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2025-03-26 01:30:06
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AI-MO/NuminaMath-TIR
AI-MO
"2024-11-25T05:32:53Z"
15,477
117
[ "task_categories:text-generation", "language:en", "license:apache-2.0", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "math", "aimo" ]
[ "text-generation" ]
"2024-07-16T07:44:24Z"
--- language: - en license: apache-2.0 task_categories: - text-generation pretty_name: NuminaMath TIR dataset_info: features: - name: problem dtype: string - name: solution dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 327147067 num_examples: 72441 - name: test num_bytes: 461331 num_examples: 99 download_size: 147557990 dataset_size: 327608398 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* tags: - math - aimo --- # Dataset Card for NuminaMath CoT ## Dataset Description - **Homepage:** https://projectnumina.ai - **Repository:** https://github.com/project-numina/aimo-progress-prize - **Paper:** https://github.com/project-numina/aimo-progress-prize/blob/main/report/numina_dataset.pdf - **Leaderboard:** - **Point of Contact:** [Jia Li]([email protected]) ### Dataset Summary Tool-integrated reasoning (TIR) plays a crucial role in this competition. However, collecting and annotating such data is both costly and time-consuming. To address this, we selected approximately 70k problems from the NuminaMath-CoT dataset, focusing on those with numerical outputs, most of which are integers. We then utilized a pipeline leveraging GPT-4 to generate TORA-like reasoning paths, executing the code and producing results until the solution was complete. We filtered out solutions where the final answer did not match the reference and repeated this process three times to ensure accuracy and consistency. This iterative approach allowed us to generate high-quality TORA data efficiently. ### Licensing Information The dataset is available under the [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0). ### Citation Information ``` @misc{numina_math_datasets, author = {Jia LI, Edward Beeching, Lewis Tunstall, Ben Lipkin, Roman Soletskyi, Shengyi Costa Huang, Kashif Rasul, Longhui Yu, Albert Jiang, Ziju Shen, Zihan Qin, Bin Dong, Li Zhou, Yann Fleureau, Guillaume Lample, and Stanislas Polu}, title = {NuminaMath TIR}, year = {2024}, publisher = {Numina}, journal = {Hugging Face repository}, howpublished = {\url{[https://huggingface.co/AI-MO/NuminaMath-TIR](https://github.com/project-numina/aimo-progress-prize/blob/main/report/numina_dataset.pdf)}} } ```
allenai/sciq
allenai
"2024-01-04T16:23:51Z"
15,458
103
[ "task_categories:question-answering", "task_ids:closed-domain-qa", "annotations_creators:no-annotation", "language_creators:crowdsourced", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:cc-by-nc-3.0", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[ "question-answering" ]
"2022-03-02T23:29:22Z"
--- 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.
lmms-lab/GQA
lmms-lab
"2024-03-08T05:02:22Z"
15,443
13
[ "license:mit", "size_categories:10M<n<100M", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
null
"2023-12-26T13:11:16Z"
--- license: mit dataset_info: - config_name: challenge_all_images features: - name: id dtype: string - name: image dtype: image splits: - name: challenge num_bytes: 261636425.25 num_examples: 1590 download_size: 261271928 dataset_size: 261636425.25 - config_name: challenge_all_instructions features: - name: id dtype: string - name: imageId dtype: string - name: question dtype: string - name: isBalanced dtype: bool splits: - name: challenge num_bytes: 50797705 num_examples: 713449 download_size: 19869828 dataset_size: 50797705 - config_name: challenge_balanced_images features: - name: id dtype: string - name: image dtype: image splits: - name: challenge num_bytes: 261636425.25 num_examples: 1590 download_size: 261333538 dataset_size: 261636425.25 - config_name: challenge_balanced_instructions features: - name: id dtype: string - name: imageId dtype: string - name: question dtype: string - name: isBalanced dtype: bool splits: - name: challenge num_bytes: 3523973 num_examples: 50726 download_size: 1787024 dataset_size: 3523973 - config_name: submission_all_images features: - name: id dtype: string - name: image dtype: image splits: - name: submission num_bytes: 2314978438.875 num_examples: 15545 download_size: 2309217874 dataset_size: 2314978438.875 - config_name: submission_all_instructions features: - name: id dtype: string - name: imageId dtype: string - name: question dtype: string - name: isBalanced dtype: bool splits: - name: submission num_bytes: 298875520 num_examples: 4237524 download_size: 121458425 dataset_size: 298875520 - config_name: test_all_images features: - name: id dtype: string - name: image dtype: image splits: - name: test num_bytes: 492571840.875 num_examples: 2993 download_size: 491611526 dataset_size: 492571840.875 - config_name: test_all_instructions features: - name: id dtype: string - name: imageId dtype: string - name: question dtype: string - name: isBalanced dtype: bool splits: - name: test num_bytes: 95588974 num_examples: 1340048 download_size: 39561711 dataset_size: 95588974 - config_name: test_balanced_images features: - name: id dtype: string - name: image dtype: image splits: - name: test num_bytes: 491210370.625 num_examples: 2987 download_size: 490293506 dataset_size: 491210370.625 - config_name: test_balanced_instructions features: - name: id dtype: string - name: imageId dtype: string - name: question dtype: string - name: isBalanced dtype: bool splits: - name: test num_bytes: 6622775 num_examples: 95336 download_size: 3401070 dataset_size: 6622775 - config_name: testdev_all_images features: - name: id dtype: string - name: image dtype: image splits: - name: testdev num_bytes: 65779269.0 num_examples: 398 download_size: 65670255 dataset_size: 65779269.0 - config_name: testdev_all_instructions features: - name: id dtype: string - name: imageId dtype: string - name: question dtype: string - name: answer dtype: string - name: fullAnswer dtype: string - name: isBalanced dtype: bool - name: groups struct: - name: global dtype: string - name: local dtype: string - name: entailed dtype: string - name: equivalent dtype: string - name: types struct: - name: structural dtype: string - name: semantic dtype: string - name: detailed dtype: string - name: annotations sequence: - name: question struct: - name: objectId dtype: string - name: value dtype: string - name: answer struct: - name: objectId dtype: string - name: value dtype: string - name: fullAnswer struct: - name: objectId dtype: string - name: value dtype: string - name: semantic list: - name: operation dtype: string - name: argument dtype: string - name: dependencies sequence: int32 - name: semanticStr dtype: string splits: - name: testdev num_bytes: 86970760 num_examples: 172174 download_size: 23385535 dataset_size: 86970760 - config_name: testdev_balanced_images features: - name: id dtype: string - name: image dtype: image splits: - name: testdev num_bytes: 65779269.0 num_examples: 398 download_size: 65647745 dataset_size: 65779269.0 - config_name: testdev_balanced_instructions features: - name: id dtype: string - name: imageId dtype: string - name: question dtype: string - name: answer dtype: string - name: fullAnswer dtype: string - name: isBalanced dtype: bool - name: groups struct: - name: global dtype: string - name: local dtype: string - name: entailed dtype: string - name: equivalent dtype: string - name: types struct: - name: structural dtype: string - name: semantic dtype: string - name: detailed dtype: string - name: annotations sequence: - name: question struct: - name: objectId dtype: string - name: value dtype: string - name: answer struct: - name: objectId dtype: string - name: value dtype: string - name: fullAnswer struct: - name: objectId dtype: string - name: value dtype: string - name: semantic list: - name: operation dtype: string - name: argument dtype: string - name: dependencies sequence: int32 - name: semanticStr dtype: string splits: - name: testdev num_bytes: 6113469 num_examples: 12578 download_size: 2090335 dataset_size: 6113469 - config_name: train_all_images features: - name: id dtype: string - name: image dtype: image splits: - name: train num_bytes: 10509758457.0 num_examples: 74256 download_size: 10480239090 dataset_size: 10509758457.0 - config_name: train_all_instructions features: - name: id dtype: string - name: imageId dtype: string - name: question dtype: string - name: answer dtype: string - name: fullAnswer dtype: string - name: isBalanced dtype: bool - name: groups struct: - name: global dtype: string - name: local dtype: string - name: entailed dtype: string - name: equivalent dtype: string - name: types struct: - name: structural dtype: string - name: semantic dtype: string - name: detailed dtype: string - name: annotations sequence: - name: question struct: - name: objectId dtype: string - name: value dtype: string - name: answer struct: - name: objectId dtype: string - name: value dtype: string - name: fullAnswer struct: - name: objectId dtype: string - name: value dtype: string - name: semantic list: - name: operation dtype: string - name: argument dtype: string - name: dependencies sequence: int32 - name: semanticStr dtype: string splits: - name: train num_bytes: 6891129609 num_examples: 14305356 download_size: 1874173198 dataset_size: 6891129609 - config_name: train_balanced_images features: - name: id dtype: string - name: image dtype: image splits: - name: train num_bytes: 10200292415.5 num_examples: 72140 download_size: 10171627271 dataset_size: 10200292415.5 - config_name: train_balanced_instructions features: - name: id dtype: string - name: imageId dtype: string - name: question dtype: string - name: answer dtype: string - name: fullAnswer dtype: string - name: isBalanced dtype: bool - name: groups struct: - name: global dtype: string - name: local dtype: string - name: entailed dtype: string - name: equivalent dtype: string - name: types struct: - name: structural dtype: string - name: semantic dtype: string - name: detailed dtype: string - name: annotations sequence: - name: question struct: - name: objectId dtype: string - name: value dtype: string - name: answer struct: - name: objectId dtype: string - name: value dtype: string - name: fullAnswer struct: - name: objectId dtype: string - name: value dtype: string - name: semantic list: - name: operation dtype: string - name: argument dtype: string - name: dependencies sequence: int32 - name: semanticStr dtype: string splits: - name: train num_bytes: 460429581 num_examples: 943000 download_size: 183979778 dataset_size: 460429581 - config_name: val_all_images features: - name: id dtype: string - name: image dtype: image splits: - name: val num_bytes: 1494990904.5 num_examples: 10564 download_size: 1490744689 dataset_size: 1494990904.5 - config_name: val_all_instructions features: - name: id dtype: string - name: imageId dtype: string - name: question dtype: string - name: answer dtype: string - name: fullAnswer dtype: string - name: isBalanced dtype: bool - name: groups struct: - name: global dtype: string - name: local dtype: string - name: entailed dtype: string - name: equivalent dtype: string - name: types struct: - name: structural dtype: string - name: semantic dtype: string - name: detailed dtype: string - name: annotations sequence: - name: question struct: - name: objectId dtype: string - name: value dtype: string - name: answer struct: - name: objectId dtype: string - name: value dtype: string - name: fullAnswer struct: - name: objectId dtype: string - name: value dtype: string - name: semantic list: - name: operation dtype: string - name: argument dtype: string - name: dependencies sequence: int32 - name: semanticStr dtype: string splits: - name: val num_bytes: 967338322 num_examples: 2011853 download_size: 266476025 dataset_size: 967338322 - config_name: val_balanced_images features: - name: id dtype: string - name: image dtype: image splits: - name: val num_bytes: 1447074448.75 num_examples: 10234 download_size: 1443033919 dataset_size: 1447074448.75 - config_name: val_balanced_instructions features: - name: id dtype: string - name: imageId dtype: string - name: question dtype: string - name: answer dtype: string - name: fullAnswer dtype: string - name: isBalanced dtype: bool - name: groups struct: - name: global dtype: string - name: local dtype: string - name: entailed dtype: string - name: equivalent dtype: string - name: types struct: - name: structural dtype: string - name: semantic dtype: string - name: detailed dtype: string - name: annotations sequence: - name: question struct: - name: objectId dtype: string - name: value dtype: string - name: answer struct: - name: objectId dtype: string - name: value dtype: string - name: fullAnswer struct: - name: objectId dtype: string - name: value dtype: string - name: semantic list: - name: operation dtype: string - name: argument dtype: string - name: dependencies sequence: int32 - name: semanticStr dtype: string splits: - name: val num_bytes: 64498952 num_examples: 132062 download_size: 25794272 dataset_size: 64498952 configs: - config_name: challenge_all_images data_files: - split: challenge path: challenge_all_images/challenge-* - config_name: challenge_all_instructions data_files: - split: challenge path: challenge_all_instructions/challenge-* - config_name: challenge_balanced_images data_files: - split: challenge path: challenge_balanced_images/challenge-* - config_name: challenge_balanced_instructions data_files: - split: challenge path: challenge_balanced_instructions/challenge-* - config_name: submission_all_images data_files: - split: submission path: submission_all_images/submission-* - config_name: submission_all_instructions data_files: - split: submission path: submission_all_instructions/submission-* - config_name: test_all_images data_files: - split: test path: test_all_images/test-* - config_name: test_all_instructions data_files: - split: test path: test_all_instructions/test-* - config_name: test_balanced_images data_files: - split: test path: test_balanced_images/test-* - config_name: test_balanced_instructions data_files: - split: test path: test_balanced_instructions/test-* - config_name: testdev_all_images data_files: - split: testdev path: testdev_all_images/testdev-* - config_name: testdev_all_instructions data_files: - split: testdev path: testdev_all_instructions/testdev-* - config_name: testdev_balanced_images data_files: - split: testdev path: testdev_balanced_images/testdev-* - config_name: testdev_balanced_instructions data_files: - split: testdev path: testdev_balanced_instructions/testdev-* - config_name: train_all_images data_files: - split: train path: train_all_images/train-* - config_name: train_all_instructions data_files: - split: train path: train_all_instructions/train-* - config_name: train_balanced_images data_files: - split: train path: train_balanced_images/train-* - config_name: train_balanced_instructions data_files: - split: train path: train_balanced_instructions/train-* - config_name: val_all_images data_files: - split: val path: val_all_images/val-* - config_name: val_all_instructions data_files: - split: val path: val_all_instructions/val-* - config_name: val_balanced_images data_files: - split: val path: val_balanced_images/val-* - config_name: val_balanced_instructions data_files: - split: val path: val_balanced_instructions/val-* --- <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 [GQA](hhttps://cs.stanford.edu/people/dorarad/gqa/about.html). It is used in our `lmms-eval` pipeline to allow for one-click evaluations of large multi-modality models. ``` @inproceedings{hudson2019gqa, title={Gqa: A new dataset for real-world visual reasoning and compositional question answering}, author={Hudson, Drew A and Manning, Christopher D}, booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition}, pages={6700--6709}, year={2019} } ```
allenai/s2-naip
allenai
"2024-05-31T21:06:47Z"
15,427
18
[ "license:apache-2.0", "size_categories:10K<n<100K", "format:webdataset", "modality:image", "modality:text", "library:datasets", "library:webdataset", "library:mlcroissant", "region:us" ]
null
"2024-03-06T03:10:43Z"
--- license: apache-2.0 --- AI2-S2-NAIP is a remote sensing dataset consisting of aligned NAIP, Sentinel-2, Sentinel-1, and Landsat images spanning the entire continental US. Data is divided into tiles. Each tile spans 512x512 pixels at 1.25 m/pixel in one of the 10 UTM projections covering the continental US. At each tile, the following data is available: - [National Agriculture Imagery Program (NAIP)](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-aerial-photography-national-agriculture-imagery-program-naip): an image from 2019-2021 at 1.25 m/pixel (512x512). - [Sentinel-2 (L1C)](https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-2): between 16 and 32 images captured within a few months of the NAIP image at 10 m/pixel (64x64). - [Sentinel-1](https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-1): between 2 and 8 images captured within a few months of the NAIP image at 10 m/pixel (64x64). - [Landsat-8/9](https://www.usgs.gov/landsat-missions/landsat-8): 4 images captured in the same year as the NAIP image at 10 m/pixel (64x64). - [OpenStreetMap](https://www.openstreetmap.org): a GeoJSON containing buildings, roads, and 30 other categories. It uses pixel coordinates relative to the 512x512 NAIP image. - [WorldCover](https://worldcover2021.esa.int/): the 2021 land cover image at 10 m/pixel (64x64). AI2-S2-NAIP is applicable to several supervised and unsupervised tasks in remote sensing, including super-resolution (e.g. NAIP -> Sentinel-2), segmentation and detection (e.g. NAIP or Sentinel-2 -> OpenStreetMap or WorldCover), and multi-modal masked autoencoder pre-training. For questions or feedback about AI2-S2-NAIP, please open an issue on Github at https://github.com/allenai/satlas. ![Example images for one tile in the dataset.](example_images/combined.png) Structure --------- Once extracted, the dataset contains the different data types in different folders. Each folder contains files named by a tile ID, which consists of the UTM projection, column, and row. The column and row are based on tiles that are 512x512 pixels with pixel coordinates at 1.25 m/pixel, e.g. `32612_960_-6049.png` spans (614400, -3871360) to (615040, -3870720) in EPSG:32612 projection units. Here is an example of NAIP data: ``` naip/ 32612_960_-6049.png 32612_960_-6050.png 32612_960_-6051.png ... ``` And an example of Sentinel-2 data: ``` sentinel2/ 32612_960_-6049_16.tif 32612_960_-6049_32.tif 32612_960_-6049_8.tif 32612_960_-6050_16.tif ... ``` The Sentinel-2, Sentinel-1, and Landsat images are GeoTIFFS so they contain georeference metadata. Other data does not have georeference metadata, but data at each tile is aligned, so the georeference metadata from the above images is applicable to the other data as well with only a resolution shift. Mapping Longitude and Latitude to Tile -------------------------------------- Here is an example of mapping longitude and latitude to a tile. First install packages: pip install rasterio shapely utm Then launch Python shell: from rasterio.crs import CRS from rasterio.warp import transform_geom import shapely import utm # Define source location. src_crs = CRS.from_epsg(4326) src_point = shapely.Point(-122.331711, 47.648450) # Get UTM zone. _, _, zone_suffix, _ = utm.from_latlon(src_point.y, src_point.x) epsg_code = 32600 + zone_suffix dst_crs = CRS.from_epsg(epsg_code) # Transform to UTM CRS. dst_point = transform_geom(src_crs, dst_crs, src_point) dst_point = shapely.geometry.shape(dst_point) # dst_point is in projection coordinates (meters). # Now convert to pixel coordinates at 1.25 m/pixel. col = int(dst_point.x/1.25) row = int(dst_point.y/-1.25) # Print the prefix for the image filenames. print(f"{epsg_code}_{col//512}_{row//512}") # Print the prefix for the tar filenames to know which one to download. # These group together many 1.25 m/pixel 512x512 tiles into one tar file. print(f"{epsg_code}_{col//512//32}_{row//512//32}") So then you would download the tar file from the second prefix, extract it, and look at the file with name matching the first prefix. See visualize_tile.py for example of visualizing the data at a particular tile. Sentinel-2 ---------- The 10 m/pixel (`_8.tif`), 20 m/pixel (`_16.tif`), and 60 m/pixel (`_32.tif`) bands are stored separately. Pixel values are the L1C 16-bit values. The band order is as follows: - _8.tif (64x64): B02, B03, B04, B08 - _16.tif (32x32): B05, B06, B07, B8A, B11, B12 - _32.tif (16x16): B01, B09, B10 The GeoTIFFs contain multiple images concatenated along the channel axis. The CSV shows the original Sentinel-2 scene ID of each image. Sentinel-1 ---------- The Sentinel-1 bands are 10 m/pixel and ordered VV then VH. Only IW VV+VH scenes are used. The pixel values are 32-bit floating point values representing decibels 10*log10(x). We obtain the radiometric-calibrated and terrain-corrected images from Google Earth Engine so see https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S1_GRD for details. The GeoTIFFs contain multiple images concatenated along the channel axis. The CSV shows the original Sentinel-1 scene ID of each image. NAIP ---- The NAIP image is 512x512 with four 8-bit bands: R, G, B, IR. It is encoded as PNG but the IR is alpha mask so cannot be visualized correctly in image viewer without removing the alpha mask. There are two NAIP images available, one under "naip" (2019-2022) and one under "oldnaip" (2015-2018). The CSV shows the original NAIP scene ID of each image. Landsat ------- We include OLI-TIRS images from Landsat-8 and Landsat-9. As with Sentinel-2, we select Landsat images that were captured within a few months of the NAIP image. We store the 15 m/pixel bands (i.e. B8) at 10 m/pixel, and the 30 m/pixel bands (all the others) at 20 m/pixel. There are separate GeoTIFFs for the 10 m/pixel (`_8.tif`) and 20 m/pixel (`_16.tif`). All pixel values are 16-bit. The band order is as follows: - _8.tif (64x64): B8 - _16.tif (32x32): B1, B2, B3, B4, B5, B6, B7, B9, B10, B11 The GeoTIFFS contain multiple images concatenated along the channel axis. The CSV shows the original Landsat scene ID of each image.
TrevorDohm/Stack_Tokenized
TrevorDohm
"2024-04-16T00:19:53Z"
15,418
0
[ "task_categories:text-generation", "language_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:multilingual", "language:code", "license:other", "size_categories:100M<n<1B", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
[ "text-generation" ]
"2024-03-10T05:49:00Z"
--- annotations_creators: [] language_creators: - crowdsourced - expert-generated language: - code license: - other multilinguality: - multilingual pretty_name: The-Stack-Tokenized size_categories: - unknown source_datasets: [] task_categories: - text-generation task_ids: [] ---
m-a-p/Matrix
m-a-p
"2025-02-25T14:39:17Z"
15,402
160
[ "task_categories:text-generation", "language:en", "language:zh", "license:apache-2.0", "size_categories:1B<n<10B", "format:json", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "region:us", "language model" ]
[ "text-generation" ]
"2024-05-08T12:49:11Z"
--- license: apache-2.0 task_categories: - text-generation language: - en - zh tags: - language model pretty_name: Matrix size_categories: - n>1T --- # Matrix An open-source pretraining dataset containing 4690 billion tokens, this bilingual dataset with both English and Chinese texts is used for training neo models. ## Dataset Composition The dataset consists of several components, each originating from different sources and serving various purposes in language modeling and processing. Below is a brief overview of each component: <p> <img src="https://cdn-uploads.huggingface.co/production/uploads/654907a4a1faff97850c4eff/1FWMF_t_Mhy0UQmu65Bb1.png" style="float: right; width: 400px; margin-left: 10px;"> <strong>Common Crawl</strong><br> Extracts from the Common Crawl project, featuring a rich diversity of internet text including websites, blogs, news articles, and more.<br> <strong>Code</strong><br> A collection of coding-related data.<be> <strong>Paper</strong><br> Consists of academic and research papers covering a broad spectrum of disciplines, offering technical and domain-specific language.<br> <strong>Book</strong><br> Comprises texts from a range of published books, encompassing literature, non-fiction, textbooks, and more.<br> <strong>Instruction</strong><br> Features a collection of texts primarily in a Q&A format.<be> <strong>Exam</strong><br> Contains various educational materials and assessments used in academic examinations.<be> <strong>News</strong><br> A collection of texts from various journalistic sources, reporting on current events and news stories.<br> <strong>Wiki</strong><br> Articles from various encyclopedic sources, not limited to Wikipedia, covering a wide array of topics and information.<br> <strong>Patent</strong><br> Includes texts from patent documents, providing detailed descriptions of inventions and their applications.<br> </p> ## Citation ``` @article{zhang2024mapneo, title = {MAP-Neo: Highly Capable and Transparent Bilingual Large Language Model Series}, author = {Ge Zhang and Scott Qu and Jiaheng Liu and Chenchen Zhang and Chenghua Lin and Chou Leuang Yu and Danny Pan and Esther Cheng and Jie Liu and Qunshu Lin and Raven Yuan and Tuney Zheng and Wei Pang and Xinrun Du and Yiming Liang and Yinghao Ma and Yizhi Li and Ziyang Ma and Bill Lin and Emmanouil Benetos and Huan Yang and Junting Zhou and Kaijing Ma and Minghao Liu and Morry Niu and Noah Wang and Quehry Que and Ruibo Liu and Sine Liu and Shawn Guo and Soren Gao and Wangchunshu Zhou and Xinyue Zhang and Yizhi Zhou and Yubo Wang and Yuelin Bai and Yuhan Zhang and Yuxiang Zhang and Zenith Wang and Zhenzhu Yang and Zijian Zhao and Jiajun Zhang and Wanli Ouyang and Wenhao Huang and Wenhu Chen}, year = {2024}, journal = {arXiv preprint arXiv: 2405.19327} } ```
rayliuca/WikidataLabels
rayliuca
"2024-01-11T04:17:57Z"
15,338
1
[ "task_categories:translation", "task_categories:text2text-generation", "language:en", "language:fr", "language:de", "language:ja", "language:zh", "language:hi", "language:ar", "language:bn", "language:ru", "language:es", "license:cc0-1.0", "size_categories:100M<n<1B", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
[ "translation", "text2text-generation" ]
"2024-01-01T00:23:08Z"
--- license: cc0-1.0 dataset_info: - config_name: aa features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13986211 num_examples: 436895 download_size: 9821312 dataset_size: 13986211 - config_name: ab features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 5012532 num_examples: 159908 download_size: 3013706 dataset_size: 5012532 - config_name: abs features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4252728 num_examples: 143986 download_size: 2567450 dataset_size: 4252728 - config_name: ace features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 19105673 num_examples: 574712 download_size: 13573374 dataset_size: 19105673 - config_name: ady features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4444259 num_examples: 148627 download_size: 2705754 dataset_size: 4444259 - config_name: ady-cyrl features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4412556 num_examples: 147884 download_size: 2682170 dataset_size: 4412556 - config_name: aeb features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4305734 num_examples: 145198 download_size: 2606368 dataset_size: 4305734 - config_name: aeb-arab features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4467930 num_examples: 148796 download_size: 2722169 dataset_size: 4467930 - config_name: aeb-latn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 12770359 num_examples: 404946 download_size: 8886489 dataset_size: 12770359 - config_name: af features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 58561042 num_examples: 1643153 download_size: 42539052 dataset_size: 58561042 - config_name: agq features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 1317 num_examples: 33 download_size: 2906 dataset_size: 1317 - config_name: ak features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14198715 num_examples: 443037 download_size: 9991525 dataset_size: 14198715 - config_name: aln features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13811116 num_examples: 432089 download_size: 9673418 dataset_size: 13811116 - config_name: als features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 20691 num_examples: 543 download_size: 17540 dataset_size: 20691 - config_name: alt features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 108390 num_examples: 1814 download_size: 59046 dataset_size: 108390 - config_name: am features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 5231176 num_examples: 163038 download_size: 3187164 dataset_size: 5231176 - config_name: ami features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 21519 num_examples: 686 download_size: 16640 dataset_size: 21519 - config_name: an features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 240345072 num_examples: 5921087 download_size: 164895205 dataset_size: 240345072 - config_name: ang features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14275715 num_examples: 443461 download_size: 10063758 dataset_size: 14275715 - config_name: anp features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 8558258 num_examples: 241612 download_size: 4381360 dataset_size: 8558258 - config_name: ar features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 291173732 num_examples: 5724064 download_size: 159369497 dataset_size: 291173732 - config_name: arc features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4473283 num_examples: 150006 download_size: 2722619 dataset_size: 4473283 - config_name: arn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13879729 num_examples: 433912 download_size: 9715431 dataset_size: 13879729 - config_name: arq features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4346991 num_examples: 146004 download_size: 2636972 dataset_size: 4346991 - config_name: ary features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 5358568 num_examples: 171568 download_size: 3313402 dataset_size: 5358568 - config_name: arz features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 81806333 num_examples: 1669699 download_size: 49423508 dataset_size: 81806333 - config_name: as features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 21658610 num_examples: 450074 download_size: 9641626 dataset_size: 21658610 - config_name: ase features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4252943 num_examples: 143986 download_size: 2568106 dataset_size: 4252943 - config_name: ast features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 1385628786 num_examples: 20696237 download_size: 955908362 dataset_size: 1385628786 - config_name: atj features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 12996229 num_examples: 411639 download_size: 9057557 dataset_size: 12996229 - config_name: av features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4722934 num_examples: 153781 download_size: 2880103 dataset_size: 4722934 - config_name: avk features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13194485 num_examples: 414598 download_size: 9200917 dataset_size: 13194485 - config_name: awa features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 8599312 num_examples: 242320 download_size: 4411751 dataset_size: 8599312 - config_name: ay features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14269432 num_examples: 443521 download_size: 10029939 dataset_size: 14269432 - config_name: az features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 21049248 num_examples: 516732 download_size: 14117527 dataset_size: 21049248 - config_name: azb features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 30781587 num_examples: 607562 download_size: 16028687 dataset_size: 30781587 - config_name: ba features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 11525351 num_examples: 261509 download_size: 6733777 dataset_size: 11525351 - config_name: ban features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13674052 num_examples: 426706 download_size: 9513747 dataset_size: 13674052 - config_name: ban-bali features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 50961 num_examples: 748 download_size: 25817 dataset_size: 50961 - config_name: bar features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 54783034 num_examples: 1566120 download_size: 40389830 dataset_size: 54783034 - config_name: bbc features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 12820895 num_examples: 406960 download_size: 8917054 dataset_size: 12820895 - config_name: bcc features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 8017228 num_examples: 241977 download_size: 4344579 dataset_size: 8017228 - config_name: be features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 30978832 num_examples: 564184 download_size: 17461174 dataset_size: 30978832 - config_name: be-tarask features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 18931909 num_examples: 374396 download_size: 10871239 dataset_size: 18931909 - config_name: bg features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 200628708 num_examples: 4383953 download_size: 137745533 dataset_size: 200628708 - config_name: bgn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 7999280 num_examples: 241566 download_size: 4331249 dataset_size: 7999280 - config_name: bi features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14040026 num_examples: 438382 download_size: 9867032 dataset_size: 14040026 - config_name: bjn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 8375348 num_examples: 254558 download_size: 5722334 dataset_size: 8375348 - config_name: bm features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 18145787 num_examples: 549694 download_size: 13129193 dataset_size: 18145787 - config_name: bn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 815803977 num_examples: 9767284 download_size: 261147329 dataset_size: 815803977 - config_name: bo features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 11671330 num_examples: 278307 download_size: 5669602 dataset_size: 11671330 - config_name: bpy features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 15497749 num_examples: 347458 download_size: 6991190 dataset_size: 15497749 - config_name: bqi features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 8017455 num_examples: 241984 download_size: 4345123 dataset_size: 8017455 - config_name: br features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 58304963 num_examples: 1653800 download_size: 42722031 dataset_size: 58304963 - config_name: brh features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 5328437 num_examples: 171504 download_size: 3376189 dataset_size: 5328437 - config_name: bs features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 30441466 num_examples: 858190 download_size: 21606575 dataset_size: 30441466 - config_name: btm features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4252525 num_examples: 143980 download_size: 2567218 dataset_size: 4252525 - config_name: bto features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 12841721 num_examples: 407470 download_size: 8934218 dataset_size: 12841721 - config_name: bug features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 7595464 num_examples: 235268 download_size: 5129941 dataset_size: 7595464 - config_name: bxr features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4713699 num_examples: 153707 download_size: 2869313 dataset_size: 4713699 - config_name: ca features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 408509932 num_examples: 9936886 download_size: 288474980 dataset_size: 408509932 - config_name: cbk-zam features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14108232 num_examples: 440345 download_size: 9920793 dataset_size: 14108232 - config_name: cdo features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 6503254 num_examples: 201362 download_size: 4137841 dataset_size: 6503254 - config_name: ce features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 28093148 num_examples: 607767 download_size: 16367596 dataset_size: 28093148 - config_name: ceb features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 332947091 num_examples: 7769402 download_size: 219525737 dataset_size: 332947091 - config_name: ch features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13983906 num_examples: 436785 download_size: 9817385 dataset_size: 13983906 - config_name: cho features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13950786 num_examples: 435869 download_size: 9791296 dataset_size: 13950786 - config_name: chr features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 5386793 num_examples: 172855 download_size: 3419676 dataset_size: 5386793 - config_name: chy features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13994916 num_examples: 437007 download_size: 9830465 dataset_size: 13994916 - config_name: ckb features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 23343034 num_examples: 511183 download_size: 11459344 dataset_size: 23343034 - config_name: co features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 47080480 num_examples: 1346929 download_size: 34551346 dataset_size: 47080480 - config_name: cps features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 12849864 num_examples: 407695 download_size: 8941921 dataset_size: 12849864 - config_name: cr features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 5516556 num_examples: 176667 download_size: 3532952 dataset_size: 5516556 - config_name: crh features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 10864382 num_examples: 336709 download_size: 7542853 dataset_size: 10864382 - config_name: crh-cyrl features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4419064 num_examples: 148046 download_size: 2688683 dataset_size: 4419064 - config_name: crh-latn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14201429 num_examples: 442905 download_size: 9986290 dataset_size: 14201429 - config_name: cs features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 140189244 num_examples: 3384048 download_size: 97516751 dataset_size: 140189244 - config_name: csb features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 20177120 num_examples: 619275 download_size: 14528772 dataset_size: 20177120 - config_name: cv features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 8047221 num_examples: 215611 download_size: 4857718 dataset_size: 8047221 - config_name: cy features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 89241808 num_examples: 2244550 download_size: 62686006 dataset_size: 89241808 - config_name: da features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 130931077 num_examples: 3448894 download_size: 98202417 dataset_size: 130931077 - config_name: dag features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 2664957 num_examples: 78534 download_size: 2052615 dataset_size: 2664957 - config_name: de features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 765398522 num_examples: 17531361 download_size: 527642124 dataset_size: 765398522 - config_name: de-at features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 53043722 num_examples: 1515373 download_size: 38761571 dataset_size: 53043722 - config_name: de-ch features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 53480908 num_examples: 1528137 download_size: 39349412 dataset_size: 53480908 - config_name: de-formal features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4256391 num_examples: 144061 download_size: 2571862 dataset_size: 4256391 - config_name: din features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 12819746 num_examples: 406591 download_size: 8922303 dataset_size: 12819746 - config_name: diq features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 7570161 num_examples: 232674 download_size: 5057742 dataset_size: 7570161 - config_name: dsb features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 16135830 num_examples: 491423 download_size: 11412316 dataset_size: 16135830 - config_name: dtp features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13867373 num_examples: 433733 download_size: 9720699 dataset_size: 13867373 - config_name: dty features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 8839082 num_examples: 246026 download_size: 4551845 dataset_size: 8839082 - config_name: dua features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 2631 num_examples: 87 download_size: 3877 dataset_size: 2631 - config_name: dv features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 81396462 num_examples: 2103276 download_size: 