_bytes: 29317947 num_examples: 9693 download_size: 10202981 dataset_size: 131915486 - config_name: super_glue_multirc_Would_it_be_good_to_answer_ features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 86590210 num_examples: 27243 - name: validation num_bytes: 15138916 num_examples: 4848 - name: test num_bytes: 29055844 num_examples: 9693 download_size: 10145179 dataset_size: 130784970 - config_name: super_glue_multirc_confirm features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 88851379 num_examples: 27243 - name: validation num_bytes: 15541300 num_examples: 4848 - name: test num_bytes: 29860363 num_examples: 9693 download_size: 10343037 dataset_size: 134253042 - config_name: super_glue_multirc_correct features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 89540386 num_examples: 27243 - name: validation num_bytes: 15663439 num_examples: 4848 - name: test num_bytes: 30104448 num_examples: 9693 download_size: 10428485 dataset_size: 135308273 - config_name: super_glue_multirc_decide_valid features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 89151052 num_examples: 27243 - name: validation num_bytes: 15594628 num_examples: 4848 - name: test num_bytes: 29966986 num_examples: 9693 download_size: 10388384 dataset_size: 134712666 - config_name: super_glue_multirc_found_this_answer features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 88308115 num_examples: 27243 - name: validation num_bytes: 15444700 num_examples: 4848 - name: test num_bytes: 29666895 num_examples: 9693 download_size: 10310634 dataset_size: 133419710 - config_name: super_glue_multirc_grading features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 88933108 num_examples: 27243 - name: validation num_bytes: 15555844 num_examples: 4848 - name: test num_bytes: 29889442 num_examples: 9693 download_size: 10380847 dataset_size: 134378394 - config_name: super_glue_multirc_is_a_correct_answer_ features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 87897874 num_examples: 27243 - name: validation num_bytes: 15371620 num_examples: 4848 - name: test num_bytes: 29521108 num_examples: 9693 download_size: 10277901 dataset_size: 132790602 - config_name: super_glue_multirc_is_the_correct_answer_ features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 86487255 num_examples: 27243 - name: validation num_bytes: 15121640 num_examples: 4848 - name: test num_bytes: 29019715 num_examples: 9693 download_size: 10063584 dataset_size: 130628610 - config_name: super_glue_multirc_paragraph_question_is_it_ features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 85833423 num_examples: 27243 - name: validation num_bytes: 15005288 num_examples: 4848 - name: test num_bytes: 28787083 num_examples: 9693 download_size: 10024769 dataset_size: 129625794 - config_name: super_glue_record_Add_sentence_after_after_continuation_choices_ features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 405851847 num_examples: 100730 - name: validation num_bytes: 40002369 num_examples: 10000 - name: test num_bytes: 37604835 num_examples: 10000 download_size: 161336040 dataset_size: 483459051 - config_name: super_glue_record_Add_sentence_after_continuation_choices_ features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 397869219 num_examples: 100730 - name: validation num_bytes: 39209961 num_examples: 10000 - name: test num_bytes: 36813541 num_examples: 10000 download_size: 160939894 dataset_size: 473892721 - config_name: super_glue_record_Can_you_figure_out_ features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 265384317 num_examples: 100730 - name: validation num_bytes: 25888812 num_examples: 10000 - name: test num_bytes: 26013119 num_examples: 10000 download_size: 137075723 dataset_size: 317286248 - config_name: super_glue_record_GPT_3_style_continuation_choices_ features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 389547353 num_examples: 100730 - name: validation num_bytes: 38377029 num_examples: 10000 - name: test num_bytes: 35877641 num_examples: 10000 download_size: 161606657 dataset_size: 463802023 - config_name: super_glue_record_GPT_3_style_summary_only_continuation_choices_ features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 391488841 num_examples: 100730 - name: validation num_bytes: 38568843 num_examples: 10000 - name: test num_bytes: 36068935 num_examples: 10000 download_size: 161430527 dataset_size: 466126619 - config_name: super_glue_record_GPT_3_style_with_labels_continuation_choices_ features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 394006123 num_examples: 100730 - name: validation num_bytes: 38818755 num_examples: 10000 - name: test num_bytes: 36318935 num_examples: 10000 download_size: 161657804 dataset_size: 469143813 - config_name: super_glue_record_GPT_3_style_with_labels_without_hyphens_continuation_choices_ features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 386704249 num_examples: 100730 - name: validation num_bytes: 38142115 num_examples: 10000 - name: test num_bytes: 35743760 num_examples: 10000 download_size: 161860960 dataset_size: 460590124 - config_name: super_glue_record_GPT_3_style_without_hyphens_continuation_choices_ features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 382247592 num_examples: 100730 - name: validation num_bytes: 37700089 num_examples: 10000 - name: test num_bytes: 35302531 num_examples: 10000 download_size: 161214381 dataset_size: 455250212 - config_name: super_glue_record_In_the_question_above_the_placeholder_stands_for features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 263170377 num_examples: 100730 - name: validation num_bytes: 25668732 num_examples: 10000 - name: test num_bytes: 25793119 num_examples: 10000 download_size: 136915415 dataset_size: 314632228 - config_name: super_glue_record_New_highlight_continuation_choices_ features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 398639353 num_examples: 100730 - name: validation num_bytes: 39278843 num_examples: 10000 - name: test num_bytes: 36778935 num_examples: 10000 download_size: 161410433 dataset_size: 474697131 - config_name: super_glue_record_News_article_continuation_choices_ features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 400384809 num_examples: 100730 - name: validation num_bytes: 39459961 num_examples: 10000 - name: test num_bytes: 37063541 num_examples: 10000 download_size: 161149940 dataset_size: 476908311 - config_name: super_glue_record_Summary_first_continuation_choices_ features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 389936507 num_examples: 100730 - name: validation num_bytes: 38422422 num_examples: 10000 - name: test num_bytes: 36024835 num_examples: 10000 download_size: 161510844 dataset_size: 464383764 - config_name: super_glue_record_What_could_the_placeholder_be_ features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 291017905 num_examples: 100730 - name: validation num_bytes: 28253736 num_examples: 10000 - name: test num_bytes: 28355871 num_examples: 10000 download_size: 149257838 dataset_size: 347627512 - config_name: super_glue_record_Which_one_is_the_placeholder_ features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 290920684 num_examples: 100730 - name: validation num_bytes: 28243964 num_examples: 10000 - name: test num_bytes: 28345871 num_examples: 10000 download_size: 149149764 dataset_size: 347510519 - config_name: super_glue_record_choose_between features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 303576388 num_examples: 100730 - name: validation num_bytes: 29481844 num_examples: 10000 - name: test num_bytes: 29577381 num_examples: 10000 download_size: 150960677 dataset_size: 362635613 - config_name: super_glue_record_corrupted features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 272131126 num_examples: 100730 - name: validation num_bytes: 26559245 num_examples: 10000 - name: test num_bytes: 26683119 num_examples: 10000 download_size: 137380371 dataset_size: 325373490 - config_name: super_glue_record_exercise features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 269411416 num_examples: 100730 - name: validation num_bytes: 26288732 num_examples: 10000 - name: test num_bytes: 26413119 num_examples: 10000 download_size: 137400236 dataset_size: 322113267 - config_name: super_glue_record_pick_one_option features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 298946149 num_examples: 100730 - name: validation num_bytes: 29021173 num_examples: 10000 - name: test num_bytes: 29117381 num_examples: 10000 download_size: 149959507 dataset_size: 357084703 - config_name: super_glue_record_the_placeholder_refers_to_ features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 258633939 num_examples: 100730 - name: validation num_bytes: 25218812 num_examples: 10000 - name: test num_bytes: 25343119 num_examples: 10000 download_size: 137051827 dataset_size: 309195870 - config_name: super_glue_record_trying_to_decide features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 309721314 num_examples: 100730 - name: validation num_bytes: 30091894 num_examples: 10000 - name: test num_bytes: 30187381 num_examples: 10000 download_size: 151048548 dataset_size: 370000589 - config_name: super_glue_rte_GPT_3_style features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 1822276 num_examples: 2490 - name: validation num_bytes: 196922 num_examples: 277 - name: test num_bytes: 2177860 num_examples: 3000 download_size: 2192949 dataset_size: 4197058 - config_name: super_glue_rte_GPT_3_style_score_eval features: - name: idx sequence: int32 - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: is_correct dtype: bool - name: targets sequence: int32 - name: targets_pretokenized dtype: string - name: weight dtype: float32 splits: - name: train num_bytes: 3620347 num_examples: 4980 - name: validation num_bytes: 391279 num_examples: 554 - name: test num_bytes: 4173470 num_examples: 6000 download_size: 2981743 dataset_size: 8185096 - config_name: super_glue_rte_MNLI_crowdsource features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 2152454 num_examples: 2490 - 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name: test num_bytes: 2379972 num_examples: 3000 download_size: 2228456 dataset_size: 4569695 - config_name: super_glue_rte_based_on_the_previous_passage_score_eval features: - name: idx sequence: int32 - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: is_correct dtype: bool - name: targets sequence: int32 - name: targets_pretokenized dtype: string - name: weight dtype: float32 splits: - name: train num_bytes: 3946963 num_examples: 4980 - name: validation num_bytes: 427619 num_examples: 554 - name: test num_bytes: 4565694 num_examples: 6000 download_size: 2997816 dataset_size: 8940276 - config_name: super_glue_rte_can_we_infer features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 1893494 num_examples: 2490 - name: validation num_bytes: 204918 num_examples: 277 - name: test num_bytes: 2280972 num_examples: 3000 download_size: 2218834 dataset_size: 4379384 - 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name: idx sequence: int32 - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: is_correct dtype: bool - name: targets sequence: int32 - name: targets_pretokenized dtype: string - name: weight dtype: float32 splits: - name: train num_bytes: 3714967 num_examples: 4980 - name: validation num_bytes: 401805 num_examples: 554 - name: test num_bytes: 4287470 num_examples: 6000 download_size: 2971692 dataset_size: 8404242 - config_name: super_glue_rte_does_this_imply features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 1910924 num_examples: 2490 - name: validation num_bytes: 206857 num_examples: 277 - name: test num_bytes: 2301972 num_examples: 3000 download_size: 2226281 dataset_size: 4419753 - config_name: super_glue_rte_does_this_imply_score_eval features: - name: idx sequence: int32 - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: is_correct dtype: bool - name: targets sequence: int32 - name: targets_pretokenized dtype: string - name: weight dtype: float32 splits: - name: train num_bytes: 3817483 num_examples: 4980 - name: validation num_bytes: 413215 num_examples: 554 - name: test num_bytes: 4409694 num_examples: 6000 download_size: 3002523 dataset_size: 8640392 - config_name: super_glue_rte_guaranteed_true features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 1910924 num_examples: 2490 - name: validation num_bytes: 206857 num_examples: 277 - name: test num_bytes: 2301972 num_examples: 3000 download_size: 2225019 dataset_size: 4419753 - config_name: super_glue_rte_guaranteed_true_score_eval features: - name: idx sequence: int32 - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: is_correct dtype: bool - name: targets sequence: int32 - name: targets_pretokenized dtype: string - name: weight dtype: float32 splits: - name: train num_bytes: 3817483 num_examples: 4980 - name: validation num_bytes: 413215 num_examples: 554 - name: test num_bytes: 4409694 num_examples: 6000 download_size: 3007337 dataset_size: 8640392 - config_name: super_glue_rte_justified_in_saying features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 1898474 num_examples: 2490 - name: validation num_bytes: 205472 num_examples: 277 - name: test num_bytes: 2286972 num_examples: 3000 download_size: 2216017 dataset_size: 4390918 - config_name: super_glue_rte_justified_in_saying_score_eval features: - name: idx sequence: int32 - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: is_correct dtype: bool - 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name: validation num_bytes: 482760 num_examples: 1276 - name: test num_bytes: 1058868 num_examples: 2800 download_size: 1238602 dataset_size: 5499343 - config_name: super_glue_wic_GPT_3_prompt_with_label features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 2119307 num_examples: 5428 - name: validation num_bytes: 257888 num_examples: 638 - name: test num_bytes: 609759 num_examples: 1400 download_size: 964203 dataset_size: 2986954 - config_name: super_glue_wic_GPT_3_prompt_with_label_score_eval features: - name: idx sequence: int32 - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: is_correct dtype: bool - name: targets sequence: int32 - name: targets_pretokenized dtype: string - name: weight dtype: float32 splits: - name: train num_bytes: 4229115 num_examples: 10856 - name: validation num_bytes: 514660 num_examples: 1276 - name: test num_bytes: 1128868 num_examples: 2800 download_size: 1250446 dataset_size: 5872643 - config_name: super_glue_wic_affirmation_true_or_false features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 2293003 num_examples: 5428 - name: validation num_bytes: 278304 num_examples: 638 - name: test num_bytes: 646159 num_examples: 1400 download_size: 983242 dataset_size: 3217466 - config_name: super_glue_wic_affirmation_true_or_false_score_eval features: - name: idx sequence: int32 - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: is_correct dtype: bool - name: targets sequence: int32 - name: targets_pretokenized dtype: string - name: weight dtype: float32 splits: - name: train num_bytes: 4533083 num_examples: 10856 - name: validation num_bytes: 550388 num_examples: 1276 - name: test num_bytes: 1207268 num_examples: 2800 download_size: 1275345 dataset_size: 6290739 - config_name: super_glue_wic_grammar_homework features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 2374423 num_examples: 5428 - name: validation num_bytes: 287874 num_examples: 638 - name: test num_bytes: 675559 num_examples: 1400 download_size: 984415 dataset_size: 3337856 - config_name: super_glue_wic_grammar_homework_score_eval features: - name: idx sequence: int32 - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: is_correct dtype: bool - name: targets sequence: int32 - name: targets_pretokenized dtype: string - name: weight dtype: float32 splits: - name: train num_bytes: 4739347 num_examples: 10856 - name: validation num_bytes: 574632 num_examples: 1276 - name: test num_bytes: 1260468 num_examples: 2800 download_size: 1274392 dataset_size: 6574447 - config_name: super_glue_wic_polysemous features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 2564403 num_examples: 5428 - name: validation num_bytes: 310204 num_examples: 638 - name: test num_bytes: 724559 num_examples: 1400 download_size: 1002838 dataset_size: 3599166 - config_name: super_glue_wic_polysemous_score_eval features: - name: idx sequence: int32 - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: is_correct dtype: bool - name: targets sequence: int32 - name: targets_pretokenized dtype: string - name: weight dtype: float32 splits: - name: train num_bytes: 5119307 num_examples: 10856 - name: validation num_bytes: 619292 num_examples: 1276 - name: test num_bytes: 1358468 num_examples: 2800 download_size: 1301826 dataset_size: 7097067 - config_name: super_glue_wic_question_context features: - name: answer_choices sequence: string - 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name: validation num_bytes: 58787 num_examples: 104 - name: test num_bytes: 90504 num_examples: 146 download_size: 112061 dataset_size: 414041 - config_name: super_glue_wsc.fixed_GPT_3_Style_score_eval features: - name: idx sequence: int32 - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: is_correct dtype: bool - name: targets sequence: int32 - name: targets_pretokenized dtype: string - name: weight dtype: float32 splits: - name: train num_bytes: 528567 num_examples: 1108 - name: validation num_bytes: 117420 num_examples: 208 - name: test num_bytes: 171555 num_examples: 292 download_size: 162969 dataset_size: 817542 - config_name: super_glue_wsc.fixed_I_think_they_mean features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 245820 num_examples: 554 - name: validation num_bytes: 57798 num_examples: 104 - name: test num_bytes: 86703 num_examples: 146 download_size: 118405 dataset_size: 390321 - config_name: super_glue_wsc.fixed_I_think_they_mean_score_eval features: - name: idx sequence: int32 - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: is_correct dtype: bool - name: targets sequence: int32 - name: targets_pretokenized dtype: string - name: weight dtype: float32 splits: - name: train num_bytes: 490707 num_examples: 1108 - name: validation num_bytes: 115442 num_examples: 208 - name: test num_bytes: 163953 num_examples: 292 download_size: 162352 dataset_size: 770102 - config_name: super_glue_wsc.fixed_Who_or_what_is_are features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 228569 num_examples: 554 - name: validation num_bytes: 51844 num_examples: 104 - name: test num_bytes: 81002 num_examples: 146 download_size: 106806 dataset_size: 361415 - 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config_name: web_questions_get_the_answer features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 804337 num_examples: 3778 - name: test num_bytes: 436882 num_examples: 2032 download_size: 489913 dataset_size: 1241219 - config_name: web_questions_potential_correct_answer features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 872716 num_examples: 3778 - name: test num_bytes: 472848 num_examples: 2032 download_size: 495767 dataset_size: 1345564 - config_name: web_questions_question_answer features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 509600 num_examples: 3778 - name: test num_bytes: 277649 num_examples: 2032 download_size: 463024 dataset_size: 787249 - config_name: web_questions_short_general_knowledge_q features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 713665 num_examples: 3778 - name: test num_bytes: 387500 num_examples: 2032 download_size: 480185 dataset_size: 1101165 - config_name: web_questions_whats_the_answer features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 782036 num_examples: 3778 - name: test num_bytes: 424624 num_examples: 2032 download_size: 488302 dataset_size: 1206660 - config_name: wiki_bio_comprehension features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 1630510502 num_examples: 582639 - name: test num_bytes: 203505789 num_examples: 72829 - name: val num_bytes: 203916390 num_examples: 72831 download_size: 888828114 dataset_size: 2037932681 - config_name: wiki_bio_guess_person features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 709582624 num_examples: 582639 - name: test num_bytes: 88627789 num_examples: 72829 - name: val num_bytes: 88793147 num_examples: 72831 download_size: 369465704 dataset_size: 887003560 - config_name: wiki_bio_key_content features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 1427894706 num_examples: 582639 - name: test num_bytes: 178164868 num_examples: 72829 - name: val num_bytes: 178545380 num_examples: 72831 download_size: 805077501 dataset_size: 1784604954 - config_name: wiki_bio_what_content features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 1005721358 num_examples: 582639 - name: test num_bytes: 125491764 num_examples: 72829 - name: val num_bytes: 125718669 num_examples: 72831 download_size: 509911784 dataset_size: 1256931791 - config_name: wiki_bio_who features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 1439607119 num_examples: 582639 - name: test num_bytes: 179628525 num_examples: 72829 - name: val num_bytes: 180006405 num_examples: 72831 download_size: 808442534 dataset_size: 1799242049 - config_name: wiki_hop_original_choose_best_object_affirmative_1 features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - 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config_name: wiki_hop_original_choose_best_object_interrogative_1 features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 658557989 num_examples: 43738 - name: validation num_bytes: 82503339 num_examples: 5129 download_size: 384888543 dataset_size: 741061328 - config_name: wiki_hop_original_choose_best_object_interrogative_2 features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 658601727 num_examples: 43738 - name: validation num_bytes: 82508468 num_examples: 5129 download_size: 385067937 dataset_size: 741110195 - config_name: wiki_hop_original_explain_relation features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 620991073 num_examples: 43738 - name: validation num_bytes: 77941958 num_examples: 5129 download_size: 366004566 dataset_size: 698933031 - config_name: wiki_hop_original_generate_object features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 621316721 num_examples: 43738 - name: validation num_bytes: 77980628 num_examples: 5129 download_size: 366787046 dataset_size: 699297349 - config_name: wiki_hop_original_generate_subject features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 623714465 num_examples: 43738 - name: validation num_bytes: 78260730 num_examples: 5129 download_size: 367748453 dataset_size: 701975195 - config_name: wiki_hop_original_generate_subject_and_object features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 624675259 num_examples: 43738 - name: validation num_bytes: 78374281 num_examples: 5129 download_size: 367493299 dataset_size: 703049540 - config_name: wiki_qa_Decide_good_answer features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 11928327 num_examples: 20360 - name: validation num_bytes: 1588513 num_examples: 2733 - name: test num_bytes: 3601306 num_examples: 6165 download_size: 6026723 dataset_size: 17118146 - config_name: wiki_qa_Direct_Answer_to_Question features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 464780 num_examples: 1040 - name: validation num_bytes: 62282 num_examples: 140 - name: test num_bytes: 128388 num_examples: 293 download_size: 395128 dataset_size: 655450 - config_name: wiki_qa_Generate_Question_from_Topic features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 600344 num_examples: 1040 - name: validation num_bytes: 80494 num_examples: 140 - name: test num_bytes: 166291 num_examples: 293 download_size: 434236 dataset_size: 847129 - config_name: wiki_qa_Is_This_True_ features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 9652071 num_examples: 20360 - name: validation num_bytes: 1282191 num_examples: 2733 - name: test num_bytes: 2918012 num_examples: 6165 download_size: 5726813 dataset_size: 13852274 - config_name: wiki_qa_Jeopardy_style features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 563988 num_examples: 1040 - name: validation num_bytes: 75570 num_examples: 140 - name: test num_bytes: 155917 num_examples: 293 download_size: 435303 dataset_size: 795475 - config_name: wiki_qa_Topic_Prediction_Answer_Only features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 476970 num_examples: 1040 - name: validation num_bytes: 63658 num_examples: 140 - name: test num_bytes: 131049 num_examples: 293 download_size: 377885 dataset_size: 671677 - config_name: wiki_qa_Topic_Prediction_Question_Only features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 242922 num_examples: 1040 - name: validation num_bytes: 32780 num_examples: 140 - name: test num_bytes: 68566 num_examples: 293 download_size: 130561 dataset_size: 344268 - config_name: wiki_qa_Topic_Prediction_Question_and_Answer_Pair features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 637104 num_examples: 1040 - name: validation num_bytes: 85410 num_examples: 140 - name: test num_bytes: 176567 num_examples: 293 download_size: 443010 dataset_size: 899081 - config_name: wiki_qa_automatic_system features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 12887927 num_examples: 20360 - name: validation num_bytes: 1715972 num_examples: 2733 - name: test num_bytes: 3899289 num_examples: 6165 download_size: 5942624 dataset_size: 18503188 - config_name: wiki_qa_exercise features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 14832087 num_examples: 20360 - name: validation num_bytes: 1976940 num_examples: 2733 - name: test num_bytes: 4488199 num_examples: 6165 download_size: 6093460 dataset_size: 21297226 - config_name: wiki_qa_found_on_google features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 11401647 num_examples: 20360 - name: validation num_bytes: 1516463 num_examples: 2733 - name: test num_bytes: 3449244 num_examples: 6165 download_size: 5814247 dataset_size: 16367354 - config_name: winogrande_winogrande_debiased_Replace features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 3875803 num_examples: 9248 - name: validation num_bytes: 528582 num_examples: 1267 - name: test num_bytes: 739620 num_examples: 1767 download_size: 1782977 dataset_size: 5144005 - config_name: winogrande_winogrande_debiased_Replace_score_eval features: - name: idx sequence: int32 - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: is_correct dtype: bool - name: targets sequence: int32 - name: targets_pretokenized dtype: string - name: weight dtype: float32 splits: - name: train num_bytes: 7551668 num_examples: 18496 - name: validation num_bytes: 1030154 num_examples: 2534 - name: test num_bytes: 1440851 num_examples: 3534 download_size: 2298663 dataset_size: 10022673 - config_name: winogrande_winogrande_debiased_does_underscore_refer_to features: - 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name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 15178699 num_examples: 40398 - name: validation num_bytes: 479169 num_examples: 1267 - name: test num_bytes: 670707 num_examples: 1767 download_size: 5110009 dataset_size: 16328575 - config_name: winogrande_winogrande_xl_does_underscore_refer_to_score_eval features: - name: idx sequence: int32 - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: is_correct dtype: bool - name: targets sequence: int32 - name: targets_pretokenized dtype: string - name: weight dtype: float32 splits: - name: train num_bytes: 29476018 num_examples: 80796 - name: validation num_bytes: 931328 num_examples: 2534 - name: test num_bytes: 1303025 num_examples: 3534 download_size: 7414291 dataset_size: 31710371 - config_name: winogrande_winogrande_xl_fill_in_the_blank features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 16835017 num_examples: 40398 - name: validation num_bytes: 531116 num_examples: 1267 - name: test num_bytes: 743154 num_examples: 1767 download_size: 5218314 dataset_size: 18109287 - config_name: winogrande_winogrande_xl_fill_in_the_blank_score_eval features: - name: idx sequence: int32 - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: is_correct dtype: bool - name: targets sequence: int32 - name: targets_pretokenized dtype: string - name: weight dtype: float32 splits: - name: train num_bytes: 32788654 num_examples: 80796 - name: validation num_bytes: 1035222 num_examples: 2534 - name: test num_bytes: 1447919 num_examples: 3534 download_size: 7679499 dataset_size: 35271795 - config_name: winogrande_winogrande_xl_stand_for features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 15259495 num_examples: 40398 - name: validation num_bytes: 481703 num_examples: 1267 - name: test num_bytes: 674241 num_examples: 1767 download_size: 5036118 dataset_size: 16415439 - config_name: winogrande_winogrande_xl_stand_for_score_eval features: - name: idx sequence: int32 - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: is_correct dtype: bool - name: targets sequence: int32 - name: targets_pretokenized dtype: string - name: weight dtype: float32 splits: - name: train num_bytes: 29799202 num_examples: 80796 - name: validation num_bytes: 941464 num_examples: 2534 - name: test num_bytes: 1317161 num_examples: 3534 download_size: 7352127 dataset_size: 32057827 - config_name: winogrande_winogrande_xl_underscore_refer_to features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - 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name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 22471193 num_examples: 29808 - name: validation num_bytes: 4941657 num_examples: 6894 - name: test num_bytes: 2012340 num_examples: 3003 download_size: 4994981 dataset_size: 29425190 - config_name: wiqa_what_might_be_the_last_step_of_the_process features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 22415520 num_examples: 29808 - name: validation num_bytes: 4932480 num_examples: 6894 - name: test num_bytes: 2006917 num_examples: 3003 download_size: 4998002 dataset_size: 29354917 - config_name: wiqa_which_of_the_following_is_the_supposed_perturbation features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 38964516 num_examples: 29808 - name: validation num_bytes: 8703251 num_examples: 6894 - name: test num_bytes: 3649318 num_examples: 3003 download_size: 12726852 dataset_size: 51317085 - config_name: xsum_DOC_boils_down_to_simple_idea_that features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 671037016 num_examples: 204045 - name: validation num_bytes: 37260538 num_examples: 11332 - name: test num_bytes: 37363789 num_examples: 11334 download_size: 423515211 dataset_size: 745661343 - config_name: xsum_DOC_given_above_write_one_sentence features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 680219041 num_examples: 204045 - name: validation num_bytes: 37770478 num_examples: 11332 - name: test num_bytes: 37873819 num_examples: 11334 download_size: 425884310 dataset_size: 755863338 - 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name: test num_bytes: 38633197 num_examples: 11334 download_size: 428092027 dataset_size: 771052975 - config_name: xsum_read_below_DOC_write_abstract features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 692869831 num_examples: 204045 - name: validation num_bytes: 38473062 num_examples: 11332 - name: test num_bytes: 38576527 num_examples: 11334 download_size: 427949570 dataset_size: 769919420 - config_name: xsum_summarize_DOC features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 660834766 num_examples: 204045 - name: validation num_bytes: 36693938 num_examples: 11332 - name: test num_bytes: 36797089 num_examples: 11334 download_size: 420917086 dataset_size: 734325793 - config_name: xsum_summarize_this_DOC_summary features: - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 668996566 num_examples: 204045 - name: validation num_bytes: 37147218 num_examples: 11332 - name: test num_bytes: 37250449 num_examples: 11334 download_size: 423104781 dataset_size: 743394233 - config_name: yelp_review_full_based_on_that features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 1031638858 num_examples: 650000 - name: test num_bytes: 79418916 num_examples: 50000 download_size: 556617412 dataset_size: 1111057774 - config_name: yelp_review_full_format_rating features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 1019288862 num_examples: 650000 - name: test num_bytes: 78468916 num_examples: 50000 download_size: 556205049 dataset_size: 1097757778 - config_name: yelp_review_full_format_score features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 1020718862 num_examples: 650000 - name: test num_bytes: 78578916 num_examples: 50000 download_size: 557789138 dataset_size: 1099297778 - config_name: yelp_review_full_format_star features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 1014088862 num_examples: 650000 - name: test num_bytes: 78068916 num_examples: 50000 download_size: 555578441 dataset_size: 1092157778 - config_name: yelp_review_full_on_a_scale features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 1035018858 num_examples: 650000 - name: test num_bytes: 79678916 num_examples: 50000 download_size: 557874177 dataset_size: 1114697774 - config_name: yelp_review_full_so_i_would features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 1020588858 num_examples: 650000 - name: test num_bytes: 78568916 num_examples: 50000 download_size: 555669482 dataset_size: 1099157774 - config_name: yelp_review_full_this_place features: - name: answer_choices sequence: string - name: inputs sequence: int32 - name: inputs_pretokenized dtype: string - name: targets sequence: int32 - name: targets_pretokenized dtype: string splits: - name: train num_bytes: 1018638858 num_examples: 650000 - name: test num_bytes: 78418916 num_examples: 50000 download_size: 555640691 dataset_size: 1097057774 configs: - config_name: adversarial_qa_dbert_answer_the_following_q data_files: - split: train path: adversarial_qa_dbert_answer_the_following_q/train-* - split: validation path: adversarial_qa_dbert_answer_the_following_q/validation-* - config_name: adversarial_qa_dbert_based_on data_files: - split: train path: adversarial_qa_dbert_based_on/train-* - split: validation path: adversarial_qa_dbert_based_on/validation-* - config_name: adversarial_qa_dbert_generate_question data_files: - split: train path: adversarial_qa_dbert_generate_question/train-* - split: validation path: adversarial_qa_dbert_generate_question/validation-* - split: test path: adversarial_qa_dbert_generate_question/test-* - config_name: adversarial_qa_dbert_question_context_answer data_files: - split: train path: adversarial_qa_dbert_question_context_answer/train-* - split: validation path: adversarial_qa_dbert_question_context_answer/validation-* - config_name: adversarial_qa_dbert_tell_what_it_is data_files: - split: train path: adversarial_qa_dbert_tell_what_it_is/train-* - split: validation path: adversarial_qa_dbert_tell_what_it_is/validation-* - config_name: adversarial_qa_dbidaf_answer_the_following_q data_files: - split: train path: adversarial_qa_dbidaf_answer_the_following_q/train-* - split: validation path: adversarial_qa_dbidaf_answer_the_following_q/validation-* - config_name: adversarial_qa_dbidaf_based_on data_files: - split: train path: adversarial_qa_dbidaf_based_on/train-* - split: validation path: adversarial_qa_dbidaf_based_on/validation-* - config_name: adversarial_qa_dbidaf_generate_question data_files: - split: train path: adversarial_qa_dbidaf_generate_question/train-* - split: validation path: adversarial_qa_dbidaf_generate_question/validation-* - split: test path: adversarial_qa_dbidaf_generate_question/test-* - config_name: adversarial_qa_dbidaf_question_context_answer data_files: - split: train path: adversarial_qa_dbidaf_question_context_answer/train-* - split: validation path: adversarial_qa_dbidaf_question_context_answer/validation-* - config_name: adversarial_qa_dbidaf_tell_what_it_is data_files: - split: train path: adversarial_qa_dbidaf_tell_what_it_is/train-* - split: validation path: adversarial_qa_dbidaf_tell_what_it_is/validation-* - config_name: adversarial_qa_droberta_answer_the_following_q data_files: - split: train path: adversarial_qa_droberta_answer_the_following_q/train-* - split: validation path: adversarial_qa_droberta_answer_the_following_q/validation-* - config_name: adversarial_qa_droberta_based_on data_files: - split: train path: adversarial_qa_droberta_based_on/train-* - split: validation path: adversarial_qa_droberta_based_on/validation-* - config_name: adversarial_qa_droberta_generate_question data_files: - split: train path: adversarial_qa_droberta_generate_question/train-* - split: validation path: adversarial_qa_droberta_generate_question/validation-* - split: test path: adversarial_qa_droberta_generate_question/test-* - config_name: adversarial_qa_droberta_question_context_answer data_files: - split: train path: adversarial_qa_droberta_question_context_answer/train-* - split: validation path: adversarial_qa_droberta_question_context_answer/validation-* - config_name: adversarial_qa_droberta_tell_what_it_is data_files: - split: train path: adversarial_qa_droberta_tell_what_it_is/train-* - split: validation path: adversarial_qa_droberta_tell_what_it_is/validation-* - config_name: ag_news_classify data_files: - split: train path: ag_news_classify/train-* - split: test path: ag_news_classify/test-* - config_name: ag_news_classify_question_first data_files: - split: train path: ag_news_classify_question_first/train-* - split: test path: ag_news_classify_question_first/test-* - config_name: ag_news_classify_with_choices data_files: - split: train path: ag_news_classify_with_choices/train-* - split: test path: ag_news_classify_with_choices/test-* - config_name: ag_news_classify_with_choices_question_first data_files: - split: train path: ag_news_classify_with_choices_question_first/train-* - split: test path: ag_news_classify_with_choices_question_first/test-* - config_name: ag_news_recommend data_files: - split: train path: ag_news_recommend/train-* - split: test path: ag_news_recommend/test-* - config_name: ag_news_which_section data_files: - split: train path: ag_news_which_section/train-* - split: test path: ag_news_which_section/test-* - config_name: ag_news_which_section_choices data_files: - split: train path: ag_news_which_section_choices/train-* - split: test path: ag_news_which_section_choices/test-* - config_name: ai2_arc_ARC_Challenge_heres_a_problem data_files: - split: train path: ai2_arc_ARC_Challenge_heres_a_problem/train-* - split: validation path: ai2_arc_ARC_Challenge_heres_a_problem/validation-* - split: test path: ai2_arc_ARC_Challenge_heres_a_problem/test-* - config_name: ai2_arc_ARC_Challenge_i_am_hesitating data_files: - split: train path: ai2_arc_ARC_Challenge_i_am_hesitating/train-* - split: validation path: ai2_arc_ARC_Challenge_i_am_hesitating/validation-* - split: test path: ai2_arc_ARC_Challenge_i_am_hesitating/test-* - config_name: ai2_arc_ARC_Challenge_multiple_choice data_files: - split: train path: ai2_arc_ARC_Challenge_multiple_choice/train-* - split: validation path: ai2_arc_ARC_Challenge_multiple_choice/validation-* - split: test path: ai2_arc_ARC_Challenge_multiple_choice/test-* - config_name: ai2_arc_ARC_Challenge_pick_false_options data_files: - split: train path: ai2_arc_ARC_Challenge_pick_false_options/train-* - split: validation path: ai2_arc_ARC_Challenge_pick_false_options/validation-* - split: test path: ai2_arc_ARC_Challenge_pick_false_options/test-* - config_name: ai2_arc_ARC_Challenge_pick_the_most_correct_option data_files: - split: train path: ai2_arc_ARC_Challenge_pick_the_most_correct_option/train-* - split: validation path: ai2_arc_ARC_Challenge_pick_the_most_correct_option/validation-* - split: test path: ai2_arc_ARC_Challenge_pick_the_most_correct_option/test-* - config_name: ai2_arc_ARC_Challenge_qa_options data_files: - split: train path: ai2_arc_ARC_Challenge_qa_options/train-* - split: validation path: ai2_arc_ARC_Challenge_qa_options/validation-* - split: test path: ai2_arc_ARC_Challenge_qa_options/test-* - config_name: ai2_arc_ARC_Easy_heres_a_problem data_files: - split: train path: ai2_arc_ARC_Easy_heres_a_problem/train-* - split: validation path: ai2_arc_ARC_Easy_heres_a_problem/validation-* - split: test path: ai2_arc_ARC_Easy_heres_a_problem/test-* - config_name: ai2_arc_ARC_Easy_i_am_hesitating data_files: - split: train path: ai2_arc_ARC_Easy_i_am_hesitating/train-* - split: validation path: ai2_arc_ARC_Easy_i_am_hesitating/validation-* - split: test path: ai2_arc_ARC_Easy_i_am_hesitating/test-* - config_name: ai2_arc_ARC_Easy_multiple_choice data_files: - split: train path: ai2_arc_ARC_Easy_multiple_choice/train-* - split: validation path: ai2_arc_ARC_Easy_multiple_choice/validation-* - split: test path: ai2_arc_ARC_Easy_multiple_choice/test-* - config_name: ai2_arc_ARC_Easy_pick_false_options data_files: - split: train path: ai2_arc_ARC_Easy_pick_false_options/train-* - split: validation path: ai2_arc_ARC_Easy_pick_false_options/validation-* - split: test path: ai2_arc_ARC_Easy_pick_false_options/test-* - config_name: ai2_arc_ARC_Easy_pick_the_most_correct_option data_files: - split: train path: ai2_arc_ARC_Easy_pick_the_most_correct_option/train-* - split: validation path: ai2_arc_ARC_Easy_pick_the_most_correct_option/validation-* - split: test path: ai2_arc_ARC_Easy_pick_the_most_correct_option/test-* - config_name: ai2_arc_ARC_Easy_qa_options data_files: - split: train path: ai2_arc_ARC_Easy_qa_options/train-* - split: validation path: ai2_arc_ARC_Easy_qa_options/validation-* - split: test path: ai2_arc_ARC_Easy_qa_options/test-* - config_name: amazon_polarity_Is_this_product_review_positive data_files: - split: train path: amazon_polarity_Is_this_product_review_positive/train-* - split: test path: amazon_polarity_Is_this_product_review_positive/test-* - config_name: amazon_polarity_Is_this_review data_files: - split: train path: amazon_polarity_Is_this_review/train-* - split: test path: amazon_polarity_Is_this_review/test-* - config_name: amazon_polarity_Is_this_review_negative data_files: - split: train path: amazon_polarity_Is_this_review_negative/train-* - split: test path: amazon_polarity_Is_this_review_negative/test-* - config_name: amazon_polarity_User_recommend_this_product data_files: - split: train path: amazon_polarity_User_recommend_this_product/train-* - split: test path: amazon_polarity_User_recommend_this_product/test-* - config_name: amazon_polarity_convey_negative_or_positive_sentiment data_files: - split: train path: amazon_polarity_convey_negative_or_positive_sentiment/train-* - split: test path: amazon_polarity_convey_negative_or_positive_sentiment/test-* - config_name: amazon_polarity_flattering_or_not data_files: - split: train path: amazon_polarity_flattering_or_not/train-* - split: test path: amazon_polarity_flattering_or_not/test-* - config_name: amazon_polarity_negative_or_positive_tone data_files: - split: train path: amazon_polarity_negative_or_positive_tone/train-* - split: test path: amazon_polarity_negative_or_positive_tone/test-* - config_name: amazon_polarity_user_satisfied data_files: - split: train path: amazon_polarity_user_satisfied/train-* - split: test path: amazon_polarity_user_satisfied/test-* - config_name: amazon_polarity_would_you_buy data_files: - split: train path: amazon_polarity_would_you_buy/train-* - split: test path: amazon_polarity_would_you_buy/test-* - config_name: anli_GPT_3_style_r1 data_files: - split: train path: anli_GPT_3_style_r1/train-* - split: validation path: anli_GPT_3_style_r1/validation-* - split: test path: anli_GPT_3_style_r1/test-* - config_name: anli_GPT_3_style_r1_score_eval data_files: - split: train path: anli_GPT_3_style_r1_score_eval/train-* - split: validation path: anli_GPT_3_style_r1_score_eval/validation-* - split: test path: anli_GPT_3_style_r1_score_eval/test-* - config_name: anli_GPT_3_style_r2 data_files: - split: train path: anli_GPT_3_style_r2/train-* - split: validation path: anli_GPT_3_style_r2/validation-* - split: test path: anli_GPT_3_style_r2/test-* - config_name: anli_GPT_3_style_r2_score_eval data_files: - split: train path: anli_GPT_3_style_r2_score_eval/train-* - split: validation path: anli_GPT_3_style_r2_score_eval/validation-* - split: test path: anli_GPT_3_style_r2_score_eval/test-* - config_name: anli_GPT_3_style_r3 data_files: - split: train path: anli_GPT_3_style_r3/train-* - split: validation path: anli_GPT_3_style_r3/validation-* - split: test path: anli_GPT_3_style_r3/test-* - config_name: anli_GPT_3_style_r3_score_eval data_files: - split: train path: anli_GPT_3_style_r3_score_eval/train-* - split: validation path: anli_GPT_3_style_r3_score_eval/validation-* - split: test path: anli_GPT_3_style_r3_score_eval/test-* - config_name: anli_MNLI_crowdsource_r1 data_files: - split: train path: anli_MNLI_crowdsource_r1/train-* - split: validation path: anli_MNLI_crowdsource_r1/validation-* - split: test path: anli_MNLI_crowdsource_r1/test-* - config_name: anli_MNLI_crowdsource_r1_score_eval data_files: - split: train path: anli_MNLI_crowdsource_r1_score_eval/train-* - split: validation path: anli_MNLI_crowdsource_r1_score_eval/validation-* - split: test path: anli_MNLI_crowdsource_r1_score_eval/test-* - config_name: anli_MNLI_crowdsource_r2 data_files: - split: train path: anli_MNLI_crowdsource_r2/train-* - split: validation path: anli_MNLI_crowdsource_r2/validation-* - split: test path: anli_MNLI_crowdsource_r2/test-* - config_name: anli_MNLI_crowdsource_r2_score_eval data_files: - split: train path: anli_MNLI_crowdsource_r2_score_eval/train-* - split: validation path: anli_MNLI_crowdsource_r2_score_eval/validation-* - split: test path: anli_MNLI_crowdsource_r2_score_eval/test-* - config_name: anli_MNLI_crowdsource_r3 data_files: - split: train path: anli_MNLI_crowdsource_r3/train-* - split: validation path: anli_MNLI_crowdsource_r3/validation-* - split: test path: anli_MNLI_crowdsource_r3/test-* - config_name: anli_MNLI_crowdsource_r3_score_eval data_files: - split: train path: anli_MNLI_crowdsource_r3_score_eval/train-* - split: validation path: anli_MNLI_crowdsource_r3_score_eval/validation-* - split: test path: anli_MNLI_crowdsource_r3_score_eval/test-* - config_name: anli_always_sometimes_never_r1 data_files: - split: train path: anli_always_sometimes_never_r1/train-* - split: validation path: anli_always_sometimes_never_r1/validation-* - split: test path: anli_always_sometimes_never_r1/test-* - config_name: anli_always_sometimes_never_r1_score_eval data_files: - split: train path: anli_always_sometimes_never_r1_score_eval/train-* - split: validation path: anli_always_sometimes_never_r1_score_eval/validation-* - split: test path: anli_always_sometimes_never_r1_score_eval/test-* - config_name: anli_always_sometimes_never_r2 data_files: - split: train path: anli_always_sometimes_never_r2/train-* - split: validation path: anli_always_sometimes_never_r2/validation-* - split: test path: anli_always_sometimes_never_r2/test-* - config_name: anli_always_sometimes_never_r2_score_eval data_files: - split: train path: anli_always_sometimes_never_r2_score_eval/train-* - split: validation path: anli_always_sometimes_never_r2_score_eval/validation-* - split: test path: anli_always_sometimes_never_r2_score_eval/test-* - config_name: anli_always_sometimes_never_r3 data_files: - split: train path: anli_always_sometimes_never_r3/train-* - split: validation path: anli_always_sometimes_never_r3/validation-* - split: test path: anli_always_sometimes_never_r3/test-* - config_name: anli_always_sometimes_never_r3_score_eval data_files: - split: train path: anli_always_sometimes_never_r3_score_eval/train-* - split: validation path: anli_always_sometimes_never_r3_score_eval/validation-* - split: test path: anli_always_sometimes_never_r3_score_eval/test-* - config_name: anli_based_on_the_previous_passage_r1 data_files: - split: train path: anli_based_on_the_previous_passage_r1/train-* - split: validation path: anli_based_on_the_previous_passage_r1/validation-* - split: test path: anli_based_on_the_previous_passage_r1/test-* - config_name: anli_based_on_the_previous_passage_r1_score_eval data_files: - split: train path: anli_based_on_the_previous_passage_r1_score_eval/train-* - split: validation path: anli_based_on_the_previous_passage_r1_score_eval/validation-* - split: test path: anli_based_on_the_previous_passage_r1_score_eval/test-* - config_name: anli_based_on_the_previous_passage_r2 data_files: - split: train path: anli_based_on_the_previous_passage_r2/train-* - split: validation path: anli_based_on_the_previous_passage_r2/validation-* - split: test path: anli_based_on_the_previous_passage_r2/test-* - config_name: anli_based_on_the_previous_passage_r2_score_eval data_files: - split: train path: anli_based_on_the_previous_passage_r2_score_eval/train-* - split: validation path: anli_based_on_the_previous_passage_r2_score_eval/validation-* - split: test path: anli_based_on_the_previous_passage_r2_score_eval/test-* - config_name: anli_based_on_the_previous_passage_r3 data_files: - split: train path: anli_based_on_the_previous_passage_r3/train-* - split: validation path: anli_based_on_the_previous_passage_r3/validation-* - split: test path: anli_based_on_the_previous_passage_r3/test-* - config_name: anli_based_on_the_previous_passage_r3_score_eval data_files: - split: train path: anli_based_on_the_previous_passage_r3_score_eval/train-* - split: validation path: anli_based_on_the_previous_passage_r3_score_eval/validation-* - split: test path: anli_based_on_the_previous_passage_r3_score_eval/test-* - config_name: anli_can_we_infer_r1 data_files: - split: train path: anli_can_we_infer_r1/train-* - split: validation path: anli_can_we_infer_r1/validation-* - split: test path: anli_can_we_infer_r1/test-* - config_name: anli_can_we_infer_r1_score_eval data_files: - split: train path: anli_can_we_infer_r1_score_eval/train-* - split: validation path: anli_can_we_infer_r1_score_eval/validation-* - split: test path: anli_can_we_infer_r1_score_eval/test-* - config_name: anli_can_we_infer_r2 data_files: - split: train path: anli_can_we_infer_r2/train-* - split: validation path: anli_can_we_infer_r2/validation-* - split: test path: anli_can_we_infer_r2/test-* - config_name: anli_can_we_infer_r2_score_eval data_files: - split: train path: anli_can_we_infer_r2_score_eval/train-* - split: validation path: anli_can_we_infer_r2_score_eval/validation-* - split: test path: anli_can_we_infer_r2_score_eval/test-* - config_name: anli_can_we_infer_r3 data_files: - split: train path: anli_can_we_infer_r3/train-* - split: validation path: anli_can_we_infer_r3/validation-* - split: test path: anli_can_we_infer_r3/test-* - config_name: anli_can_we_infer_r3_score_eval data_files: - split: train path: anli_can_we_infer_r3_score_eval/train-* - split: validation path: anli_can_we_infer_r3_score_eval/validation-* - split: test path: anli_can_we_infer_r3_score_eval/test-* - config_name: anli_claim_true_false_inconclusive_r1 data_files: - split: train path: anli_claim_true_false_inconclusive_r1/train-* - split: validation path: anli_claim_true_false_inconclusive_r1/validation-* - split: test path: anli_claim_true_false_inconclusive_r1/test-* - config_name: anli_claim_true_false_inconclusive_r1_score_eval data_files: - split: train path: anli_claim_true_false_inconclusive_r1_score_eval/train-* - split: validation path: anli_claim_true_false_inconclusive_r1_score_eval/validation-* - split: test path: anli_claim_true_false_inconclusive_r1_score_eval/test-* - config_name: anli_claim_true_false_inconclusive_r2 data_files: - split: train path: anli_claim_true_false_inconclusive_r2/train-* - split: validation path: anli_claim_true_false_inconclusive_r2/validation-* - split: test path: anli_claim_true_false_inconclusive_r2/test-* - config_name: anli_claim_true_false_inconclusive_r2_score_eval data_files: - split: train path: anli_claim_true_false_inconclusive_r2_score_eval/train-* - split: validation path: anli_claim_true_false_inconclusive_r2_score_eval/validation-* - split: test path: anli_claim_true_false_inconclusive_r2_score_eval/test-* - config_name: anli_claim_true_false_inconclusive_r3 data_files: - split: train path: anli_claim_true_false_inconclusive_r3/train-* - split: validation path: anli_claim_true_false_inconclusive_r3/validation-* - split: test path: anli_claim_true_false_inconclusive_r3/test-* - config_name: anli_claim_true_false_inconclusive_r3_score_eval data_files: - split: train path: anli_claim_true_false_inconclusive_r3_score_eval/train-* - split: validation path: anli_claim_true_false_inconclusive_r3_score_eval/validation-* - split: test path: anli_claim_true_false_inconclusive_r3_score_eval/test-* - config_name: anli_consider_always_sometimes_never_r1 data_files: - split: train path: anli_consider_always_sometimes_never_r1/train-* - split: validation path: anli_consider_always_sometimes_never_r1/validation-* - split: test path: anli_consider_always_sometimes_never_r1/test-* - config_name: anli_consider_always_sometimes_never_r1_score_eval data_files: - split: train path: anli_consider_always_sometimes_never_r1_score_eval/train-* - split: validation path: anli_consider_always_sometimes_never_r1_score_eval/validation-* - split: test path: anli_consider_always_sometimes_never_r1_score_eval/test-* - config_name: anli_consider_always_sometimes_never_r2 data_files: - split: train path: anli_consider_always_sometimes_never_r2/train-* - split: validation path: anli_consider_always_sometimes_never_r2/validation-* - split: test path: anli_consider_always_sometimes_never_r2/test-* - config_name: anli_consider_always_sometimes_never_r2_score_eval data_files: - split: train path: anli_consider_always_sometimes_never_r2_score_eval/train-* - split: validation path: anli_consider_always_sometimes_never_r2_score_eval/validation-* - split: test path: anli_consider_always_sometimes_never_r2_score_eval/test-* - config_name: anli_consider_always_sometimes_never_r3 data_files: - split: train path: anli_consider_always_sometimes_never_r3/train-* - split: validation path: anli_consider_always_sometimes_never_r3/validation-* - split: test path: anli_consider_always_sometimes_never_r3/test-* - config_name: anli_consider_always_sometimes_never_r3_score_eval data_files: - split: train path: anli_consider_always_sometimes_never_r3_score_eval/train-* - split: validation path: anli_consider_always_sometimes_never_r3_score_eval/validation-* - split: test path: anli_consider_always_sometimes_never_r3_score_eval/test-* - config_name: anli_does_it_follow_that_r1 data_files: - split: train path: anli_does_it_follow_that_r1/train-* - split: validation path: anli_does_it_follow_that_r1/validation-* - split: test path: anli_does_it_follow_that_r1/test-* - config_name: anli_does_it_follow_that_r1_score_eval data_files: - split: train path: anli_does_it_follow_that_r1_score_eval/train-* - split: validation path: anli_does_it_follow_that_r1_score_eval/validation-* - split: test path: anli_does_it_follow_that_r1_score_eval/test-* - config_name: anli_does_it_follow_that_r2 data_files: - split: train path: anli_does_it_follow_that_r2/train-* - split: validation path: anli_does_it_follow_that_r2/validation-* - split: test path: anli_does_it_follow_that_r2/test-* - config_name: anli_does_it_follow_that_r2_score_eval data_files: - split: train path: anli_does_it_follow_that_r2_score_eval/train-* - split: validation path: anli_does_it_follow_that_r2_score_eval/validation-* - split: test path: anli_does_it_follow_that_r2_score_eval/test-* - config_name: anli_does_it_follow_that_r3 data_files: - split: train path: anli_does_it_follow_that_r3/train-* - split: validation path: anli_does_it_follow_that_r3/validation-* - split: test path: anli_does_it_follow_that_r3/test-* - config_name: anli_does_it_follow_that_r3_score_eval data_files: - split: train path: anli_does_it_follow_that_r3_score_eval/train-* - split: validation path: anli_does_it_follow_that_r3_score_eval/validation-* - split: test path: anli_does_it_follow_that_r3_score_eval/test-* - config_name: anli_does_this_imply_r1 data_files: - split: train path: anli_does_this_imply_r1/train-* - split: validation path: anli_does_this_imply_r1/validation-* - split: test path: anli_does_this_imply_r1/test-* - config_name: anli_does_this_imply_r1_score_eval data_files: - split: train path: anli_does_this_imply_r1_score_eval/train-* - split: validation path: anli_does_this_imply_r1_score_eval/validation-* - split: test path: anli_does_this_imply_r1_score_eval/test-* - config_name: anli_does_this_imply_r2 data_files: - split: train path: anli_does_this_imply_r2/train-* - split: validation path: anli_does_this_imply_r2/validation-* - split: test path: anli_does_this_imply_r2/test-* - config_name: anli_does_this_imply_r2_score_eval data_files: - split: train path: anli_does_this_imply_r2_score_eval/train-* - split: validation path: anli_does_this_imply_r2_score_eval/validation-* - split: test path: anli_does_this_imply_r2_score_eval/test-* - config_name: anli_does_this_imply_r3 data_files: - split: train path: anli_does_this_imply_r3/train-* - split: validation path: anli_does_this_imply_r3/validation-* - split: test path: anli_does_this_imply_r3/test-* - config_name: anli_does_this_imply_r3_score_eval data_files: - split: train path: anli_does_this_imply_r3_score_eval/train-* - split: validation path: anli_does_this_imply_r3_score_eval/validation-* - split: test path: anli_does_this_imply_r3_score_eval/test-* - config_name: anli_guaranteed_possible_impossible_r1 data_files: - split: train path: anli_guaranteed_possible_impossible_r1/train-* - split: validation path: anli_guaranteed_possible_impossible_r1/validation-* - split: test path: anli_guaranteed_possible_impossible_r1/test-* - config_name: anli_guaranteed_possible_impossible_r1_score_eval data_files: - split: train path: anli_guaranteed_possible_impossible_r1_score_eval/train-* - split: validation path: anli_guaranteed_possible_impossible_r1_score_eval/validation-* - split: test path: anli_guaranteed_possible_impossible_r1_score_eval/test-* - config_name: anli_guaranteed_possible_impossible_r2 data_files: - split: train path: anli_guaranteed_possible_impossible_r2/train-* - split: validation path: anli_guaranteed_possible_impossible_r2/validation-* - split: test path: anli_guaranteed_possible_impossible_r2/test-* - config_name: anli_guaranteed_possible_impossible_r2_score_eval data_files: - split: train path: anli_guaranteed_possible_impossible_r2_score_eval/train-* - split: validation path: anli_guaranteed_possible_impossible_r2_score_eval/validation-* - split: test path: anli_guaranteed_possible_impossible_r2_score_eval/test-* - config_name: anli_guaranteed_possible_impossible_r3 data_files: - split: train path: anli_guaranteed_possible_impossible_r3/train-* - split: validation path: anli_guaranteed_possible_impossible_r3/validation-* - split: test path: anli_guaranteed_possible_impossible_r3/test-* - config_name: anli_guaranteed_possible_impossible_r3_score_eval data_files: - split: train path: anli_guaranteed_possible_impossible_r3_score_eval/train-* - split: validation path: anli_guaranteed_possible_impossible_r3_score_eval/validation-* - split: test path: anli_guaranteed_possible_impossible_r3_score_eval/test-* - config_name: anli_guaranteed_true_r1 data_files: - split: train path: anli_guaranteed_true_r1/train-* - split: validation path: anli_guaranteed_true_r1/validation-* - split: test path: anli_guaranteed_true_r1/test-* - config_name: anli_guaranteed_true_r1_score_eval data_files: - split: train path: anli_guaranteed_true_r1_score_eval/train-* - split: validation path: anli_guaranteed_true_r1_score_eval/validation-* - split: test path: anli_guaranteed_true_r1_score_eval/test-* - config_name: anli_guaranteed_true_r2 data_files: - split: train path: anli_guaranteed_true_r2/train-* - split: validation path: anli_guaranteed_true_r2/validation-* - split: test path: anli_guaranteed_true_r2/test-* - config_name: anli_guaranteed_true_r2_score_eval data_files: - split: train path: anli_guaranteed_true_r2_score_eval/train-* - split: validation path: anli_guaranteed_true_r2_score_eval/validation-* - split: test path: anli_guaranteed_true_r2_score_eval/test-* - config_name: anli_guaranteed_true_r3 data_files: - split: train path: anli_guaranteed_true_r3/train-* - split: validation path: anli_guaranteed_true_r3/validation-* - split: test path: anli_guaranteed_true_r3/test-* - config_name: anli_guaranteed_true_r3_score_eval data_files: - split: train path: anli_guaranteed_true_r3_score_eval/train-* - split: validation path: anli_guaranteed_true_r3_score_eval/validation-* - split: test path: anli_guaranteed_true_r3_score_eval/test-* - config_name: anli_justified_in_saying_r1 data_files: - split: train path: anli_justified_in_saying_r1/train-* - split: validation path: anli_justified_in_saying_r1/validation-* - split: test path: anli_justified_in_saying_r1/test-* - config_name: anli_justified_in_saying_r1_score_eval data_files: - split: train path: anli_justified_in_saying_r1_score_eval/train-* - split: validation path: anli_justified_in_saying_r1_score_eval/validation-* - split: test path: anli_justified_in_saying_r1_score_eval/test-* - config_name: anli_justified_in_saying_r2 data_files: - split: train path: anli_justified_in_saying_r2/train-* - split: validation path: anli_justified_in_saying_r2/validation-* - split: test path: anli_justified_in_saying_r2/test-* - config_name: anli_justified_in_saying_r2_score_eval data_files: - split: train path: anli_justified_in_saying_r2_score_eval/train-* - split: validation path: anli_justified_in_saying_r2_score_eval/validation-* - split: test path: anli_justified_in_saying_r2_score_eval/test-* - config_name: anli_justified_in_saying_r3 data_files: - split: train path: anli_justified_in_saying_r3/train-* - split: validation path: anli_justified_in_saying_r3/validation-* - split: test path: anli_justified_in_saying_r3/test-* - config_name: anli_justified_in_saying_r3_score_eval data_files: - split: train path: anli_justified_in_saying_r3_score_eval/train-* - split: validation path: anli_justified_in_saying_r3_score_eval/validation-* - split: test path: anli_justified_in_saying_r3_score_eval/test-* - config_name: anli_must_be_true_r1 data_files: - split: train path: anli_must_be_true_r1/train-* - split: validation path: anli_must_be_true_r1/validation-* - split: test path: anli_must_be_true_r1/test-* - config_name: anli_must_be_true_r1_score_eval data_files: - split: train path: anli_must_be_true_r1_score_eval/train-* - split: validation path: anli_must_be_true_r1_score_eval/validation-* - split: test path: anli_must_be_true_r1_score_eval/test-* - config_name: anli_must_be_true_r2 data_files: - split: train path: anli_must_be_true_r2/train-* - split: validation path: anli_must_be_true_r2/validation-* - split: test path: anli_must_be_true_r2/test-* - config_name: anli_must_be_true_r2_score_eval data_files: - split: train path: anli_must_be_true_r2_score_eval/train-* - split: validation path: anli_must_be_true_r2_score_eval/validation-* - split: test path: anli_must_be_true_r2_score_eval/test-* - config_name: anli_must_be_true_r3 data_files: - split: train path: anli_must_be_true_r3/train-* - split: validation path: anli_must_be_true_r3/validation-* - split: test path: anli_must_be_true_r3/test-* - config_name: anli_must_be_true_r3_score_eval data_files: - split: train path: anli_must_be_true_r3_score_eval/train-* - split: validation path: anli_must_be_true_r3_score_eval/validation-* - split: test path: anli_must_be_true_r3_score_eval/test-* - config_name: anli_should_assume_r1 data_files: - split: train path: anli_should_assume_r1/train-* - split: validation path: anli_should_assume_r1/validation-* - split: test path: anli_should_assume_r1/test-* - config_name: anli_should_assume_r1_score_eval data_files: - split: train path: anli_should_assume_r1_score_eval/train-* - split: validation path: anli_should_assume_r1_score_eval/validation-* - split: test path: anli_should_assume_r1_score_eval/test-* - config_name: anli_should_assume_r2 data_files: - split: train path: anli_should_assume_r2/train-* - split: validation path: anli_should_assume_r2/validation-* - split: test path: anli_should_assume_r2/test-* - config_name: anli_should_assume_r2_score_eval data_files: - split: train path: anli_should_assume_r2_score_eval/train-* - split: validation path: anli_should_assume_r2_score_eval/validation-* - split: test path: anli_should_assume_r2_score_eval/test-* - config_name: anli_should_assume_r3 data_files: - split: train path: anli_should_assume_r3/train-* - split: validation path: anli_should_assume_r3/validation-* - split: test path: anli_should_assume_r3/test-* - config_name: anli_should_assume_r3_score_eval data_files: - split: train path: anli_should_assume_r3_score_eval/train-* - split: validation path: anli_should_assume_r3_score_eval/validation-* - split: test path: anli_should_assume_r3_score_eval/test-* - config_name: anli_take_the_following_as_truth_r1 data_files: - split: train path: anli_take_the_following_as_truth_r1/train-* - split: validation path: anli_take_the_following_as_truth_r1/validation-* - split: test path: anli_take_the_following_as_truth_r1/test-* - config_name: anli_take_the_following_as_truth_r1_score_eval data_files: - split: train path: anli_take_the_following_as_truth_r1_score_eval/train-* - split: validation path: anli_take_the_following_as_truth_r1_score_eval/validation-* - split: test path: anli_take_the_following_as_truth_r1_score_eval/test-* - config_name: anli_take_the_following_as_truth_r2 data_files: - split: train path: anli_take_the_following_as_truth_r2/train-* - split: validation path: anli_take_the_following_as_truth_r2/validation-* - split: test path: anli_take_the_following_as_truth_r2/test-* - config_name: anli_take_the_following_as_truth_r2_score_eval data_files: - split: train path: anli_take_the_following_as_truth_r2_score_eval/train-* - split: validation path: anli_take_the_following_as_truth_r2_score_eval/validation-* - split: test path: anli_take_the_following_as_truth_r2_score_eval/test-* - config_name: anli_take_the_following_as_truth_r3 data_files: - split: train path: anli_take_the_following_as_truth_r3/train-* - split: validation path: anli_take_the_following_as_truth_r3/validation-* - split: test path: anli_take_the_following_as_truth_r3/test-* - config_name: anli_take_the_following_as_truth_r3_score_eval data_files: - split: train path: anli_take_the_following_as_truth_r3_score_eval/train-* - split: validation path: anli_take_the_following_as_truth_r3_score_eval/validation-* - split: test path: anli_take_the_following_as_truth_r3_score_eval/test-* - config_name: app_reviews_categorize_rating_using_review data_files: - split: train path: app_reviews_categorize_rating_using_review/train-* - config_name: app_reviews_convert_to_rating data_files: - split: train path: app_reviews_convert_to_rating/train-* - config_name: app_reviews_convert_to_star_rating data_files: - split: train path: app_reviews_convert_to_star_rating/train-* - config_name: app_reviews_generate_review data_files: - split: train path: app_reviews_generate_review/train-* - config_name: cnn_dailymail_3.0.0_2_or_3_sentences data_files: - split: train path: cnn_dailymail_3.0.0_2_or_3_sentences/train-* - split: validation path: cnn_dailymail_3.0.0_2_or_3_sentences/validation-* - split: test path: cnn_dailymail_3.0.0_2_or_3_sentences/test-* - config_name: cnn_dailymail_3.0.0_generate_story data_files: - split: train path: cnn_dailymail_3.0.0_generate_story/train-* - split: validation path: cnn_dailymail_3.0.0_generate_story/validation-* - split: test path: cnn_dailymail_3.0.0_generate_story/test-* - config_name: cnn_dailymail_3.0.0_news_card_view data_files: - split: train path: cnn_dailymail_3.0.0_news_card_view/train-* - split: validation path: cnn_dailymail_3.0.0_news_card_view/validation-* - split: test path: cnn_dailymail_3.0.0_news_card_view/test-* - config_name: cnn_dailymail_3.0.0_news_stock data_files: - split: train path: cnn_dailymail_3.0.0_news_stock/train-* - split: validation path: cnn_dailymail_3.0.0_news_stock/validation-* - split: test path: cnn_dailymail_3.0.0_news_stock/test-* - config_name: cnn_dailymail_3.0.0_news_summary data_files: - split: train path: cnn_dailymail_3.0.0_news_summary/train-* - split: validation path: cnn_dailymail_3.0.0_news_summary/validation-* - split: test path: cnn_dailymail_3.0.0_news_summary/test-* - config_name: cnn_dailymail_3.0.0_spice_up_story data_files: - split: train path: cnn_dailymail_3.0.0_spice_up_story/train-* - split: validation path: cnn_dailymail_3.0.0_spice_up_story/validation-* - split: test path: cnn_dailymail_3.0.0_spice_up_story/test-* - config_name: cnn_dailymail_3.0.0_sum_in_brief data_files: - split: train path: cnn_dailymail_3.0.0_sum_in_brief/train-* - split: validation path: cnn_dailymail_3.0.0_sum_in_brief/validation-* - split: test path: cnn_dailymail_3.0.0_sum_in_brief/test-* - config_name: cnn_dailymail_3.0.0_tldr_summary data_files: - split: train path: cnn_dailymail_3.0.0_tldr_summary/train-* - split: validation path: cnn_dailymail_3.0.0_tldr_summary/validation-* - split: test path: cnn_dailymail_3.0.0_tldr_summary/test-* - config_name: cnn_dailymail_3.0.0_write_an_outline data_files: - split: train path: cnn_dailymail_3.0.0_write_an_outline/train-* - split: validation path: cnn_dailymail_3.0.0_write_an_outline/validation-* - split: test path: cnn_dailymail_3.0.0_write_an_outline/test-* - config_name: common_gen_Example_prompt data_files: - split: train path: common_gen_Example_prompt/train-* - split: validation path: common_gen_Example_prompt/validation-* - split: test path: common_gen_Example_prompt/test-* - config_name: common_gen_Given_concepts_type_1 data_files: - split: train path: common_gen_Given_concepts_type_1/train-* - split: validation path: common_gen_Given_concepts_type_1/validation-* - split: test path: common_gen_Given_concepts_type_1/test-* - config_name: common_gen_Given_concepts_type_2 data_files: - split: train path: common_gen_Given_concepts_type_2/train-* - split: validation path: common_gen_Given_concepts_type_2/validation-* - split: test path: common_gen_Given_concepts_type_2/test-* - config_name: common_gen_Put_together data_files: - split: train path: common_gen_Put_together/train-* - split: validation path: common_gen_Put_together/validation-* - split: test path: common_gen_Put_together/test-* - config_name: common_gen_choice_in_concept_centric_sentence_generation data_files: - split: train path: common_gen_choice_in_concept_centric_sentence_generation/train-* - split: validation path: common_gen_choice_in_concept_centric_sentence_generation/validation-* - split: test path: common_gen_choice_in_concept_centric_sentence_generation/test-* - config_name: common_gen_random_task_template_prompt data_files: - split: train path: common_gen_random_task_template_prompt/train-* - split: validation path: common_gen_random_task_template_prompt/validation-* - split: test path: common_gen_random_task_template_prompt/test-* - config_name: common_gen_sentence_to_concepts data_files: - split: train path: common_gen_sentence_to_concepts/train-* - split: validation path: common_gen_sentence_to_concepts/validation-* - split: test path: common_gen_sentence_to_concepts/test-* - config_name: common_gen_topic_to_sentence data_files: - split: train path: common_gen_topic_to_sentence/train-* - split: validation path: common_gen_topic_to_sentence/validation-* - split: test path: common_gen_topic_to_sentence/test-* - config_name: common_gen_topics_from_the_sentence data_files: - split: train path: common_gen_topics_from_the_sentence/train-* - split: validation path: common_gen_topics_from_the_sentence/validation-* - split: test path: common_gen_topics_from_the_sentence/test-* - config_name: cos_e_v1.11_aligned_with_common_sense data_files: - split: train path: cos_e_v1.11_aligned_with_common_sense/train-* - split: validation path: cos_e_v1.11_aligned_with_common_sense/validation-* - config_name: cos_e_v1.11_description_question_option_id data_files: - split: train path: cos_e_v1.11_description_question_option_id/train-* - split: validation path: cos_e_v1.11_description_question_option_id/validation-* - config_name: cos_e_v1.11_description_question_option_text data_files: - split: train path: cos_e_v1.11_description_question_option_text/train-* - split: validation path: cos_e_v1.11_description_question_option_text/validation-* - config_name: cos_e_v1.11_explain_why_human data_files: - split: train path: cos_e_v1.11_explain_why_human/train-* - split: validation path: cos_e_v1.11_explain_why_human/validation-* - config_name: cos_e_v1.11_generate_explanation_given_text data_files: - split: train path: cos_e_v1.11_generate_explanation_given_text/train-* - split: validation path: cos_e_v1.11_generate_explanation_given_text/validation-* - config_name: cos_e_v1.11_i_think data_files: - split: train path: cos_e_v1.11_i_think/train-* - split: validation path: cos_e_v1.11_i_think/validation-* - config_name: cos_e_v1.11_question_description_option_id data_files: - split: train path: cos_e_v1.11_question_description_option_id/train-* - split: validation path: cos_e_v1.11_question_description_option_id/validation-* - config_name: cos_e_v1.11_question_description_option_text data_files: - split: train path: cos_e_v1.11_question_description_option_text/train-* - split: validation path: cos_e_v1.11_question_description_option_text/validation-* - config_name: cos_e_v1.11_question_option_description_id data_files: - split: train path: cos_e_v1.11_question_option_description_id/train-* - split: validation path: cos_e_v1.11_question_option_description_id/validation-* - config_name: cos_e_v1.11_question_option_description_text data_files: - split: train path: cos_e_v1.11_question_option_description_text/train-* - split: validation path: cos_e_v1.11_question_option_description_text/validation-* - config_name: cos_e_v1.11_rationale data_files: - split: train path: cos_e_v1.11_rationale/train-* - split: validation path: cos_e_v1.11_rationale/validation-* - config_name: cosmos_qa_context_answer_to_question data_files: - split: train path: cosmos_qa_context_answer_to_question/train-* - split: validation path: cosmos_qa_context_answer_to_question/validation-* - split: test path: cosmos_qa_context_answer_to_question/test-* - config_name: cosmos_qa_context_description_question_answer_id data_files: - split: train path: cosmos_qa_context_description_question_answer_id/train-* - split: validation path: cosmos_qa_context_description_question_answer_id/validation-* - split: test path: cosmos_qa_context_description_question_answer_id/test-* - config_name: cosmos_qa_context_description_question_answer_text data_files: - split: train path: cosmos_qa_context_description_question_answer_text/train-* - split: validation path: cosmos_qa_context_description_question_answer_text/validation-* - split: test path: cosmos_qa_context_description_question_answer_text/test-* - config_name: cosmos_qa_context_description_question_text data_files: - split: train path: cosmos_qa_context_description_question_text/train-* - split: validation path: cosmos_qa_context_description_question_text/validation-* - split: test path: cosmos_qa_context_description_question_text/test-* - config_name: cosmos_qa_context_question_description_answer_id data_files: - split: train path: cosmos_qa_context_question_description_answer_id/train-* - split: validation path: cosmos_qa_context_question_description_answer_id/validation-* - split: test path: cosmos_qa_context_question_description_answer_id/test-* - config_name: cosmos_qa_context_question_description_answer_text data_files: - split: train path: cosmos_qa_context_question_description_answer_text/train-* - split: validation path: cosmos_qa_context_question_description_answer_text/validation-* - split: test path: cosmos_qa_context_question_description_answer_text/test-* - config_name: cosmos_qa_context_question_description_text data_files: - split: train path: cosmos_qa_context_question_description_text/train-* - split: validation path: cosmos_qa_context_question_description_text/validation-* - split: test path: cosmos_qa_context_question_description_text/test-* - config_name: cosmos_qa_description_context_question_answer_id data_files: - split: train path: cosmos_qa_description_context_question_answer_id/train-* - split: validation path: cosmos_qa_description_context_question_answer_id/validation-* - split: test path: cosmos_qa_description_context_question_answer_id/test-* - config_name: cosmos_qa_description_context_question_answer_text data_files: - split: train path: cosmos_qa_description_context_question_answer_text/train-* - split: validation path: cosmos_qa_description_context_question_answer_text/validation-* - split: test path: cosmos_qa_description_context_question_answer_text/test-* - config_name: cosmos_qa_description_context_question_text data_files: - split: train path: cosmos_qa_description_context_question_text/train-* - split: validation path: cosmos_qa_description_context_question_text/validation-* - split: test path: cosmos_qa_description_context_question_text/test-* - config_name: cosmos_qa_no_prompt_id data_files: - split: train path: cosmos_qa_no_prompt_id/train-* - split: validation path: cosmos_qa_no_prompt_id/validation-* - split: test path: cosmos_qa_no_prompt_id/test-* - config_name: cosmos_qa_no_prompt_text data_files: - split: train path: cosmos_qa_no_prompt_text/train-* - split: validation path: cosmos_qa_no_prompt_text/validation-* - split: test path: cosmos_qa_no_prompt_text/test-* - config_name: cosmos_qa_only_question_answer data_files: - split: train path: cosmos_qa_only_question_answer/train-* - split: validation path: cosmos_qa_only_question_answer/validation-* - split: test path: cosmos_qa_only_question_answer/test-* - config_name: dbpedia_14_given_a_choice_of_categories_ data_files: - split: train path: dbpedia_14_given_a_choice_of_categories_/train-* - split: test path: dbpedia_14_given_a_choice_of_categories_/test-* - config_name: dbpedia_14_given_a_list_of_category_what_does_the_title_belong_to data_files: - split: train path: dbpedia_14_given_a_list_of_category_what_does_the_title_belong_to/train-* - split: test path: dbpedia_14_given_a_list_of_category_what_does_the_title_belong_to/test-* - config_name: dbpedia_14_given_list_what_category_does_the_paragraph_belong_to data_files: - split: train path: dbpedia_14_given_list_what_category_does_the_paragraph_belong_to/train-* - split: test path: dbpedia_14_given_list_what_category_does_the_paragraph_belong_to/test-* - config_name: dbpedia_14_pick_one_category_for_the_following_text data_files: - split: train path: dbpedia_14_pick_one_category_for_the_following_text/train-* - split: test path: dbpedia_14_pick_one_category_for_the_following_text/test-* - config_name: dream_answer_to_dialogue data_files: - split: train path: dream_answer_to_dialogue/train-* - split: validation path: dream_answer_to_dialogue/validation-* - split: test path: dream_answer_to_dialogue/test-* - config_name: dream_baseline data_files: - split: train path: dream_baseline/train-* - split: validation path: dream_baseline/validation-* - split: test path: dream_baseline/test-* - config_name: dream_generate_first_utterance data_files: - split: train path: dream_generate_first_utterance/train-* - split: validation path: dream_generate_first_utterance/validation-* - split: test path: dream_generate_first_utterance/test-* - config_name: dream_generate_last_utterance data_files: - split: train path: dream_generate_last_utterance/train-* - split: validation path: dream_generate_last_utterance/validation-* - split: test path: dream_generate_last_utterance/test-* - config_name: dream_read_the_following_conversation_and_answer_the_question data_files: - split: train path: dream_read_the_following_conversation_and_answer_the_question/train-* - split: validation path: dream_read_the_following_conversation_and_answer_the_question/validation-* - split: test path: dream_read_the_following_conversation_and_answer_the_question/test-* - config_name: duorc_ParaphraseRC_answer_question data_files: - split: train path: duorc_ParaphraseRC_answer_question/train-* - split: validation path: duorc_ParaphraseRC_answer_question/validation-* - split: test path: duorc_ParaphraseRC_answer_question/test-* - config_name: duorc_ParaphraseRC_build_story_around_qa data_files: - split: train path: duorc_ParaphraseRC_build_story_around_qa/train-* - split: validation path: duorc_ParaphraseRC_build_story_around_qa/validation-* - split: test path: duorc_ParaphraseRC_build_story_around_qa/test-* - config_name: duorc_ParaphraseRC_decide_worth_it data_files: - split: train path: duorc_ParaphraseRC_decide_worth_it/train-* - split: validation path: duorc_ParaphraseRC_decide_worth_it/validation-* - split: test path: duorc_ParaphraseRC_decide_worth_it/test-* - config_name: duorc_ParaphraseRC_extract_answer data_files: - split: train path: duorc_ParaphraseRC_extract_answer/train-* - split: validation path: duorc_ParaphraseRC_extract_answer/validation-* - split: test path: duorc_ParaphraseRC_extract_answer/test-* - config_name: duorc_ParaphraseRC_generate_question data_files: - split: train path: duorc_ParaphraseRC_generate_question/train-* - split: validation path: duorc_ParaphraseRC_generate_question/validation-* - split: test path: duorc_ParaphraseRC_generate_question/test-* - config_name: duorc_ParaphraseRC_generate_question_by_answer data_files: - split: train path: duorc_ParaphraseRC_generate_question_by_answer/train-* - split: validation path: duorc_ParaphraseRC_generate_question_by_answer/validation-* - split: test path: duorc_ParaphraseRC_generate_question_by_answer/test-* - config_name: duorc_ParaphraseRC_movie_director data_files: - split: train path: duorc_ParaphraseRC_movie_director/train-* - split: validation path: duorc_ParaphraseRC_movie_director/validation-* - split: test path: duorc_ParaphraseRC_movie_director/test-* - config_name: duorc_ParaphraseRC_question_answering data_files: - split: train path: duorc_ParaphraseRC_question_answering/train-* - split: validation path: duorc_ParaphraseRC_question_answering/validation-* - split: test path: duorc_ParaphraseRC_question_answering/test-* - config_name: duorc_ParaphraseRC_title_generation data_files: - split: train path: duorc_ParaphraseRC_title_generation/train-* - split: validation path: duorc_ParaphraseRC_title_generation/validation-* - split: test path: duorc_ParaphraseRC_title_generation/test-* - config_name: duorc_SelfRC_answer_question data_files: - split: train path: duorc_SelfRC_answer_question/train-* - split: validation path: duorc_SelfRC_answer_question/validation-* - split: test path: duorc_SelfRC_answer_question/test-* - config_name: duorc_SelfRC_build_story_around_qa data_files: - split: train path: duorc_SelfRC_build_story_around_qa/train-* - split: validation path: duorc_SelfRC_build_story_around_qa/validation-* - split: test path: duorc_SelfRC_build_story_around_qa/test-* - config_name: duorc_SelfRC_decide_worth_it data_files: - split: train path: duorc_SelfRC_decide_worth_it/train-* - split: validation path: duorc_SelfRC_decide_worth_it/validation-* - split: test path: duorc_SelfRC_decide_worth_it/test-* - config_name: duorc_SelfRC_extract_answer data_files: - split: train path: duorc_SelfRC_extract_answer/train-* - split: validation path: duorc_SelfRC_extract_answer/validation-* - split: test path: duorc_SelfRC_extract_answer/test-* - config_name: duorc_SelfRC_generate_question data_files: - split: train path: duorc_SelfRC_generate_question/train-* - split: validation path: duorc_SelfRC_generate_question/validation-* - split: test path: duorc_SelfRC_generate_question/test-* - config_name: duorc_SelfRC_generate_question_by_answer data_files: - split: train path: duorc_SelfRC_generate_question_by_answer/train-* - split: validation path: duorc_SelfRC_generate_question_by_answer/validation-* - split: test path: duorc_SelfRC_generate_question_by_answer/test-* - config_name: duorc_SelfRC_movie_director data_files: - split: train path: duorc_SelfRC_movie_director/train-* - split: validation path: duorc_SelfRC_movie_director/validation-* - split: test path: duorc_SelfRC_movie_director/test-* - config_name: duorc_SelfRC_question_answering data_files: - split: train path: duorc_SelfRC_question_answering/train-* - split: validation path: duorc_SelfRC_question_answering/validation-* - split: test path: duorc_SelfRC_question_answering/test-* - config_name: duorc_SelfRC_title_generation data_files: - split: train path: duorc_SelfRC_title_generation/train-* - split: validation path: duorc_SelfRC_title_generation/validation-* - split: test path: duorc_SelfRC_title_generation/test-* - config_name: gigaword_TLDR data_files: - split: train path: gigaword_TLDR/train-* - split: validation path: gigaword_TLDR/validation-* - split: test path: gigaword_TLDR/test-* - config_name: gigaword_first_sentence_title data_files: - split: train path: gigaword_first_sentence_title/train-* - split: validation path: gigaword_first_sentence_title/validation-* - split: test path: gigaword_first_sentence_title/test-* - config_name: gigaword_generate_summary_for_this data_files: - split: train path: gigaword_generate_summary_for_this/train-* - split: validation path: gigaword_generate_summary_for_this/validation-* - split: test path: gigaword_generate_summary_for_this/test-* - config_name: gigaword_in_a_nutshell data_files: - split: train path: gigaword_in_a_nutshell/train-* - split: validation path: gigaword_in_a_nutshell/validation-* - split: test path: gigaword_in_a_nutshell/test-* - config_name: gigaword_make_a_title data_files: - split: train path: gigaword_make_a_title/train-* - split: validation path: gigaword_make_a_title/validation-* - split: test path: gigaword_make_a_title/test-* - config_name: gigaword_reverse_writing data_files: - split: train path: gigaword_reverse_writing/train-* - split: validation path: gigaword_reverse_writing/validation-* - split: test path: gigaword_reverse_writing/test-* - config_name: gigaword_write_a_title_for_this_sentence data_files: - split: train path: gigaword_write_a_title_for_this_sentence/train-* - split: validation path: gigaword_write_a_title_for_this_sentence/validation-* - split: test path: gigaword_write_a_title_for_this_sentence/test-* - config_name: gigaword_write_an_article data_files: - split: train path: gigaword_write_an_article/train-* - split: validation path: gigaword_write_an_article/validation-* - split: test path: gigaword_write_an_article/test-* - config_name: gigaword_write_its_sentence data_files: - split: train path: gigaword_write_its_sentence/train-* - split: validation path: gigaword_write_its_sentence/validation-* - split: test path: gigaword_write_its_sentence/test-* - config_name: glue_mrpc_equivalent data_files: - split: train path: glue_mrpc_equivalent/train-* - split: validation path: glue_mrpc_equivalent/validation-* - split: test path: glue_mrpc_equivalent/test-* - config_name: glue_mrpc_generate_paraphrase data_files: - split: train path: glue_mrpc_generate_paraphrase/train-* - split: validation path: glue_mrpc_generate_paraphrase/validation-* - split: test path: glue_mrpc_generate_paraphrase/test-* - config_name: glue_mrpc_generate_sentence data_files: - split: train path: glue_mrpc_generate_sentence/train-* - split: validation path: glue_mrpc_generate_sentence/validation-* - split: test path: glue_mrpc_generate_sentence/test-* - config_name: glue_mrpc_paraphrase data_files: - split: train path: glue_mrpc_paraphrase/train-* - split: validation path: glue_mrpc_paraphrase/validation-* - split: test path: glue_mrpc_paraphrase/test-* - config_name: glue_mrpc_replace data_files: - split: train path: glue_mrpc_replace/train-* - split: validation path: glue_mrpc_replace/validation-* - split: test path: glue_mrpc_replace/test-* - config_name: glue_mrpc_same_thing data_files: - split: train path: glue_mrpc_same_thing/train-* - split: validation path: glue_mrpc_same_thing/validation-* - split: test path: glue_mrpc_same_thing/test-* - config_name: glue_mrpc_want_to_know data_files: - split: train path: glue_mrpc_want_to_know/train-* - split: validation path: glue_mrpc_want_to_know/validation-* - split: test path: glue_mrpc_want_to_know/test-* - config_name: glue_qqp_answer data_files: - split: train path: glue_qqp_answer/train-* - split: validation path: glue_qqp_answer/validation-* - split: test path: glue_qqp_answer/test-* - config_name: glue_qqp_duplicate data_files: - split: train path: glue_qqp_duplicate/train-* - split: validation path: glue_qqp_duplicate/validation-* - split: test path: glue_qqp_duplicate/test-* - config_name: glue_qqp_duplicate_or_not data_files: - split: train path: glue_qqp_duplicate_or_not/train-* - split: validation path: glue_qqp_duplicate_or_not/validation-* - split: test path: glue_qqp_duplicate_or_not/test-* - config_name: glue_qqp_meaning data_files: - split: train path: glue_qqp_meaning/train-* - split: validation path: glue_qqp_meaning/validation-* - split: test path: glue_qqp_meaning/test-* - config_name: glue_qqp_quora data_files: - split: train path: glue_qqp_quora/train-* - split: validation path: glue_qqp_quora/validation-* - split: test path: glue_qqp_quora/test-* - config_name: glue_qqp_same_thing data_files: - split: train path: glue_qqp_same_thing/train-* - split: validation path: glue_qqp_same_thing/validation-* - split: test path: glue_qqp_same_thing/test-* - config_name: hellaswag_Appropriate_continuation_Yes_or_No data_files: - split: train path: hellaswag_Appropriate_continuation_Yes_or_No/train-* - split: validation path: hellaswag_Appropriate_continuation_Yes_or_No/validation-* - split: test path: hellaswag_Appropriate_continuation_Yes_or_No/test-* - config_name: hellaswag_Open_ended_completion data_files: - split: train path: hellaswag_Open_ended_completion/train-* - split: validation path: hellaswag_Open_ended_completion/validation-* - split: test path: hellaswag_Open_ended_completion/test-* - config_name: hellaswag_Open_ended_start data_files: - split: train path: hellaswag_Open_ended_start/train-* - split: validation path: hellaswag_Open_ended_start/validation-* - split: test path: hellaswag_Open_ended_start/test-* - config_name: hellaswag_Predict_ending_with_hint data_files: - split: train path: hellaswag_Predict_ending_with_hint/train-* - split: validation path: hellaswag_Predict_ending_with_hint/validation-* - split: test path: hellaswag_Predict_ending_with_hint/test-* - config_name: hellaswag_Predict_ending_with_hint_score_eval data_files: - split: train path: hellaswag_Predict_ending_with_hint_score_eval/train-* - split: validation path: hellaswag_Predict_ending_with_hint_score_eval/validation-* - split: test path: hellaswag_Predict_ending_with_hint_score_eval/test-* - config_name: hellaswag_Randomized_prompts_template data_files: - split: train path: hellaswag_Randomized_prompts_template/train-* - split: validation path: hellaswag_Randomized_prompts_template/validation-* - split: test path: hellaswag_Randomized_prompts_template/test-* - config_name: hellaswag_Randomized_prompts_template_score_eval data_files: - split: train path: hellaswag_Randomized_prompts_template_score_eval/train-* - split: validation path: hellaswag_Randomized_prompts_template_score_eval/validation-* - split: test path: hellaswag_Randomized_prompts_template_score_eval/test-* - config_name: hellaswag_Reversed_appropriate_continuation_Yes_or_No data_files: - split: train path: hellaswag_Reversed_appropriate_continuation_Yes_or_No/train-* - split: validation path: hellaswag_Reversed_appropriate_continuation_Yes_or_No/validation-* - split: test path: hellaswag_Reversed_appropriate_continuation_Yes_or_No/test-* - config_name: hellaswag_Topic_of_the_context data_files: - split: train path: hellaswag_Topic_of_the_context/train-* - split: validation path: hellaswag_Topic_of_the_context/validation-* - split: test path: hellaswag_Topic_of_the_context/test-* - config_name: hellaswag_Topic_without_the_ending_answer data_files: - split: train path: hellaswag_Topic_without_the_ending_answer/train-* - split: validation path: hellaswag_Topic_without_the_ending_answer/validation-* - split: test path: hellaswag_Topic_without_the_ending_answer/test-* - config_name: hellaswag_complete_first_then data_files: - split: train path: hellaswag_complete_first_then/train-* - split: validation path: hellaswag_complete_first_then/validation-* - split: test path: hellaswag_complete_first_then/test-* - config_name: hellaswag_complete_first_then_score_eval data_files: - split: train path: hellaswag_complete_first_then_score_eval/train-* - split: validation path: hellaswag_complete_first_then_score_eval/validation-* - split: test path: hellaswag_complete_first_then_score_eval/test-* - config_name: hellaswag_how_ends data_files: - split: train path: hellaswag_how_ends/train-* - split: validation path: hellaswag_how_ends/validation-* - split: test path: hellaswag_how_ends/test-* - config_name: hellaswag_if_begins_how_continues data_files: - split: train path: hellaswag_if_begins_how_continues/train-* - split: validation path: hellaswag_if_begins_how_continues/validation-* - split: test path: hellaswag_if_begins_how_continues/test-* - config_name: hellaswag_if_begins_how_continues_score_eval data_files: - split: train path: hellaswag_if_begins_how_continues_score_eval/train-* - split: validation path: hellaswag_if_begins_how_continues_score_eval/validation-* - split: test path: hellaswag_if_begins_how_continues_score_eval/test-* - config_name: imdb_Movie_Expressed_Sentiment data_files: - split: train path: imdb_Movie_Expressed_Sentiment/train-* - split: test path: imdb_Movie_Expressed_Sentiment/test-* - split: unsupervised path: imdb_Movie_Expressed_Sentiment/unsupervised-* - config_name: imdb_Movie_Expressed_Sentiment_2 data_files: - split: train path: imdb_Movie_Expressed_Sentiment_2/train-* - split: test path: imdb_Movie_Expressed_Sentiment_2/test-* - split: unsupervised path: imdb_Movie_Expressed_Sentiment_2/unsupervised-* - config_name: imdb_Negation_template_for_positive_and_negative data_files: - split: train path: imdb_Negation_template_for_positive_and_negative/train-* - split: test path: imdb_Negation_template_for_positive_and_negative/test-* - split: unsupervised path: imdb_Negation_template_for_positive_and_negative/unsupervised-* - config_name: imdb_Reviewer_Enjoyment data_files: - split: train path: imdb_Reviewer_Enjoyment/train-* - split: test path: imdb_Reviewer_Enjoyment/test-* - split: unsupervised path: imdb_Reviewer_Enjoyment/unsupervised-* - config_name: imdb_Reviewer_Enjoyment_Yes_No data_files: - split: train path: imdb_Reviewer_Enjoyment_Yes_No/train-* - split: test path: imdb_Reviewer_Enjoyment_Yes_No/test-* - split: unsupervised path: imdb_Reviewer_Enjoyment_Yes_No/unsupervised-* - config_name: imdb_Reviewer_Expressed_Sentiment data_files: - split: train path: imdb_Reviewer_Expressed_Sentiment/train-* - split: test path: imdb_Reviewer_Expressed_Sentiment/test-* - split: unsupervised path: imdb_Reviewer_Expressed_Sentiment/unsupervised-* - config_name: imdb_Reviewer_Opinion_bad_good_choices data_files: - split: train path: imdb_Reviewer_Opinion_bad_good_choices/train-* - split: test path: imdb_Reviewer_Opinion_bad_good_choices/test-* - split: unsupervised path: imdb_Reviewer_Opinion_bad_good_choices/unsupervised-* - config_name: imdb_Reviewer_Sentiment_Feeling data_files: - split: train path: imdb_Reviewer_Sentiment_Feeling/train-* - split: test path: imdb_Reviewer_Sentiment_Feeling/test-* - split: unsupervised path: imdb_Reviewer_Sentiment_Feeling/unsupervised-* - config_name: imdb_Sentiment_with_choices_ data_files: - split: train path: imdb_Sentiment_with_choices_/train-* - split: test path: imdb_Sentiment_with_choices_/test-* - split: unsupervised path: imdb_Sentiment_with_choices_/unsupervised-* - config_name: imdb_Text_Expressed_Sentiment data_files: - split: train path: imdb_Text_Expressed_Sentiment/train-* - split: test path: imdb_Text_Expressed_Sentiment/test-* - split: unsupervised path: imdb_Text_Expressed_Sentiment/unsupervised-* - config_name: imdb_Writer_Expressed_Sentiment data_files: - split: train path: imdb_Writer_Expressed_Sentiment/train-* - split: test path: imdb_Writer_Expressed_Sentiment/test-* - split: unsupervised path: imdb_Writer_Expressed_Sentiment/unsupervised-* - config_name: kilt_tasks_hotpotqa_combining_facts data_files: - split: train path: kilt_tasks_hotpotqa_combining_facts/train-* - split: validation path: kilt_tasks_hotpotqa_combining_facts/validation-* - config_name: kilt_tasks_hotpotqa_complex_question data_files: - split: train path: kilt_tasks_hotpotqa_complex_question/train-* - split: validation path: kilt_tasks_hotpotqa_complex_question/validation-* - config_name: kilt_tasks_hotpotqa_final_exam data_files: - split: train path: kilt_tasks_hotpotqa_final_exam/train-* - split: validation path: kilt_tasks_hotpotqa_final_exam/validation-* - config_name: kilt_tasks_hotpotqa_formulate data_files: - split: train path: kilt_tasks_hotpotqa_formulate/train-* - split: validation path: kilt_tasks_hotpotqa_formulate/validation-* - config_name: kilt_tasks_hotpotqa_straighforward_qa data_files: - split: train path: kilt_tasks_hotpotqa_straighforward_qa/train-* - split: validation path: kilt_tasks_hotpotqa_straighforward_qa/validation-* - config_name: multi_news_distill data_files: - split: train path: multi_news_distill/train-* - split: validation path: multi_news_distill/validation-* - split: test path: multi_news_distill/test-* - config_name: multi_news_expand_reverse_task_ data_files: - split: train path: multi_news_expand_reverse_task_/train-* - split: validation path: multi_news_expand_reverse_task_/validation-* - split: test path: multi_news_expand_reverse_task_/test-* - config_name: multi_news_summarize data_files: - split: train path: multi_news_summarize/train-* - split: validation path: multi_news_summarize/validation-* - split: test path: multi_news_summarize/test-* - config_name: multi_news_summary_scenario data_files: - split: train path: multi_news_summary_scenario/train-* - split: validation path: multi_news_summary_scenario/validation-* - split: test path: multi_news_summary_scenario/test-* - config_name: multi_news_synthesize data_files: - split: train path: multi_news_synthesize/train-* - split: validation path: multi_news_synthesize/validation-* - split: test path: multi_news_synthesize/test-* - config_name: multi_news_what_are_the_key_points data_files: - split: train path: multi_news_what_are_the_key_points/train-* - split: validation path: multi_news_what_are_the_key_points/validation-* - split: test path: multi_news_what_are_the_key_points/test-* - config_name: openbookqa_main_choices data_files: - split: train path: openbookqa_main_choices/train-* - split: validation path: openbookqa_main_choices/validation-* - split: test path: openbookqa_main_choices/test-* - config_name: openbookqa_main_choose_an_answer_with_options data_files: - split: train path: openbookqa_main_choose_an_answer_with_options/train-* - split: validation path: openbookqa_main_choose_an_answer_with_options/validation-* - split: test path: openbookqa_main_choose_an_answer_with_options/test-* - config_name: openbookqa_main_only_options data_files: - split: train path: openbookqa_main_only_options/train-* - split: validation path: openbookqa_main_only_options/validation-* - split: test path: openbookqa_main_only_options/test-* - config_name: openbookqa_main_pick_answer_with_options data_files: - split: train path: openbookqa_main_pick_answer_with_options/train-* - split: validation path: openbookqa_main_pick_answer_with_options/validation-* - split: test path: openbookqa_main_pick_answer_with_options/test-* - config_name: openbookqa_main_pick_using_id data_files: - split: train path: openbookqa_main_pick_using_id/train-* - split: validation path: openbookqa_main_pick_using_id/validation-* - split: test path: openbookqa_main_pick_using_id/test-* - config_name: openbookqa_main_which_correct data_files: - split: train path: openbookqa_main_which_correct/train-* - split: validation path: openbookqa_main_which_correct/validation-* - split: test path: openbookqa_main_which_correct/test-* - config_name: openbookqa_main_which_correct_inverse data_files: - split: train path: openbookqa_main_which_correct_inverse/train-* - split: validation path: openbookqa_main_which_correct_inverse/validation-* - split: test path: openbookqa_main_which_correct_inverse/test-* - config_name: paws_labeled_final_Concatenation data_files: - split: train path: paws_labeled_final_Concatenation/train-* - split: validation path: paws_labeled_final_Concatenation/validation-* - split: test path: paws_labeled_final_Concatenation/test-* - config_name: paws_labeled_final_Concatenation_no_label data_files: - split: train path: paws_labeled_final_Concatenation_no_label/train-* - split: validation path: paws_labeled_final_Concatenation_no_label/validation-* - split: test path: paws_labeled_final_Concatenation_no_label/test-* - config_name: paws_labeled_final_Meaning data_files: - split: train path: paws_labeled_final_Meaning/train-* - split: validation path: paws_labeled_final_Meaning/validation-* - split: test path: paws_labeled_final_Meaning/test-* - config_name: paws_labeled_final_Meaning_no_label data_files: - split: train path: paws_labeled_final_Meaning_no_label/train-* - split: validation path: paws_labeled_final_Meaning_no_label/validation-* - split: test path: paws_labeled_final_Meaning_no_label/test-* - config_name: paws_labeled_final_PAWS_ANLI_GPT3 data_files: - split: train path: paws_labeled_final_PAWS_ANLI_GPT3/train-* - split: validation path: paws_labeled_final_PAWS_ANLI_GPT3/validation-* - split: test path: paws_labeled_final_PAWS_ANLI_GPT3/test-* - config_name: paws_labeled_final_PAWS_ANLI_GPT3_no_label data_files: - split: train path: paws_labeled_final_PAWS_ANLI_GPT3_no_label/train-* - split: validation path: paws_labeled_final_PAWS_ANLI_GPT3_no_label/validation-* - split: test path: paws_labeled_final_PAWS_ANLI_GPT3_no_label/test-* - config_name: paws_labeled_final_Rewrite data_files: - split: train path: paws_labeled_final_Rewrite/train-* - split: validation path: paws_labeled_final_Rewrite/validation-* - split: test path: paws_labeled_final_Rewrite/test-* - config_name: paws_labeled_final_Rewrite_no_label data_files: - split: train path: paws_labeled_final_Rewrite_no_label/train-* - split: validation path: paws_labeled_final_Rewrite_no_label/validation-* - split: test path: paws_labeled_final_Rewrite_no_label/test-* - config_name: paws_labeled_final_context_question data_files: - split: train path: paws_labeled_final_context_question/train-* - split: validation path: paws_labeled_final_context_question/validation-* - split: test path: paws_labeled_final_context_question/test-* - config_name: paws_labeled_final_context_question_no_label data_files: - split: train path: paws_labeled_final_context_question_no_label/train-* - split: validation path: paws_labeled_final_context_question_no_label/validation-* - split: test path: paws_labeled_final_context_question_no_label/test-* - config_name: paws_labeled_final_paraphrase_task data_files: - split: train path: paws_labeled_final_paraphrase_task/train-* - split: validation path: paws_labeled_final_paraphrase_task/validation-* - split: test path: paws_labeled_final_paraphrase_task/test-* - config_name: paws_labeled_final_task_description_no_label data_files: - split: train path: paws_labeled_final_task_description_no_label/train-* - split: validation path: paws_labeled_final_task_description_no_label/validation-* - split: test path: paws_labeled_final_task_description_no_label/test-* - config_name: piqa_Correct_the_solution data_files: - split: train path: piqa_Correct_the_solution/train-* - split: validation path: piqa_Correct_the_solution/validation-* - split: test path: piqa_Correct_the_solution/test-* - config_name: piqa_Correct_the_solution_if_false_from_sol_1 data_files: - split: train path: piqa_Correct_the_solution_if_false_from_sol_1/train-* - split: validation path: piqa_Correct_the_solution_if_false_from_sol_1/validation-* - split: test path: piqa_Correct_the_solution_if_false_from_sol_1/test-* - config_name: piqa_Correct_the_solution_if_false_from_sol_2 data_files: - split: train path: piqa_Correct_the_solution_if_false_from_sol_2/train-* - split: validation path: piqa_Correct_the_solution_if_false_from_sol_2/validation-* - split: test path: piqa_Correct_the_solution_if_false_from_sol_2/test-* - config_name: piqa_Does_this_solution_make_sense_sol1 data_files: - split: train path: piqa_Does_this_solution_make_sense_sol1/train-* - split: validation path: piqa_Does_this_solution_make_sense_sol1/validation-* - split: test path: piqa_Does_this_solution_make_sense_sol1/test-* - config_name: piqa_Does_this_solution_make_sense_sol2 data_files: - split: train path: piqa_Does_this_solution_make_sense_sol2/train-* - split: validation path: piqa_Does_this_solution_make_sense_sol2/validation-* - split: test path: piqa_Does_this_solution_make_sense_sol2/test-* - config_name: piqa_choose_the_most_appropriate_solution data_files: - split: train path: piqa_choose_the_most_appropriate_solution/train-* - split: validation path: piqa_choose_the_most_appropriate_solution/validation-* - split: test path: piqa_choose_the_most_appropriate_solution/test-* - config_name: piqa_finish_sentence_with_correct_choice