45332104 dataset_size: 81396462 - config_name: dz features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 8590239 num_examples: 242196 download_size: 4406353 dataset_size: 8590239 - config_name: ee features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14377017 num_examples: 447208 download_size: 10136064 dataset_size: 14377017 - config_name: egl features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13068224 num_examples: 413551 download_size: 9121776 dataset_size: 13068224 - config_name: el features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 32978562 num_examples: 592016 download_size: 19577876 dataset_size: 32978562 - config_name: eml features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14768563 num_examples: 458847 download_size: 10453636 dataset_size: 14768563 - config_name: en features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 6327454281 num_examples: 81801560 download_size: 4224231068 dataset_size: 6327454281 - config_name: en-ca features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 73305274 num_examples: 1909970 download_size: 53060194 dataset_size: 73305274 - config_name: en-gb features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 115978412 num_examples: 2520405 download_size: 78924421 dataset_size: 115978412 - config_name: en-us features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14815 num_examples: 332 download_size: 9953 dataset_size: 14815 - config_name: eo features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 256196064 num_examples: 6285304 download_size: 177219679 dataset_size: 256196064 - config_name: es features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 730214298 num_examples: 17233968 download_size: 514588069 dataset_size: 730214298 - config_name: es-419 features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4355180 num_examples: 146476 download_size: 2659218 dataset_size: 4355180 - config_name: es-formal features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4280933 num_examples: 144717 download_size: 2592085 dataset_size: 4280933 - config_name: et features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 65123623 num_examples: 1820762 download_size: 48197302 dataset_size: 65123623 - config_name: eu features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 290282374 num_examples: 7109758 download_size: 197889378 dataset_size: 290282374 - config_name: ext features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 223257222 num_examples: 5359047 download_size: 147078789 dataset_size: 223257222 - config_name: fa features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 123727757 num_examples: 2142642 download_size: 65952114 dataset_size: 123727757 - config_name: ff features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14116652 num_examples: 440614 download_size: 9920388 dataset_size: 14116652 - config_name: fi features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 286539944 num_examples: 6905698 download_size: 209916638 dataset_size: 286539944 - config_name: fit features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 20217258 num_examples: 620391 download_size: 14566702 dataset_size: 20217258 - config_name: fj features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14159041 num_examples: 441745 download_size: 9956108 dataset_size: 14159041 - config_name: fkv features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4328482 num_examples: 145988 download_size: 2619845 dataset_size: 4328482 - config_name: fo features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 24474476 num_examples: 731732 download_size: 17876981 dataset_size: 24474476 - config_name: fr features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 774128723 num_examples: 17908351 download_size: 534489308 dataset_size: 774128723 - config_name: frc features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 17896106 num_examples: 547258 download_size: 12953740 dataset_size: 17896106 - config_name: frp features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 40902510 num_examples: 1191134 download_size: 29778105 dataset_size: 40902510 - config_name: frr features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 16979214 num_examples: 515350 download_size: 12069637 dataset_size: 16979214 - config_name: fur features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 42077410 num_examples: 1221071 download_size: 30714082 dataset_size: 42077410 - config_name: ga features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 471527543 num_examples: 11524282 download_size: 320967189 dataset_size: 471527543 - config_name: gag features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14149375 num_examples: 440732 download_size: 9940551 dataset_size: 14149375 - config_name: gan features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 31572161 num_examples: 905186 download_size: 18909564 dataset_size: 31572161 - config_name: gan-hans features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 31004794 num_examples: 889875 download_size: 18566811 dataset_size: 31004794 - config_name: gan-hant features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4374444 num_examples: 147098 download_size: 2657182 dataset_size: 4374444 - config_name: gcr features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4311409 num_examples: 145829 download_size: 2618211 dataset_size: 4311409 - config_name: gd features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 49316935 num_examples: 1429457 download_size: 36220978 dataset_size: 49316935 - config_name: gl features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 289484839 num_examples: 7052226 download_size: 197315151 dataset_size: 289484839 - config_name: glk features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 8327018 num_examples: 249115 download_size: 4538325 dataset_size: 8327018 - config_name: gn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14212974 num_examples: 442765 download_size: 10004863 dataset_size: 14212974 - config_name: gom features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4584575 num_examples: 150273 download_size: 2780570 dataset_size: 4584575 - config_name: gom-deva features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 8585678 num_examples: 242131 download_size: 4400578 dataset_size: 8585678 - config_name: gom-latn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 12783006 num_examples: 405302 download_size: 8897342 dataset_size: 12783006 - config_name: gor features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14667616 num_examples: 454512 download_size: 10319196 dataset_size: 14667616 - config_name: got features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 5432139 num_examples: 172951 download_size: 3435531 dataset_size: 5432139 - config_name: grc features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4494817 num_examples: 149631 download_size: 2746170 dataset_size: 4494817 - config_name: gu features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 23788894 num_examples: 486140 download_size: 10779200 dataset_size: 23788894 - config_name: guc features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 1419 num_examples: 38 download_size: 3054 dataset_size: 1419 - config_name: guw features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 118 num_examples: 4 download_size: 1864 dataset_size: 118 - config_name: gv features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 20683485 num_examples: 631005 download_size: 14894590 dataset_size: 20683485 - config_name: ha features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14716168 num_examples: 455836 download_size: 10421790 dataset_size: 14716168 - config_name: hak features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 6128644 num_examples: 193036 download_size: 3991729 dataset_size: 6128644 - config_name: haw features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14158084 num_examples: 441511 download_size: 9952975 dataset_size: 14158084 - config_name: he features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 43629050 num_examples: 884809 download_size: 27221301 dataset_size: 43629050 - config_name: hi features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 37237187 num_examples: 668964 download_size: 17804873 dataset_size: 37237187 - config_name: hif features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14457954 num_examples: 449009 download_size: 10166264 dataset_size: 14457954 - config_name: hif-latn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14519845 num_examples: 454037 download_size: 10240704 dataset_size: 14519845 - config_name: hil features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 12928914 num_examples: 409962 download_size: 9009705 dataset_size: 12928914 - config_name: ho features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13950504 num_examples: 435857 download_size: 9790849 dataset_size: 13950504 - config_name: hr features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 61272623 num_examples: 1720527 download_size: 45307411 dataset_size: 61272623 - config_name: hrx features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 12869295 num_examples: 407823 download_size: 8964114 dataset_size: 12869295 - config_name: hsb features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 23720349 num_examples: 707100 download_size: 17145693 dataset_size: 23720349 - config_name: ht features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 16835529 num_examples: 509955 download_size: 11880404 dataset_size: 16835529 - config_name: hu features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 85054175 num_examples: 2200589 download_size: 64143342 dataset_size: 85054175 - config_name: hu-formal features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4252810 num_examples: 143986 download_size: 2567582 dataset_size: 4252810 - config_name: hy features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 39339286 num_examples: 773925 download_size: 22108994 dataset_size: 39339286 - config_name: hyw features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 5443608 num_examples: 166902 download_size: 3238370 dataset_size: 5443608 - config_name: hz features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13948574 num_examples: 435804 download_size: 9788697 dataset_size: 13948574 - config_name: ia features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 229143237 num_examples: 5616433 download_size: 155877454 dataset_size: 229143237 - config_name: id features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 95220928 num_examples: 2512331 download_size: 69525046 dataset_size: 95220928 - config_name: ie features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 225725262 num_examples: 5533032 download_size: 153371930 dataset_size: 225725262 - config_name: ig features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 20109388 num_examples: 617044 download_size: 14475407 dataset_size: 20109388 - config_name: ii features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4310418 num_examples: 145332 download_size: 2609723 dataset_size: 4310418 - config_name: ik features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13989609 num_examples: 436958 download_size: 9823174 dataset_size: 13989609 - config_name: ike-cans features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4352278 num_examples: 146355 download_size: 2645174 dataset_size: 4352278 - config_name: ike-latn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13851135 num_examples: 432932 download_size: 9714057 dataset_size: 13851135 - config_name: ilo features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 15955483 num_examples: 480555 download_size: 11141942 dataset_size: 15955483 - config_name: inh features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4634360 num_examples: 152226 download_size: 2831580 dataset_size: 4634360 - config_name: io features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 233656822 num_examples: 5757440 download_size: 159720058 dataset_size: 233656822 - config_name: is features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 51679396 num_examples: 1483610 download_size: 37965494 dataset_size: 51679396 - config_name: it features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 536601426 num_examples: 12631487 download_size: 375025347 dataset_size: 536601426 - config_name: iu features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 5360588 num_examples: 172215 download_size: 3402239 dataset_size: 5360588 - config_name: ja features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 140641579 num_examples: 2917962 download_size: 92145329 dataset_size: 140641579 - config_name: jam features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 18849751 num_examples: 571777 download_size: 13684422 dataset_size: 18849751 - config_name: jbo features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14301985 num_examples: 446512 download_size: 9994516 dataset_size: 14301985 - config_name: jv features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 27232302 num_examples: 794181 download_size: 19651565 dataset_size: 27232302 - config_name: ka features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 24073345 num_examples: 399546 download_size: 11679979 dataset_size: 24073345 - config_name: kaa features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14082184 num_examples: 439411 download_size: 9902820 dataset_size: 14082184 - config_name: kab features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 18459676 num_examples: 557857 download_size: 13384218 dataset_size: 18459676 - config_name: kbd features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4594409 num_examples: 149733 download_size: 2759503 dataset_size: 4594409 - config_name: kbd-cyrl features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4417661 num_examples: 148017 download_size: 2687531 dataset_size: 4417661 - config_name: kbp features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 12873178 num_examples: 408039 download_size: 8965474 dataset_size: 12873178 - config_name: kea features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 12793700 num_examples: 405901 download_size: 8896866 dataset_size: 12793700 - config_name: kg features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 40949149 num_examples: 1193499 download_size: 29766747 dataset_size: 40949149 - config_name: khw features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4308653 num_examples: 145279 download_size: 2608581 dataset_size: 4308653 - config_name: ki features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14056900 num_examples: 439015 download_size: 9875534 dataset_size: 14056900 - config_name: kj features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13881723 num_examples: 433861 download_size: 9733715 dataset_size: 13881723 - config_name: kjp features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 8504302 num_examples: 240339 download_size: 4341523 dataset_size: 8504302 - config_name: kk features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 19216115 num_examples: 428880 download_size: 11577682 dataset_size: 19216115 - config_name: kk-arab features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 7241749 num_examples: 211731 download_size: 4487032 dataset_size: 7241749 - config_name: kk-kz features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4937945 num_examples: 160027 download_size: 3062906 dataset_size: 4937945 - config_name: kk-latn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 22197825 num_examples: 677162 download_size: 16072332 dataset_size: 22197825 - config_name: kk-tr features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 20060635 num_examples: 616521 download_size: 14438929 dataset_size: 20060635 - config_name: ko features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 60335212 num_examples: 1364440 download_size: 39186630 dataset_size: 60335212 - config_name: ko-kp features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4338717 num_examples: 146150 download_size: 2630925 dataset_size: 4338717 - config_name: koi features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4737590 num_examples: 155082 download_size: 2894674 dataset_size: 4737590 - config_name: kr features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13886057 num_examples: 433990 download_size: 9737602 dataset_size: 13886057 - config_name: krc features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4646136 num_examples: 151026 download_size: 2785454 dataset_size: 4646136 - config_name: kri features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 12798530 num_examples: 406032 download_size: 8902330 dataset_size: 12798530 - config_name: krj features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13850324 num_examples: 433444 download_size: 9703460 dataset_size: 13850324 - config_name: krl features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 12788020 num_examples: 405729 download_size: 8893337 dataset_size: 12788020 - config_name: ks features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4390604 num_examples: 147033 download_size: 2671069 dataset_size: 4390604 - config_name: ks-deva features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 8567518 num_examples: 241832 download_size: 4387687 dataset_size: 8567518 - config_name: ksh features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 20394712 num_examples: 624523 download_size: 14698860 dataset_size: 20394712 - config_name: ku features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 8037777 num_examples: 239515 download_size: 5306097 dataset_size: 8037777 - config_name: ku-arab features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4577826 num_examples: 151290 download_size: 2796159 dataset_size: 4577826 - config_name: ku-latn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14683841 num_examples: 458802 download_size: 10371977 dataset_size: 14683841 - config_name: kum features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4252739 num_examples: 143985 download_size: 2567503 dataset_size: 4252739 - config_name: kv features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4946978 num_examples: 158888 download_size: 2997865 dataset_size: 4946978 - config_name: kw features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 20245535 num_examples: 621432 download_size: 14581378 dataset_size: 20245535 - config_name: ky features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 8909613 num_examples: 235165 download_size: 5462115 dataset_size: 8909613 - config_name: la features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 299766395 num_examples: 7085082 download_size: 201477460 dataset_size: 299766395 - config_name: lad features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 20336417 num_examples: 622775 download_size: 14653199 dataset_size: 20336417 - config_name: lb features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 56473066 num_examples: 1601093 download_size: 41410732 dataset_size: 56473066 - config_name: lbe features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4501470 num_examples: 149898 download_size: 2744786 dataset_size: 4501470 - config_name: lez features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4890798 num_examples: 155936 download_size: 2959653 dataset_size: 4890798 - config_name: lfn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14709210 num_examples: 456719 download_size: 10408539 dataset_size: 14709210 - config_name: lg features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13979286 num_examples: 436009 download_size: 9802779 dataset_size: 13979286 - config_name: li features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 43476868 num_examples: 1253970 download_size: 31750932 dataset_size: 43476868 - config_name: lij features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 42327066 num_examples: 1227346 download_size: 30898971 dataset_size: 42327066 - config_name: liv features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 12781331 num_examples: 405236 download_size: 8895889 dataset_size: 12781331 - config_name: lki features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 8039166 num_examples: 242526 download_size: 4363703 dataset_size: 8039166 - config_name: lld features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 90305 num_examples: 2634 download_size: 69672 dataset_size: 90305 - config_name: lmo