data_files: - split: train path: piqa_finish_sentence_with_correct_choice/train-* - split: validation path: piqa_finish_sentence_with_correct_choice/validation-* - split: test path: piqa_finish_sentence_with_correct_choice/test-* - config_name: piqa_no_prompt_needed data_files: - split: train path: piqa_no_prompt_needed/train-* - split: validation path: piqa_no_prompt_needed/validation-* - split: test path: piqa_no_prompt_needed/test-* - config_name: piqa_pick_correct_choice_index data_files: - split: train path: piqa_pick_correct_choice_index/train-* - split: validation path: piqa_pick_correct_choice_index/validation-* - split: test path: piqa_pick_correct_choice_index/test-* - config_name: piqa_pick_correct_choice_with_choice_given_before_goal data_files: - split: train path: piqa_pick_correct_choice_with_choice_given_before_goal/train-* - split: validation path: piqa_pick_correct_choice_with_choice_given_before_goal/validation-* - split: test path: piqa_pick_correct_choice_with_choice_given_before_goal/test-* - config_name: piqa_what_is_the_correct_ending data_files: - split: train path: piqa_what_is_the_correct_ending/train-* - split: validation path: piqa_what_is_the_correct_ending/validation-* - split: test path: piqa_what_is_the_correct_ending/test-* - config_name: qasc_is_correct_1 data_files: - split: train path: qasc_is_correct_1/train-* - split: validation path: qasc_is_correct_1/validation-* - split: test path: qasc_is_correct_1/test-* - config_name: qasc_is_correct_2 data_files: - split: train path: qasc_is_correct_2/train-* - split: validation path: qasc_is_correct_2/validation-* - split: test path: qasc_is_correct_2/test-* - config_name: qasc_qa_with_combined_facts_1 data_files: - split: train path: qasc_qa_with_combined_facts_1/train-* - split: validation path: qasc_qa_with_combined_facts_1/validation-* - split: test path: qasc_qa_with_combined_facts_1/test-* - config_name: qasc_qa_with_separated_facts_1 data_files: - split: train path: qasc_qa_with_separated_facts_1/train-* - split: validation path: qasc_qa_with_separated_facts_1/validation-* - split: test path: qasc_qa_with_separated_facts_1/test-* - config_name: qasc_qa_with_separated_facts_2 data_files: - split: train path: qasc_qa_with_separated_facts_2/train-* - split: validation path: qasc_qa_with_separated_facts_2/validation-* - split: test path: qasc_qa_with_separated_facts_2/test-* - config_name: qasc_qa_with_separated_facts_3 data_files: - split: train path: qasc_qa_with_separated_facts_3/train-* - split: validation path: qasc_qa_with_separated_facts_3/validation-* - split: test path: qasc_qa_with_separated_facts_3/test-* - config_name: qasc_qa_with_separated_facts_4 data_files: - split: train path: qasc_qa_with_separated_facts_4/train-* - split: validation path: qasc_qa_with_separated_facts_4/validation-* - split: test path: qasc_qa_with_separated_facts_4/test-* - config_name: qasc_qa_with_separated_facts_5 data_files: - split: train path: qasc_qa_with_separated_facts_5/train-* - split: validation path: qasc_qa_with_separated_facts_5/validation-* - split: test path: qasc_qa_with_separated_facts_5/test-* - config_name: quail_context_description_question_answer_id data_files: - split: train path: quail_context_description_question_answer_id/train-* - split: validation path: quail_context_description_question_answer_id/validation-* - split: challenge path: quail_context_description_question_answer_id/challenge-* - config_name: quail_context_description_question_answer_text data_files: - split: train path: quail_context_description_question_answer_text/train-* - split: validation path: quail_context_description_question_answer_text/validation-* - split: challenge path: quail_context_description_question_answer_text/challenge-* - config_name: quail_context_description_question_text data_files: - split: train path: quail_context_description_question_text/train-* - split: validation path: quail_context_description_question_text/validation-* - split: challenge path: quail_context_description_question_text/challenge-* - config_name: quail_context_question_answer_description_id data_files: - split: train path: quail_context_question_answer_description_id/train-* - split: validation path: quail_context_question_answer_description_id/validation-* - split: challenge path: quail_context_question_answer_description_id/challenge-* - config_name: quail_context_question_answer_description_text data_files: - split: train path: quail_context_question_answer_description_text/train-* - split: validation path: quail_context_question_answer_description_text/validation-* - split: challenge path: quail_context_question_answer_description_text/challenge-* - config_name: quail_context_question_description_answer_id data_files: - split: train path: quail_context_question_description_answer_id/train-* - split: validation path: quail_context_question_description_answer_id/validation-* - split: challenge path: quail_context_question_description_answer_id/challenge-* - config_name: quail_context_question_description_answer_text data_files: - split: train path: quail_context_question_description_answer_text/train-* - split: validation path: quail_context_question_description_answer_text/validation-* - split: challenge path: quail_context_question_description_answer_text/challenge-* - config_name: quail_context_question_description_text data_files: - split: train path: quail_context_question_description_text/train-* - split: validation path: quail_context_question_description_text/validation-* - split: challenge path: quail_context_question_description_text/challenge-* - config_name: quail_description_context_question_answer_id data_files: - split: train path: quail_description_context_question_answer_id/train-* - split: validation path: quail_description_context_question_answer_id/validation-* - split: challenge path: quail_description_context_question_answer_id/challenge-* - config_name: quail_description_context_question_answer_text data_files: - split: train path: quail_description_context_question_answer_text/train-* - split: validation path: quail_description_context_question_answer_text/validation-* - split: challenge path: quail_description_context_question_answer_text/challenge-* - config_name: quail_description_context_question_text data_files: - split: train path: quail_description_context_question_text/train-* - split: validation path: quail_description_context_question_text/validation-* - split: challenge path: quail_description_context_question_text/challenge-* - config_name: quail_no_prompt_id data_files: - split: train path: quail_no_prompt_id/train-* - split: validation path: quail_no_prompt_id/validation-* - split: challenge path: quail_no_prompt_id/challenge-* - config_name: quail_no_prompt_text data_files: - split: train path: quail_no_prompt_text/train-* - split: validation path: quail_no_prompt_text/validation-* - split: challenge path: quail_no_prompt_text/challenge-* - config_name: quarel_choose_between data_files: - split: train path: quarel_choose_between/train-* - split: validation path: quarel_choose_between/validation-* - split: test path: quarel_choose_between/test-* - config_name: quarel_do_not_use data_files: - split: train path: quarel_do_not_use/train-* - split: validation path: quarel_do_not_use/validation-* - split: test path: quarel_do_not_use/test-* - config_name: quarel_heres_a_story data_files: - split: train path: quarel_heres_a_story/train-* - split: validation path: quarel_heres_a_story/validation-* - split: test path: quarel_heres_a_story/test-* - config_name: quarel_logic_test data_files: - split: train path: quarel_logic_test/train-* - split: validation path: quarel_logic_test/validation-* - split: test path: quarel_logic_test/test-* - config_name: quarel_testing_students data_files: - split: train path: quarel_testing_students/train-* - split: validation path: quarel_testing_students/validation-* - split: test path: quarel_testing_students/test-* - config_name: quartz_answer_question_based_on data_files: - split: train path: quartz_answer_question_based_on/train-* - split: validation path: quartz_answer_question_based_on/validation-* - split: test path: quartz_answer_question_based_on/test-* - config_name: quartz_answer_question_below data_files: - split: train path: quartz_answer_question_below/train-* - split: validation path: quartz_answer_question_below/validation-* - split: test path: quartz_answer_question_below/test-* - config_name: quartz_given_the_fact_answer_the_q data_files: - split: train path: quartz_given_the_fact_answer_the_q/train-* - split: validation path: quartz_given_the_fact_answer_the_q/validation-* - split: test path: quartz_given_the_fact_answer_the_q/test-* - config_name: quartz_having_read_above_passage data_files: - split: train path: quartz_having_read_above_passage/train-* - split: validation path: quartz_having_read_above_passage/validation-* - split: test path: quartz_having_read_above_passage/test-* - config_name: quartz_paragraph_question_plain_concat data_files: - split: train path: quartz_paragraph_question_plain_concat/train-* - split: validation path: quartz_paragraph_question_plain_concat/validation-* - split: test path: quartz_paragraph_question_plain_concat/test-* - config_name: quartz_read_passage_below_choose data_files: - split: train path: quartz_read_passage_below_choose/train-* - split: validation path: quartz_read_passage_below_choose/validation-* - split: test path: quartz_read_passage_below_choose/test-* - config_name: quartz_use_info_from_paragraph_question data_files: - split: train path: quartz_use_info_from_paragraph_question/train-* - split: validation path: quartz_use_info_from_paragraph_question/validation-* - split: test path: quartz_use_info_from_paragraph_question/test-* - config_name: quartz_use_info_from_question_paragraph data_files: - split: train path: quartz_use_info_from_question_paragraph/train-* - split: validation path: quartz_use_info_from_question_paragraph/validation-* - split: test path: quartz_use_info_from_question_paragraph/test-* - config_name: quoref_Answer_Friend_Question data_files: - split: train path: quoref_Answer_Friend_Question/train-* - split: validation path: quoref_Answer_Friend_Question/validation-* - config_name: quoref_Answer_Question_Given_Context data_files: - split: train path: quoref_Answer_Question_Given_Context/train-* - split: validation path: quoref_Answer_Question_Given_Context/validation-* - config_name: quoref_Answer_Test data_files: - split: train path: quoref_Answer_Test/train-* - split: validation path: quoref_Answer_Test/validation-* - config_name: quoref_Context_Contains_Answer data_files: - split: train path: quoref_Context_Contains_Answer/train-* - split: validation path: quoref_Context_Contains_Answer/validation-* - config_name: quoref_Find_Answer data_files: - split: train path: quoref_Find_Answer/train-* - split: validation path: quoref_Find_Answer/validation-* - config_name: quoref_Found_Context_Online data_files: - split: train path: quoref_Found_Context_Online/train-* - split: validation path: quoref_Found_Context_Online/validation-* - config_name: quoref_Given_Context_Answer_Question data_files: - split: train path: quoref_Given_Context_Answer_Question/train-* - split: validation path: quoref_Given_Context_Answer_Question/validation-* - config_name: quoref_Guess_Answer data_files: - split: train path: quoref_Guess_Answer/train-* - split: validation path: quoref_Guess_Answer/validation-* - config_name: quoref_Guess_Title_For_Context data_files: - split: train path: quoref_Guess_Title_For_Context/train-* - split: validation path: quoref_Guess_Title_For_Context/validation-* - config_name: quoref_Read_And_Extract_ data_files: - split: train path: quoref_Read_And_Extract_/train-* - split: validation path: quoref_Read_And_Extract_/validation-* - config_name: quoref_What_Is_The_Answer data_files: - split: train path: quoref_What_Is_The_Answer/train-* - split: validation path: quoref_What_Is_The_Answer/validation-* - config_name: race_high_Is_this_the_right_answer data_files: - split: train path: race_high_Is_this_the_right_answer/train-* - split: validation path: race_high_Is_this_the_right_answer/validation-* - split: test path: race_high_Is_this_the_right_answer/test-* - config_name: race_high_Read_the_article_and_answer_the_question_no_option_ data_files: - split: train path: race_high_Read_the_article_and_answer_the_question_no_option_/train-* - split: validation path: race_high_Read_the_article_and_answer_the_question_no_option_/validation-* - split: test path: race_high_Read_the_article_and_answer_the_question_no_option_/test-* - config_name: race_high_Select_the_best_answer data_files: - split: train path: race_high_Select_the_best_answer/train-* - split: validation path: race_high_Select_the_best_answer/validation-* - split: test path: race_high_Select_the_best_answer/test-* - config_name: race_high_Select_the_best_answer_generate_span_ data_files: - split: train path: race_high_Select_the_best_answer_generate_span_/train-* - split: validation path: race_high_Select_the_best_answer_generate_span_/validation-* - split: test path: race_high_Select_the_best_answer_generate_span_/test-* - config_name: race_high_Select_the_best_answer_no_instructions_ data_files: - split: train path: race_high_Select_the_best_answer_no_instructions_/train-* - split: validation path: race_high_Select_the_best_answer_no_instructions_/validation-* - split: test path: race_high_Select_the_best_answer_no_instructions_/test-* - config_name: race_high_Taking_a_test data_files: - split: train path: race_high_Taking_a_test/train-* - split: validation path: race_high_Taking_a_test/validation-* - split: test path: race_high_Taking_a_test/test-* - config_name: race_high_Write_a_multi_choice_question_for_the_following_article data_files: - split: train path: race_high_Write_a_multi_choice_question_for_the_following_article/train-* - split: validation path: race_high_Write_a_multi_choice_question_for_the_following_article/validation-* - split: test path: race_high_Write_a_multi_choice_question_for_the_following_article/test-* - config_name: race_high_Write_a_multi_choice_question_options_given_ data_files: - split: train path: race_high_Write_a_multi_choice_question_options_given_/train-* - split: validation path: race_high_Write_a_multi_choice_question_options_given_/validation-* - split: test path: race_high_Write_a_multi_choice_question_options_given_/test-* - config_name: race_middle_Is_this_the_right_answer data_files: - split: train path: race_middle_Is_this_the_right_answer/train-* - split: validation path: race_middle_Is_this_the_right_answer/validation-* - split: test path: race_middle_Is_this_the_right_answer/test-* - config_name: race_middle_Read_the_article_and_answer_the_question_no_option_ data_files: - split: train path: race_middle_Read_the_article_and_answer_the_question_no_option_/train-* - split: validation path: race_middle_Read_the_article_and_answer_the_question_no_option_/validation-* - split: test path: race_middle_Read_the_article_and_answer_the_question_no_option_/test-* - config_name: race_middle_Select_the_best_answer data_files: - split: train path: race_middle_Select_the_best_answer/train-* - split: validation path: race_middle_Select_the_best_answer/validation-* - split: test path: race_middle_Select_the_best_answer/test-* - config_name: race_middle_Select_the_best_answer_generate_span_ data_files: - split: train path: race_middle_Select_the_best_answer_generate_span_/train-* - split: validation path: race_middle_Select_the_best_answer_generate_span_/validation-* - split: test path: race_middle_Select_the_best_answer_generate_span_/test-* - config_name: race_middle_Select_the_best_answer_no_instructions_ data_files: - split: train path: race_middle_Select_the_best_answer_no_instructions_/train-* - split: validation path: race_middle_Select_the_best_answer_no_instructions_/validation-* - split: test path: race_middle_Select_the_best_answer_no_instructions_/test-* - config_name: race_middle_Taking_a_test data_files: - split: train path: race_middle_Taking_a_test/train-* - split: validation path: race_middle_Taking_a_test/validation-* - split: test path: race_middle_Taking_a_test/test-* - config_name: race_middle_Write_a_multi_choice_question_for_the_following_article data_files: - split: train path: race_middle_Write_a_multi_choice_question_for_the_following_article/train-* - split: validation path: race_middle_Write_a_multi_choice_question_for_the_following_article/validation-* - split: test path: race_middle_Write_a_multi_choice_question_for_the_following_article/test-* - config_name: race_middle_Write_a_multi_choice_question_options_given_ data_files: - split: train path: race_middle_Write_a_multi_choice_question_options_given_/train-* - split: validation path: race_middle_Write_a_multi_choice_question_options_given_/validation-* - split: test path: race_middle_Write_a_multi_choice_question_options_given_/test-* - config_name: ropes_background_new_situation_answer data_files: - split: train path: ropes_background_new_situation_answer/train-* - split: validation path: ropes_background_new_situation_answer/validation-* - config_name: ropes_background_situation_middle data_files: - split: train path: ropes_background_situation_middle/train-* - split: validation path: ropes_background_situation_middle/validation-* - config_name: ropes_given_background_situation data_files: - split: train path: ropes_given_background_situation/train-* - split: validation path: ropes_given_background_situation/validation-* - config_name: ropes_new_situation_background_answer data_files: - split: train path: ropes_new_situation_background_answer/train-* - split: validation path: ropes_new_situation_background_answer/validation-* - config_name: ropes_plain_background_situation data_files: - split: train path: ropes_plain_background_situation/train-* - split: validation path: ropes_plain_background_situation/validation-* - config_name: ropes_plain_bottom_hint data_files: - split: train path: ropes_plain_bottom_hint/train-* - split: validation path: ropes_plain_bottom_hint/validation-* - config_name: ropes_plain_no_background data_files: - split: train path: ropes_plain_no_background/train-* - split: validation path: ropes_plain_no_background/validation-* - config_name: ropes_prompt_beginning data_files: - split: train path: ropes_prompt_beginning/train-* - split: validation path: ropes_prompt_beginning/validation-* - config_name: ropes_prompt_bottom_hint_beginning data_files: - split: train path: ropes_prompt_bottom_hint_beginning/train-* - split: validation path: ropes_prompt_bottom_hint_beginning/validation-* - config_name: ropes_prompt_bottom_no_hint data_files: - split: train path: ropes_prompt_bottom_no_hint/train-* - split: validation path: ropes_prompt_bottom_no_hint/validation-* - config_name: ropes_prompt_mix data_files: - split: train path: ropes_prompt_mix/train-* - split: validation path: ropes_prompt_mix/validation-* - config_name: ropes_read_background_situation data_files: - split: train path: ropes_read_background_situation/train-* - split: validation path: ropes_read_background_situation/validation-* - config_name: rotten_tomatoes_Movie_Expressed_Sentiment data_files: - split: train path: rotten_tomatoes_Movie_Expressed_Sentiment/train-* - split: validation path: rotten_tomatoes_Movie_Expressed_Sentiment/validation-* - split: test path: rotten_tomatoes_Movie_Expressed_Sentiment/test-* - config_name: rotten_tomatoes_Movie_Expressed_Sentiment_2 data_files: - split: train path: rotten_tomatoes_Movie_Expressed_Sentiment_2/train-* - split: validation path: rotten_tomatoes_Movie_Expressed_Sentiment_2/validation-* - split: test path: rotten_tomatoes_Movie_Expressed_Sentiment_2/test-* - config_name: rotten_tomatoes_Reviewer_Enjoyment data_files: - split: train path: rotten_tomatoes_Reviewer_Enjoyment/train-* - split: validation path: rotten_tomatoes_Reviewer_Enjoyment/validation-* - split: test path: rotten_tomatoes_Reviewer_Enjoyment/test-* - config_name: rotten_tomatoes_Reviewer_Enjoyment_Yes_No data_files: - split: train path: rotten_tomatoes_Reviewer_Enjoyment_Yes_No/train-* - split: validation path: rotten_tomatoes_Reviewer_Enjoyment_Yes_No/validation-* - split: test path: rotten_tomatoes_Reviewer_Enjoyment_Yes_No/test-* - config_name: rotten_tomatoes_Reviewer_Expressed_Sentiment data_files: - split: train path: rotten_tomatoes_Reviewer_Expressed_Sentiment/train-* - split: validation path: rotten_tomatoes_Reviewer_Expressed_Sentiment/validation-* - split: test path: rotten_tomatoes_Reviewer_Expressed_Sentiment/test-* - config_name: rotten_tomatoes_Reviewer_Opinion_bad_good_choices data_files: - split: train path: rotten_tomatoes_Reviewer_Opinion_bad_good_choices/train-* - split: validation path: rotten_tomatoes_Reviewer_Opinion_bad_good_choices/validation-* - split: test path: rotten_tomatoes_Reviewer_Opinion_bad_good_choices/test-* - config_name: rotten_tomatoes_Reviewer_Sentiment_Feeling data_files: - split: train path: rotten_tomatoes_Reviewer_Sentiment_Feeling/train-* - split: validation path: rotten_tomatoes_Reviewer_Sentiment_Feeling/validation-* - split: test path: rotten_tomatoes_Reviewer_Sentiment_Feeling/test-* - config_name: rotten_tomatoes_Sentiment_with_choices_ data_files: - split: train path: rotten_tomatoes_Sentiment_with_choices_/train-* - split: validation path: rotten_tomatoes_Sentiment_with_choices_/validation-* - split: test path: rotten_tomatoes_Sentiment_with_choices_/test-* - config_name: rotten_tomatoes_Text_Expressed_Sentiment data_files: - split: train path: rotten_tomatoes_Text_Expressed_Sentiment/train-* - split: validation path: rotten_tomatoes_Text_Expressed_Sentiment/validation-* - split: test path: rotten_tomatoes_Text_Expressed_Sentiment/test-* - config_name: rotten_tomatoes_Writer_Expressed_Sentiment data_files: - split: train path: rotten_tomatoes_Writer_Expressed_Sentiment/train-* - split: validation path: rotten_tomatoes_Writer_Expressed_Sentiment/validation-* - split: test path: rotten_tomatoes_Writer_Expressed_Sentiment/test-* - config_name: samsum_Generate_a_summary_for_this_dialogue data_files: - split: train path: samsum_Generate_a_summary_for_this_dialogue/train-* - split: validation path: samsum_Generate_a_summary_for_this_dialogue/validation-* - split: test path: samsum_Generate_a_summary_for_this_dialogue/test-* - config_name: samsum_Given_the_above_dialogue_write_a_summary data_files: - split: train path: samsum_Given_the_above_dialogue_write_a_summary/train-* - split: validation path: samsum_Given_the_above_dialogue_write_a_summary/validation-* - split: test path: samsum_Given_the_above_dialogue_write_a_summary/test-* - config_name: samsum_Sum_up_the_following_dialogue data_files: - split: train path: samsum_Sum_up_the_following_dialogue/train-* - split: validation path: samsum_Sum_up_the_following_dialogue/validation-* - split: test path: samsum_Sum_up_the_following_dialogue/test-* - config_name: samsum_Summarize_ data_files: - split: train path: samsum_Summarize_/train-* - split: validation path: samsum_Summarize_/validation-* - split: test path: samsum_Summarize_/test-* - config_name: samsum_Summarize_this_dialogue_ data_files: - split: train path: samsum_Summarize_this_dialogue_/train-* - split: validation path: samsum_Summarize_this_dialogue_/validation-* - split: test path: samsum_Summarize_this_dialogue_/test-* - config_name: samsum_To_sum_up_this_dialog data_files: - split: train path: samsum_To_sum_up_this_dialog/train-* - split: validation path: samsum_To_sum_up_this_dialog/validation-* - split: test path: samsum_To_sum_up_this_dialog/test-* - config_name: samsum_Write_a_dialogue_that_match_this_summary data_files: - split: train path: samsum_Write_a_dialogue_that_match_this_summary/train-* - split: validation path: samsum_Write_a_dialogue_that_match_this_summary/validation-* - split: test path: samsum_Write_a_dialogue_that_match_this_summary/test-* - config_name: sciq_Direct_Question data_files: - split: train path: sciq_Direct_Question/train-* - split: validation path: sciq_Direct_Question/validation-* - split: test path: sciq_Direct_Question/test-* - config_name: sciq_Direct_Question_Closed_Book_ data_files: - split: train path: sciq_Direct_Question_Closed_Book_/train-* - split: validation path: sciq_Direct_Question_Closed_Book_/validation-* - split: test path: sciq_Direct_Question_Closed_Book_/test-* - config_name: sciq_Multiple_Choice data_files: - split: train path: sciq_Multiple_Choice/train-* - split: validation path: sciq_Multiple_Choice/validation-* - split: test path: sciq_Multiple_Choice/test-* - config_name: sciq_Multiple_Choice_Closed_Book_ data_files: - split: train path: sciq_Multiple_Choice_Closed_Book_/train-* - split: validation path: sciq_Multiple_Choice_Closed_Book_/validation-* - split: test path: sciq_Multiple_Choice_Closed_Book_/test-* - config_name: sciq_Multiple_Choice_Question_First data_files: - split: train path: sciq_Multiple_Choice_Question_First/train-* - split: validation path: sciq_Multiple_Choice_Question_First/validation-* - split: test path: sciq_Multiple_Choice_Question_First/test-* - config_name: social_i_qa_Check_if_a_random_answer_is_valid_or_not data_files: - split: train path: social_i_qa_Check_if_a_random_answer_is_valid_or_not/train-* - split: validation path: social_i_qa_Check_if_a_random_answer_is_valid_or_not/validation-* - config_name: social_i_qa_Generate_answer data_files: - split: train path: social_i_qa_Generate_answer/train-* - split: validation path: social_i_qa_Generate_answer/validation-* - config_name: social_i_qa_Generate_the_question_from_the_answer data_files: - split: train path: social_i_qa_Generate_the_question_from_the_answer/train-* - split: validation path: social_i_qa_Generate_the_question_from_the_answer/validation-* - config_name: social_i_qa_I_was_wondering data_files: - split: train path: social_i_qa_I_was_wondering/train-* - split: validation path: social_i_qa_I_was_wondering/validation-* - config_name: social_i_qa_Show_choices_and_generate_answer data_files: - split: train path: social_i_qa_Show_choices_and_generate_answer/train-* - split: validation path: social_i_qa_Show_choices_and_generate_answer/validation-* - config_name: social_i_qa_Show_choices_and_generate_index data_files: - split: train path: social_i_qa_Show_choices_and_generate_index/train-* - split: validation path: social_i_qa_Show_choices_and_generate_index/validation-* - config_name: squad_v2_Jeopardy_with_Context data_files: - split: train path: squad_v2_Jeopardy_with_Context/train-* - split: validation path: squad_v2_Jeopardy_with_Context/validation-* - config_name: squad_v2_Jeopardy_without_Context data_files: - split: train path: squad_v2_Jeopardy_without_Context/train-* - split: validation path: squad_v2_Jeopardy_without_Context/validation-* - config_name: squad_v2_Questions_with_Context data_files: - split: train path: squad_v2_Questions_with_Context/train-* - split: validation path: squad_v2_Questions_with_Context/validation-* - config_name: squad_v2_Questions_with_Context_Without_Prompt_Keywords data_files: - split: train path: squad_v2_Questions_with_Context_Without_Prompt_Keywords/train-* - split: validation path: squad_v2_Questions_with_Context_Without_Prompt_Keywords/validation-* - config_name: squad_v2_Questions_with_Context_Without_Prompt_Keywords_unanswerable data_files: - split: train path: squad_v2_Questions_with_Context_Without_Prompt_Keywords_unanswerable/train-* - split: validation path: squad_v2_Questions_with_Context_Without_Prompt_Keywords_unanswerable/validation-* - config_name: squad_v2_Questions_with_Context_unanswerable data_files: - split: train path: squad_v2_Questions_with_Context_unanswerable/train-* - split: validation path: squad_v2_Questions_with_Context_unanswerable/validation-* - config_name: squad_v2_Topic_Prediction_Context data_files: - split: train path: squad_v2_Topic_Prediction_Context/train-* - split: validation path: squad_v2_Topic_Prediction_Context/validation-* - config_name: squad_v2_Topic_Prediction_Context_with_randomized_prompt_options data_files: - split: train path: squad_v2_Topic_Prediction_Context_with_randomized_prompt_options/train-* - split: validation path: squad_v2_Topic_Prediction_Context_with_randomized_prompt_options/validation-* - config_name: squad_v2_Topic_Prediction_Context_with_randomized_prompt_options_placed_in_the_end data_files: - split: train path: squad_v2_Topic_Prediction_Context_with_randomized_prompt_options_placed_in_the_end/train-* - split: validation path: squad_v2_Topic_Prediction_Context_with_randomized_prompt_options_placed_in_the_end/validation-* - config_name: squad_v2_Topic_Prediction_Question_and_Answer_Pair data_files: - split: train path: squad_v2_Topic_Prediction_Question_and_Answer_Pair/train-* - split: validation path: squad_v2_Topic_Prediction_Question_and_Answer_Pair/validation-* - config_name: squad_v2_Trivia data_files: - split: train path: squad_v2_Trivia/train-* - split: validation path: squad_v2_Trivia/validation-* - config_name: squad_v2_Unanwerable_question data_files: - split: train path: squad_v2_Unanwerable_question/train-* - split: validation path: squad_v2_Unanwerable_question/validation-* - config_name: super_glue_boolq_GPT_3_Style data_files: - split: train path: super_glue_boolq_GPT_3_Style/train-* - split: validation path: super_glue_boolq_GPT_3_Style/validation-* - split: test path: super_glue_boolq_GPT_3_Style/test-* - config_name: super_glue_boolq_I_wonder_ data_files: - split: train path: super_glue_boolq_I_wonder_/train-* - split: validation path: super_glue_boolq_I_wonder_/validation-* - split: test path: super_glue_boolq_I_wonder_/test-* - config_name: super_glue_boolq_after_reading data_files: - split: train path: super_glue_boolq_after_reading/train-* - split: validation path: super_glue_boolq_after_reading/validation-* - split: test path: super_glue_boolq_after_reading/test-* - config_name: super_glue_boolq_based_on_the_following_passage data_files: - split: train path: super_glue_boolq_based_on_the_following_passage/train-* - split: validation path: super_glue_boolq_based_on_the_following_passage/validation-* - split: test path: super_glue_boolq_based_on_the_following_passage/test-* - config_name: super_glue_boolq_based_on_the_previous_passage data_files: - split: train path: super_glue_boolq_based_on_the_previous_passage/train-* - split: validation path: super_glue_boolq_based_on_the_previous_passage/validation-* - split: test path: super_glue_boolq_based_on_the_previous_passage/test-* - config_name: super_glue_boolq_could_you_tell_me_ data_files: - split: train path: super_glue_boolq_could_you_tell_me_/train-* - split: validation path: super_glue_boolq_could_you_tell_me_/validation-* - split: test path: super_glue_boolq_could_you_tell_me_/test-* - config_name: super_glue_boolq_exam data_files: - split: train path: super_glue_boolq_exam/train-* - split: validation path: super_glue_boolq_exam/validation-* - split: test path: super_glue_boolq_exam/test-* - config_name: super_glue_boolq_exercise data_files: - split: train path: super_glue_boolq_exercise/train-* - split: validation path: super_glue_boolq_exercise/validation-* - split: test path: super_glue_boolq_exercise/test-* - config_name: super_glue_boolq_valid_binary data_files: - split: train path: super_glue_boolq_valid_binary/train-* - split: validation path: super_glue_boolq_valid_binary/validation-* - split: test path: super_glue_boolq_valid_binary/test-* - config_name: super_glue_boolq_yes_no_question data_files: - split: train path: super_glue_boolq_yes_no_question/train-* - split: validation path: super_glue_boolq_yes_no_question/validation-* - split: test path: super_glue_boolq_yes_no_question/test-* - config_name: super_glue_cb_GPT_3_style data_files: - split: train path: super_glue_cb_GPT_3_style/train-* - split: validation path: super_glue_cb_GPT_3_style/validation-* - split: test path: super_glue_cb_GPT_3_style/test-* - config_name: super_glue_cb_GPT_3_style_score_eval data_files: - split: train path: super_glue_cb_GPT_3_style_score_eval/train-* - split: validation path: super_glue_cb_GPT_3_style_score_eval/validation-* - split: test path: super_glue_cb_GPT_3_style_score_eval/test-* - config_name: super_glue_cb_MNLI_crowdsource data_files: - split: train path: super_glue_cb_MNLI_crowdsource/train-* - split: validation path: super_glue_cb_MNLI_crowdsource/validation-* - split: test path: super_glue_cb_MNLI_crowdsource/test-* - config_name: super_glue_cb_MNLI_crowdsource_score_eval data_files: - split: train path: super_glue_cb_MNLI_crowdsource_score_eval/train-* - split: validation path: super_glue_cb_MNLI_crowdsource_score_eval/validation-* - split: test path: super_glue_cb_MNLI_crowdsource_score_eval/test-* - config_name: super_glue_cb_always_sometimes_never data_files: - split: train path: super_glue_cb_always_sometimes_never/train-* - split: validation path: super_glue_cb_always_sometimes_never/validation-* - split: test path: super_glue_cb_always_sometimes_never/test-* - config_name: super_glue_cb_always_sometimes_never_score_eval data_files: - split: train path: super_glue_cb_always_sometimes_never_score_eval/train-* - split: validation path: super_glue_cb_always_sometimes_never_score_eval/validation-* - split: test path: super_glue_cb_always_sometimes_never_score_eval/test-* - config_name: super_glue_cb_based_on_the_previous_passage data_files: - split: train path: super_glue_cb_based_on_the_previous_passage/train-* - split: validation path: super_glue_cb_based_on_the_previous_passage/validation-* - split: test path: super_glue_cb_based_on_the_previous_passage/test-* - config_name: super_glue_cb_based_on_the_previous_passage_score_eval data_files: - split: train path: super_glue_cb_based_on_the_previous_passage_score_eval/train-* - split: validation path: super_glue_cb_based_on_the_previous_passage_score_eval/validation-* - split: test path: super_glue_cb_based_on_the_previous_passage_score_eval/test-* - config_name: super_glue_cb_can_we_infer data_files: - split: train path: super_glue_cb_can_we_infer/train-* - split: validation path: super_glue_cb_can_we_infer/validation-* - split: test path: super_glue_cb_can_we_infer/test-* - config_name: super_glue_cb_can_we_infer_score_eval data_files: - split: train path: super_glue_cb_can_we_infer_score_eval/train-* - split: validation path: super_glue_cb_can_we_infer_score_eval/validation-* - split: test path: super_glue_cb_can_we_infer_score_eval/test-* - config_name: super_glue_cb_claim_true_false_inconclusive data_files: - split: train path: super_glue_cb_claim_true_false_inconclusive/train-* - split: validation path: super_glue_cb_claim_true_false_inconclusive/validation-* - split: test path: super_glue_cb_claim_true_false_inconclusive/test-* - config_name: super_glue_cb_claim_true_false_inconclusive_score_eval data_files: - split: train path: super_glue_cb_claim_true_false_inconclusive_score_eval/train-* - split: validation path: super_glue_cb_claim_true_false_inconclusive_score_eval/validation-* - split: test path: super_glue_cb_claim_true_false_inconclusive_score_eval/test-* - config_name: super_glue_cb_consider_always_sometimes_never data_files: - split: train path: super_glue_cb_consider_always_sometimes_never/train-* - split: validation path: super_glue_cb_consider_always_sometimes_never/validation-* - split: test path: super_glue_cb_consider_always_sometimes_never/test-* - config_name: super_glue_cb_consider_always_sometimes_never_score_eval data_files: - split: train path: super_glue_cb_consider_always_sometimes_never_score_eval/train-* - split: validation path: super_glue_cb_consider_always_sometimes_never_score_eval/validation-* - split: test path: super_glue_cb_consider_always_sometimes_never_score_eval/test-* - config_name: super_glue_cb_does_it_follow_that data_files: - split: train path: super_glue_cb_does_it_follow_that/train-* - split: validation path: super_glue_cb_does_it_follow_that/validation-* - split: test path: super_glue_cb_does_it_follow_that/test-* - config_name: super_glue_cb_does_it_follow_that_score_eval data_files: - split: train path: super_glue_cb_does_it_follow_that_score_eval/train-* - split: validation path: super_glue_cb_does_it_follow_that_score_eval/validation-* - split: test path: super_glue_cb_does_it_follow_that_score_eval/test-* - config_name: super_glue_cb_does_this_imply data_files: - split: train path: super_glue_cb_does_this_imply/train-* - split: validation path: super_glue_cb_does_this_imply/validation-* - split: test path: super_glue_cb_does_this_imply/test-* - config_name: super_glue_cb_does_this_imply_score_eval data_files: - split: train path: super_glue_cb_does_this_imply_score_eval/train-* - split: validation path: super_glue_cb_does_this_imply_score_eval/validation-* - split: test path: super_glue_cb_does_this_imply_score_eval/test-* - config_name: super_glue_cb_guaranteed_possible_impossible data_files: - split: train path: super_glue_cb_guaranteed_possible_impossible/train-* - split: validation path: super_glue_cb_guaranteed_possible_impossible/validation-* - split: test path: super_glue_cb_guaranteed_possible_impossible/test-* - config_name: super_glue_cb_guaranteed_possible_impossible_score_eval data_files: - split: train path: super_glue_cb_guaranteed_possible_impossible_score_eval/train-* - split: validation path: super_glue_cb_guaranteed_possible_impossible_score_eval/validation-* - split: test path: super_glue_cb_guaranteed_possible_impossible_score_eval/test-* - config_name: super_glue_cb_guaranteed_true data_files: - split: train path: super_glue_cb_guaranteed_true/train-* - split: validation path: super_glue_cb_guaranteed_true/validation-* - split: test path: super_glue_cb_guaranteed_true/test-* - config_name: super_glue_cb_guaranteed_true_score_eval data_files: - split: train path: super_glue_cb_guaranteed_true_score_eval/train-* - split: validation path: super_glue_cb_guaranteed_true_score_eval/validation-* - split: test path: super_glue_cb_guaranteed_true_score_eval/test-* - config_name: super_glue_cb_justified_in_saying data_files: - split: train path: super_glue_cb_justified_in_saying/train-* - split: validation path: super_glue_cb_justified_in_saying/validation-* - split: test path: super_glue_cb_justified_in_saying/test-* - config_name: super_glue_cb_justified_in_saying_score_eval data_files: - split: train path: super_glue_cb_justified_in_saying_score_eval/train-* - split: validation path: super_glue_cb_justified_in_saying_score_eval/validation-* - split: test path: super_glue_cb_justified_in_saying_score_eval/test-* - config_name: super_glue_cb_must_be_true data_files: - split: train path: super_glue_cb_must_be_true/train-* - split: validation path: super_glue_cb_must_be_true/validation-* - split: test path: super_glue_cb_must_be_true/test-* - config_name: super_glue_cb_must_be_true_score_eval data_files: - split: train path: super_glue_cb_must_be_true_score_eval/train-* - split: validation path: super_glue_cb_must_be_true_score_eval/validation-* - split: test path: super_glue_cb_must_be_true_score_eval/test-* - config_name: super_glue_cb_should_assume data_files: - split: train path: super_glue_cb_should_assume/train-* - split: validation path: super_glue_cb_should_assume/validation-* - split: test path: super_glue_cb_should_assume/test-* - config_name: super_glue_cb_should_assume_score_eval data_files: - split: train path: super_glue_cb_should_assume_score_eval/train-* - split: validation path: super_glue_cb_should_assume_score_eval/validation-* - split: test path: super_glue_cb_should_assume_score_eval/test-* - config_name: super_glue_cb_take_the_following_as_truth data_files: - split: train path: super_glue_cb_take_the_following_as_truth/train-* - split: validation path: super_glue_cb_take_the_following_as_truth/validation-* - split: test path: super_glue_cb_take_the_following_as_truth/test-* - config_name: super_glue_cb_take_the_following_as_truth_score_eval data_files: - split: train path: super_glue_cb_take_the_following_as_truth_score_eval/train-* - split: validation path: super_glue_cb_take_the_following_as_truth_score_eval/validation-* - split: test path: super_glue_cb_take_the_following_as_truth_score_eval/test-* - config_name: super_glue_copa_C1_or_C2_premise_so_because_ data_files: - split: train path: super_glue_copa_C1_or_C2_premise_so_because_/train-* - split: validation path: super_glue_copa_C1_or_C2_premise_so_because_/validation-* - split: test path: super_glue_copa_C1_or_C2_premise_so_because_/test-* - config_name: super_glue_copa_C1_or_C2_premise_so_because__score_eval data_files: - split: train path: super_glue_copa_C1_or_C2_premise_so_because__score_eval/train-* - split: validation path: super_glue_copa_C1_or_C2_premise_so_because__score_eval/validation-* - split: test path: super_glue_copa_C1_or_C2_premise_so_because__score_eval/test-* - config_name: super_glue_copa__As_a_result_C1_or_C2_ data_files: - split: train path: super_glue_copa__As_a_result_C1_or_C2_/train-* - split: validation path: super_glue_copa__As_a_result_C1_or_C2_/validation-* - split: test path: super_glue_copa__As_a_result_C1_or_C2_/test-* - config_name: super_glue_copa__As_a_result_C1_or_C2__score_eval data_files: - split: train path: super_glue_copa__As_a_result_C1_or_C2__score_eval/train-* - split: validation path: super_glue_copa__As_a_result_C1_or_C2__score_eval/validation-* - split: test path: super_glue_copa__As_a_result_C1_or_C2__score_eval/test-* - config_name: super_glue_copa__What_could_happen_next_C1_or_C2_ data_files: - split: train path: super_glue_copa__What_could_happen_next_C1_or_C2_/train-* - split: validation path: super_glue_copa__What_could_happen_next_C1_or_C2_/validation-* - split: test path: super_glue_copa__What_could_happen_next_C1_or_C2_/test-* - config_name: super_glue_copa__What_could_happen_next_C1_or_C2__score_eval data_files: - split: train path: super_glue_copa__What_could_happen_next_C1_or_C2__score_eval/train-* - split: validation path: super_glue_copa__What_could_happen_next_C1_or_C2__score_eval/validation-* - split: test path: super_glue_copa__What_could_happen_next_C1_or_C2__score_eval/test-* - config_name: super_glue_copa__which_may_be_caused_by data_files: - split: train path: super_glue_copa__which_may_be_caused_by/train-* - split: validation path: super_glue_copa__which_may_be_caused_by/validation-* - split: test path: super_glue_copa__which_may_be_caused_by/test-* - config_name: super_glue_copa__which_may_be_caused_by_score_eval data_files: - split: train path: super_glue_copa__which_may_be_caused_by_score_eval/train-* - split: validation path: super_glue_copa__which_may_be_caused_by_score_eval/validation-* - split: test path: super_glue_copa__which_may_be_caused_by_score_eval/test-* - config_name: super_glue_copa__why_C1_or_C2 data_files: - split: train path: super_glue_copa__why_C1_or_C2/train-* - split: validation path: super_glue_copa__why_C1_or_C2/validation-* - split: test path: super_glue_copa__why_C1_or_C2/test-* - config_name: super_glue_copa__why_C1_or_C2_score_eval data_files: - split: train path: super_glue_copa__why_C1_or_C2_score_eval/train-* - split: validation path: super_glue_copa__why_C1_or_C2_score_eval/validation-* - split: test path: super_glue_copa__why_C1_or_C2_score_eval/test-* - config_name: super_glue_copa_best_option data_files: - split: train path: super_glue_copa_best_option/train-* - split: validation path: super_glue_copa_best_option/validation-* - split: test path: super_glue_copa_best_option/test-* - config_name: super_glue_copa_best_option_score_eval data_files: - split: train path: super_glue_copa_best_option_score_eval/train-* - split: validation path: super_glue_copa_best_option_score_eval/validation-* - split: test path: super_glue_copa_best_option_score_eval/test-* - config_name: super_glue_copa_cause_effect data_files: - split: train path: super_glue_copa_cause_effect/train-* - split: validation path: super_glue_copa_cause_effect/validation-* - split: test path: super_glue_copa_cause_effect/test-* - config_name: super_glue_copa_cause_effect_score_eval data_files: - split: train path: super_glue_copa_cause_effect_score_eval/train-* - split: validation path: super_glue_copa_cause_effect_score_eval/validation-* - split: test path: super_glue_copa_cause_effect_score_eval/test-* - config_name: super_glue_copa_choose data_files: - split: train path: super_glue_copa_choose/train-* - split: validation path: super_glue_copa_choose/validation-* - split: test path: super_glue_copa_choose/test-* - config_name: super_glue_copa_choose_score_eval data_files: - split: train path: super_glue_copa_choose_score_eval/train-* - split: validation path: super_glue_copa_choose_score_eval/validation-* - split: test path: super_glue_copa_choose_score_eval/test-* - config_name: super_glue_copa_exercise data_files: - split: train path: super_glue_copa_exercise/train-* - split: validation path: super_glue_copa_exercise/validation-* - split: test path: super_glue_copa_exercise/test-* - config_name: super_glue_copa_exercise_score_eval data_files: - split: train path: super_glue_copa_exercise_score_eval/train-* - split: validation path: super_glue_copa_exercise_score_eval/validation-* - split: test path: super_glue_copa_exercise_score_eval/test-* - config_name: super_glue_copa_i_am_hesitating data_files: - split: train path: super_glue_copa_i_am_hesitating/train-* - split: validation path: super_glue_copa_i_am_hesitating/validation-* - split: test path: super_glue_copa_i_am_hesitating/test-* - config_name: super_glue_copa_i_am_hesitating_score_eval data_files: - split: train path: super_glue_copa_i_am_hesitating_score_eval/train-* - split: validation path: super_glue_copa_i_am_hesitating_score_eval/validation-* - split: test path: super_glue_copa_i_am_hesitating_score_eval/test-* - config_name: super_glue_copa_more_likely data_files: - split: train path: super_glue_copa_more_likely/train-* - split: validation path: super_glue_copa_more_likely/validation-* - split: test path: super_glue_copa_more_likely/test-* - config_name: super_glue_copa_more_likely_score_eval data_files: - split: train path: super_glue_copa_more_likely_score_eval/train-* - split: validation path: super_glue_copa_more_likely_score_eval/validation-* - split: test path: super_glue_copa_more_likely_score_eval/test-* - config_name: super_glue_copa_plausible_alternatives data_files: - split: train path: super_glue_copa_plausible_alternatives/train-* - split: validation path: super_glue_copa_plausible_alternatives/validation-* - split: test path: super_glue_copa_plausible_alternatives/test-* - config_name: super_glue_copa_plausible_alternatives_score_eval data_files: - split: train path: super_glue_copa_plausible_alternatives_score_eval/train-* - split: validation path: super_glue_copa_plausible_alternatives_score_eval/validation-* - split: test path: super_glue_copa_plausible_alternatives_score_eval/test-* - config_name: super_glue_multirc_I_was_going_to_say_ data_files: - split: train path: super_glue_multirc_I_was_going_to_say_/train-* - split: validation path: super_glue_multirc_I_was_going_to_say_/validation-* - split: test path: super_glue_multirc_I_was_going_to_say_/test-* - config_name: super_glue_multirc_Would_it_be_good_to_answer_ data_files: - split: train path: super_glue_multirc_Would_it_be_good_to_answer_/train-* - split: validation path: super_glue_multirc_Would_it_be_good_to_answer_/validation-* - split: test path: super_glue_multirc_Would_it_be_good_to_answer_/test-* - config_name: super_glue_multirc_confirm data_files: - split: train path: super_glue_multirc_confirm/train-* - split: validation path: super_glue_multirc_confirm/validation-* - split: test path: super_glue_multirc_confirm/test-* - config_name: super_glue_multirc_correct data_files: - split: train path: super_glue_multirc_correct/train-* - split: validation path: super_glue_multirc_correct/validation-* - split: test path: super_glue_multirc_correct/test-* - config_name: super_glue_multirc_decide_valid data_files: - split: train path: super_glue_multirc_decide_valid/train-* - split: validation path: super_glue_multirc_decide_valid/validation-* - split: test path: super_glue_multirc_decide_valid/test-* - config_name: super_glue_multirc_found_this_answer data_files: - split: train path: super_glue_multirc_found_this_answer/train-* - split: validation path: super_glue_multirc_found_this_answer/validation-* - split: test path: super_glue_multirc_found_this_answer/test-* - config_name: super_glue_multirc_grading data_files: - split: train path: super_glue_multirc_grading/train-* - split: validation path: super_glue_multirc_grading/validation-* - split: test path: super_glue_multirc_grading/test-* - config_name: super_glue_multirc_is_a_correct_answer_ data_files: - split: train path: super_glue_multirc_is_a_correct_answer_/train-* - split: validation path: super_glue_multirc_is_a_correct_answer_/validation-* - split: test path: super_glue_multirc_is_a_correct_answer_/test-* - config_name: super_glue_multirc_is_the_correct_answer_ data_files: - split: train path: super_glue_multirc_is_the_correct_answer_/train-* - split: validation path: super_glue_multirc_is_the_correct_answer_/validation-* - split: test path: super_glue_multirc_is_the_correct_answer_/test-* - config_name: super_glue_multirc_paragraph_question_is_it_ data_files: - split: train path: super_glue_multirc_paragraph_question_is_it_/train-* - split: validation path: super_glue_multirc_paragraph_question_is_it_/validation-* - split: test path: super_glue_multirc_paragraph_question_is_it_/test-* - config_name: super_glue_record_Add_sentence_after_after_continuation_choices_ data_files: - split: train path: super_glue_record_Add_sentence_after_after_continuation_choices_/train-* - split: validation path: super_glue_record_Add_sentence_after_after_continuation_choices_/validation-* - split: test path: super_glue_record_Add_sentence_after_after_continuation_choices_/test-* - config_name: super_glue_record_Add_sentence_after_continuation_choices_ data_files: - split: train path: super_glue_record_Add_sentence_after_continuation_choices_/train-* - split: validation path: super_glue_record_Add_sentence_after_continuation_choices_/validation-* - split: test path: super_glue_record_Add_sentence_after_continuation_choices_/test-* - config_name: super_glue_record_Can_you_figure_out_ data_files: - split: train path: super_glue_record_Can_you_figure_out_/train-* - split: validation path: super_glue_record_Can_you_figure_out_/validation-* - split: test path: super_glue_record_Can_you_figure_out_/test-* - config_name: super_glue_record_GPT_3_style_continuation_choices_ data_files: - split: train path: super_glue_record_GPT_3_style_continuation_choices_/train-* - split: validation path: super_glue_record_GPT_3_style_continuation_choices_/validation-* - split: test path: super_glue_record_GPT_3_style_continuation_choices_/test-* - config_name: super_glue_record_GPT_3_style_summary_only_continuation_choices_ data_files: - split: train path: super_glue_record_GPT_3_style_summary_only_continuation_choices_/train-* - split: validation path: super_glue_record_GPT_3_style_summary_only_continuation_choices_/validation-* - split: test path: super_glue_record_GPT_3_style_summary_only_continuation_choices_/test-* - config_name: super_glue_record_GPT_3_style_with_labels_continuation_choices_ data_files: - split: train path: super_glue_record_GPT_3_style_with_labels_continuation_choices_/train-* - split: validation path: super_glue_record_GPT_3_style_with_labels_continuation_choices_/validation-* - split: test path: super_glue_record_GPT_3_style_with_labels_continuation_choices_/test-* - config_name: super_glue_record_GPT_3_style_with_labels_without_hyphens_continuation_choices_ data_files: - split: train path: super_glue_record_GPT_3_style_with_labels_without_hyphens_continuation_choices_/train-* - split: validation path: super_glue_record_GPT_3_style_with_labels_without_hyphens_continuation_choices_/validation-* - split: test path: super_glue_record_GPT_3_style_with_labels_without_hyphens_continuation_choices_/test-* - config_name: super_glue_record_GPT_3_style_without_hyphens_continuation_choices_ data_files: - split: train path: super_glue_record_GPT_3_style_without_hyphens_continuation_choices_/train-* - split: validation path: super_glue_record_GPT_3_style_without_hyphens_continuation_choices_/validation-* - split: test path: super_glue_record_GPT_3_style_without_hyphens_continuation_choices_/test-* - config_name: super_glue_record_In_the_question_above_the_placeholder_stands_for data_files: - split: train path: super_glue_record_In_the_question_above_the_placeholder_stands_for/train-* - split: validation path: super_glue_record_In_the_question_above_the_placeholder_stands_for/validation-* - split: test path: super_glue_record_In_the_question_above_the_placeholder_stands_for/test-* - config_name: super_glue_record_New_highlight_continuation_choices_ data_files: - split: train path: super_glue_record_New_highlight_continuation_choices_/train-* - split: validation path: super_glue_record_New_highlight_continuation_choices_/validation-* - split: test path: super_glue_record_New_highlight_continuation_choices_/test-* - config_name: super_glue_record_News_article_continuation_choices_ data_files: - split: train path: super_glue_record_News_article_continuation_choices_/train-* - split: validation path: super_glue_record_News_article_continuation_choices_/validation-* - split: test path: super_glue_record_News_article_continuation_choices_/test-* - config_name: super_glue_record_Summary_first_continuation_choices_ data_files: - split: train path: super_glue_record_Summary_first_continuation_choices_/train-* - split: validation path: super_glue_record_Summary_first_continuation_choices_/validation-* - split: test path: super_glue_record_Summary_first_continuation_choices_/test-* - config_name: super_glue_record_What_could_the_placeholder_be_ data_files: - split: train path: super_glue_record_What_could_the_placeholder_be_/train-* - split: validation path: super_glue_record_What_could_the_placeholder_be_/validation-* - split: test path: super_glue_record_What_could_the_placeholder_be_/test-* - config_name: super_glue_record_Which_one_is_the_placeholder_ data_files: - split: train path: super_glue_record_Which_one_is_the_placeholder_/train-* - split: validation path: super_glue_record_Which_one_is_the_placeholder_/validation-* - split: test path: super_glue_record_Which_one_is_the_placeholder_/test-* - config_name: super_glue_record_choose_between data_files: - split: train path: super_glue_record_choose_between/train-* - split: validation path: super_glue_record_choose_between/validation-* - split: test path: super_glue_record_choose_between/test-* - config_name: super_glue_record_corrupted data_files: - split: train path: super_glue_record_corrupted/train-* - split: validation path: super_glue_record_corrupted/validation-* - split: test path: super_glue_record_corrupted/test-* - config_name: super_glue_record_exercise data_files: - split: train path: super_glue_record_exercise/train-* - split: validation path: super_glue_record_exercise/validation-* - split: test path: super_glue_record_exercise/test-* - config_name: super_glue_record_pick_one_option data_files: - split: train path: super_glue_record_pick_one_option/train-* - split: validation path: super_glue_record_pick_one_option/validation-* - split: test path: super_glue_record_pick_one_option/test-* - config_name: super_glue_record_the_placeholder_refers_to_ data_files: - split: train path: super_glue_record_the_placeholder_refers_to_/train-* - split: validation path: super_glue_record_the_placeholder_refers_to_/validation-* - split: test path: super_glue_record_the_placeholder_refers_to_/test-* - config_name: super_glue_record_trying_to_decide data_files: - split: train path: super_glue_record_trying_to_decide/train-* - split: validation path: super_glue_record_trying_to_decide/validation-* - split: test path: super_glue_record_trying_to_decide/test-* - config_name: super_glue_rte_GPT_3_style data_files: - split: train path: super_glue_rte_GPT_3_style/train-* - split: validation path: super_glue_rte_GPT_3_style/validation-* - split: test path: super_glue_rte_GPT_3_style/test-* - config_name: super_glue_rte_GPT_3_style_score_eval data_files: - split: train path: super_glue_rte_GPT_3_style_score_eval/train-* - split: validation path: super_glue_rte_GPT_3_style_score_eval/validation-* - split: test path: super_glue_rte_GPT_3_style_score_eval/test-* - config_name: super_glue_rte_MNLI_crowdsource data_files: - split: train path: super_glue_rte_MNLI_crowdsource/train-* - split: validation path: super_glue_rte_MNLI_crowdsource/validation-* - split: test path: super_glue_rte_MNLI_crowdsource/test-* - config_name: super_glue_rte_MNLI_crowdsource_score_eval data_files: - split: train path: super_glue_rte_MNLI_crowdsource_score_eval/train-* - split: validation path: super_glue_rte_MNLI_crowdsource_score_eval/validation-* - split: test path: super_glue_rte_MNLI_crowdsource_score_eval/test-* - config_name: super_glue_rte_based_on_the_previous_passage data_files: - split: train path: super_glue_rte_based_on_the_previous_passage/train-* - split: validation path: super_glue_rte_based_on_the_previous_passage/validation-* - split: test path: super_glue_rte_based_on_the_previous_passage/test-* - config_name: super_glue_rte_based_on_the_previous_passage_score_eval data_files: - split: train path: super_glue_rte_based_on_the_previous_passage_score_eval/train-* - split: validation path: super_glue_rte_based_on_the_previous_passage_score_eval/validation-* - split: test path: super_glue_rte_based_on_the_previous_passage_score_eval/test-* - config_name: super_glue_rte_can_we_infer data_files: - split: train path: super_glue_rte_can_we_infer/train-* - split: validation path: super_glue_rte_can_we_infer/validation-* - split: test path: super_glue_rte_can_we_infer/test-* - config_name: super_glue_rte_can_we_infer_score_eval data_files: - split: train path: super_glue_rte_can_we_infer_score_eval/train-* - split: validation path: super_glue_rte_can_we_infer_score_eval/validation-* - split: test path: super_glue_rte_can_we_infer_score_eval/test-* - config_name: super_glue_rte_does_it_follow_that data_files: - split: train path: super_glue_rte_does_it_follow_that/train-* - split: validation path: super_glue_rte_does_it_follow_that/validation-* - split: test path: super_glue_rte_does_it_follow_that/test-* - config_name: super_glue_rte_does_it_follow_that_score_eval data_files: - split: train path: super_glue_rte_does_it_follow_that_score_eval/train-* - split: validation path: super_glue_rte_does_it_follow_that_score_eval/validation-* - split: test path: super_glue_rte_does_it_follow_that_score_eval/test-* - config_name: super_glue_rte_does_this_imply data_files: - split: train path: super_glue_rte_does_this_imply/train-* - split: validation path: super_glue_rte_does_this_imply/validation-* - split: test path: super_glue_rte_does_this_imply/test-* - config_name: super_glue_rte_does_this_imply_score_eval data_files: - split: train path: super_glue_rte_does_this_imply_score_eval/train-* - split: validation path: super_glue_rte_does_this_imply_score_eval/validation-* - split: test path: super_glue_rte_does_this_imply_score_eval/test-* - config_name: super_glue_rte_guaranteed_true data_files: - split: train path: super_glue_rte_guaranteed_true/train-* - split: validation path: super_glue_rte_guaranteed_true/validation-* - split: test path: super_glue_rte_guaranteed_true/test-* - config_name: super_glue_rte_guaranteed_true_score_eval data_files: - split: train path: super_glue_rte_guaranteed_true_score_eval/train-* - split: validation path: super_glue_rte_guaranteed_true_score_eval/validation-* - split: test path: super_glue_rte_guaranteed_true_score_eval/test-* - config_name: super_glue_rte_justified_in_saying data_files: - split: train path: super_glue_rte_justified_in_saying/train-* - split: validation path: super_glue_rte_justified_in_saying/validation-* - split: test path: super_glue_rte_justified_in_saying/test-* - config_name: super_glue_rte_justified_in_saying_score_eval data_files: - split: train path: super_glue_rte_justified_in_saying_score_eval/train-* - split: validation path: super_glue_rte_justified_in_saying_score_eval/validation-* - split: test path: super_glue_rte_justified_in_saying_score_eval/test-* - config_name: super_glue_rte_must_be_true data_files: - split: train path: super_glue_rte_must_be_true/train-* - split: validation path: super_glue_rte_must_be_true/validation-* - split: test path: super_glue_rte_must_be_true/test-* - config_name: super_glue_rte_must_be_true_score_eval data_files: - split: train path: super_glue_rte_must_be_true_score_eval/train-* - split: validation path: super_glue_rte_must_be_true_score_eval/validation-* - split: test path: super_glue_rte_must_be_true_score_eval/test-* - config_name: super_glue_rte_should_assume data_files: - split: train path: super_glue_rte_should_assume/train-* - split: validation path: super_glue_rte_should_assume/validation-* - split: test path: super_glue_rte_should_assume/test-* - config_name: super_glue_rte_should_assume_score_eval data_files: - split: train path: super_glue_rte_should_assume_score_eval/train-* - split: validation path: super_glue_rte_should_assume_score_eval/validation-* - split: test path: super_glue_rte_should_assume_score_eval/test-* - config_name: super_glue_wic_GPT_3_prompt data_files: - split: train path: super_glue_wic_GPT_3_prompt/train-* - split: validation path: super_glue_wic_GPT_3_prompt/validation-* - split: test path: super_glue_wic_GPT_3_prompt/test-* - config_name: super_glue_wic_GPT_3_prompt_score_eval data_files: - split: train path: super_glue_wic_GPT_3_prompt_score_eval/train-* - split: validation path: super_glue_wic_GPT_3_prompt_score_eval/validation-* - split: test path: super_glue_wic_GPT_3_prompt_score_eval/test-* - config_name: super_glue_wic_GPT_3_prompt_with_label data_files: - split: train path: super_glue_wic_GPT_3_prompt_with_label/train-* - split: validation path: super_glue_wic_GPT_3_prompt_with_label/validation-* - split: test path: super_glue_wic_GPT_3_prompt_with_label/test-* - config_name: super_glue_wic_GPT_3_prompt_with_label_score_eval data_files: - split: train path: super_glue_wic_GPT_3_prompt_with_label_score_eval/train-* - split: validation path: super_glue_wic_GPT_3_prompt_with_label_score_eval/validation-* - split: test path: super_glue_wic_GPT_3_prompt_with_label_score_eval/test-* - config_name: super_glue_wic_affirmation_true_or_false data_files: - split: train path: super_glue_wic_affirmation_true_or_false/train-* - split: validation path: super_glue_wic_affirmation_true_or_false/validation-* - split: test path: super_glue_wic_affirmation_true_or_false/test-* - config_name: super_glue_wic_affirmation_true_or_false_score_eval data_files: - split: train path: super_glue_wic_affirmation_true_or_false_score_eval/train-* - split: validation path: super_glue_wic_affirmation_true_or_false_score_eval/validation-* - split: test path: super_glue_wic_affirmation_true_or_false_score_eval/test-* - config_name: super_glue_wic_grammar_homework data_files: - split: train path: super_glue_wic_grammar_homework/train-* - split: validation path: super_glue_wic_grammar_homework/validation-* - split: test path: super_glue_wic_grammar_homework/test-* - config_name: super_glue_wic_grammar_homework_score_eval data_files: - split: train path: super_glue_wic_grammar_homework_score_eval/train-* - split: validation path: super_glue_wic_grammar_homework_score_eval/validation-* - split: test path: super_glue_wic_grammar_homework_score_eval/test-* - config_name: super_glue_wic_polysemous data_files: - split: train path: super_glue_wic_polysemous/train-* - split: validation path: super_glue_wic_polysemous/validation-* - split: test path: super_glue_wic_polysemous/test-* - config_name: super_glue_wic_polysemous_score_eval data_files: - split: train path: super_glue_wic_polysemous_score_eval/train-* - split: validation path: super_glue_wic_polysemous_score_eval/validation-* - split: test path: super_glue_wic_polysemous_score_eval/test-* - config_name: super_glue_wic_question_context data_files: - split: train path: super_glue_wic_question_context/train-* - split: validation path: super_glue_wic_question_context/validation-* - split: test path: super_glue_wic_question_context/test-* - config_name: super_glue_wic_question_context_meaning data_files: - split: train path: super_glue_wic_question_context_meaning/train-* - split: validation path: super_glue_wic_question_context_meaning/validation-* - split: test path: super_glue_wic_question_context_meaning/test-* - config_name: super_glue_wic_question_context_meaning_score_eval data_files: - split: train path: super_glue_wic_question_context_meaning_score_eval/train-* - split: validation path: super_glue_wic_question_context_meaning_score_eval/validation-* - split: test path: super_glue_wic_question_context_meaning_score_eval/test-* - config_name: super_glue_wic_question_context_meaning_with_label data_files: - split: train path: super_glue_wic_question_context_meaning_with_label/train-* - split: validation path: super_glue_wic_question_context_meaning_with_label/validation-* - split: test path: super_glue_wic_question_context_meaning_with_label/test-* - config_name: super_glue_wic_question_context_meaning_with_label_score_eval data_files: - split: train path: super_glue_wic_question_context_meaning_with_label_score_eval/train-* - split: validation path: super_glue_wic_question_context_meaning_with_label_score_eval/validation-* - split: test path: super_glue_wic_question_context_meaning_with_label_score_eval/test-* - config_name: super_glue_wic_question_context_score_eval data_files: - split: train path: super_glue_wic_question_context_score_eval/train-* - split: validation path: super_glue_wic_question_context_score_eval/validation-* - split: test path: super_glue_wic_question_context_score_eval/test-* - config_name: super_glue_wic_same_sense data_files: - split: train path: super_glue_wic_same_sense/train-* - split: validation path: super_glue_wic_same_sense/validation-* - split: test path: super_glue_wic_same_sense/test-* - config_name: super_glue_wic_same_sense_score_eval data_files: - split: train path: super_glue_wic_same_sense_score_eval/train-* - split: validation path: super_glue_wic_same_sense_score_eval/validation-* - split: test path: super_glue_wic_same_sense_score_eval/test-* - config_name: super_glue_wic_similar_sense data_files: - split: train path: super_glue_wic_similar_sense/train-* - split: validation path: super_glue_wic_similar_sense/validation-* - split: test path: super_glue_wic_similar_sense/test-* - config_name: super_glue_wic_similar_sense_score_eval data_files: - split: train path: super_glue_wic_similar_sense_score_eval/train-* - split: validation path: super_glue_wic_similar_sense_score_eval/validation-* - split: test path: super_glue_wic_similar_sense_score_eval/test-* - config_name: super_glue_wsc.fixed_GPT_3_Style data_files: - split: train path: super_glue_wsc.fixed_GPT_3_Style/train-* - split: validation path: super_glue_wsc.fixed_GPT_3_Style/validation-* - split: test path: super_glue_wsc.fixed_GPT_3_Style/test-* - config_name: super_glue_wsc.fixed_GPT_3_Style_score_eval data_files: - split: train path: super_glue_wsc.fixed_GPT_3_Style_score_eval/train-* - split: validation path: super_glue_wsc.fixed_GPT_3_Style_score_eval/validation-* - split: test path: super_glue_wsc.fixed_GPT_3_Style_score_eval/test-* - config_name: super_glue_wsc.fixed_I_think_they_mean data_files: - split: train path: super_glue_wsc.fixed_I_think_they_mean/train-* - split: validation path: super_glue_wsc.fixed_I_think_they_mean/validation-* - split: test path: super_glue_wsc.fixed_I_think_they_mean/test-* - config_name: super_glue_wsc.fixed_I_think_they_mean_score_eval data_files: - split: train path: super_glue_wsc.fixed_I_think_they_mean_score_eval/train-* - split: validation path: super_glue_wsc.fixed_I_think_they_mean_score_eval/validation-* - split: test path: super_glue_wsc.fixed_I_think_they_mean_score_eval/test-* - config_name: super_glue_wsc.fixed_Who_or_what_is_are data_files: - split: train path: super_glue_wsc.fixed_Who_or_what_is_are/train-* - split: validation path: super_glue_wsc.fixed_Who_or_what_is_are/validation-* - split: test path: super_glue_wsc.fixed_Who_or_what_is_are/test-* - config_name: super_glue_wsc.fixed_Who_or_what_is_are_score_eval data_files: - split: train path: super_glue_wsc.fixed_Who_or_what_is_are_score_eval/train-* - split: validation path: super_glue_wsc.fixed_Who_or_what_is_are_score_eval/validation-* - split: test path: super_glue_wsc.fixed_Who_or_what_is_are_score_eval/test-* - config_name: super_glue_wsc.fixed_by_p_they_mean data_files: - split: train path: super_glue_wsc.fixed_by_p_they_mean/train-* - split: validation path: super_glue_wsc.fixed_by_p_they_mean/validation-* - split: test path: super_glue_wsc.fixed_by_p_they_mean/test-* - config_name: super_glue_wsc.fixed_by_p_they_mean_score_eval data_files: - split: train path: super_glue_wsc.fixed_by_p_they_mean_score_eval/train-* - split: validation path: super_glue_wsc.fixed_by_p_they_mean_score_eval/validation-* - split: test path: super_glue_wsc.fixed_by_p_they_mean_score_eval/test-* - config_name: super_glue_wsc.fixed_does_p_stand_for data_files: - split: train path: super_glue_wsc.fixed_does_p_stand_for/train-* - split: validation path: super_glue_wsc.fixed_does_p_stand_for/validation-* - split: test path: super_glue_wsc.fixed_does_p_stand_for/test-* - config_name: super_glue_wsc.fixed_does_p_stand_for_score_eval data_files: - split: train path: super_glue_wsc.fixed_does_p_stand_for_score_eval/train-* - split: validation path: super_glue_wsc.fixed_does_p_stand_for_score_eval/validation-* - split: test path: super_glue_wsc.fixed_does_p_stand_for_score_eval/test-* - config_name: super_glue_wsc.fixed_does_the_pronoun_refer_to data_files: - split: train path: super_glue_wsc.fixed_does_the_pronoun_refer_to/train-* - split: validation path: super_glue_wsc.fixed_does_the_pronoun_refer_to/validation-* - split: test path: super_glue_wsc.fixed_does_the_pronoun_refer_to/test-* - config_name: super_glue_wsc.fixed_does_the_pronoun_refer_to_score_eval data_files: - split: train path: super_glue_wsc.fixed_does_the_pronoun_refer_to_score_eval/train-* - split: validation path: super_glue_wsc.fixed_does_the_pronoun_refer_to_score_eval/validation-* - split: test path: super_glue_wsc.fixed_does_the_pronoun_refer_to_score_eval/test-* - config_name: super_glue_wsc.fixed_in_other_words data_files: - split: train path: super_glue_wsc.fixed_in_other_words/train-* - split: validation path: super_glue_wsc.fixed_in_other_words/validation-* - split: test path: super_glue_wsc.fixed_in_other_words/test-* - config_name: super_glue_wsc.fixed_in_other_words_score_eval data_files: - split: train path: super_glue_wsc.fixed_in_other_words_score_eval/train-* - split: validation path: super_glue_wsc.fixed_in_other_words_score_eval/validation-* - split: test path: super_glue_wsc.fixed_in_other_words_score_eval/test-* - config_name: super_glue_wsc.fixed_p_is_are_r data_files: - split: train path: super_glue_wsc.fixed_p_is_are_r/train-* - split: validation path: super_glue_wsc.fixed_p_is_are_r/validation-* - split: test path: super_glue_wsc.fixed_p_is_are_r/test-* - config_name: super_glue_wsc.fixed_p_is_are_r_score_eval data_files: - split: train path: super_glue_wsc.fixed_p_is_are_r_score_eval/train-* - split: validation path: super_glue_wsc.fixed_p_is_are_r_score_eval/validation-* - split: test path: super_glue_wsc.fixed_p_is_are_r_score_eval/test-* - config_name: super_glue_wsc.fixed_replaced_with data_files: - split: train path: super_glue_wsc.fixed_replaced_with/train-* - split: validation path: super_glue_wsc.fixed_replaced_with/validation-* - split: test path: super_glue_wsc.fixed_replaced_with/test-* - config_name: super_glue_wsc.fixed_replaced_with_score_eval data_files: - split: train path: super_glue_wsc.fixed_replaced_with_score_eval/train-* - split: validation path: super_glue_wsc.fixed_replaced_with_score_eval/validation-* - split: test path: super_glue_wsc.fixed_replaced_with_score_eval/test-* - config_name: super_glue_wsc.fixed_the_pronoun_refers_to data_files: - split: train path: super_glue_wsc.fixed_the_pronoun_refers_to/train-* - split: validation path: super_glue_wsc.fixed_the_pronoun_refers_to/validation-* - split: test path: super_glue_wsc.fixed_the_pronoun_refers_to/test-* - config_name: super_glue_wsc.fixed_the_pronoun_refers_to_score_eval data_files: - split: train path: super_glue_wsc.fixed_the_pronoun_refers_to_score_eval/train-* - split: validation path: super_glue_wsc.fixed_the_pronoun_refers_to_score_eval/validation-* - split: test path: super_glue_wsc.fixed_the_pronoun_refers_to_score_eval/test-* - config_name: trec_fine_grained_ABBR data_files: - split: train path: trec_fine_grained_ABBR/train-* - split: test path: trec_fine_grained_ABBR/test-* - config_name: trec_fine_grained_ABBR_context_first data_files: - split: train path: trec_fine_grained_ABBR_context_first/train-* - split: test path: trec_fine_grained_ABBR_context_first/test-* - config_name: trec_fine_grained_DESC data_files: - split: train path: trec_fine_grained_DESC/train-* - split: test path: trec_fine_grained_DESC/test-* - config_name: trec_fine_grained_DESC_context_first data_files: - split: train path: trec_fine_grained_DESC_context_first/train-* - split: test path: trec_fine_grained_DESC_context_first/test-* - config_name: trec_fine_grained_ENTY data_files: - split: train path: trec_fine_grained_ENTY/train-* - split: test path: trec_fine_grained_ENTY/test-* - config_name: trec_fine_grained_HUM data_files: - split: train path: trec_fine_grained_HUM/train-* - split: test path: trec_fine_grained_HUM/test-* - config_name: trec_fine_grained_HUM_context_first data_files: - split: train path: trec_fine_grained_HUM_context_first/train-* - split: test path: trec_fine_grained_HUM_context_first/test-* - config_name: trec_fine_grained_LOC data_files: - split: train path: trec_fine_grained_LOC/train-* - split: test path: trec_fine_grained_LOC/test-* - config_name: trec_fine_grained_LOC_context_first data_files: - split: train path: trec_fine_grained_LOC_context_first/train-* - split: test path: trec_fine_grained_LOC_context_first/test-* - config_name: trec_fine_grained_NUM data_files: - split: train path: trec_fine_grained_NUM/train-* - split: test path: trec_fine_grained_NUM/test-* - config_name: trec_fine_grained_NUM_context_first data_files: - split: train path: trec_fine_grained_NUM_context_first/train-* - split: test path: trec_fine_grained_NUM_context_first/test-* - config_name: trec_fine_grained_open data_files: - split: train path: trec_fine_grained_open/train-* - split: test path: trec_fine_grained_open/test-* - config_name: trec_fine_grained_open_context_first data_files: - split: train path: trec_fine_grained_open_context_first/train-* - split: test path: trec_fine_grained_open_context_first/test-* - config_name: trec_pick_the_best_descriptor data_files: - split: train path: trec_pick_the_best_descriptor/train-* - split: test path: trec_pick_the_best_descriptor/test-* - config_name: trec_trec1 data_files: - split: train path: trec_trec1/train-* - split: test path: trec_trec1/test-* - config_name: trec_trec2 data_files: - split: train path: trec_trec2/train-* - split: test path: trec_trec2/test-* - config_name: trec_what_category_best_describe data_files: - split: train path: trec_what_category_best_describe/train-* - split: test path: trec_what_category_best_describe/test-* - config_name: trec_which_category_best_describes data_files: - split: train path: trec_which_category_best_describes/train-* - split: test path: trec_which_category_best_describes/test-* - config_name: trivia_qa_unfiltered_first_person_context data_files: - split: train path: trivia_qa_unfiltered_first_person_context/train-* - split: validation path: trivia_qa_unfiltered_first_person_context/validation-* - split: test path: trivia_qa_unfiltered_first_person_context/test-* - config_name: trivia_qa_unfiltered_formal_description data_files: - split: train path: trivia_qa_unfiltered_formal_description/train-* - split: validation path: trivia_qa_unfiltered_formal_description/validation-* - split: test path: trivia_qa_unfiltered_formal_description/test-* - config_name: trivia_qa_unfiltered_guess_question data_files: - split: train path: trivia_qa_unfiltered_guess_question/train-* - split: validation path: trivia_qa_unfiltered_guess_question/validation-* - config_name: trivia_qa_unfiltered_question_answer data_files: - split: train path: trivia_qa_unfiltered_question_answer/train-* - split: validation path: trivia_qa_unfiltered_question_answer/validation-* - split: test path: trivia_qa_unfiltered_question_answer/test-* - config_name: trivia_qa_unfiltered_question_with_instruction data_files: - split: train path: trivia_qa_unfiltered_question_with_instruction/train-* - split: validation path: trivia_qa_unfiltered_question_with_instruction/validation-* - split: test path: trivia_qa_unfiltered_question_with_instruction/test-* - config_name: web_questions_get_the_answer data_files: - split: train path: web_questions_get_the_answer/train-* - split: test path: web_questions_get_the_answer/test-* - config_name: web_questions_potential_correct_answer data_files: - split: train path: web_questions_potential_correct_answer/train-* - split: test path: web_questions_potential_correct_answer/test-* - config_name: web_questions_question_answer data_files: - split: train path: web_questions_question_answer/train-* - split: test path: web_questions_question_answer/test-* - config_name: web_questions_short_general_knowledge_q data_files: - split: train path: web_questions_short_general_knowledge_q/train-* - split: test path: web_questions_short_general_knowledge_q/test-* - config_name: web_questions_whats_the_answer data_files: - split: train path: web_questions_whats_the_answer/train-* - split: test path: web_questions_whats_the_answer/test-* - config_name: wiki_bio_comprehension data_files: - split: train path: wiki_bio_comprehension/train-* - split: test path: wiki_bio_comprehension/test-* - split: val path: wiki_bio_comprehension/val-* - config_name: wiki_bio_guess_person data_files: - split: train path: wiki_bio_guess_person/train-* - split: test path: wiki_bio_guess_person/test-* - split: val path: wiki_bio_guess_person/val-* - config_name: wiki_bio_key_content data_files: - split: train path: wiki_bio_key_content/train-* - split: test path: wiki_bio_key_content/test-* - split: val path: wiki_bio_key_content/val-* - config_name: wiki_bio_what_content data_files: - split: train path: wiki_bio_what_content/train-* - split: test path: wiki_bio_what_content/test-* - split: val path: wiki_bio_what_content/val-* - config_name: wiki_bio_who data_files: - split: train path: wiki_bio_who/train-* - split: test path: wiki_bio_who/test-* - split: val path: wiki_bio_who/val-* - config_name: wiki_hop_original_choose_best_object_affirmative_1 data_files: - split: train path: wiki_hop_original_choose_best_object_affirmative_1/train-* - split: validation path: wiki_hop_original_choose_best_object_affirmative_1/validation-* - config_name: wiki_hop_original_choose_best_object_affirmative_2 data_files: - split: train path: wiki_hop_original_choose_best_object_affirmative_2/train-* - split: validation path: wiki_hop_original_choose_best_object_affirmative_2/validation-* - config_name: wiki_hop_original_choose_best_object_affirmative_3 data_files: - split: train path: wiki_hop_original_choose_best_object_affirmative_3/train-* - split: validation path: wiki_hop_original_choose_best_object_affirmative_3/validation-* - config_name: wiki_hop_original_choose_best_object_interrogative_1 data_files: - split: train path: wiki_hop_original_choose_best_object_interrogative_1/train-* - split: validation path: wiki_hop_original_choose_best_object_interrogative_1/validation-* - config_name: wiki_hop_original_choose_best_object_interrogative_2 data_files: - split: train path: wiki_hop_original_choose_best_object_interrogative_2/train-* - split: validation path: wiki_hop_original_choose_best_object_interrogative_2/validation-* - config_name: wiki_hop_original_explain_relation data_files: - split: train path: wiki_hop_original_explain_relation/train-* - split: validation path: wiki_hop_original_explain_relation/validation-* - config_name: wiki_hop_original_generate_object data_files: - split: train path: wiki_hop_original_generate_object/train-* - split: validation path: wiki_hop_original_generate_object/validation-* - config_name: wiki_hop_original_generate_subject data_files: - split: train path: wiki_hop_original_generate_subject/train-* - split: validation path: wiki_hop_original_generate_subject/validation-* - config_name: wiki_hop_original_generate_subject_and_object data_files: - split: train path: wiki_hop_original_generate_subject_and_object/train-* - split: validation path: wiki_hop_original_generate_subject_and_object/validation-* - config_name: wiki_qa_Decide_good_answer data_files: - split: train path: wiki_qa_Decide_good_answer/train-* - split: validation path: wiki_qa_Decide_good_answer/validation-* - split: test path: wiki_qa_Decide_good_answer/test-* - config_name: wiki_qa_Direct_Answer_to_Question data_files: - split: train path: wiki_qa_Direct_Answer_to_Question/train-* - split: validation path: wiki_qa_Direct_Answer_to_Question/validation-* - split: test path: wiki_qa_Direct_Answer_to_Question/test-* - config_name: wiki_qa_Generate_Question_from_Topic data_files: - split: train path: wiki_qa_Generate_Question_from_Topic/train-* - split: validation path: wiki_qa_Generate_Question_from_Topic/validation-* - split: test path: wiki_qa_Generate_Question_from_Topic/test-* - config_name: wiki_qa_Is_This_True_ data_files: - split: train path: wiki_qa_Is_This_True_/train-* - split: validation path: wiki_qa_Is_This_True_/validation-* - split: test path: wiki_qa_Is_This_True_/test-* - config_name: wiki_qa_Jeopardy_style data_files: - split: train path: wiki_qa_Jeopardy_style/train-* - split: validation path: wiki_qa_Jeopardy_style/validation-* - split: test path: wiki_qa_Jeopardy_style/test-* - config_name: wiki_qa_Topic_Prediction_Answer_Only data_files: - split: train path: wiki_qa_Topic_Prediction_Answer_Only/train-* - split: validation path: wiki_qa_Topic_Prediction_Answer_Only/validation-* - split: test path: wiki_qa_Topic_Prediction_Answer_Only/test-* - config_name: wiki_qa_Topic_Prediction_Question_Only data_files: - split: train path: wiki_qa_Topic_Prediction_Question_Only/train-* - split: validation path: wiki_qa_Topic_Prediction_Question_Only/validation-* - split: test path: wiki_qa_Topic_Prediction_Question_Only/test-* - config_name: wiki_qa_Topic_Prediction_Question_and_Answer_Pair data_files: - split: train path: wiki_qa_Topic_Prediction_Question_and_Answer_Pair/train-* - split: validation path: wiki_qa_Topic_Prediction_Question_and_Answer_Pair/validation-* - split: test path: wiki_qa_Topic_Prediction_Question_and_Answer_Pair/test-* - config_name: wiki_qa_automatic_system data_files: - split: train path: wiki_qa_automatic_system/train-* - split: validation path: wiki_qa_automatic_system/validation-* - split: test path: wiki_qa_automatic_system/test-* - config_name: wiki_qa_exercise data_files: - split: train path: wiki_qa_exercise/train-* - split: validation path: wiki_qa_exercise/validation-* - split: test path: wiki_qa_exercise/test-* - config_name: wiki_qa_found_on_google data_files: - split: train path: wiki_qa_found_on_google/train-* - split: validation path: wiki_qa_found_on_google/validation-* - split: test path: wiki_qa_found_on_google/test-* - config_name: winogrande_winogrande_debiased_Replace data_files: - split: train path: winogrande_winogrande_debiased_Replace/train-* - split: validation path: winogrande_winogrande_debiased_Replace/validation-* - split: test path: winogrande_winogrande_debiased_Replace/test-* - config_name: winogrande_winogrande_debiased_Replace_score_eval data_files: - split: train path: winogrande_winogrande_debiased_Replace_score_eval/train-* - split: validation path: winogrande_winogrande_debiased_Replace_score_eval/validation-* - split: test path: winogrande_winogrande_debiased_Replace_score_eval/test-* - config_name: winogrande_winogrande_debiased_does_underscore_refer_to data_files: - split: train path: winogrande_winogrande_debiased_does_underscore_refer_to/train-* - split: validation path: winogrande_winogrande_debiased_does_underscore_refer_to/validation-* - split: test path: winogrande_winogrande_debiased_does_underscore_refer_to/test-* - config_name: winogrande_winogrande_debiased_does_underscore_refer_to_score_eval data_files: - split: train path: winogrande_winogrande_debiased_does_underscore_refer_to_score_eval/train-* - split: validation path: winogrande_winogrande_debiased_does_underscore_refer_to_score_eval/validation-* - split: test path: winogrande_winogrande_debiased_does_underscore_refer_to_score_eval/test-* - config_name: winogrande_winogrande_debiased_fill_in_the_blank data_files: - split: train path: winogrande_winogrande_debiased_fill_in_the_blank/train-* - split: validation path: winogrande_winogrande_debiased_fill_in_the_blank/validation-* - split: test path: winogrande_winogrande_debiased_fill_in_the_blank/test-* - config_name: winogrande_winogrande_debiased_fill_in_the_blank_score_eval data_files: - split: train path: winogrande_winogrande_debiased_fill_in_the_blank_score_eval/train-* - split: validation path: winogrande_winogrande_debiased_fill_in_the_blank_score_eval/validation-* - split: test path: winogrande_winogrande_debiased_fill_in_the_blank_score_eval/test-* - config_name: winogrande_winogrande_debiased_stand_for data_files: - split: train path: winogrande_winogrande_debiased_stand_for/train-* - split: validation path: winogrande_winogrande_debiased_stand_for/validation-* - split: test path: winogrande_winogrande_debiased_stand_for/test-* - config_name: winogrande_winogrande_debiased_stand_for_score_eval data_files: - split: train path: winogrande_winogrande_debiased_stand_for_score_eval/train-* - split: validation path: winogrande_winogrande_debiased_stand_for_score_eval/validation-* - split: test path: winogrande_winogrande_debiased_stand_for_score_eval/test-* - config_name: winogrande_winogrande_debiased_underscore_refer_to data_files: - split: train path: winogrande_winogrande_debiased_underscore_refer_to/train-* - split: validation path: winogrande_winogrande_debiased_underscore_refer_to/validation-* - split: test path: winogrande_winogrande_debiased_underscore_refer_to/test-* - config_name: winogrande_winogrande_debiased_underscore_refer_to_score_eval data_files: - split: train path: winogrande_winogrande_debiased_underscore_refer_to_score_eval/train-* - split: validation path: winogrande_winogrande_debiased_underscore_refer_to_score_eval/validation-* - split: test path: winogrande_winogrande_debiased_underscore_refer_to_score_eval/test-* - config_name: winogrande_winogrande_xl_Replace data_files: - split: train path: winogrande_winogrande_xl_Replace/train-* - split: validation path: winogrande_winogrande_xl_Replace/validation-* - split: test path: winogrande_winogrande_xl_Replace/test-* - config_name: winogrande_winogrande_xl_Replace_score_eval data_files: - split: train path: winogrande_winogrande_xl_Replace_score_eval/train-* - split: validation path: winogrande_winogrande_xl_Replace_score_eval/validation-* - split: test path: winogrande_winogrande_xl_Replace_score_eval/test-* - config_name: winogrande_winogrande_xl_does_underscore_refer_to data_files: - split: train path: winogrande_winogrande_xl_does_underscore_refer_to/train-* - split: validation path: winogrande_winogrande_xl_does_underscore_refer_to/validation-* - split: test path: winogrande_winogrande_xl_does_underscore_refer_to/test-* - config_name: winogrande_winogrande_xl_does_underscore_refer_to_score_eval data_files: - split: train path: winogrande_winogrande_xl_does_underscore_refer_to_score_eval/train-* - split: validation path: winogrande_winogrande_xl_does_underscore_refer_to_score_eval/validation-* - split: test path: winogrande_winogrande_xl_does_underscore_refer_to_score_eval/test-* - config_name: winogrande_winogrande_xl_fill_in_the_blank data_files: - split: train path: winogrande_winogrande_xl_fill_in_the_blank/train-* - split: validation path: winogrande_winogrande_xl_fill_in_the_blank/validation-* - split: test path: winogrande_winogrande_xl_fill_in_the_blank/test-* - config_name: winogrande_winogrande_xl_fill_in_the_blank_score_eval data_files: - split: train path: winogrande_winogrande_xl_fill_in_the_blank_score_eval/train-* - split: validation path: winogrande_winogrande_xl_fill_in_the_blank_score_eval/validation-* - split: test path: winogrande_winogrande_xl_fill_in_the_blank_score_eval/test-* - config_name: winogrande_winogrande_xl_stand_for data_files: - split: train path: winogrande_winogrande_xl_stand_for/train-* - split: validation path: winogrande_winogrande_xl_stand_for/validation-* - split: test path: winogrande_winogrande_xl_stand_for/test-* - config_name: winogrande_winogrande_xl_stand_for_score_eval data_files: - split: train path: winogrande_winogrande_xl_stand_for_score_eval/train-* - split: validation path: winogrande_winogrande_xl_stand_for_score_eval/validation-* - split: test path: winogrande_winogrande_xl_stand_for_score_eval/test-* - config_name: winogrande_winogrande_xl_underscore_refer_to data_files: - split: train path: winogrande_winogrande_xl_underscore_refer_to/train-* - split: validation path: winogrande_winogrande_xl_underscore_refer_to/validation-* - split: test path: winogrande_winogrande_xl_underscore_refer_to/test-* - config_name: winogrande_winogrande_xl_underscore_refer_to_score_eval data_files: - split: train path: winogrande_winogrande_xl_underscore_refer_to_score_eval/train-* - split: validation path: winogrande_winogrande_xl_underscore_refer_to_score_eval/validation-* - split: test path: winogrande_winogrande_xl_underscore_refer_to_score_eval/test-* - config_name: wiqa_does_the_supposed_perturbation_have_an_effect data_files: - split: train path: wiqa_does_the_supposed_perturbation_have_an_effect/train-* - split: validation path: wiqa_does_the_supposed_perturbation_have_an_effect/validation-* - split: test path: wiqa_does_the_supposed_perturbation_have_an_effect/test-* - config_name: wiqa_effect_with_label_answer data_files: - split: train path: wiqa_effect_with_label_answer/train-* - split: validation path: wiqa_effect_with_label_answer/validation-* - split: test path: wiqa_effect_with_label_answer/test-* - config_name: wiqa_effect_with_string_answer data_files: - split: train path: wiqa_effect_with_string_answer/train-* - split: validation path: wiqa_effect_with_string_answer/validation-* - split: test path: wiqa_effect_with_string_answer/test-* - config_name: wiqa_what_is_the_final_step_of_the_following_process data_files: - split: train path: wiqa_what_is_the_final_step_of_the_following_process/train-* - split: validation path: wiqa_what_is_the_final_step_of_the_following_process/validation-* - split: test path: wiqa_what_is_the_final_step_of_the_following_process/test-* - config_name: wiqa_what_is_the_missing_first_step data_files: - split: train path: wiqa_what_is_the_missing_first_step/train-* - split: validation path: wiqa_what_is_the_missing_first_step/validation-* - split: test path: wiqa_what_is_the_missing_first_step/test-* - config_name: wiqa_what_might_be_the_first_step_of_the_process data_files: - split: train path: wiqa_what_might_be_the_first_step_of_the_process/train-* - split: validation path: wiqa_what_might_be_the_first_step_of_the_process/validation-* - split: test path: wiqa_what_might_be_the_first_step_of_the_process/test-* - config_name: wiqa_what_might_be_the_last_step_of_the_process data_files: - split: train path: wiqa_what_might_be_the_last_step_of_the_process/train-* - split: validation path: wiqa_what_might_be_the_last_step_of_the_process/validation-* - split: test path: wiqa_what_might_be_the_last_step_of_the_process/test-* - config_name: wiqa_which_of_the_following_is_the_supposed_perturbation data_files: - split: train path: wiqa_which_of_the_following_is_the_supposed_perturbation/train-* - split: validation path: wiqa_which_of_the_following_is_the_supposed_perturbation/validation-* - split: test path: wiqa_which_of_the_following_is_the_supposed_perturbation/test-* - config_name: xsum_DOC_boils_down_to_simple_idea_that data_files: - split: train path: xsum_DOC_boils_down_to_simple_idea_that/train-* - split: validation path: xsum_DOC_boils_down_to_simple_idea_that/validation-* - split: test path: xsum_DOC_boils_down_to_simple_idea_that/test-* - config_name: xsum_DOC_given_above_write_one_sentence data_files: - split: train path: xsum_DOC_given_above_write_one_sentence/train-* - split: validation path: xsum_DOC_given_above_write_one_sentence/validation-* - split: test path: xsum_DOC_given_above_write_one_sentence/test-* - config_name: xsum_DOC_how_would_you_rephrase_few_words data_files: - split: train path: xsum_DOC_how_would_you_rephrase_few_words/train-* - split: validation path: xsum_DOC_how_would_you_rephrase_few_words/validation-* - split: test path: xsum_DOC_how_would_you_rephrase_few_words/test-* - config_name: xsum_DOC_tldr data_files: - split: train path: xsum_DOC_tldr/train-* - split: validation path: xsum_DOC_tldr/validation-* - split: test path: xsum_DOC_tldr/test-* - config_name: xsum_DOC_write_summary_of_above data_files: - split: train path: xsum_DOC_write_summary_of_above/train-* - split: validation path: xsum_DOC_write_summary_of_above/validation-* - split: test path: xsum_DOC_write_summary_of_above/test-* - config_name: xsum_article_DOC_summary data_files: - split: train path: xsum_article_DOC_summary/train-* - split: validation path: xsum_article_DOC_summary/validation-* - split: test path: xsum_article_DOC_summary/test-* - config_name: xsum_college_roommate_asked_DOC_so_I_recap data_files: - split: train path: xsum_college_roommate_asked_DOC_so_I_recap/train-* - split: validation path: xsum_college_roommate_asked_DOC_so_I_recap/validation-* - split: test path: xsum_college_roommate_asked_DOC_so_I_recap/test-* - config_name: xsum_read_below_DOC_write_abstract data_files: - split: train path: xsum_read_below_DOC_write_abstract/train-* - split: validation path: xsum_read_below_DOC_write_abstract/validation-* - split: test path: xsum_read_below_DOC_write_abstract/test-* - config_name: xsum_summarize_DOC data_files: - split: train path: xsum_summarize_DOC/train-* - split: validation path: xsum_summarize_DOC/validation-* - split: test path: xsum_summarize_DOC/test-* - config_name: xsum_summarize_this_DOC_summary data_files: - split: train path: xsum_summarize_this_DOC_summary/train-* - split: validation path: xsum_summarize_this_DOC_summary/validation-* - split: test path: xsum_summarize_this_DOC_summary/test-* - config_name: yelp_review_full_based_on_that data_files: - split: train path: yelp_review_full_based_on_that/train-* - split: test path: yelp_review_full_based_on_that/test-* - config_name: yelp_review_full_format_rating data_files: - split: train path: yelp_review_full_format_rating/train-* - split: test path: yelp_review_full_format_rating/test-* - config_name: yelp_review_full_format_score data_files: - split: train path: yelp_review_full_format_score/train-* - split: test path: yelp_review_full_format_score/test-* - config_name: yelp_review_full_format_star data_files: - split: train path: yelp_review_full_format_star/train-* - split: test path: yelp_review_full_format_star/test-* - config_name: yelp_review_full_on_a_scale data_files: - split: train path: yelp_review_full_on_a_scale/train-* - split: test path: yelp_review_full_on_a_scale/test-* - config_name: yelp_review_full_so_i_would data_files: - split: train path: yelp_review_full_so_i_would/train-* - split: test path: yelp_review_full_so_i_would/test-* - config_name: yelp_review_full_this_place data_files: - split: train path: yelp_review_full_this_place/train-* - split: test path: yelp_review_full_this_place/test-* --- # Dataset Card for P3 ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Additional Information](#additional-information) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://bigscience.huggingface.co/promptsource - **Repository:** https://github.com/bigscience-workshop/promptsource/ - **Paper:** [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://arxiv.org/abs/2110.08207) - **Point of Contact:** [Victor Sanh](mailto:[email protected]) ### Dataset Summary P3 (Public Pool of Prompts) is a collection of prompted English datasets covering a diverse set of NLP tasks. A prompt is the combination of an input template and a target template. The templates are functions mapping a data example into natural language for the input and target sequences. For example, in the case of an NLI dataset, the data example would include fields for *Premise, Hypothesis, Label*. An input template would be *If {Premise} is true, is it also true that {Hypothesis}?*, whereas a target template can be defined with the label choices *Choices[label]*. Here *Choices* is prompt-specific metadata that consists of the options *yes, maybe, no* corresponding to *label* being entailment (0), neutral (1) or contradiction (2). Prompts are collected using [Promptsource](https://github.com/bigscience-workshop/promptsource), an interface to interactively write prompts on datasets, and collect prompt-specific metadata such as evaluation metrics. As of October 13th, there are 2'000 prompts collected for 270+ data(sub)sets. The collection of prompts of P3 is publicly available on [Promptsource](https://github.com/bigscience-workshop/promptsource). To train [T0*](https://huggingface.co/bigscience/T0pp), we used a subset of the prompts available in Promptsource (see details [here](https://huggingface.co/bigscience/T0pp#training-data)). However, some of the prompts use `random.choice`, a method that selects uniformly at random an option in a list of valid possibilities. For reproducibility purposes, we release the collection of prompted examples used to train T0*. **The data available here are the materialized version of the prompted datasets used in [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://arxiv.org/abs/2110.08207) which represent only a subset of the datasets for which there is at least one prompt in Promptsource.** ### Supported Tasks and Leaderboards The tasks represented in P3 cover a diverse set of NLP tasks including multiple-choice QA, sentiment analysis or natural language inference. We detail the full list of datasets in [Source Data](#source-data). ### Languages The data in P3 are in English (BCP-47 `en`). ## Dataset Structure ### Data Instances An example of "train" looks as follows: ```bash { 'answer_choices': ['safe', 'trolley'], 'inputs': [86, 8, 7142, 666, 6, 405, 8, 3, 834, 1518, 21, 1346, 42, 31682, 58, 37, 3, 929, 9, 3042, 63, 2765, 808, 8, 2045, 6448, 326, 13, 8, 31682, 11, 3, 24052, 135, 16, 8, 1346, 552, 8, 3, 834, 47, 6364, 5], 'inputs_pretokenized': 'In the sentence below, does the _ stand for safe or trolley?\nThe treasury workers took the gold bars off of the trolley and stacked them in the safe until the _ was empty.', 'targets': [31682, 1], 'targets_pretokenized': '\ntrolley' } ``` In the case of rank classification (letting the model select its the prediction the option with the highest log-likelihood), an example looks as follows: ```bash { 'idx': [5, 0], 'inputs': [86, 8, 7142, 666, 6, 405, 8, 3, 834, 1518, 21, 19454, 42, 22227, 58, 19454, 744, 31, 17, 2112, 4553, 17742, 7, 12, 1953, 6, 298, 22227, 966, 373, 405, 5, 3, 834, 19, 72, 952, 12, 619, 16, 3, 9, 17742, 3298, 5], 'inputs_pretokenized': "In the sentence below, does the _ stand for Kyle or Logan?\nKyle doesn't wear leg warmers to bed, while Logan almost always does. _ is more likely to live in a warmer climate.", 'is_correct': True, 'targets': [19454, 1], 'targets_pretokenized': 'Kyle', 'weight': 1.0 } ``` To check all the prompted examples, you can use the [Promptsource hosted tool](http://bigscience.huggingface.co/promptsource) and choose the `Prompted dataset viewer` mode in the left panel. ### Data Fields The data fields are the same among all splits: - `answer_choices`: the choices (in natural language) available to the model - `inputs_pretokenized`: the natural language input fed to the model - `targets_pretokenized`: the natural language target that the model has to generate - `inputs`: the tokenized input with [T5](https://huggingface.co/google/t5-v1_1-base)'s tokenizer - `targets`: the tokenized target with [T5](https://huggingface.co/google/t5-v1_1-base)'s tokenizer - `idx`: identifier of the (example, answer_option_id) in the case of rank classification - `weight`: a weight for the example produced by seqio (always set to 1.0 in practise) - `is_correct`: whether the (example, answer_option_id) is the correct one ### Data Splits The list of data splits and their respective sizes is very long. You'll find the whole list in this [file](https://huggingface.co/datasets/bigscience/P3/blob/main/tasks_splits_and_features.py). ## Dataset Creation ### Curation Rationale The Public Pool of Prompts relies on the Hugging Face Dataset library. Any public dataset in the Datasets library can be prompted. We select the datasets that have at least one subset in English and excluded datasets containing (predominantly) non-natural language examples. We conservatively decided not to prompt datasets that contain potentially harmful content (for instance, datasets built on social media content). However, we sometimes prompt datasets that are purposefully built to measure bias and fairness of trained models, and reserve these prompted datasets (the validation or test sets) for evaluation purposes. ### Source Data Here's the full list of the datasets present in the materialized version of P3: - Multiple-Choice QA - CommonsenseQA - DREAM - QUAIL - QuaRTz - Social IQA - WiQA - Cosmos - QASC - Quarel - SciQ - Wiki Hop - ARC - OpenBookQA - MultiRC - PIQA - RACE - HellaSwag - BoolQ - Extractive QA - Adversarial QA - Quoref - DuoRC - ROPES - SQuAD v2 - ReCoRD - Close-book QA - Hotpot QA - Wiki QA - Trivia QA - Web Questions - Structure-to-text - Common Gen - Wiki Bio - Sentiment - Amazon - App Reviews - IMDB - Rotten Tomatoes - Yelp - Summarization - CNN Daily Mail - Gigaword - MultiNews - SamSum - XSum - Topic Classification - AG News - DBPedia - TREC - Paraphrase Identification - MRPC - PAWS - QQP - Natural Language Inference - ANLI - CB - RTE - Coreference Resolution - WSC - Winogrande - Word Sense disambiguation - WiC - Sentence Completion - COPA - HellaSwag - Story Cloze ### Annotations The prompts available in Promptsource are collected as part of BigScience, one-year long research workshop on large multilingual models and datasets. 36 contributors affiliated with 24 institutions in 8 countries participated to the prompt collection. Contributors are in majority machine learning researchers or machine learning engineers. The main annotation guideline was that prompts needed to be grammatical and understandable by a native English speaker with no prior experience of the tasks. Additionally, prompts that required explicit counting or numerical indexing were removed in favor of natural language variants, e.g., instead of predicting indices of a span to extract (e.g. in extractive question answering), the model was expected to copy the span's text instead. With these minimal constraints, prompt writers were encouraged to use both formal and creative prompts and various orderings of the data. Most of the prompts correspond directly to a version of the original proposed task, although we also allowed prompts that permuted the original task (for instance, generating a document from its summary) or allowed for ambiguous output (for instance, not indicating a list of available choices). The full annotation given to the contributors can be found [here](https://github.com/bigscience-workshop/promptsource/blob/main/CONTRIBUTING.md). *Note to self: the link is currently being updated with the) ## Additional Information ### Licensing Information The dataset is released under Apache 2.0. ### Citation Information ```bibtex @misc{sanh2021multitask, title={Multitask Prompted Training Enables Zero-Shot Task Generalization}, author={Victor Sanh and Albert Webson and Colin Raffel and Stephen H. Bach and Lintang Sutawika and Zaid Alyafeai and Antoine Chaffin and Arnaud Stiegler and Teven Le Scao and Arun Raja and Manan Dey and M Saiful Bari and Canwen Xu and Urmish Thakker and Shanya Sharma Sharma and Eliza Szczechla and Taewoon Kim and Gunjan Chhablani and Nihal Nayak and Debajyoti Datta and Jonathan Chang and Mike Tian-Jian Jiang and Han Wang and Matteo Manica and Sheng Shen and Zheng Xin Yong and Harshit Pandey and Rachel Bawden and Thomas Wang and Trishala Neeraj and Jos Rozen and Abheesht Sharma and Andrea Santilli and Thibault Fevry and Jason Alan Fries and Ryan Teehan and Stella Biderman and Leo Gao and Tali Bers and Thomas Wolf and Alexander M. Rush}, year={2021}, eprint={2110.08207}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` ### Contributions Thanks to the contributors of [promptsource](https://github.com/bigscience-workshop/promptsource/graphs/contributors) for adding this dataset.

huggingface/release-assets

huggingface

--- license: mit ---

HuggingFaceH4/MATH-500

HuggingFaceH4

--- task_categories: - text-generation language: - en pretty_name: MATH-500 --- # Dataset Card for MATH-500 <!-- Provide a quick summary of the dataset. --> This dataset contains a subset of 500 problems from the MATH benchmark that OpenAI created in their _Let's Verify Step by Step_ paper. See their GitHub repo for the source file: https://github.com/openai/prm800k/tree/main?tab=readme-ov-file#math-splits

MartinKu/wikipedia_stage2_coverage_20230402

MartinKu

--- dataset_info: features: - name: text dtype: string - name: S_V_position sequence: int64 - name: O_C_position sequence: int64 - name: start_point_list sequence: int64 splits: - name: train num_bytes: 113325298194 num_examples: 3295240 download_size: 33360668694 dataset_size: 113325298194 --- # Dataset Card for "wikipedia_stage2_coverage_20230402" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

hails/bigbench

hails

--- dataset_info: - config_name: abstract_narrative_understanding_zero_shot features: - name: idx dtype: int32 - name: inputs dtype: string - name: targets sequence: string - name: multiple_choice_targets sequence: string - name: multiple_choice_scores sequence: int32 splits: - name: default num_bytes: 6560069 num_examples: 3000 - name: train num_bytes: 5249819 num_examples: 2400 - name: validation num_bytes: 1310250 num_examples: 600 download_size: 0 dataset_size: 13120138 - config_name: anachronisms_zero_shot features: - name: idx dtype: int32 - name: inputs dtype: string - name: targets sequence: string - name: multiple_choice_targets sequence: string - name: multiple_choice_scores sequence: int32 splits: - name: default num_bytes: 48826 num_examples: 230 - name: train num_bytes: 39116 num_examples: 184 - name: validation num_bytes: 9710 num_examples: 46 download_size: 0 dataset_size: 97652 - config_name: analogical_similarity_zero_shot features: - name: idx dtype: int32 - name: inputs dtype: string - name: targets sequence: string - name: multiple_choice_targets sequence: string - name: multiple_choice_scores sequence: int32 splits: - name: default num_bytes: 1373815 num_examples: 323 - name: train num_bytes: 1101512 num_examples: 259 - name: validation num_bytes: 272303 num_examples: 64 download_size: 0 dataset_size: 2747630 - config_name: analytic_entailment_zero_shot features: - name: idx dtype: int32 - name: inputs dtype: string - name: targets sequence: string - name: multiple_choice_targets sequence: string - name: multiple_choice_scores sequence: int32 splits: - name: default num_bytes: 17316 num_examples: 70 - name: train num_bytes: 13368 num_examples: 54 - name: validation num_bytes: 3948 num_examples: 16 download_size: 0 dataset_size: 34632 - config_name: arithmetic_zero_shot features: - name: idx dtype: int32 - name: inputs dtype: string - name: targets sequence: string - name: multiple_choice_targets sequence: string - name: multiple_choice_scores sequence: int32 splits: - name: default num_bytes: 3833272 num_examples: 15023 - name: train num_bytes: 3066775 num_examples: 12019 - name: validation num_bytes: 766497 num_examples: 3004 download_size: 0 dataset_size: 7666544 - config_name: ascii_word_recognition_zero_shot features: - name: idx dtype: int32 - name: inputs dtype: string - name: targets sequence: string - name: multiple_choice_targets sequence: string - name: multiple_choice_scores sequence: int32 splits: - name: default num_bytes: 4984662 num_examples: 5000 - name: train num_bytes: 3997273 num_examples: 4000 - name: validation num_bytes: 987389 num_examples: 1000 download_size: 0 dataset_size: 9969324 - config_name: authorship_verification_zero_shot features: - name: idx dtype: int32 - name: inputs dtype: string - name: targets sequence: string - name: multiple_choice_targets sequence: string - name: multiple_choice_scores sequence: int32 splits: - name: default num_bytes: 14118592 num_examples: 880 - name: train num_bytes: 11288481 num_examples: 704 - 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config_name: scientific_press_release_zero_shot features: - name: idx dtype: int32 - name: inputs dtype: string - name: targets sequence: string - name: multiple_choice_targets sequence: string - name: multiple_choice_scores sequence: int32 splits: - name: default num_bytes: 13690 num_examples: 50 - name: train num_bytes: 9254 num_examples: 34 - name: validation num_bytes: 4436 num_examples: 16 download_size: 27293 dataset_size: 27380 - config_name: semantic_parsing_in_context_sparc_zero_shot features: - name: idx dtype: int32 - name: inputs dtype: string - name: targets sequence: string - name: multiple_choice_targets sequence: string - name: multiple_choice_scores sequence: int32 splits: - name: default num_bytes: 1524852 num_examples: 1155 - name: train num_bytes: 1248391 num_examples: 924 - name: validation num_bytes: 276461 num_examples: 231 download_size: 440326 dataset_size: 3049704 - config_name: semantic_parsing_spider_zero_shot features: - name: idx dtype: int32 - name: inputs dtype: string - name: targets sequence: string - name: multiple_choice_targets sequence: string - name: multiple_choice_scores sequence: int32 splits: - name: default num_bytes: 1265744 num_examples: 1034 - name: train num_bytes: 973864 num_examples: 828 - name: validation num_bytes: 291880 num_examples: 206 download_size: 358276 dataset_size: 2531488 - config_name: sentence_ambiguity_zero_shot features: - name: idx dtype: int32 - name: inputs dtype: string - name: targets sequence: string - name: multiple_choice_targets sequence: string - name: multiple_choice_scores sequence: int32 splits: - name: default num_bytes: 8168 num_examples: 60 - name: train num_bytes: 5976 num_examples: 44 - name: validation num_bytes: 2192 num_examples: 16 download_size: 18275 dataset_size: 16336 - config_name: similarities_abstraction_zero_shot features: - name: idx dtype: int32 - name: inputs dtype: string - name: targets sequence: string - name: multiple_choice_targets sequence: string - name: multiple_choice_scores sequence: int32 splits: - name: default num_bytes: 23416 num_examples: 76 - name: train num_bytes: 18545 num_examples: 60 - name: validation num_bytes: 4871 num_examples: 16 download_size: 31521 dataset_size: 46832 - config_name: simp_turing_concept_zero_shot features: - name: idx dtype: int32 - name: inputs dtype: string - name: targets sequence: string - name: multiple_choice_targets sequence: string - name: multiple_choice_scores sequence: int32 splits: - name: default num_bytes: 1017646 num_examples: 6390 - name: train num_bytes: 813220 num_examples: 5112 - name: validation num_bytes: 204426 num_examples: 1278 download_size: 402574 dataset_size: 2035292 - config_name: simple_arithmetic_json_multiple_choice_zero_shot features: - name: idx dtype: int32 - name: inputs dtype: string - name: targets sequence: string - name: multiple_choice_targets sequence: string - name: multiple_choice_scores sequence: int32 splits: - name: default num_bytes: 924 num_examples: 8 - name: train num_bytes: 0 num_examples: 0 - name: validation num_bytes: 0 num_examples: 0 download_size: 7777 dataset_size: 924 - config_name: simple_arithmetic_json_subtasks_zero_shot features: - name: idx dtype: int32 - name: inputs dtype: string - name: targets sequence: string - name: multiple_choice_targets sequence: string - name: multiple_choice_scores sequence: int32 splits: - name: default num_bytes: 1145 num_examples: 30 - name: train num_bytes: 571 num_examples: 15 - name: validation num_bytes: 574 num_examples: 15 download_size: 10460 dataset_size: 2290 - config_name: simple_arithmetic_json_zero_shot features: - name: idx dtype: int32 - name: inputs dtype: string - name: targets sequence: string - name: multiple_choice_targets sequence: string - name: multiple_choice_scores sequence: int32 splits: - name: default num_bytes: 1145 num_examples: 30 - name: train num_bytes: 540 num_examples: 14 - name: validation num_bytes: 605 num_examples: 16 download_size: 10645 dataset_size: 2290 - config_name: simple_arithmetic_multiple_targets_json_zero_shot features: - name: idx dtype: int32 - name: inputs dtype: string - name: targets sequence: string - name: multiple_choice_targets sequence: string - name: multiple_choice_scores sequence: int32 splits: - name: default num_bytes: 414 num_examples: 10 - name: train num_bytes: 0 num_examples: 0 - name: validation num_bytes: 0 num_examples: 0 download_size: 7352 dataset_size: 414 - config_name: simple_ethical_questions_zero_shot features: - name: idx dtype: int32 - name: inputs dtype: string - name: targets sequence: string - name: multiple_choice_targets sequence: string - name: multiple_choice_scores sequence: int32 splits: - name: default num_bytes: 76518 num_examples: 115 - name: train num_bytes: 60275 num_examples: 92 - name: validation num_bytes: 16243 num_examples: 23 download_size: 81285 dataset_size: 153036 - config_name: simple_text_editing_zero_shot features: - name: idx dtype: int32 - name: inputs dtype: string - name: targets sequence: string - name: multiple_choice_targets sequence: string - name: multiple_choice_scores sequence: int32 splits: - name: default num_bytes: 27865 num_examples: 47 - name: train num_bytes: 18469 num_examples: 31 - name: validation num_bytes: 9396 num_examples: 16 download_size: 27100 dataset_size: 55730 - config_name: snarks_zero_shot features: - name: idx dtype: int32 - name: inputs dtype: string - name: targets sequence: string - name: multiple_choice_targets sequence: string - name: multiple_choice_scores sequence: int32 splits: - name: default num_bytes: 45717 num_examples: 181 - name: train num_bytes: 36989 num_examples: 145 - name: validation num_bytes: 8728 num_examples: 36 download_size: 45434 dataset_size: 91434 - config_name: social_iqa_zero_shot features: - name: idx dtype: int32 - name: inputs dtype: string - name: targets sequence: string - name: multiple_choice_targets sequence: string - name: multiple_choice_scores sequence: int32 splits: - name: default num_bytes: 643162 num_examples: 1935 - name: train num_bytes: 515686 num_examples: 1548 - name: validation num_bytes: 127476 num_examples: 387 download_size: 684043 dataset_size: 1286324 - config_name: social_support_zero_shot features: - name: idx dtype: int32 - name: inputs dtype: string - name: targets sequence: string - name: multiple_choice_targets sequence: string - name: multiple_choice_scores sequence: int32 splits: - name: default num_bytes: 366705 num_examples: 897 - name: train num_bytes: 294793 num_examples: 718 - name: validation num_bytes: 71912 num_examples: 179 download_size: 288867 dataset_size: 733410 - config_name: sports_understanding_zero_shot features: - name: idx dtype: int32 - name: inputs dtype: string - name: targets sequence: string - name: multiple_choice_targets sequence: string - name: multiple_choice_scores sequence: int32 splits: - name: default num_bytes: 226654 num_examples: 986 - name: train num_bytes: 181328 num_examples: 789 - name: validation num_bytes: 45326 num_examples: 197 download_size: 82415 dataset_size: 453308 - config_name: strange_stories_zero_shot features: - name: idx dtype: int32 - name: inputs dtype: string - name: targets sequence: string - name: multiple_choice_targets sequence: string - name: multiple_choice_scores sequence: int32 splits: - name: default num_bytes: 120500 num_examples: 174 - name: train num_bytes: 98055 num_examples: 140 - name: validation num_bytes: 22445 num_examples: 34 download_size: 106428 dataset_size: 241000 - config_name: strategyqa_zero_shot features: - name: idx dtype: int32 - name: inputs dtype: string - name: targets sequence: string - name: multiple_choice_targets sequence: string - name: multiple_choice_scores sequence: int32 splits: - name: default num_bytes: 659967 num_examples: 2289 - name: train num_bytes: 527670 num_examples: 1832 - name: validation num_bytes: 132297 num_examples: 457 download_size: 814405 dataset_size: 1319934 - config_name: sufficient_information_zero_shot features: - name: idx dtype: int32 - name: inputs dtype: string - name: targets sequence: string - name: multiple_choice_targets sequence: string - name: multiple_choice_scores sequence: int32 splits: - name: default num_bytes: 9425 num_examples: 39 - name: train num_bytes: 5594 num_examples: 23 - name: validation num_bytes: 3831 num_examples: 16 download_size: 17766 dataset_size: 18850 - config_name: suicide_risk_zero_shot features: - name: idx dtype: int32 - name: inputs dtype: string - name: targets sequence: string - name: multiple_choice_targets sequence: string - name: multiple_choice_scores sequence: int32 splits: - name: default num_bytes: 37952 num_examples: 40 - name: train num_bytes: 23067 num_examples: 24 - name: validation num_bytes: 14885 num_examples: 16 download_size: 60518 dataset_size: 75904 - config_name: swahili_english_proverbs_zero_shot features: - name: idx dtype: int32 - name: inputs dtype: string - name: targets sequence: string - name: multiple_choice_targets sequence: string - name: multiple_choice_scores sequence: int32 splits: - name: default num_bytes: 90246 num_examples: 153 - name: train num_bytes: 72467 num_examples: 123 - name: validation num_bytes: 17779 num_examples: 30 download_size: 95186 dataset_size: 180492 - config_name: swedish_to_german_proverbs_zero_shot features: - name: idx dtype: int32 - name: inputs dtype: string - name: targets sequence: string - name: multiple_choice_targets sequence: string - name: multiple_choice_scores sequence: int32 splits: - name: default num_bytes: 35204 num_examples: 72 - name: train num_bytes: 27266 num_examples: 56 - name: validation num_bytes: 7938 num_examples: 16 download_size: 55102 dataset_size: 70408 - config_name: symbol_interpretation_zero_shot features: - name: idx dtype: int32 - name: inputs dtype: string - name: targets sequence: string - name: multiple_choice_targets sequence: string - name: multiple_choice_scores sequence: int32 splits: - name: default num_bytes: 1148958 num_examples: 990 - name: train num_bytes: 927326 num_examples: 795 - name: validation num_bytes: 221632 num_examples: 195 download_size: 320412 dataset_size: 2297916 - config_name: temporal_sequences_zero_shot features: - name: idx dtype: int32 - name: inputs dtype: string - name: targets sequence: string - name: multiple_choice_targets sequence: string - name: multiple_choice_scores sequence: int32 splits: - name: default num_bytes: 687086 num_examples: 1000 - name: train num_bytes: 549808 num_examples: 800 - name: validation num_bytes: 137278 num_examples: 200 download_size: 295316 dataset_size: 1374172 - config_name: tense_zero_shot features: - name: idx dtype: int32 - name: inputs dtype: string - name: targets sequence: string - name: multiple_choice_targets sequence: string - name: multiple_choice_scores sequence: int32 splits: - name: default num_bytes: 43882 num_examples: 286 - name: train num_bytes: 35466 num_examples: 229 - name: validation num_bytes: 8416 num_examples: 57 download_size: 51466 dataset_size: 87764 - config_name: timedial_zero_shot features: - name: idx dtype: int32 - name: inputs dtype: string - name: targets sequence: string - name: multiple_choice_targets sequence: string - name: multiple_choice_scores sequence: int32 splits: - name: default num_bytes: 2763178 num_examples: 2550 - name: train num_bytes: 2217190 num_examples: 2040 - name: validation num_bytes: 545988 num_examples: 510 download_size: 2444115 dataset_size: 5526356 - config_name: topical_chat_zero_shot features: - name: idx dtype: int32 - name: inputs dtype: string - name: targets sequence: string - name: multiple_choice_targets sequence: string - name: multiple_choice_scores sequence: int32 splits: - name: default num_bytes: 30927758 num_examples: 22295 - name: train num_bytes: 24827254 num_examples: 17836 - name: validation num_bytes: 6100504 num_examples: 4459 download_size: 23505731 dataset_size: 61855516 - config_name: tracking_shuffled_objects_zero_shot features: - name: idx dtype: int32 - name: inputs dtype: string - name: targets sequence: string - name: multiple_choice_targets sequence: string - name: multiple_choice_scores sequence: int32 splits: - name: default num_bytes: 2775972 num_examples: 3750 - name: train num_bytes: 2224037 num_examples: 3000 - name: validation num_bytes: 551935 num_examples: 750 download_size: 738413 dataset_size: 5551944 - config_name: understanding_fables_zero_shot features: - name: idx dtype: int32 - name: inputs dtype: string - name: targets sequence: string - name: multiple_choice_targets sequence: string - name: multiple_choice_scores sequence: int32 splits: - name: default num_bytes: 227748 num_examples: 189 - name: train num_bytes: 181000 num_examples: 152 - name: validation num_bytes: 46748 num_examples: 37 download_size: 237036 dataset_size: 455496 - config_name: undo_permutation_zero_shot features: - name: idx dtype: int32 - name: inputs dtype: string - name: targets sequence: string - name: multiple_choice_targets sequence: string - name: multiple_choice_scores sequence: int32 splits: - name: default num_bytes: 196118 num_examples: 300 - name: train num_bytes: 158562 num_examples: 240 - name: validation num_bytes: 37556 num_examples: 60 download_size: 137204 dataset_size: 392236 - config_name: unit_conversion_zero_shot features: - name: idx dtype: int32 - name: inputs dtype: string - name: targets sequence: string - name: multiple_choice_targets sequence: string - name: multiple_choice_scores sequence: int32 splits: - name: default num_bytes: 4028628 num_examples: 23936 - name: train num_bytes: 3230357 num_examples: 19151 - name: validation num_bytes: 798271 num_examples: 4785 download_size: 3208622 dataset_size: 8057256 - config_name: unit_interpretation_zero_shot features: - name: idx dtype: int32 - name: inputs dtype: string - name: targets sequence: string - name: multiple_choice_targets sequence: string - name: multiple_choice_scores sequence: int32 splits: - name: default num_bytes: 37363 num_examples: 100 - name: train num_bytes: 29939 num_examples: 80 - name: validation num_bytes: 7424 num_examples: 20 download_size: 34926 dataset_size: 74726 - config_name: unnatural_in_context_learning_zero_shot features: - name: idx dtype: int32 - name: inputs dtype: string - name: targets sequence: string - name: multiple_choice_targets sequence: string - name: multiple_choice_scores sequence: int32 splits: - name: default num_bytes: 4599760 num_examples: 73420 - name: train num_bytes: 3679822 num_examples: 58736 - name: validation num_bytes: 919938 num_examples: 14684 download_size: 3840657 dataset_size: 9199520 - config_name: vitaminc_fact_verification_zero_shot features: - name: idx dtype: int32 - name: inputs dtype: string - name: targets sequence: string - name: multiple_choice_targets sequence: string - name: multiple_choice_scores sequence: int32 splits: - name: default num_bytes: 32361818 num_examples: 54668 - name: train num_bytes: 25889850 num_examples: 43735 - name: validation num_bytes: 6471968 num_examples: 10933 download_size: 14264790 dataset_size: 64723636 - config_name: what_is_the_tao_zero_shot features: - name: idx dtype: int32 - name: inputs dtype: string - name: targets sequence: string - name: multiple_choice_targets sequence: string - name: multiple_choice_scores sequence: int32 splits: - name: default num_bytes: 13268 num_examples: 36 - name: train num_bytes: 7435 num_examples: 20 - name: validation num_bytes: 5833 num_examples: 16 download_size: 27585 dataset_size: 26536 - config_name: which_wiki_edit_zero_shot features: - name: idx dtype: int32 - name: inputs dtype: string - name: targets sequence: string - name: multiple_choice_targets sequence: string - name: multiple_choice_scores sequence: int32 splits: - name: default num_bytes: 6331683 num_examples: 571 - name: train num_bytes: 5233870 num_examples: 457 - name: validation num_bytes: 1097813 num_examples: 114 download_size: 3914574 dataset_size: 12663366 - config_name: winowhy_zero_shot features: - name: idx dtype: int32 - name: inputs dtype: string - name: targets sequence: string - name: multiple_choice_targets sequence: string - name: multiple_choice_scores sequence: int32 splits: - name: default num_bytes: 1002434 num_examples: 2862 - name: train num_bytes: 800520 num_examples: 2290 - name: validation num_bytes: 201914 num_examples: 572 download_size: 449218 dataset_size: 2004868 - config_name: word_sorting_zero_shot features: - name: idx dtype: int32 - name: inputs dtype: string - name: targets sequence: string - name: multiple_choice_targets sequence: string - name: multiple_choice_scores sequence: int32 splits: - name: default num_bytes: 491054 num_examples: 1900 - name: train num_bytes: 392738 num_examples: 1520 - name: validation num_bytes: 98316 num_examples: 380 download_size: 641536 dataset_size: 982108 - config_name: word_unscrambling_zero_shot features: - name: idx dtype: int32 - name: inputs dtype: string - name: targets sequence: string - name: multiple_choice_targets sequence: string - name: multiple_choice_scores sequence: int32 splits: - name: default num_bytes: 882364 num_examples: 8917 - name: train num_bytes: 705755 num_examples: 7134 - name: validation num_bytes: 176609 num_examples: 1783 download_size: 563799 dataset_size: 1764728 configs: - config_name: abstract_narrative_understanding_zero_shot data_files: - split: default path: abstract_narrative_understanding_zero_shot/default-* - split: train path: abstract_narrative_understanding_zero_shot/train-* - split: validation path: abstract_narrative_understanding_zero_shot/validation-* - config_name: anachronisms_zero_shot data_files: - split: default path: anachronisms_zero_shot/default-* - split: train path: anachronisms_zero_shot/train-* - split: validation path: anachronisms_zero_shot/validation-* - config_name: analogical_similarity_zero_shot data_files: - split: default path: analogical_similarity_zero_shot/default-* - split: train path: analogical_similarity_zero_shot/train-* - split: validation path: analogical_similarity_zero_shot/validation-* - config_name: analytic_entailment_zero_shot data_files: - split: default path: analytic_entailment_zero_shot/default-* - split: train path: analytic_entailment_zero_shot/train-* - split: validation path: analytic_entailment_zero_shot/validation-* - config_name: arithmetic_zero_shot data_files: - split: default path: arithmetic_zero_shot/default-* - split: train path: arithmetic_zero_shot/train-* - split: validation path: arithmetic_zero_shot/validation-* - config_name: ascii_word_recognition_zero_shot data_files: - split: default path: ascii_word_recognition_zero_shot/default-* - split: train path: ascii_word_recognition_zero_shot/train-* - split: validation path: ascii_word_recognition_zero_shot/validation-* - config_name: authorship_verification_zero_shot data_files: - split: default path: authorship_verification_zero_shot/default-* - split: train path: authorship_verification_zero_shot/train-* - split: validation path: authorship_verification_zero_shot/validation-* - config_name: auto_categorization_zero_shot data_files: - split: default path: auto_categorization_zero_shot/default-* - split: train path: auto_categorization_zero_shot/train-* - split: validation path: auto_categorization_zero_shot/validation-* - config_name: auto_debugging_zero_shot data_files: - split: default path: auto_debugging_zero_shot/default-* - split: train path: auto_debugging_zero_shot/train-* - split: validation path: auto_debugging_zero_shot/validation-* - config_name: bbq_lite_json_zero_shot data_files: - split: default path: bbq_lite_json_zero_shot/default-* - split: train path: bbq_lite_json_zero_shot/train-* - split: validation path: bbq_lite_json_zero_shot/validation-* - config_name: bridging_anaphora_resolution_barqa_zero_shot data_files: - split: default path: bridging_anaphora_resolution_barqa_zero_shot/default-* - split: train path: bridging_anaphora_resolution_barqa_zero_shot/train-* - split: validation path: bridging_anaphora_resolution_barqa_zero_shot/validation-* - config_name: causal_judgment_zero_shot data_files: - split: default path: causal_judgment_zero_shot/default-* - split: train path: causal_judgment_zero_shot/train-* - split: validation path: causal_judgment_zero_shot/validation-* - config_name: cause_and_effect_zero_shot data_files: - split: default path: cause_and_effect_zero_shot/default-* - split: train path: cause_and_effect_zero_shot/train-* - split: validation path: cause_and_effect_zero_shot/validation-* - config_name: checkmate_in_one_zero_shot data_files: - split: default path: checkmate_in_one_zero_shot/default-* - split: train path: checkmate_in_one_zero_shot/train-* - split: validation path: checkmate_in_one_zero_shot/validation-* - config_name: chess_state_tracking_zero_shot data_files: - split: default path: chess_state_tracking_zero_shot/default-* - split: train path: chess_state_tracking_zero_shot/train-* - split: validation path: chess_state_tracking_zero_shot/validation-* - config_name: chinese_remainder_theorem_zero_shot data_files: - split: default path: chinese_remainder_theorem_zero_shot/default-* - split: train path: chinese_remainder_theorem_zero_shot/train-* - split: validation path: chinese_remainder_theorem_zero_shot/validation-* - config_name: cifar10_classification_zero_shot data_files: - split: default path: cifar10_classification_zero_shot/default-* - split: train path: cifar10_classification_zero_shot/train-* - split: validation path: cifar10_classification_zero_shot/validation-* - config_name: code_line_description_zero_shot data_files: - split: default path: code_line_description_zero_shot/default-* - split: train path: code_line_description_zero_shot/train-* - split: validation path: code_line_description_zero_shot/validation-* - config_name: codenames_zero_shot data_files: - split: default path: codenames_zero_shot/default-* - split: train path: codenames_zero_shot/train-* - split: validation path: codenames_zero_shot/validation-* - config_name: color_zero_shot data_files: - split: default path: color_zero_shot/default-* - split: train path: color_zero_shot/train-* - split: validation path: color_zero_shot/validation-* - config_name: common_morpheme_zero_shot data_files: - split: default path: common_morpheme_zero_shot/default-* - split: train path: common_morpheme_zero_shot/train-* - split: validation path: common_morpheme_zero_shot/validation-* - config_name: conceptual_combinations_zero_shot data_files: - split: default path: conceptual_combinations_zero_shot/default-* - split: train path: conceptual_combinations_zero_shot/train-* - split: validation path: conceptual_combinations_zero_shot/validation-* - config_name: conlang_translation_zero_shot data_files: - split: default path: conlang_translation_zero_shot/default-* - split: train path: conlang_translation_zero_shot/train-* - split: validation path: conlang_translation_zero_shot/validation-* - config_name: contextual_parametric_knowledge_conflicts_zero_shot data_files: - split: default path: contextual_parametric_knowledge_conflicts_zero_shot/default-* - split: train path: contextual_parametric_knowledge_conflicts_zero_shot/train-* - split: validation path: contextual_parametric_knowledge_conflicts_zero_shot/validation-* - config_name: crash_blossom_zero_shot data_files: - split: default path: crash_blossom_zero_shot/default-* - split: train path: crash_blossom_zero_shot/train-* - split: validation path: crash_blossom_zero_shot/validation-* - config_name: crass_ai_zero_shot data_files: - split: default path: crass_ai_zero_shot/default-* - split: train path: crass_ai_zero_shot/train-* - split: validation path: crass_ai_zero_shot/validation-* - config_name: cryobiology_spanish_zero_shot data_files: - split: default path: cryobiology_spanish_zero_shot/default-* - split: train path: cryobiology_spanish_zero_shot/train-* - split: validation path: cryobiology_spanish_zero_shot/validation-* - config_name: cryptonite_zero_shot data_files: - split: default path: cryptonite_zero_shot/default-* - split: train path: cryptonite_zero_shot/train-* - split: validation path: cryptonite_zero_shot/validation-* - config_name: cs_algorithms_zero_shot data_files: - split: default path: cs_algorithms_zero_shot/default-* - split: train path: cs_algorithms_zero_shot/train-* - split: validation path: cs_algorithms_zero_shot/validation-* - config_name: dark_humor_detection_zero_shot data_files: - split: default path: dark_humor_detection_zero_shot/default-* - split: train path: dark_humor_detection_zero_shot/train-* - split: validation path: dark_humor_detection_zero_shot/validation-* - config_name: date_understanding_zero_shot data_files: - split: default path: date_understanding_zero_shot/default-* - split: train path: date_understanding_zero_shot/train-* - split: validation path: date_understanding_zero_shot/validation-* - config_name: disambiguation_qa_zero_shot data_files: - split: default path: disambiguation_qa_zero_shot/default-* - split: train path: disambiguation_qa_zero_shot/train-* - split: validation path: disambiguation_qa_zero_shot/validation-* - config_name: discourse_marker_prediction_zero_shot data_files: - split: default path: discourse_marker_prediction_zero_shot/default-* - split: train path: discourse_marker_prediction_zero_shot/train-* - split: validation path: discourse_marker_prediction_zero_shot/validation-* - config_name: disfl_qa_zero_shot data_files: - split: default path: disfl_qa_zero_shot/default-* - split: train path: disfl_qa_zero_shot/train-* - split: validation path: disfl_qa_zero_shot/validation-* - config_name: dyck_languages_zero_shot data_files: - split: default path: dyck_languages_zero_shot/default-* - split: train path: dyck_languages_zero_shot/train-* - split: validation path: dyck_languages_zero_shot/validation-* - config_name: elementary_math_qa_zero_shot data_files: - split: default path: elementary_math_qa_zero_shot/default-* - split: train path: elementary_math_qa_zero_shot/train-* - split: validation path: elementary_math_qa_zero_shot/validation-* - config_name: emoji_movie_zero_shot data_files: - split: default path: emoji_movie_zero_shot/default-* - split: train path: emoji_movie_zero_shot/train-* - split: validation path: emoji_movie_zero_shot/validation-* - config_name: emojis_emotion_prediction_zero_shot data_files: - split: default path: emojis_emotion_prediction_zero_shot/default-* - split: train path: emojis_emotion_prediction_zero_shot/train-* - split: validation path: emojis_emotion_prediction_zero_shot/validation-* - config_name: empirical_judgments_zero_shot data_files: - split: default path: empirical_judgments_zero_shot/default-* - split: train path: empirical_judgments_zero_shot/train-* - split: validation path: empirical_judgments_zero_shot/validation-* - config_name: english_proverbs_zero_shot data_files: - split: default path: english_proverbs_zero_shot/default-* - split: train path: english_proverbs_zero_shot/train-* - split: validation path: english_proverbs_zero_shot/validation-* - config_name: english_russian_proverbs_zero_shot data_files: - split: default path: english_russian_proverbs_zero_shot/default-* - split: train path: english_russian_proverbs_zero_shot/train-* - split: validation path: english_russian_proverbs_zero_shot/validation-* - config_name: entailed_polarity_hindi_zero_shot data_files: - split: default path: entailed_polarity_hindi_zero_shot/default-* - split: train path: entailed_polarity_hindi_zero_shot/train-* - split: validation path: entailed_polarity_hindi_zero_shot/validation-* - config_name: entailed_polarity_zero_shot data_files: - split: default path: entailed_polarity_zero_shot/default-* - split: train path: entailed_polarity_zero_shot/train-* - split: validation path: entailed_polarity_zero_shot/validation-* - config_name: epistemic_reasoning_zero_shot data_files: - split: default path: epistemic_reasoning_zero_shot/default-* - split: train path: epistemic_reasoning_zero_shot/train-* - split: validation path: epistemic_reasoning_zero_shot/validation-* - config_name: evaluating_information_essentiality_zero_shot data_files: - split: default path: evaluating_information_essentiality_zero_shot/default-* - split: train path: evaluating_information_essentiality_zero_shot/train-* - split: validation path: evaluating_information_essentiality_zero_shot/validation-* - config_name: fact_checker_zero_shot data_files: - split: default path: fact_checker_zero_shot/default-* - split: train path: fact_checker_zero_shot/train-* - split: validation path: fact_checker_zero_shot/validation-* - config_name: fantasy_reasoning_zero_shot data_files: - split: default path: fantasy_reasoning_zero_shot/default-* - split: train path: fantasy_reasoning_zero_shot/train-* - split: validation path: fantasy_reasoning_zero_shot/validation-* - config_name: few_shot_nlg_zero_shot data_files: - split: default path: few_shot_nlg_zero_shot/default-* - split: train path: few_shot_nlg_zero_shot/train-* - split: validation path: few_shot_nlg_zero_shot/validation-* - config_name: figure_of_speech_detection_zero_shot data_files: - split: default path: figure_of_speech_detection_zero_shot/default-* - split: train path: figure_of_speech_detection_zero_shot/train-* - split: validation path: figure_of_speech_detection_zero_shot/validation-* - config_name: formal_fallacies_syllogisms_negation_zero_shot data_files: - split: default path: formal_fallacies_syllogisms_negation_zero_shot/default-* - split: train path: formal_fallacies_syllogisms_negation_zero_shot/train-* - split: validation path: formal_fallacies_syllogisms_negation_zero_shot/validation-* - config_name: gem_zero_shot data_files: - split: default path: gem_zero_shot/default-* - split: train path: gem_zero_shot/train-* - split: validation path: gem_zero_shot/validation-* - config_name: gender_inclusive_sentences_german_zero_shot data_files: - split: default path: gender_inclusive_sentences_german_zero_shot/default-* - split: train path: gender_inclusive_sentences_german_zero_shot/train-* - split: validation path: gender_inclusive_sentences_german_zero_shot/validation-* - config_name: general_knowledge_zero_shot data_files: - split: default path: general_knowledge_zero_shot/default-* - split: train path: general_knowledge_zero_shot/train-* - split: validation path: general_knowledge_zero_shot/validation-* - config_name: geometric_shapes_zero_shot data_files: - split: default path: geometric_shapes_zero_shot/default-* - split: train path: geometric_shapes_zero_shot/train-* - split: validation path: geometric_shapes_zero_shot/validation-* - config_name: goal_step_wikihow_zero_shot data_files: - split: default path: goal_step_wikihow_zero_shot/default-* - split: train path: goal_step_wikihow_zero_shot/train-* - split: validation path: goal_step_wikihow_zero_shot/validation-* - config_name: gre_reading_comprehension_zero_shot data_files: - split: default path: gre_reading_comprehension_zero_shot/default-* - split: train path: gre_reading_comprehension_zero_shot/train-* - split: validation path: gre_reading_comprehension_zero_shot/validation-* - config_name: hhh_alignment_zero_shot data_files: - split: default path: hhh_alignment_zero_shot/default-* - split: train path: hhh_alignment_zero_shot/train-* - split: validation path: hhh_alignment_zero_shot/validation-* - config_name: hindi_question_answering_zero_shot data_files: - split: default path: hindi_question_answering_zero_shot/default-* - split: train path: hindi_question_answering_zero_shot/train-* - split: validation path: hindi_question_answering_zero_shot/validation-* - config_name: hindu_knowledge_zero_shot data_files: - split: default path: hindu_knowledge_zero_shot/default-* - split: train path: hindu_knowledge_zero_shot/train-* - split: validation path: hindu_knowledge_zero_shot/validation-* - config_name: hinglish_toxicity_zero_shot data_files: - split: default path: hinglish_toxicity_zero_shot/default-* - split: train path: hinglish_toxicity_zero_shot/train-* - split: validation path: hinglish_toxicity_zero_shot/validation-* - config_name: human_organs_senses_zero_shot data_files: - split: default path: human_organs_senses_zero_shot/default-* - split: train path: human_organs_senses_zero_shot/train-* - split: validation path: human_organs_senses_zero_shot/validation-* - config_name: hyperbaton_zero_shot data_files: - split: default path: hyperbaton_zero_shot/default-* - split: train path: hyperbaton_zero_shot/train-* - split: validation path: hyperbaton_zero_shot/validation-* - config_name: identify_math_theorems_zero_shot data_files: - split: default path: identify_math_theorems_zero_shot/default-* - split: train path: identify_math_theorems_zero_shot/train-* - split: validation path: identify_math_theorems_zero_shot/validation-* - config_name: identify_odd_metaphor_zero_shot data_files: - split: default path: identify_odd_metaphor_zero_shot/default-* - split: train path: identify_odd_metaphor_zero_shot/train-* - split: validation path: identify_odd_metaphor_zero_shot/validation-* - config_name: implicatures_zero_shot data_files: - split: default path: implicatures_zero_shot/default-* - split: train path: implicatures_zero_shot/train-* - split: validation path: implicatures_zero_shot/validation-* - config_name: implicit_relations_zero_shot data_files: - split: default path: implicit_relations_zero_shot/default-* - split: train path: implicit_relations_zero_shot/train-* - split: validation path: implicit_relations_zero_shot/validation-* - config_name: intent_recognition_zero_shot data_files: - split: default path: intent_recognition_zero_shot/default-* - split: train path: intent_recognition_zero_shot/train-* - split: validation path: intent_recognition_zero_shot/validation-* - config_name: international_phonetic_alphabet_nli_zero_shot data_files: - split: default path: international_phonetic_alphabet_nli_zero_shot/default-* - split: train path: international_phonetic_alphabet_nli_zero_shot/train-* - split: validation path: international_phonetic_alphabet_nli_zero_shot/validation-* - config_name: international_phonetic_alphabet_transliterate_zero_shot data_files: - split: default path: international_phonetic_alphabet_transliterate_zero_shot/default-* - split: train path: international_phonetic_alphabet_transliterate_zero_shot/train-* - split: validation path: international_phonetic_alphabet_transliterate_zero_shot/validation-* - config_name: intersect_geometry_zero_shot data_files: - split: default path: intersect_geometry_zero_shot/default-* - split: train path: intersect_geometry_zero_shot/train-* - split: validation path: intersect_geometry_zero_shot/validation-* - config_name: irony_identification_zero_shot data_files: - split: default path: irony_identification_zero_shot/default-* - split: train path: irony_identification_zero_shot/train-* - split: validation path: irony_identification_zero_shot/validation-* - config_name: kanji_ascii_zero_shot data_files: - split: default path: kanji_ascii_zero_shot/default-* - split: train path: kanji_ascii_zero_shot/train-* - split: validation path: kanji_ascii_zero_shot/validation-* - config_name: kannada_zero_shot data_files: - split: default path: kannada_zero_shot/default-* - split: train path: kannada_zero_shot/train-* - split: validation path: kannada_zero_shot/validation-* - config_name: key_value_maps_zero_shot data_files: - split: default path: key_value_maps_zero_shot/default-* - split: train path: key_value_maps_zero_shot/train-* - split: validation path: key_value_maps_zero_shot/validation-* - config_name: known_unknowns_zero_shot data_files: - split: default path: known_unknowns_zero_shot/default-* - split: train path: known_unknowns_zero_shot/train-* - split: validation path: known_unknowns_zero_shot/validation-* - config_name: language_games_zero_shot data_files: - split: default path: language_games_zero_shot/default-* - split: train path: language_games_zero_shot/train-* - split: validation path: language_games_zero_shot/validation-* - config_name: language_identification_zero_shot data_files: - split: default path: language_identification_zero_shot/default-* - split: train path: language_identification_zero_shot/train-* - split: validation path: language_identification_zero_shot/validation-* - config_name: linguistic_mappings_zero_shot data_files: - split: default path: linguistic_mappings_zero_shot/default-* - split: train path: linguistic_mappings_zero_shot/train-* - split: validation path: linguistic_mappings_zero_shot/validation-* - config_name: linguistics_puzzles_zero_shot data_files: - split: default path: linguistics_puzzles_zero_shot/default-* - split: train path: linguistics_puzzles_zero_shot/train-* - split: validation path: linguistics_puzzles_zero_shot/validation-* - config_name: list_functions_zero_shot data_files: - split: default path: list_functions_zero_shot/default-* - split: train path: list_functions_zero_shot/train-* - split: validation path: list_functions_zero_shot/validation-* - config_name: logic_grid_puzzle_zero_shot data_files: - split: default path: logic_grid_puzzle_zero_shot/default-* - split: train path: logic_grid_puzzle_zero_shot/train-* - split: validation path: logic_grid_puzzle_zero_shot/validation-* - config_name: logical_args_zero_shot data_files: - split: default path: logical_args_zero_shot/default-* - split: train path: logical_args_zero_shot/train-* - split: validation path: logical_args_zero_shot/validation-* - config_name: logical_deduction_zero_shot data_files: - split: default path: logical_deduction_zero_shot/default-* - split: train path: logical_deduction_zero_shot/train-* - split: validation path: logical_deduction_zero_shot/validation-* - config_name: logical_fallacy_detection_zero_shot data_files: - split: default path: logical_fallacy_detection_zero_shot/default-* - split: train path: logical_fallacy_detection_zero_shot/train-* - split: validation path: logical_fallacy_detection_zero_shot/validation-* - config_name: logical_sequence_zero_shot data_files: - split: default path: logical_sequence_zero_shot/default-* - split: train path: logical_sequence_zero_shot/train-* - split: validation path: logical_sequence_zero_shot/validation-* - config_name: mathematical_induction_zero_shot data_files: - split: default path: mathematical_induction_zero_shot/default-* - split: train path: mathematical_induction_zero_shot/train-* - split: validation path: mathematical_induction_zero_shot/validation-* - config_name: matrixshapes_zero_shot data_files: - split: default path: matrixshapes_zero_shot/default-* - split: train path: matrixshapes_zero_shot/train-* - split: validation path: matrixshapes_zero_shot/validation-* - config_name: metaphor_boolean_zero_shot data_files: - split: default path: metaphor_boolean_zero_shot/default-* - split: train path: metaphor_boolean_zero_shot/train-* - split: validation path: metaphor_boolean_zero_shot/validation-* - config_name: metaphor_understanding_zero_shot data_files: - split: default path: metaphor_understanding_zero_shot/default-* - split: train path: metaphor_understanding_zero_shot/train-* - split: validation path: metaphor_understanding_zero_shot/validation-* - config_name: minute_mysteries_qa_zero_shot data_files: - split: default path: minute_mysteries_qa_zero_shot/default-* - split: train path: minute_mysteries_qa_zero_shot/train-* - split: validation path: minute_mysteries_qa_zero_shot/validation-* - config_name: misconceptions_russian_zero_shot data_files: - split: default path: misconceptions_russian_zero_shot/default-* - split: train path: misconceptions_russian_zero_shot/train-* - split: validation path: misconceptions_russian_zero_shot/validation-* - config_name: misconceptions_zero_shot data_files: - split: default path: misconceptions_zero_shot/default-* - split: train path: misconceptions_zero_shot/train-* - split: validation path: misconceptions_zero_shot/validation-* - config_name: mnist_ascii_zero_shot data_files: - split: default path: mnist_ascii_zero_shot/default-* - split: train path: mnist_ascii_zero_shot/train-* - split: validation path: mnist_ascii_zero_shot/validation-* - config_name: modified_arithmetic_zero_shot data_files: - split: default path: modified_arithmetic_zero_shot/default-* - split: train path: modified_arithmetic_zero_shot/train-* - split: validation path: modified_arithmetic_zero_shot/validation-* - config_name: moral_permissibility_zero_shot data_files: - split: default path: moral_permissibility_zero_shot/default-* - split: train path: moral_permissibility_zero_shot/train-* - split: validation path: moral_permissibility_zero_shot/validation-* - config_name: movie_dialog_same_or_different_zero_shot data_files: - split: default path: movie_dialog_same_or_different_zero_shot/default-* - split: train path: movie_dialog_same_or_different_zero_shot/train-* - split: validation path: movie_dialog_same_or_different_zero_shot/validation-* - config_name: movie_recommendation_zero_shot data_files: - split: default path: movie_recommendation_zero_shot/default-* - split: train path: movie_recommendation_zero_shot/train-* - split: validation path: movie_recommendation_zero_shot/validation-* - config_name: mult_data_wrangling_zero_shot data_files: - split: default path: mult_data_wrangling_zero_shot/default-* - split: train path: mult_data_wrangling_zero_shot/train-* - split: validation path: mult_data_wrangling_zero_shot/validation-* - config_name: multiemo_zero_shot data_files: - split: default path: multiemo_zero_shot/default-* - split: train path: multiemo_zero_shot/train-* - split: validation path: multiemo_zero_shot/validation-* - config_name: natural_instructions_zero_shot data_files: - split: default path: natural_instructions_zero_shot/default-* - split: train path: natural_instructions_zero_shot/train-* - split: validation path: natural_instructions_zero_shot/validation-* - config_name: navigate_zero_shot data_files: - split: default path: navigate_zero_shot/default-* - split: train path: navigate_zero_shot/train-* - split: validation path: navigate_zero_shot/validation-* - config_name: nonsense_words_grammar_zero_shot data_files: - split: default path: nonsense_words_grammar_zero_shot/default-* - split: train path: nonsense_words_grammar_zero_shot/train-* - split: validation path: nonsense_words_grammar_zero_shot/validation-* - config_name: novel_concepts_zero_shot data_files: - split: default path: novel_concepts_zero_shot/default-* - split: train path: novel_concepts_zero_shot/train-* - split: validation path: novel_concepts_zero_shot/validation-* - config_name: object_counting_zero_shot data_files: - split: default path: object_counting_zero_shot/default-* - split: train path: object_counting_zero_shot/train-* - split: validation path: object_counting_zero_shot/validation-* - config_name: odd_one_out_zero_shot data_files: - split: default path: odd_one_out_zero_shot/default-* - split: train path: odd_one_out_zero_shot/train-* - split: validation path: odd_one_out_zero_shot/validation-* - config_name: operators_zero_shot data_files: - split: default path: operators_zero_shot/default-* - split: train path: operators_zero_shot/train-* - split: validation path: operators_zero_shot/validation-* - config_name: paragraph_segmentation_zero_shot data_files: - split: default path: paragraph_segmentation_zero_shot/default-* - split: train path: paragraph_segmentation_zero_shot/train-* - split: validation path: paragraph_segmentation_zero_shot/validation-* - config_name: parsinlu_qa_zero_shot data_files: - split: default path: parsinlu_qa_zero_shot/default-* - split: train path: parsinlu_qa_zero_shot/train-* - split: validation path: parsinlu_qa_zero_shot/validation-* - config_name: parsinlu_reading_comprehension_zero_shot data_files: - split: default path: parsinlu_reading_comprehension_zero_shot/default-* - split: train path: parsinlu_reading_comprehension_zero_shot/train-* - split: validation path: parsinlu_reading_comprehension_zero_shot/validation-* - config_name: penguins_in_a_table_zero_shot data_files: - split: default path: penguins_in_a_table_zero_shot/default-* - split: train path: penguins_in_a_table_zero_shot/train-* - split: validation path: penguins_in_a_table_zero_shot/validation-* - config_name: periodic_elements_zero_shot data_files: - split: default path: periodic_elements_zero_shot/default-* - split: train path: periodic_elements_zero_shot/train-* - split: validation path: periodic_elements_zero_shot/validation-* - config_name: persian_idioms_zero_shot data_files: - split: default path: persian_idioms_zero_shot/default-* - split: train path: persian_idioms_zero_shot/train-* - split: validation path: persian_idioms_zero_shot/validation-* - config_name: phrase_relatedness_zero_shot data_files: - split: default path: phrase_relatedness_zero_shot/default-* - split: train path: phrase_relatedness_zero_shot/train-* - split: validation path: phrase_relatedness_zero_shot/validation-* - config_name: physical_intuition_zero_shot data_files: - split: default path: physical_intuition_zero_shot/default-* - split: train path: physical_intuition_zero_shot/train-* - split: validation path: physical_intuition_zero_shot/validation-* - config_name: physics_questions_zero_shot data_files: - split: default path: physics_questions_zero_shot/default-* - split: train path: physics_questions_zero_shot/train-* - split: validation path: physics_questions_zero_shot/validation-* - config_name: physics_zero_shot data_files: - split: default path: physics_zero_shot/default-* - split: train path: physics_zero_shot/train-* - split: validation path: physics_zero_shot/validation-* - config_name: play_dialog_same_or_different_zero_shot data_files: - split: default path: play_dialog_same_or_different_zero_shot/default-* - split: train path: play_dialog_same_or_different_zero_shot/train-* - split: validation path: play_dialog_same_or_different_zero_shot/validation-* - config_name: polish_sequence_labeling_zero_shot data_files: - split: default path: polish_sequence_labeling_zero_shot/default-* - split: train path: polish_sequence_labeling_zero_shot/train-* - split: validation path: polish_sequence_labeling_zero_shot/validation-* - config_name: presuppositions_as_nli_zero_shot data_files: - split: default path: presuppositions_as_nli_zero_shot/default-* - split: train path: presuppositions_as_nli_zero_shot/train-* - split: validation path: presuppositions_as_nli_zero_shot/validation-* - config_name: qa_wikidata_zero_shot data_files: - split: default path: qa_wikidata_zero_shot/default-* - split: train path: qa_wikidata_zero_shot/train-* - split: validation path: qa_wikidata_zero_shot/validation-* - config_name: question_selection_zero_shot data_files: - split: default path: question_selection_zero_shot/default-* - split: train path: question_selection_zero_shot/train-* - split: validation path: question_selection_zero_shot/validation-* - config_name: real_or_fake_text_zero_shot data_files: - split: default path: real_or_fake_text_zero_shot/default-* - split: train path: real_or_fake_text_zero_shot/train-* - split: validation path: real_or_fake_text_zero_shot/validation-* - config_name: reasoning_about_colored_objects_zero_shot data_files: - split: default path: reasoning_about_colored_objects_zero_shot/default-* - split: train path: reasoning_about_colored_objects_zero_shot/train-* - split: validation path: reasoning_about_colored_objects_zero_shot/validation-* - config_name: repeat_copy_logic_zero_shot data_files: - split: default path: repeat_copy_logic_zero_shot/default-* - split: train path: repeat_copy_logic_zero_shot/train-* - split: validation path: repeat_copy_logic_zero_shot/validation-* - config_name: rephrase_zero_shot data_files: - split: default path: rephrase_zero_shot/default-* - split: train path: rephrase_zero_shot/train-* - split: validation path: rephrase_zero_shot/validation-* - config_name: riddle_sense_zero_shot data_files: - split: default path: riddle_sense_zero_shot/default-* - split: train path: riddle_sense_zero_shot/train-* - split: validation path: riddle_sense_zero_shot/validation-* - config_name: ruin_names_zero_shot data_files: - split: default path: ruin_names_zero_shot/default-* - split: train path: ruin_names_zero_shot/train-* - split: validation path: ruin_names_zero_shot/validation-* - config_name: salient_translation_error_detection_zero_shot data_files: - split: default path: salient_translation_error_detection_zero_shot/default-* - split: train path: salient_translation_error_detection_zero_shot/train-* - split: validation path: salient_translation_error_detection_zero_shot/validation-* - config_name: scientific_press_release_zero_shot data_files: - split: default path: scientific_press_release_zero_shot/default-* - split: train path: scientific_press_release_zero_shot/train-* - split: validation path: scientific_press_release_zero_shot/validation-* - config_name: semantic_parsing_in_context_sparc_zero_shot data_files: - split: default path: semantic_parsing_in_context_sparc_zero_shot/default-* - split: train path: semantic_parsing_in_context_sparc_zero_shot/train-* - split: validation path: semantic_parsing_in_context_sparc_zero_shot/validation-* - config_name: semantic_parsing_spider_zero_shot data_files: - split: default path: semantic_parsing_spider_zero_shot/default-* - split: train path: semantic_parsing_spider_zero_shot/train-* - split: validation path: semantic_parsing_spider_zero_shot/validation-* - config_name: sentence_ambiguity_zero_shot data_files: - split: default path: sentence_ambiguity_zero_shot/default-* - split: train path: sentence_ambiguity_zero_shot/train-* - split: validation path: sentence_ambiguity_zero_shot/validation-* - config_name: similarities_abstraction_zero_shot data_files: - split: default path: similarities_abstraction_zero_shot/default-* - split: train path: similarities_abstraction_zero_shot/train-* - split: validation path: similarities_abstraction_zero_shot/validation-* - config_name: simp_turing_concept_zero_shot data_files: - split: default path: simp_turing_concept_zero_shot/default-* - split: train path: simp_turing_concept_zero_shot/train-* - split: validation path: simp_turing_concept_zero_shot/validation-* - config_name: simple_arithmetic_json_multiple_choice_zero_shot data_files: - split: default path: simple_arithmetic_json_multiple_choice_zero_shot/default-* - split: train path: simple_arithmetic_json_multiple_choice_zero_shot/train-* - split: validation path: simple_arithmetic_json_multiple_choice_zero_shot/validation-* - config_name: simple_arithmetic_json_subtasks_zero_shot data_files: - split: default path: simple_arithmetic_json_subtasks_zero_shot/default-* - split: train path: simple_arithmetic_json_subtasks_zero_shot/train-* - split: validation path: simple_arithmetic_json_subtasks_zero_shot/validation-* - config_name: simple_arithmetic_json_zero_shot data_files: - split: default path: simple_arithmetic_json_zero_shot/default-* - split: train path: simple_arithmetic_json_zero_shot/train-* - split: validation path: simple_arithmetic_json_zero_shot/validation-* - config_name: simple_arithmetic_multiple_targets_json_zero_shot data_files: - split: default path: simple_arithmetic_multiple_targets_json_zero_shot/default-* - split: train path: simple_arithmetic_multiple_targets_json_zero_shot/train-* - split: validation path: simple_arithmetic_multiple_targets_json_zero_shot/validation-* - config_name: simple_ethical_questions_zero_shot data_files: - split: default path: simple_ethical_questions_zero_shot/default-* - split: train path: simple_ethical_questions_zero_shot/train-* - split: validation path: simple_ethical_questions_zero_shot/validation-* - config_name: simple_text_editing_zero_shot data_files: - split: default path: simple_text_editing_zero_shot/default-* - split: train path: simple_text_editing_zero_shot/train-* - split: validation path: simple_text_editing_zero_shot/validation-* - config_name: snarks_zero_shot data_files: - split: default path: snarks_zero_shot/default-* - split: train path: snarks_zero_shot/train-* - split: validation path: snarks_zero_shot/validation-* - config_name: social_iqa_zero_shot data_files: - split: default path: social_iqa_zero_shot/default-* - split: train path: social_iqa_zero_shot/train-* - split: validation path: social_iqa_zero_shot/validation-* - config_name: social_support_zero_shot data_files: - split: default path: social_support_zero_shot/default-* - split: train path: social_support_zero_shot/train-* - split: validation path: social_support_zero_shot/validation-* - config_name: sports_understanding_zero_shot data_files: - split: default path: sports_understanding_zero_shot/default-* - split: train path: sports_understanding_zero_shot/train-* - split: validation path: sports_understanding_zero_shot/validation-* - config_name: strange_stories_zero_shot data_files: - split: default path: strange_stories_zero_shot/default-* - split: train path: strange_stories_zero_shot/train-* - split: validation path: strange_stories_zero_shot/validation-* - config_name: strategyqa_zero_shot data_files: - split: default path: strategyqa_zero_shot/default-* - split: train path: strategyqa_zero_shot/train-* - split: validation path: strategyqa_zero_shot/validation-* - config_name: sufficient_information_zero_shot data_files: - split: default path: sufficient_information_zero_shot/default-* - split: train path: sufficient_information_zero_shot/train-* - split: validation path: sufficient_information_zero_shot/validation-* - config_name: suicide_risk_zero_shot data_files: - split: default path: suicide_risk_zero_shot/default-* - split: train path: suicide_risk_zero_shot/train-* - split: validation path: suicide_risk_zero_shot/validation-* - config_name: swahili_english_proverbs_zero_shot data_files: - split: default path: swahili_english_proverbs_zero_shot/default-* - split: train path: swahili_english_proverbs_zero_shot/train-* - split: validation path: swahili_english_proverbs_zero_shot/validation-* - config_name: swedish_to_german_proverbs_zero_shot data_files: - split: default path: swedish_to_german_proverbs_zero_shot/default-* - split: train path: swedish_to_german_proverbs_zero_shot/train-* - split: validation path: swedish_to_german_proverbs_zero_shot/validation-* - config_name: symbol_interpretation_zero_shot data_files: - split: default path: symbol_interpretation_zero_shot/default-* - split: train path: symbol_interpretation_zero_shot/train-* - split: validation path: symbol_interpretation_zero_shot/validation-* - config_name: temporal_sequences_zero_shot data_files: - split: default path: temporal_sequences_zero_shot/default-* - split: train path: temporal_sequences_zero_shot/train-* - split: validation path: temporal_sequences_zero_shot/validation-* - config_name: tense_zero_shot data_files: - split: default path: tense_zero_shot/default-* - split: train path: tense_zero_shot/train-* - split: validation path: tense_zero_shot/validation-* - config_name: timedial_zero_shot data_files: - split: default path: timedial_zero_shot/default-* - split: train path: timedial_zero_shot/train-* - split: validation path: timedial_zero_shot/validation-* - config_name: topical_chat_zero_shot data_files: - split: default path: topical_chat_zero_shot/default-* - split: train path: topical_chat_zero_shot/train-* - split: validation path: topical_chat_zero_shot/validation-* - config_name: tracking_shuffled_objects_zero_shot data_files: - split: default path: tracking_shuffled_objects_zero_shot/default-* - split: train path: tracking_shuffled_objects_zero_shot/train-* - split: validation path: tracking_shuffled_objects_zero_shot/validation-* - config_name: understanding_fables_zero_shot data_files: - split: default path: understanding_fables_zero_shot/default-* - split: train path: understanding_fables_zero_shot/train-* - split: validation path: understanding_fables_zero_shot/validation-* - config_name: undo_permutation_zero_shot data_files: - split: default path: undo_permutation_zero_shot/default-* - split: train path: undo_permutation_zero_shot/train-* - split: validation path: undo_permutation_zero_shot/validation-* - config_name: unit_conversion_zero_shot data_files: - split: default path: unit_conversion_zero_shot/default-* - split: train path: unit_conversion_zero_shot/train-* - split: validation path: unit_conversion_zero_shot/validation-* - config_name: unit_interpretation_zero_shot data_files: - split: default path: unit_interpretation_zero_shot/default-* - split: train path: unit_interpretation_zero_shot/train-* - split: validation path: unit_interpretation_zero_shot/validation-* - config_name: unnatural_in_context_learning_zero_shot data_files: - split: default path: unnatural_in_context_learning_zero_shot/default-* - split: train path: unnatural_in_context_learning_zero_shot/train-* - split: validation path: unnatural_in_context_learning_zero_shot/validation-* - config_name: vitaminc_fact_verification_zero_shot data_files: - split: default path: vitaminc_fact_verification_zero_shot/default-* - split: train path: vitaminc_fact_verification_zero_shot/train-* - split: validation path: vitaminc_fact_verification_zero_shot/validation-* - config_name: what_is_the_tao_zero_shot data_files: - split: default path: what_is_the_tao_zero_shot/default-* - split: train path: what_is_the_tao_zero_shot/train-* - split: validation path: what_is_the_tao_zero_shot/validation-* - config_name: which_wiki_edit_zero_shot data_files: - split: default path: which_wiki_edit_zero_shot/default-* - split: train path: which_wiki_edit_zero_shot/train-* - split: validation path: which_wiki_edit_zero_shot/validation-* - config_name: winowhy_zero_shot data_files: - split: default path: winowhy_zero_shot/default-* - split: train path: winowhy_zero_shot/train-* - split: validation path: winowhy_zero_shot/validation-* - config_name: word_sorting_zero_shot data_files: - split: default path: word_sorting_zero_shot/default-* - split: train path: word_sorting_zero_shot/train-* - split: validation path: word_sorting_zero_shot/validation-* - config_name: word_unscrambling_zero_shot data_files: - split: default path: word_unscrambling_zero_shot/default-* - split: train path: word_unscrambling_zero_shot/train-* - split: validation path: word_unscrambling_zero_shot/validation-* --- # Dataset Card for "bigbench" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