features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 18287638 num_examples: 545398 download_size: 13130119 dataset_size: 18287638 - config_name: ln features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14123637 num_examples: 439731 download_size: 9915851 dataset_size: 14123637 - config_name: lo features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 9905189 num_examples: 271710 download_size: 5313218 dataset_size: 9905189 - config_name: loz features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13695602 num_examples: 428723 download_size: 9581113 dataset_size: 13695602 - config_name: lt features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 39902419 num_examples: 1096727 download_size: 29185765 dataset_size: 39902419 - config_name: ltg features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13884707 num_examples: 433453 download_size: 9736637 dataset_size: 13884707 - config_name: lus features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13695197 num_examples: 428712 download_size: 9580538 dataset_size: 13695197 - config_name: luz features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 8459036 num_examples: 253454 download_size: 4687414 dataset_size: 8459036 - config_name: lv features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 27242119 num_examples: 764753 download_size: 19676667 dataset_size: 27242119 - config_name: lzh features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 25067538 num_examples: 685152 download_size: 14998856 dataset_size: 25067538 - config_name: mdf features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4634268 num_examples: 152141 download_size: 2820744 dataset_size: 4634268 - config_name: mg features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 43863002 num_examples: 1271074 download_size: 32016826 dataset_size: 43863002 - config_name: mh features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13775721 num_examples: 431162 download_size: 9644397 dataset_size: 13775721 - config_name: mi features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 20857040 num_examples: 637118 download_size: 15060301 dataset_size: 20857040 - config_name: min features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 53044258 num_examples: 1464128 download_size: 38587450 dataset_size: 53044258 - config_name: mk features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 24087229 num_examples: 449241 download_size: 12217912 dataset_size: 24087229 - config_name: ml features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 189266798 num_examples: 2664923 download_size: 71344031 dataset_size: 189266798 - config_name: mn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 9311543 num_examples: 219695 download_size: 5272784 dataset_size: 9311543 - config_name: mni features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 8696893 num_examples: 243616 download_size: 4470994 dataset_size: 8696893 - config_name: mnw features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 8861861 num_examples: 244906 download_size: 4517726 dataset_size: 8861861 - config_name: mo features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 5377009 num_examples: 172144 download_size: 3405661 dataset_size: 5377009 - config_name: mr features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 26855182 num_examples: 526220 download_size: 12358679 dataset_size: 26855182 - config_name: mrh features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 68 num_examples: 2 download_size: 1820 dataset_size: 68 - config_name: mrj features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 5007903 num_examples: 160889 download_size: 3073431 dataset_size: 5007903 - config_name: ms features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 64674328 num_examples: 1803714 download_size: 47165217 dataset_size: 64674328 - config_name: ms-arab features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 136496 num_examples: 2961 download_size: 92316 dataset_size: 136496 - config_name: mt features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 22632686 num_examples: 682867 download_size: 16352572 dataset_size: 22632686 - config_name: mus features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14013416 num_examples: 437688 download_size: 9835239 dataset_size: 14013416 - config_name: mwl features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14493299 num_examples: 448926 download_size: 10225888 dataset_size: 14493299 - config_name: my features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 16182182 num_examples: 345096 download_size: 7981905 dataset_size: 16182182 - config_name: mzn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 17973941 num_examples: 447870 download_size: 9174617 dataset_size: 17973941 - config_name: na features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13992666 num_examples: 436956 download_size: 9823328 dataset_size: 13992666 - config_name: nah features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14490294 num_examples: 449748 download_size: 10192501 dataset_size: 14490294 - config_name: nan-hani features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 191 num_examples: 6 download_size: 1925 dataset_size: 191 - config_name: nap features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 42362346 num_examples: 1229161 download_size: 30918265 dataset_size: 42362346 - config_name: nb features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 142554768 num_examples: 3688026 download_size: 105549981 dataset_size: 142554768 - config_name: nds features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 58766114 num_examples: 1666813 download_size: 43421948 dataset_size: 58766114 - config_name: nds-nl features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 44121756 num_examples: 1273149 download_size: 32201410 dataset_size: 44121756 - config_name: ne features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 11925386 num_examples: 295006 download_size: 6265232 dataset_size: 11925386 - config_name: new features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 16906308 num_examples: 350362 download_size: 7680329 dataset_size: 16906308 - config_name: ng features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13870754 num_examples: 433582 download_size: 9723795 dataset_size: 13870754 - config_name: nia features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 20649 num_examples: 515 download_size: 16535 dataset_size: 20649 - config_name: niu features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 12794247 num_examples: 405902 download_size: 8897260 dataset_size: 12794247 - config_name: nl features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 5016576732 num_examples: 61931959 download_size: 3380404239 dataset_size: 5016576732 - config_name: nn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 99997815 num_examples: 2708994 download_size: 74736304 dataset_size: 99997815 - config_name: 'no' features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 2934 num_examples: 64 download_size: 4108 dataset_size: 2934 - config_name: nod features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4322068 num_examples: 145566 download_size: 2618106 dataset_size: 4322068 - config_name: nov features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14150434 num_examples: 440903 download_size: 9947798 dataset_size: 14150434 - config_name: nqo features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 8094271 num_examples: 243184 download_size: 4398836 dataset_size: 8094271 - config_name: nrm features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 41330956 num_examples: 1203295 download_size: 30084065 dataset_size: 41330956 - config_name: nso features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14178321 num_examples: 443205 download_size: 9959708 dataset_size: 14178321 - config_name: nv features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 15351770 num_examples: 455188 download_size: 10472240 dataset_size: 15351770 - config_name: ny features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13989813 num_examples: 436764 download_size: 9821588 dataset_size: 13989813 - config_name: nys features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13092059 num_examples: 413241 download_size: 9153100 dataset_size: 13092059 - config_name: oc features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 266612548 num_examples: 6569770 download_size: 180156462 dataset_size: 266612548 - config_name: olo features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13200388 num_examples: 416935 download_size: 9214968 dataset_size: 13200388 - config_name: om features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 5476389 num_examples: 175314 download_size: 3496637 dataset_size: 5476389 - config_name: or features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 22798709 num_examples: 470237 download_size: 10322832 dataset_size: 22798709 - config_name: os features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 5946062 num_examples: 177054 download_size: 3583703 dataset_size: 5946062 - config_name: ota features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 8015024 num_examples: 241903 download_size: 4343478 dataset_size: 8015024 - config_name: pa features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 20505754 num_examples: 481522 download_size: 10552147 dataset_size: 20505754 - config_name: pam features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14527964 num_examples: 451253 download_size: 10242443 dataset_size: 14527964 - config_name: pap features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 54505401 num_examples: 1449881 download_size: 40415776 dataset_size: 54505401 - config_name: pcd features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 42132826 num_examples: 1221362 download_size: 30766812 dataset_size: 42132826 - config_name: pdc features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14435256 num_examples: 448055 download_size: 10178322 dataset_size: 14435256 - config_name: pdt features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13994892 num_examples: 437200 download_size: 9819388 dataset_size: 13994892 - config_name: pfl