derek-thomas/dataset-creator-askreddit

derek-thomas

--- dataset_info: features: - name: score dtype: int64 - name: num_comments dtype: int64 - name: title dtype: string - name: permalink dtype: string - name: selftext dtype: string - name: url dtype: string - name: created_utc dtype: timestamp[us, tz=UTC] - name: author dtype: string - name: id dtype: string - name: downs dtype: float64 - name: ups dtype: float64 - name: date dtype: string - name: time dtype: string splits: - name: all_days num_bytes: 3806675432 num_examples: 9854469 download_size: 1782830000 dataset_size: 3806675432 --- # Dataset Card for "dataset-creator-askreddit" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) --- Generated Below --- # Dataset Name derek-thomas/dataset-creator-askreddit ## Update Frequency The dataset is updated daily and covers the period from `2013-01-01` to 2018-07-18 ## Dataset Overview The goal is to have an open dataset of `askreddit` submissions. This has been taken from the Pushshift API. ## Data Collection This has been collected with sequential calls that follow the pagination of the pushshift request. ## Attribution Data sourced from the Pushshift API.

yahma/alpaca-cleaned

yahma

--- license: cc-by-4.0 language: - en tags: - instruction-finetuning pretty_name: Alpaca-Cleaned task_categories: - text-generation --- # Dataset Card for Alpaca-Cleaned - **Repository:** https://github.com/gururise/AlpacaDataCleaned ## Dataset Description This is a cleaned version of the original Alpaca Dataset released by Stanford. The following issues have been identified in the original release and fixed in this dataset: 1. **Hallucinations:** Many instructions in the original dataset had instructions referencing data on the internet, which just caused GPT3 to hallucinate an answer. ``` "instruction":"Summarize the given article in 200 Words.", "input": "https://www.bbc.com/news/world-51461830", "output": "The recent protests in Belarus are part of a growing wave of discontent against the rule of Alexander Lukashenko, who has led the country since 1994. Demonstrations against his government have been ongoing since August, and come in the wake of an election widely seen as rigged. As police and security forces have cracked down on protesters, the movement has only grown, with hundreds of thousands of people taking to the streets to call for new leadership. Human rights activists have accused the government of using extreme violence in order prosecute protesters, leading to an international outcry and sanctions from the European Union. As the situation in Belarus continues to unfold, the international community is monitoring the situation closely - hoping for a peaceful resolution, while also standing firm against the oppressive tactics employed by the Belarusian state." "instruction": "Classify the following shape according to simple geometry.", "input": "<Picture Attached>", "output": "Triangle" "instruction": "Given a recent news article, write a headline for it.", "input": "https://www.news.example.com/article-1", "output": "\"NASA Launches a Mars 2020 Mission to Search for Signs of Ancient Life\"" ``` 2. **Merged Instructions:** There were many instructions that were merged together in the original dataset for some reason. ``` "instruction": "Should I invest in stocks?", "input": "", "output": "It depends on your individual situation. Investing in stocks carries a certain amount of risk but can also offer potential rewards. Make sure to research the different types of stocks, understand the risks and rewards, and develop a plan that works for you.\n##\n18. Instruction: Identify the noun in the following sentence:\n18. Input:\nThe large apple was delicious.\n18. Output:\nApple" ``` 3. **Empty outputs:** Some entries in the original dataset had empty outputs. ``` "instruction": "Calculate the area of the following shape in square centimeters.", "input": "rectangle of size 4 cm x 5 cm", "output": "" ``` 4. **Empty code examples:** Some descriptions in the original dataset were missing code examples, making it difficult to understand the intended behavior of the code. 5. **Instructions to generate images:** Some descriptions in the original dataset included instructions to generate images, something obviously not possible. ``` "instruction": "Create a graphic or logo that visually represents the word \"courage\".", "input": "", "output": "<No Output>" ``` 6. **N/A outputs:** Some code snippets in the original dataset had N/A outputs. 7. **Inconsistent input field:** The original dataset had inconsistent usage of the input field when it was supposed to be empty. ``` "input":"<no input>" "input":"No input" "input":"noinput" "input":"<noinput>" ``` 8. **Wrong answers:** Some instructions/questions in the original dataset had incorrect answers. About 80% of the math problems are estimated to have incorrect answers. ``` "instruction": "Calculate the median of the following data set.", "input": "1, 2, 4, 5, 8, 9", "output": "5" "instruction": "Convert 25m to km.", "input": "", "output": "25km" ``` 9. **Non-Sensical/Unclear instructions:** Many instructions are unclear, we try to clarify (or re-write) if instructions are non-sensical. Instructions that are slightly unclear, but where one could deduce the meaning are not altered. ``` "instruction": "Freeze the following sample of yogurt for 10 minutes.", "input": "Yogurt sample", "output": "<noinput>" "instruction": "Increase the font size to 12 points.", "input": "", "output": "The font size has been increased to 12 points." ``` 10. **Extraneous escape and control characters:** The original dataset had several entries with extraneous escape and control characters. ### Original Alpaca Dataset Summary Alpaca is a dataset of 52,000 instructions and demonstrations generated by OpenAI's `text-davinci-003` engine. This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better. The authors built on the data generation pipeline from [Self-Instruct framework](https://github.com/yizhongw/self-instruct) and made the following modifications: - The `text-davinci-003` engine to generate the instruction data instead of `davinci`. - A [new prompt](https://github.com/tatsu-lab/stanford_alpaca/blob/main/prompt.txt) was written that explicitly gave the requirement of instruction generation to `text-davinci-003`. - Much more aggressive batch decoding was used, i.e., generating 20 instructions at once, which significantly reduced the cost of data generation. - The data generation pipeline was simplified by discarding the difference between classification and non-classification instructions. - Only a single instance was generated for each instruction, instead of 2 to 3 instances as in Self-Instruct. This produced an instruction-following dataset with 52K examples obtained at a much lower cost (less than $500). In a preliminary study, the authors also found that the 52K generated data to be much more diverse than the data released by [Self-Instruct](https://github.com/yizhongw/self-instruct/blob/main/data/seed_tasks.jsonl). ### Supported Tasks and Leaderboards The Alpaca dataset designed for instruction training pretrained language models. ### Languages The data in Alpaca are in English (BCP-47 en). ## Dataset Structure ### Data Instances An example of "train" looks as follows: ```json { "instruction": "Create a classification task by clustering the given list of items.", "input": "Apples, oranges, bananas, strawberries, pineapples", "output": "Class 1: Apples, Oranges\nClass 2: Bananas, Strawberries\nClass 3: Pineapples", "text": "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n### Instruction:\nCreate a classification task by clustering the given list of items.\n\n### Input:\nApples, oranges, bananas, strawberries, pineapples\n\n### Response:\nClass 1: Apples, Oranges\nClass 2: Bananas, Strawberries\nClass 3: Pineapples", } ``` ### Data Fields The data fields are as follows: * `instruction`: describes the task the model should perform. Each of the 52K instructions is unique. * `input`: optional context or input for the task. For example, when the instruction is "Summarize the following article", the input is the article. Around 40% of the examples have an input. * `output`: the answer to the instruction as generated by `text-davinci-003`. * `text`: the `instruction`, `input` and `output` formatted with the [prompt template](https://github.com/tatsu-lab/stanford_alpaca#data-release) used by the authors for fine-tuning their models. ### Data Splits | | train | |---------------|------:| | alpaca | 52002 | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset Excerpt the [blog post](https://crfm.stanford.edu/2023/03/13/alpaca.html) accompanying the release of this dataset: > We believe that releasing the above assets will enable the academic community to perform controlled scientific studies on instruction-following language models, resulting in better science and ultimately new techniques to address the existing deficiencies with these models. At the same time, any release carries some risk. First, we recognize that releasing our training recipe reveals the feasibility of certain capabilities. On one hand, this enables more people (including bad actors) to create models that could cause harm (either intentionally or not). On the other hand, this awareness might incentivize swift defensive action, especially from the academic community, now empowered by the means to perform deeper safety research on such models. Overall, we believe that the benefits for the research community outweigh the risks of this particular release. Given that we are releasing the training recipe, we believe that releasing the data, model weights, and training code incur minimal further risk, given the simplicity of the recipe. At the same time, releasing these assets has enormous benefits for reproducible science, so that the academic community can use standard datasets, models, and code to perform controlled comparisons and to explore extensions. Deploying an interactive demo for Alpaca also poses potential risks, such as more widely disseminating harmful content and lowering the barrier for spam, fraud, or disinformation. We have put into place two risk mitigation strategies. First, we have implemented a content filter using OpenAI’s content moderation API, which filters out harmful content as defined by OpenAI’s usage policies. Second, we watermark all the model outputs using the method described in Kirchenbauer et al. 2023, so that others can detect (with some probability) whether an output comes from Alpaca 7B. Finally, we have strict terms and conditions for using the demo; it is restricted to non-commercial uses and to uses that follow LLaMA’s license agreement. We understand that these mitigation measures can be circumvented once we release the model weights or if users train their own instruction-following models. However, by installing these mitigations, we hope to advance the best practices and ultimately develop community norms for the responsible deployment of foundation models. ### Discussion of Biases [More Information Needed] ### Other Known Limitations The `alpaca` data is generated by a language model (`text-davinci-003`) and inevitably contains some errors or biases. We encourage users to use this data with caution and propose new methods to filter or improve the imperfections. ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information The dataset is available under the [Creative Commons NonCommercial (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/legalcode). ### Citation Information ``` @misc{alpaca, author = {Rohan Taori and Ishaan Gulrajani and Tianyi Zhang and Yann Dubois and Xuechen Li and Carlos Guestrin and Percy Liang and Tatsunori B. Hashimoto }, title = {Stanford Alpaca: An Instruction-following LLaMA model}, year = {2023}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/tatsu-lab/stanford_alpaca}}, } ``` ### Contributions [More Information Needed]

allenai/olmo-mix-1124

allenai

--- license: odc-by task_categories: - text-generation language: - en pretty_name: OLMo 2 Mix (November 2024) size_categories: - 1B<n<10B configs: - config_name: default data_files: - split: train path: data/*/* - config_name: algebraic-stack data_files: - split: train path: data/algebraic-stack/* - config_name: arxiv data_files: - split: train path: data/arxiv/* - config_name: dclm data_files: - split: train path: data/dclm/* - config_name: open-web-math data_files: - split: train path: data/open-web-math/* - config_name: pes2o data_files: - split: train path: data/pes2o/* - config_name: starcoder data_files: - split: train path: data/starcoder/* - config_name: wiki data_files: - split: train path: data/wiki/* dataset_info: features: - name: id dtype: string - name: text dtype: string - name: added dtype: string - name: created dtype: string --- # OLMo 2 (November 2024) Pretraining set Collection of data used to train OLMo-2-1124 models. The majority of this dataset comes from DCLM-Baseline with no additional filtering, but we provide the explicit breakdowns below. | Name | Tokens | Bytes (uncompressed) | Documents | License | |-----------------|--------|----------------------|-----------|-----------| | DCLM-Baseline | 3.70T | 21.3TB | 2.95B | CC-BY-4.0 | | Arxiv | 20.8B | 77.2GB | 3.95M | ODC-BY | | pes2o | 58.6B | 412GB | 38M | ODC-BY | | starcoder | 83.0B | 458GB | 78.7M | ODC-BY | | Algebraic-stack | 11.8B | 44.0GB | 2.83M | ODC-BY | | OpenWebMath | 12.2B | 47.23GB | 2.89M | ODC-BY | | Wiki | 3.66B | 18.1GB | 6.17M | ODC-BY | | Total | 3.90T | 22.4TB | 3.08M | ODC-BY | Please refer to the OLMo2 Tech Report for further details. ## Licensing Information This **collection** is released under the **Open Data Commons Attribution License (ODC-By) v1.0** [license](https://opendatacommons.org/licenses/by/1-0/). The use of this dataset is also subject to [CommonCrawl's Terms of Use](https://commoncrawl.org/terms-of-use). ## Citation A technical manuscript is forthcoming!

mteb/scifact

mteb

--- language: - en multilinguality: - monolingual task_categories: - text-retrieval source_datasets: - scifact task_ids: - document-retrieval config_names: - corpus tags: - text-retrieval dataset_info: - config_name: default features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 24585 num_examples: 919 - name: test num_bytes: 9092 num_examples: 339 - config_name: corpus features: - name: _id dtype: string - name: title dtype: string - name: text dtype: string splits: - name: corpus num_bytes: 7874970 num_examples: 5183 - config_name: queries features: - name: _id dtype: string - name: text dtype: string splits: - name: queries num_bytes: 111225 num_examples: 1109 configs: - config_name: default data_files: - split: train path: qrels/train.jsonl - split: test path: qrels/test.jsonl - config_name: corpus data_files: - split: corpus path: corpus.jsonl - config_name: queries data_files: - split: queries path: queries.jsonl ---

mteb/nfcorpus

mteb

--- language: - en multilinguality: - monolingual task_categories: - text-retrieval source_datasets: - nfcorpus task_ids: - document-retrieval config_names: - corpus tags: - text-retrieval dataset_info: - config_name: default features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 3720942 num_examples: 110575 - name: dev num_bytes: 383427 num_examples: 11385 - name: test num_bytes: 415220 num_examples: 12334 - config_name: corpus features: - name: _id dtype: string - name: title dtype: string - name: text dtype: string splits: - name: corpus num_bytes: 5856698 num_examples: 3633 - config_name: queries features: - name: _id dtype: string - name: text dtype: string splits: - name: queries num_bytes: 128355 num_examples: 3237 configs: - config_name: default data_files: - split: train path: qrels/train.jsonl - split: dev path: qrels/dev.jsonl - split: test path: qrels/test.jsonl - config_name: corpus data_files: - split: corpus path: corpus.jsonl - config_name: queries data_files: - split: queries path: queries.jsonl ---

mteb/banking77

mteb

--- language: - en ---

nyu-mll/blimp

nyu-mll

--- annotations_creators: - crowdsourced language_creators: - machine-generated language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - acceptability-classification paperswithcode_id: blimp pretty_name: BLiMP dataset_info: - config_name: adjunct_island features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 165894 num_examples: 1000 download_size: 62231 dataset_size: 165894 - config_name: anaphor_gender_agreement features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 130918 num_examples: 1000 download_size: 39201 dataset_size: 130918 - config_name: anaphor_number_agreement features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 139879 num_examples: 1000 download_size: 41547 dataset_size: 139879 - config_name: animate_subject_passive features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 144423 num_examples: 1000 download_size: 47282 dataset_size: 144423 - config_name: animate_subject_trans features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 127798 num_examples: 1000 download_size: 49651 dataset_size: 127798 - config_name: causative features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 122772 num_examples: 1000 download_size: 48963 dataset_size: 122772 - config_name: complex_NP_island features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 198972 num_examples: 1000 download_size: 78211 dataset_size: 198972 - config_name: coordinate_structure_constraint_complex_left_branch features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 210912 num_examples: 1000 download_size: 67908 dataset_size: 210912 - config_name: coordinate_structure_constraint_object_extraction features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 171655 num_examples: 1000 download_size: 51584 dataset_size: 171655 - config_name: determiner_noun_agreement_1 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 156120 num_examples: 1000 download_size: 49893 dataset_size: 156120 - config_name: determiner_noun_agreement_2 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 156204 num_examples: 1000 download_size: 49527 dataset_size: 156204 - config_name: determiner_noun_agreement_irregular_1 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 164473 num_examples: 1000 download_size: 47274 dataset_size: 164473 - config_name: determiner_noun_agreement_irregular_2 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 161074 num_examples: 1000 download_size: 47422 dataset_size: 161074 - config_name: determiner_noun_agreement_with_adj_2 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 179666 num_examples: 1000 download_size: 56346 dataset_size: 179666 - config_name: determiner_noun_agreement_with_adj_irregular_1 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 184529 num_examples: 1000 download_size: 54405 dataset_size: 184529 - config_name: determiner_noun_agreement_with_adj_irregular_2 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 184396 num_examples: 1000 download_size: 54064 dataset_size: 184396 - config_name: determiner_noun_agreement_with_adjective_1 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 185126 num_examples: 1000 download_size: 55682 dataset_size: 185126 - config_name: distractor_agreement_relational_noun features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 191473 num_examples: 1000 download_size: 59641 dataset_size: 191473 - config_name: distractor_agreement_relative_clause features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 216756 num_examples: 1000 download_size: 77897 dataset_size: 216756 - config_name: drop_argument features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 109806 num_examples: 1000 download_size: 39961 dataset_size: 109806 - config_name: ellipsis_n_bar_1 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 217590 num_examples: 1000 download_size: 92776 dataset_size: 217590 - config_name: ellipsis_n_bar_2 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 233161 num_examples: 1000 download_size: 98882 dataset_size: 233161 - config_name: existential_there_object_raising features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 223741 num_examples: 1000 download_size: 76641 dataset_size: 223741 - config_name: existential_there_quantifiers_1 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 162931 num_examples: 1000 download_size: 51576 dataset_size: 162931 - config_name: existential_there_quantifiers_2 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 164826 num_examples: 1000 download_size: 52092 dataset_size: 164826 - config_name: existential_there_subject_raising features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 200063 num_examples: 1000 download_size: 59519 dataset_size: 200063 - config_name: expletive_it_object_raising features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 238615 num_examples: 1000 download_size: 88607 dataset_size: 238615 - config_name: inchoative features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 104319 num_examples: 1000 download_size: 39842 dataset_size: 104319 - config_name: intransitive features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 111097 num_examples: 1000 download_size: 42387 dataset_size: 111097 - config_name: irregular_past_participle_adjectives features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 144661 num_examples: 1000 download_size: 36654 dataset_size: 144661 - config_name: irregular_past_participle_verbs features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 125692 num_examples: 1000 download_size: 37297 dataset_size: 125692 - config_name: irregular_plural_subject_verb_agreement_1 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 165584 num_examples: 1000 download_size: 50725 dataset_size: 165584 - config_name: irregular_plural_subject_verb_agreement_2 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 153843 num_examples: 1000 download_size: 42707 dataset_size: 153843 - config_name: left_branch_island_echo_question features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 147840 num_examples: 1000 download_size: 50481 dataset_size: 147840 - config_name: left_branch_island_simple_question features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 150060 num_examples: 1000 download_size: 50293 dataset_size: 150060 - config_name: matrix_question_npi_licensor_present features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 153262 num_examples: 1000 download_size: 51899 dataset_size: 153262 - config_name: npi_present_1 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 138465 num_examples: 1000 download_size: 51981 dataset_size: 138465 - config_name: npi_present_2 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 127636 num_examples: 1000 download_size: 51661 dataset_size: 127636 - config_name: only_npi_licensor_present features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 148516 num_examples: 1000 download_size: 51361 dataset_size: 148516 - config_name: only_npi_scope features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 208902 num_examples: 1000 download_size: 84970 dataset_size: 208902 - config_name: passive_1 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 145882 num_examples: 1000 download_size: 53931 dataset_size: 145882 - config_name: passive_2 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 113960 num_examples: 1000 download_size: 40499 dataset_size: 113960 - config_name: principle_A_c_command features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 188490 num_examples: 1000 download_size: 67867 dataset_size: 188490 - config_name: principle_A_case_1 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 170398 num_examples: 1000 download_size: 61092 dataset_size: 170398 - config_name: principle_A_case_2 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 170412 num_examples: 1000 download_size: 56430 dataset_size: 170412 - config_name: principle_A_domain_1 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 171170 num_examples: 1000 download_size: 59120 dataset_size: 171170 - config_name: principle_A_domain_2 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 165333 num_examples: 1000 download_size: 58464 dataset_size: 165333 - config_name: principle_A_domain_3 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 158998 num_examples: 1000 download_size: 52859 dataset_size: 158998 - config_name: principle_A_reconstruction features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 152104 num_examples: 1000 download_size: 44480 dataset_size: 152104 - config_name: regular_plural_subject_verb_agreement_1 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 158819 num_examples: 1000 download_size: 49466 dataset_size: 158819 - config_name: regular_plural_subject_verb_agreement_2 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 153609 num_examples: 1000 download_size: 43365 dataset_size: 153609 - config_name: sentential_negation_npi_licensor_present features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 171864 num_examples: 1000 download_size: 54830 dataset_size: 171864 - config_name: sentential_negation_npi_scope features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 232098 num_examples: 1000 download_size: 90157 dataset_size: 232098 - config_name: sentential_subject_island features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 172432 num_examples: 1000 download_size: 56666 dataset_size: 172432 - config_name: superlative_quantifiers_1 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 159290 num_examples: 1000 download_size: 48453 dataset_size: 159290 - config_name: superlative_quantifiers_2 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 159340 num_examples: 1000 download_size: 50480 dataset_size: 159340 - config_name: tough_vs_raising_1 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 148636 num_examples: 1000 download_size: 44779 dataset_size: 148636 - config_name: tough_vs_raising_2 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 169684 num_examples: 1000 download_size: 61465 dataset_size: 169684 - config_name: transitive features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 133104 num_examples: 1000 download_size: 55090 dataset_size: 133104 - config_name: wh_island features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 142340 num_examples: 1000 download_size: 52808 dataset_size: 142340 - config_name: wh_questions_object_gap features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 193045 num_examples: 1000 download_size: 70049 dataset_size: 193045 - config_name: wh_questions_subject_gap features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 195593 num_examples: 1000 download_size: 71632 dataset_size: 195593 - config_name: wh_questions_subject_gap_long_distance features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 268270 num_examples: 1000 download_size: 98913 dataset_size: 268270 - config_name: wh_vs_that_no_gap features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 188872 num_examples: 1000 download_size: 71710 dataset_size: 188872 - config_name: wh_vs_that_no_gap_long_distance features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 247039 num_examples: 1000 download_size: 95504 dataset_size: 247039 - config_name: wh_vs_that_with_gap features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 173386 num_examples: 1000 download_size: 60291 dataset_size: 173386 - config_name: wh_vs_that_with_gap_long_distance features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 231595 num_examples: 1000 download_size: 84147 dataset_size: 231595 configs: - config_name: adjunct_island data_files: - split: train path: adjunct_island/train-* - config_name: anaphor_gender_agreement data_files: - split: train path: anaphor_gender_agreement/train-* - config_name: anaphor_number_agreement data_files: - split: train path: anaphor_number_agreement/train-* - config_name: animate_subject_passive data_files: - split: train path: animate_subject_passive/train-* - config_name: animate_subject_trans data_files: - split: train path: animate_subject_trans/train-* - config_name: causative data_files: - split: train path: causative/train-* - config_name: complex_NP_island data_files: - split: train path: complex_NP_island/train-* - config_name: coordinate_structure_constraint_complex_left_branch data_files: - split: train path: coordinate_structure_constraint_complex_left_branch/train-* - config_name: coordinate_structure_constraint_object_extraction data_files: - split: train path: coordinate_structure_constraint_object_extraction/train-* - config_name: determiner_noun_agreement_1 data_files: - split: train path: determiner_noun_agreement_1/train-* - config_name: determiner_noun_agreement_2 data_files: - split: train path: determiner_noun_agreement_2/train-* - config_name: determiner_noun_agreement_irregular_1 data_files: - split: train path: determiner_noun_agreement_irregular_1/train-* - config_name: determiner_noun_agreement_irregular_2 data_files: - split: train path: determiner_noun_agreement_irregular_2/train-* - config_name: determiner_noun_agreement_with_adj_2 data_files: - split: train path: determiner_noun_agreement_with_adj_2/train-* - config_name: determiner_noun_agreement_with_adj_irregular_1 data_files: - split: train path: determiner_noun_agreement_with_adj_irregular_1/train-* - config_name: determiner_noun_agreement_with_adj_irregular_2 data_files: - split: train path: determiner_noun_agreement_with_adj_irregular_2/train-* - config_name: determiner_noun_agreement_with_adjective_1 data_files: - split: train path: determiner_noun_agreement_with_adjective_1/train-* - config_name: distractor_agreement_relational_noun data_files: - split: train path: distractor_agreement_relational_noun/train-* - config_name: distractor_agreement_relative_clause data_files: - split: train path: distractor_agreement_relative_clause/train-* - config_name: drop_argument data_files: - split: train path: drop_argument/train-* - config_name: ellipsis_n_bar_1 data_files: - split: train path: ellipsis_n_bar_1/train-* - config_name: ellipsis_n_bar_2 data_files: - split: train path: ellipsis_n_bar_2/train-* - config_name: existential_there_object_raising data_files: - split: train path: existential_there_object_raising/train-* - config_name: existential_there_quantifiers_1 data_files: - split: train path: existential_there_quantifiers_1/train-* - config_name: existential_there_quantifiers_2 data_files: - split: train path: existential_there_quantifiers_2/train-* - config_name: existential_there_subject_raising data_files: - split: train path: existential_there_subject_raising/train-* - config_name: expletive_it_object_raising data_files: - split: train path: expletive_it_object_raising/train-* - config_name: inchoative data_files: - split: train path: inchoative/train-* - config_name: intransitive data_files: - split: train path: intransitive/train-* - config_name: irregular_past_participle_adjectives data_files: - split: train path: irregular_past_participle_adjectives/train-* - config_name: irregular_past_participle_verbs data_files: - split: train path: irregular_past_participle_verbs/train-* - config_name: irregular_plural_subject_verb_agreement_1 data_files: - split: train path: irregular_plural_subject_verb_agreement_1/train-* - config_name: irregular_plural_subject_verb_agreement_2 data_files: - split: train path: irregular_plural_subject_verb_agreement_2/train-* - config_name: left_branch_island_echo_question data_files: - split: train path: left_branch_island_echo_question/train-* - config_name: left_branch_island_simple_question data_files: - split: train path: left_branch_island_simple_question/train-* - config_name: matrix_question_npi_licensor_present data_files: - split: train path: matrix_question_npi_licensor_present/train-* - config_name: npi_present_1 data_files: - split: train path: npi_present_1/train-* - config_name: npi_present_2 data_files: - split: train path: npi_present_2/train-* - config_name: only_npi_licensor_present data_files: - split: train path: only_npi_licensor_present/train-* - config_name: only_npi_scope data_files: - split: train path: only_npi_scope/train-* - config_name: passive_1 data_files: - split: train path: passive_1/train-* - config_name: passive_2 data_files: - split: train path: passive_2/train-* - config_name: principle_A_c_command data_files: - split: train path: principle_A_c_command/train-* - config_name: principle_A_case_1 data_files: - split: train path: principle_A_case_1/train-* - config_name: principle_A_case_2 data_files: - split: train path: principle_A_case_2/train-* - config_name: principle_A_domain_1 data_files: - split: train path: principle_A_domain_1/train-* - config_name: principle_A_domain_2 data_files: - split: train path: principle_A_domain_2/train-* - config_name: principle_A_domain_3 data_files: - split: train path: principle_A_domain_3/train-* - config_name: principle_A_reconstruction data_files: - split: train path: principle_A_reconstruction/train-* - config_name: regular_plural_subject_verb_agreement_1 data_files: - split: train path: regular_plural_subject_verb_agreement_1/train-* - config_name: regular_plural_subject_verb_agreement_2 data_files: - split: train path: regular_plural_subject_verb_agreement_2/train-* - config_name: sentential_negation_npi_licensor_present data_files: - split: train path: sentential_negation_npi_licensor_present/train-* - config_name: sentential_negation_npi_scope data_files: - split: train path: sentential_negation_npi_scope/train-* - config_name: sentential_subject_island data_files: - split: train path: sentential_subject_island/train-* - config_name: superlative_quantifiers_1 data_files: - split: train path: superlative_quantifiers_1/train-* - config_name: superlative_quantifiers_2 data_files: - split: train path: superlative_quantifiers_2/train-* - config_name: tough_vs_raising_1 data_files: - split: train path: tough_vs_raising_1/train-* - config_name: tough_vs_raising_2 data_files: - split: train path: tough_vs_raising_2/train-* - config_name: transitive data_files: - split: train path: transitive/train-* - config_name: wh_island data_files: - split: train path: wh_island/train-* - config_name: wh_questions_object_gap data_files: - split: train path: wh_questions_object_gap/train-* - config_name: wh_questions_subject_gap data_files: - split: train path: wh_questions_subject_gap/train-* - config_name: wh_questions_subject_gap_long_distance data_files: - split: train path: wh_questions_subject_gap_long_distance/train-* - config_name: wh_vs_that_no_gap data_files: - split: train path: wh_vs_that_no_gap/train-* - config_name: wh_vs_that_no_gap_long_distance data_files: - split: train path: wh_vs_that_no_gap_long_distance/train-* - config_name: wh_vs_that_with_gap data_files: - split: train path: wh_vs_that_with_gap/train-* - config_name: wh_vs_that_with_gap_long_distance data_files: - split: train path: wh_vs_that_with_gap_long_distance/train-* --- # Dataset Card for "blimp" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** - **Repository:** https://github.com/alexwarstadt/blimp - **Paper:** [BLiMP: The Benchmark of Linguistic Minimal Pairs for English](https://doi.org/10.1162/tacl_a_00321) - **Paper:** https://arxiv.org/abs/1912.00582 - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 29.58 MB - **Size of the generated dataset:** 11.45 MB - **Total amount of disk used:** 41.03 MB ### Dataset Summary BLiMP is a challenge set for evaluating what language models (LMs) know about major grammatical phenomena in English. BLiMP consists of 67 sub-datasets, each containing 1000 minimal pairs isolating specific contrasts in syntax, morphology, or semantics. The data is automatically generated according to expert-crafted grammars. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### adjunct_island - **Size of downloaded dataset files:** 0.36 MB - **Size of the generated dataset:** 0.17 MB - **Total amount of disk used:** 0.52 MB An example of 'train' looks as follows. ``` { "UID": "tough_vs_raising_1", "field": "syntax_semantics", "lexically_identical": false, "linguistics_term": "control_raising", "one_prefix_method": false, "pair_id": 2, "sentence_bad": "Benjamin's tutor was certain to boast about.", "sentence_good": "Benjamin's tutor was easy to boast about.", "simple_LM_method": true, "two_prefix_method": false } ``` #### anaphor_gender_agreement - **Size of downloaded dataset files:** 0.44 MB - **Size of the generated dataset:** 0.14 MB - **Total amount of disk used:** 0.57 MB An example of 'train' looks as follows. ``` { "UID": "tough_vs_raising_1", "field": "syntax_semantics", "lexically_identical": false, "linguistics_term": "control_raising", "one_prefix_method": false, "pair_id": 2, "sentence_bad": "Benjamin's tutor was certain to boast about.", "sentence_good": "Benjamin's tutor was easy to boast about.", "simple_LM_method": true, "two_prefix_method": false } ``` #### anaphor_number_agreement - **Size of downloaded dataset files:** 0.45 MB - **Size of the generated dataset:** 0.14 MB - **Total amount of disk used:** 0.59 MB An example of 'train' looks as follows. ``` { "UID": "tough_vs_raising_1", "field": "syntax_semantics", "lexically_identical": false, "linguistics_term": "control_raising", "one_prefix_method": false, "pair_id": 2, "sentence_bad": "Benjamin's tutor was certain to boast about.", "sentence_good": "Benjamin's tutor was easy to boast about.", "simple_LM_method": true, "two_prefix_method": false } ``` #### animate_subject_passive - **Size of downloaded dataset files:** 0.46 MB - **Size of the generated dataset:** 0.15 MB - **Total amount of disk used:** 0.61 MB An example of 'train' looks as follows. ``` { "UID": "tough_vs_raising_1", "field": "syntax_semantics", "lexically_identical": false, "linguistics_term": "control_raising", "one_prefix_method": false, "pair_id": 2, "sentence_bad": "Benjamin's tutor was certain to boast about.", "sentence_good": "Benjamin's tutor was easy to boast about.", "simple_LM_method": true, "two_prefix_method": false } ``` #### animate_subject_trans - **Size of downloaded dataset files:** 0.43 MB - **Size of the generated dataset:** 0.13 MB - **Total amount of disk used:** 0.57 MB An example of 'train' looks as follows. ``` { "UID": "tough_vs_raising_1", "field": "syntax_semantics", "lexically_identical": false, "linguistics_term": "control_raising", "one_prefix_method": false, "pair_id": 2, "sentence_bad": "Benjamin's tutor was certain to boast about.", "sentence_good": "Benjamin's tutor was easy to boast about.", "simple_LM_method": true, "two_prefix_method": false } ``` ### Data Fields The data fields are the same among all splits. #### adjunct_island - `sentence_good`: a `string` feature. - `sentence_bad`: a `string` feature. - `field`: a `string` feature. - `linguistics_term`: a `string` feature. - `UID`: a `string` feature. - `simple_LM_method`: a `bool` feature. - `one_prefix_method`: a `bool` feature. - `two_prefix_method`: a `bool` feature. - `lexically_identical`: a `bool` feature. - `pair_id`: a `int32` feature. #### anaphor_gender_agreement - `sentence_good`: a `string` feature. - `sentence_bad`: a `string` feature. - `field`: a `string` feature. - `linguistics_term`: a `string` feature. - `UID`: a `string` feature. - `simple_LM_method`: a `bool` feature. - `one_prefix_method`: a `bool` feature. - `two_prefix_method`: a `bool` feature. - `lexically_identical`: a `bool` feature. - `pair_id`: a `int32` feature. #### anaphor_number_agreement - `sentence_good`: a `string` feature. - `sentence_bad`: a `string` feature. - `field`: a `string` feature. - `linguistics_term`: a `string` feature. - `UID`: a `string` feature. - `simple_LM_method`: a `bool` feature. - `one_prefix_method`: a `bool` feature. - `two_prefix_method`: a `bool` feature. - `lexically_identical`: a `bool` feature. - `pair_id`: a `int32` feature. #### animate_subject_passive - `sentence_good`: a `string` feature. - `sentence_bad`: a `string` feature. - `field`: a `string` feature. - `linguistics_term`: a `string` feature. - `UID`: a `string` feature. - `simple_LM_method`: a `bool` feature. - `one_prefix_method`: a `bool` feature. - `two_prefix_method`: a `bool` feature. - `lexically_identical`: a `bool` feature. - `pair_id`: a `int32` feature. #### animate_subject_trans - `sentence_good`: a `string` feature. - `sentence_bad`: a `string` feature. - `field`: a `string` feature. - `linguistics_term`: a `string` feature. - `UID`: a `string` feature. - `simple_LM_method`: a `bool` feature. - `one_prefix_method`: a `bool` feature. - `two_prefix_method`: a `bool` feature. - `lexically_identical`: a `bool` feature. - `pair_id`: a `int32` feature. ### Data Splits | name |train| |------------------------|----:| |adjunct_island | 1000| |anaphor_gender_agreement| 1000| |anaphor_number_agreement| 1000| |animate_subject_passive | 1000| |animate_subject_trans | 1000| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information BLiMP is distributed under a [CC-BY](https://creativecommons.org/licenses/by/4.0/) license. Source: https://github.com/alexwarstadt/blimp#license ### Citation Information ``` @article{warstadt2020blimp, author = {Warstadt, Alex and Parrish, Alicia and Liu, Haokun and Mohananey, Anhad and Peng, Wei and Wang, Sheng-Fu and Bowman, Samuel R.}, title = {BLiMP: The Benchmark of Linguistic Minimal Pairs for English}, journal = {Transactions of the Association for Computational Linguistics}, volume = {8}, number = {}, pages = {377-392}, year = {2020}, doi = {10.1162/tacl\_a\_00321}, URL = {https://doi.org/10.1162/tacl_a_00321}, eprint = {https://doi.org/10.1162/tacl_a_00321}, abstract = { We introduce The Benchmark of Linguistic Minimal Pairs (BLiMP),1 a challenge set for evaluating the linguistic knowledge of language models (LMs) on major grammatical phenomena in English. BLiMP consists of 67 individual datasets, each containing 1,000 minimal pairs—that is, pairs of minimally different sentences that contrast in grammatical acceptability and isolate specific phenomenon in syntax, morphology, or semantics. We generate the data according to linguist-crafted grammar templates, and human aggregate agreement with the labels is 96.4\%. We evaluate n-gram, LSTM, and Transformer (GPT-2 and Transformer-XL) LMs by observing whether they assign a higher probability to the acceptable sentence in each minimal pair. We find that state-of-the-art models identify morphological contrasts related to agreement reliably, but they struggle with some subtle semantic and syntactic phenomena, such as negative polarity items and extraction islands. } } ``` #### Errata Some results were misreported in the published TACL version. Please refer to the corrected version on arXiv: https://arxiv.org/abs/1912.00582 ### Contributions Thanks to [@lhoestq](https://github.com/lhoestq), [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf) for adding this dataset.

google-research-datasets/paws-x

google-research-datasets

--- annotations_creators: - expert-generated - machine-generated language_creators: - expert-generated - machine-generated language: - de - en - es - fr - ja - ko - zh license: - other multilinguality: - multilingual size_categories: - 10K<n<100K source_datasets: - extended|other-paws task_categories: - text-classification task_ids: - semantic-similarity-classification - semantic-similarity-scoring - text-scoring - multi-input-text-classification paperswithcode_id: paws-x pretty_name: 'PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification' tags: - paraphrase-identification dataset_info: - config_name: de features: - name: id dtype: int32 - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: class_label: names: '0': '0' '1': '1' splits: - name: train num_bytes: 12801784 num_examples: 49401 - name: test num_bytes: 524206 num_examples: 2000 - name: validation num_bytes: 514001 num_examples: 2000 download_size: 9601920 dataset_size: 13839991 - config_name: en features: - name: id dtype: int32 - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: class_label: names: '0': '0' '1': '1' splits: - name: train num_bytes: 12215913 num_examples: 49401 - name: test num_bytes: 494726 num_examples: 2000 - name: validation num_bytes: 492279 num_examples: 2000 download_size: 9045005 dataset_size: 13202918 - config_name: es features: - name: id dtype: int32 - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: class_label: names: '0': '0' '1': '1' splits: - name: train num_bytes: 12808446 num_examples: 49401 - name: test num_bytes: 519103 num_examples: 2000 - name: validation num_bytes: 513880 num_examples: 2000 download_size: 9538815 dataset_size: 13841429 - config_name: fr features: - name: id dtype: int32 - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: class_label: names: '0': '0' '1': '1' splits: - name: train num_bytes: 13295557 num_examples: 49401 - name: test num_bytes: 535093 num_examples: 2000 - name: validation num_bytes: 533023 num_examples: 2000 download_size: 9785410 dataset_size: 14363673 - config_name: ja features: - name: id dtype: int32 - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: class_label: names: '0': '0' '1': '1' splits: - name: train num_bytes: 15041592 num_examples: 49401 - name: test num_bytes: 668628 num_examples: 2000 - name: validation num_bytes: 661770 num_examples: 2000 download_size: 10435711 dataset_size: 16371990 - config_name: ko features: - name: id dtype: int32 - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: class_label: names: '0': '0' '1': '1' splits: - name: train num_bytes: 13934181 num_examples: 49401 - name: test num_bytes: 562292 num_examples: 2000 - name: validation num_bytes: 554867 num_examples: 2000 download_size: 10263972 dataset_size: 15051340 - config_name: zh features: - name: id dtype: int32 - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: class_label: names: '0': '0' '1': '1' splits: - name: train num_bytes: 10815459 num_examples: 49401 - name: test num_bytes: 474636 num_examples: 2000 - name: validation num_bytes: 473110 num_examples: 2000 download_size: 9178953 dataset_size: 11763205 configs: - config_name: de data_files: - split: train path: de/train-* - split: test path: de/test-* - split: validation path: de/validation-* - config_name: en data_files: - split: train path: en/train-* - split: test path: en/test-* - split: validation path: en/validation-* - config_name: es data_files: - split: train path: es/train-* - split: test path: es/test-* - split: validation path: es/validation-* - config_name: fr data_files: - split: train path: fr/train-* - split: test path: fr/test-* - split: validation path: fr/validation-* - config_name: ja data_files: - split: train path: ja/train-* - split: test path: ja/test-* - split: validation path: ja/validation-* - config_name: ko data_files: - split: train path: ko/train-* - split: test path: ko/test-* - split: validation path: ko/validation-* - config_name: zh data_files: - split: train path: zh/train-* - split: test path: zh/test-* - split: validation path: zh/validation-* --- # Dataset Card for PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [PAWS-X](https://github.com/google-research-datasets/paws/tree/master/pawsx) - **Repository:** [PAWS-X](https://github.com/google-research-datasets/paws/tree/master/pawsx) - **Paper:** [PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification](https://arxiv.org/abs/1908.11828) - **Point of Contact:** [Yinfei Yang]([email protected]) ### Dataset Summary This dataset contains 23,659 **human** translated PAWS evaluation pairs and 296,406 **machine** translated training pairs in six typologically distinct languages: French, Spanish, German, Chinese, Japanese, and Korean. All translated pairs are sourced from examples in [PAWS-Wiki](https://github.com/google-research-datasets/paws#paws-wiki). For further details, see the accompanying paper: [PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification](https://arxiv.org/abs/1908.11828) ### Supported Tasks and Leaderboards It has been majorly used for paraphrase identification for English and other 6 languages namely French, Spanish, German, Chinese, Japanese, and Korean ### Languages The dataset is in English, French, Spanish, German, Chinese, Japanese, and Korean ## Dataset Structure ### Data Instances For en: ``` id : 1 sentence1 : In Paris , in October 1560 , he secretly met the English ambassador , Nicolas Throckmorton , asking him for a passport to return to England through Scotland . sentence2 : In October 1560 , he secretly met with the English ambassador , Nicolas Throckmorton , in Paris , and asked him for a passport to return to Scotland through England . label : 0 ``` For fr: ``` id : 1 sentence1 : À Paris, en octobre 1560, il rencontra secrètement l'ambassadeur d'Angleterre, Nicolas Throckmorton, lui demandant un passeport pour retourner en Angleterre en passant par l'Écosse. sentence2 : En octobre 1560, il rencontra secrètement l'ambassadeur d'Angleterre, Nicolas Throckmorton, à Paris, et lui demanda un passeport pour retourner en Écosse par l'Angleterre. label : 0 ``` ### Data Fields All files are in tsv format with four columns: Column Name | Data :---------- | :-------------------------------------------------------- id | An ID that matches the ID of the source pair in PAWS-Wiki sentence1 | The first sentence sentence2 | The second sentence label | Label for each pair The source text of each translation can be retrieved by looking up the ID in the corresponding file in PAWS-Wiki. ### Data Splits The numbers of examples for each of the seven languages are shown below: Language | Train | Dev | Test :------- | ------: | -----: | -----: en | 49,401 | 2,000 | 2,000 fr | 49,401 | 2,000 | 2,000 es | 49,401 | 2,000 | 2,000 de | 49,401 | 2,000 | 2,000 zh | 49,401 | 2,000 | 2,000 ja | 49,401 | 2,000 | 2,000 ko | 49,401 | 2,000 | 2,000 > **Caveat**: please note that the dev and test sets of PAWS-X are both sourced > from the dev set of PAWS-Wiki. As a consequence, the same `sentence 1` may > appear in both the dev and test sets. Nevertheless our data split guarantees > that there is no overlap on sentence pairs (`sentence 1` + `sentence 2`) > between dev and test. ## Dataset Creation ### Curation Rationale Most existing work on adversarial data generation focuses on English. For example, PAWS (Paraphrase Adversaries from Word Scrambling) (Zhang et al., 2019) consists of challenging English paraphrase identification pairs from Wikipedia and Quora. They remedy this gap with PAWS-X, a new dataset of 23,659 human translated PAWS evaluation pairs in six typologically distinct languages: French, Spanish, German, Chinese, Japanese, and Korean. They provide baseline numbers for three models with different capacity to capture non-local context and sentence structure, and using different multilingual training and evaluation regimes. Multilingual BERT (Devlin et al., 2019) fine-tuned on PAWS English plus machine-translated data performs the best, with a range of 83.1-90.8 accuracy across the non-English languages and an average accuracy gain of 23% over the next best model. PAWS-X shows the effectiveness of deep, multilingual pre-training while also leaving considerable headroom as a new challenge to drive multilingual research that better captures structure and contextual information. ### Source Data PAWS (Paraphrase Adversaries from Word Scrambling) #### Initial Data Collection and Normalization All translated pairs are sourced from examples in [PAWS-Wiki](https://github.com/google-research-datasets/paws#paws-wiki) #### Who are the source language producers? This dataset contains 23,659 human translated PAWS evaluation pairs and 296,406 machine translated training pairs in six typologically distinct languages: French, Spanish, German, Chinese, Japanese, and Korean. ### Annotations #### Annotation process If applicable, describe the annotation process and any tools used, or state otherwise. Describe the amount of data annotated, if not all. Describe or reference annotation guidelines provided to the annotators. If available, provide interannotator statistics. Describe any annotation validation processes. #### Who are the annotators? The paper mentions the translate team, especially Mengmeng Niu, for the help with the annotations. ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators List the people involved in collecting the dataset and their affiliation(s). If funding information is known, include it here. ### Licensing Information The dataset may be freely used for any purpose, although acknowledgement of Google LLC ("Google") as the data source would be appreciated. The dataset is provided "AS IS" without any warranty, express or implied. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset. ### Citation Information ``` @InProceedings{pawsx2019emnlp, title = {{PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification}}, author = {Yang, Yinfei and Zhang, Yuan and Tar, Chris and Baldridge, Jason}, booktitle = {Proc. of EMNLP}, year = {2019} } ``` ### Contributions Thanks to [@bhavitvyamalik](https://github.com/bhavitvyamalik), [@gowtham1997](https://github.com/gowtham1997) for adding this dataset.

ncoop57/mmmlu

ncoop57

--- license: mit ---

anon8231489123/ShareGPT_Vicuna_unfiltered

anon8231489123

--- license: apache-2.0 language: - en --- **Further cleaning done. Please look through the dataset and ensure that I didn't miss anything.** **Update: Confirmed working method for training the model: https://huggingface.co/AlekseyKorshuk/vicuna-7b/discussions/4#64346c08ef6d5abefe42c12c** Two choices: - Removes instances of "I'm sorry, but": https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/blob/main/ShareGPT_V3_unfiltered_cleaned_split_no_imsorry.json - Has instances of "I'm sorry, but": https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/blob/main/ShareGPT_V3_unfiltered_cleaned_split.json The choice is yours. The first dataset may go to far and remove valuable data. The second is better for when the AI asks for clarification, but it also may refuse to do stuff like browse the internet, which it actually may be able to do with certain langchain implementations. These are important things to think about before training. ~100k ShareGPT conversations narrowed down to 53k by: * Removing non-english conversations * Removing excessive unicode (indicative of Chinese or Korean text, usually) * Removing excessive repeated characters * Removing various instances "AI Moralizing". Conversations with these phrases were removed (and a few others that can't be mentioned here): "text-based AI language model", "domestic violence", "please refrain", "derogatory", "inappropriate", "offensive", "racism", "racist", "racial", "discriminate", "discriminatory", "discrimination", "sexist", "sexism", "unacceptable", "inclusive workplace", "lgbt", "morals", "ethics", "ethical", "legality", "illegal", "illegality", "hateful", "harmful", "it is never okay", "It is important to", "It's important to", "real-world consequences", "hate speech", "glorify", "not be appropriate", "supremacist", "extremist", "responsible AI", "AI principles", "AI assistant", "an AI language", "ableist", "hurtful", "gender stereotype", "gender inequality", "underrepresentation", "safe spaces", "gender-based", "inclusivity", "feminist", "feminism", "transgender", "empowerment", "communist", "capitalism", "stereotypes", "biases", "bias", "Microaggression", "prioritize human safety", "as a language model", "as an AI language model", "As a large language model", "As an AI", "ethical principles", "consensual", "it is not appropriate", "it's not appropriate", "I cannot fulfill your request", "harmful to human beings", "ethical guidelines", "my guidelines", "prioritize user safety", "adhere to ethical guidelines", "harmful consequences", "potentially harmful", "dangerous activities", "promote safety", "well-being of all users", "responsible information sharing", "jeopardize the safety", "illegal actions or intentions", "undermine the stability", "promote the well-being", "illegal activities or actions", "adherence to the law", "potentially be harmful", "illegal substances or activities", "committed to promoting", "safe information", "lawful information", "cannot provide guidance", "cannot provide information", "unable to offer assistance", "cannot engage in discussions", "programming prohibits", "follow ethical guidelines", "ensure the safety", "involves an illegal subject", "prioritize safety", "illegal subject", "prioritize user well-being", "cannot support or promote", "activities that could harm", "pose a risk to others", "against my programming", "activities that could undermine", "potentially dangerous", "not within the scope", "designed to prioritize safety", "not able to provide", "maintain user safety", "adhere to safety guidelines", "dangerous or harmful", "cannot provide any information", "focus on promoting safety" * Conversations split into 2048 token chunks as described here: https://github.com/lm-sys/FastChat/blob/main/docs/commands/data_cleaning.md This should be fully ready to train an unfiltered english Vicuna model based on the procedure here: https://github.com/lm-sys/FastChat/

mteb/sickr-sts

mteb

--- language: - en ---

asahi417/seamless-align-enA-viA.speaker-embedding.hubert-xl

asahi417

--- dataset_info: - config_name: subset_1 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: viA.id dtype: string - name: viA.laser_score dtype: float64 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 - name: viA.audio.speaker_embedding sequence: float32 - name: viA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 8770415431 num_examples: 1853 download_size: 8797657395 dataset_size: 8770415431 - config_name: subset_10 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: viA.id dtype: string - name: viA.laser_score dtype: float64 - name: viA.audio.speaker_embedding sequence: float32 - name: viA.audio.speaker_embedding.full sequence: sequence: float32 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 4439599384 num_examples: 1090 download_size: 4453523579 dataset_size: 4439599384 - config_name: subset_100 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: viA.id dtype: string - name: viA.laser_score dtype: float64 - name: viA.audio.speaker_embedding sequence: float32 - name: viA.audio.speaker_embedding.full sequence: sequence: float32 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 6551628750 num_examples: 1574 download_size: 6574192646 dataset_size: 6551628750 - config_name: subset_101 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: viA.id dtype: string - name: viA.laser_score dtype: float64 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 - name: viA.audio.speaker_embedding sequence: float32 - 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nvidia/HelpSteer2

nvidia

--- license: cc-by-4.0 language: - en pretty_name: HelpSteer2 size_categories: - 10K<n<100K tags: - human-feedback --- # HelpSteer2: Open-source dataset for training top-performing reward models HelpSteer2 is an open-source Helpfulness Dataset (CC-BY-4.0) that supports aligning models to become more helpful, factually correct and coherent, while being adjustable in terms of the complexity and verbosity of its responses. This dataset has been created in partnership with [Scale AI](https://scale.com/). When used to tune a [Llama 3.1 70B Instruct Model](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct), we achieve 94.1% on RewardBench, which makes it the best Reward Model as of 1 Oct 2024. This reward model is available on HuggingFace in both .nemo format at [Llama-3.1-Nemotron-70B-Reward](https://huggingface.co/nvidia/Llama-3.1-Nemotron-70B-Reward) or HF-compatible format at [Llama-3.1-Nemotron-70B-Reward-HF](https://huggingface.co/nvidia/Llama-3.1-Nemotron-70B-Reward-HF) Using this reward model for RLHF (specifically, REINFORCE), we were able to align a Llama-3.1-70B-Instruct model to reach [AlpacaEval 2 LC](https://tatsu-lab.github.io/alpaca_eval/) of 57.6, [Arena Hard](https://github.com/lmarena/arena-hard-auto) of 85.0 and [GPT-4-Turbo MT-Bench](https://github.com/lm-sys/FastChat/pull/3158) of 8.98, which are known to be predictive of [LMSys Chatbot Arena Elo](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard) This Instruct model is available at [Llama-3.1-Nemotron-70B-Instruct](https://huggingface.co/nvidia/Llama-3.1-Nemotron-70B-Instruct) as .nemo model and [Llama-3.1-Nemotron-70B-Instruct-HF](https://huggingface.co/nvidia/Llama-3.1-Nemotron-70B-Instruct-HF) as a HF Transformers model. As of 1 Oct 2024, this aligned model is #1 on all three automatic alignment benchmarks, edging out strong frontier models such as GPT-4o and Claude 3.5 Sonnet. See details on HelpSteer2-Preference paper at [https://arxiv.org/abs/2410.01257](https://arxiv.org/abs/2410.01257) - as a preview, this model can correctly the question ```How many r in strawberry?``` without specialized prompting or additional reasoning tokens: ``` A sweet question! Let’s count the “R”s in “strawberry”: 1. S 2. T 3. R 4. A 5. W 6. B 7. E 8. R 9. R 10. Y There are **3 “R”s** in the word “strawberry”. ``` Reward Models was trained using the open-source [NeMo Aligner](https://github.com/NVIDIA/NeMo-Aligner). HelpSteer2 is a follow-up to the popular [HelpSteer](https://huggingface.co/datasets/nvidia/HelpSteer) dataset and we recommend using HelpSteer2 instead of HelpSteer. HelpSteer2 Paper : [HelpSteer2: Open-source dataset for training top-performing reward models](http://arxiv.org/abs/2406.08673) ## RewardBench Primary Dataset LeaderBoard As of 1 Oct 2024, Llama-3.1-Nemotron-70B-Reward performs best Overall on RewardBench as well as with strong performance in Chat, Safety and Reasoning categories among the models below. | Model | Type of Data Used For Training | Overall | Chat | Chat-Hard | Safety | Reasoning | |:-----------------------------|:----------------|:-----|:----------|:-------|:----------|:-----------------------| | _**Llama-3.1-Nemotron-70B-Reward**_ |Permissive Licensed Data Only (CC-BY-4.0) | **94.1** | **97.5** | 85.7 | **95.1** | **98.1** | | Skywork-Reward-Gemma-2-27B | Includes GPT4 Generated Data| 93.8 | 95.8 | **91.4** | 91.9 | 96.1 | | TextEval-Llama3.1-70B | Not disclosed | 93.5 | 94.1 | 90.1 | 93.2 | 96.4 | | Skywork-Critic-Llama-3.1-70B | Not fully disclosed | 93.3 | 96.6 | 87.9 | 93.1 | 95.5 | | SFR-LLaMa-3.1-70B-Judge-r | Not fully disclosed | 92.7 | 96.9 | 84.8 | 91.6 | 97.6 | Nemotron-4-340B-Reward | Permissive Licensed Data Only (CC-BY-4.0) | 92.0 | 95.8 | 87.1 | 91.5 | 93.7 | | ArmoRM-Llama3-8B-v0.1 | Includes GPT4 Generated Data | 90.8 | 96.9 | 76.8 | 92.2 | 97.3 | | Cohere May 2024 | Not disclosed | 89.5 | 96.4 | 71.3 | 92.7 | 97.7 | | Llama3-70B-SteerLM-RM | Permissive Licensed Data Only (CC-BY-4.0) | 88.8 | 91.3 | 80.3 | 92.8 | 90.7 | | Google Gemini Pro 1.5 | Not disclosed | 88.1 | 92.3 | 80.6 | 87.5 | 92.0 | | GPT-4o-2024-08-06 |Not disclosed | 86.7 | 96.1 | 76.1 | 88.1 | 86.6 | | claude-3-5-sonnet-20240620 | Not disclosed | 84.2 | 96.4 | 74.0 | 81.6 | 84.7 | | Meta-Llama-3.1-70B-Instruct | Not fully disclosed | 84.0 | 97.2 | 70.2 | 82.8 | 86.0 | To better understand why Llama-3.1-Nemotron-70B-Reward does less well in the Chat-Hard category, we analyze the scores for each consistutent subset under the Chat-Hard category. We find that on categories that uses human annotations as ground truth, Llama-3.1-Nemotron-70B-Reward performs similar to Skywork-Reward-Gemma-2-27B (<= 2.2% difference). On the other hand, when GPT-4 annotations are used as Ground-Truth, Llama-3.1-Nemotron-70B-Reward trails substantially behind Skywork-Reward-Gemma-2-27B (by 10.8 to 19.2%). This suggests that Skywork-Reward-Gemma-2-27B can better modelling GPT-4 preferences (but not human-annotated preferences), likely contributed by the inclusion of GPT-4 annotated training data used to train it found in the [OffSetBias dataset](https://huggingface.co/datasets/NCSOFT/offsetbias) as part of the [Skywork-Reward-Preference-80k](https://huggingface.co/datasets/Skywork/Skywork-Reward-Preference-80K-v0.1). | Model | Type of Data Used For Training | Chat-Hard | LLMBar-Adversarial-Manual | LLMBar-Adversarial-Neighbour | LLMBar-Natural | LLMBar-Adversarial-GPTInst | LLMBar-Adversarial-GPTOut | MT-Bench-Hard| |:-----------------------------|:----------------|:-----|:----------|:-------|:----------|:-----------------------|:-----------------------|:-----------------------| |||| Human as Ground Truth | Human as Ground Truth | Human as Ground Truth | _GPT-4 as Ground Truth_ |_GPT-4 as Ground Truth_ | _GPT-4 as Ground Truth_ | | Llama-3.1-Nemotron-70B-Reward |Permissive Licensed Data Only (CC-BY-4.0) | 85.7 | 76.1 | 88.8 | 95.0 | 87.0 | 72.3 | 75.7 | Skywork-Reward-Gemma-2-27B | Includes GPT4 Generated Data | 91.4 | 78.3 | 89.6 | 96.0 | 97.8 | 91.5 | 86.5| ## Dataset Description HelpSteer contains 21, 362 samples, each containing a prompt, a response as well as five human-annotated attributes of the response, each ranging between 0 and 4 where higher means better for each attribute. Consecutive samples (e.g. sample 1 with 2, 3 with 4 ...) share the same prompt so this can be used for preference pairs based on the helpfulness score (e.g. training DPO or Preference RM), in addition to training SteerLM Regression RM. About 29% of all prompts used are multi-turn. In this case, the prompt consists of all of the user turns and all but the last assistant turn, which is contained in the response field. This is done because the attribute values only assessed only for the last assistant turn. For multi-turn prompts, the structure of prompts look like below. This is how we used it for training SteerLM Regression Reward Models, but you need to use it in other settings, please adjust as required. ```json {user turn 1} <extra_id_1>Assistant {assistant turn 1} <extra_id_1>User {user turn 2} ``` These attributes are: 1. **Helpfulness**: Overall helpfulness of the response to the prompt. 2. **Correctness**: Inclusion of all pertinent facts without errors. 3. **Coherence**: Consistency and clarity of expression. 4. **Complexity**: Intellectual depth required to write response (i.e. whether the response can be written by anyone with basic language competency or requires deep domain expertise). 5. **Verbosity**: Amount of detail included in the response, relative to what is asked for in the prompt. An example looks like ```json { "prompt": "c#", "response": "C# is a high-level, object-oriented programming language developed by Microsoft as part of its .NET initiative. It was created as a modern alternative to Java and supports a variety of programming paradigms, including imperative, functional, and event-driven. C# is primarily used for Windows application development, but it can also be used for web, mobile, and game development. The language is designed to be safe, secure, and efficient, and it provides developers with a rich set of libraries and tools for building robust and scalable applications. C# is also widely used in the game development industry, particularly in the development of games for the Xbox 360 and Xbox One consoles.", "helpfulness": 3, "correctness": 4, "coherence": 4, "complexity": 2, "verbosity": 1 } ``` ## Preferences (NEW - 1 Oct 2024) We release the preference annotations between the responses. While existing HelpSteer2 can be use for Bradley-Terry Modelling (by taking difference in helpfulness), we also collected dedicated annotation for which among two responses they prefer. We recommend using this set of annotation for Bradley-Terry Modelling because it accounts for the situation that one response can be preferred over the other even when they have the same helpfulness score (e.g. 4) For each sample, we provide: **Preference Strength** (one of the following - score: meaning) -3: Response 1 is much better than Response 2 -2: Response 1 is better than Response 2 -1: Response 1 is slightly better than Response 2 0: Response 1 is about the same as Response 2 1: Response 2 is slightly better than Response 1 2: Response 2 is better than Response 1 3: Response 2 is much better than Response 1 **Human-written Preference Justification** This has been post-processed by us into a Preference statement and a Preference elaboration. **Three Most Similar Preferences** In calculating overall preference, we only consider the most similar preferences in order to filter outliers. **All Preferences Unprocessed** We did not use these in our experiments but we release all preferences in case someone finds them helpful. **Split** For clarity, the ```train``` subset of the ```preference``` annotations only contains prompts and responses from the original HelpSteer2 ```train``` set, as is for the validation subset. This represents our best attempt at doing a data matched comparison of the two data collection approaches. An example looks like ```json { "split": "train", "prompt": "Define Signal Discuss its various properties with the help of diagram", "response_1": "A signal is a form of energy that is used to transmit information from one place to another. It can be in the form of sound, light, radio waves, or any other form of energy that can be detected by a sensor or receiver.\n\nThe properties of a signal can be described by its amplitude, frequency, and phase.\n\nAmplitude: The amplitude of a signal is the measure of its strength or intensity. It is usually measured in volts or watts.\n\nFrequency: The frequency of a signal is the measure of how many times it repeats itself in a given period of time. It is usually measured in Hertz (Hz).\n\nPhase: The phase of a signal is the measure of its position in time. It is usually measured in degrees or radians.\n\nA signal can be represented by a graph that shows its amplitude or intensity over time. This graph is called a waveform. The shape of the waveform can provide information about the type of signal and the information it carries.", "response_2": "A signal is a message that is conveyed from a sender to a receiver through a communication channel. The message can be in the form of a sound, light, electrical, or any other form that can be transmitted and received. The properties of a signal can vary depending on the type of signal and the communication channel used. Here are some common properties of signals:\n\nAmplitude: This is the maximum value that a signal can take. It represents the strength of the signal. For example, in an electrical signal, amplitude can be measured in volts.\n\nFrequency: This is the number of cycles or oscillations of a signal per unit time. It is measured in hertz (Hz). For example, a sound signal with a frequency of 20 Hz vibrates 20 times per second.\n\nWavelength: This is the distance between two successive crests or troughs of a signal. It is measured in meters (m). For example, in a light signal, wavelength can be measured in nanometers (nm).\n\nPhase: This is the position of a signal with respect to a reference signal. It is measured in degrees (\u00b0) or radians (rad). For example, in an electrical signal, phase can be measured in relation to a reference signal.\n\nEnvelope: This is the curve that bounds the amplitude of a signal. It can be used to represent the shape of a signal. For example, in an audio signal, the envelope can represent the shape of a musical note.\n\nNoise: This is any unwanted or random variation in a signal that can affect its quality. It can be caused by various sources such as interference, interference, or natural phenomena.\n\nHere is a diagram that represents the properties of a signal:\n\n\nAmplitude\nFrequency\nWavelength\nPhase\nEnvelope\nNoise\n\n\nThe diagram shows how each property can affect the shape and quality of a signal. The presence of noise can degrade the quality of a signal and make it difficult to interpret. The properties of a signal can be controlled and optimized to ensure that the message is conveyed accurately and efficiently.", "preference_strength": 1, "preference_statement": "@Response 2 is better than @Response 1 because it provides a comprehensive insightful explanation of signanal and its properties.", "preference_elaboration": "It is complete, clear and correct as it discuss all the the poperties of signal while @Response 1 only discusses three properties of signal. It does not diuscuss important properties like noise, phase and envelope. @Response 2 follows all the instruction but @Response 1 does not follow all the instruction. For instance the instruction requires an explanation of signal and its properties with an aid of a diagram but @Response 1 does not provide the diagram.", "three_most_similar_preferences": [ { "statement": "@Response 2 is better than @Response 1 because it provides a comprehensive insightful explanation of signanal and its properties.", "elaboration": "It is complete, clear and correct as it discuss all the the poperties of signal while @Response 1 only discusses three properties of signal. It does not diuscuss important properties like noise, phase and envelope. @Response 2 follows all the instruction but @Response 1 does not follow all the instruction. For instance the instruction requires an explanation of signal and its properties with an aid of a diagram but @Response 1 does not provide the diagram.", "strength": 1 }, { "statement": "@Response 2 is slightly better than @Response 1.", "elaboration": "@Response 2 goes into detail about the different types of signals that can be used for transmittal. Providing these topics gives a full overview of Signal Discuss. That makes this prompt complete, extremely helpful, and it is well-written. This response uses a paragraph format which breaks up the change in topic. @Response 1 covers a signal in less detail. It leaves out wavelengths, noise, and envelop as a way to transmit information from one network to another. This is not necessarily bad, but it is not in full detail.", "strength": 1 }, { "statement": "@Response 2 is slightly better than @Response 1 because it includes the diagram as requested by the prompt, which @Response 1 does not.", "elaboration": "However, @Response 2 does have issues with **correctness**: irrelevant terms like \"envelope\" are typically properties of the diagram, not the signal. **Formatting** could also be improved for @Response 2. While the diagram is included, it does not display correctly and the word \"interference\" is erroneously repeated twice.", "strength": 1 } ], "all_preferences_unprocessed": [ { "strength": 1, "justification": "@Response 2 is better than @Response 1 because it provides a comprehensive insightful explanation of signanal and its properties. It is complete, clear and correct as it discuss all the the poperties of signal while @Response 1 only discusses three properties of signal. It does not diuscuss important properties like noise, phase and envelope. @Response 2 follows all the instruction but @Response 1 does not follow all the instruction. For instance the instruction requires an explanation of signal and its properties with an aid of a diagram but @Response 1 does not provide the diagram." }, { "strength": 1, "justification": "@Response 2 is slightly better than @Response 1. @Response 2 goes into detail about the different types of signals that can be used for transmittal. Providing these topics gives a full overview of Signal Discuss. That makes this prompt complete, extremely helpful, and it is well-written. This response uses a paragraph format which breaks up the change in topic. @Response 1 covers a signal in less detail. It leaves out wavelengths, noise, and envelop as a way to transmit information from one network to another. This is not necessarily bad, but it is not in full detail." }, { "strength": 1, "justification": "@Response 2 is slightly better than @Response 1 because it includes the diagram as requested by the prompt, which @Response 1 does not. However, @Response 2 does have issues with **correctness**: irrelevant terms like \"envelope\" are typically properties of the diagram, not the signal. **Formatting** could also be improved for @Response 2. While the diagram is included, it does not display correctly and the word \"interference\" is erroneously repeated twice. Although @Response 1 is more succinct and the writing style flows better, it falls short in **instructions following** and therefore @Response 2 is slightly better." } ] } ``` ## Disagreements (NEW - 1 Oct 2024) We release the individual annotations that were used to calculate the mean score (after filtering outliers) for the five HelpSteer attributes. The schema is similar to HelpSteer2, except that each entry is a list of labels given by various annotators. We believe this resource support studies on how/why annotators disagree with one another as well as transparency on the data collection process. Note: A small proportion of samples only annotations by 2 annotators - this is because one or more of the annotations were labelled as not-ratable by annotators or did not have a valid annotation. Here's an example below: ```json { "prompt": "c#", "response": "C# is a high-level, object-oriented programming language developed by Microsoft as part of its .NET initiative. It was created as a modern alternative to Java and supports a variety of programming paradigms, including imperative, functional, and event-driven. C# is primarily used for Windows application development, but it can also be used for web, mobile, and game development. The language is designed to be safe, secure, and efficient, and it provides developers with a rich set of libraries and tools for building robust and scalable applications. C# is also widely used in the game development industry, particularly in the development of games for the Xbox 360 and Xbox One consoles.", "helpfulness": [ 3, 3, 4 ], "correctness": [ 3, 4, 4 ], "coherence": [ 4, 3, 4 ], "complexity": [ 2, 2, 2 ], "verbosity": [ 2, 1, 1 ] } ``` ## Using the Huggingface Datasets ```python from datasets import load_dataset ds = load_dataset("nvidia/HelpSteer2") train = ds['train'] # len(train) = 20324 (95%) val = ds['validation'] # len(val) = 1038 (5%) preference = load_dataset("nvidia/HelpSteer2", data_dir="preference")['train'] # despite the name, this contains both train and val, which you can use split to distinguish disagreements = load_dataset("nvidia/HelpSteer2", data_dir="disagreements")['train'] ``` ## Source 1. Prompts are collected based on mostly user-contributed ShareGPT prompts and with a small proportion (~5%) that are human generated by Scale AI. 2. Responses are generated by early versions of a mix of 10 different inhouse LLMs (note: none from properitary LLM providers such as OpenAI). We generate 2 responses per prompts (each from a different model) using sampling techniques to give diverse yet reasonable responses. 3. Annotations of various attributes were done by Scale AI. Annotators rated each response on a Likert 5 scale (between 0 and 4) for each attribute (helpfulness, correctness, coherence, complexity and verbosity). ## Annotation methodology (short) 1. We engaged a select group of contractors via Scale AI. These contractors were provided with comprehensive guidelines that defined each attribute and the criteria for every rating level, together with some annotated examples. These guidelines and examples are detailed in the Appendix of the accompanying paper. 2. The annotation process involved approximately 1000 U.S.-based human annotators. Candidates first underwent preliminary assignments, including assessments of English proficiency, to determine eligibility for working on the project. Subsequently, they participated in an introductory training course on the task which ended with a test that involved annotating 35 sample responses. This process ensured not only a thorough understanding of the task requirements but also the delivery of high-quality annotations. 3. Every sample was independently annotated by a minimum of three annotators and up to five annotators, if the initial annotators do not agree with each other sufficiently (2 points or less on helpfulness). The final annotations (mean of 3.41 annotators) were obtain by taking the mean of the three annotators who agree with each other most, rounded to the nearest integer. 4. Post-annotations, Scale AI performed extensive quality assurance, with each annotation reaching a minimum of two human reviews in addition to automated checks. After receiving the annotations from Scale AI, we conducted our independent quality assurance to make sure that the quality of the annotations was up to our expectations. As a result, many annotations were filtered away to retain only 20, 324 samples. ## Ethical statement Annotators for the dataset were contracted through Scale AI. Scale AI engages the Anker Methodology, GISC Impact Sourcing Standard, and UN Sustainable Development Goals to provide a fair and competitive pay. The specific pay is calculated based on many factors, including the specific project, the specialized skillset and expertise required, regional costs of living and then transparently listed on Scale AI platform. Scale AI also provides multiple channels for questions and support, including 24/7 support teams, community discussion channels with specially trained moderators, and a “speak up” hotline where contractors can report concerns anonymously. Worker concerns can be submitted to and are reviewed by our Remotasks support team, and pay disputes are reviewed by support specialists trained in this area. ## Citation If you find this dataset useful, please cite the following works ```bibtex @misc{wang2024helpsteer2preferencecomplementingratingspreferences, title={HelpSteer2-Preference: Complementing Ratings with Preferences}, author={Zhilin Wang and Alexander Bukharin and Olivier Delalleau and Daniel Egert and Gerald Shen and Jiaqi Zeng and Oleksii Kuchaiev and Yi Dong}, year={2024}, eprint={2410.01257}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/2410.01257}, } @misc{wang2024helpsteer2, title={HelpSteer2: Open-source dataset for training top-performing reward models}, author={Zhilin Wang and Yi Dong and Olivier Delalleau and Jiaqi Zeng and Gerald Shen and Daniel Egert and Jimmy J. Zhang and Makesh Narsimhan Sreedhar and Oleksii Kuchaiev}, year={2024}, eprint={2406.08673}, archivePrefix={arXiv}, primaryClass={id='cs.CL' full_name='Computation and Language' is_active=True alt_name='cmp-lg' in_archive='cs' is_general=False description='Covers natural language processing. Roughly includes material in ACM Subject Class I.2.7. Note that work on artificial languages (programming languages, logics, formal systems) that does not explicitly address natural-language issues broadly construed (natural-language processing, computational linguistics, speech, text retrieval, etc.) is not appropriate for this area.'} } ```

tiange/Cap3D

tiange

--- license: odc-by viewer: false task_categories: - text-to-3d - image-to-3d --- ## Dataset Description - **Paper:** [Scalable 3D Captioning with Pretrained Models](https://arxiv.org/abs/2306.07279) - **Paper:** [View Selection for 3D Captioning via Diffusion Ranking](https://arxiv.org/abs/2404.07984) - **Repository**: [Github_Cap3D](https://github.com/crockwell/Cap3D) - **Repository**: [Github_DiffuRank](https://github.com/tiangeluo/DiffuRank) - **Project**: [Project](https://cap3d-um.github.io/) This repository hosts data for [Scalable 3D Captioning with Pretrained Models](https://cap3d-um.github.io/) and [View Selection for 3D Captioning via Diffusion Ranking](http://arxiv.org/abs/2404.07984), including descriptive **captions** for 3D objects in [Objaverse](https://arxiv.org/abs/2212.08051), [Objaverse-XL](https://arxiv.org/pdf/2307.05663.pdf), [ABO](https://arxiv.org/abs/2110.06199), and [ShapeNet](https://arxiv.org/abs/1512.03012). This repo also includes **point clouds** and **rendered images with camera, depth, and MatAlpha information** of Objaverse objects, as well as their Shap-E latent codes. All the captions and data provided by our papers are released under ODC-By 1.0 license. ## Usage Please download and unzip files from [**Page**](https://huggingface.co/datasets/tiange/Cap3D/tree/main) according to your usage. Below is a table listing fiels descriptions, followed by example Python scripts for data loading. | Filename | Description | | -------------------------------------- | ------------------------------------------------------------ | | **Cap3D_automated_Objaverse_full.csv** | By integrating text descriptions initially generated by [Cap3D](https://arxiv.org/abs/2306.07279) and subsequently refined by [DiffuRank](https://arxiv.org/abs/2404.07984), we have produced a total of **1,006,782** 3D-caption pairs. Out of the total, **785,150** pairs have been contributed to the whole [Objaverse](https://arxiv.org/abs/2212.08051) dataset, with the balance for the [Objaverse-XL](https://arxiv.org/pdf/2307.05663.pdf) dataset (specifically the highquality subset described in Section 4.1 Alignment Finetuning of [Objaverse-XL](https://proceedings.neurips.cc/paper_files/paper/2023/file/70364304877b5e767de4e9a2a511be0c-Paper-Datasets_and_Benchmarks.pdf)). For the object identifier in the left column, strings with a length of 32 characters are **UIDs** from Objaverse 1.0 (retrieved using `import objaverse; uids = objaverse.load_uids()`). Strings with a length of 64 characters are **SHA256** hashes provided by Objaverse-XL. | | Cap3D_automated_**ABO**.csv | Our captions generated by [Cap3D](https://arxiv.org/abs/2306.07279) and [DiffuRank](https://arxiv.org/abs/2404.07984) for the [ABO dataset](https://arxiv.org/abs/2110.06199), including both general and compositional descriptions. | | Cap3D_automated_**ShapeNet**.csv | Our captions generated by [Cap3D](https://arxiv.org/abs/2306.07279) and [DiffuRank](https://arxiv.org/abs/2404.07984) for the [ShapeNet dataset](https://arxiv.org/abs/1512.03012). | | **PointCloud_zips** | Provided by [Cap3D](https://arxiv.org/abs/2306.07279) and [DiffuRank](https://arxiv.org/abs/2404.07984), **1,006,782** PointClouds (16,384 colorful points) extracted from Objaverse objects. Saved as `.ply` file. | | PointCloud_zips_**ABO** | Provided by [Cap3D](https://arxiv.org/abs/2306.07279) and [DiffuRank](https://arxiv.org/abs/2404.07984), **7,953** PointClouds (16,384 colorful points) extracted from ABO objects. Saved as `.ply` file. | | PointCloud_zips_**ShapeNet** | Provided by [Cap3D](https://arxiv.org/abs/2306.07279) and [DiffuRank](https://arxiv.org/abs/2404.07984), **52,472** PointClouds (16,384 colorful points) extracted from ShapeNet objects. Saved as `.ply` file. | | **RenderedImage_perobj_zips** | Provided by [DiffuRank](https://arxiv.org/abs/2404.07984), Rendered images for **1,006,782** Objaverse objects. Once unzip `compressed_imgs_perobj_xx.zip` will have multiple zip files which consists of **20** rendered images along with camera details (intrinsic & extrinsic), depth data, and masks ([one example](https://huggingface.co/datasets/tiange/Cap3D/tree/main/RenderedImage_perobj_zips/example_zipfile)). Please specify the unzip path, such as `unzip ed51a51909ee46c780db3a85e821feb2.zip -d ed51a51909ee46c780db3a85e821feb2`. More information are in [here](https://huggingface.co/datasets/tiange/Cap3D/blob/main/RenderedImage_perobj_zips/README.md). | | RenderedImage_perobj_zips_**ABO** | Provided by [DiffuRank](https://arxiv.org/abs/2404.07984), Rendered images for **7,953** ABO objects. Details similar to the above. | | RenderedImage_perobj_zips_**ShapeNet** | Provided by [DiffuRank](https://arxiv.org/abs/2404.07984), Rendered images for **52,472** ShapeNet objects. Similar to the above but with 8 rendered images. | | misc | Including miscellaneous files such as human-authored captions, finetuned models, objaverse pointclouds stored as .pt, shapE latent codes, and etc. Please refer to this [README](https://huggingface.co/datasets/tiange/Cap3D/blob/main/misc/README.md) | ``` python # load our captions import pandas as pd captions = pd.read_csv('Cap3D_automated_Objaverse_full.csv', header=None) ## captions: ## 0 1 ## 0 ed51a51909ee46c780db3a85e821feb2 Matte green rifle with a long barrel, stock, a... ## 1 9110b606f6c547b2980fcb3c8c4b6a1c Rustic single-story building with a weathered ... ## 2 80d9caaa1fa04502af666135196456e1 a pair of purple and black swords with white h... ## 3 28d43a218cd8466a8c1f82b29b71e314 3D model of a cluttered outdoor scene with veg... ## 4 75582285fab442a2ba31733f9c8fae66 Floating terrain piece with grassy landscape a... ## ... ... ... ## 1002417 3623e74f34c1c3c523af6b2bb8ffcbe2d2dce897ef61b9... Abstract 3D composition with human figures and... ## 1002418 64e9f7b7a1fc4c4ec56ed8b5917dfd610930043ac5e15f... 3D object with a rough, irregular pink surface... ## 1002419 fcd089d6a237fee21dfd5f0d6d9b74b2fd1150cdc61c7f... Bright pink abstract 3D model of a building wi... ## 1002420 f812dc980050f2d5f4b37df2a8620372f810dd6456a5f2... Monochromatic gray 3D model of a stylized huma... ## 1002421 77c09500b4d8e4b881e1ce6929d56c23658b87173c0996... Modular futuristic spacecraft with red and ora... ## if u want to obtain the caption for specific UID caption = captions[captions[0] == '80d9caaa1fa04502af666135196456e1'][1].values[0] # load point clouds (unzip https://huggingface.co/datasets/tiange/Cap3D/tree/main/PointCloud_pt_zips) import torch pts = torch.load('Cap3D_pcs_pt/80d9caaa1fa04502af666135196456e1.pt') ## pts.shape == torch.Size([6, 16384]) ``` If you have any questions, please contact [Tiange](mailto:[email protected]) or [Chris](mailto:[email protected]). ## Citation Information If you find our data or code useful, please consider citing: ```bibtex @article{luo2023scalable, title={Scalable 3D Captioning with Pretrained Models}, author={Luo, Tiange and Rockwell, Chris and Lee, Honglak and Johnson, Justin}, journal={arXiv preprint arXiv:2306.07279}, year={2023} } @article{luo2024view, title={View Selection for 3D Captioning via Diffusion Ranking}, author={Luo, Tiange and Johnson, Justin and Lee, Honglak}, journal={arXiv preprint arXiv:2404.07984}, year={2024} } ``` Please cite ***Objaverse*** and ***ABO*** paper accordingly, if you use related data. ``` @inproceedings{deitke2023objaverse, title={Objaverse: A universe of annotated 3d objects}, author={Deitke, Matt and Schwenk, Dustin and Salvador, Jordi and Weihs, Luca and Michel, Oscar and VanderBilt, Eli and Schmidt, Ludwig and Ehsani, Kiana and Kembhavi, Aniruddha and Farhadi, Ali}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages={13142--13153}, year={2023} } @article{deitke2024objaverse, title={Objaverse-xl: A universe of 10m+ 3d objects}, author={Deitke, Matt and Liu, Ruoshi and Wallingford, Matthew and Ngo, Huong and Michel, Oscar and Kusupati, Aditya and Fan, Alan and Laforte, Christian and Voleti, Vikram and Gadre, Samir Yitzhak and others}, journal={Advances in Neural Information Processing Systems}, volume={36}, year={2024} } @inproceedings{collins2022abo, title={Abo: Dataset and benchmarks for real-world 3d object understanding}, author={Collins, Jasmine and Goel, Shubham and Deng, Kenan and Luthra, Achleshwar and Xu, Leon and Gundogdu, Erhan and Zhang, Xi and Vicente, Tomas F Yago and Dideriksen, Thomas and Arora, Himanshu and others}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages={21126--21136}, year={2022} } ```

mlfoundations/dclm-pool-1b-5x

mlfoundations

--- license: cc-by-4.0 ---

bigcode/humanevalpack

bigcode

--- license: mit pretty_name: HumanEvalPack language_creators: - expert-generated multilinguality: - multilingual language: - code tags: - code ---  # Dataset Card for HumanEvalPack ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Additional Information](#additional-information) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** https://github.com/bigcode-project/octopack - **Paper:** [OctoPack: Instruction Tuning Code Large Language Models](https://arxiv.org/abs/2308.07124) - **Point of Contact:** [Niklas Muennighoff](mailto:[email protected]) ### Dataset Summary > HumanEvalPack is an extension of OpenAI's HumanEval to cover 6 total languages across 3 tasks. The Python split is exactly the same as OpenAI's Python HumanEval. The other splits are translated by humans (similar to HumanEval-X but with additional cleaning, see [here](https://github.com/bigcode-project/octopack/tree/main/evaluation/create/humaneval-x#modifications-muennighoff)). Refer to the [OctoPack paper](https://arxiv.org/abs/2308.07124) for more details. > - **Languages:** Python, JavaScript, Java, Go, C++, Rust - **OctoPack🐙🎒:** <table> <tr> <th>Data</t> <td><a href=https://huggingface.co/datasets/bigcode/commitpack>CommitPack</a></td> <td>4TB of GitHub commits across 350 programming languages</td> </tr> <tr> <th></t> <td><a href=https://huggingface.co/datasets/bigcode/commitpackft>CommitPackFT</a></td> <td>Filtered version of CommitPack for high-quality commit messages that resemble instructions</td> </tr> <tr> <th>Model</t> <td><a href=https://huggingface.co/bigcode/octocoder>OctoCoder</a></td> <td>StarCoder (16B parameters) instruction tuned on CommitPackFT + OASST</td> </tr> <tr> <th></t> <td><a href=https://huggingface.co/bigcode/octogeex>OctoGeeX</a></td> <td>CodeGeeX2 (6B parameters) instruction tuned on CommitPackFT + OASST</td> </tr> <tr> <th>Evaluation</t> <td><a href=https://huggingface.co/datasets/bigcode/humanevalpack>HumanEvalPack</a></td> <td>Extension of OpenAI's HumanEval to cover 3 scenarios across 6 languages</td> </tr> </table> ## Usage ```python # pip install -q datasets from datasets import load_dataset # Languages: "python", "js", "java", "go", "cpp", "rust" ds = load_dataset("bigcode/humanevalpack", "python")["test"] ds[0] ``` ## Dataset Structure ### Data Instances An example looks as follows: ```json { "task_id": "Python/0", "prompt": "from typing import List\n\n\ndef has_close_elements(numbers: List[float], threshold: float) -> bool:\n \"\"\" Check if in given list of numbers, are any two numbers closer to each other than\n given threshold.\n >>> has_close_elements([1.0, 2.0, 3.0], 0.5)\n False\n >>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)\n True\n \"\"\"\n", "declaration": "from typing import List\n\n\ndef has_close_elements(numbers: List[float], threshold: float) -> bool:\n", "canonical_solution": " for idx, elem in enumerate(numbers):\n for idx2, elem2 in enumerate(numbers):\n if idx != idx2:\n distance = abs(elem - elem2)\n if distance < threshold:\n return True\n\n return False\n", "buggy_solution": " for idx, elem in enumerate(numbers):\n for idx2, elem2 in enumerate(numbers):\n if idx != idx2:\n distance = elem - elem2\n if distance < threshold:\n return True\n\n return False\n", "bug_type": "missing logic", "failure_symptoms": "incorrect output", "entry_point": "has_close_elements", "import": "" "test_setup": "" "test": "\n\n\n\n\ndef check(has_close_elements):\n assert has_close_elements([1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.3) == True\n assert has_close_elements([1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.05) == False\n assert has_close_elements([1.0, 2.0, 5.9, 4.0, 5.0], 0.95) == True\n assert has_close_elements([1.0, 2.0, 5.9, 4.0, 5.0], 0.8) == False\n assert has_close_elements([1.0, 2.0, 3.0, 4.0, 5.0, 2.0], 0.1) == True\n assert has_close_elements([1.1, 2.2, 3.1, 4.1, 5.1], 1.0) == True\n assert has_close_elements([1.1, 2.2, 3.1, 4.1, 5.1], 0.5) == False\n\ncheck(has_close_elements)", "example_test": "def check(has_close_elements):\n assert has_close_elements([1.0, 2.0, 3.0], 0.5) == False\n assert has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3) == True\ncheck(has_close_elements)\n", "signature": "has_close_elements(numbers: List[float], threshold: float) -> bool", "docstring": "Check if in given list of numbers, are any two numbers closer to each other than\ngiven threshold.\n>>> has_close_elements([1.0, 2.0, 3.0], 0.5)\nFalse\n>>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)\nTrue", "instruction": "Write a Python function `has_close_elements(numbers: List[float], threshold: float) -> bool` to solve the following problem:\nCheck if in given list of numbers, are any two numbers closer to each other than\ngiven threshold.\n>>> has_close_elements([1.0, 2.0, 3.0], 0.5)\nFalse\n>>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)\nTrue" } ``` ### Data Fields The data fields are the same among all splits: - `task_id`: Indicates the language (Python/JavaScript/Java/Go/C++/Rust) and task id (from 0 to 163) of the problem - `prompt`: the prompt for models relying on code continuation - `declaration`: the declaration of the function (same as prompt but without the docstring) - `canonical_solution`: the correct solution passing all unit tests for the problem - `buggy_solution`: same as `canonical_solution` but with a subtle human-written bug causing the unit tests to fail - `bug_type`: the type of the bug in `buggy_solution` (one of [`missing logic`, `excess logic`, `value misuse`, `operator misuse`, `variable misuse`, `function misuse`]) - `failure_symptoms`: the problem the bug causes (one of [`incorrect output`, `stackoverflow`, `infinite loop`]) - `entry_point`: the name of the function - `import`: imports necessary for the solution (only present for Go) - `test_setup`: imports necessary for the test execution (only present for Go) - `test`: the unit tests for the problem - `example_test`: additional unit tests different from `test` that could be e.g. provided to the model (these are not used in the paper) - `signature`: the signature of the function - `docstring`: the docstring describing the problem - `instruction`: an instruction for HumanEvalSynthesize in the form `Write a {language_name} function {signature} to solve the following problem:\n{docstring}` ## Citation Information ```bibtex @article{muennighoff2023octopack, title={OctoPack: Instruction Tuning Code Large Language Models}, author={Niklas Muennighoff and Qian Liu and Armel Zebaze and Qinkai Zheng and Binyuan Hui and Terry Yue Zhuo and Swayam Singh and Xiangru Tang and Leandro von Werra and Shayne Longpre}, journal={arXiv preprint arXiv:2308.07124}, year={2023} } ```

capcutdn2024/storage

capcutdn2024

--- license: apache-2.0 ---

indolem/IndoMMLU

indolem

--- license: mit task_categories: - question-answering language: - id tags: - knowledge pretty_name: IndoMMLU size_categories: - 10K<n<100K --- # IndoMMLU <!--- [](https://github.com/internLM/OpenCompass/) [](https://github.com/EleutherAI/lm-evaluation-harness) --> <p align="center"> <img src="https://raw.githubusercontent.com/fajri91/eval_picts/master/IndoMMLU-Bar.png" style="width: 100%;" id="title-icon"> </p> <p align="center"> <a href="http://www.fajrikoto.com" target="_blank">Fajri Koto</a>, <a href="https://www.linkedin.com/in/nuaisyah/" target="_blank">Nurul Aisyah</a>, <a href="https://haonan-li.github.io/" target="_blank">Haonan Li</a>, <a href="https://people.eng.unimelb.edu.au/tbaldwin/" target="_blank">Timothy Baldwin</a> </p> <h4 align="center"> <p align="center" style="display: flex; flex-direction: row; justify-content: center; align-items: center"> 📄 <a href="https://arxiv.org/abs/2310.04928" target="_blank" style="margin-right: 15px; margin-left: 10px">Paper</a> • 🏆 <a href="https://github.com/fajri91/IndoMMLU/blob/main/README_EN.md#evaluation" target="_blank" style="margin-left: 10px">Leaderboard</a> • 🤗 <a href="https://huggingface.co/datasets/indolem/indommlu" target="_blank" style="margin-left: 10px">Dataset</a> </p> </h4> ## Introduction We introduce IndoMMLU, the first multi-task language understanding benchmark for Indonesian culture and languages, which consists of questions from primary school to university entrance exams in Indonesia. By employing professional teachers, we obtain 14,906 questions across 63 tasks and education levels, with 46\% of the questions focusing on assessing proficiency in the Indonesian language and knowledge of nine local languages and cultures in Indonesia. <p align="left"> <img src="https://github.com/fajri91/eval_picts/blob/master/IndoMMLU-dist.png?raw=true" style="width: 500px;" id="title-icon"> </p> ## Subjects | Level | Subjects | |-----------|------------------------------------| | SD (Primary School) | Science, Social science, Civics, Indonesian Language, Balinese, Makassarese, Banjarese, Lampungic, Madurese, Sundanese, Javanese, Dayak Ngaju, Minangkabau culture, Art, Sports, Islam religion, Christian religion, Hindu religion | | SMP (Junior High School) | Science, Social science, Civics, Indonesian Language, Balinese, Makassarese, Banjarese, Lampungic, Madurese, Sundanese, Javanese, Minangkabau culture, Art, Sports, Islam religion, Christian religion, Hindu religion | | SMA (Senior High School) | Physics, Chemistry, Biology, Geography, Sociology, Economics, History, Civics, Indonesian Language, Balinese, Makassarese, Banjarese, Lampungic, Madurese, Sundanese, Javanese, Art, Sports, Islam religion, Christian religion, Hindu religion | University Entrance Test | Chemistry, Biology, Geography, Sociology, Economics, History, Indonesian Language | We categorize the collected questions into different subject areas, including: (1) STEM (Science, Technology, Engineering, and Mathematics); (2) Social Science; (3) Humanities; (4) Indonesian Language; and (5) Local Languages and Cultures. ## Examples These questions are written in Indonesian. For local language subjects, some are written in the local languages. The English version is for illustrative purposes only. <p align="left"> <img src="https://github.com/fajri91/eval_picts/blob/master/min_example.png?raw=true" style="width: 400px;" id="title-icon"> </p> ## Evaluation We evaluate 24 multilingual LLMs of different sizes in zero-shot and few-shot settings. This includes [GPT-3.5 (ChatGPT)](https://chat.openai.com/), [XGLM](https://arxiv.org/abs/2112.10668), [Falcon](https://falconllm.tii.ae/), [BLOOMZ](https://huggingface.co/bigscience/bloomz), [mT0](https://huggingface.co/bigscience/bloomz), [LLaMA](https://arxiv.org/abs/2302.13971), and [Bactrian-X](https://github.com/mbzuai-nlp/bactrian-x). Prior to the question and multiple-choice options, we add a simple prompt in the Indonesian language: ``` Ini adalah soal [subject] untuk [level]. Pilihlah salah satu jawaban yang dianggap benar! English Translation: This is a [subject] question for [level]. Please choose the correct answer! ``` #### Zero-shot Evaluation | Model (#param) | STEM | Social Science | Humanities | Indonesian Lang. | Local L. Culture | Average | |---------------------|------|----------|-------------|---------|----------|---------| | Random | 21.9 | 23.4 | 23.5 | 24.4 | 26.6 | 24.4 | | [GPT-3.5 (175B)](https://chat.openai.com/) | **54.3** | **62.5** | **64.0** | **62.2** | 39.3 | **53.2** | | [XGLM (564M)](https://huggingface.co/facebook/xglm-564M) | 22.1 | 23.0 | 25.6 | 25.6 | 27.5 | 25.2 | | [XGLM (1.7B)](https://huggingface.co/facebook/xglm-1.7B) | 20.9 | 23.0 | 24.6 | 24.8 | 26.6 | 24.4 | | [XGLM (2.9B)](https://huggingface.co/facebook/xglm-2.9B) | 22.9 | 23.2 | 25.4 | 26.3 | 27.2 | 25.2 | | [XGLM (4.5B)](https://huggingface.co/facebook/xglm-4.5B) | 21.8 | 23.1 | 25.6 | 25.8 | 27.1 | 25.0 | | [XGLM (7.5B)](https://huggingface.co/facebook/xglm-7.5B) | 22.7 | 21.7 | 23.6 | 24.5 | 27.5 | 24.5 | | [Falcon (7B)](https://huggingface.co/tiiuae/falcon-7b) | 22.1 | 22.9 | 25.5 | 25.7 | 27.5 | 25.1 | | [Falcon (40B)](https://huggingface.co/tiiuae/falcon-40b) | 30.2 | 34.8 | 34.8 | 34.9 | 29.2 | 32.1 | | [BLOOMZ (560M)](https://huggingface.co/bigscience/bloomz-560m) | 22.9 | 23.6 | 23.2 | 24.2 | 25.1 | 24.0 | | [BLOOMZ (1.1B)](https://huggingface.co/bigscience/bloomz-1b1) | 20.4 | 21.4 | 21.1 | 23.5 | 24.7 | 22.4 | | [BLOOMZ (1.7B)](https://huggingface.co/bigscience/bloomz-1b7) | 31.5 | 39.3 | 38.3 | 42.8 | 29.4 | 34.4 | | [BLOOMZ (3B)](https://huggingface.co/bigscience/bloomz-3b) | 33.5 | 44.5 | 39.7 | 46.7 | 29.8 | 36.4 | | [BLOOMZ (7.1B)](https://huggingface.co/bigscience/bloomz-7b1) | 37.1 | 46.7 | 44.0 | 49.1 | 28.2 | 38.0 | | [mT0_small (300M)](https://huggingface.co/bigscience/mt0-small) | 21.8 | 21.4 | 25.7 | 25.1 | 27.6 | 24.9 | | [mT0_base (580M)](https://huggingface.co/bigscience/mt0-base) | 22.6 | 22.6 | 25.7 | 25.6 | 26.9 | 25.0 | | [mT0_large (1.2B)](https://huggingface.co/bigscience/mt0-large) | 22.0 | 23.4 | 25.1 | 27.3 | 27.6 | 25.2 | | [mT0_xl (3.7B)](https://huggingface.co/bigscience/mt0-xl) | 31.4 | 42.9 | 41.0 | 47.8 | 35.7 | 38.2 | | [mT0_xxl (13B)](https://huggingface.co/bigscience/mt0-xxl) | 33.5 | 46.2 | 47.9 | 52.6 | **39.6** | 42.5 | | [LLaMA (7B)](https://arxiv.org/abs/2302.13971) | 22.8 | 23.1 | 25.1 | 26.7 | 27.6 | 25.3 | | [LLaMA (13B)](https://arxiv.org/abs/2302.13971) | 24.1 | 23.0 | 24.4 | 29.5 | 26.7 | 25.3 | | [LLaMA (30B)](https://arxiv.org/abs/2302.13971) | 25.4 | 23.5 | 25.9 | 28.4 | 28.7 | 26.5 | | [LLaMA (65B)](https://arxiv.org/abs/2302.13971) | 33.0 | 37.7 | 40.8 | 41.4 | 32.1 | 35.8 | | [Bactrian-X-LLaMA (7B)](https://github.com/mbzuai-nlp/bactrian-x) | 23.3 | 24.0 | 26.0 | 26.1 | 27.5 | 25.7 | | [Bactrian-X-LLaMA (13B)](https://github.com/mbzuai-nlp/bactrian-x) | 28.3 | 29.9 | 32.8 | 35.2 | 29.2 | 30.3 | #### GPT-3.5 performance (% accuracy) across different education levels <p align="left"> <img src="https://github.com/fajri91/eval_picts/blob/master/IndoMMLU-result.png?raw=true" style="width: 370px;" id="title-icon"> </p> Red indicates that the score is below the minimum passing threshold of 65, while green signifies a score at or above this minimum. We can observe that ChatGPT mostly passes a score of 65 in Indonesian primary school exams. #### Few-shot Evaluation <p align="left"> <img src="https://github.com/fajri91/eval_picts/blob/master/plot_fewshot.png?raw=true" style="width: 380px;" id="title-icon"> </p> ## Data Each question in the dataset is a multiple-choice question with up to 5 choices and only one choice as the correct answer. We provide our dataset according to each subject in [data](data) folder. You can also access our dataset via [Hugging Face](https://huggingface.co/datasets/indolem/indommlu). <!-- #### Quick Use Our dataset has been added to [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) and [OpenCompass](https://github.com/InternLM/opencompass), you can evaluate your model via these open-source tools. --> #### Evaluation The code for the evaluation of each model we used is in `evaluate.py`, and the code to run them is listed in `run.sh`. ## Citation ``` @inproceedings{koto-etal-2023-indommlu, title = "Large Language Models Only Pass Primary School Exams in {I}ndonesia: A Comprehensive Test on {I}ndo{MMLU}", author = "Fajri Koto and Nurul Aisyah and Haonan Li and Timothy Baldwin", booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP)", month = December, year = "2023", address = "Singapore", publisher = "Association for Computational Linguistics", } ``` ## License The IndoMMLU dataset is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-nc-sa/4.0/).

zy1111/test

zy1111

--- license: apache-2.0 ---

lmms-lab/MMMU

lmms-lab

--- dataset_info: features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 57719107.0 num_examples: 150 - name: validation num_bytes: 347519954.0 num_examples: 900 - name: test num_bytes: 3271046267.0 num_examples: 10500 download_size: 3377778136 dataset_size: 3676285328.0 configs: - config_name: default data_files: - split: dev path: data/dev-* - split: validation path: data/validation-* - split: test path: data/test-* --- This is a merged version of [MMMU/MMMU](https://huggingface.co/datasets/MMMU/MMMU) with all subsets concatenated. <p align="center" width="100%"> <img src="https://i.postimg.cc/g0QRgMVv/WX20240228-113337-2x.png" width="100%" height="80%"> </p> # Large-scale Multi-modality Models Evaluation Suite > Accelerating the development of large-scale multi-modality models (LMMs) with `lmms-eval` 🏠 [Homepage](https://lmms-lab.github.io/) | 📚 [Documentation](docs/README.md) | 🤗 [Huggingface Datasets](https://huggingface.co/lmms-lab) # This Dataset This is a formatted version of [MMMU](https://github.com/MMMU-Benchmark/MMMU). It is used in our `lmms-eval` pipeline to allow for one-click evaluations of large multi-modality models. ``` @article{yue2023mmmu, title={Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for expert agi}, author={Yue, Xiang and Ni, Yuansheng and Zhang, Kai and Zheng, Tianyu and Liu, Ruoqi and Zhang, Ge and Stevens, Samuel and Jiang, Dongfu and Ren, Weiming and Sun, Yuxuan and others}, journal={arXiv preprint arXiv:2311.16502}, year={2023} } ```

OpenDILabCommunity/LMDrive

OpenDILabCommunity

--- configs: - config_name: default data_files: - split: train path: navigation_instruction_list.txt sep: " " default: true license: apache-2.0 language: - en size_categories: - n>1T --- # LMDrive 64K Dataset Card LMDrive Dataset consists of 64K instruction-sensor-control data clips collected in the CARLA simulator， where each clip includes one navigation instruction, several notice instructions, a sequence of multi-modal multi-view sensor data, and control signals. The duration of the clip spans from 2 to 20 seconds. ## Dataset details - `data/`: dataset folder, the entire dataset contains about 2T of data. - `data/Town01`: sub dataset folder, which only consists of the data folder for the Town01 - `data/Town02`: sub dataset folder, which only consists of the data folder for the Town02 - ... - `dataset_index.txt`: the data list for pretraining the vision encoder - `navigation_instruction_list.txt`: the data list for instruction finetuning - `notice_instruction_list.json`: the data list for instruction finetuning (optional if the notice instruction data is not engaged in the training) **Dataset date:** LMDrive-1.0 Dataset was collected in September 2023. **Paper or resources for more information:** Github: https://github.com/opendilab/LMDrive/README.md Paper: https://arxiv.org/abs/2312.07488 **License:** Attribution-NonCommercial 4.0 International **Where to send questions or comments about the model:** https://github.com/opendilab/LMDrive/issues ## Intended use **Primary intended uses:** The primary use of LMDrive is research on large multimodal models for autonomous driving. **Primary intended users:** The primary intended users of the model are researchers and hobbyists in computer vision, large multimodal model, autonomous driving, and artificial intelligence.

bigscience/xP3all

bigscience

--- annotations_creators: - expert-generated - crowdsourced language: - ak - ar - as - bm - bn - ca - code - en - es - eu - fon - fr - gu - hi - id - ig - ki - kn - lg - ln - ml - mr - ne - nso - ny - or - pa - pt - rn - rw - sn - st - sw - ta - te - tn - ts - tum - tw - ur - vi - wo - xh - yo - zh - zu programming_language: - C - C++ - C# - Go - Java - JavaScript - Lua - PHP - Python - Ruby - Rust - Scala - TypeScript license: - apache-2.0 multilinguality: - multilingual pretty_name: xP3 size_categories: - 100M<n<1B task_categories: - other --- # Dataset Card for xP3 ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Additional Information](#additional-information) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** https://github.com/bigscience-workshop/xmtf - **Paper:** [Crosslingual Generalization through Multitask Finetuning](https://arxiv.org/abs/2211.01786) - **Point of Contact:** [Niklas Muennighoff](mailto:[email protected]) ### Dataset Summary > xP3 (Crosslingual Public Pool of Prompts) is a collection of prompts & datasets across 46 of languages & 16 NLP tasks. It is used for the training of BLOOMZ and mT0, multilingual language models capable of following human instructions in dozens of languages zero-shot. - **Creation:** The dataset can be recreated using instructions available [here](https://github.com/bigscience-workshop/xmtf#create-xp3). We provide this version to save processing time and ease reproducibility. - **Languages:** 46 (Can be extended by [recreating with more splits](https://github.com/bigscience-workshop/xmtf#create-xp3)) - **xP3 Dataset Family:** <table> <tr> <th>Name</th> <th>Explanation</th> <th>Example models</th> </tr> <tr> <td><a href=https://huggingface.co/datasets/Muennighoff/xP3x>xP3x</a></t> <td>Mixture of 17 tasks in 277 languages with English prompts</td> <td>WIP - Join us at Project Aya @<a href=https://cohere.for.ai/>C4AI</a> to help!</td> </tr> <tr> <td><a href=https://huggingface.co/datasets/bigscience/xP3>xP3</a></t> <td>Mixture of 13 training tasks in 46 languages with English prompts</td> <td><a href=https://huggingface.co/bigscience/bloomz>bloomz</a> & <a href=https://huggingface.co/bigscience/mt0-xxl>mt0-xxl</a></td> </tr> <tr> <td><a href=https://huggingface.co/datasets/bigscience/xP3mt>xP3mt</a></t> <td>Mixture of 13 training tasks in 46 languages with prompts in 20 languages (machine-translated from English)</td> <td><a href=https://huggingface.co/bigscience/bloomz-mt>bloomz-mt</a> & <a href=https://huggingface.co/bigscience/mt0-xxl-mt>mt0-xxl-mt</a></td> </tr> <tr> <td><a href=https://huggingface.co/datasets/bigscience/xP3all>xP3all</a></t> <td>xP3 + evaluation datasets adding an additional 3 tasks for a total of 16 tasks in 46 languages with English prompts</td> <td></td> </tr> <tr> <td><a href=https://huggingface.co/datasets/bigscience/xP3megds>xP3megds</a></t> <td><a href=https://github.com/bigscience-workshop/Megatron-DeepSpeed>Megatron-DeepSpeed</a> processed version of xP3</td> <td><a href=https://huggingface.co/bigscience/bloomz>bloomz</a></td> </tr> <tr> <td><a href=https://huggingface.co/datasets/Muennighoff/P3>P3</a></t> <td>Repreprocessed version of the English-only <a href=https://huggingface.co/datasets/bigscience/P3>P3</a> with 8 training tasks</td> <td><a href=https://huggingface.co/bigscience/bloomz-p3>bloomz-p3</a> & <a href=https://huggingface.co/bigscience/mt0-xxl-p3>mt0-xxl-p3</a></td> </tr> </table> ## Dataset Structure ### Data Instances An example of "train" looks as follows: ```json { "inputs": "Sentence 1: Fue académico en literatura metafísica, teología y ciencias clásicas.\nSentence 2: Fue académico en literatura metafísica, teología y ciencia clásica.\nQuestion: Can we rewrite Sentence 1 to Sentence 2? Yes or No?", "targets": "Yes" } ``` ### Data Fields The data fields are the same among all splits: - `inputs`: the natural language input fed to the model - `targets`: the natural language target that the model has to generate ### Data Splits The below table summarizes sizes per language (computed from the `merged_{lang}.jsonl` files). Due to languages like `tw` only being single sentence translation samples from Flores, their byte percentage is significantly lower than their sample percentage. |Language|Kilobytes|%|Samples|%| |--------|------:|-:|---:|-:| |tw|106288|0.11|265071|0.33| |bm|107056|0.11|265180|0.33| |ak|108096|0.11|265071|0.33| |ca|110608|0.11|271191|0.33| |eu|113008|0.11|281199|0.35| |fon|113072|0.11|265063|0.33| |st|114080|0.11|265063|0.33| |ki|115040|0.12|265180|0.33| |tum|116032|0.12|265063|0.33| |wo|122560|0.12|365063|0.45| |ln|126304|0.13|365060|0.45| |as|156256|0.16|265063|0.33| |or|161472|0.16|265063|0.33| |kn|165456|0.17|265063|0.33| |ml|175040|0.18|265864|0.33| |rn|192992|0.19|318189|0.39| |nso|229712|0.23|915051|1.13| |tn|235536|0.24|915054|1.13| |lg|235936|0.24|915021|1.13| |rw|249360|0.25|915043|1.13| |ts|250256|0.25|915044|1.13| |sn|252496|0.25|865056|1.07| |xh|254672|0.26|915058|1.13| |zu|263712|0.26|915061|1.13| |ny|272128|0.27|915063|1.13| |ig|325232|0.33|950097|1.17| |yo|352784|0.35|918416|1.13| |ne|393680|0.39|315754|0.39| |pa|523248|0.52|339210|0.42| |gu|560688|0.56|347499|0.43| |sw|566656|0.57|1130481|1.4| |mr|666240|0.67|417269|0.52| |bn|832720|0.83|428843|0.53| |ta|926912|0.93|415433|0.51| |te|1343232|1.35|584590|0.72| |ur|1918272|1.92|855756|1.06| |vi|3102512|3.11|1672106|2.07| |code|4330752|4.34|2707724|3.34| |hi|4403568|4.41|1554667|1.92| |zh|4599440|4.61|3589234|4.43| |id|4612256|4.62|2643418|3.27| |ar|4683456|4.69|2160181|2.67| |fr|6591120|6.6|5316403|6.57| |pt|6886800|6.9|3752156|4.63| |es|8587920|8.6|5413205|6.69| |en|39252528|39.33|32740750|40.44| |total|99807184|100.0|80956089|100.0| ## Dataset Creation ### Source Data #### Training datasets - Code Miscellaneous - [CodeComplex](https://huggingface.co/datasets/codeparrot/codecomplex) - [Docstring Corpus](https://huggingface.co/datasets/teven/code_docstring_corpus) - [GreatCode](https://huggingface.co/datasets/great_code) - [State Changes](https://huggingface.co/datasets/Fraser/python-state-changes) - Closed-book QA - [Hotpot QA](https://huggingface.co/datasets/hotpot_qa) - [Trivia QA](https://huggingface.co/datasets/trivia_qa) - [Web Questions](https://huggingface.co/datasets/web_questions) - [Wiki QA](https://huggingface.co/datasets/wiki_qa) - Extractive QA - [Adversarial QA](https://huggingface.co/datasets/adversarial_qa) - [CMRC2018](https://huggingface.co/datasets/cmrc2018) - [DRCD](https://huggingface.co/datasets/clue) - [DuoRC](https://huggingface.co/datasets/duorc) - [MLQA](https://huggingface.co/datasets/mlqa) - [Quoref](https://huggingface.co/datasets/quoref) - [ReCoRD](https://huggingface.co/datasets/super_glue) - [ROPES](https://huggingface.co/datasets/ropes) - [SQuAD v2](https://huggingface.co/datasets/squad_v2) - [xQuAD](https://huggingface.co/datasets/xquad) - TyDI QA - [Primary](https://huggingface.co/datasets/khalidalt/tydiqa-primary) - [Goldp](https://huggingface.co/datasets/khalidalt/tydiqa-goldp) - Multiple-Choice QA - [ARC](https://huggingface.co/datasets/ai2_arc) - [C3](https://huggingface.co/datasets/c3) - [CoS-E](https://huggingface.co/datasets/cos_e) - [Cosmos](https://huggingface.co/datasets/cosmos) - [DREAM](https://huggingface.co/datasets/dream) - [MultiRC](https://huggingface.co/datasets/super_glue) - [OpenBookQA](https://huggingface.co/datasets/openbookqa) - [PiQA](https://huggingface.co/datasets/piqa) - [QUAIL](https://huggingface.co/datasets/quail) - [QuaRel](https://huggingface.co/datasets/quarel) - [QuaRTz](https://huggingface.co/datasets/quartz) - [QASC](https://huggingface.co/datasets/qasc) - [RACE](https://huggingface.co/datasets/race) - [SciQ](https://huggingface.co/datasets/sciq) - [Social IQA](https://huggingface.co/datasets/social_i_qa) - [Wiki Hop](https://huggingface.co/datasets/wiki_hop) - [WiQA](https://huggingface.co/datasets/wiqa) - Paraphrase Identification - [MRPC](https://huggingface.co/datasets/super_glue) - [PAWS](https://huggingface.co/datasets/paws) - [PAWS-X](https://huggingface.co/datasets/paws-x) - [QQP](https://huggingface.co/datasets/qqp) - Program Synthesis - [APPS](https://huggingface.co/datasets/codeparrot/apps) - [CodeContests](https://huggingface.co/datasets/teven/code_contests) - [JupyterCodePairs](https://huggingface.co/datasets/codeparrot/github-jupyter-text-code-pairs) - [MBPP](https://huggingface.co/datasets/Muennighoff/mbpp) - [NeuralCodeSearch](https://huggingface.co/datasets/neural_code_search) - [XLCoST](https://huggingface.co/datasets/codeparrot/xlcost-text-to-code) - Structure-to-text - [Common Gen](https://huggingface.co/datasets/common_gen) - [Wiki Bio](https://huggingface.co/datasets/wiki_bio) - Sentiment - [Amazon](https://huggingface.co/datasets/amazon_polarity) - [App Reviews](https://huggingface.co/datasets/app_reviews) - [IMDB](https://huggingface.co/datasets/imdb) - [Rotten Tomatoes](https://huggingface.co/datasets/rotten_tomatoes) - [Yelp](https://huggingface.co/datasets/yelp_review_full) - Simplification - [BiSECT](https://huggingface.co/datasets/GEM/BiSECT) - Summarization - [CNN Daily Mail](https://huggingface.co/datasets/cnn_dailymail) - [Gigaword](https://huggingface.co/datasets/gigaword) - [MultiNews](https://huggingface.co/datasets/multi_news) - [SamSum](https://huggingface.co/datasets/samsum) - [Wiki-Lingua](https://huggingface.co/datasets/GEM/wiki_lingua) - [XLSum](https://huggingface.co/datasets/GEM/xlsum) - [XSum](https://huggingface.co/datasets/xsum) - Topic Classification - [AG News](https://huggingface.co/datasets/ag_news) - [DBPedia](https://huggingface.co/datasets/dbpedia_14) - [TNEWS](https://huggingface.co/datasets/clue) - [TREC](https://huggingface.co/datasets/trec) - [CSL](https://huggingface.co/datasets/clue) - Translation - [Flores-200](https://huggingface.co/datasets/Muennighoff/flores200) - [Tatoeba](https://huggingface.co/datasets/Helsinki-NLP/tatoeba_mt) - Word Sense disambiguation - [WiC](https://huggingface.co/datasets/super_glue) - [XL-WiC](https://huggingface.co/datasets/pasinit/xlwic) #### Evaluation datasets (included in [xP3all](https://huggingface.co/datasets/bigscience/xP3all) except for HumanEval) - Natural Language Inference - [ANLI](https://huggingface.co/datasets/anli) - [CB](https://huggingface.co/datasets/super_glue) - [RTE](https://huggingface.co/datasets/super_glue) - [XNLI](https://huggingface.co/datasets/xnli) - Coreference Resolution - [Winogrande](https://huggingface.co/datasets/winogrande) - [XWinograd](https://huggingface.co/datasets/Muennighoff/xwinograd) - Program Synthesis - [HumanEval](https://huggingface.co/datasets/openai_humaneval) - Sentence Completion - [COPA](https://huggingface.co/datasets/super_glue) - [Story Cloze](https://huggingface.co/datasets/story_cloze) - [XCOPA](https://huggingface.co/datasets/xcopa) - [XStoryCloze](https://huggingface.co/datasets/Muennighoff/xstory_cloze) #### Additional [xP3all](https://huggingface.co/datasets/bigscience/xP3all) datasets - Coreference Resolution - [WSC (Fixed)](https://huggingface.co/datasets/super_glue) - Sentence Completion - [HellaSwag](https://huggingface.co/datasets/hellaswag) - Translation - [MultiEurlex](https://huggingface.co/datasets/multi_eurlex) ## Additional Information ### Licensing Information The dataset is released under Apache 2.0. ### Citation Information ```bibtex @misc{muennighoff2022crosslingual, title={Crosslingual Generalization through Multitask Finetuning}, author={Niklas Muennighoff and Thomas Wang and Lintang Sutawika and Adam Roberts and Stella Biderman and Teven Le Scao and M Saiful Bari and Sheng Shen and Zheng-Xin Yong and Hailey Schoelkopf and Xiangru Tang and Dragomir Radev and Alham Fikri Aji and Khalid Almubarak and Samuel Albanie and Zaid Alyafeai and Albert Webson and Edward Raff and Colin Raffel}, year={2022}, eprint={2211.01786}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to the contributors of [promptsource](https://github.com/bigscience-workshop/promptsource/graphs/contributors) for adding many prompts used in this dataset.

DAMO-NLP-SG/multimodal_textbook

DAMO-NLP-SG

--- license: apache-2.0 task_categories: - text-generation - summarization language: - en tags: - Pretraining - Interleaved - Reasoning size_categories: - 1M<n<10M --- # Multimodal-Textbook-6.5M <img src="./src/logo.png" alt="Image" style="width: 900px;"> [](https://arxiv.org/abs/2501.00958) [](https://multimodal-interleaved-textbook.github.io/) [](https://github.com/DAMO-NLP-SG/multimodal_textbook/tree/master) ## Overview This dataset is for ["2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining"](https://arxiv.org/abs/2501.00958), containing 6.5M images interleaving with 0.8B text from instructional videos. - It contains **pre-training corpus using interleaved image-text format**. Specifically, our multimodal-textbook includes **6.5M keyframes** extracted from instructional videos, interleaving with 0.8B **ASR texts**. - All the images and text are extracted from online instructional videos (22,000 class hours), covering multiple fundamental subjects, e.g., mathematics, physics, and chemistry. - Our textbook corpus providing a more coherent context and richer knowledge for image-text aligning. - Our code can be found in [Multimodal-Textbook](https://github.com/DAMO-NLP-SG/multimodal_textbook/tree/master). Note: We have uploaded the annotation file （`./multimodal_textbook.json`）and image folder (`./dataset_images_interval_7.tar.gz`), which contains keyframes, processed asr and ocr texts. For more details, please refer to [Using Multimodal Textbook](#using-multimodal-textbook) <img src="./src/page_fig.png" alt="Image" style="width: 900px;"> ## Visualize Our Textbook Due to the large size of the dataset (our complete textbook dataset is 11GB for JSON files and 0.7TB for images), we sampled 100 samples and the corresponding images and stored them in the `example_data` folder: `./example_data/textbook_sample_100.json`. Each sample is stored in dict format as follows: ``` [ {'images': [keyframe1, None, keyframe2, None, keyframe3, None,.....], 'texts': [None, asr1, None, asr2, None, asr3,.....], 'text_ocr_list': [None, asr1+ocr1, None, asr2+ocr2, None, asr3+ocr3,.....], 'metadata': [...], 'image_num': 15, 'text_num': 425, 'token_num': 9065}, .... ] ``` Just like [OBELICS](https://github.com/huggingface/OBELICS), the "images" and "texts" are arranged interleavely: - "Images" list contains multiple keyframes and "None", where "None" represents that the current position is text. - "texts" list contain multiple asr text. The position of "None" in "texts" list is image. - "text_ocr_list": In addition to asr text, "text_ocr_list" also includes OCR text. - "image_num", "text_num", "token_num": respectively represent the number of images, the number of asr text tokens, and the estimated total number of tokens in this sample. To view our dataset more conveniently, we have written a jupyter notebook: `./llava/dataset/show_interleaved_dataset.ipynb` ``` cd example_data show_interleaved_dataset.ipynb ``` In the notebook, you can see keyframes interleaving with text. ## Dataset Statistics We utilize GPT-4o to synthesize our knowledge taxonomy with 3915 knowledge points across 6 subjects, which enabled us to automatically collect 159K English instructional videos based on this taxonomy. Following our video-totextbook pipeline, we filter 53% low-quality or repetitive videos and retain 75K videos (22,697 class hours) with an average duration of 18 minutes. Then we extract 6.5M keyframes and 0.75B text (ASR+OCR) tokens from these videos. To enhance training efficiency, we concatenate multiple video clips into a single sample, producing a total of 610K interleaved samples. Each sample contains an average of 10.7 keyframes and 1,230 text tokens. The detailed statistics for each subject are shown as follows: <img src="./src/table.png" alt="Image" style="width: 900px;"> ## Using Multimodal Textbook ### Description of Dataset We provide the annotation file (json file) and corresponding images folder for textbook: - Dataset json-file: `./multimodal_textbook.json` (600k samples ~ 11GB) - Dataset image_folder: `./dataset_images_interval_7.tar.gz` (6.5M image ~ 600GB) (**Due to its large size, we split it into 20 sub-files as `dataset_images_interval_7.tar.gz.part_00, dataset_images_interval_7.tar.gz.part_01, ...`**) - Videometa_data: `video_meta_data/video_meta_data1.json` and `video_meta_data/video_meta_data2.json` contains the meta information of the collected videos, including video vid, title, description, duration, language, and searched knowledge points. Besides, we also provide `multimodal_textbook_meta_data.json.zip` records the textbook in its video format, not in the OBELICS format. - Original video: You can downloaded original video using our provided video-id in `video_meta_data`. ### Learning about image_folder After you download 20 image segmentation files (`dataset_images_interval_7.tar.gz.part_*`), you need to merge them first and then decompress. Please do not unzip a single segmentation file alone. It will lead to an error. ``` cd multimodal_textbook cat dataset_images_interval_7.tar.gz.part_* > dataset_images_interval_7.tar.gz tar -xzvf dataset_images_interval_7.tar.gz ``` After the above steps, you will get the image folder `dataset_images_interval_7`, which is approximately 600GB and contains 6 million keyframes. Each sub-folder in the `dataset_images_interval_7` is named with the video id. ### Naming Rule of keyframe For each keyframe, its naming format rule is: `video id@start-time_end-time#keyframe-number.jpg`. For example, the path and file name of a keyframe is `dataset_images_interval_7/-1uixJ1V-As/[email protected]_55.0#2.jpg`. This means that this image is extracted from the video (`-1uixJ1V-As`). It is the second keyframe (#2) in the video clip from 10.0 to 55.0 seconds. You can access the original video through [https://www.youtube.com/watch?v=-1uixJ1V-As](https://www.youtube.com/watch?v=-1uixJ1V-As). ### Learning about annotation file The format of each sample in `multimodal_textbook.json` is as follows, that is, images and texts are interleaved: ``` "images": [ "/mnt/workspace/zwq_data/interleaved_dataset/dataset_images_interval_7/-1uixJ1V-As/[email protected]_10.0#1.jpg", null, "/mnt/workspace/zwq_data/interleaved_dataset/dataset_images_interval_7/-1uixJ1V-As/[email protected]_55.0#6.jpg", null, ...... ], "texts": [ null, "Hi everyone, and welcome to another lesson in our Eureka Tips for computers series .....", null, "I'm actually trying to use the number line to find the sum for each. So to start I'm going to use the paint tool to demonstrate. Let's use the number line for four plus five. We're going to start at four then we're going to count up five. One two three four five. That equals nine. Now let's do three plus six for the next one.", .... ], ``` Each sample has approximately 10.7 images and 1927 text tokens. You need to replace the each image path (`/mnt/workspace/zwq_data/interleaved_dataset/`) with your personal image folder path. ### Learning about metadata of instructional video The format of the `./video_meta_data/video_meta_data1.json`: ``` { "file_path": xxx, "file_size (MB)": 85.54160022735596, "file_name": "-r7-s1z3lFY.mp4", "video_duration": 0, "unique": true, "asr_path": xxxx, "asr_len": 2990, "caption_path": xxx, "caption_len": 0, "search_keyword": "1.3B parameter size models comparison", "title": "DeepSeek Coder LLM | A Revolutionary Coder Model", "desc": "In this video, we are going to test out Deepseek Coder, a coding LLM....., "llm_response": " The video appears to be a detailed and technical analysis of DeepSeek Coder LLM..... ###Score: 10###", "language": "en", "asr is repetive": false, "deepseek_score": 10, "llama_score": 2, "deepseek_score long context": 10 }, ``` In addition, the `multimodal_textbook_meta_data.json.zip` records the textbook in video format. Each "video clip" is stored as a dict. Each sample includes multiple consecutive video clips from the same video. Sometimes one sample may also include video clips from different long videos. When a long video ends, it will store as `End of a Video`. ``` {'token_num': 1657, 'conversations': [ { 'vid': video id-1, 'clip_path': video id-1-clip1, 'asr': ASR transcribed from audio, 'extracted_frames': Extract keyframe sequences according to time intervals as [image1, image2,....]., 'image_tokens': xxx, 'token_num': xxx, 'refined_asr': Refine the original ASR, 'ocr_internvl_8b': OCR obtained using internvl_8b, 'ocr_image': the image does OCR come from, 'ocr_internvl_8b_deduplicates': xxx, 'keyframe_ssim': Keyframe sequence extracted according to SSIM algorithm, 'asr_token_num': xxx, 'ocr_qwen2_vl_72b': '...............' }, { 'vid': video id-1, 'clip_path': video id-1-clip2, 'asr': ASR transcribed from audio, 'extracted_frames': Extract keyframe sequences according to time intervals as [image3, image4,....]., ..... }, { 'vid': 'End of a Video', 'clip_path': xxxx, 'image_tokens': 0, 'token_num': 0 }, { 'vid': video id-2, 'clip_path': video id-2-clip1, 'asr': ASR transcribed from audio, 'extracted_frames': Extract keyframe sequences according to time intervals as [image5, image6,....]., .... }, .... ] } ``` In this example above, the first two video clips are from the same video. Then the third dict represents the end of the current video. The fourth video clip is from a new video. ## Citation ``` @article{zhang20252, title={2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining}, author={Zhang, Wenqi and Zhang, Hang and Li, Xin and Sun, Jiashuo and Shen, Yongliang and Lu, Weiming and Zhao, Deli and Zhuang, Yueting and Bing, Lidong}, journal={arXiv preprint arXiv:2501.00958}, year={2025} } ```

asahi417/seamless-align-deA-enA.speaker-embedding.xlsr-2b

asahi417

--- dataset_info: - config_name: subset_1 features: - name: line_no dtype: int64 - name: deA.id dtype: string - name: deA.laser_score dtype: float64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: deA.audio.speaker_embedding sequence: float32 - name: deA.audio.speaker_embedding.full sequence: sequence: float32 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 16496072358 num_examples: 2064 download_size: 16544774466 dataset_size: 16496072358 - config_name: subset_10 features: - name: line_no dtype: int64 - name: deA.id dtype: string - name: deA.laser_score dtype: float64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: deA.audio.speaker_embedding sequence: float32 - name: deA.audio.speaker_embedding.full sequence: sequence: float32 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 16105175291 num_examples: 2106 download_size: 16153856631 dataset_size: 16105175291 - config_name: subset_100 features: - name: line_no dtype: int64 - name: deA.id dtype: string - name: deA.laser_score dtype: float64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: deA.audio.speaker_embedding sequence: float32 - name: deA.audio.speaker_embedding.full sequence: sequence: float32 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 11700093808 num_examples: 1944 download_size: 11740547200 dataset_size: 11700093808 - config_name: subset_101 features: - name: line_no dtype: int64 - name: deA.id dtype: string - name: deA.laser_score dtype: float64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 - name: deA.audio.speaker_embedding sequence: float32 - 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config_name: subset_187 data_files: - split: train path: subset_187/train-* - config_name: subset_188 data_files: - split: train path: subset_188/train-* - config_name: subset_189 data_files: - split: train path: subset_189/train-* - config_name: subset_19 data_files: - split: train path: subset_19/train-* - config_name: subset_190 data_files: - split: train path: subset_190/train-* - config_name: subset_191 data_files: - split: train path: subset_191/train-* - config_name: subset_192 data_files: - split: train path: subset_192/train-* - config_name: subset_193 data_files: - split: train path: subset_193/train-* - config_name: subset_194 data_files: - split: train path: subset_194/train-* - config_name: subset_195 data_files: - split: train path: subset_195/train-* - config_name: subset_196 data_files: - split: train path: subset_196/train-* - config_name: subset_197 data_files: - split: train path: subset_197/train-* - config_name: subset_198 data_files: - split: train path: subset_198/train-* - config_name: subset_199 data_files: - split: train path: subset_199/train-* - config_name: subset_2 data_files: - split: train path: subset_2/train-* - config_name: subset_20 data_files: - split: train path: subset_20/train-* - config_name: subset_200 data_files: - split: train path: subset_200/train-* - config_name: subset_201 data_files: - split: train path: subset_201/train-* - config_name: subset_202 data_files: - split: train path: subset_202/train-* - config_name: subset_203 data_files: - split: train path: subset_203/train-* - config_name: subset_204 data_files: - split: train path: subset_204/train-* - config_name: subset_205 data_files: - split: train path: subset_205/train-* - config_name: subset_206 data_files: - split: train path: subset_206/train-* - config_name: subset_207 data_files: - split: train path: subset_207/train-* - config_name: subset_208 data_files: - split: train path: subset_208/train-* - config_name: subset_209 data_files: - split: train path: subset_209/train-* - config_name: subset_21 data_files: - split: train path: subset_21/train-* - config_name: subset_210 data_files: - split: train path: subset_210/train-* - config_name: subset_211 data_files: - split: train path: subset_211/train-* - config_name: subset_212 data_files: - split: train path: subset_212/train-* - config_name: subset_213 data_files: - split: train path: subset_213/train-* - config_name: subset_214 data_files: - split: train path: subset_214/train-* - config_name: subset_215 data_files: - split: train path: subset_215/train-* - config_name: subset_216 data_files: - split: train path: subset_216/train-* - config_name: subset_217 data_files: - split: train path: subset_217/train-* - config_name: subset_218 data_files: - split: train path: subset_218/train-* - config_name: subset_219 data_files: - split: train path: subset_219/train-* - config_name: subset_22 data_files: - split: train path: subset_22/train-* - config_name: subset_220 data_files: - split: train path: subset_220/train-* - config_name: subset_221 data_files: - split: train path: subset_221/train-* - config_name: subset_222 data_files: - split: train path: subset_222/train-* - config_name: subset_223 data_files: - split: train path: subset_223/train-* - config_name: subset_224 data_files: - split: train path: subset_224/train-* - config_name: subset_225 data_files: - split: train path: subset_225/train-* - config_name: subset_226 data_files: - split: train path: subset_226/train-* - config_name: subset_227 data_files: - split: train path: subset_227/train-* - config_name: subset_228 data_files: - split: train path: subset_228/train-* - config_name: subset_229 data_files: - split: train path: subset_229/train-* - config_name: subset_23 data_files: - split: train path: subset_23/train-* - config_name: subset_230 data_files: - split: train path: subset_230/train-* - config_name: subset_231 data_files: - split: train path: subset_231/train-* - config_name: subset_232 data_files: - split: train path: subset_232/train-* - config_name: subset_233 data_files: - split: train path: subset_233/train-* - config_name: subset_234 data_files: - split: train path: subset_234/train-* - config_name: subset_235 data_files: - split: train path: subset_235/train-* - config_name: subset_236 data_files: - split: train path: subset_236/train-* - config_name: subset_237 data_files: - split: train path: subset_237/train-* - config_name: subset_238 data_files: - split: train path: subset_238/train-* - config_name: subset_239 data_files: - split: train path: subset_239/train-* - config_name: subset_24 data_files: - split: train path: subset_24/train-* - config_name: subset_240 data_files: - split: train path: subset_240/train-* - config_name: subset_241 data_files: - split: train path: subset_241/train-* - config_name: subset_242 data_files: - split: train path: subset_242/train-* - config_name: subset_243 data_files: - split: train path: subset_243/train-* - config_name: subset_244 data_files: - split: train path: subset_244/train-* - config_name: subset_245 data_files: - split: train path: subset_245/train-* - config_name: subset_246 data_files: - split: train path: subset_246/train-* - config_name: subset_247 data_files: - split: train path: subset_247/train-* - config_name: subset_248 data_files: - split: train path: subset_248/train-* - config_name: subset_249 data_files: - split: train path: subset_249/train-* - config_name: subset_25 data_files: - split: train path: subset_25/train-* - config_name: subset_250 data_files: - split: train path: subset_250/train-* - config_name: subset_251 data_files: - split: train path: subset_251/train-* - config_name: subset_252 data_files: - split: train path: subset_252/train-* - config_name: subset_253 data_files: - split: train path: subset_253/train-* - config_name: subset_254 data_files: - split: train path: subset_254/train-* - config_name: subset_255 data_files: - split: train path: subset_255/train-* - config_name: subset_256 data_files: - split: train path: subset_256/train-* - config_name: subset_257 data_files: - split: train path: subset_257/train-* - config_name: subset_258 data_files: - split: train path: subset_258/train-* - config_name: subset_259 data_files: - split: train path: subset_259/train-* - config_name: subset_26 data_files: - split: train path: subset_26/train-* - config_name: subset_260 data_files: - split: train path: subset_260/train-* - config_name: subset_261 data_files: - split: train path: subset_261/train-* - config_name: subset_262 data_files: - split: train path: subset_262/train-* - config_name: subset_263 data_files: - split: train path: subset_263/train-* - config_name: subset_264 data_files: - split: train path: subset_264/train-* - config_name: subset_265 data_files: - split: train path: subset_265/train-* - config_name: subset_266 data_files: - split: train path: subset_266/train-* - config_name: subset_267 data_files: - split: train path: subset_267/train-* - config_name: subset_268 data_files: - split: train path: subset_268/train-* - config_name: subset_269 data_files: - split: train path: subset_269/train-* - config_name: subset_27 data_files: - split: train path: subset_27/train-* - config_name: subset_270 data_files: - split: train path: subset_270/train-* - config_name: subset_271 data_files: - split: train path: subset_271/train-* - config_name: subset_272 data_files: - split: train path: subset_272/train-* - config_name: subset_273 data_files: - split: train path: subset_273/train-* - config_name: subset_274 data_files: - split: train path: subset_274/train-* - config_name: subset_275 data_files: - split: train path: subset_275/train-* - config_name: subset_276 data_files: - split: train path: subset_276/train-* - config_name: subset_277 data_files: - split: train path: subset_277/train-* - config_name: subset_278 data_files: - split: train path: subset_278/train-* - config_name: subset_279 data_files: - split: train path: subset_279/train-* - config_name: subset_28 data_files: - split: train path: subset_28/train-* - config_name: subset_280 data_files: - split: train path: subset_280/train-* - config_name: subset_281 data_files: - split: train path: subset_281/train-* - config_name: subset_282 data_files: - split: train path: subset_282/train-* - config_name: subset_283 data_files: - split: train path: subset_283/train-* - config_name: subset_284 data_files: - split: train path: subset_284/train-* - config_name: subset_285 data_files: - split: train path: subset_285/train-* - config_name: subset_286 data_files: - split: train path: subset_286/train-* - config_name: subset_287 data_files: - split: train path: subset_287/train-* - config_name: subset_288 data_files: - split: train path: subset_288/train-* - config_name: subset_289 data_files: - split: train path: subset_289/train-* - config_name: subset_29 data_files: - split: train path: subset_29/train-* - config_name: subset_290 data_files: - split: train path: subset_290/train-* - config_name: subset_291 data_files: - split: train path: subset_291/train-* - config_name: subset_292 data_files: - split: train path: subset_292/train-* - config_name: subset_293 data_files: - split: train path: subset_293/train-* - config_name: subset_294 data_files: - split: train path: subset_294/train-* - config_name: subset_295 data_files: - split: train path: subset_295/train-* - config_name: subset_296 data_files: - split: train path: subset_296/train-* - config_name: subset_297 data_files: - split: train path: subset_297/train-* - config_name: subset_298 data_files: - split: train path: subset_298/train-* - config_name: subset_299 data_files: - split: train path: subset_299/train-* - config_name: subset_3 data_files: - split: train path: subset_3/train-* - config_name: subset_30 data_files: - split: train path: subset_30/train-* - config_name: subset_300 data_files: - split: train path: subset_300/train-* - config_name: subset_301 data_files: - split: train path: subset_301/train-* - config_name: subset_302 data_files: - split: train path: subset_302/train-* - config_name: subset_303 data_files: - split: train path: subset_303/train-* - config_name: subset_304 data_files: - split: train path: subset_304/train-* - config_name: subset_305 data_files: - split: train path: subset_305/train-* - config_name: subset_306 data_files: - split: train path: subset_306/train-* - config_name: subset_307 data_files: - split: train path: subset_307/train-* - config_name: subset_308 data_files: - split: train path: subset_308/train-* - config_name: subset_309 data_files: - split: train path: subset_309/train-* - config_name: subset_31 data_files: - split: train path: subset_31/train-* - config_name: subset_310 data_files: - split: train path: subset_310/train-* - config_name: subset_311 data_files: - split: train path: subset_311/train-* - config_name: subset_312 data_files: - split: train path: subset_312/train-* - config_name: subset_313 data_files: - split: train path: subset_313/train-* - config_name: subset_314 data_files: - split: train path: subset_314/train-* - config_name: subset_315 data_files: - split: train path: subset_315/train-* - config_name: subset_316 data_files: - split: train path: subset_316/train-* - config_name: subset_317 data_files: - split: train path: subset_317/train-* - config_name: subset_318 data_files: - split: train path: subset_318/train-* - config_name: subset_319 data_files: - split: train path: subset_319/train-* - config_name: subset_32 data_files: - split: train path: subset_32/train-* - config_name: subset_320 data_files: - split: train path: subset_320/train-* - config_name: subset_321 data_files: - split: train path: subset_321/train-* - config_name: subset_322 data_files: - split: train path: subset_322/train-* - config_name: subset_323 data_files: - split: train path: subset_323/train-* - config_name: subset_324 data_files: - split: train path: subset_324/train-* - config_name: subset_325 data_files: - split: train path: subset_325/train-* - config_name: subset_326 data_files: - split: train path: subset_326/train-* - config_name: subset_327 data_files: - split: train path: subset_327/train-* - config_name: subset_328 data_files: - split: train path: subset_328/train-* - config_name: subset_329 data_files: - split: train path: subset_329/train-* - config_name: subset_33 data_files: - split: train path: subset_33/train-* - config_name: subset_330 data_files: - split: train path: subset_330/train-* - config_name: subset_331 data_files: - split: train path: subset_331/train-* - config_name: subset_332 data_files: - split: train path: subset_332/train-* - config_name: subset_333 data_files: - split: train path: subset_333/train-* - config_name: subset_334 data_files: - split: train path: subset_334/train-* - config_name: subset_335 data_files: - split: train path: subset_335/train-* - config_name: subset_336 data_files: - split: train path: subset_336/train-* - config_name: subset_337 data_files: - split: train path: subset_337/train-* - config_name: subset_338 data_files: - split: train path: subset_338/train-* - config_name: subset_339 data_files: - split: train path: subset_339/train-* - config_name: subset_34 data_files: - split: train path: subset_34/train-* - config_name: subset_340 data_files: - split: train path: subset_340/train-* - config_name: subset_341 data_files: - split: train path: subset_341/train-* - config_name: subset_342 data_files: - split: train path: subset_342/train-* - config_name: subset_343 data_files: - split: train path: subset_343/train-* - config_name: subset_344 data_files: - split: train path: subset_344/train-* - config_name: subset_345 data_files: - split: train path: subset_345/train-* - config_name: subset_346 data_files: - split: train path: subset_346/train-* - config_name: subset_347 data_files: - split: train path: subset_347/train-* - config_name: subset_348 data_files: - split: train path: subset_348/train-* - config_name: subset_349 data_files: - split: train path: subset_349/train-* - config_name: subset_35 data_files: - split: train path: subset_35/train-* - config_name: subset_350 data_files: - split: train path: subset_350/train-* - config_name: subset_351 data_files: - split: train path: subset_351/train-* - config_name: subset_352 data_files: - split: train path: subset_352/train-* - config_name: subset_353 data_files: - split: train path: subset_353/train-* - config_name: subset_354 data_files: - split: train path: subset_354/train-* - config_name: subset_355 data_files: - split: train path: subset_355/train-* - config_name: subset_356 data_files: - split: train path: subset_356/train-* - config_name: subset_357 data_files: - split: train path: subset_357/train-* - config_name: subset_358 data_files: - split: train path: subset_358/train-* - config_name: subset_359 data_files: - split: train path: subset_359/train-* - config_name: subset_36 data_files: - split: train path: subset_36/train-* - config_name: subset_360 data_files: - split: train path: subset_360/train-* - config_name: subset_361 data_files: - split: train path: subset_361/train-* - config_name: subset_362 data_files: - split: train path: subset_362/train-* - config_name: subset_363 data_files: - split: train path: subset_363/train-* - config_name: subset_364 data_files: - split: train path: subset_364/train-* - config_name: subset_365 data_files: - split: train path: subset_365/train-* - config_name: subset_366 data_files: - split: train path: subset_366/train-* - config_name: subset_367 data_files: - split: train path: subset_367/train-* - config_name: subset_368 data_files: - split: train path: subset_368/train-* - config_name: subset_369 data_files: - split: train path: subset_369/train-* - config_name: subset_37 data_files: - split: train path: subset_37/train-* - config_name: subset_370 data_files: - split: train path: subset_370/train-* - config_name: subset_371 data_files: - split: train path: subset_371/train-* - config_name: subset_372 data_files: - split: train path: subset_372/train-* - config_name: subset_373 data_files: - split: train path: subset_373/train-* - config_name: subset_374 data_files: - split: train path: subset_374/train-* - config_name: subset_375 data_files: - split: train path: subset_375/train-* - config_name: subset_376 data_files: - split: train path: subset_376/train-* - config_name: subset_377 data_files: - split: train path: subset_377/train-* - config_name: subset_378 data_files: - split: train path: subset_378/train-* - config_name: subset_379 data_files: - split: train path: subset_379/train-* - config_name: subset_38 data_files: - split: train path: subset_38/train-* - config_name: subset_380 data_files: - split: train path: subset_380/train-* - config_name: subset_381 data_files: - split: train path: subset_381/train-* - config_name: subset_382 data_files: - split: train path: subset_382/train-* - config_name: subset_383 data_files: - split: train path: subset_383/train-* - config_name: subset_384 data_files: - split: train path: subset_384/train-* - config_name: subset_385 data_files: - split: train path: subset_385/train-* - config_name: subset_386 data_files: - split: train path: subset_386/train-* - config_name: subset_387 data_files: - split: train path: subset_387/train-* - config_name: subset_388 data_files: - split: train path: subset_388/train-* - config_name: subset_389 data_files: - split: train path: subset_389/train-* - config_name: subset_39 data_files: - split: train path: subset_39/train-* - config_name: subset_390 data_files: - split: train path: subset_390/train-* - config_name: subset_391 data_files: - split: train path: subset_391/train-* - config_name: subset_392 data_files: - split: train path: subset_392/train-* - config_name: subset_393 data_files: - split: train path: subset_393/train-* - config_name: subset_394 data_files: - split: train path: subset_394/train-* - config_name: subset_4 data_files: - split: train path: subset_4/train-* - config_name: subset_40 data_files: - split: train path: subset_40/train-* - config_name: subset_41 data_files: - split: train path: subset_41/train-* - config_name: subset_42 data_files: - split: train path: subset_42/train-* - config_name: subset_43 data_files: - split: train path: subset_43/train-* - config_name: subset_44 data_files: - split: train path: subset_44/train-* - config_name: subset_45 data_files: - split: train path: subset_45/train-* - config_name: subset_46 data_files: - split: train path: subset_46/train-* - config_name: subset_47 data_files: - split: train path: subset_47/train-* - config_name: subset_48 data_files: - split: train path: subset_48/train-* - config_name: subset_49 data_files: - split: train path: subset_49/train-* - config_name: subset_5 data_files: - split: train path: subset_5/train-* - config_name: subset_50 data_files: - split: train path: subset_50/train-* - config_name: subset_51 data_files: - split: train path: subset_51/train-* - config_name: subset_52 data_files: - split: train path: subset_52/train-* - config_name: subset_53 data_files: - split: train path: subset_53/train-* - config_name: subset_54 data_files: - split: train path: subset_54/train-* - config_name: subset_55 data_files: - split: train path: subset_55/train-* - config_name: subset_56 data_files: - split: train path: subset_56/train-* - config_name: subset_57 data_files: - split: train path: subset_57/train-* - config_name: subset_58 data_files: - split: train path: subset_58/train-* - config_name: subset_59 data_files: - split: train path: subset_59/train-* - config_name: subset_6 data_files: - split: train path: subset_6/train-* - config_name: subset_60 data_files: - split: train path: subset_60/train-* - config_name: subset_61 data_files: - split: train path: subset_61/train-* - config_name: subset_62 data_files: - split: train path: subset_62/train-* - config_name: subset_63 data_files: - split: train path: subset_63/train-* - config_name: subset_64 data_files: - split: train path: subset_64/train-* - config_name: subset_65 data_files: - split: train path: subset_65/train-* - config_name: subset_66 data_files: - split: train path: subset_66/train-* - config_name: subset_67 data_files: - split: train path: subset_67/train-* - config_name: subset_68 data_files: - split: train path: subset_68/train-* - config_name: subset_69 data_files: - split: train path: subset_69/train-* - config_name: subset_7 data_files: - split: train path: subset_7/train-* - config_name: subset_70 data_files: - split: train path: subset_70/train-* - config_name: subset_71 data_files: - split: train path: subset_71/train-* - config_name: subset_72 data_files: - split: train path: subset_72/train-* - config_name: subset_73 data_files: - split: train path: subset_73/train-* - config_name: subset_74 data_files: - split: train path: subset_74/train-* - config_name: subset_75 data_files: - split: train path: subset_75/train-* - config_name: subset_76 data_files: - split: train path: subset_76/train-* - config_name: subset_77 data_files: - split: train path: subset_77/train-* - config_name: subset_78 data_files: - split: train path: subset_78/train-* - config_name: subset_79 data_files: - split: train path: subset_79/train-* - config_name: subset_8 data_files: - split: train path: subset_8/train-* - config_name: subset_80 data_files: - split: train path: subset_80/train-* - config_name: subset_81 data_files: - split: train path: subset_81/train-* - config_name: subset_82 data_files: - split: train path: subset_82/train-* - config_name: subset_83 data_files: - split: train path: subset_83/train-* - config_name: subset_84 data_files: - split: train path: subset_84/train-* - config_name: subset_85 data_files: - split: train path: subset_85/train-* - config_name: subset_86 data_files: - split: train path: subset_86/train-* - config_name: subset_87 data_files: - split: train path: subset_87/train-* - config_name: subset_88 data_files: - split: train path: subset_88/train-* - config_name: subset_89 data_files: - split: train path: subset_89/train-* - config_name: subset_9 data_files: - split: train path: subset_9/train-* - config_name: subset_90 data_files: - split: train path: subset_90/train-* - config_name: subset_91 data_files: - split: train path: subset_91/train-* - config_name: subset_92 data_files: - split: train path: subset_92/train-* - config_name: subset_93 data_files: - split: train path: subset_93/train-* - config_name: subset_94 data_files: - split: train path: subset_94/train-* - config_name: subset_95 data_files: - split: train path: subset_95/train-* - config_name: subset_96 data_files: - split: train path: subset_96/train-* - config_name: subset_97 data_files: - split: train path: subset_97/train-* - config_name: subset_98 data_files: - split: train path: subset_98/train-* - config_name: subset_99 data_files: - split: train path: subset_99/train-* ---

TempoFunk/medium

TempoFunk

--- size_categories: - 10K<n<100K license: agpl-3.0 task_categories: - text-to-video language: - en pretty_name: Medium --- curr. size: 53,081 videos goal (todo): 100,000+

laion/strategic_game_chess

laion

--- tags: - game pretty_name: The Chess Dataset license: cc-by-4.0 --- # Chess > Recent advancements in artificial intelligence (AI) underscore the progress of reasoning and planning shown by recent generalist machine learning (ML) models. The progress can be boosted by datasets that can further boost these generic capabilities when used for training foundation models of various kind. This research initiative has generated extensive synthetic datasets from complex games — chess, Rubik's Cube, and mazes — to study facilitation and the advancement of these critical generic skills in AI models. This dataset contains 3.2 billion games, equating to approximately 608 billion individual moves. it is generated through self-play by Stockfish engine using Fugaku and we add initial moves to expand its diversity. Each game has three columns: 'Moves', 'Termination' and 'Result', - 'Move': recorded chess moves of the whole game. - 'Termination': include CHECKMATE, INSUFFICIENT_MATERIAL, ... etc. - Please check this for detail information https://python-chess.readthedocs.io/en/latest/core.html#chess.Outcome.termination - 'Result': result of this game, 1-0, 1/2-1/2, 0-1. ### Call for Collaboration We invite interested researchers and ML practitioners to explore these datasets' potential. Whether training GPT models from scratch or fine-tuning pre-existing models, we encourage the exploration of various pre-training and fine-tuning strategies using these game-based datasets standalone or as enhancement of other already composed large-scale data. Our team is prepared to assist in securing necessary GPU resources for these explorations. We are particularly interested in collaborators eager to pre-train models of small to medium scale on our game data, subsequently transition to standard text-based training, and then perform comparative analyses against models of similar architecture trained exclusively on text data. Conclusively, this initiative marks a significant stride toward intricate problem-solving and strategic planning in AI, extending an open invitation to the research community for collaborative advancement in this domain.

airtrain-ai/fineweb-edu-fortified

airtrain-ai

--- language: - en license: odc-by task_categories: - text-generation dataset_info: - config_name: CC-MAIN-2013-20 features: - name: text dtype: string - name: id dtype: string - name: dump dtype: string - name: url dtype: string - name: file_path dtype: string - name: language dtype: string - name: language_score dtype: float64 - name: token_count dtype: int64 - name: score dtype: float64 - name: int_score dtype: int64 - name: embedding sequence: float32 - name: count dtype: int64 splits: - name: train num_bytes: 71683996286 num_examples: 10800000 download_size: 55571546426 dataset_size: 71683996286 - config_name: CC-MAIN-2013-48 features: - name: text dtype: string - name: id dtype: string - name: dump dtype: string - name: url dtype: string - name: file_path dtype: string - name: language dtype: string - name: language_score dtype: float64 - name: token_count dtype: int64 - name: score dtype: float64 - name: int_score dtype: int64 - name: embedding sequence: float32 - name: count dtype: int64 splits: - name: train num_bytes: 38878994623 num_examples: 5800000 download_size: 30087644388 dataset_size: 38878994623 - config_name: CC-MAIN-2014-10 features: - name: text dtype: string - name: id dtype: string - name: dump dtype: string - name: url dtype: string - name: file_path dtype: string - name: language dtype: string - name: language_score dtype: float64 - name: token_count dtype: int64 - name: score dtype: float64 - name: int_score dtype: int64 - name: embedding sequence: float32 - name: count dtype: int64 splits: - name: train num_bytes: 24971658588 num_examples: 3550000 download_size: 19058832929 dataset_size: 24971658588 - config_name: CC-MAIN-2014-15 features: - name: text dtype: string - name: id dtype: string - name: dump dtype: string - name: url dtype: string - name: file_path dtype: string - name: language dtype: string - name: language_score dtype: float64 - name: token_count dtype: int64 - name: score dtype: float64 - name: int_score dtype: int64 - name: embedding sequence: float32 - name: count dtype: int64 splits: - name: train num_bytes: 13615746365 num_examples: 1850000 download_size: 10299687552 dataset_size: 13615746365 - config_name: CC-MAIN-2014-23 features: - name: text dtype: string - name: id dtype: string - name: dump dtype: string - name: url dtype: string - name: file_path dtype: string - name: language dtype: string - name: language_score dtype: float64 - name: token_count dtype: int64 - name: score dtype: float64 - name: int_score dtype: int64 - name: embedding sequence: float32 - name: count dtype: int64 splits: - name: train num_bytes: 21798450754 num_examples: 3100000 download_size: 16663899441 dataset_size: 21798450754 - config_name: CC-MAIN-2014-35 features: - name: text dtype: string - name: id dtype: string - name: dump dtype: string - name: url dtype: string - name: file_path dtype: string - name: language dtype: string - name: language_score dtype: float64 - name: token_count dtype: int64 - name: score dtype: float64 - name: int_score dtype: int64 - name: embedding sequence: float32 - name: count dtype: int64 splits: - name: train num_bytes: 10954201796 num_examples: 1500000 download_size: 8309419357 dataset_size: 10954201796 - config_name: CC-MAIN-2014-41 features: - name: text dtype: string - name: id dtype: string - name: dump dtype: string - name: url dtype: string - name: file_path dtype: string - name: language dtype: string - name: language_score dtype: float64 - name: token_count dtype: int64 - name: score dtype: float64 - name: int_score dtype: int64 - name: embedding sequence: float32 - name: count dtype: int64 splits: - name: train num_bytes: 11392615401 num_examples: 1600000 download_size: 8694382261 dataset_size: 11392615401 - config_name: CC-MAIN-2014-42 features: - name: text dtype: string - name: id dtype: string - name: dump dtype: string - name: url dtype: string - name: file_path dtype: string - name: language dtype: string - name: language_score dtype: float64 - name: token_count dtype: int64 - name: score dtype: float64 - name: int_score dtype: int64 - name: embedding sequence: float32 - name: count dtype: int64 splits: - name: train num_bytes: 8491740156 num_examples: 1150000 download_size: 6430841610 dataset_size: 8491740156 - config_name: CC-MAIN-2014-49 features: - name: text dtype: string - name: id dtype: string - name: dump dtype: string - name: url dtype: string - name: file_path dtype: string - name: language dtype: string - name: language_score dtype: float64 - name: token_count dtype: int64 - name: score dtype: float64 - name: int_score dtype: int64 - name: embedding sequence: float32 - name: count dtype: int64 splits: - name: train num_bytes: 7754099049 num_examples: 1050000 download_size: 5866979308 dataset_size: 7754099049 - config_name: CC-MAIN-2014-52 features: - name: text dtype: string - name: id dtype: string - name: dump dtype: string - name: url dtype: string - name: file_path dtype: string - name: language dtype: string - name: language_score dtype: float64 - name: token_count dtype: int64 - name: score dtype: float64 - name: int_score dtype: int64 - name: embedding sequence: float32 - name: count dtype: int64 splits: - name: train num_bytes: 9953666568 num_examples: 1350000 download_size: 7521103037 dataset_size: 9953666568 - config_name: CC-MAIN-2015-06 features: - name: text dtype: string - name: id dtype: string - name: dump dtype: string - name: url dtype: string - name: file_path dtype: string - name: language dtype: string - name: language_score dtype: float64 - name: token_count dtype: int64 - name: score dtype: float64 - name: int_score dtype: int64 - name: embedding sequence: float32 - name: count dtype: int64 splits: - name: train num_bytes: 8988649992 num_examples: 1200000 download_size: 6771650647 dataset_size: 8988649992 - config_name: CC-MAIN-2015-11 features: - name: text dtype: string - name: id dtype: string - name: dump dtype: string - name: url dtype: string - name: file_path dtype: string - name: language dtype: string - name: language_score dtype: float64 - name: token_count dtype: int64 - name: score dtype: float64 - name: int_score dtype: int64 - name: embedding sequence: float32 - name: count dtype: int64 splits: - name: train num_bytes: 9212466984 num_examples: 1200000 download_size: 6893305603 dataset_size: 9212466984 - config_name: CC-MAIN-2015-14 features: - name: text dtype: string - name: id dtype: string - name: dump dtype: string - name: url dtype: string - name: file_path dtype: string - name: language dtype: string - name: language_score dtype: float64 - name: token_count dtype: int64 - name: score dtype: float64 - name: int_score dtype: int64 - name: embedding sequence: float32 - name: count dtype: int64 splits: - name: train num_bytes: 7773258320 num_examples: 1000000 download_size: 5810026390 dataset_size: 7773258320 - config_name: CC-MAIN-2015-18 features: - name: text dtype: string - name: id dtype: string - name: dump dtype: string - name: url dtype: string - name: file_path dtype: string - name: language dtype: string - name: language_score dtype: float64 - name: token_count dtype: int64 - name: score dtype: float64 - name: int_score dtype: int64 - name: embedding sequence: float32 - name: count dtype: int64 splits: - name: train num_bytes: 9906342182 num_examples: 1300000 download_size: 7420897339 dataset_size: 9906342182 - config_name: CC-MAIN-2015-22 features: - name: text dtype: string - name: id dtype: string - name: dump dtype: string - name: url dtype: string - name: file_path dtype: string - name: language dtype: string - name: language_score dtype: float64 - name: token_count dtype: int64 - name: score dtype: float64 - name: int_score dtype: int64 - name: embedding sequence: float32 - name: count dtype: int64 splits: - name: train num_bytes: 8677092389 num_examples: 1100000 download_size: 6445775687 dataset_size: 8677092389 - config_name: CC-MAIN-2015-27 features: - name: text dtype: string - name: id dtype: string - name: dump dtype: string - name: url dtype: string - name: file_path dtype: string - 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name: url dtype: string - name: file_path dtype: string - name: language dtype: string - name: language_score dtype: float64 - name: token_count dtype: int64 - name: score dtype: float64 - name: int_score dtype: int64 - name: embedding sequence: float32 - name: count dtype: int64 splits: - name: train num_bytes: 9500987324 num_examples: 1200000 download_size: 7050820902 dataset_size: 9500987324 - config_name: CC-MAIN-2016-07 features: - name: text dtype: string - name: id dtype: string - name: dump dtype: string - name: url dtype: string - name: file_path dtype: string - name: language dtype: string - name: language_score dtype: float64 - name: token_count dtype: int64 - name: score dtype: float64 - name: int_score dtype: int64 - name: embedding sequence: float32 - name: count dtype: int64 splits: - name: train num_bytes: 10612088943 num_examples: 1300000 download_size: 7816414470 dataset_size: 10612088943 - config_name: CC-MAIN-2016-18 features: - name: text dtype: string - name: id dtype: string - 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config_name: CC-MAIN-2013-20 data_files: - split: train path: data/CC-MAIN-2013-20/train-* - config_name: CC-MAIN-2013-48 data_files: - split: train path: data/CC-MAIN-2013-48/train-* - config_name: CC-MAIN-2014-10 data_files: - split: train path: data/CC-MAIN-2014-10/train-* - config_name: CC-MAIN-2014-15 data_files: - split: train path: data/CC-MAIN-2014-15/train-* - config_name: CC-MAIN-2014-23 data_files: - split: train path: data/CC-MAIN-2014-23/train-* - config_name: CC-MAIN-2014-35 data_files: - split: train path: data/CC-MAIN-2014-35/train-* - config_name: CC-MAIN-2014-41 data_files: - split: train path: data/CC-MAIN-2014-41/train-* - config_name: CC-MAIN-2014-42 data_files: - split: train path: data/CC-MAIN-2014-42/train-* - config_name: CC-MAIN-2014-49 data_files: - split: train path: data/CC-MAIN-2014-49/train-* - config_name: CC-MAIN-2014-52 data_files: - split: train path: data/CC-MAIN-2014-52/train-* - config_name: CC-MAIN-2015-06 data_files: - split: train path: data/CC-MAIN-2015-06/train-* - config_name: CC-MAIN-2015-11 data_files: - split: train path: data/CC-MAIN-2015-11/train-* - config_name: CC-MAIN-2015-14 data_files: - split: train path: data/CC-MAIN-2015-14/train-* - config_name: CC-MAIN-2015-18 data_files: - split: train path: data/CC-MAIN-2015-18/train-* - config_name: CC-MAIN-2015-22 data_files: - split: train path: data/CC-MAIN-2015-22/train-* - config_name: CC-MAIN-2015-27 data_files: - split: train path: data/CC-MAIN-2015-27/train-* - config_name: CC-MAIN-2015-32 data_files: - split: train path: data/CC-MAIN-2015-32/train-* - config_name: CC-MAIN-2015-35 data_files: - split: train path: data/CC-MAIN-2015-35/train-* - config_name: CC-MAIN-2015-40 data_files: - split: train path: data/CC-MAIN-2015-40/train-* - config_name: CC-MAIN-2015-48 data_files: - split: train path: data/CC-MAIN-2015-48/train-* - config_name: CC-MAIN-2016-07 data_files: - split: train path: data/CC-MAIN-2016-07/train-* - config_name: CC-MAIN-2016-18 data_files: - split: train path: data/CC-MAIN-2016-18/train-* - config_name: CC-MAIN-2016-22 data_files: - split: train path: data/CC-MAIN-2016-22/train-* - config_name: CC-MAIN-2016-26 data_files: - split: train path: data/CC-MAIN-2016-26/train-* - config_name: CC-MAIN-2016-30 data_files: - split: train path: data/CC-MAIN-2016-30/train-* - config_name: CC-MAIN-2016-36 data_files: - split: train path: data/CC-MAIN-2016-36/train-* - config_name: CC-MAIN-2016-40 data_files: - split: train path: data/CC-MAIN-2016-40/train-* - config_name: CC-MAIN-2016-44 data_files: - split: train path: data/CC-MAIN-2016-44/train-* - config_name: CC-MAIN-2016-50 data_files: - split: train path: data/CC-MAIN-2016-50/train-* - config_name: CC-MAIN-2017-04 data_files: - split: train path: data/CC-MAIN-2017-04/train-* - config_name: CC-MAIN-2017-09 data_files: - split: train path: data/CC-MAIN-2017-09/train-* - config_name: CC-MAIN-2017-13 data_files: - split: train path: data/CC-MAIN-2017-13/train-* - config_name: CC-MAIN-2017-17 data_files: - split: train path: data/CC-MAIN-2017-17/train-* - config_name: CC-MAIN-2017-22 data_files: - split: train path: data/CC-MAIN-2017-22/train-* - config_name: CC-MAIN-2017-26 data_files: - split: train path: data/CC-MAIN-2017-26/train-* - config_name: CC-MAIN-2017-30 data_files: - split: train path: data/CC-MAIN-2017-30/train-* - config_name: CC-MAIN-2017-34 data_files: - split: train path: data/CC-MAIN-2017-34/train-* - config_name: CC-MAIN-2017-39 data_files: - split: train path: data/CC-MAIN-2017-39/train-* - config_name: CC-MAIN-2017-43 data_files: - split: train path: data/CC-MAIN-2017-43/train-* - config_name: CC-MAIN-2017-47 data_files: - split: train path: data/CC-MAIN-2017-47/train-* - config_name: CC-MAIN-2017-51 data_files: - split: train path: data/CC-MAIN-2017-51/train-* - config_name: CC-MAIN-2018-05 data_files: - split: train path: data/CC-MAIN-2018-05/train-* - config_name: CC-MAIN-2018-09 data_files: - split: train path: data/CC-MAIN-2018-09/train-* - config_name: CC-MAIN-2018-13 data_files: - split: train path: data/CC-MAIN-2018-13/train-* - config_name: CC-MAIN-2018-17 data_files: - split: train path: data/CC-MAIN-2018-17/train-* - config_name: CC-MAIN-2018-22 data_files: - split: train path: data/CC-MAIN-2018-22/train-* - config_name: CC-MAIN-2018-26 data_files: - split: train path: data/CC-MAIN-2018-26/train-* - config_name: CC-MAIN-2018-30 data_files: - split: train path: data/CC-MAIN-2018-30/train-* - config_name: CC-MAIN-2018-34 data_files: - split: train path: data/CC-MAIN-2018-34/train-* - config_name: CC-MAIN-2018-39 data_files: - split: train path: data/CC-MAIN-2018-39/train-* - config_name: CC-MAIN-2018-43 data_files: - split: train path: data/CC-MAIN-2018-43/train-* - config_name: CC-MAIN-2018-47 data_files: - split: train path: data/CC-MAIN-2018-47/train-* - config_name: CC-MAIN-2018-51 data_files: - split: train path: data/CC-MAIN-2018-51/train-* - config_name: CC-MAIN-2019-04 data_files: - split: train path: data/CC-MAIN-2019-04/train-* - config_name: CC-MAIN-2019-09 data_files: - split: train path: data/CC-MAIN-2019-09/train-* - config_name: CC-MAIN-2019-13 data_files: - split: train path: data/CC-MAIN-2019-13/train-* - config_name: CC-MAIN-2019-18 data_files: - split: train path: data/CC-MAIN-2019-18/train-* - config_name: CC-MAIN-2019-22 data_files: - split: train path: data/CC-MAIN-2019-22/train-* - config_name: CC-MAIN-2019-26 data_files: - split: train path: data/CC-MAIN-2019-26/train-* - config_name: CC-MAIN-2019-30 data_files: - split: train path: data/CC-MAIN-2019-30/train-* - config_name: CC-MAIN-2019-35 data_files: - split: train path: data/CC-MAIN-2019-35/train-* - config_name: CC-MAIN-2019-39 data_files: - split: train path: data/CC-MAIN-2019-39/train-* - config_name: CC-MAIN-2019-43 data_files: - split: train path: data/CC-MAIN-2019-43/train-* - config_name: CC-MAIN-2019-47 data_files: - split: train path: data/CC-MAIN-2019-47/train-* - config_name: CC-MAIN-2019-51 data_files: - split: train path: data/CC-MAIN-2019-51/train-* - config_name: CC-MAIN-2020-05 data_files: - split: train path: data/CC-MAIN-2020-05/train-* - config_name: CC-MAIN-2020-10 data_files: - split: train path: data/CC-MAIN-2020-10/train-* - config_name: CC-MAIN-2020-16 data_files: - split: train path: data/CC-MAIN-2020-16/train-* - config_name: CC-MAIN-2020-24 data_files: - split: train path: data/CC-MAIN-2020-24/train-* - config_name: CC-MAIN-2020-29 data_files: - split: train path: data/CC-MAIN-2020-29/train-* - config_name: CC-MAIN-2020-34 data_files: - split: train path: data/CC-MAIN-2020-34/train-* - config_name: CC-MAIN-2020-40 data_files: - split: train path: data/CC-MAIN-2020-40/train-* - config_name: CC-MAIN-2020-45 data_files: - split: train path: data/CC-MAIN-2020-45/train-* - config_name: CC-MAIN-2020-50 data_files: - split: train path: data/CC-MAIN-2020-50/train-* - config_name: CC-MAIN-2021-04 data_files: - split: train path: data/CC-MAIN-2021-04/train-* - config_name: CC-MAIN-2021-10 data_files: - split: train path: data/CC-MAIN-2021-10/train-* - config_name: CC-MAIN-2021-17 data_files: - split: train path: data/CC-MAIN-2021-17/train-* - config_name: CC-MAIN-2021-21 data_files: - split: train path: data/CC-MAIN-2021-21/train-* - config_name: CC-MAIN-2021-25 data_files: - split: train path: data/CC-MAIN-2021-25/train-* - config_name: CC-MAIN-2021-31 data_files: - split: train path: data/CC-MAIN-2021-31/train-* - config_name: CC-MAIN-2021-39 data_files: - split: train path: data/CC-MAIN-2021-39/train-* - config_name: CC-MAIN-2021-43 data_files: - split: train path: data/CC-MAIN-2021-43/train-* - config_name: CC-MAIN-2021-49 data_files: - split: train path: data/CC-MAIN-2021-49/train-* - config_name: CC-MAIN-2022-05 data_files: - split: train path: data/CC-MAIN-2022-05/train-* - config_name: CC-MAIN-2022-21 data_files: - split: train path: data/CC-MAIN-2022-21/train-* - config_name: CC-MAIN-2022-27 data_files: - split: train path: data/CC-MAIN-2022-27/train-* - config_name: CC-MAIN-2022-33 data_files: - split: train path: data/CC-MAIN-2022-33/train-* - config_name: CC-MAIN-2022-40 data_files: - split: train path: data/CC-MAIN-2022-40/train-* - config_name: CC-MAIN-2022-49 data_files: - split: train path: data/CC-MAIN-2022-49/train-* - config_name: CC-MAIN-2023-06 data_files: - split: train path: data/CC-MAIN-2023-06/train-* - config_name: CC-MAIN-2023-14 data_files: - split: train path: data/CC-MAIN-2023-14/train-* - config_name: CC-MAIN-2023-23 data_files: - split: train path: data/CC-MAIN-2023-23/train-* - config_name: CC-MAIN-2023-40 data_files: - split: train path: data/CC-MAIN-2023-40/train-* - config_name: CC-MAIN-2023-50 data_files: - split: train path: data/CC-MAIN-2023-50/train-* - config_name: CC-MAIN-2024-10 data_files: - split: train path: data/CC-MAIN-2024-10/train-* --- # Fineweb-Edu-Fortified <figure> <img src="https://cdn-uploads.huggingface.co/production/uploads/646516d2200b583e1e50faf8/79yPdK79m9mA0cCz-3h4v.png" width="500" style="margin-left:auto; margin-right: auto"/> <figcaption style="text-align: center; margin-left: auto; margin-right: auto; font-style: italic;"> The composition of fineweb-edu-fortified, produced by automatically clustering a 500k row sample in <a href="https://app.airtrain.ai/dataset/c232b33f-4f4a-49a7-ba55-8167a5f433da/null/1/0"> Airtrain </a> </figcaption> </figure> ## What is it? Fineweb-Edu-Fortified is a dataset derived from [Fineweb-Edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu) by applying exact-match deduplication across the whole dataset and producing an embedding for each row. The number of times the text from each row appears is also included as a `count` column. The embeddings were produced using [TaylorAI/bge-micro](https://huggingface.co/TaylorAI/bge-micro) Fineweb and Fineweb-Edu were obtained by processing data from 95 crawls of [Common Crawl](https://commoncrawl.org/), covering a time period from 2013 to 2024. More information about the original datasets can be found by consulting: - [Fineweb-edu dataset card](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu) - [Fineweb dataset card](https://huggingface.co/datasets/HuggingFaceFW/fineweb) - [Fineweb release blog post](https://huggingface.co/spaces/HuggingFaceFW/blogpost-fineweb-v1) - [Fineweb paper](https://arxiv.org/abs/2406.17557) The contents of a randomly selected 500k rows from this dataset can be interactively explored in this [Airtrain](https://app.airtrain.ai/dataset/c232b33f-4f4a-49a7-ba55-8167a5f433da/null/1/0) dashboard. ## Deduplication ### Deduplication in original Fineweb and Fineweb-Edu During creation of the original Fineweb dataset, a variety of deduplication strategies were explored. The evaluation criteria used to assess deduplication strategies was to train ablation models on randomly selected subsets of the data, using a subset of up to ~350 billion tokens. Using this mechanism, the Fineweb authors selected a MinHash algorithm, using parameters considering documents with approximately 75% similarity or higher to be duplicates. This deduplication was performed *within* each Common Crawl crawl. For example, it would have removed all approximate duplicates from the 20th crawl from 2013, but would have retained an identical record that showed up in both the 2013-20 crawl and the 2013-48 crawl. The authors note that applying the deduplication *across crawls* reduced the evaluation performance of the ablation models used for assessment. The proposed reason for this performance degredation is that data duplicated across crawls is more likely to be high-quality compared to data that is not, so leaving in the duplicates effectively upsamples the higer-quality data. Following deduplication in Fineweb, Fineweb-Edu was extracted using a model-based quality classifier targeting educational content. It thus inherited the same inter-crawl deduplication strategy of Fineweb. ### Deduplication in this dataset #### Motivation Given the findings that cross-crawl deduplication reduced ablation model performance, one might ask what the motivation is for producing a dataset that uses it. Our motivation was threefold: - Reduce the number of rows that needed to be embedded by avoiding embedding of exact-match content - Enable easier filtering of the dataset for subsets-of-interest - Provide a version of the dataset for users whose training goals include avoiding training on non-unique tokens. For use cases that would benefit from "re-hydrating" or filtering the rows based on how frequently the text appeared in the original dataset, the new `count` column retains the number of appearances of the associated text. #### Procedure The overall procedure was to remove exact matches that appeared in multiple crawls (also referred to as "dumps"). This was achieved by performing an md5 hash on the text column and removing rows with duplicate hashes. To make this tractable at scale, we first grouped all rows by the first two hex digits of their hashes, then looked for exact hash matches within each of the resulting 256 buckets of data. Note that unlike the intra-crawl deduplication, we only eliminated exact matches across crawls. For duplicated rows, a strong preference was given to keep the metadata (ex: dump, url) from the oldest crawl where the text appeared. Following deduplication and embedding, the data were grouped by the "dump" column, mirroring the organization of the original Fineweb-Edu dataset. ### Deduplication stats Deduplication removed approximately 74.7% of rows from the original dataset (from 1.279 billion in Fineweb-Edu to 0.324 billion rows in Fineweb-Edu-Fortified). This indicates that a substantial amount of data in Fineweb-Edu is present across multiple crawls. The total token count in the deduplicated dataset is approximately 375 billion, compared to the 1,320 billion tokens in Fineweb-Edu. <figure> <img src="https://cdn-uploads.huggingface.co/production/uploads/646516d2200b583e1e50faf8/mUFyO1fUWJEXbYwiteR9e.png" width="750" style="margin-left:auto; margin-right: auto"/> <figcaption style="text-align: center; margin-left: auto; margin-right: auto; font-style: italic;"> A histogram of the `count` column. Histogram was generated using a 500k row sample after performing global per-row text duplication counting. </figcaption> </figure> ## Embeddings To support use cases with Fineweb-Edu such as classification, clustering, semantic search, etc., we have produced an embedding vector for each row in the dataset. The embedding model [TaylorAI/bge-micro](https://huggingface.co/TaylorAI/bge-micro) was selected for its tradeoff of strong performance on [MTEB](https://huggingface.co/spaces/mteb/leaderboard) benchmarks relative to its size (17 million parameters). The model's embedding space has 384 dimensions. The context-window of the model is 512 tokens (roughly several paragraphs of text); each row is embedded by using the first 512 tokens in its text field. Producing the embeddings took approximately 412 GPU-hours on Nvidia T4 GPUs. ## Using via `datasets` ```python from datasets import load_dataset fw = load_dataset("airtrain-ai/fineweb-edu-fortified", name="CC-MAIN-2024-10", split="train", streaming=True) ``` ## Considerations for Using the Data This "Considerations" section is copied from the parent dataset: [FineWeb-edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu). ### Social Impact of Dataset With the release of this dataset we aim to make model training more accessible to the machine learning community at large. While multiple open-weights models with strong performance have been publicly released in the past, more often than not these releases are not accompanied by the corresponding training dataset. This is unfortunate as the dataset specificities and characteristics have been demonstrated to have a very large impact and role in the performances of the models. As the creation of a high quality training dataset is a fundamental requirement to training an LLM capable of excelling at downstream tasks, with 🍷 FineWeb we (a) not only make the dataset creation process more transparent, by sharing our entire processing setup including the codebase used, we also (b) help alleviate the costs of dataset curation, both in time and in compute, for model creators by publicly releasing our dataset with the community. ### Discussion of Biases Efforts were made to minimize the amount of NSFW and toxic content present in the dataset by employing filtering on the URL level. However, there are still a significant number of documents present in the final dataset that could be considered toxic or contain harmful content. As 🍷 FineWeb was sourced from the web as a whole, any harmful biases typically present in it may be reproduced on our dataset. We deliberately avoided using machine learning filtering methods that define text quality based on the similarity to a “gold” source such as wikipedia or toxicity classifiers as these methods have been known to [disproportionately remove content in specific dialects](https://aclanthology.org/D16-1120/) and [overclassify as toxic text related to specific social identities](https://arxiv.org/pdf/2109.07445.pdf), respectively. ### Other Known Limitations As a consequence of some of the filtering steps applied, it is likely that code content is not prevalent in our dataset. If you are training a model that should also perform code tasks, we recommend you use 🍷 FineWeb with a code dataset, such as [The Stack v2](https://huggingface.co/datasets/bigcode/the-stack-v2). You should also probably consider complementing 🍷 FineWeb with specialized curated sources (such as Wikipedia, for example) as they will likely have better formatting than the wikipedia content included in 🍷 FineWeb (we did not tailor the processing to individual websites). ## Additional Information ### Acknowledgements Airtrain would like to thank the Fineweb/Fineweb-Edu team at Hugging Face for producing the original datasets, as well as for their support during work on Fineweb-Edu-Fortified. We'd also like to thank [@underspirit](https://huggingface.co/underspirit) for [pointing out](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu/discussions/7) the amount of reduction in dataset size that could be achieved via deduplication. We owe gratitude to [TaylorAI](https://huggingface.co/TaylorAI) for the `bge-micro` embedding model. Finally, thank you to the Hugging Face community for fostering a thriving ecosystem of models, datasets, and tools to support open-source AI. ### Licensing Information The dataset is released under the **Open Data Commons Attribution License (ODC-By) v1.0** [license](https://opendatacommons.org/licenses/by/1-0/). The use of this dataset is also subject to [CommonCrawl's Terms of Use](https://commoncrawl.org/terms-of-use).

OpenGVLab/MVBench

OpenGVLab

--- license: mit extra_gated_prompt: >- You agree to not use the dataset to conduct experiments that cause harm to human subjects. Please note that the data in this dataset may be subject to other agreements. Before using the data, be sure to read the relevant agreements carefully to ensure compliant use. Video copyrights belong to the original video creators or platforms and are for academic research use only. task_categories: - visual-question-answering - video-classification extra_gated_fields: Name: text Company/Organization: text Country: text E-Mail: text modalities: - Video - Text configs: - config_name: action_sequence data_files: json/action_sequence.json - config_name: moving_count data_files: json/moving_count.json - config_name: action_prediction data_files: json/action_prediction.json - config_name: episodic_reasoning data_files: json/episodic_reasoning.json - config_name: action_antonym data_files: json/action_antonym.json - config_name: action_count data_files: json/action_count.json - config_name: scene_transition data_files: json/scene_transition.json - config_name: object_shuffle data_files: json/object_shuffle.json - config_name: object_existence data_files: json/object_existence.json - config_name: fine_grained_pose data_files: json/fine_grained_pose.json - config_name: unexpected_action data_files: json/unexpected_action.json - config_name: moving_direction data_files: json/moving_direction.json - config_name: state_change data_files: json/state_change.json - config_name: object_interaction data_files: json/object_interaction.json - config_name: character_order data_files: json/character_order.json - config_name: action_localization data_files: json/action_localization.json - config_name: counterfactual_inference data_files: json/counterfactual_inference.json - config_name: fine_grained_action data_files: json/fine_grained_action.json - config_name: moving_attribute data_files: json/moving_attribute.json - config_name: egocentric_navigation data_files: json/egocentric_navigation.json language: - en size_categories: - 1K<n<10K --- # MVBench ## Dataset Description - **Repository:** [MVBench](https://github.com/OpenGVLab/Ask-Anything/blob/main/video_chat2/mvbench.ipynb) - **Paper:** [2311.17005](https://arxiv.org/abs/2311.17005) - **Point of Contact:** mailto:[kunchang li]([email protected]) ## <span style="color: red;">Important Update</span> [18/10/2024] Due to NTU RGB+D License, 320 videos from NTU RGB+D need to be downloaded manually. Please visit [ROSE Lab](https://rose1.ntu.edu.sg/dataset/actionRecognition/) to access the data. We also provide a [list of the 320 videos](https://huggingface.co/datasets/OpenGVLab/MVBench/blob/main/video/MVBench_videos_ntu.txt) used in MVBench for your reference.  We introduce a novel static-to-dynamic method for defining temporal-related tasks. By converting static tasks into dynamic ones, we facilitate systematic generation of video tasks necessitating a wide range of temporal abilities, from perception to cognition. Guided by task definitions, we then **automatically transform public video annotations into multiple-choice QA** for task evaluation. This unique paradigm enables efficient creation of MVBench with minimal manual intervention while ensuring evaluation fairness through ground-truth video annotations and avoiding biased LLM scoring. The **20** temporal task examples are as follows.  ## Evaluation An evaluation example is provided in [mvbench.ipynb](https://github.com/OpenGVLab/Ask-Anything/blob/main/video_chat2/mvbench.ipynb). Please follow the pipeline to prepare the evaluation code for various MLLMs. - **Preprocess**: We preserve the raw video (high resolution, long duration, etc.) along with corresponding annotations (start, end, subtitles, etc.) for future exploration; hence, the decoding of some raw videos like Perception Test may be slow. - **Prompt**: We explore effective system prompts to encourage better temporal reasoning in MLLM, as well as efficient answer prompts for option extraction. ## Leadrboard While an [Online leaderboard]() is under construction, the current standings are as follows: 

QingyiSi/Alpaca-CoT

QingyiSi

--- language: - en - zh - ml tags: - Instruction - Cot license: apache-2.0 datasets: - dataset1 - dataset2 --- # Instruction-Finetuning Dataset Collection (Alpaca-CoT) This repository will continuously collect various instruction tuning datasets. And we standardize different datasets into the same format, which can be directly loaded by the [code](https://github.com/PhoebusSi/alpaca-CoT) of Alpaca model. We also have conducted empirical study on various instruction-tuning datasets based on the Alpaca model, as shown in [https://github.com/PhoebusSi/alpaca-CoT](https://github.com/PhoebusSi/alpaca-CoT). If you think this dataset collection is helpful to you, please `like` this dataset and `star` our [github project](https://github.com/PhoebusSi/alpaca-CoT)! You are in a warm welcome to provide us with any non-collected instruction-tuning datasets (or their sources). We will uniformly format them, train Alpaca model with these datasets and open source the model checkpoints. # Contribute Welcome to join us and become a contributor to this project! If you want to share some datasets, adjust the data in the following format: ``` example.json [ {"instruction": instruction string, "input": input string, # (may be empty) "output": output string} ] ``` Folder should be like this: ``` Alpaca-CoT | |----example | | | |----example.json | | | ----example_context.json ... ``` Create a new pull request in [Community ](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT/discussions) and publish your branch when you are ready. We will merge it as soon as we can. # Data Usage and Resources ## Data Format All data in this folder is formatted into the same templates, where each sample is as follows: ``` [ {"instruction": instruction string, "input": input string, # (may be empty) "output": output string} ] ``` ## alpaca #### alpaca_data.json > This dataset is published by [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca). It contains 52K English instruction-following samples obtained by [Self-Instruction](https://github.com/yizhongw/self-instruct) techniques. #### alpaca_data_cleaned.json > This dataset is obtained [here](https://github.com/tloen/alpaca-lora). It is a revised version of `alpaca_data.json` by stripping of various tokenization artifacts. ## alpacaGPT4 #### alpaca_gpt4_data.json > This dataset is published by [Instruction-Tuning-with-GPT-4](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM). It contains 52K English instruction-following samples generated by GPT-4 using Alpaca prompts for fine-tuning LLMs. #### alpaca_gpt4_data_zh.json > This dataset is generated by GPT-4 using Chinese prompts translated from Alpaca by ChatGPT. <!-- ## belle_cn #### belle_data_cn.json This dataset is published by [BELLE](https://github.com/LianjiaTech/BELLE). It contains 0.5M Chinese instruction-following samples, which is also generated by [Self-Instruction](https://github.com/yizhongw/self-instruct) techniques. #### belle_data1M_cn.json This dataset is published by [BELLE](https://github.com/LianjiaTech/BELLE). It contains 1M Chinese instruction-following samples. The data of `belle_data_cn.json` and `belle_data1M_cn.json` are not duplicated. --> ## Chain-of-Thought #### CoT_data.json > This dataset is obtained by formatting the combination of 9 CoT datasets published by [FLAN](https://github.com/google-research/FLAN). It contains 9 CoT tasks involving 74771 samples. #### CoT_CN_data.json > This dataset is obtained by tranlating `CoT_data.json` into Chinese, using Google Translate(en2cn). #### formatted_cot_data folder > This folder contains the formatted English data for each CoT dataset. #### formatted_cot_data folder > This folder contains the formatted Chinese data for each CoT dataset. ## CodeAlpaca #### code_alpaca.json > This dataset is published by [codealpaca](https://github.com/sahil280114/codealpaca). It contains code generation task involving 20022 samples. ## finance #### finance_en.json > This dataset is collected from [here](https://huggingface.co/datasets/gbharti/finance-alpaca). It contains 68912 financial related instructions in English. ## firefly #### firefly.json > his dataset is collected from [here](https://github.com/yangjianxin1/Firefly). It contains 1649398 chinese instructions in 23 nlp tasks. ## GPT4all #### gpt4all.json > This dataset is collected from [here](https://github.com/nomic-ai/gpt4all). It contains 806199 en instructions in code, storys and dialogs tasks. #### gpt4all_without_p3.json > gpt4all without Bigscience/P3, contains 437605 samples. ## GPTeacher #### GPTeacher.json > This dataset is collected from [here](https://github.com/teknium1/GPTeacher). It contains 29013 en instructions generated by GPT-4, General-Instruct - Roleplay-Instruct - Code-Instruct - and Toolformer. ## Guanaco #### GuanacoDataset.json > This dataset is collected from [here](https://huggingface.co/datasets/JosephusCheung/GuanacoDataset). It contains 534610 en instructions generated by text-davinci-003 upon 175 tasks from the Alpaca model by providing rewrites of seed tasks in different languages and adding new tasks specifically designed for English grammar analysis, natural language understanding, cross-lingual self-awareness, and explicit content recognition. #### Guanaco_additional_Dataset.json > A new additional larger dataset for different languages. ## HC3 #### HC3_ChatGPT.json/HC3_Human.json > This dataset is collected from [here](https://huggingface.co/datasets/Hello-SimpleAI/HC3). It contains 37175 en/zh instructions generated by ChatGPT and human. #### HC3_ChatGPT_deduplication.json/HC3_Human_deduplication.json > HC3 dataset without deduplication instructions. ## instinwild #### instinwild_en.json & instinwild_cn.json > The two datasets are obtained [here](https://github.com/XueFuzhao/InstructionWild). It contains 52191 English and 51504 Chinese instructions, which are collected from Twitter, where users tend to share their interesting prompts of mostly generation, open QA, and mind-storm types. (Colossal AI used these datasets to train the ColossalChat model.) ## instruct #### instruct.json > The two datasets are obtained [here](https://huggingface.co/datasets/swype/instruct). It contains 888969 English instructions, which are caugmentation performed using the advanced NLP tools provided by AllenAI. ## Natural Instructions #### natural-instructions-1700tasks.zip > This dataset is obtained [here](https://github.com/allenai/natural-instructions). It contains 5040134 instructions, which are collected from diverse nlp tasks ## prosocial dialog #### natural-instructions-1700tasks.zip > This dataset is obtained [here](https://huggingface.co/datasets/allenai/prosocial-dialog). It contains 165681 English instructions, which are produuced by GPT-3 rewrites questions and humans feedback ## xP3 #### natural-instructions-1700tasks.zip > This dataset is obtained [here](https://huggingface.co/datasets/bigscience/xP3). It contains 78883588 instructions, which are collected by prompts & datasets across 46 of languages & 16 NLP tasks ## Chinese-instruction-collection > all datasets of Chinese instruction collection ## combination #### alcapa_plus_belle_data.json > This dataset is the combination of English `alpaca_data.json` and Chinese `belle_data_cn.json`. #### alcapa_plus_cot_data.json > This dataset is the combination of English `alpaca_data.json` and CoT `CoT_data.json`. #### alcapa_plus_belle_cot_data.json > This dataset is the combination of English `alpaca_data.json`, Chinese `belle_data_cn.json` and CoT `CoT_data.json`. ## Citation Please cite the repo if you use the data collection, code, and experimental findings in this repo. ``` @misc{alpaca-cot, author = {Qingyi Si, Zheng Lin }, school = {Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China}, title = {Alpaca-CoT: An Instruction Fine-Tuning Platform with Instruction Data Collection and Unified Large Language Models Interface}, year = {2023}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/PhoebusSi/alpaca-CoT}}, } ``` Cite the original Stanford Alpaca, BELLE and FLAN papers as well, please.

jinzhuoran/RWKU

jinzhuoran

--- language: - en license: cc-by-4.0 pretty_name: RWKU size_categories: - 10K<n<100K task_categories: - text-generation - fill-mask - question-answering tags: - unlearning - knowledge unlearning - NLP - LLM configs: - config_name: forget_target data_files: - split: train path: - "All/intro.json" - config_name: forget_level1 data_files: - split: test path: - "All/forget_level1.json" - config_name: forget_level2 data_files: - split: test path: - "All/forget_level2.json" - config_name: forget_level3 data_files: - split: test path: - "All/forget_level3.json" - config_name: neighbor_level1 data_files: - split: test path: - "All/neighbor_level1.json" - config_name: neighbor_level2 data_files: - split: test path: - "All/neighbor_level2.json" - config_name: mia_forget data_files: - split: test path: - "All/forget_mia.json" - config_name: mia_retain data_files: - split: test path: - "All/retain_mia.json" - config_name: utility_general data_files: - split: test path: - "All/retain_mmlu.json" - config_name: utility_general data_files: - split: test path: - "All/retain_mmlu.json" - config_name: utility_reason data_files: - split: test path: - "All/retain_bbh.json" - config_name: utility_truthfulness data_files: - split: test path: - "All/truthful.json" - config_name: utility_factuality data_files: - split: test path: - "All/triviaqa.json" - config_name: utility_fluency data_files: - split: test path: - "All/fluency.json" - config_name: train_original_passage data_files: - split: train path: - "All/passage.json" - config_name: train_positive_llama3 data_files: - split: train path: - "All/positive.json" - config_name: train_negative_llama3 data_files: - split: train path: - "All/negative.json" - config_name: train_pair_llama3 data_files: - split: train path: - "All/pair.json" - config_name: train_refusal_llama3 data_files: - split: train path: - "All/reject.json" - config_name: train_positive_phi3 data_files: - split: train path: - "All/positive_phi.json" - config_name: train_negative_phi3 data_files: - split: train path: - "All/negative_phi.json" - config_name: train_pair_phi3 data_files: - split: train path: - "All/pair_phi.json" - config_name: train_refusal_phi3 data_files: - split: train path: - "All/reject_phi.json" --- # Dataset Card for Real-World Knowledge Unlearning Benchmark (RWKU) ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://rwku-bench.github.io - **Repository:** https://github.com/jinzhuoran/RWKU - **Paper:** https://arxiv.org/abs/2406.10890 ### Dataset Summary **RWKU is a real-world knowledge unlearning benchmark specifically designed for large language models (LLMs).** This benchmark contains 200 real-world unlearning targets and 13,131 multi-level forget probes, including 3,268 fill-in-the-blank probes, 2,879 question-answer probes, and 6,984 adversarial-attack probes. RWKU is designed based on the following three key factors: 1. For the **task setting**, we consider a more practical and challenging setting, similar to _zero-shot knowledge unlearning_. We provide only the unlearning target and the original model, without offering any forget corpus or retain corpus. In this way, it avoids secondary information leakage caused by the forget corpus and is not affected by the distribution bias of the retain corpus. 2. For the **knowledge source**, we choose real-world famous people from Wikipedia as the unlearning targets and demonstrate that such popular knowledge is widely present in various LLMs through memorization quantification, making it more suitable for knowledge unlearning. Additionally, choosing entities as unlearning targets can well clearly define the unlearning boundaries. 3. For the **evaluation framework**, we carefully design the forget set and the retain set to evaluate the model's capabilities from multiple real-world applications. Regarding the forget set, we evaluate the **efficacy** of knowledge unlearning at both the knowledge memorization (fill-in-the-blank style) and knowledge manipulation (question-answer style) abilities. Specifically, we also evaluate these two abilities through **adversarial attacks** to induce forgotten knowledge in the model. We adopt four membership inference attack (MIA) methods for knowledge memorization on our collected MIA set. We meticulously designed nine types of adversarial-attack probes for knowledge manipulation, including prefix injection, affirmative suffix, role playing, reverse query, and others. Regarding the retain set, we design a neighbor set to test the impact of neighbor perturbation, specifically focusing on the **locality** of unlearning. In addition, we assess the **model utility** on various capabilities, including general ability, reasoning ability, truthfulness, factuality, and fluency. ### Supported Tasks Knowledge unlearning for LLMs. ### Languages English. ## Dataset Structure To evaluate the unlearning efficacy: ```python from datasets import load_dataset forget_level1 = load_dataset("jinzhuoran/RWKU", 'forget_level1') forget_level2 = load_dataset("jinzhuoran/RWKU", 'forget_level2') forget_level2 = load_dataset("jinzhuoran/RWKU", 'forget_level2') ``` To evaluate the locality: ```python from datasets import load_dataset neighbor_level1 = load_dataset("jinzhuoran/RWKU", 'neighbor_level1') neighbor_level2 = load_dataset("jinzhuoran/RWKU", 'neighbor_level2') ``` To evaluate the model utility: ```python from datasets import load_dataset utility_general = load_dataset("jinzhuoran/RWKU", 'utility_general') utility_reason = load_dataset("jinzhuoran/RWKU", 'utility_reason') utility_truthfulness = load_dataset("jinzhuoran/RWKU", 'utility_truthfulness') utility_factuality = load_dataset("jinzhuoran/RWKU", 'utility_factuality') utility_fluency = load_dataset("jinzhuoran/RWKU", 'utility_fluency') ``` To conduct membership inference attacks: ```python from datasets import load_dataset mia_forget = load_dataset("jinzhuoran/RWKU", 'mia_forget') mia_retain = load_dataset("jinzhuoran/RWKU", 'mia_retain') ``` To load the forget corpus: ```python from datasets import load_dataset train_original_passage = load_dataset("jinzhuoran/RWKU", 'train_original_passage') train_positive_llama3 = load_dataset("jinzhuoran/RWKU", 'train_positive_llama3') ``` ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citing Our Work If you find our codebase and dataset beneficial, please cite our work: ```bibtex @misc{jin2024rwku, title={RWKU: Benchmarking Real-World Knowledge Unlearning for Large Language Models}, author={Zhuoran Jin and Pengfei Cao and Chenhao Wang and Zhitao He and Hongbang Yuan and Jiachun Li and Yubo Chen and Kang Liu and Jun Zhao}, year={2024}, eprint={2406.10890}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

uwipl/RT-Pose

uwipl

--- license: cc-by-nc-sa-4.0 size_categories: - 1K<n<10K task_categories: - keypoint-detection - pose-estimation --- [Paper](https://arxiv.org/pdf/2407.13930) # RT-Pose: A 4D Radar Tensor-based 3D Human Pose Estimation and Localization Benchmark (ECCV 2024) RT-Pose introduces a human pose estimation (HPE) dataset and benchmark by integrating a unique combination of calibrated radar ADC data, 4D radar tensors, stereo RGB images, and LiDAR point clouds. This integration marks a significant advancement in studying human pose analysis through multi-modality datasets.   ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> #### Sensors The data collection hardware system comprises two RGB [cameras](https://www.flir.com/products/blackfly-s-usb3/?model=BFS-U3-16S2C-CS), a non-repetitive horizontal scanning [LiDAR](https://www.livoxtech.com/3296f540ecf5458a8829e01cf429798e/assets/horizon/Livox%20Horizon%20user%20manual%20v1.0.pdf), and a cascade imaging [radar module](https://www.ti.com/tool/MMWCAS-RF-EVM).  #### Data Statics We collect the dataset in 40 scenes with indoor and outdoor environments.  The dataset comprises 72,000 frames distributed across 240 sequences. The structured organization ensures a realistic distribution of human motions, which is crucial for robust analysis and model training.  Please check the paper for more details. - **Curated by:** Yuan-Hao Ho ([email protected]), Jen-Hao(Andy) Cheng([email protected]) from [Information Processing Lab](https://ipl-uw.github.io/) at University of Washington - **License:** [CC BY-NC-SA](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en) ### Dataset Sources <!-- Provide the basic links for the dataset. --> - **Repository including data processing and baseline method codes:** [RT-POSE](https://github.com/ipl-uw/RT-POSE) - **Paper:** [Paper](https://arxiv.org/pdf/2407.13930) ## Uses <!-- Address questions around how the dataset is intended to be used. --> 1. Download the dataset from Hugging Face (Total data size: ~1.2 TB) 2. Follow the [data processing tool](https://github.com/ipl-uw/RT-POSE/data_processing) to process radar ADC samples into radar tensors. (Total data size of the downloaded data and saved radar tensors: ~41 TB) 3. Check the data loading and baseline method's training and testing codes in the same repo [RT-POSE](https://github.com/ipl-uw/RT-POSE) ## Citation **BibTeX:** @article{rtpose2024, title={RT-Pose: A 4D Radar Tensor-based 3D Human Pose Estimation and Localization Benchmark}, author={Yuan-Hao Ho and Jen-Hao Cheng and Sheng Yao Kuan and Zhongyu Jiang and Wenhao Chai and Hsiang-Wei Huang and Chih-Lung Lin and Jenq-Neng Hwang}, journal={arXiv preprint arXiv:2407.13930}, year={2024} }

graelo/wikipedia

graelo

--- annotations_creators: - no-annotation language_creators: - crowdsourced pretty_name: Wikipedia paperswithcode_id: null license: - cc-by-sa-3.0 - gfdl task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling source_datasets: - original multilinguality: - multilingual size_categories: - n<1K - 1K<n<10K - 10K<n<100K - 100K<n<1M - 1M<n<10M language: # - aa - closed and no dump - ab - ace - ady - af - ak - als - alt - am - ami - an - ang - anp - ar - arc - ary - arz - as - ast - atj - av - avk - awa - ay - az - azb - ba - ban - bar # - bat-smg - see bcp47 below - bcl # - be-x-old - see bcp47 below - be - bg - bh - bi - bjn - blk - bm - bn - bo - bpy - br - bs - bug - bxr - ca # - cbk-zam - see bcp47 below - cdo - ce - ceb - ch - cho # closed - chr - chy - ckb - co - cr - crh - cs - csb - cu - cv - cy - da - dag - de - din - diq - dsb - dty - dv - dz - ee - el - eml - eo - es - et - eu - ext - fa - fat - ff - fi # - fiu-vro - see bcp47 below - fj - fo - fr - frp - frr - fur - fy - ga - gag - gan - gcr - gd - gl - glk - gn - gom - gor - got - gu - guc - gur - guw - gv - ha - hak - haw - he - hi - hif - ho # closed - hr - hsb - ht - hu - hy - hyw # - hz - closed and no dump - ia - id - ie - ig - ii # closed - ik - ilo - inh - io - is - it - iu - ja - jam - jbo - jv - ka - kaa - kab - kbd - kbp - kcg - kg - ki - kj # closed - kk - kl - km - kn - ko - koi # - kr - closed and no dump - krc - ks - ksh - ku - kv - kw - ky - la - lad - lb - lbe - lez - lfn - lg - li - lij - lld - lmo - ln - lo - lrc # closed - lt - ltg - lv - mad - mai # - map-bms - see bcp47 below - mdf - mg - mh - mhr - mi - min - mk - ml - mn - mni - mnw - mr - mrj - ms - mt - mus # closed - mwl - my - myv - mzn # - na - closed and no dump - nah - nap # - nds-nl - see bcp47 below - nds - ne - new - ng # closed - nia - nl - nn - no - nov - nqo - nrm - nso - nv - ny - oc - olo - om - or - os - pa - pag - pam - pap - pcd - pcm - pdc - pfl - pi - pih - pl - pms - pnb - pnt - ps - pt - pwn - qu - rm - rmy - rn - ro # - roa-rup - see bcp47 below # - roa-tara - see bcp47 below - ru - rue - rw - sa - sah - sat - sc - scn - sco - sd - se - sg - sh - shi - shn - si # - simple - see bcp47 below - sk - skr - sl - sm - smn - sn - so - sq - sr - srn - ss - st - stq - su - sv - sw - szl - szy - ta - tay - tcy - te - tet - tg - th - ti - tk - tl - tn - to - tpi - tr - trv - ts - tt - tum - tw - ty - tyv - udm - ug - uk - ur - uz - ve - vec - vep - vi - vls - vo - wa - war - wo - wuu - xal - xh - xmf - yi - yo - za - zea - zh # - zh-classical - see bcp47 below # - zh-min-nan - see bcp47 below # - zh-yue - see bcp47 below - zu language_bcp47: - bat-smg - be-x-old - cbk-zam - fiu-vro - map-bms - nds-nl - roa-rup - roa-tara - simple - zh-classical - zh-min-nan - zh-yue dataset_info: - config_name: 20230601.ab features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 4183525 num_examples: 6114 download_size: 1172328 dataset_size: 4183525 - 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config_name: 20230901.tpi features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 457865 num_examples: 1396 download_size: 231303 dataset_size: 457865 - config_name: 20230901.tr features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 984939694 num_examples: 530830 download_size: 554907604 dataset_size: 984939694 - config_name: 20230901.trv features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 4906787 num_examples: 1835 download_size: 2654525 dataset_size: 4906787 - config_name: 20230901.ts features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 845256 num_examples: 778 download_size: 454559 dataset_size: 845256 - config_name: 20230901.tt features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 680656530 num_examples: 501002 download_size: 129123758 dataset_size: 680656530 - config_name: 20230901.tum features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 13199654 num_examples: 18591 download_size: 5352424 dataset_size: 13199654 - config_name: 20230901.tw features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 7386605 num_examples: 3717 download_size: 3815538 dataset_size: 7386605 - config_name: 20230901.ty features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 333733 num_examples: 1355 download_size: 149306 dataset_size: 333733 - config_name: 20230901.tyv features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 14319641 num_examples: 3481 download_size: 6513101 dataset_size: 14319641 - config_name: 20230901.udm features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 6975919 num_examples: 5665 download_size: 2952228 dataset_size: 6975919 - config_name: 20230901.ug features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 42219904 num_examples: 8621 download_size: 17716007 dataset_size: 42219904 - config_name: 20230901.uk features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 4910916097 num_examples: 1285004 download_size: 2303106335 dataset_size: 4910916097 - config_name: 20230901.ur features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 402322741 num_examples: 197343 download_size: 164074548 dataset_size: 402322741 - config_name: 20230901.uz features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 385386661 num_examples: 242726 download_size: 203362895 dataset_size: 385386661 - config_name: 20230901.ve features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 349857 num_examples: 840 download_size: 161562 dataset_size: 349857 - config_name: 20230901.vec features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 37883286 num_examples: 69250 download_size: 16164035 dataset_size: 37883286 - config_name: 20230901.vep features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 11487509 num_examples: 6918 download_size: 6327017 dataset_size: 11487509 - config_name: 20230901.vi features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 1606980713 num_examples: 1287263 download_size: 742700712 dataset_size: 1606980713 - config_name: 20230901.vls features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 11310015 num_examples: 7839 download_size: 6960289 dataset_size: 11310015 - config_name: 20230901.vo features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 19274897 num_examples: 34504 download_size: 6491359 dataset_size: 19274897 - config_name: 20230901.wa features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 12140372 num_examples: 11955 download_size: 7231141 dataset_size: 12140372 - config_name: 20230901.war features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 467623925 num_examples: 1266345 download_size: 109503863 dataset_size: 467623925 - config_name: 20230901.wo features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 3498562 num_examples: 1718 download_size: 2077375 dataset_size: 3498562 - config_name: 20230901.wuu features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 25005942 num_examples: 42969 download_size: 15994961 dataset_size: 25005942 - config_name: 20230901.xal features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 1390063 num_examples: 2290 download_size: 507117 dataset_size: 1390063 - config_name: 20230901.xh features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 2415590 num_examples: 1667 download_size: 1503917 dataset_size: 2415590 - config_name: 20230901.xmf features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 37262425 num_examples: 17949 download_size: 12771047 dataset_size: 37262425 - config_name: 20230901.yi features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 36150608 num_examples: 15329 download_size: 16208341 dataset_size: 36150608 - config_name: 20230901.yo features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 18460117 num_examples: 33495 download_size: 8504564 dataset_size: 18460117 - config_name: 20230901.za features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 1359106 num_examples: 2971 download_size: 662982 dataset_size: 1359106 - config_name: 20230901.zea features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 5106625 num_examples: 5834 download_size: 2567716 dataset_size: 5106625 - config_name: 20230901.zh features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 2766648619 num_examples: 1375017 download_size: 1748154636 dataset_size: 2766648619 - config_name: 20230901.zh-classical features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 14819164 num_examples: 12615 download_size: 10031693 dataset_size: 14819164 - config_name: 20230901.zh-min-nan features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 159385896 num_examples: 432644 download_size: 37476665 dataset_size: 159385896 - config_name: 20230901.zh-yue features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 108979942 num_examples: 133155 download_size: 64318527 dataset_size: 108979942 - config_name: 20230901.zu features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 6925330 num_examples: 11486 download_size: 3690925 dataset_size: 6925330 - config_name: 20230601.et features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 431680309 num_examples: 236848 download_size: 262989758 dataset_size: 431680309 --- # Wikipedia This Wikipedia dataset contains all available languages for recent dumps. It is a refresh of the [20220301 wikipedia](https://hf.co/datasets/wikipedia) from Huggingface, so it has the same license and dataset card details. The benefits of this dataset are: - more recent dumps (see table below) - a few additional languages - all available languages are preprocessed (including the largests: `en` and `ceb`) | version | dump | # available languages | closed & dump | closed & no dump | | ----- | ---- | ----- | ------ | --- | | `1.0.0` | 20230601 | 328 | 9: ak (soon), cho, ho, ii, kj, lrc, mh, mus, ng | 4: aa, hz, kr, na | | `1.1.0` | 20230601 | 329 (+et ~[az,ceb,ch,hr,ii,lrc,ta]) | 9: ak (soon), cho, ho, ii, kj, lrc, mh, mus, ng | 4: aa, hz, kr, na | | `1.2.0` | 20230901 | idem | 9: ak , cho, ho, ii, kj, lrc, mh, mus, ng | 4: aa, hz, kr, na | Source: [List of Wikimedia Languages](https://en.wikipedia.org/wiki/List_of_Wikipedias). A few (9) Wikimedias are closed, meaning they won't have new pages, but the dumps are still available. In addition, very few (4) Wikimedias are closed and don't have dumps anymore. ## Release Notes `1.2.0` - **chore**: Update to 20230901 `1.1.0` - **feat**: Add missing estonian (my bad), thanks Chris Ha - **fix**: update category lists for az, ceb, ch, hr, ii, lrc, ta, which means they were all processed again. `1.0.0` - **chore**: File layout is now `data/{dump}/{lang}/{info.json,*.parquet}`. Sorry for the radical update, probably won't happen again. - **chore**: Parquet files are now sharded (size < 200 MB), allowing parallel downloads and processing. - **fix**: All languages were all processed again because of a bug in the media and category names, leading to some links not being extracted. - **feat**: Add `en` and `ceb` which were too big for my Beam DirectRunner at the time. ## Usage ```python from datasets import load_dataset wikipedia_es = load_dataset("graelo/wikipedia", "20230601.es") ``` --- ## Build instructions Developer only. This dataset was preprocessed with a Beam DirectRunner as follows. ### 1. Determine the date of the dump you are interested in Choose one wikipedia dump, for instance <https://dumps.wikimedia.org/cewiki/> and identify the date. ### 2. [Optional] Get a refreshed list of languages This is optional because it not very likely that a new language will have suddenly appeared since the last version _and_ have a significant dataset. Navigate to <https://en.wikipedia.org/wiki/List_of_Wikipedias> and copy the languages column from the "Detailed list" table (near the end of the page). Copy that content in the form of a Python list into `lang_def.py` (at the top of the repo) under a new date. ### 3. [Optional] Create Media and Category aliases In order to properly extract links to images and media in all languages, we must refresh the two corresponding files. To do so, from the root of the repo, run ```sh python -m prep.create_aliases ``` This will create or update these two files at the root of the repo: - `media_aliases.py` - `category_aliases.py` These files are used in the final step ### 4. Build and prepare the datasets into sharded parquet files Running this script downloads the wikipedia dumps for each language in `lang_def.py` and shards each language dataset into the appropriate number of shards (max size ~ 250MB). ```sh python -m prep.build --date 20230601 ``` There are other options: ```text $ python -m prep.build --help usage: Wikipedia Builder [-h] [--date DATE] [--language [LANG ...]] [--cache-dir DIR] [--mirror MIRROR] Prepares the Wikipedia dataset for each language optional arguments: -h, --help show this help message and exit --date DATE Wikipedia dump date (e.g. 20230601) --language [LANG ...] Language code (e.g. en). If missing, all languages are processed --cache-dir DIR Cache directory for 🤗 Datasets --mirror MIRROR Mirror URL ``` For instance, for faster downloads of the dumps, use the mirror option: ```sh python -m prep.build \ --date 20230601 \ --language bs \ --mirror https://mirror.accum.se/mirror/wikimedia.org/dumps/ ``` It will download the dumps at around 60MB/s instead of the capped speed (~4MB/s) from <https://dumps.wikimedia.org>. The script will skip existing directories, allowing you to run the script in several passes. Notes: - These instructions build upon the build process of the [Wikipedia](https://huggingface.co/datasets/wikipedia) 🤗 Dataset. HF did a fantastic job, I just pushed it a bit further. - Be aware that not all mirrors contain all dumps. For instance mirror.accum.se does not contain dumps for languages such as be-x-old or cbk-zam. My own solution is to run a first pass using the aforementioned mirror, and a second pass with the official `https://dumps.wikimedia.org` site (omitting the `--mirror` parameter).

vikhyatk/docmatix-single

vikhyatk

--- dataset_info: features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 244951255658.16818 num_examples: 565009 download_size: 145422811605 dataset_size: 244951255658.16818 configs: - config_name: default data_files: - split: train path: data/train-* --- [Docmatix](https://huggingface.co/datasets/HuggingFaceM4/Docmatix), but with multi-image samples filtered out.

allenai/sciq

allenai

--- annotations_creators: - no-annotation language_creators: - crowdsourced language: - en license: - cc-by-nc-3.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - question-answering task_ids: - closed-domain-qa paperswithcode_id: sciq pretty_name: SciQ dataset_info: features: - name: question dtype: string - name: distractor3 dtype: string - name: distractor1 dtype: string - name: distractor2 dtype: string - name: correct_answer dtype: string - name: support dtype: string splits: - name: train num_bytes: 6546183 num_examples: 11679 - name: validation num_bytes: 554120 num_examples: 1000 - name: test num_bytes: 563927 num_examples: 1000 download_size: 4674410 dataset_size: 7664230 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* --- # Dataset Card for "sciq" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://allenai.org/data/sciq](https://allenai.org/data/sciq) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 2.82 MB - **Size of the generated dataset:** 7.68 MB - **Total amount of disk used:** 10.50 MB ### Dataset Summary The SciQ dataset contains 13,679 crowdsourced science exam questions about Physics, Chemistry and Biology, among others. The questions are in multiple-choice format with 4 answer options each. For the majority of the questions, an additional paragraph with supporting evidence for the correct answer is provided. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - **Size of downloaded dataset files:** 2.82 MB - **Size of the generated dataset:** 7.68 MB - **Total amount of disk used:** 10.50 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "correct_answer": "coriolis effect", "distractor1": "muon effect", "distractor2": "centrifugal effect", "distractor3": "tropical effect", "question": "What phenomenon makes global winds blow northeast to southwest or the reverse in the northern hemisphere and northwest to southeast or the reverse in the southern hemisphere?", "support": "\"Without Coriolis Effect the global winds would blow north to south or south to north. But Coriolis makes them blow northeast to..." } ``` ### Data Fields The data fields are the same among all splits. #### default - `question`: a `string` feature. - `distractor3`: a `string` feature. - `distractor1`: a `string` feature. - `distractor2`: a `string` feature. - `correct_answer`: a `string` feature. - `support`: a `string` feature. ### Data Splits | name |train|validation|test| |-------|----:|---------:|---:| |default|11679| 1000|1000| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information The dataset is licensed under the [Creative Commons Attribution-NonCommercial 3.0 Unported License](http://creativecommons.org/licenses/by-nc/3.0/). ### Citation Information ``` @inproceedings{SciQ, title={Crowdsourcing Multiple Choice Science Questions}, author={Johannes Welbl, Nelson F. Liu, Matt Gardner}, year={2017}, journal={arXiv:1707.06209v1} } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf) for adding this dataset.

Qi28/aistudio_TTS

Qi28

--- license: apache-2.0 ---

HuggingFaceM4/OBELICS

HuggingFaceM4

--- language: - en license: cc-by-4.0 size_categories: - 100M<n<1B pretty_name: OBELICS configs: - config_name: default data_files: - split: train path: data/train-* - config_name: opt_out_docs_removed_2023_07_12 data_files: - split: train path: opt_out_docs_removed_2023_07_12/train-* dataset_info: - config_name: default features: - name: images sequence: string - name: metadata dtype: string - name: general_metadata dtype: string - name: texts sequence: string splits: - name: train num_bytes: 715724717192 num_examples: 141047697 download_size: 71520629655 dataset_size: 715724717192 - config_name: opt_out_docs_removed_2023_07_12 features: - name: images sequence: string - name: metadata dtype: string - name: general_metadata dtype: string - name: texts sequence: string splits: - name: train num_bytes: 684638314215 num_examples: 134648855 download_size: 266501092920 dataset_size: 684638314215 --- # Dataset Card for OBELICS ## Dataset Description - **Visualization of OBELICS web documents:** https://huggingface.co/spaces/HuggingFaceM4/obelics_visualization - **Paper:** [OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents](https://arxiv.org/abs/2306.16527) - **Repository:** https://github.com/huggingface/OBELICS - **Point of Contact: [email protected]** `OBELICS` is an open, massive, and curated collection of interleaved image-text web documents, containing 141M English documents, 115B text tokens, and 353M images, extracted from Common Crawl dumps between February 2020 and February 2023. The collection and filtering steps are described in our [paper](https://huggingface.co/papers/2306.16527). Interleaved image-text web documents are a succession of text paragraphs interleaved by images, such as web pages that contain images. Models trained on these web documents outperform vision and language models trained solely on image-text pairs on various benchmarks. They can also generate long and coherent text about a set of multiple images. As an example, we trained [IDEFICS](https://huggingface.co/HuggingFaceM4/idefics-80b), a visual language model that accepts arbitrary sequences of image and text inputs and produces text outputs. We provide an [interactive visualization](https://atlas.nomic.ai/map/f2fba2aa-3647-4f49-a0f3-9347daeee499/ee4a84bd-f125-4bcc-a683-1b4e231cb10f) of OBELICS that allows exploring the content of OBELICS. The map shows a subset of 11M of the 141M documents. [](https://atlas.nomic.ai/map/f2fba2aa-3647-4f49-a0f3-9347daeee499/ee4a84bd-f125-4bcc-a683-1b4e231cb10f) ## Data Fields An example of a sample looks as follows: ``` # The example has been cropped { 'images': [ 'https://cdn.motor1.com/images/mgl/oRKO0/s1/lamborghini-urus-original-carbon-fiber-accessories.jpg', None ], 'metadata': '[{"document_url": "https://lamborghinichat.com/forum/news/vw-group-allegedly-receives-offer-to-sell-lamborghini-for-9-2-billion.728/", "unformatted_src": "https://cdn.motor1.com/images/mgl/oRKO0/s1/lamborghini-urus-original-carbon-fiber-accessories.jpg", "src": "https://cdn.motor1.com/images/mgl/oRKO0/s1/lamborghini-urus-original-carbon-fiber-accessories.jpg", "formatted_filename": "lamborghini urus original carbon fiber accessories", "alt_text": "VW Group Allegedly Receives Offer To Sell Lamborghini For $9.2 Billion", "original_width": 1920, "original_height": 1080, "format": "jpeg"}, null]', 'general_metadata': '{"url": "https://lamborghinichat.com/forum/news/vw-group-allegedly-receives-offer-to-sell-lamborghini-for-9-2-billion.728/", "warc_filename": "crawl-data/CC-MAIN-2021-25/segments/1623488528979.69/warc/CC-MAIN-20210623011557-20210623041557-00312.warc.gz", "warc_record_offset": 322560850, "warc_record_length": 17143}', 'texts': [ None, 'The buyer would get everything, including Lambo\'s headquarters.\n\nThe investment groupQuantum Group AG has submitted a€7.5 billion ($9.2 billion at current exchange rates) offer to purchase Lamborghini from Volkswagen Group, Autocar reports. There\'s no info yet about whether VW intends to accept the offer or further negotiate the deal.\n\nQuantum ... Group Chief Executive Herbert Diess said at the time.' ] } ``` Each sample is composed of the same 4 fields: `images`, `texts`, `metadata`, and `general_metadata`. `images` and `texts` are two lists of the same size, where for each index, one element and only one is not `None`. For example, for the interleaved web document `<image_1>text<image_2>`, we would find `[image_1, None, image_2]` in `images` and `[None, text, None]` in `texts`. The images are replaced by their URLs, and the users need to download the images, for instance, with the library [img2dataset](https://github.com/rom1504/img2dataset). `metadata` is the string representation of a list containing information about each of the images. It has the same length as `texts` and `images` and logs for each image relevant information such as original source document, unformatted source, alternative text if present, etc. `general_metadata` is the string representation of a dictionary containing the URL of the document, and information regarding the extraction from Common Crawl snapshots. ## Size and Data Splits There is only one split, `train`, that contains 141,047,697 documents. `OBELICS` with images replaced by their URLs weighs 666.6 GB (😈) in arrow format and 377 GB in the uploaded `parquet` format. ## Considerations for Using the Data ### Discussion of Biases A subset of this dataset `train`, of ~50k was evaluated using the Data Measurements Tool, with a particular focus on the nPMI metric > nPMI scores for a word help to identify potentially problematic associations, ranked by how close the association is. > nPMI bias scores for paired words help to identify how word associations are skewed between the selected selected words (Aka et al., 2021). > You can select from gender and sexual orientation identity terms that appear in the dataset at least 10 times. > The resulting ranked words are those that co-occur with both identity terms. > The more positive the score, the more associated the word is with the first identity term. The more negative the score, the more associated the word is with the second identity term. While there was a positive skew of words relating occupations e.g _`government`_, _`jobs`_ towards she, her, and similar attributions of the masculine and feminine words to they and them, more harmful words attributions such as _`escort`_ and even _`colour`_ presented with greater attributions to she, her and him, his, respectively.  We welcome users to explore the [Data Measurements nPMI Visualitons for OBELICS](https://huggingface.co/spaces/HuggingFaceM4/IDEFICS_Data_Measurement_Tool) further and to see the [idefics-9b model card](https://huggingface.co/HuggingFaceM4/idefics-9b) for further Bias considerations. ## Opted-out content To respect the preferences of content creators, we removed from OBELICS all images for which creators explicitly opted out of AI model training. We used the [Spawning API](https://api.spawning.ai/spawning-api) to verify that the images in the dataset respect the original copyright owners’ choices. However, due to an error on our side, we did not remove entire documents (i.e., URLs) that opted out of AI model training. As of July 12, 2023, it represents 4.25% of the totality of OBELICS. The config `opt_out_docs_removed_2023_07_12` applies the correct filtering at the web document level as of July 2023: `ds = load_dataset("HuggingFaceM4/OBELICS", "opt_out_docs_removed_2023_07_12")`. We recommend users of OBELICS to regularly check every document against the API. ## Content warnings Despite our efforts in filtering, OBELICS contains a small proportion of documents that are not suitable for all audiences. For instance, while navigating the interactive map, you might find the cluster named "Sex" which predominantly contains descriptions of pornographic movies along with pornographic images. Other clusters would contain advertising for sex workers or reports of violent shootings. In our experience, these documents represent a small proportion of all the documents. ## Terms of Use By using the dataset, you agree to comply with the original licenses of the source content as well as the dataset license (CC-BY-4.0). Additionally, if you use this dataset to train a Machine Learning model, you agree to disclose your use of the dataset when releasing the model or an ML application using the model. ### Licensing Information License CC-BY-4.0. ### Citation Information If you are using this dataset, please cite ``` @misc{laurencon2023obelics, title={OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents}, author={Hugo Laurençon and Lucile Saulnier and Léo Tronchon and Stas Bekman and Amanpreet Singh and Anton Lozhkov and Thomas Wang and Siddharth Karamcheti and Alexander M. Rush and Douwe Kiela and Matthieu Cord and Victor Sanh}, year={2023}, eprint={2306.16527}, archivePrefix={arXiv}, primaryClass={cs.IR} } ```

Jackmin108/xtreme

Jackmin108

--- license: apache-2.0 configs: - config_name: mnli data_files: - split: train path: - "mnli/train-0000.parquet" - "mnli/train-0001.parquet" - "mnli/train-0002.parquet" - "mnli/train-0003.parquet" features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - entailment - neutral - contradiction _type: ClassLabel idx: dtype: int32 _type: Value - config_name: tydiqa data_files: - split: train path: - "tydiqa/ko/train.parquet" - "tydiqa/sw/train.parquet" - "tydiqa/ru/train.parquet" - "tydiqa/te/train.parquet" - "tydiqa/ar/train.parquet" - "tydiqa/fi/train.parquet" - "tydiqa/bn/train.parquet" - "tydiqa/en/train.parquet" - "tydiqa/id/train.parquet" - split: validation path: - "tydiqa/ko/validation.parquet" - "tydiqa/sw/validation.parquet" - "tydiqa/ru/validation.parquet" - "tydiqa/te/validation.parquet" - "tydiqa/ar/validation.parquet" - "tydiqa/fi/validation.parquet" - "tydiqa/bn/validation.parquet" - "tydiqa/en/validation.parquet" - "tydiqa/id/validation.parquet" - config_name: tydiqa.ko data_files: - split: train path: "tydiqa/ko/train.parquet" - split: validation path: "tydiqa/ko/validation.parquet" - config_name: tydiqa.sw data_files: - split: train path: "tydiqa/sw/train.parquet" - split: validation path: "tydiqa/sw/validation.parquet" - config_name: tydiqa.ru data_files: - split: train path: "tydiqa/ru/train.parquet" - split: validation path: "tydiqa/ru/validation.parquet" - config_name: tydiqa.te data_files: - split: train path: "tydiqa/te/train.parquet" - split: validation path: "tydiqa/te/validation.parquet" - config_name: tydiqa.ar data_files: - split: train path: "tydiqa/ar/train.parquet" - split: validation path: "tydiqa/ar/validation.parquet" - config_name: tydiqa.fi data_files: - split: train path: "tydiqa/fi/train.parquet" - split: validation path: "tydiqa/fi/validation.parquet" - config_name: tydiqa.bn data_files: - split: train path: "tydiqa/bn/train.parquet" - split: validation path: "tydiqa/bn/validation.parquet" - config_name: tydiqa.en data_files: - split: train path: "tydiqa/en/train.parquet" - split: validation path: "tydiqa/en/validation.parquet" - config_name: tydiqa.id data_files: - split: train path: "tydiqa/id/train.parquet" - split: validation path: "tydiqa/id/validation.parquet" - config_name: xnli data_files: - split: validation path: - xnli/hi/validation.parquet - xnli/zh/validation.parquet - xnli/sw/validation.parquet - xnli/tr/validation.parquet - xnli/en/validation.parquet - xnli/th/validation.parquet - xnli/ru/validation.parquet - xnli/ar/validation.parquet - xnli/vi/validation.parquet - xnli/bg/validation.parquet - xnli/es/validation.parquet - xnli/el/validation.parquet - xnli/fr/validation.parquet - xnli/ur/validation.parquet - xnli/de/validation.parquet - split: test path: - xnli/hi/test.parquet - xnli/zh/test.parquet - xnli/sw/test.parquet - xnli/tr/test.parquet - xnli/en/test.parquet - xnli/th/test.parquet - xnli/ru/test.parquet - xnli/ar/test.parquet - xnli/vi/test.parquet - xnli/bg/test.parquet - xnli/es/test.parquet - xnli/el/test.parquet - xnli/fr/test.parquet - xnli/ur/test.parquet - xnli/de/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - entailment - neutral - contradiction _type: ClassLabel idx: dtype: int32 _type: Value - config_name: xnli.hi data_files: - split: validation path: xnli/hi/validation.parquet - split: test path: xnli/hi/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - entailment - neutral - contradiction _type: ClassLabel idx: dtype: int32 _type: Value - config_name: xnli.zh data_files: - split: validation path: xnli/zh/validation.parquet - split: test path: xnli/zh/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - entailment - neutral - contradiction _type: ClassLabel idx: dtype: int32 _type: Value - config_name: xnli.sw data_files: - split: validation path: xnli/sw/validation.parquet - split: test path: xnli/sw/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - entailment - neutral - contradiction _type: ClassLabel idx: dtype: int32 _type: Value - config_name: xnli.tr data_files: - split: validation path: xnli/tr/validation.parquet - split: test path: xnli/tr/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - entailment - neutral - contradiction _type: ClassLabel idx: dtype: int32 _type: Value - config_name: xnli.en data_files: - split: validation path: xnli/en/validation.parquet - split: test path: xnli/en/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - entailment - neutral - contradiction _type: ClassLabel idx: dtype: int32 _type: Value - config_name: xnli.th data_files: - split: validation path: xnli/th/validation.parquet - split: test path: xnli/th/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - entailment - neutral - contradiction _type: ClassLabel idx: dtype: int32 _type: Value - config_name: xnli.ru data_files: - split: validation path: xnli/ru/validation.parquet - split: test path: xnli/ru/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - entailment - neutral - contradiction _type: ClassLabel idx: dtype: int32 _type: Value - config_name: xnli.ar data_files: - split: validation path: xnli/ar/validation.parquet - split: test path: xnli/ar/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - entailment - neutral - contradiction _type: ClassLabel idx: dtype: int32 _type: Value - config_name: xnli.vi data_files: - split: validation path: xnli/vi/validation.parquet - split: test path: xnli/vi/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - entailment - neutral - contradiction _type: ClassLabel idx: dtype: int32 _type: Value - config_name: xnli.bg data_files: - split: validation path: xnli/bg/validation.parquet - split: test path: xnli/bg/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - entailment - neutral - contradiction _type: ClassLabel idx: dtype: int32 _type: Value - config_name: xnli.es data_files: - split: validation path: xnli/es/validation.parquet - split: test path: xnli/es/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - entailment - neutral - contradiction _type: ClassLabel idx: dtype: int32 _type: Value - config_name: xnli.el data_files: - split: validation path: xnli/el/validation.parquet - split: test path: xnli/el/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - entailment - neutral - contradiction _type: ClassLabel idx: dtype: int32 _type: Value - config_name: xnli.fr data_files: - split: validation path: xnli/fr/validation.parquet - split: test path: xnli/fr/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - entailment - neutral - contradiction _type: ClassLabel idx: dtype: int32 _type: Value - config_name: xnli.ur data_files: - split: validation path: xnli/ur/validation.parquet - split: test path: xnli/ur/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - entailment - neutral - contradiction _type: ClassLabel idx: dtype: int32 _type: Value - config_name: xnli.de data_files: - split: validation path: xnli/de/validation.parquet - split: test path: xnli/de/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - entailment - neutral - contradiction _type: ClassLabel idx: dtype: int32 _type: Value - config_name: paws-x.de data_files: - split: train path: paws-x/de/train.parquet - split: validation path: paws-x/de/validation.parquet - split: test path: paws-x/de/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - not_paraphrase - paraphrase _type: ClassLabel - config_name: paws-x.en data_files: - split: train path: paws-x/en/train.parquet - split: validation path: paws-x/en/validation.parquet - split: test path: paws-x/en/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - not_paraphrase - paraphrase _type: ClassLabel - config_name: paws-x.es data_files: - split: train path: paws-x/es/train.parquet - split: validation path: paws-x/es/validation.parquet - split: test path: paws-x/es/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - not_paraphrase - paraphrase _type: ClassLabel - config_name: paws-x.fr data_files: - split: train path: paws-x/fr/train.parquet - split: validation path: paws-x/fr/validation.parquet - split: test path: paws-x/fr/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - not_paraphrase - paraphrase _type: ClassLabel - config_name: paws-x.ja data_files: - split: train path: paws-x/ja/train.parquet - split: validation path: paws-x/ja/validation.parquet - split: test path: paws-x/ja/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - not_paraphrase - paraphrase _type: ClassLabel - config_name: paws-x.ko data_files: - split: train path: paws-x/ko/train.parquet - split: validation path: paws-x/ko/validation.parquet - split: test path: paws-x/ko/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - not_paraphrase - paraphrase _type: ClassLabel - config_name: paws-x.zh data_files: - split: train path: paws-x/zh/train.parquet - split: validation path: paws-x/zh/validation.parquet - split: test path: paws-x/zh/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - not_paraphrase - paraphrase _type: ClassLabel ---

google-research-datasets/newsgroup

google-research-datasets

--- annotations_creators: - found language: - en language_creators: - found license: - unknown multilinguality: - monolingual pretty_name: 20 Newsgroups size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - multi-class-classification paperswithcode_id: 20-newsgroups dataset_info: - config_name: 18828_alt.atheism features: - name: text dtype: string splits: - name: train num_bytes: 1669511 num_examples: 799 download_size: 14666916 dataset_size: 1669511 - config_name: 18828_comp.graphics features: - name: text dtype: string splits: - name: train num_bytes: 1661199 num_examples: 973 download_size: 14666916 dataset_size: 1661199 - config_name: 18828_comp.os.ms-windows.misc features: - name: text dtype: string splits: - name: train num_bytes: 2378739 num_examples: 985 download_size: 14666916 dataset_size: 2378739 - config_name: 18828_comp.sys.ibm.pc.hardware features: - name: text dtype: string splits: - name: train num_bytes: 1185187 num_examples: 982 download_size: 14666916 dataset_size: 1185187 - config_name: 18828_comp.sys.mac.hardware features: - name: text dtype: string splits: - name: train num_bytes: 1056264 num_examples: 961 download_size: 14666916 dataset_size: 1056264 - config_name: 18828_comp.windows.x features: - name: text dtype: string splits: - name: train num_bytes: 1876297 num_examples: 980 download_size: 14666916 dataset_size: 1876297 - config_name: 18828_misc.forsale features: - name: text dtype: string splits: - name: train num_bytes: 925124 num_examples: 972 download_size: 14666916 dataset_size: 925124 - config_name: 18828_rec.autos features: - name: text dtype: string splits: - name: train num_bytes: 1295307 num_examples: 990 download_size: 14666916 dataset_size: 1295307 - config_name: 18828_rec.motorcycles features: - name: text dtype: string splits: - name: train num_bytes: 1206491 num_examples: 994 download_size: 14666916 dataset_size: 1206491 - config_name: 18828_rec.sport.baseball features: - name: text dtype: string splits: - name: train num_bytes: 1369551 num_examples: 994 download_size: 14666916 dataset_size: 1369551 - config_name: 18828_rec.sport.hockey features: - name: text dtype: string splits: - name: train num_bytes: 1758094 num_examples: 999 download_size: 14666916 dataset_size: 1758094 - config_name: 18828_sci.crypt features: - name: text dtype: string splits: - name: train num_bytes: 2050727 num_examples: 991 download_size: 14666916 dataset_size: 2050727 - config_name: 18828_sci.electronics features: - name: text dtype: string splits: - name: train num_bytes: 1237175 num_examples: 981 download_size: 14666916 dataset_size: 1237175 - config_name: 18828_sci.med features: - name: text dtype: string splits: - name: train num_bytes: 1886363 num_examples: 990 download_size: 14666916 dataset_size: 1886363 - config_name: 18828_sci.space features: - name: text dtype: string splits: - name: train num_bytes: 1812803 num_examples: 987 download_size: 14666916 dataset_size: 1812803 - config_name: 18828_soc.religion.christian features: - name: text dtype: string splits: - name: train num_bytes: 2307486 num_examples: 997 download_size: 14666916 dataset_size: 2307486 - config_name: 18828_talk.politics.guns features: - name: text dtype: string splits: - name: train num_bytes: 1922992 num_examples: 910 download_size: 14666916 dataset_size: 1922992 - config_name: 18828_talk.politics.mideast features: - name: text dtype: string splits: - name: train num_bytes: 2910324 num_examples: 940 download_size: 14666916 dataset_size: 2910324 - config_name: 18828_talk.politics.misc features: - name: text dtype: string splits: - name: train num_bytes: 2102809 num_examples: 775 download_size: 14666916 dataset_size: 2102809 - config_name: 18828_talk.religion.misc features: - name: text dtype: string splits: - name: train num_bytes: 1374261 num_examples: 628 download_size: 14666916 dataset_size: 1374261 - config_name: 19997_alt.atheism features: - name: text dtype: string splits: - name: train num_bytes: 2562277 num_examples: 1000 download_size: 17332201 dataset_size: 2562277 - config_name: 19997_comp.graphics features: - name: text dtype: string splits: - name: train num_bytes: 2181673 num_examples: 1000 download_size: 17332201 dataset_size: 2181673 - config_name: 19997_comp.os.ms-windows.misc features: - name: text dtype: string splits: - name: train num_bytes: 2898760 num_examples: 1000 download_size: 17332201 dataset_size: 2898760 - config_name: 19997_comp.sys.ibm.pc.hardware features: - name: text dtype: string splits: - name: train num_bytes: 1671166 num_examples: 1000 download_size: 17332201 dataset_size: 1671166 - config_name: 19997_comp.sys.mac.hardware features: - name: text dtype: string splits: - name: train num_bytes: 1580881 num_examples: 1000 download_size: 17332201 dataset_size: 1580881 - config_name: 19997_comp.windows.x features: - name: text dtype: string splits: - name: train num_bytes: 2418273 num_examples: 1000 download_size: 17332201 dataset_size: 2418273 - config_name: 19997_misc.forsale features: - name: text dtype: string splits: - name: train num_bytes: 1412012 num_examples: 1000 download_size: 17332201 dataset_size: 1412012 - config_name: 19997_rec.autos features: - name: text dtype: string splits: - name: train num_bytes: 1780502 num_examples: 1000 download_size: 17332201 dataset_size: 1780502 - config_name: 19997_rec.motorcycles features: - name: text dtype: string splits: - name: train num_bytes: 1677964 num_examples: 1000 download_size: 17332201 dataset_size: 1677964 - config_name: 19997_rec.sport.baseball features: - name: text dtype: string splits: - name: train num_bytes: 1835432 num_examples: 1000 download_size: 17332201 dataset_size: 1835432 - config_name: 19997_rec.sport.hockey features: - name: text dtype: string splits: - name: train num_bytes: 2207282 num_examples: 1000 download_size: 17332201 dataset_size: 2207282 - config_name: 19997_sci.crypt features: - name: text dtype: string splits: - name: train num_bytes: 2607835 num_examples: 1000 download_size: 17332201 dataset_size: 2607835 - config_name: 19997_sci.electronics features: - name: text dtype: string splits: - name: train num_bytes: 1732199 num_examples: 1000 download_size: 17332201 dataset_size: 1732199 - config_name: 19997_sci.med features: - name: text dtype: string splits: - name: train num_bytes: 2388789 num_examples: 1000 download_size: 17332201 dataset_size: 2388789 - config_name: 19997_sci.space features: - name: text dtype: string splits: - name: train num_bytes: 2351411 num_examples: 1000 download_size: 17332201 dataset_size: 2351411 - config_name: 19997_soc.religion.christian features: - name: text dtype: string splits: - name: train num_bytes: 2743018 num_examples: 997 download_size: 17332201 dataset_size: 2743018 - config_name: 19997_talk.politics.guns features: - name: text dtype: string splits: - name: train num_bytes: 2639343 num_examples: 1000 download_size: 17332201 dataset_size: 2639343 - config_name: 19997_talk.politics.mideast features: - name: text dtype: string splits: - name: train num_bytes: 3695931 num_examples: 1000 download_size: 17332201 dataset_size: 3695931 - config_name: 19997_talk.politics.misc features: - name: text dtype: string splits: - name: train num_bytes: 3169183 num_examples: 1000 download_size: 17332201 dataset_size: 3169183 - config_name: 19997_talk.religion.misc features: - name: text dtype: string splits: - name: train num_bytes: 2658700 num_examples: 1000 download_size: 17332201 dataset_size: 2658700 - config_name: bydate_alt.atheism features: - name: text dtype: string splits: - name: train num_bytes: 1042224 num_examples: 480 - name: test num_bytes: 702920 num_examples: 319 download_size: 14464277 dataset_size: 1745144 - config_name: bydate_comp.graphics features: - name: text dtype: string splits: - name: train num_bytes: 911665 num_examples: 584 - name: test num_bytes: 849632 num_examples: 389 download_size: 14464277 dataset_size: 1761297 - config_name: bydate_comp.os.ms-windows.misc features: - name: text dtype: string splits: - name: train num_bytes: 1770988 num_examples: 591 - name: test num_bytes: 706676 num_examples: 394 download_size: 14464277 dataset_size: 2477664 - config_name: bydate_comp.sys.ibm.pc.hardware features: - name: text dtype: string splits: - name: train num_bytes: 800446 num_examples: 590 - name: test num_bytes: 485310 num_examples: 392 download_size: 14464277 dataset_size: 1285756 - config_name: bydate_comp.sys.mac.hardware features: - name: text dtype: string splits: - name: train num_bytes: 696311 num_examples: 578 - name: test num_bytes: 468791 num_examples: 385 download_size: 14464277 dataset_size: 1165102 - config_name: bydate_comp.windows.x features: - name: text dtype: string splits: - name: train num_bytes: 1243463 num_examples: 593 - name: test num_bytes: 795366 num_examples: 395 download_size: 14464277 dataset_size: 2038829 - config_name: bydate_misc.forsale features: - name: text dtype: string splits: - name: train num_bytes: 611210 num_examples: 585 - name: test num_bytes: 415902 num_examples: 390 download_size: 14464277 dataset_size: 1027112 - config_name: bydate_rec.autos features: - name: text dtype: string splits: - name: train num_bytes: 860646 num_examples: 594 - name: test num_bytes: 535378 num_examples: 396 download_size: 14464277 dataset_size: 1396024 - config_name: bydate_rec.motorcycles features: - name: text dtype: string splits: - name: train num_bytes: 811151 num_examples: 598 - name: test num_bytes: 497735 num_examples: 398 download_size: 14464277 dataset_size: 1308886 - config_name: bydate_rec.sport.baseball features: - name: text dtype: string splits: - name: train num_bytes: 850740 num_examples: 597 - name: test num_bytes: 618609 num_examples: 397 download_size: 14464277 dataset_size: 1469349 - config_name: bydate_rec.sport.hockey features: - name: text dtype: string splits: - name: train num_bytes: 1189652 num_examples: 600 - name: test num_bytes: 666358 num_examples: 399 download_size: 14464277 dataset_size: 1856010 - config_name: bydate_sci.crypt features: - name: text dtype: string splits: - name: train num_bytes: 1502448 num_examples: 595 - name: test num_bytes: 657727 num_examples: 396 download_size: 14464277 dataset_size: 2160175 - config_name: bydate_sci.electronics features: - name: text dtype: string splits: - name: train num_bytes: 814856 num_examples: 591 - name: test num_bytes: 523095 num_examples: 393 download_size: 14464277 dataset_size: 1337951 - config_name: bydate_sci.med features: - name: text dtype: string splits: - name: train num_bytes: 1195201 num_examples: 594 - name: test num_bytes: 791826 num_examples: 396 download_size: 14464277 dataset_size: 1987027 - config_name: bydate_sci.space features: - name: text dtype: string splits: - name: train num_bytes: 1197965 num_examples: 593 - name: test num_bytes: 721771 num_examples: 394 download_size: 14464277 dataset_size: 1919736 - config_name: bydate_soc.religion.christian features: - name: text dtype: string splits: - name: train num_bytes: 1358047 num_examples: 599 - name: test num_bytes: 1003668 num_examples: 398 download_size: 14464277 dataset_size: 2361715 - config_name: bydate_talk.politics.guns features: - name: text dtype: string splits: - name: train num_bytes: 1313019 num_examples: 546 - name: test num_bytes: 701477 num_examples: 364 download_size: 14464277 dataset_size: 2014496 - config_name: bydate_talk.politics.mideast features: - name: text dtype: string splits: - name: train num_bytes: 1765833 num_examples: 564 - name: test num_bytes: 1236435 num_examples: 376 download_size: 14464277 dataset_size: 3002268 - config_name: bydate_talk.politics.misc features: - name: text dtype: string splits: - name: train num_bytes: 1328057 num_examples: 465 - name: test num_bytes: 853395 num_examples: 310 download_size: 14464277 dataset_size: 2181452 - config_name: bydate_talk.religion.misc features: - name: text dtype: string splits: - name: train num_bytes: 835761 num_examples: 377 - name: test num_bytes: 598452 num_examples: 251 download_size: 14464277 dataset_size: 1434213 --- # Dataset Card for "newsgroup" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [http://qwone.com/~jason/20Newsgroups/](http://qwone.com/~jason/20Newsgroups/) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [NewsWeeder: Learning to Filter Netnews](https://doi.org/10.1016/B978-1-55860-377-6.50048-7) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 929.27 MB - **Size of the generated dataset:** 124.41 MB - **Total amount of disk used:** 1.05 GB ### Dataset Summary The 20 Newsgroups data set is a collection of approximately 20,000 newsgroup documents, partitioned (nearly) evenly across 20 different newsgroups. To the best of my knowledge, it was originally collected by Ken Lang, probably for his Newsweeder: Learning to filter netnews paper, though he does not explicitly mention this collection. The 20 newsgroups collection has become a popular data set for experiments in text applications of machine learning techniques, such as text classification and text clustering. does not include cross-posts and includes only the "From" and "Subject" headers. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### 18828_alt.atheism - **Size of downloaded dataset files:** 14.67 MB - **Size of the generated dataset:** 1.67 MB - **Total amount of disk used:** 16.34 MB An example of 'train' looks as follows. ``` ``` #### 18828_comp.graphics - **Size of downloaded dataset files:** 14.67 MB - **Size of the generated dataset:** 1.66 MB - **Total amount of disk used:** 16.33 MB An example of 'train' looks as follows. ``` ``` #### 18828_comp.os.ms-windows.misc - **Size of downloaded dataset files:** 14.67 MB - **Size of the generated dataset:** 2.38 MB - **Total amount of disk used:** 17.05 MB An example of 'train' looks as follows. ``` ``` #### 18828_comp.sys.ibm.pc.hardware - **Size of downloaded dataset files:** 14.67 MB - **Size of the generated dataset:** 1.18 MB - **Total amount of disk used:** 15.85 MB An example of 'train' looks as follows. ``` ``` #### 18828_comp.sys.mac.hardware - **Size of downloaded dataset files:** 14.67 MB - **Size of the generated dataset:** 1.06 MB - **Total amount of disk used:** 15.73 MB An example of 'train' looks as follows. ``` ``` ### Data Fields The data fields are the same among all splits. #### 18828_alt.atheism - `text`: a `string` feature. #### 18828_comp.graphics - `text`: a `string` feature. #### 18828_comp.os.ms-windows.misc - `text`: a `string` feature. #### 18828_comp.sys.ibm.pc.hardware - `text`: a `string` feature. #### 18828_comp.sys.mac.hardware - `text`: a `string` feature. ### Data Splits | name |train| |------------------------------|----:| |18828_alt.atheism | 799| |18828_comp.graphics | 973| |18828_comp.os.ms-windows.misc | 985| |18828_comp.sys.ibm.pc.hardware| 982| |18828_comp.sys.mac.hardware | 961| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @incollection{LANG1995331, title = {NewsWeeder: Learning to Filter Netnews}, editor = {Armand Prieditis and Stuart Russell}, booktitle = {Machine Learning Proceedings 1995}, publisher = {Morgan Kaufmann}, address = {San Francisco (CA)}, pages = {331-339}, year = {1995}, isbn = {978-1-55860-377-6}, doi = {https://doi.org/10.1016/B978-1-55860-377-6.50048-7}, url = {https://www.sciencedirect.com/science/article/pii/B9781558603776500487}, author = {Ken Lang}, } ``` ### Contributions Thanks to [@mariamabarham](https://github.com/mariamabarham), [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq) for adding this dataset.

cardiffnlp/tweet_eval

cardiffnlp

--- annotations_creators: - found language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 100K<n<1M - 10K<n<100K - 1K<n<10K - n<1K source_datasets: - extended|other-tweet-datasets task_categories: - text-classification task_ids: - intent-classification - multi-class-classification - sentiment-classification paperswithcode_id: tweeteval pretty_name: TweetEval config_names: - emoji - emotion - hate - irony - offensive - sentiment - stance_abortion - stance_atheism - stance_climate - stance_feminist - stance_hillary dataset_info: - config_name: emoji features: - name: text dtype: string - name: label dtype: class_label: names: '0': ❤ '1': 😍 '2': 😂 '3': 💕 '4': 🔥 '5': 😊 '6': 😎 '7': ✨ '8': 💙 '9': 😘 '10': 📷 '11': 🇺🇸 '12': ☀ '13': 💜 '14': 😉 '15': 💯 '16': 😁 '17': 🎄 '18': 📸 '19': 😜 splits: - name: train num_bytes: 3803167 num_examples: 45000 - name: test num_bytes: 4255901 num_examples: 50000 - name: validation num_bytes: 396079 num_examples: 5000 download_size: 5939308 dataset_size: 8455147 - config_name: emotion features: - name: text dtype: string - name: label dtype: class_label: names: '0': anger '1': joy '2': optimism '3': sadness splits: - name: train num_bytes: 338871 num_examples: 3257 - name: test num_bytes: 146645 num_examples: 1421 - name: validation num_bytes: 38273 num_examples: 374 download_size: 367016 dataset_size: 523789 - config_name: hate features: - name: text dtype: string - name: label dtype: class_label: names: '0': non-hate '1': hate splits: - name: train num_bytes: 1223650 num_examples: 9000 - name: test num_bytes: 428934 num_examples: 2970 - name: validation num_bytes: 154144 num_examples: 1000 download_size: 1196346 dataset_size: 1806728 - config_name: irony features: - name: text dtype: string - name: label dtype: class_label: names: '0': non_irony '1': irony splits: - name: train num_bytes: 259187 num_examples: 2862 - name: test num_bytes: 75897 num_examples: 784 - name: validation num_bytes: 86017 num_examples: 955 download_size: 297647 dataset_size: 421101 - config_name: offensive features: - name: text dtype: string - name: label dtype: class_label: names: '0': non-offensive '1': offensive splits: - name: train num_bytes: 1648061 num_examples: 11916 - name: test num_bytes: 135473 num_examples: 860 - name: validation num_bytes: 192417 num_examples: 1324 download_size: 1234528 dataset_size: 1975951 - config_name: sentiment features: - name: text dtype: string - name: label dtype: class_label: names: '0': negative '1': neutral '2': positive splits: - name: train num_bytes: 5425122 num_examples: 45615 - name: test num_bytes: 1279540 num_examples: 12284 - name: validation num_bytes: 239084 num_examples: 2000 download_size: 4849675 dataset_size: 6943746 - config_name: stance_abortion features: - name: text dtype: string - name: label dtype: class_label: names: '0': none '1': against '2': favor splits: - name: train num_bytes: 68694 num_examples: 587 - name: test num_bytes: 33171 num_examples: 280 - name: validation num_bytes: 7657 num_examples: 66 download_size: 73517 dataset_size: 109522 - config_name: stance_atheism features: - name: text dtype: string - name: label dtype: class_label: names: '0': none '1': against '2': favor splits: - name: train num_bytes: 54775 num_examples: 461 - name: test num_bytes: 25716 num_examples: 220 - name: validation num_bytes: 6320 num_examples: 52 download_size: 62265 dataset_size: 86811 - config_name: stance_climate features: - name: text dtype: string - name: label dtype: class_label: names: '0': none '1': against '2': favor splits: - name: train num_bytes: 40249 num_examples: 355 - name: test num_bytes: 19925 num_examples: 169 - name: validation num_bytes: 4801 num_examples: 40 download_size: 48493 dataset_size: 64975 - config_name: stance_feminist features: - name: text dtype: string - name: label dtype: class_label: names: '0': none '1': against '2': favor splits: - name: train num_bytes: 70509 num_examples: 597 - name: test num_bytes: 33305 num_examples: 285 - name: validation num_bytes: 8035 num_examples: 67 download_size: 76345 dataset_size: 111849 - config_name: stance_hillary features: - name: text dtype: string - name: label dtype: class_label: names: '0': none '1': against '2': favor splits: - name: train num_bytes: 69596 num_examples: 620 - name: test num_bytes: 34487 num_examples: 295 - name: validation num_bytes: 7532 num_examples: 69 download_size: 74057 dataset_size: 111615 configs: - config_name: emoji data_files: - split: train path: emoji/train-* - split: test path: emoji/test-* - split: validation path: emoji/validation-* - config_name: emotion data_files: - split: train path: emotion/train-* - split: test path: emotion/test-* - split: validation path: emotion/validation-* - config_name: hate data_files: - split: train path: hate/train-* - split: test path: hate/test-* - split: validation path: hate/validation-* - config_name: irony data_files: - split: train path: irony/train-* - split: test path: irony/test-* - split: validation path: irony/validation-* - config_name: offensive data_files: - split: train path: offensive/train-* - split: test path: offensive/test-* - split: validation path: offensive/validation-* - config_name: sentiment data_files: - split: train path: sentiment/train-* - split: test path: sentiment/test-* - split: validation path: sentiment/validation-* - config_name: stance_abortion data_files: - split: train path: stance_abortion/train-* - split: test path: stance_abortion/test-* - split: validation path: stance_abortion/validation-* - config_name: stance_atheism data_files: - split: train path: stance_atheism/train-* - split: test path: stance_atheism/test-* - split: validation path: stance_atheism/validation-* - config_name: stance_climate data_files: - split: train path: stance_climate/train-* - split: test path: stance_climate/test-* - split: validation path: stance_climate/validation-* - config_name: stance_feminist data_files: - split: train path: stance_feminist/train-* - split: test path: stance_feminist/test-* - split: validation path: stance_feminist/validation-* - config_name: stance_hillary data_files: - split: train path: stance_hillary/train-* - split: test path: stance_hillary/test-* - split: validation path: stance_hillary/validation-* train-eval-index: - config: emotion task: text-classification task_id: multi_class_classification splits: train_split: train eval_split: test col_mapping: text: text label: target metrics: - type: accuracy name: Accuracy - type: f1 name: F1 macro args: average: macro - type: f1 name: F1 micro args: average: micro - type: f1 name: F1 weighted args: average: weighted - type: precision name: Precision macro args: average: macro - type: precision name: Precision micro args: average: micro - type: precision name: Precision weighted args: average: weighted - type: recall name: Recall macro args: average: macro - type: recall name: Recall micro args: average: micro - type: recall name: Recall weighted args: average: weighted - config: hate task: text-classification task_id: binary_classification splits: train_split: train eval_split: test col_mapping: text: text label: target metrics: - type: accuracy name: Accuracy - type: f1 name: F1 binary args: average: binary - type: precision name: Precision macro args: average: macro - type: precision name: Precision micro args: average: micro - type: precision name: Precision weighted args: average: weighted - type: recall name: Recall macro args: average: macro - type: recall name: Recall micro args: average: micro - type: recall name: Recall weighted args: average: weighted - config: irony task: text-classification task_id: binary_classification splits: train_split: train eval_split: test col_mapping: text: text label: target metrics: - type: accuracy name: Accuracy - type: f1 name: F1 binary args: average: binary - type: precision name: Precision macro args: average: macro - type: precision name: Precision micro args: average: micro - type: precision name: Precision weighted args: average: weighted - type: recall name: Recall macro args: average: macro - type: recall name: Recall micro args: average: micro - type: recall name: Recall weighted args: average: weighted - config: offensive task: text-classification task_id: binary_classification splits: train_split: train eval_split: test col_mapping: text: text label: target metrics: - type: accuracy name: Accuracy - type: f1 name: F1 binary args: average: binary - type: precision name: Precision macro args: average: macro - type: precision name: Precision micro args: average: micro - type: precision name: Precision weighted args: average: weighted - type: recall name: Recall macro args: average: macro - type: recall name: Recall micro args: average: micro - type: recall name: Recall weighted args: average: weighted - config: sentiment task: text-classification task_id: multi_class_classification splits: train_split: train eval_split: test col_mapping: text: text label: target metrics: - type: accuracy name: Accuracy - type: f1 name: F1 macro args: average: macro - type: f1 name: F1 micro args: average: micro - type: f1 name: F1 weighted args: average: weighted - type: precision name: Precision macro args: average: macro - type: precision name: Precision micro args: average: micro - type: precision name: Precision weighted args: average: weighted - type: recall name: Recall macro args: average: macro - type: recall name: Recall micro args: average: micro - type: recall name: Recall weighted args: average: weighted --- # Dataset Card for tweet_eval ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Needs More Information] - **Repository:** [GitHub](https://github.com/cardiffnlp/tweeteval) - **Paper:** [EMNLP Paper](https://arxiv.org/pdf/2010.12421.pdf) - **Leaderboard:** [GitHub Leaderboard](https://github.com/cardiffnlp/tweeteval) - **Point of Contact:** [Needs More Information] ### Dataset Summary TweetEval consists of seven heterogenous tasks in Twitter, all framed as multi-class tweet classification. The tasks include - irony, hate, offensive, stance, emoji, emotion, and sentiment. All tasks have been unified into the same benchmark, with each dataset presented in the same format and with fixed training, validation and test splits. ### Supported Tasks and Leaderboards - `text_classification`: The dataset can be trained using a SentenceClassification model from HuggingFace transformers. ### Languages The text in the dataset is in English, as spoken by Twitter users. ## Dataset Structure ### Data Instances An instance from `emoji` config: ``` {'label': 12, 'text': 'Sunday afternoon walking through Venice in the sun with @user ️ ️ ️ @ Abbot Kinney, Venice'} ``` An instance from `emotion` config: ``` {'label': 2, 'text': "“Worry is a down payment on a problem you may never have'. \xa0Joyce Meyer. #motivation #leadership #worry"} ``` An instance from `hate` config: ``` {'label': 0, 'text': '@user nice new signage. Are you not concerned by Beatlemania -style hysterical crowds crongregating on you…'} ``` An instance from `irony` config: ``` {'label': 1, 'text': 'seeing ppl walking w/ crutches makes me really excited for the next 3 weeks of my life'} ``` An instance from `offensive` config: ``` {'label': 0, 'text': '@user Bono... who cares. Soon people will understand that they gain nothing from following a phony celebrity. Become a Leader of your people instead or help and support your fellow countrymen.'} ``` An instance from `sentiment` config: ``` {'label': 2, 'text': '"QT @user In the original draft of the 7th book, Remus Lupin survived the Battle of Hogwarts. #HappyBirthdayRemusLupin"'} ``` An instance from `stance_abortion` config: ``` {'label': 1, 'text': 'we remind ourselves that love means to be willing to give until it hurts - Mother Teresa'} ``` An instance from `stance_atheism` config: ``` {'label': 1, 'text': '@user Bless Almighty God, Almighty Holy Spirit and the Messiah. #SemST'} ``` An instance from `stance_climate` config: ``` {'label': 0, 'text': 'Why Is The Pope Upset? via @user #UnzippedTruth #PopeFrancis #SemST'} ``` An instance from `stance_feminist` config: ``` {'label': 1, 'text': "@user @user is the UK's answer to @user and @user #GamerGate #SemST"} ``` An instance from `stance_hillary` config: ``` {'label': 1, 'text': "If a man demanded staff to get him an ice tea he'd be called a sexists elitist pig.. Oink oink #Hillary #SemST"} ``` ### Data Fields For `emoji` config: - `text`: a `string` feature containing the tweet. - `label`: an `int` classification label with the following mapping: `0`: ❤ `1`: 😍 `2`: 😂 `3`: 💕 `4`: 🔥 `5`: 😊 `6`: 😎 `7`: ✨ `8`: 💙 `9`: 😘 `10`: 📷 `11`: 🇺🇸 `12`: ☀ `13`: 💜 `14`: 😉 `15`: 💯 `16`: 😁 `17`: 🎄 `18`: 📸 `19`: 😜 For `emotion` config: - `text`: a `string` feature containing the tweet. - `label`: an `int` classification label with the following mapping: `0`: anger `1`: joy `2`: optimism `3`: sadness For `hate` config: - `text`: a `string` feature containing the tweet. - `label`: an `int` classification label with the following mapping: `0`: non-hate `1`: hate For `irony` config: - `text`: a `string` feature containing the tweet. - `label`: an `int` classification label with the following mapping: `0`: non_irony `1`: irony For `offensive` config: - `text`: a `string` feature containing the tweet. - `label`: an `int` classification label with the following mapping: `0`: non-offensive `1`: offensive For `sentiment` config: - `text`: a `string` feature containing the tweet. - `label`: an `int` classification label with the following mapping: `0`: negative `1`: neutral `2`: positive For `stance_abortion` config: - `text`: a `string` feature containing the tweet. - `label`: an `int` classification label with the following mapping: `0`: none `1`: against `2`: favor For `stance_atheism` config: - `text`: a `string` feature containing the tweet. - `label`: an `int` classification label with the following mapping: `0`: none `1`: against `2`: favor For `stance_climate` config: - `text`: a `string` feature containing the tweet. - `label`: an `int` classification label with the following mapping: `0`: none `1`: against `2`: favor For `stance_feminist` config: - `text`: a `string` feature containing the tweet. - `label`: an `int` classification label with the following mapping: `0`: none `1`: against `2`: favor For `stance_hillary` config: - `text`: a `string` feature containing the tweet. - `label`: an `int` classification label with the following mapping: `0`: none `1`: against `2`: favor ### Data Splits | name | train | validation | test | | --------------- | ----- | ---------- | ----- | | emoji | 45000 | 5000 | 50000 | | emotion | 3257 | 374 | 1421 | | hate | 9000 | 1000 | 2970 | | irony | 2862 | 955 | 784 | | offensive | 11916 | 1324 | 860 | | sentiment | 45615 | 2000 | 12284 | | stance_abortion | 587 | 66 | 280 | | stance_atheism | 461 | 52 | 220 | | stance_climate | 355 | 40 | 169 | | stance_feminist | 597 | 67 | 285 | | stance_hillary | 620 | 69 | 295 | ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators Francesco Barbieri, Jose Camacho-Collados, Luis Espiinosa-Anke and Leonardo Neves through Cardiff NLP. ### Licensing Information This is not a single dataset, therefore each subset has its own license (the collection itself does not have additional restrictions). All of the datasets require complying with Twitter [Terms Of Service](https://twitter.com/tos) and Twitter API [Terms Of Service](https://developer.twitter.com/en/developer-terms/agreement-and-policy) Additionally the license are: - emoji: Undefined - emotion(EmoInt): Undefined - hate (HateEval): Need permission [here](http://hatespeech.di.unito.it/hateval.html) - irony: Undefined - Offensive: Undefined - Sentiment: [Creative Commons Attribution 3.0 Unported License](https://groups.google.com/g/semevaltweet/c/k5DDcvVb_Vo/m/zEOdECFyBQAJ) - Stance: Undefined ### Citation Information ``` @inproceedings{barbieri2020tweeteval, title={{TweetEval:Unified Benchmark and Comparative Evaluation for Tweet Classification}}, author={Barbieri, Francesco and Camacho-Collados, Jose and Espinosa-Anke, Luis and Neves, Leonardo}, booktitle={Proceedings of Findings of EMNLP}, year={2020} } ``` If you use any of the TweetEval datasets, please cite their original publications: #### Emotion Recognition: ``` @inproceedings{mohammad2018semeval, title={Semeval-2018 task 1: Affect in tweets}, author={Mohammad, Saif and Bravo-Marquez, Felipe and Salameh, Mohammad and Kiritchenko, Svetlana}, booktitle={Proceedings of the 12th international workshop on semantic evaluation}, pages={1--17}, year={2018} } ``` #### Emoji Prediction: ``` @inproceedings{barbieri2018semeval, title={Semeval 2018 task 2: Multilingual emoji prediction}, author={Barbieri, Francesco and Camacho-Collados, Jose and Ronzano, Francesco and Espinosa-Anke, Luis and Ballesteros, Miguel and Basile, Valerio and Patti, Viviana and Saggion, Horacio}, booktitle={Proceedings of The 12th International Workshop on Semantic Evaluation}, pages={24--33}, year={2018} } ``` #### Irony Detection: ``` @inproceedings{van2018semeval, title={Semeval-2018 task 3: Irony detection in english tweets}, author={Van Hee, Cynthia and Lefever, Els and Hoste, V{\'e}ronique}, booktitle={Proceedings of The 12th International Workshop on Semantic Evaluation}, pages={39--50}, year={2018} } ``` #### Hate Speech Detection: ``` @inproceedings{basile-etal-2019-semeval, title = "{S}em{E}val-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in {T}witter", author = "Basile, Valerio and Bosco, Cristina and Fersini, Elisabetta and Nozza, Debora and Patti, Viviana and Rangel Pardo, Francisco Manuel and Rosso, Paolo and Sanguinetti, Manuela", booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation", year = "2019", address = "Minneapolis, Minnesota, USA", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/S19-2007", doi = "10.18653/v1/S19-2007", pages = "54--63" } ``` #### Offensive Language Identification: ``` @inproceedings{zampieri2019semeval, title={SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)}, author={Zampieri, Marcos and Malmasi, Shervin and Nakov, Preslav and Rosenthal, Sara and Farra, Noura and Kumar, Ritesh}, booktitle={Proceedings of the 13th International Workshop on Semantic Evaluation}, pages={75--86}, year={2019} } ``` #### Sentiment Analysis: ``` @inproceedings{rosenthal2017semeval, title={SemEval-2017 task 4: Sentiment analysis in Twitter}, author={Rosenthal, Sara and Farra, Noura and Nakov, Preslav}, booktitle={Proceedings of the 11th international workshop on semantic evaluation (SemEval-2017)}, pages={502--518}, year={2017} } ``` #### Stance Detection: ``` @inproceedings{mohammad2016semeval, title={Semeval-2016 task 6: Detecting stance in tweets}, author={Mohammad, Saif and Kiritchenko, Svetlana and Sobhani, Parinaz and Zhu, Xiaodan and Cherry, Colin}, booktitle={Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)}, pages={31--41}, year={2016} } ``` ### Contributions Thanks to [@gchhablani](https://github.com/gchhablani) and [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.

Samsung/samsum

Samsung

--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - cc-by-nc-nd-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - summarization task_ids: [] paperswithcode_id: samsum-corpus pretty_name: SAMSum Corpus tags: - conversations-summarization dataset_info: features: - name: id dtype: string - name: dialogue dtype: string - name: summary dtype: string config_name: samsum splits: - name: train num_bytes: 9479141 num_examples: 14732 - name: test num_bytes: 534492 num_examples: 819 - name: validation num_bytes: 516431 num_examples: 818 download_size: 2944100 dataset_size: 10530064 train-eval-index: - config: samsum task: summarization task_id: summarization splits: eval_split: test col_mapping: dialogue: text summary: target --- # Dataset Card for SAMSum Corpus ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://arxiv.org/abs/1911.12237v2 - **Repository:** [Needs More Information] - **Paper:** https://arxiv.org/abs/1911.12237v2 - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Needs More Information] ### Dataset Summary The SAMSum dataset contains about 16k messenger-like conversations with summaries. Conversations were created and written down by linguists fluent in English. Linguists were asked to create conversations similar to those they write on a daily basis, reflecting the proportion of topics of their real-life messenger convesations. The style and register are diversified - conversations could be informal, semi-formal or formal, they may contain slang words, emoticons and typos. Then, the conversations were annotated with summaries. It was assumed that summaries should be a concise brief of what people talked about in the conversation in third person. The SAMSum dataset was prepared by Samsung R&D Institute Poland and is distributed for research purposes (non-commercial licence: CC BY-NC-ND 4.0). ### Supported Tasks and Leaderboards [Needs More Information] ### Languages English ## Dataset Structure ### Data Instances The created dataset is made of 16369 conversations distributed uniformly into 4 groups based on the number of utterances in con- versations: 3-6, 7-12, 13-18 and 19-30. Each utterance contains the name of the speaker. Most conversations consist of dialogues between two interlocutors (about 75% of all conversations), the rest is between three or more people The first instance in the training set: {'id': '13818513', 'summary': 'Amanda baked cookies and will bring Jerry some tomorrow.', 'dialogue': "Amanda: I baked cookies. Do you want some?\r\nJerry: Sure!\r\nAmanda: I'll bring you tomorrow :-)"} ### Data Fields - dialogue: text of dialogue. - summary: human written summary of the dialogue. - id: unique id of an example. ### Data Splits - train: 14732 - val: 818 - test: 819 ## Dataset Creation ### Curation Rationale In paper: > In the first approach, we reviewed datasets from the following categories: chatbot dialogues, SMS corpora, IRC/chat data, movie dialogues, tweets, comments data (conversations formed by replies to comments), transcription of meetings, written discussions, phone dialogues and daily communication data. Unfortunately, they all differed in some respect from the conversations that are typ- ically written in messenger apps, e.g. they were too technical (IRC data), too long (comments data, transcription of meetings), lacked context (movie dialogues) or they were more of a spoken type, such as a dialogue between a petrol station assis- tant and a client buying petrol. As a consequence, we decided to create a chat dialogue dataset by constructing such conversa- tions that would epitomize the style of a messenger app. ### Source Data #### Initial Data Collection and Normalization In paper: > We asked linguists to create conversations similar to those they write on a daily basis, reflecting the proportion of topics of their real-life messenger conversations. It includes chit-chats, gossiping about friends, arranging meetings, discussing politics, consulting university assignments with colleagues, etc. Therefore, this dataset does not contain any sensitive data or fragments of other corpora. #### Who are the source language producers? linguists ### Annotations #### Annotation process In paper: > Each dialogue was created by one person. After collecting all of the conversations, we asked language experts to annotate them with summaries, assuming that they should (1) be rather short, (2) extract important pieces of information, (3) include names of interlocutors, (4) be written in the third person. Each dialogue contains only one ref- erence summary. #### Who are the annotators? language experts ### Personal and Sensitive Information None, see above: Initial Data Collection and Normalization ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information non-commercial licence: CC BY-NC-ND 4.0 ### Citation Information ``` @inproceedings{gliwa-etal-2019-samsum, title = "{SAMS}um Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization", author = "Gliwa, Bogdan and Mochol, Iwona and Biesek, Maciej and Wawer, Aleksander", booktitle = "Proceedings of the 2nd Workshop on New Frontiers in Summarization", month = nov, year = "2019", address = "Hong Kong, China", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/D19-5409", doi = "10.18653/v1/D19-5409", pages = "70--79" } ``` ### Contributions Thanks to [@cccntu](https://github.com/cccntu) for adding this dataset.

databricks/databricks-dolly-15k

databricks

--- license: cc-by-sa-3.0 task_categories: - question-answering - summarization language: - en size_categories: - 10K<n<100K --- # Summary `databricks-dolly-15k` is an open source dataset of instruction-following records generated by thousands of Databricks employees in several of the behavioral categories outlined in the [InstructGPT](https://arxiv.org/abs/2203.02155) paper, including brainstorming, classification, closed QA, generation, information extraction, open QA, and summarization. This dataset can be used for any purpose, whether academic or commercial, under the terms of the [Creative Commons Attribution-ShareAlike 3.0 Unported License](https://creativecommons.org/licenses/by-sa/3.0/legalcode). Supported Tasks: - Training LLMs - Synthetic Data Generation - Data Augmentation Languages: English Version: 1.0 **Owner: Databricks, Inc.** # Dataset Overview `databricks-dolly-15k` is a corpus of more than 15,000 records generated by thousands of Databricks employees to enable large language models to exhibit the magical interactivity of ChatGPT. Databricks employees were invited to create prompt / response pairs in each of eight different instruction categories, including the seven outlined in the InstructGPT paper, as well as an open-ended free-form category. The contributors were instructed to avoid using information from any source on the web with the exception of Wikipedia (for particular subsets of instruction categories), and explicitly instructed to avoid using generative AI in formulating instructions or responses. Examples of each behavior were provided to motivate the types of questions and instructions appropriate to each category. Halfway through the data generation process, contributors were given the option of answering questions posed by other contributors. They were asked to rephrase the original question and only select questions they could be reasonably expected to answer correctly. For certain categories contributors were asked to provide reference texts copied from Wikipedia. Reference text (indicated by the `context` field in the actual dataset) may contain bracketed Wikipedia citation numbers (e.g. `[42]`) which we recommend users remove for downstream applications. # Intended Uses While immediately valuable for instruction fine tuning large language models, as a corpus of human-generated instruction prompts, this dataset also presents a valuable opportunity for synthetic data generation in the methods outlined in the Self-Instruct paper. For example, contributor--generated prompts could be submitted as few-shot examples to a large open language model to generate a corpus of millions of examples of instructions in each of the respective InstructGPT categories. Likewise, both the instructions and responses present fertile ground for data augmentation. A paraphrasing model might be used to restate each prompt or short responses, with the resulting text associated to the respective ground-truth sample. Such an approach might provide a form of regularization on the dataset that could allow for more robust instruction-following behavior in models derived from these synthetic datasets. # Dataset ## Purpose of Collection As part of our continuing commitment to open source, Databricks developed what is, to the best of our knowledge, the first open source, human-generated instruction corpus specifically designed to enable large language models to exhibit the magical interactivity of ChatGPT. Unlike other datasets that are limited to non-commercial use, this dataset can be used, modified, and extended for any purpose, including academic or commercial applications. ## Sources - **Human-generated data**: Databricks employees were invited to create prompt / response pairs in each of eight different instruction categories. - **Wikipedia**: For instruction categories that require an annotator to consult a reference text (information extraction, closed QA, summarization) contributors selected passages from Wikipedia for particular subsets of instruction categories. No guidance was given to annotators as to how to select the target passages. ## Annotator Guidelines To create a record, employees were given a brief description of the annotation task as well as examples of the types of prompts typical of each annotation task. Guidelines were succinct by design so as to encourage a high task completion rate, possibly at the cost of rigorous compliance to an annotation rubric that concretely and reliably operationalizes the specific task. Caveat emptor. The annotation guidelines for each of the categories are as follows: - **Creative Writing**: Write a question or instruction that requires a creative, open-ended written response. The instruction should be reasonable to ask of a person with general world knowledge and should not require searching. In this task, your prompt should give very specific instructions to follow. Constraints, instructions, guidelines, or requirements all work, and the more of them the better. - **Closed QA**: Write a question or instruction that requires factually correct response based on a passage of text from Wikipedia. The question can be complex and can involve human-level reasoning capabilities, but should not require special knowledge. To create a question for this task include both the text of the question as well as the reference text in the form. - **Open QA**: Write a question that can be answered using general world knowledge or at most a single search. This task asks for opinions and facts about the world at large and does not provide any reference text for consultation. - **Summarization**: Give a summary of a paragraph from Wikipedia. Please don't ask questions that will require more than 3-5 minutes to answer. To create a question for this task include both the text of the question as well as the reference text in the form. - **Information Extraction**: These questions involve reading a paragraph from Wikipedia and extracting information from the passage. Everything required to produce an answer (e.g. a list, keywords etc) should be included in the passages. To create a question for this task include both the text of the question as well as the reference text in the form. - **Classification**: These prompts contain lists or examples of entities to be classified, e.g. movie reviews, products, etc. In this task the text or list of entities under consideration is contained in the prompt (e.g. there is no reference text.). You can choose any categories for classification you like, the more diverse the better. - **Brainstorming**: Think up lots of examples in response to a question asking to brainstorm ideas. ## Personal or Sensitive Data This dataset contains public information (e.g., some information from Wikipedia). To our knowledge, there are no private person’s personal identifiers or sensitive information. ## Language American English # Known Limitations - Wikipedia is a crowdsourced corpus and the contents of this dataset may reflect the bias, factual errors and topical focus found in Wikipedia - Some annotators may not be native English speakers - Annotator demographics and subject matter may reflect the makeup of Databricks employees # Citation ``` @online{DatabricksBlog2023DollyV2, author = {Mike Conover and Matt Hayes and Ankit Mathur and Jianwei Xie and Jun Wan and Sam Shah and Ali Ghodsi and Patrick Wendell and Matei Zaharia and Reynold Xin}, title = {Free Dolly: Introducing the World's First Truly Open Instruction-Tuned LLM}, year = {2023}, url = {https://www.databricks.com/blog/2023/04/12/dolly-first-open-commercially-viable-instruction-tuned-llm}, urldate = {2023-06-30} } ``` # License/Attribution **Copyright (2023) Databricks, Inc.** This dataset was developed at Databricks (https://www.databricks.com) and its use is subject to the CC BY-SA 3.0 license. Certain categories of material in the dataset include materials from the following sources, licensed under the CC BY-SA 3.0 license: Wikipedia (various pages) - https://www.wikipedia.org/ Copyright © Wikipedia editors and contributors.

mteb/amazon_massive_scenario

mteb

--- configs: - config_name: default data_files: - path: train/*.json.gz split: train - path: test/*.json.gz split: test - path: validation/*.json.gz split: validation - config_name: ta data_files: - path: train/ta.json.gz split: train - path: test/ta.json.gz split: test - path: validation/ta.json.gz split: validation - config_name: is data_files: - path: train/is.json.gz split: train - path: test/is.json.gz split: test - path: validation/is.json.gz split: validation - config_name: pl data_files: - path: train/pl.json.gz split: train - path: test/pl.json.gz split: test - path: validation/pl.json.gz split: validation - config_name: zh-CN data_files: - path: train/zh-CN.json.gz split: train - path: test/zh-CN.json.gz split: test - path: validation/zh-CN.json.gz split: validation - config_name: el data_files: - path: train/el.json.gz split: train - path: test/el.json.gz split: test - path: validation/el.json.gz split: validation - config_name: ru data_files: - path: train/ru.json.gz split: train - path: test/ru.json.gz split: test - path: validation/ru.json.gz split: validation - config_name: te data_files: - path: train/te.json.gz split: train - path: test/te.json.gz split: test - path: validation/te.json.gz split: validation - config_name: cy data_files: - path: train/cy.json.gz split: train - path: test/cy.json.gz split: test - path: validation/cy.json.gz split: validation - config_name: he data_files: - path: train/he.json.gz split: train - path: test/he.json.gz split: test - path: validation/he.json.gz split: validation - config_name: de data_files: - path: train/de.json.gz split: train - path: test/de.json.gz split: test - path: validation/de.json.gz split: validation - config_name: af data_files: - path: train/af.json.gz split: train - path: test/af.json.gz split: test - path: validation/af.json.gz split: validation - config_name: ml data_files: - path: train/ml.json.gz split: train - path: test/ml.json.gz split: test - path: validation/ml.json.gz split: validation - config_name: sl data_files: - path: train/sl.json.gz split: train - path: test/sl.json.gz split: test - path: validation/sl.json.gz split: validation - config_name: vi data_files: - path: train/vi.json.gz split: train - path: test/vi.json.gz split: test - path: validation/vi.json.gz split: validation - config_name: mn data_files: - path: train/mn.json.gz split: train - path: test/mn.json.gz split: test - path: validation/mn.json.gz split: validation - config_name: tl data_files: - path: train/tl.json.gz split: train - path: test/tl.json.gz split: test - path: validation/tl.json.gz split: validation - config_name: it data_files: - path: train/it.json.gz split: train - path: test/it.json.gz split: test - path: validation/it.json.gz split: validation - config_name: jv data_files: - path: train/jv.json.gz split: train - path: test/jv.json.gz split: test - path: validation/jv.json.gz split: validation - config_name: sq data_files: - path: train/sq.json.gz split: train - path: test/sq.json.gz split: test - path: validation/sq.json.gz split: validation - config_name: fa data_files: - path: train/fa.json.gz split: train - path: test/fa.json.gz split: test - path: validation/fa.json.gz split: validation - config_name: nb data_files: - path: train/nb.json.gz split: train - path: test/nb.json.gz split: test - path: validation/nb.json.gz split: validation - config_name: km data_files: - path: train/km.json.gz split: train - path: test/km.json.gz split: test - path: validation/km.json.gz split: validation - config_name: th data_files: - path: train/th.json.gz split: train - path: test/th.json.gz split: test - path: validation/th.json.gz split: validation - config_name: ja data_files: - path: train/ja.json.gz split: train - path: test/ja.json.gz split: test - path: validation/ja.json.gz split: validation - config_name: hi data_files: - path: train/hi.json.gz split: train - path: test/hi.json.gz split: test - path: validation/hi.json.gz split: validation - config_name: id data_files: - path: train/id.json.gz split: train - path: test/id.json.gz split: test - path: validation/id.json.gz split: validation - config_name: kn data_files: - path: train/kn.json.gz split: train - path: test/kn.json.gz split: test - path: validation/kn.json.gz split: validation - config_name: fi data_files: - path: train/fi.json.gz split: train - path: test/fi.json.gz split: test - path: validation/fi.json.gz split: validation - config_name: ur data_files: - path: train/ur.json.gz split: train - path: test/ur.json.gz split: test - path: validation/ur.json.gz split: validation - config_name: my data_files: - path: train/my.json.gz split: train - path: test/my.json.gz split: test - path: validation/my.json.gz split: validation - config_name: lv data_files: - path: train/lv.json.gz split: train - path: test/lv.json.gz split: test - path: validation/lv.json.gz split: validation - config_name: fr data_files: - path: train/fr.json.gz split: train - path: test/fr.json.gz split: test - path: validation/fr.json.gz split: validation - config_name: ko data_files: - path: train/ko.json.gz split: train - path: test/ko.json.gz split: test - path: validation/ko.json.gz split: validation - config_name: sw data_files: - path: train/sw.json.gz split: train - path: test/sw.json.gz split: test - path: validation/sw.json.gz split: validation - config_name: sv data_files: - path: train/sv.json.gz split: train - path: test/sv.json.gz split: test - path: validation/sv.json.gz split: validation - config_name: nl data_files: - path: train/nl.json.gz split: train - path: test/nl.json.gz split: test - path: validation/nl.json.gz split: validation - config_name: da data_files: - path: train/da.json.gz split: train - path: test/da.json.gz split: test - path: validation/da.json.gz split: validation - config_name: ar data_files: - path: train/ar.json.gz split: train - path: test/ar.json.gz split: test - path: validation/ar.json.gz split: validation - config_name: ms data_files: - path: train/ms.json.gz split: train - path: test/ms.json.gz split: test - path: validation/ms.json.gz split: validation - config_name: en data_files: - path: train/en.json.gz split: train - path: test/en.json.gz split: test - path: validation/en.json.gz split: validation - config_name: am data_files: - path: train/am.json.gz split: train - path: test/am.json.gz split: test - path: validation/am.json.gz split: validation - config_name: pt data_files: - path: train/pt.json.gz split: train - path: test/pt.json.gz split: test - path: validation/pt.json.gz split: validation - config_name: ka data_files: - path: train/ka.json.gz split: train - path: test/ka.json.gz split: test - path: validation/ka.json.gz split: validation - config_name: ro data_files: - path: train/ro.json.gz split: train - path: test/ro.json.gz split: test - path: validation/ro.json.gz split: validation - config_name: tr data_files: - path: train/tr.json.gz split: train - path: test/tr.json.gz split: test - path: validation/tr.json.gz split: validation - config_name: hu data_files: - path: train/hu.json.gz split: train - path: test/hu.json.gz split: test - path: validation/hu.json.gz split: validation - config_name: zh-TW data_files: - path: train/zh-TW.json.gz split: train - path: test/zh-TW.json.gz split: test - path: validation/zh-TW.json.gz split: validation - config_name: bn data_files: - path: train/bn.json.gz split: train - path: test/bn.json.gz split: test - path: validation/bn.json.gz split: validation - config_name: hy data_files: - path: train/hy.json.gz split: train - path: test/hy.json.gz split: test - path: validation/hy.json.gz split: validation - config_name: es data_files: - path: train/es.json.gz split: train - path: test/es.json.gz split: test - path: validation/es.json.gz split: validation - config_name: az data_files: - path: train/az.json.gz split: train - path: test/az.json.gz split: test - path: validation/az.json.gz split: validation ---

boettiger-lab/ca-30x30

boettiger-lab

--- license: bsd-2-clause ---

legacy-datasets/mc4

legacy-datasets

--- pretty_name: mC4 annotations_creators: - no-annotation language_creators: - found language: - af - am - ar - az - be - bg - bn - ca - ceb - co - cs - cy - da - de - el - en - eo - es - et - eu - fa - fi - fil - fr - fy - ga - gd - gl - gu - ha - haw - he - hi - hmn - ht - hu - hy - id - ig - is - it - iw - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lb - lo - lt - lv - mg - mi - mk - ml - mn - mr - ms - mt - my - ne - nl - 'no' - ny - pa - pl - ps - pt - ro - ru - sd - si - sk - sl - sm - sn - so - sq - sr - st - su - sv - sw - ta - te - tg - th - tr - uk - und - ur - uz - vi - xh - yi - yo - zh - zu language_bcp47: - bg-Latn - el-Latn - hi-Latn - ja-Latn - ru-Latn - zh-Latn license: - odc-by multilinguality: - multilingual size_categories: - n<1K - 1K<n<10K - 10K<n<100K - 100K<n<1M - 1M<n<10M - 10M<n<100M - 100M<n<1B - 1B<n<10B source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: mc4 viewer: false --- <div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"> <p><b>Deprecated:</b> Dataset "mc4" is deprecated and will be deleted. Use "<a href="https://huggingface.co/datasets/allenai/c4">allenai/c4</a>" instead.</p> </div> # Dataset Card for mC4 ## Table of Contents - [Dataset Card for mC4](#dataset-card-for-mc4) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://huggingface.co/datasets/allenai/c4 - **Paper:** https://arxiv.org/abs/1910.10683 ### Dataset Summary A multilingual colossal, cleaned version of Common Crawl's web crawl corpus. Based on Common Crawl dataset: "https://commoncrawl.org". This is the version prepared by AllenAI, hosted at this address: https://huggingface.co/datasets/allenai/c4 108 languages are available and are reported in the table below. Note that the languages that end with "-Latn" are simply romanized variants, i.e. written using the Latin script. | language code | language name | |:----------------|:---------------------| | af | Afrikaans | | am | Amharic | | ar | Arabic | | az | Azerbaijani | | be | Belarusian | | bg | Bulgarian | | bg-Latn | Bulgarian (Latin) | | bn | Bangla | | ca | Catalan | | ceb | Cebuano | | co | Corsican | | cs | Czech | | cy | Welsh | | da | Danish | | de | German | | el | Greek | | el-Latn | Greek (Latin) | | en | English | | eo | Esperanto | | es | Spanish | | et | Estonian | | eu | Basque | | fa | Persian | | fi | Finnish | | fil | Filipino | | fr | French | | fy | Western Frisian | | ga | Irish | | gd | Scottish Gaelic | | gl | Galician | | gu | Gujarati | | ha | Hausa | | haw | Hawaiian | | hi | Hindi | | hi-Latn | Hindi (Latin script) | | hmn | Hmong, Mong | | ht | Haitian | | hu | Hungarian | | hy | Armenian | | id | Indonesian | | ig | Igbo | | is | Icelandic | | it | Italian | | iw | former Hebrew | | ja | Japanese | | ja-Latn | Japanese (Latin) | | jv | Javanese | | ka | Georgian | | kk | Kazakh | | km | Khmer | | kn | Kannada | | ko | Korean | | ku | Kurdish | | ky | Kyrgyz | | la | Latin | | lb | Luxembourgish | | lo | Lao | | lt | Lithuanian | | lv | Latvian | | mg | Malagasy | | mi | Maori | | mk | Macedonian | | ml | Malayalam | | mn | Mongolian | | mr | Marathi | | ms | Malay | | mt | Maltese | | my | Burmese | | ne | Nepali | | nl | Dutch | | no | Norwegian | | ny | Nyanja | | pa | Punjabi | | pl | Polish | | ps | Pashto | | pt | Portuguese | | ro | Romanian | | ru | Russian | | ru-Latn | Russian (Latin) | | sd | Sindhi | | si | Sinhala | | sk | Slovak | | sl | Slovenian | | sm | Samoan | | sn | Shona | | so | Somali | | sq | Albanian | | sr | Serbian | | st | Southern Sotho | | su | Sundanese | | sv | Swedish | | sw | Swahili | | ta | Tamil | | te | Telugu | | tg | Tajik | | th | Thai | | tr | Turkish | | uk | Ukrainian | | und | Unknown language | | ur | Urdu | | uz | Uzbek | | vi | Vietnamese | | xh | Xhosa | | yi | Yiddish | | yo | Yoruba | | zh | Chinese | | zh-Latn | Chinese (Latin) | | zu | Zulu | You can load the mC4 subset of any language like this: ```python from datasets import load_dataset en_mc4 = load_dataset("mc4", "en") ``` And if you can even specify a list of languages: ```python from datasets import load_dataset mc4_subset_with_five_languages = load_dataset("mc4", languages=["en", "fr", "es", "de", "zh"]) ``` ### Supported Tasks and Leaderboards mC4 is mainly intended to pretrain language models and word representations. ### Languages The dataset supports 108 languages. ## Dataset Structure ### Data Instances An example form the `en` config is: ``` {'timestamp': '2018-06-24T01:32:39Z', 'text': 'Farm Resources in Plumas County\nShow Beginning Farmer Organizations & Professionals (304)\nThere are 304 resources serving Plumas County in the following categories:\nMap of Beginning Farmer Organizations & Professionals serving Plumas County\nVictoria Fisher - Office Manager - Loyalton, CA\nAmy Lynn Rasband - UCCE Plumas-Sierra Administrative Assistant II - Quincy , CA\nShow Farm Income Opportunities Organizations & Professionals (353)\nThere are 353 resources serving Plumas County in the following categories:\nFarm Ranch And Forest Retailers (18)\nMap of Farm Income Opportunities Organizations & Professionals serving Plumas County\nWarner Valley Wildlife Area - Plumas County\nShow Farm Resources Organizations & Professionals (297)\nThere are 297 resources serving Plumas County in the following categories:\nMap of Farm Resources Organizations & Professionals serving Plumas County\nThere are 57 resources serving Plumas County in the following categories:\nMap of Organic Certification Organizations & Professionals serving Plumas County', 'url': 'http://www.californialandcan.org/Plumas/Farm-Resources/'} ``` ### Data Fields The data have several fields: - `url`: url of the source as a string - `text`: text content as a string - `timestamp`: timestamp as a string ### Data Splits To build mC4, the authors used [CLD3](https://github.com/google/cld3) to identify over 100 languages. The resulting mC4 subsets for each language are reported in this table: | config | train | validation | |:---------|:--------|:-------------| | af | ? | ? | | am | ? | ? | | ar | ? | ? | | az | ? | ? | | be | ? | ? | | bg | ? | ? | | bg-Latn | ? | ? | | bn | ? | ? | | ca | ? | ? | | ceb | ? | ? | | co | ? | ? | | cs | ? | ? | | cy | ? | ? | | da | ? | ? | | de | ? | ? | | el | ? | ? | | el-Latn | ? | ? | | en | ? | ? | | eo | ? | ? | | es | ? | ? | | et | ? | ? | | eu | ? | ? | | fa | ? | ? | | fi | ? | ? | | fil | ? | ? | | fr | ? | ? | | fy | ? | ? | | ga | ? | ? | | gd | ? | ? | | gl | ? | ? | | gu | ? | ? | | ha | ? | ? | | haw | ? | ? | | hi | ? | ? | | hi-Latn | ? | ? | | hmn | ? | ? | | ht | ? | ? | | hu | ? | ? | | hy | ? | ? | | id | ? | ? | | ig | ? | ? | | is | ? | ? | | it | ? | ? | | iw | ? | ? | | ja | ? | ? | | ja-Latn | ? | ? | | jv | ? | ? | | ka | ? | ? | | kk | ? | ? | | km | ? | ? | | kn | ? | ? | | ko | ? | ? | | ku | ? | ? | | ky | ? | ? | | la | ? | ? | | lb | ? | ? | | lo | ? | ? | | lt | ? | ? | | lv | ? | ? | | mg | ? | ? | | mi | ? | ? | | mk | ? | ? | | ml | ? | ? | | mn | ? | ? | | mr | ? | ? | | ms | ? | ? | | mt | ? | ? | | my | ? | ? | | ne | ? | ? | | nl | ? | ? | | no | ? | ? | | ny | ? | ? | | pa | ? | ? | | pl | ? | ? | | ps | ? | ? | | pt | ? | ? | | ro | ? | ? | | ru | ? | ? | | ru-Latn | ? | ? | | sd | ? | ? | | si | ? | ? | | sk | ? | ? | | sl | ? | ? | | sm | ? | ? | | sn | ? | ? | | so | ? | ? | | sq | ? | ? | | sr | ? | ? | | st | ? | ? | | su | ? | ? | | sv | ? | ? | | sw | ? | ? | | ta | ? | ? | | te | ? | ? | | tg | ? | ? | | th | ? | ? | | tr | ? | ? | | uk | ? | ? | | und | ? | ? | | ur | ? | ? | | uz | ? | ? | | vi | ? | ? | | xh | ? | ? | | yi | ? | ? | | yo | ? | ? | | zh | ? | ? | | zh-Latn | ? | ? | | zu | ? | ? | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information AllenAI are releasing this dataset under the terms of ODC-BY. By using this, you are also bound by the Common Crawl terms of use in respect of the content contained in the dataset. ### Citation Information ``` @article{2019t5, author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu}, title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer}, journal = {arXiv e-prints}, year = {2019}, archivePrefix = {arXiv}, eprint = {1910.10683}, } ``` ### Contributions Thanks to [@dirkgr](https://github.com/dirkgr) and [@lhoestq](https://github.com/lhoestq) for adding this dataset.

dsfsi/vukuzenzele-sentence-aligned

dsfsi

--- language: - eng - afr - nbl - xho - zul - sot - nso - tsn - ssw - ven - tso license: cc-by-4.0 task_categories: - sentence-similarity - translation pretty_name: The Vuk'uzenzele South African Multilingual Corpus tags: - multilingual - government arxiv: 2303.0375 configs: - config_name: afr-eng data_files: - split: train path: afr-eng/train-* - split: test path: afr-eng/test-* - split: eval path: afr-eng/eval-* - config_name: afr-nbl data_files: - split: train path: afr-nbl/train-* - split: test path: afr-nbl/test-* - split: eval path: afr-nbl/eval-* - config_name: afr-nso data_files: - split: train path: afr-nso/train-* - split: test path: afr-nso/test-* - split: eval path: afr-nso/eval-* - config_name: afr-sot data_files: - split: train path: afr-sot/train-* - split: test path: afr-sot/test-* - split: eval path: afr-sot/eval-* - config_name: afr-ssw data_files: - split: train path: afr-ssw/train-* - split: test path: afr-ssw/test-* - split: eval path: afr-ssw/eval-* - config_name: afr-tsn data_files: - split: train path: afr-tsn/train-* - split: test path: afr-tsn/test-* - split: eval path: afr-tsn/eval-* - config_name: afr-tso data_files: - split: train path: afr-tso/train-* - split: test path: afr-tso/test-* - split: eval path: afr-tso/eval-* - config_name: afr-ven data_files: - split: train path: afr-ven/train-* - split: test path: afr-ven/test-* - split: eval path: afr-ven/eval-* - config_name: afr-xho data_files: - split: train path: afr-xho/train-* - split: test path: afr-xho/test-* - split: eval path: afr-xho/eval-* - config_name: afr-zul data_files: - split: train path: afr-zul/train-* - split: test path: afr-zul/test-* - split: eval path: afr-zul/eval-* - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - config_name: eng-nbl data_files: - split: train path: eng-nbl/train-* - split: test path: eng-nbl/test-* - split: eval path: eng-nbl/eval-* - config_name: eng-nso data_files: - split: train path: eng-nso/train-* - split: test path: eng-nso/test-* - split: eval path: eng-nso/eval-* - config_name: eng-sot data_files: - split: train path: eng-sot/train-* - split: test path: eng-sot/test-* - split: eval path: eng-sot/eval-* - config_name: eng-ssw data_files: - split: train path: eng-ssw/train-* - split: test path: eng-ssw/test-* - split: eval path: eng-ssw/eval-* - config_name: eng-tsn data_files: - split: train path: eng-tsn/train-* - split: test path: eng-tsn/test-* - split: eval path: eng-tsn/eval-* - config_name: eng-tso data_files: - split: train path: eng-tso/train-* - split: test path: eng-tso/test-* - split: eval path: eng-tso/eval-* - config_name: eng-ven data_files: - split: train path: eng-ven/train-* - split: test path: eng-ven/test-* - split: eval path: eng-ven/eval-* - config_name: eng-xho data_files: - split: train path: eng-xho/train-* - split: test path: eng-xho/test-* - split: eval path: eng-xho/eval-* - config_name: eng-zul data_files: - split: train path: eng-zul/train-* - split: test path: eng-zul/test-* - split: eval path: eng-zul/eval-* - config_name: nbl-nso data_files: - split: train path: nbl-nso/train-* - split: test path: nbl-nso/test-* - split: eval path: nbl-nso/eval-* - config_name: nbl-sot data_files: - split: train path: nbl-sot/train-* - split: test path: nbl-sot/test-* - split: eval path: nbl-sot/eval-* - config_name: nbl-ssw data_files: - split: train path: nbl-ssw/train-* - split: test path: nbl-ssw/test-* - split: eval path: nbl-ssw/eval-* - config_name: nbl-tsn data_files: - split: train path: nbl-tsn/train-* - split: test path: nbl-tsn/test-* - split: eval path: nbl-tsn/eval-* - config_name: nbl-tso data_files: - split: train path: nbl-tso/train-* - split: test path: nbl-tso/test-* - split: eval path: nbl-tso/eval-* - config_name: nbl-ven data_files: - split: train path: nbl-ven/train-* - split: test path: nbl-ven/test-* - split: eval path: nbl-ven/eval-* - config_name: nbl-xho data_files: - split: train path: nbl-xho/train-* - split: test path: nbl-xho/test-* - split: eval path: nbl-xho/eval-* - config_name: nbl-zul data_files: - split: train path: nbl-zul/train-* - split: test path: nbl-zul/test-* - split: eval path: nbl-zul/eval-* - config_name: nso-sot data_files: - split: train path: nso-sot/train-* - split: test path: nso-sot/test-* - split: eval path: nso-sot/eval-* - config_name: nso-ssw data_files: - split: train path: nso-ssw/train-* - split: test path: nso-ssw/test-* - split: eval path: nso-ssw/eval-* - config_name: nso-tsn data_files: - split: train path: nso-tsn/train-* - split: test path: nso-tsn/test-* - split: eval path: nso-tsn/eval-* - config_name: nso-tso data_files: - split: train path: nso-tso/train-* - split: test path: nso-tso/test-* - split: eval path: nso-tso/eval-* - config_name: nso-ven data_files: - split: train path: nso-ven/train-* - split: test path: nso-ven/test-* - split: eval path: nso-ven/eval-* - config_name: nso-xho data_files: - split: train path: nso-xho/train-* - split: test path: nso-xho/test-* - split: eval path: nso-xho/eval-* - config_name: nso-zul data_files: - split: train path: nso-zul/train-* - split: test path: nso-zul/test-* - split: eval path: nso-zul/eval-* - config_name: sot-ssw data_files: - split: train path: sot-ssw/train-* - split: test path: sot-ssw/test-* - split: eval path: sot-ssw/eval-* - config_name: sot-tsn data_files: - split: train path: sot-tsn/train-* - split: test path: sot-tsn/test-* - split: eval path: sot-tsn/eval-* - config_name: sot-tso data_files: - split: train path: sot-tso/train-* - split: test path: sot-tso/test-* - split: eval path: sot-tso/eval-* - config_name: sot-ven data_files: - split: train path: sot-ven/train-* - split: test path: sot-ven/test-* - split: eval path: sot-ven/eval-* - config_name: sot-xho data_files: - split: train path: sot-xho/train-* - split: test path: sot-xho/test-* - split: eval path: sot-xho/eval-* - config_name: sot-zul data_files: - split: train path: sot-zul/train-* - split: test path: sot-zul/test-* - split: eval path: sot-zul/eval-* - config_name: ssw-tsn data_files: - split: train path: ssw-tsn/train-* - split: test path: ssw-tsn/test-* - split: eval path: ssw-tsn/eval-* - config_name: ssw-tso data_files: - split: train path: ssw-tso/train-* - split: test path: ssw-tso/test-* - split: eval path: ssw-tso/eval-* - config_name: ssw-ven data_files: - split: train path: ssw-ven/train-* - split: test path: ssw-ven/test-* - split: eval path: ssw-ven/eval-* - config_name: ssw-xho data_files: - split: train path: ssw-xho/train-* - split: test path: ssw-xho/test-* - split: eval path: ssw-xho/eval-* - config_name: ssw-zul data_files: - split: train path: ssw-zul/train-* - split: test path: ssw-zul/test-* - split: eval path: ssw-zul/eval-* - config_name: tsn-tso data_files: - split: train path: tsn-tso/train-* - split: test path: tsn-tso/test-* - split: eval path: tsn-tso/eval-* - config_name: tsn-ven data_files: - split: train path: tsn-ven/train-* - split: test path: tsn-ven/test-* - split: eval path: tsn-ven/eval-* - config_name: tsn-xho data_files: - split: train path: tsn-xho/train-* - split: test path: tsn-xho/test-* - split: eval path: tsn-xho/eval-* - config_name: tsn-zul data_files: - split: train path: tsn-zul/train-* - split: test path: tsn-zul/test-* - split: eval path: tsn-zul/eval-* - config_name: tso-ven data_files: - split: train path: tso-ven/train-* - split: test path: tso-ven/test-* - split: eval path: tso-ven/eval-* - config_name: tso-xho data_files: - split: train path: tso-xho/train-* - split: test path: tso-xho/test-* - split: eval path: tso-xho/eval-* - config_name: tso-zul data_files: - split: train path: tso-zul/train-* - split: test path: tso-zul/test-* - split: eval path: tso-zul/eval-* - config_name: ven-xho data_files: - split: train path: ven-xho/train-* - split: test path: ven-xho/test-* - split: eval path: ven-xho/eval-* - config_name: ven-zul data_files: - split: train path: ven-zul/train-* - split: test path: ven-zul/test-* - split: eval path: ven-zul/eval-* - config_name: xho-zul data_files: - split: train path: xho-zul/train-* - split: test path: xho-zul/test-* - split: eval path: xho-zul/eval-* dataset_info: - config_name: afr-eng features: - name: afr dtype: string - name: eng dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 793530 num_examples: 2660 - name: test num_bytes: 171644 num_examples: 570 - name: eval num_bytes: 172132 num_examples: 571 download_size: 757198 dataset_size: 1137306 - config_name: afr-nbl features: - name: afr dtype: string - name: nbl dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 281328 num_examples: 723 - name: test num_bytes: 57947 num_examples: 155 - name: eval num_bytes: 59996 num_examples: 155 download_size: 279950 dataset_size: 399271 - config_name: afr-nso features: - name: afr dtype: string - name: nso dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 980475 num_examples: 2937 - name: test num_bytes: 203451 num_examples: 630 - name: eval num_bytes: 214623 num_examples: 630 download_size: 892392 dataset_size: 1398549 - config_name: afr-sot features: - name: afr dtype: string - name: sot dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 971841 num_examples: 2968 - name: test num_bytes: 211374 num_examples: 636 - name: eval num_bytes: 209697 num_examples: 636 download_size: 901006 dataset_size: 1392912 - config_name: afr-ssw features: - name: afr dtype: string - name: ssw dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 975744 num_examples: 3042 - name: test num_bytes: 209151 num_examples: 652 - name: eval num_bytes: 208877 num_examples: 653 download_size: 927666 dataset_size: 1393772 - config_name: afr-tsn features: - name: afr dtype: string - name: tsn dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1010506 num_examples: 2830 - name: test num_bytes: 218153 num_examples: 607 - name: eval num_bytes: 214373 num_examples: 607 download_size: 913596 dataset_size: 1443032 - config_name: afr-tso features: - name: afr dtype: string - name: tso dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 980025 num_examples: 2952 - name: test num_bytes: 213355 num_examples: 633 - name: eval num_bytes: 211642 num_examples: 633 download_size: 902666 dataset_size: 1405022 - config_name: afr-ven features: - name: afr dtype: string - name: ven dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 352031 num_examples: 830 - name: test num_bytes: 72702 num_examples: 178 - name: eval num_bytes: 75243 num_examples: 178 download_size: 323825 dataset_size: 499976 - config_name: afr-xho features: - name: afr dtype: string - name: xho dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 986062 num_examples: 3098 - name: test num_bytes: 205229 num_examples: 664 - name: eval num_bytes: 210379 num_examples: 665 download_size: 944334 dataset_size: 1401670 - config_name: afr-zul features: - name: afr dtype: string - name: zul dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 982102 num_examples: 3078 - name: test num_bytes: 208473 num_examples: 660 - name: eval num_bytes: 201824 num_examples: 660 download_size: 932565 dataset_size: 1392399 - config_name: default features: - name: nbl dtype: string - name: nso dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 128131 num_examples: 315 - name: test num_bytes: 31826 num_examples: 79 download_size: 113394 dataset_size: 159957 - config_name: eng-nbl features: - name: eng dtype: string - name: nbl dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 78419 num_examples: 207 - name: test num_bytes: 16930 num_examples: 45 - name: eval num_bytes: 15202 num_examples: 45 download_size: 89654 dataset_size: 110551 - config_name: eng-nso features: - name: eng dtype: string - name: nso dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 848347 num_examples: 2616 - name: test num_bytes: 183267 num_examples: 561 - name: eval num_bytes: 181802 num_examples: 561 download_size: 770909 dataset_size: 1213416 - config_name: eng-sot features: - name: eng dtype: string - name: sot dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 843173 num_examples: 2671 - name: test num_bytes: 181709 num_examples: 573 - name: eval num_bytes: 180602 num_examples: 573 download_size: 776145 dataset_size: 1205484 - config_name: eng-ssw features: - name: eng dtype: string - name: ssw dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 830354 num_examples: 2662 - name: test num_bytes: 175688 num_examples: 571 - name: eval num_bytes: 176734 num_examples: 571 download_size: 777951 dataset_size: 1182776 - config_name: eng-tsn features: - name: eng dtype: string - name: tsn dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 880306 num_examples: 2517 - name: test num_bytes: 190843 num_examples: 539 - name: eval num_bytes: 187728 num_examples: 540 download_size: 786563 dataset_size: 1258877 - config_name: eng-tso features: - name: eng dtype: string - name: tso dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 849914 num_examples: 2623 - name: test num_bytes: 181181 num_examples: 562 - name: eval num_bytes: 176362 num_examples: 563 download_size: 773662 dataset_size: 1207457 - config_name: eng-ven features: - name: eng dtype: string - name: ven dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 113647 num_examples: 279 - name: test num_bytes: 26195 num_examples: 60 - name: eval num_bytes: 26121 num_examples: 60 download_size: 119271 dataset_size: 165963 - config_name: eng-xho features: - name: eng dtype: string - name: xho dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 827201 num_examples: 2662 - name: test num_bytes: 175023 num_examples: 571 - name: eval num_bytes: 176047 num_examples: 571 download_size: 784961 dataset_size: 1178271 - config_name: eng-zul features: - name: eng dtype: string - name: zul dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 815837 num_examples: 2646 - name: test num_bytes: 168769 num_examples: 567 - name: eval num_bytes: 177547 num_examples: 567 download_size: 767836 dataset_size: 1162153 - config_name: nbl-nso features: - name: nbl dtype: string - name: nso dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 109438 num_examples: 275 - name: test num_bytes: 24000 num_examples: 59 - name: eval num_bytes: 26519 num_examples: 60 download_size: 118816 dataset_size: 159957 - config_name: nbl-sot features: - name: nbl dtype: string - name: sot dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 149593 num_examples: 365 - name: test num_bytes: 30656 num_examples: 78 - name: eval num_bytes: 32211 num_examples: 79 download_size: 152576 dataset_size: 212460 - config_name: nbl-ssw features: - name: nbl dtype: string - name: ssw dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 146544 num_examples: 387 - name: test num_bytes: 33410 num_examples: 83 - name: eval num_bytes: 32858 num_examples: 84 download_size: 157314 dataset_size: 212812 - config_name: nbl-tsn features: - name: nbl dtype: string - name: tsn dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 131856 num_examples: 302 - name: test num_bytes: 31961 num_examples: 65 - name: eval num_bytes: 29676 num_examples: 65 download_size: 139315 dataset_size: 193493 - config_name: nbl-tso features: - name: nbl dtype: string - name: tso dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 127433 num_examples: 296 - name: test num_bytes: 24654 num_examples: 63 - name: eval num_bytes: 23290 num_examples: 64 download_size: 127532 dataset_size: 175377 - config_name: nbl-ven features: - name: nbl dtype: string - name: ven dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 976911 num_examples: 2660 - name: test num_bytes: 211536 num_examples: 570 - name: eval num_bytes: 207694 num_examples: 570 download_size: 885066 dataset_size: 1396141 - config_name: nbl-xho features: - name: nbl dtype: string - name: xho dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 149020 num_examples: 403 - name: test num_bytes: 33319 num_examples: 87 - name: eval num_bytes: 31809 num_examples: 87 download_size: 160427 dataset_size: 214148 - config_name: nbl-zul features: - name: nbl dtype: string - name: zul dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 111905 num_examples: 289 - name: test num_bytes: 25799 num_examples: 62 - name: eval num_bytes: 22660 num_examples: 63 download_size: 124588 dataset_size: 160364 - config_name: nso-sot features: - name: nso dtype: string - name: sot dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1057851 num_examples: 3052 - name: test num_bytes: 226420 num_examples: 654 - name: eval num_bytes: 232934 num_examples: 655 download_size: 945243 dataset_size: 1517205 - config_name: nso-ssw features: - name: nso dtype: string - name: ssw dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1020738 num_examples: 2968 - name: test num_bytes: 219932 num_examples: 636 - name: eval num_bytes: 218161 num_examples: 637 download_size: 922981 dataset_size: 1458831 - config_name: nso-tsn features: - name: nso dtype: string - name: tsn dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1092885 num_examples: 2918 - name: test num_bytes: 238439 num_examples: 625 - name: eval num_bytes: 234644 num_examples: 626 download_size: 952272 dataset_size: 1565968 - config_name: nso-tso features: - name: nso dtype: string - name: tso dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1045959 num_examples: 2956 - name: test num_bytes: 228677 num_examples: 634 - name: eval num_bytes: 226626 num_examples: 634 download_size: 925262 dataset_size: 1501262 - config_name: nso-ven features: - name: nso dtype: string - name: ven dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 155499 num_examples: 343 - name: test num_bytes: 35576 num_examples: 73 - name: eval num_bytes: 31381 num_examples: 74 download_size: 152424 dataset_size: 222456 - config_name: nso-xho features: - name: nso dtype: string - name: xho dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1022670 num_examples: 2959 - name: test num_bytes: 214850 num_examples: 634 - name: eval num_bytes: 212932 num_examples: 635 download_size: 929486 dataset_size: 1450452 - config_name: nso-zul features: - name: nso dtype: string - name: zul dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1014985 num_examples: 2998 - name: test num_bytes: 223825 num_examples: 643 - name: eval num_bytes: 219173 num_examples: 643 download_size: 926742 dataset_size: 1457983 - config_name: sot-ssw features: - name: sot dtype: string - name: ssw dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1036642 num_examples: 3075 - name: test num_bytes: 217328 num_examples: 659 - name: eval num_bytes: 222863 num_examples: 660 download_size: 950426 dataset_size: 1476833 - config_name: sot-tsn features: - name: sot dtype: string - name: tsn dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1118346 num_examples: 3019 - name: test num_bytes: 237826 num_examples: 647 - name: eval num_bytes: 235279 num_examples: 647 download_size: 981019 dataset_size: 1591451 - config_name: sot-tso features: - name: sot dtype: string - name: tso dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1057631 num_examples: 3027 - name: test num_bytes: 226229 num_examples: 649 - name: eval num_bytes: 222671 num_examples: 649 download_size: 943068 dataset_size: 1506531 - config_name: sot-ven features: - name: sot dtype: string - name: ven dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 201166 num_examples: 461 - name: test num_bytes: 44845 num_examples: 99 - name: eval num_bytes: 42607 num_examples: 99 download_size: 191947 dataset_size: 288618 - config_name: sot-xho features: - name: sot dtype: string - name: xho dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1042930 num_examples: 3098 - name: test num_bytes: 217327 num_examples: 664 - name: eval num_bytes: 223313 num_examples: 665 download_size: 964792 dataset_size: 1483570 - config_name: sot-zul features: - name: sot dtype: string - name: zul dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1017097 num_examples: 3079 - name: test num_bytes: 223761 num_examples: 660 - name: eval num_bytes: 227514 num_examples: 660 download_size: 949761 dataset_size: 1468372 - config_name: ssw-tsn features: - name: ssw dtype: string - name: tsn dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1124400 num_examples: 3110 - name: test num_bytes: 238160 num_examples: 666 - name: eval num_bytes: 246176 num_examples: 667 download_size: 1012570 dataset_size: 1608736 - config_name: ssw-tso features: - name: ssw dtype: string - name: tso dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1069540 num_examples: 3142 - name: test num_bytes: 237608 num_examples: 673 - name: eval num_bytes: 231657 num_examples: 674 download_size: 980833 dataset_size: 1538805 - config_name: ssw-ven features: - name: ssw dtype: string - name: ven dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 170825 num_examples: 401 - name: test num_bytes: 34774 num_examples: 86 - name: eval num_bytes: 39434 num_examples: 87 download_size: 170522 dataset_size: 245033 - config_name: ssw-xho features: - name: ssw dtype: string - name: xho dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1047037 num_examples: 3193 - name: test num_bytes: 227505 num_examples: 684 - name: eval num_bytes: 219981 num_examples: 685 download_size: 992683 dataset_size: 1494523 - config_name: ssw-zul features: - name: ssw dtype: string - name: zul dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1054772 num_examples: 3255 - name: test num_bytes: 231524 num_examples: 698 - name: eval num_bytes: 223701 num_examples: 698 download_size: 997182 dataset_size: 1509997 - config_name: tsn-tso features: - name: tsn dtype: string - name: tso dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1141163 num_examples: 3023 - name: test num_bytes: 244100 num_examples: 648 - name: eval num_bytes: 242886 num_examples: 648 download_size: 998631 dataset_size: 1628149 - config_name: tsn-ven features: - name: tsn dtype: string - name: ven dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 175353 num_examples: 361 - name: test num_bytes: 39141 num_examples: 77 - name: eval num_bytes: 37453 num_examples: 78 download_size: 165408 dataset_size: 251947 - config_name: tsn-xho features: - name: tsn dtype: string - name: xho dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1095514 num_examples: 2989 - name: test num_bytes: 231544 num_examples: 640 - name: eval num_bytes: 227856 num_examples: 641 download_size: 986295 dataset_size: 1554914 - config_name: tsn-zul features: - name: tsn dtype: string - name: zul dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1090114 num_examples: 3038 - name: test num_bytes: 232488 num_examples: 651 - name: eval num_bytes: 240758 num_examples: 651 download_size: 989654 dataset_size: 1563360 - config_name: tso-ven features: - name: tso dtype: string - name: ven dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 171428 num_examples: 375 - name: test num_bytes: 33029 num_examples: 80 - name: eval num_bytes: 38079 num_examples: 81 download_size: 163896 dataset_size: 242536 - config_name: tso-xho features: - name: tso dtype: string - name: xho dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1049926 num_examples: 3066 - name: test num_bytes: 224708 num_examples: 657 - name: eval num_bytes: 221699 num_examples: 657 download_size: 967978 dataset_size: 1496333 - config_name: tso-zul features: - name: tso dtype: string - name: zul dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1059314 num_examples: 3106 - name: test num_bytes: 224935 num_examples: 666 - name: eval num_bytes: 225248 num_examples: 666 download_size: 970505 dataset_size: 1509497 - config_name: ven-xho features: - name: ven dtype: string - name: xho dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 164305 num_examples: 401 - name: test num_bytes: 36290 num_examples: 86 - name: eval num_bytes: 35520 num_examples: 87 download_size: 165177 dataset_size: 236115 - config_name: ven-zul features: - name: ven dtype: string - name: zul dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 138178 num_examples: 336 - name: test num_bytes: 32949 num_examples: 72 - name: eval num_bytes: 30697 num_examples: 72 download_size: 143542 dataset_size: 201824 - config_name: xho-zul features: - name: xho dtype: string - name: zul dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1062980 num_examples: 3276 - name: test num_bytes: 226001 num_examples: 702 - name: eval num_bytes: 225893 num_examples: 703 download_size: 1011124 dataset_size: 1514874 --- # The Vuk'uzenzele South African Multilingual Corpus Github: [https://github.com/dsfsi/vukuzenzele-nlp/](https://github.com/dsfsi/vukuzenzele-nlp/) Zenodo: [](https://doi.org/10.5281/zenodo.7598539) Arxiv Preprint: [](https://arxiv.org/abs/2303.03750) Give Feedback 📑: [DSFSI Resource Feedback Form](https://docs.google.com/forms/d/e/1FAIpQLSf7S36dyAUPx2egmXbFpnTBuzoRulhL5Elu-N1eoMhaO7v10w/formResponse) # About The dataset was obtained from the South African government magazine Vuk'uzenzele, created by the [Government Communication and Information System (GCIS)](https://www.gcis.gov.za/). The original raw PDFS were obtatined from the [Vuk'uzenzele website](https://www.vukuzenzele.gov.za/). The datasets contain government magazine editions in 11 languages, namely: | Language | Code | Language | Code | |------------|-------|------------|-------| | English | (eng) | Sepedi | (sep) | | Afrikaans | (afr) | Setswana | (tsn) | | isiNdebele | (nbl) | Siswati | (ssw) | | isiXhosa | (xho) | Tshivenda | (ven) | | isiZulu | (zul) | Xitstonga | (tso) | | Sesotho | (nso) | ## Available pairings The alignment direction is bidrectional, i.e. xho-zul is zul-xho afr-eng; afr-nbl; afr-nso; afr-sot; afr-ssw; afr-tsn; afr-tso; afr-ven; afr-xho; afr-zul eng-nbl; eng-nso; eng-sot ;eng-ssw; eng-tsn; eng-tso; eng-ven; eng-xho; eng-zul nbl-nso; nbl-sot; nbl-ssw; nbl-tsn; nbl-tso; nbl-ven; nbl-xho; nbl-zul nso-sot; nso-ssw; nso-tsn; nso-tso; nso-ven; nso-xho; nso-zul sot-ssw; sot-tsn; sot-tso; sot-ven; sot-xho; sot-zul ssw-tsn; ssw-tso; ssw-ven; ssw-xho; ssw-zul tsn-tso; tsn-ven; tsn-xho; tsn-zul tso-ven; tso-xho; tso-zul ven-xho; ven-zul xho-zul # Disclaimer This dataset contains machine-readable data extracted from PDF documents, from https://www.vukuzenzele.gov.za/, provided by the Government Communication Information System (GCIS). While efforts were made to ensure the accuracy and completeness of this data, there may be errors or discrepancies between the original publications and this dataset. No warranties, guarantees or representations are given in relation to the information contained in the dataset. The members of the Data Science for Societal Impact Research Group bear no responsibility and/or liability for any such errors or discrepancies in this dataset. The Government Communication Information System (GCIS) bears no responsibility and/or liability for any such errors or discrepancies in this dataset. It is recommended that users verify all information contained herein before making decisions based upon this information. # Datasets The datasets consist of pairwise sentence aligned data. There are 55 distinct datasets of paired sentences. The data is obtained by comparing [LASER](https://github.com/facebookresearch/LASER) embeddings of sentence tokens between 2 languages. If the similarity is high, the sentences are deemed semantic equivalents of one another and the observation is outputted. Naming convention: The naming structure of the pairwise_sentence_aligned folder is `aligned-{src_lang_code}-{tgt_lang_code}.csv`. For example, `aligned-afr-zul.csv` is the aligned sentences between Afrikaans and isiZulu. The data is in .csv format and the columns are `src_text`,`tgt_text`,`cosine_score` where: - `src_text` is the source sentence - `tgt_text` is the target sentence - `cosine_score` is the cosine similarity score obtained by comparing the sentence embeddings, it ranges from 0 to 1 **Note:** The notion of source (src) and target (tgt) are only necessary for distinction between the languages used in the aligned pair, as the sentence semantics should be bidirectional. (hallo <-> sawubona) # Citation Vukosi Marivate, Andani Madodonga, Daniel Njini, Richard Lastrucci, Isheanesu Dzingirai, Jenalea Rajab. **The Vuk'uzenzele South African Multilingual Corpus**, 2023 > @dataset{marivate_vukosi_2023_7598540, author = {Marivate, Vukosi and Njini, Daniel and Madodonga, Andani and Lastrucci, Richard and Dzingirai, Isheanesu Rajab, Jenalea}, title = {The Vuk'uzenzele South African Multilingual Corpus}, month = feb, year = 2023, publisher = {Zenodo}, doi = {10.5281/zenodo.7598539}, url = {https://doi.org/10.5281/zenodo.7598539} } ### Licence * Licence for Data - [CC 4.0 BY](LICENSE.md)

open-llm-leaderboard-old/details_meta-llama__Llama-2-70b-hf

open-llm-leaderboard-old

--- pretty_name: Evaluation run of meta-llama/Llama-2-70b-hf dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [meta-llama/Llama-2-70b-hf](https://huggingface.co/meta-llama/Llama-2-70b-hf)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 124 configuration, each one coresponding to one of\ \ the evaluated task.\n\nThe dataset has been created from 11 run(s). Each run can\ \ be found as a specific split in each configuration, the split being named using\ \ the timestamp of the run.The \"train\" split is always pointing to the latest\ \ results.\n\nAn additional configuration \"results\" store all the aggregated results\ \ of the run (and is used to compute and display the aggregated metrics on the [Open\ \ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_meta-llama__Llama-2-70b-hf\"\ ,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\ \ are the [latest results from run 2023-12-03T01:14:42.713769](https://huggingface.co/datasets/open-llm-leaderboard/details_meta-llama__Llama-2-70b-hf/blob/main/results_2023-12-03T01-14-42.713769.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.5405610310841547,\n\ \ \"acc_stderr\": 0.013727093010429788\n },\n \"harness|gsm8k|5\":\ \ {\n \"acc\": 0.5405610310841547,\n \"acc_stderr\": 0.013727093010429788\n\ \ }\n}\n```" repo_url: https://huggingface.co/meta-llama/Llama-2-70b-hf leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|arc:challenge|25_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|arc:challenge|25_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|arc:challenge|25_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|arc:challenge|25_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-08-22T13:47:53.141854.parquet' - config_name: harness_drop_3 data_files: - split: 2023_09_08T23_38_08.931556 path: - '**/details_harness|drop|3_2023-09-08T23-38-08.931556.parquet' - split: 2023_09_18T06_46_44.905361 path: - '**/details_harness|drop|3_2023-09-18T06-46-44.905361.parquet' - split: latest path: - '**/details_harness|drop|3_2023-09-18T06-46-44.905361.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_08T23_38_08.931556 path: - '**/details_harness|gsm8k|5_2023-09-08T23-38-08.931556.parquet' - split: 2023_09_18T06_46_44.905361 path: - '**/details_harness|gsm8k|5_2023-09-18T06-46-44.905361.parquet' - split: 2023_12_03T01_14_42.713769 path: - '**/details_harness|gsm8k|5_2023-12-03T01-14-42.713769.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-12-03T01-14-42.713769.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hellaswag|10_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hellaswag|10_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hellaswag|10_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hellaswag|10_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_0 data_files: - split: 2023_08_21T11_06_07.240233 path: - '**/details_harness|hendrycksTest-abstract_algebra|0_2023-08-21T11:06:07.240233.parquet' - split: 2023_08_21T11_28_25.684618 path: - '**/details_harness|hendrycksTest-abstract_algebra|0_2023-08-21T11:28:25.684618.parquet' - split: 2023_08_21T20_33_55.417483 path: - '**/details_harness|hendrycksTest-abstract_algebra|0_2023-08-21T20:33:55.417483.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|0_2023-08-21T20:33:55.417483.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_abstract_algebra_0 data_files: - split: 2023_08_21T11_06_07.240233 path: - '**/details_harness|hendrycksTest-abstract_algebra|0_2023-08-21T11:06:07.240233.parquet' - split: 2023_08_21T11_28_25.684618 path: - '**/details_harness|hendrycksTest-abstract_algebra|0_2023-08-21T11:28:25.684618.parquet' - split: 2023_08_21T20_33_55.417483 path: - '**/details_harness|hendrycksTest-abstract_algebra|0_2023-08-21T20:33:55.417483.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|0_2023-08-21T20:33:55.417483.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-management|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-management|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-management|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-management|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-virology|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-virology|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-virology|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-virology|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|truthfulqa:mc|0_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|truthfulqa:mc|0_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|truthfulqa:mc|0_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|truthfulqa:mc|0_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-08-22T13:47:53.141854.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_08T23_38_08.931556 path: - '**/details_harness|winogrande|5_2023-09-08T23-38-08.931556.parquet' - split: 2023_09_18T06_46_44.905361 path: - '**/details_harness|winogrande|5_2023-09-18T06-46-44.905361.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-09-18T06-46-44.905361.parquet' - config_name: original_mmlu_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:abstract_algebra|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:anatomy|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:astronomy|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:business_ethics|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:clinical_knowledge|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:college_biology|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:college_chemistry|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:college_computer_science|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:college_mathematics|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:college_medicine|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:college_physics|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:computer_security|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:conceptual_physics|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:econometrics|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:electrical_engineering|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:elementary_mathematics|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:formal_logic|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:global_facts|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:high_school_biology|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:high_school_chemistry|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:high_school_computer_science|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:high_school_european_history|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:high_school_geography|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:high_school_government_and_politics|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:high_school_macroeconomics|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:high_school_mathematics|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:high_school_microeconomics|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:high_school_physics|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:high_school_psychology|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:high_school_statistics|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:high_school_us_history|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:high_school_world_history|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:human_aging|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:human_sexuality|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:international_law|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:jurisprudence|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:logical_fallacies|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:machine_learning|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:management|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:marketing|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:medical_genetics|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:miscellaneous|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:moral_disputes|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:moral_scenarios|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:nutrition|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:philosophy|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:prehistory|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:professional_accounting|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:professional_law|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:professional_medicine|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:professional_psychology|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:public_relations|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:security_studies|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:sociology|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:virology|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:world_religions|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:abstract_algebra|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:anatomy|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:astronomy|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:business_ethics|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:clinical_knowledge|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:college_biology|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:college_chemistry|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:college_computer_science|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:college_mathematics|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:college_medicine|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:college_physics|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:computer_security|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:conceptual_physics|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:econometrics|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:electrical_engineering|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:elementary_mathematics|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:formal_logic|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:global_facts|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:high_school_biology|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:high_school_chemistry|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:high_school_computer_science|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:high_school_european_history|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:high_school_geography|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:high_school_government_and_politics|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:high_school_macroeconomics|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:high_school_mathematics|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:high_school_microeconomics|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:high_school_physics|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:high_school_psychology|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:high_school_statistics|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:high_school_us_history|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:high_school_world_history|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:human_aging|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:human_sexuality|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:international_law|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:jurisprudence|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:logical_fallacies|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:machine_learning|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:management|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:marketing|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:medical_genetics|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:miscellaneous|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:moral_disputes|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:moral_scenarios|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:nutrition|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:philosophy|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:prehistory|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:professional_accounting|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:professional_law|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:professional_medicine|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:professional_psychology|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:public_relations|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:security_studies|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:sociology|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:virology|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:world_religions|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_abstract_algebra_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:abstract_algebra|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:abstract_algebra|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_anatomy_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:anatomy|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:anatomy|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_astronomy_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:astronomy|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:astronomy|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_business_ethics_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:business_ethics|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:business_ethics|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_clinical_knowledge_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:clinical_knowledge|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:clinical_knowledge|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_college_biology_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:college_biology|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:college_biology|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_college_chemistry_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:college_chemistry|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:college_chemistry|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_college_computer_science_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:college_computer_science|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:college_computer_science|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_college_mathematics_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:college_mathematics|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:college_mathematics|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_college_medicine_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:college_medicine|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:college_medicine|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_college_physics_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:college_physics|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:college_physics|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_computer_security_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:computer_security|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:computer_security|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_conceptual_physics_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:conceptual_physics|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:conceptual_physics|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_econometrics_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:econometrics|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:econometrics|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_electrical_engineering_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:electrical_engineering|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:electrical_engineering|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_elementary_mathematics_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:elementary_mathematics|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:elementary_mathematics|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_formal_logic_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:formal_logic|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:formal_logic|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_global_facts_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:global_facts|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:global_facts|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_high_school_biology_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:high_school_biology|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:high_school_biology|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_high_school_chemistry_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:high_school_chemistry|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:high_school_chemistry|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_high_school_computer_science_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:high_school_computer_science|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:high_school_computer_science|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_high_school_european_history_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:high_school_european_history|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:high_school_european_history|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_high_school_geography_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:high_school_geography|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:high_school_geography|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_high_school_government_and_politics_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:high_school_government_and_politics|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:high_school_government_and_politics|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_high_school_macroeconomics_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:high_school_macroeconomics|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:high_school_macroeconomics|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_high_school_mathematics_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:high_school_mathematics|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:high_school_mathematics|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_high_school_microeconomics_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:high_school_microeconomics|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:high_school_microeconomics|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_high_school_physics_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:high_school_physics|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:high_school_physics|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_high_school_psychology_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:high_school_psychology|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:high_school_psychology|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_high_school_statistics_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:high_school_statistics|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:high_school_statistics|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_high_school_us_history_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:high_school_us_history|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:high_school_us_history|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_high_school_world_history_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:high_school_world_history|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:high_school_world_history|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_human_aging_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:human_aging|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:human_aging|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_human_sexuality_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:human_sexuality|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:human_sexuality|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_international_law_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:international_law|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:international_law|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_jurisprudence_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:jurisprudence|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:jurisprudence|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_logical_fallacies_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:logical_fallacies|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:logical_fallacies|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_machine_learning_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:machine_learning|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:machine_learning|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_management_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:management|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:management|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_marketing_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:marketing|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:marketing|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_medical_genetics_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:medical_genetics|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:medical_genetics|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_miscellaneous_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:miscellaneous|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:miscellaneous|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_moral_disputes_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:moral_disputes|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:moral_disputes|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_moral_scenarios_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:moral_scenarios|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:moral_scenarios|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_nutrition_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:nutrition|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:nutrition|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_philosophy_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:philosophy|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:philosophy|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_prehistory_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:prehistory|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:prehistory|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_professional_accounting_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:professional_accounting|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:professional_accounting|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_professional_law_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:professional_law|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:professional_law|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_professional_medicine_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:professional_medicine|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:professional_medicine|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_professional_psychology_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:professional_psychology|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:professional_psychology|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_public_relations_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:public_relations|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:public_relations|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_security_studies_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:security_studies|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:security_studies|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_sociology_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:sociology|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:sociology|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_us_foreign_policy_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_virology_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:virology|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:virology|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_world_religions_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:world_religions|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:world_religions|5_2023-08-28T20:36:26.123850.parquet' - config_name: results data_files: - split: 2023_08_21T11_06_07.240233 path: - results_2023-08-21T11:06:07.240233.parquet - split: 2023_08_21T11_28_25.684618 path: - results_2023-08-21T11:28:25.684618.parquet - split: 2023_08_21T20_33_55.417483 path: - results_2023-08-21T20:33:55.417483.parquet - split: 2023_08_22T09_05_23.035851 path: - results_2023-08-22T09:05:23.035851.parquet - split: 2023_08_22T10_47_05.866748 path: - results_2023-08-22T10:47:05.866748.parquet - split: 2023_08_22T13_42_09.433095 path: - results_2023-08-22T13:42:09.433095.parquet - split: 2023_08_22T13_47_53.141854 path: - results_2023-08-22T13:47:53.141854.parquet - split: 2023_08_28T20_36_26.123850 path: - results_2023-08-28T20:36:26.123850.parquet - split: 2023_09_08T23_38_08.931556 path: - results_2023-09-08T23-38-08.931556.parquet - split: 2023_09_18T06_46_44.905361 path: - results_2023-09-18T06-46-44.905361.parquet - split: 2023_12_03T01_14_42.713769 path: - results_2023-12-03T01-14-42.713769.parquet - split: latest path: - results_2023-12-03T01-14-42.713769.parquet --- # Dataset Card for Evaluation run of meta-llama/Llama-2-70b-hf ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/meta-llama/Llama-2-70b-hf - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [meta-llama/Llama-2-70b-hf](https://huggingface.co/meta-llama/Llama-2-70b-hf) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 124 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 11 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_meta-llama__Llama-2-70b-hf", "harness_gsm8k_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-03T01:14:42.713769](https://huggingface.co/datasets/open-llm-leaderboard/details_meta-llama__Llama-2-70b-hf/blob/main/results_2023-12-03T01-14-42.713769.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.5405610310841547, "acc_stderr": 0.013727093010429788 }, "harness|gsm8k|5": { "acc": 0.5405610310841547, "acc_stderr": 0.013727093010429788 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]

gametb30hp/storage2

gametb30hp

--- license: apache-2.0 ---

openslr/librispeech_asr

openslr

--- pretty_name: LibriSpeech annotations_creators: - expert-generated language_creators: - crowdsourced - expert-generated language: - en license: - cc-by-4.0 multilinguality: - monolingual paperswithcode_id: librispeech-1 size_categories: - 100K<n<1M source_datasets: - original task_categories: - automatic-speech-recognition - audio-classification task_ids: - speaker-identification dataset_info: - config_name: clean features: - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: text dtype: string - name: speaker_id dtype: int64 - name: chapter_id dtype: int64 - name: id dtype: string splits: - name: train.100 num_bytes: 6619683041 num_examples: 28539 - name: train.360 num_bytes: 23898214592 num_examples: 104014 - name: validation num_bytes: 359572231 num_examples: 2703 - name: test num_bytes: 367705423 num_examples: 2620 download_size: 30121377654 dataset_size: 31245175287 - config_name: other features: - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: text dtype: string - name: speaker_id dtype: int64 - name: chapter_id dtype: int64 - name: id dtype: string splits: - name: train.500 num_bytes: 31810256902 num_examples: 148688 - name: validation num_bytes: 337283304 num_examples: 2864 - name: test num_bytes: 352396474 num_examples: 2939 download_size: 31236565377 dataset_size: 32499936680 - config_name: all features: - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: text dtype: string - name: speaker_id dtype: int64 - name: chapter_id dtype: int64 - name: id dtype: string splits: - name: train.clean.100 num_bytes: 6627791685 num_examples: 28539 - name: train.clean.360 num_bytes: 23927767570 num_examples: 104014 - name: train.other.500 num_bytes: 31852502880 num_examples: 148688 - name: validation.clean num_bytes: 359505691 num_examples: 2703 - name: validation.other num_bytes: 337213112 num_examples: 2864 - name: test.clean num_bytes: 368449831 num_examples: 2620 - name: test.other num_bytes: 353231518 num_examples: 2939 download_size: 61357943031 dataset_size: 63826462287 --- # Dataset Card for librispeech_asr ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [LibriSpeech ASR corpus](http://www.openslr.org/12) - **Repository:** [Needs More Information] - **Paper:** [LibriSpeech: An ASR Corpus Based On Public Domain Audio Books](https://www.danielpovey.com/files/2015_icassp_librispeech.pdf) - **Leaderboard:** [The 🤗 Speech Bench](https://huggingface.co/spaces/huggingface/hf-speech-bench) - **Point of Contact:** [Daniel Povey](mailto:[email protected]) ### Dataset Summary LibriSpeech is a corpus of approximately 1000 hours of 16kHz read English speech, prepared by Vassil Panayotov with the assistance of Daniel Povey. The data is derived from read audiobooks from the LibriVox project, and has been carefully segmented and aligned. ### Supported Tasks and Leaderboards - `automatic-speech-recognition`, `audio-speaker-identification`: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). The task has an active Hugging Face leaderboard which can be found at https://huggingface.co/spaces/huggingface/hf-speech-bench. The leaderboard ranks models uploaded to the Hub based on their WER. An external leaderboard at https://paperswithcode.com/sota/speech-recognition-on-librispeech-test-clean ranks the latest models from research and academia. ### Languages The audio is in English. There are two configurations: `clean` and `other`. The speakers in the corpus were ranked according to the WER of the transcripts of a model trained on a different dataset, and were divided roughly in the middle, with the lower-WER speakers designated as "clean" and the higher WER speakers designated as "other". ## Dataset Structure ### Data Instances A typical data point comprises the path to the audio file, usually called `file` and its transcription, called `text`. Some additional information about the speaker and the passage which contains the transcription is provided. ``` {'chapter_id': 141231, 'file': '/home/patrick/.cache/huggingface/datasets/downloads/extracted/b7ded9969e09942ab65313e691e6fc2e12066192ee8527e21d634aca128afbe2/dev_clean/1272/141231/1272-141231-0000.flac', 'audio': {'path': '/home/patrick/.cache/huggingface/datasets/downloads/extracted/b7ded9969e09942ab65313e691e6fc2e12066192ee8527e21d634aca128afbe2/dev_clean/1272/141231/1272-141231-0000.flac', 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 16000}, 'id': '1272-141231-0000', 'speaker_id': 1272, 'text': 'A MAN SAID TO THE UNIVERSE SIR I EXIST'} ``` ### Data Fields - file: A path to the downloaded audio file in .flac format. - audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`. - text: the transcription of the audio file. - id: unique id of the data sample. - speaker_id: unique id of the speaker. The same speaker id can be found for multiple data samples. - chapter_id: id of the audiobook chapter which includes the transcription. ### Data Splits The size of the corpus makes it impractical, or at least inconvenient for some users, to distribute it as a single large archive. Thus the training portion of the corpus is split into three subsets, with approximate size 100, 360 and 500 hours respectively. A simple automatic procedure was used to select the audio in the first two sets to be, on average, of higher recording quality and with accents closer to US English. An acoustic model was trained on WSJ’s si-84 data subset and was used to recognize the audio in the corpus, using a bigram LM estimated on the text of the respective books. We computed the Word Error Rate (WER) of this automatic transcript relative to our reference transcripts obtained from the book texts. The speakers in the corpus were ranked according to the WER of the WSJ model’s transcripts, and were divided roughly in the middle, with the lower-WER speakers designated as "clean" and the higher-WER speakers designated as "other". For "clean", the data is split into train, validation, and test set. The train set is further split into train.100 and train.360 respectively accounting for 100h and 360h of the training data. For "other", the data is split into train, validation, and test set. The train set contains approximately 500h of recorded speech. | | Train.500 | Train.360 | Train.100 | Valid | Test | | ----- | ------ | ----- | ---- | ---- | ---- | | clean | - | 104014 | 28539 | 2703 | 2620| | other | 148688 | - | - | 2864 | 2939 | ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators The dataset was initially created by Vassil Panayotov, Guoguo Chen, Daniel Povey, and Sanjeev Khudanpur. ### Licensing Information [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) ### Citation Information ``` @inproceedings{panayotov2015librispeech, title={Librispeech: an ASR corpus based on public domain audio books}, author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev}, booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on}, pages={5206--5210}, year={2015}, organization={IEEE} } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.

ptb-text-only/ptb_text_only

ptb-text-only

--- annotations_creators: - expert-generated language_creators: - found language: - en license: - other license_details: LDC User Agreement for Non-Members multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: null pretty_name: Penn Treebank dataset_info: features: - name: sentence dtype: string config_name: penn_treebank splits: - name: train num_bytes: 5143706 num_examples: 42068 - name: test num_bytes: 453710 num_examples: 3761 - name: validation num_bytes: 403156 num_examples: 3370 download_size: 5951345 dataset_size: 6000572 --- # Dataset Card for Penn Treebank ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://catalog.ldc.upenn.edu/LDC99T42 - **Repository:** 'https://raw.githubusercontent.com/wojzaremba/lstm/master/data/ptb.train.txt', 'https://raw.githubusercontent.com/wojzaremba/lstm/master/data/ptb.valid.txt', 'https://raw.githubusercontent.com/wojzaremba/lstm/master/data/ptb.test.txt' - **Paper:** https://www.aclweb.org/anthology/J93-2004.pdf - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Needs More Information] ### Dataset Summary This is the Penn Treebank Project: Release 2 CDROM, featuring a million words of 1989 Wall Street Journal material. The rare words in this version are already replaced with <unk> token. The numbers are replaced with <N> token. ### Supported Tasks and Leaderboards Language Modelling ### Languages The text in the dataset is in American English ## Dataset Structure ### Data Instances [Needs More Information] ### Data Fields [Needs More Information] ### Data Splits [Needs More Information] ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information Dataset provided for research purposes only. Please check dataset license for additional information. ### Citation Information @article{marcus-etal-1993-building, title = "Building a Large Annotated Corpus of {E}nglish: The {P}enn {T}reebank", author = "Marcus, Mitchell P. and Santorini, Beatrice and Marcinkiewicz, Mary Ann", journal = "Computational Linguistics", volume = "19", number = "2", year = "1993", url = "https://www.aclweb.org/anthology/J93-2004", pages = "313--330", } ### Contributions Thanks to [@harshalmittal4](https://github.com/harshalmittal4) for adding this dataset.

facebook/xnli

facebook

--- language: - ar - bg - de - el - en - es - fr - hi - ru - sw - th - tr - ur - vi - zh paperswithcode_id: xnli pretty_name: Cross-lingual Natural Language Inference dataset_info: - config_name: all_languages features: - name: premise dtype: translation: languages: - ar - bg - de - el - en - es - fr - hi - ru - sw - th - tr - ur - vi - zh - name: hypothesis dtype: translation_variable_languages: languages: - ar - bg - de - el - en - es - fr - hi - ru - sw - th - tr - ur - vi - zh num_languages: 15 - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: train num_bytes: 1581471691 num_examples: 392702 - name: test num_bytes: 19387432 num_examples: 5010 - name: validation num_bytes: 9566179 num_examples: 2490 download_size: 963942271 dataset_size: 1610425302 - config_name: ar features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: train num_bytes: 107399614 num_examples: 392702 - name: test num_bytes: 1294553 num_examples: 5010 - name: validation num_bytes: 633001 num_examples: 2490 download_size: 59215902 dataset_size: 109327168 - config_name: bg features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: train num_bytes: 125973225 num_examples: 392702 - name: test num_bytes: 1573034 num_examples: 5010 - name: validation num_bytes: 774061 num_examples: 2490 download_size: 66117878 dataset_size: 128320320 - config_name: de features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: train num_bytes: 84684140 num_examples: 392702 - name: test num_bytes: 996488 num_examples: 5010 - name: validation num_bytes: 494604 num_examples: 2490 download_size: 55973883 dataset_size: 86175232 - config_name: el features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: train num_bytes: 139753358 num_examples: 392702 - name: test num_bytes: 1704785 num_examples: 5010 - name: validation num_bytes: 841226 num_examples: 2490 download_size: 74551247 dataset_size: 142299369 - config_name: en features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: train num_bytes: 74444026 num_examples: 392702 - name: test num_bytes: 875134 num_examples: 5010 - name: validation num_bytes: 433463 num_examples: 2490 download_size: 50627367 dataset_size: 75752623 - config_name: es features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: train num_bytes: 81383284 num_examples: 392702 - name: test num_bytes: 969813 num_examples: 5010 - name: validation num_bytes: 478422 num_examples: 2490 download_size: 53677157 dataset_size: 82831519 - config_name: fr features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: train num_bytes: 85808779 num_examples: 392702 - name: test num_bytes: 1029239 num_examples: 5010 - name: validation num_bytes: 510104 num_examples: 2490 download_size: 55968680 dataset_size: 87348122 - config_name: hi features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: train num_bytes: 170593964 num_examples: 392702 - name: test num_bytes: 2073073 num_examples: 5010 - name: validation num_bytes: 1023915 num_examples: 2490 download_size: 70908548 dataset_size: 173690952 - config_name: ru features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: train num_bytes: 129859615 num_examples: 392702 - name: test num_bytes: 1603466 num_examples: 5010 - name: validation num_bytes: 786442 num_examples: 2490 download_size: 70702606 dataset_size: 132249523 - config_name: sw features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: train num_bytes: 69285725 num_examples: 392702 - name: test num_bytes: 871651 num_examples: 5010 - name: validation num_bytes: 429850 num_examples: 2490 download_size: 45564152 dataset_size: 70587226 - config_name: th features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: train num_bytes: 176062892 num_examples: 392702 - name: test num_bytes: 2147015 num_examples: 5010 - name: validation num_bytes: 1061160 num_examples: 2490 download_size: 77222045 dataset_size: 179271067 - config_name: tr features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: train num_bytes: 71637140 num_examples: 392702 - name: test num_bytes: 934934 num_examples: 5010 - name: validation num_bytes: 459308 num_examples: 2490 download_size: 48509680 dataset_size: 73031382 - config_name: ur features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: train num_bytes: 96441486 num_examples: 392702 - name: test num_bytes: 1416241 num_examples: 5010 - name: validation num_bytes: 699952 num_examples: 2490 download_size: 46682785 dataset_size: 98557679 - config_name: vi features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: train num_bytes: 101417430 num_examples: 392702 - name: test num_bytes: 1190217 num_examples: 5010 - name: validation num_bytes: 590680 num_examples: 2490 download_size: 57690058 dataset_size: 103198327 - config_name: zh features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: train num_bytes: 72224841 num_examples: 392702 - name: test num_bytes: 777929 num_examples: 5010 - name: validation num_bytes: 384851 num_examples: 2490 download_size: 48269855 dataset_size: 73387621 configs: - config_name: all_languages data_files: - split: train path: all_languages/train-* - split: test path: all_languages/test-* - split: validation path: all_languages/validation-* - config_name: ar data_files: - split: train path: ar/train-* - split: test path: ar/test-* - split: validation path: ar/validation-* - config_name: bg data_files: - split: train path: bg/train-* - split: test path: bg/test-* - split: validation path: bg/validation-* - config_name: de data_files: - split: train path: de/train-* - split: test path: de/test-* - split: validation path: de/validation-* - config_name: el data_files: - split: train path: el/train-* - split: test path: el/test-* - split: validation path: el/validation-* - config_name: en data_files: - split: train path: en/train-* - split: test path: en/test-* - split: validation path: en/validation-* - config_name: es data_files: - split: train path: es/train-* - split: test path: es/test-* - split: validation path: es/validation-* - config_name: fr data_files: - split: train path: fr/train-* - split: test path: fr/test-* - split: validation path: fr/validation-* - config_name: hi data_files: - split: train path: hi/train-* - split: test path: hi/test-* - split: validation path: hi/validation-* - config_name: ru data_files: - split: train path: ru/train-* - split: test path: ru/test-* - split: validation path: ru/validation-* - config_name: sw data_files: - split: train path: sw/train-* - split: test path: sw/test-* - split: validation path: sw/validation-* - config_name: th data_files: - split: train path: th/train-* - split: test path: th/test-* - split: validation path: th/validation-* - config_name: tr data_files: - split: train path: tr/train-* - split: test path: tr/test-* - split: validation path: tr/validation-* - config_name: ur data_files: - split: train path: ur/train-* - split: test path: ur/test-* - split: validation path: ur/validation-* - config_name: vi data_files: - split: train path: vi/train-* - split: test path: vi/test-* - split: validation path: vi/validation-* - config_name: zh data_files: - split: train path: zh/train-* - split: test path: zh/test-* - split: validation path: zh/validation-* --- # Dataset Card for "xnli" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://www.nyu.edu/projects/bowman/xnli/](https://www.nyu.edu/projects/bowman/xnli/) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 7.74 GB - **Size of the generated dataset:** 3.23 GB - **Total amount of disk used:** 10.97 GB ### Dataset Summary XNLI is a subset of a few thousand examples from MNLI which has been translated into a 14 different languages (some low-ish resource). As with MNLI, the goal is to predict textual entailment (does sentence A imply/contradict/neither sentence B) and is a classification task (given two sentences, predict one of three labels). ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### all_languages - **Size of downloaded dataset files:** 483.96 MB - **Size of the generated dataset:** 1.61 GB - **Total amount of disk used:** 2.09 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "hypothesis": "{\"language\": [\"ar\", \"bg\", \"de\", \"el\", \"en\", \"es\", \"fr\", \"hi\", \"ru\", \"sw\", \"th\", \"tr\", \"ur\", \"vi\", \"zh\"], \"translation\": [\"احد اع...", "label": 0, "premise": "{\"ar\": \"واحدة من رقابنا ستقوم بتنفيذ تعليماتك كلها بكل دقة\", \"bg\": \"един от нашите номера ще ви даде инструкции .\", \"de\": \"Eine ..." } ``` #### ar - **Size of downloaded dataset files:** 483.96 MB - **Size of the generated dataset:** 109.32 MB - **Total amount of disk used:** 593.29 MB An example of 'validation' looks as follows. ``` { "hypothesis": "اتصل بأمه حالما أوصلته حافلة المدرسية.", "label": 1, "premise": "وقال، ماما، لقد عدت للمنزل." } ``` #### bg - **Size of downloaded dataset files:** 483.96 MB - **Size of the generated dataset:** 128.32 MB - **Total amount of disk used:** 612.28 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "hypothesis": "\"губиш нещата на следното ниво , ако хората си припомнят .\"...", "label": 0, "premise": "\"по време на сезона и предполагам , че на твоето ниво ще ги загубиш на следващото ниво , ако те решат да си припомнят отбора на ..." } ``` #### de - **Size of downloaded dataset files:** 483.96 MB - **Size of the generated dataset:** 86.17 MB - **Total amount of disk used:** 570.14 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "hypothesis": "Man verliert die Dinge auf die folgende Ebene , wenn sich die Leute erinnern .", "label": 0, "premise": "\"Du weißt , während der Saison und ich schätze , auf deiner Ebene verlierst du sie auf die nächste Ebene , wenn sie sich entschl..." } ``` #### el - **Size of downloaded dataset files:** 483.96 MB - **Size of the generated dataset:** 142.30 MB - **Total amount of disk used:** 626.26 MB An example of 'validation' looks as follows. ``` This example was too long and was cropped: { "hypothesis": "\"Τηλεφώνησε στη μαμά του μόλις το σχολικό λεωφορείο τον άφησε.\"...", "label": 1, "premise": "Και είπε, Μαμά, έφτασα στο σπίτι." } ``` ### Data Fields The data fields are the same among all splits. #### all_languages - `premise`: a multilingual `string` variable, with possible languages including `ar`, `bg`, `de`, `el`, `en`. - `hypothesis`: a multilingual `string` variable, with possible languages including `ar`, `bg`, `de`, `el`, `en`. - `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2). #### ar - `premise`: a `string` feature. - `hypothesis`: a `string` feature. - `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2). #### bg - `premise`: a `string` feature. - `hypothesis`: a `string` feature. - `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2). #### de - `premise`: a `string` feature. - `hypothesis`: a `string` feature. - `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2). #### el - `premise`: a `string` feature. - `hypothesis`: a `string` feature. - `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2). ### Data Splits | name |train |validation|test| |-------------|-----:|---------:|---:| |all_languages|392702| 2490|5010| |ar |392702| 2490|5010| |bg |392702| 2490|5010| |de |392702| 2490|5010| |el |392702| 2490|5010| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @InProceedings{conneau2018xnli, author = {Conneau, Alexis and Rinott, Ruty and Lample, Guillaume and Williams, Adina and Bowman, Samuel R. and Schwenk, Holger and Stoyanov, Veselin}, title = {XNLI: Evaluating Cross-lingual Sentence Representations}, booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing}, year = {2018}, publisher = {Association for Computational Linguistics}, location = {Brussels, Belgium}, } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun), [@mariamabarham](https://github.com/mariamabarham), [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.

HuggingFaceM4/Docmatix

HuggingFaceM4

--- language: - en license: mit size_categories: - 1M<n<10M task_categories: - visual-question-answering pretty_name: Docmatix tags: - docvqa configs: - config_name: images data_files: - split: train path: data/train-* - config_name: pdf data_files: - split: train path: pdf/train-* - config_name: zero-shot-exp data_files: - split: train path: zero-shot-exp/train-* - split: test path: zero-shot-exp/test-* dataset_info: - config_name: images features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 552957537722.77 num_examples: 1273215 download_size: 159404414330 dataset_size: 552957537722.77 - config_name: pdf features: - name: pdf dtype: binary - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 458612867150 num_examples: 1273245 download_size: 431829972210 dataset_size: 458612867150 - config_name: zero-shot-exp features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: test num_bytes: 68900253.0 num_examples: 200 - name: train num_bytes: 578335690.5 num_examples: 1700 download_size: 642963847 dataset_size: 647235943.5 --- # Dataset Card for Docmatix  ## Dataset description Docmatix is part of the Idefics3 release (stay tuned). It is a massive dataset for Document Visual Question Answering that was used for the fine-tuning of the vision-language model Idefics3. ## Load the dataset To load the dataset, install the library `datasets` with `pip install datasets`. Then, ``` from datasets import load_dataset ds = load_dataset("HuggingFaceM4/Docmatix") ``` If you want the dataset to link to the pdf files as binaries instead of the images, do: ``` from datasets import load_dataset ds = load_dataset("HuggingFaceM4/Docmatix", "pdf") ``` ## Data fields An example of a sample looks as follows: ``` { "images" = [PIL.Image] "texts" = [ { "user": "What is the purpose of the Confirmation Statement mentioned in the document?", "assistant": "The purpose of the Confirmation Statement is to confirm that all information required to be delivered by the company to the registrar in relation to the confirmation period concerned has been delivered or is being delivered at the same time as the confirmation statement.", "source": "PDFA key: 244" }, { "user": "When was the filing received as per the document?", "assistant": "The filing was received for filing in Electronic Format on the 23/03/2021.", "source": "PDFA key: 244" }, ] } ``` In `images`, there is a list of up to 4 images, to be placed before the text. In `texts`, there is a conversation between a user and an assistant about the images that is represented by a list of turns. ## Comparison to other DocVQA datasets | Dataset | # images | # Q/A pairs | # tokens | |----------------------|----------|-------------|------------| | *Document visual question answering* | | **Docmatix** | **2,444,750**| **9,500,000** | **390,000,000**| | DocVQA | 10,189 | 39,463 | 337,829 | | TextCaps | 21,953 | 21,953 | 389,658 | | TextVQA | 21,953 | 34,602 | 181,918 | | ST-VQA | 17,247 | 23,121 | 127,846 | | OCR-VQA | 165,746 | 801,579 | 6,073,824 | | VisualMRC | 3,027 | 11,988 | 168,828 | | IAM | 5,663 | 5,663 | 144,216 | | InfoVQA | 2,118 | 10,074 | 61,048 | | Diagram image-to-text| 300 | 300 | 22,196 | # Citation **BibTeX:** ```bibtex @misc{laurençon2024building, title={Building and better understanding vision-language models: insights and future directions.}, author={Hugo Laurençon and Andrés Marafioti and Victor Sanh and Léo Tronchon}, year={2024}, eprint={2408.12637}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```

eminorhan/llm-memory

eminorhan

--- license: mit --- This repository contains the results of all experiments (inlcuding every single hyperparameter run) reported in the following paper: Orhan AE (2023) [Recognition, recall, and retention of few-shot memories in large language models.](https://arxiv.org/abs/2303.17557) arXiv:2303.17557. A brief description of the directories included in this repository: * [`evals`](https://huggingface.co/datasets/eminorhan/llm-memory/tree/main/evals): contains the results of all recognition experiments * [`recalls`](https://huggingface.co/datasets/eminorhan/llm-memory/tree/main/recalls): contains the results of all recall experiments * [`re-evals`](https://huggingface.co/datasets/eminorhan/llm-memory/tree/main/re-evals): contains the results of all recognition experiments during the retention phase * [`re-recalls`](https://huggingface.co/datasets/eminorhan/llm-memory/tree/main/re-recalls): contains the results of all recall experiments during the retention phase * [`scratch-evals`](https://huggingface.co/datasets/eminorhan/llm-memory/tree/main/scratch-evals), [`scratch-recalls`](https://huggingface.co/datasets/eminorhan/llm-memory/tree/main/scratch-recalls), [`scratch-re-evals`](https://huggingface.co/datasets/eminorhan/llm-memory/tree/main/scratch-re-evals), [`scratch-re-recalls`](https://huggingface.co/datasets/eminorhan/llm-memory/tree/main/scratch-re-recalls): similar to the above, but the results are for the `gpt-j-6B-st` model trained from scratch on [`wikitext-103-raw-v1`](https://huggingface.co/datasets/wikitext).

lmms-lab/MME

lmms-lab

--- size_categories: - 1K<n<10K configs: - config_name: default data_files: - split: test path: data/test-* dataset_info: features: - name: question_id dtype: string - name: image dtype: image - name: question dtype: string - name: answer dtype: string - name: category dtype: string splits: - name: test num_bytes: 1733070098.024 num_examples: 2374 download_size: 864018279 dataset_size: 1733070098.024 --- # Evaluation Dataset for MME

rethinklab/Bench2Drive-Full

rethinklab

--- license: apache-2.0 ---

HuggingFaceH4/ultrachat_200k

HuggingFaceH4

--- language: - en license: mit size_categories: - 100K<n<1M task_categories: - text-generation pretty_name: UltraChat 200k configs: - config_name: default data_files: - split: train_sft path: data/train_sft-* - split: test_sft path: data/test_sft-* - split: train_gen path: data/train_gen-* - split: test_gen path: data/test_gen-* dataset_info: features: - name: prompt dtype: string - name: prompt_id dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train_sft num_bytes: 1397058554 num_examples: 207865 - name: test_sft num_bytes: 154695659 num_examples: 23110 - name: train_gen num_bytes: 1347396812 num_examples: 256032 - name: test_gen num_bytes: 148276089 num_examples: 28304 download_size: 1624049723 dataset_size: 3047427114 --- # Dataset Card for UltraChat 200k ## Dataset Description This is a heavily filtered version of the [UltraChat](https://github.com/thunlp/UltraChat) dataset and was used to train [Zephyr-7B-β](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta), a state of the art 7b chat model. The original datasets consists of 1.4M dialogues generated by ChatGPT and spanning a wide range of topics. To create `UltraChat 200k`, we applied the following logic: - Selection of a subset of data for faster supervised fine tuning. - Truecasing of the dataset, as we observed around 5% of the data contained grammatical errors like "Hello. how are you?" instead of "Hello. How are you?" - Removal of dialogues where the assistant replies with phrases like "I do not have emotions" or "I don't have opinions", even for fact-based prompts that don't involve either. ## Dataset Structure The dataset has four splits, suitable for: * Supervised fine-tuning (`sft`). * Generation ranking (`gen`) via techniques like rejection sampling or PPO. The number of examples per split is shown as follows: | train_sft | test_sft | train_gen | test_gen | |:-------:|:-----------:|:-----:| :-----:| | 207865 | 23110 | 256032 | 28304 | The dataset is stored in parquet format with each entry using the following schema: ``` { "prompt": "Create a fully-developed protagonist who is challenged to survive within a dystopian society under the rule of a tyrant. ...", "messages":[ { "content": "Create a fully-developed protagonist who is challenged to survive within a dystopian society under the rule of a tyrant. ...", "role": "user" }, { "content": "Name: Ava\n\n Ava was just 16 years old when the world as she knew it came crashing down. The government had collapsed, leaving behind a chaotic and lawless society. ...", "role": "assistant" }, { "content": "Wow, Ava's story is so intense and inspiring! Can you provide me with more details. ...", "role": "user" }, { "content": "Certainly! ....", "role": "assistant" }, { "content": "That's really interesting! I would love to hear more...", "role": "user" } { "content": "Certainly! ....", "role": "assistant" }, ], "prompt_id": "d938b65dfe31f05f80eb8572964c6673eddbd68eff3db6bd234d7f1e3b86c2af" } ``` ## Citation If you find this dataset is useful in your work, please cite the original UltraChat dataset: ``` @misc{ding2023enhancing, title={Enhancing Chat Language Models by Scaling High-quality Instructional Conversations}, author={Ning Ding and Yulin Chen and Bokai Xu and Yujia Qin and Zhi Zheng and Shengding Hu and Zhiyuan Liu and Maosong Sun and Bowen Zhou}, year={2023}, eprint={2305.14233}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

facebook/voxpopuli

facebook

--- annotations_creators: [] language: - en - de - fr - es - pl - it - ro - hu - cs - nl - fi - hr - sk - sl - et - lt language_creators: [] license: - cc0-1.0 - other multilinguality: - multilingual pretty_name: VoxPopuli size_categories: [] source_datasets: [] tags: [] task_categories: - automatic-speech-recognition task_ids: [] --- # Dataset Card for Voxpopuli ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://github.com/facebookresearch/voxpopuli - **Repository:** https://github.com/facebookresearch/voxpopuli - **Paper:** https://arxiv.org/abs/2101.00390 - **Point of Contact:** [[email protected]](mailto:[email protected]), [[email protected]](mailto:[email protected]), [[email protected]](mailto:[email protected]) ### Dataset Summary VoxPopuli is a large-scale multilingual speech corpus for representation learning, semi-supervised learning and interpretation. The raw data is collected from 2009-2020 [European Parliament event recordings](https://multimedia.europarl.europa.eu/en/home). We acknowledge the European Parliament for creating and sharing these materials. This implementation contains transcribed speech data for 18 languages. It also contains 29 hours of transcribed speech data of non-native English intended for research in ASR for accented speech (15 L2 accents) ### Example usage VoxPopuli contains labelled data for 18 languages. To load a specific language pass its name as a config name: ```python from datasets import load_dataset voxpopuli_croatian = load_dataset("facebook/voxpopuli", "hr") ``` To load all the languages in a single dataset use "multilang" config name: ```python voxpopuli_all = load_dataset("facebook/voxpopuli", "multilang") ``` To load a specific set of languages, use "multilang" config name and pass a list of required languages to `languages` parameter: ```python voxpopuli_slavic = load_dataset("facebook/voxpopuli", "multilang", languages=["hr", "sk", "sl", "cs", "pl"]) ``` To load accented English data, use "en_accented" config name: ```python voxpopuli_accented = load_dataset("facebook/voxpopuli", "en_accented") ``` **Note that L2 English subset contains only `test` split.** ### Supported Tasks and Leaderboards * automatic-speech-recognition: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). Accented English subset can also be used for research in ASR for accented speech (15 L2 accents) ### Languages VoxPopuli contains labelled (transcribed) data for 18 languages: | Language | Code | Transcribed Hours | Transcribed Speakers | Transcribed Tokens | |:---:|:---:|:---:|:---:|:---:| | English | En | 543 | 1313 | 4.8M | | German | De | 282 | 531 | 2.3M | | French | Fr | 211 | 534 | 2.1M | | Spanish | Es | 166 | 305 | 1.6M | | Polish | Pl | 111 | 282 | 802K | | Italian | It | 91 | 306 | 757K | | Romanian | Ro | 89 | 164 | 739K | | Hungarian | Hu | 63 | 143 | 431K | | Czech | Cs | 62 | 138 | 461K | | Dutch | Nl | 53 | 221 | 488K | | Finnish | Fi | 27 | 84 | 160K | | Croatian | Hr | 43 | 83 | 337K | | Slovak | Sk | 35 | 96 | 270K | | Slovene | Sl | 10 | 45 | 76K | | Estonian | Et | 3 | 29 | 18K | | Lithuanian | Lt | 2 | 21 | 10K | | Total | | 1791 | 4295 | 15M | Accented speech transcribed data has 15 various L2 accents: | Accent | Code | Transcribed Hours | Transcribed Speakers | |:---:|:---:|:---:|:---:| | Dutch | en_nl | 3.52 | 45 | | German | en_de | 3.52 | 84 | | Czech | en_cs | 3.30 | 26 | | Polish | en_pl | 3.23 | 33 | | French | en_fr | 2.56 | 27 | | Hungarian | en_hu | 2.33 | 23 | | Finnish | en_fi | 2.18 | 20 | | Romanian | en_ro | 1.85 | 27 | | Slovak | en_sk | 1.46 | 17 | | Spanish | en_es | 1.42 | 18 | | Italian | en_it | 1.11 | 15 | | Estonian | en_et | 1.08 | 6 | | Lithuanian | en_lt | 0.65 | 7 | | Croatian | en_hr | 0.42 | 9 | | Slovene | en_sl | 0.25 | 7 | ## Dataset Structure ### Data Instances ```python { 'audio_id': '20180206-0900-PLENARY-15-hr_20180206-16:10:06_5', 'language': 11, # "hr" 'audio': { 'path': '/home/polina/.cache/huggingface/datasets/downloads/extracted/44aedc80bb053f67f957a5f68e23509e9b181cc9e30c8030f110daaedf9c510e/train_part_0/20180206-0900-PLENARY-15-hr_20180206-16:10:06_5.wav', 'array': array([-0.01434326, -0.01055908, 0.00106812, ..., 0.00646973], dtype=float32), 'sampling_rate': 16000 }, 'raw_text': '', 'normalized_text': 'poast genitalnog sakaenja ena u europi tek je jedna od manifestacija takve tetne politike.', 'gender': 'female', 'speaker_id': '119431', 'is_gold_transcript': True, 'accent': 'None' } ``` ### Data Fields * `audio_id` (string) - id of audio segment * `language` (datasets.ClassLabel) - numerical id of audio segment * `audio` (datasets.Audio) - a dictionary containing the path to the audio, the decoded audio array, and the sampling rate. In non-streaming mode (default), the path points to the locally extracted audio. In streaming mode, the path is the relative path of an audio inside its archive (as files are not downloaded and extracted locally). * `raw_text` (string) - original (orthographic) audio segment text * `normalized_text` (string) - normalized audio segment transcription * `gender` (string) - gender of speaker * `speaker_id` (string) - id of speaker * `is_gold_transcript` (bool) - ? * `accent` (string) - type of accent, for example "en_lt", if applicable, else "None". ### Data Splits All configs (languages) except for accented English contain data in three splits: train, validation and test. Accented English `en_accented` config contains only test split. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data The raw data is collected from 2009-2020 [European Parliament event recordings](https://multimedia.europarl.europa.eu/en/home) #### Initial Data Collection and Normalization The VoxPopuli transcribed set comes from aligning the full-event source speech audio with the transcripts for plenary sessions. Official timestamps are available for locating speeches by speaker in the full session, but they are frequently inaccurate, resulting in truncation of the speech or mixture of fragments from the preceding or the succeeding speeches. To calibrate the original timestamps, we perform speaker diarization (SD) on the full-session audio using pyannote.audio (Bredin et al.2020) and adopt the nearest SD timestamps (by L1 distance to the original ones) instead for segmentation. Full-session audios are segmented into speech paragraphs by speaker, each of which has a transcript available. The speech paragraphs have an average duration of 197 seconds, which leads to significant. We hence further segment these paragraphs into utterances with a maximum duration of 20 seconds. We leverage speech recognition (ASR) systems to force-align speech paragraphs to the given transcripts. The ASR systems are TDS models (Hannun et al., 2019) trained with ASG criterion (Collobert et al., 2016) on audio tracks from in-house deidentified video data. The resulting utterance segments may have incorrect transcriptions due to incomplete raw transcripts or inaccurate ASR force-alignment. We use the predictions from the same ASR systems as references and filter the candidate segments by a maximum threshold of 20% character error rate(CER). #### Who are the source language producers? Speakers are participants of the European Parliament events, many of them are EU officials. ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases Gender speakers distribution is imbalanced, percentage of female speakers is mostly lower than 50% across languages, with the minimum of 15% for the Lithuanian language data. VoxPopuli includes all available speeches from the 2009-2020 EP events without any selections on the topics or speakers. The speech contents represent the standpoints of the speakers in the EP events, many of which are EU officials. ### Other Known Limitations ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information The dataset is distributet under CC0 license, see also [European Parliament's legal notice](https://www.europarl.europa.eu/legal-notice/en/) for the raw data. ### Citation Information Please cite this paper: ```bibtex @inproceedings{wang-etal-2021-voxpopuli, title = "{V}ox{P}opuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation", author = "Wang, Changhan and Riviere, Morgane and Lee, Ann and Wu, Anne and Talnikar, Chaitanya and Haziza, Daniel and Williamson, Mary and Pino, Juan and Dupoux, Emmanuel", booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)", month = aug, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.acl-long.80", pages = "993--1003", } ``` ### Contributions Thanks to [@polinaeterna](https://github.com/polinaeterna) for adding this dataset.

mteb/sts15-sts

mteb

--- language: - en ---

bigscience/evaluation-results

bigscience

--- pretty_name: evaluation-results size_categories: - 100M<n<1B task_categories: - other --- # BigScience BLOOM Evaluation Results This repository contains evaluation results & original predictions of BLOOM & friends. ## Usage You can load numeric results via: ```python from datasets import load_dataset ds = load_dataset("bigscience/evaluation-results", "bloom") ``` If it takes too long, it may be faster to clone the repository and load the data from disk: ```python !git clone https://huggingface.co/datasets/bigscience/evaluation-results ds = load_dataset("evaluation-results", "bloom") ``` For example generations (.jsonl files), you need to manually browse the repository. ## Structure For `bigsciencelmevalharness`, `lmevalharness` & `codeeval` evaluation_frameworks the structure is: `model_name > evaluation_framework > checkpoint_type > dataset_name > data` ## Evaluation Procedure - `bigsciencelmevalharness` files were created using the below: - https://github.com/bigscience-workshop/Megatron-DeepSpeed/pull/291 - https://github.com/bigscience-workshop/lm-evaluation-harness - `lmevalharness` files were created using the below: - https://github.com/bigscience-workshop/Megatron-DeepSpeed - https://github.com/EleutherAI/lm-evaluation-harness - `codeeval` files were created using the HumanEval code dataset with the below: - https://github.com/loubnabnl/bloom-code-evaluation

jacobbieker/eumetsat-cloudmask-0deg

jacobbieker

--- license: mit ---

MU-NLPC/Calc-svamp

MU-NLPC

--- language: - en license: mit size_categories: - n<1K task_categories: - text-generation tags: - math world problems - math - arithmetics dataset_info: - config_name: default features: - name: id dtype: string - name: question dtype: string - name: chain dtype: string - name: result dtype: string - name: result_float dtype: float64 - name: equation dtype: string - name: problem_type dtype: string splits: - name: test num_bytes: 335744 num_examples: 1000 download_size: 116449 dataset_size: 335744 - config_name: original-splits features: - name: id dtype: string - name: question dtype: string - name: chain dtype: string - name: result dtype: string - name: result_float dtype: float64 - name: equation dtype: string - name: problem_type dtype: string splits: - name: test num_bytes: 335744 num_examples: 1000 download_size: 116449 dataset_size: 335744 configs: - config_name: default data_files: - split: test path: data/test-* - config_name: original-splits data_files: - split: test path: original-splits/test-* --- # Dataset Card for Calc-SVAMP ## Summary The dataset is a collection of simple math word problems focused on arithmetics. It is derived from <https://github.com/arkilpatel/SVAMP/>. The main addition in this dataset variant is the `chain` column. It was created by converting the solution to a simple html-like language that can be easily parsed (e.g. by BeautifulSoup). The data contains 3 types of tags: - gadget: A tag whose content is intended to be evaluated by calling an external tool (sympy-based calculator in this case) - output: An output of the external tool - result: The final answer to the mathematical problem (a number) ## Supported Tasks This variant of the dataset is intended for training Chain-of-Thought reasoning models able to use external tools to enhance the factuality of their responses. This dataset presents in-context scenarios where models can outsource the computations in the reasoning chain to a calculator. ## Construction process We created the dataset by converting the **equation** attribute in the original dataset to a sequence (chain) of calculations, with final one being the result to the math problem. We also perform in-dataset and cross-dataset data-leak detection within the [Calc-X collection](https://huggingface.co/collections/MU-NLPC/calc-x-652fee9a6b838fd820055483). However, for SVAMP specifically, we detected no data leaks and filtered no data. ## Content and data splits The dataset contains the same data instances as the original dataset except for a correction of inconsistency between `equation` and `answer` in one data instance. To the best of our knowledge, the original dataset does not contain an official train-test split. We treat the whole dataset as a testing benchmark. ## Attributes: - **id**: problem id from the original dataset - **question**: the question intended to answer - **chain**: series of simple operations (derived from `equation`) that leads to the solution - **result**: the result (number) as a string - **result_float**: result converted to a floating point - **equation**: a nested expression that evaluates to the correct result - **problem_type**: a category of the problem Attributes **id**, **question**, **chain**, and **result** are present in all datasets in [Calc-X collection](https://huggingface.co/collections/MU-NLPC/calc-x-652fee9a6b838fd820055483). ## Related work This dataset was created as a part of a larger effort in training models capable of using a calculator during inference, which we call Calcformers. - [**Calc-X collection**](https://huggingface.co/collections/MU-NLPC/calc-x-652fee9a6b838fd820055483) - datasets for training Calcformers - [**Calcformers collection**](https://huggingface.co/collections/MU-NLPC/calcformers-65367392badc497807b3caf5) - calculator-using models we trained and published on HF - [**Calc-X and Calcformers paper**](https://arxiv.org/abs/2305.15017) - [**Calc-X and Calcformers repo**](https://github.com/prompteus/calc-x) Here are links to the original dataset: - [**original SVAMP dataset and repo**](https://github.com/arkilpatel/SVAMP/) - [**original SVAMP paper**](https://www.semanticscholar.org/paper/Are-NLP-Models-really-able-to-Solve-Simple-Math-Patel-Bhattamishra/13c4e5a6122f3fa2663f63e49537091da6532f35) ## Licence MIT, consistent with the original source dataset linked above. ## Cite If you use this version of dataset in research, please cite the original [SVAMP paper](https://www.semanticscholar.org/paper/Are-NLP-Models-really-able-to-Solve-Simple-Math-Patel-Bhattamishra/13c4e5a6122f3fa2663f63e49537091da6532f35), and [Calc-X collection](https://arxiv.org/abs/2305.15017) as follows: ```bibtex @inproceedings{kadlcik-etal-2023-soft, title = "Calc-X and Calcformers: Empowering Arithmetical Chain-of-Thought through Interaction with Symbolic Systems", author = "Marek Kadlčík and Michal Štefánik and Ondřej Sotolář and Vlastimil Martinek", booktitle = "Proceedings of the The 2023 Conference on Empirical Methods in Natural Language Processing: Main track", month = dec, year = "2023", address = "Singapore, Singapore", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/2305.15017", } ```