features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 15461023 num_examples: 474198 download_size: 10893651 dataset_size: 15461023 - config_name: pi features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 8913354 num_examples: 250251 download_size: 4651392 dataset_size: 8913354 - config_name: pih features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13971081 num_examples: 436214 download_size: 9810653 dataset_size: 13971081 - config_name: pl features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 426030491 num_examples: 10025139 download_size: 295767506 dataset_size: 426030491 - config_name: pms features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 51268512 num_examples: 1477043 download_size: 37698831 dataset_size: 51268512 - config_name: pnb features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 16192682 num_examples: 409037 download_size: 9196626 dataset_size: 16192682 - config_name: pnt features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4439173 num_examples: 148336 download_size: 2703117 dataset_size: 4439173 - config_name: prg features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 17940420 num_examples: 544030 download_size: 12958482 dataset_size: 17940420 - config_name: ps features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 8860902 num_examples: 259186 download_size: 4916502 dataset_size: 8860902 - config_name: pt features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 491184040 num_examples: 11574568 download_size: 340831923 dataset_size: 491184040 - config_name: pt-br features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 318857431 num_examples: 7782980 download_size: 223442911 dataset_size: 318857431 - config_name: pwn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 8500 num_examples: 269 download_size: 8738 dataset_size: 8500 - config_name: qu features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 15254702 num_examples: 468823 download_size: 10750388 dataset_size: 15254702 - config_name: quc features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 32 num_examples: 1 download_size: 1772 dataset_size: 32 - config_name: qug features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13798264 num_examples: 431733 download_size: 9661685 dataset_size: 13798264 - config_name: rgn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 17001688 num_examples: 519871 download_size: 12258201 dataset_size: 17001688 - config_name: rif features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13792951 num_examples: 431588 download_size: 9657698 dataset_size: 13792951 - config_name: rm features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 44450577 num_examples: 1284908 download_size: 32519630 dataset_size: 44450577 - config_name: rmc features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 159 num_examples: 4 download_size: 1963 dataset_size: 159 - config_name: rmy features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 5610156 num_examples: 179191 download_size: 3608283 dataset_size: 5610156 - config_name: rn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13935534 num_examples: 435271 download_size: 9779486 dataset_size: 13935534 - config_name: ro features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 247469452 num_examples: 5878366 download_size: 177525205 dataset_size: 247469452 - config_name: roa-tara features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14425120 num_examples: 448972 download_size: 10152875 dataset_size: 14425120 - config_name: ru features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 405103215 num_examples: 7485811 download_size: 257215625 dataset_size: 405103215 - config_name: rue features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4953403 num_examples: 159530 download_size: 3037824 dataset_size: 4953403 - config_name: rup features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14459686 num_examples: 450345 download_size: 10198398 dataset_size: 14459686 - config_name: ruq-cyrl features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4434290 num_examples: 148404 download_size: 2700920 dataset_size: 4434290 - config_name: ruq-latn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13783683 num_examples: 430978 download_size: 9656941 dataset_size: 13783683 - config_name: rw features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14090196 num_examples: 439172 download_size: 9901257 dataset_size: 14090196 - config_name: rwr features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 8568706 num_examples: 241841 download_size: 4388475 dataset_size: 8568706 - config_name: ryu features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 2852 num_examples: 82 download_size: 4237 dataset_size: 2852 - config_name: sa features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 21404327 num_examples: 455674 download_size: 9692464 dataset_size: 21404327 - config_name: sat features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 10810040 num_examples: 284911 download_size: 5750917 dataset_size: 10810040 - config_name: sc features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 47195572 num_examples: 1348137 download_size: 34521764 dataset_size: 47195572 - config_name: scn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 43458983 num_examples: 1259067 download_size: 31775157 dataset_size: 43458983 - config_name: sco features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 56960413 num_examples: 1611092 download_size: 41724559 dataset_size: 56960413 - config_name: sd features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14257513 num_examples: 363318 download_size: 7844047 dataset_size: 14257513 - config_name: sdc features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13975497 num_examples: 436913 download_size: 9800517 dataset_size: 13975497 - config_name: se features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 23962268 num_examples: 711439 download_size: 17409387 dataset_size: 23962268 - config_name: sei features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13827581 num_examples: 432520 download_size: 9684192 dataset_size: 13827581 - config_name: sg features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13913524 num_examples: 434751 download_size: 9761739 dataset_size: 13913524 - config_name: sh features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 30173635 num_examples: 746207 download_size: 20133594 dataset_size: 30173635 - config_name: shi-latn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13783218 num_examples: 430968 download_size: 9656828 dataset_size: 13783218 - config_name: shi-tfng features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4308577 num_examples: 145279 download_size: 2608525 dataset_size: 4308577 - config_name: shn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 10139002 num_examples: 260808 download_size: 4952168 dataset_size: 10139002 - config_name: shy-latn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4255322 num_examples: 144058 download_size: 2570625 dataset_size: 4255322 - config_name: si features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 7405400 num_examples: 189718 download_size: 4270591 dataset_size: 7405400 - config_name: sjd features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4300688 num_examples: 145047 download_size: 2604357 dataset_size: 4300688 - config_name: sje features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 20970223 num_examples: 637639 download_size: 15120381 dataset_size: 20970223 - config_name: sju features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4315103 num_examples: 145655 download_size: 2620763 dataset_size: 4315103 - config_name: sk features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 75586366 num_examples: 2050873 download_size: 54951330 dataset_size: 75586366 - config_name: skr features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4274062 num_examples: 144443 download_size: 2585286 dataset_size: 4274062 - config_name: sl features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 157883240 num_examples: 4112048 download_size: 118047353 dataset_size: 157883240 - config_name: sli features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13909208 num_examples: 434986 download_size: 9745964 dataset_size: 13909208 - config_name: sm features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13984823 num_examples: 436830 download_size: 9817472 dataset_size: 13984823 - config_name: sma features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 20653595 num_examples: 630437 download_size: 14902319 dataset_size: 20653595 - config_name: smj features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 19640206 num_examples: 604326 download_size: 14133964 dataset_size: 19640206 - config_name: smn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 10902411 num_examples: 337543 download_size: 7576850 dataset_size: 10902411 - config_name: sms features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4462345 num_examples: 149355 download_size: 2741038 dataset_size: 4462345 - config_name: sn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 20116601 num_examples: 618231 download_size: 14463728 dataset_size: 20116601 - config_name: sq features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 304708913 num_examples: 7311820 download_size: 225592169 dataset_size: 304708913 - config_name: sr features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 52787253 num_examples: 1018361 download_size: 31364006 dataset_size: 52787253 - config_name: sr-ec features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 9237541 num_examples: 248556 download_size: 5875548 dataset_size: 9237541 - config_name: sr-el features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 48848162 num_examples: 1418824 download_size: 35859120 dataset_size: 48848162 - config_name: srq features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 12796525 num_examples: 405957 download_size: 8899493 dataset_size: 12796525 - config_name: ss features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13823630 num_examples: 432423 download_size: 9682165 dataset_size: 13823630 - config_name: st features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13938937 num_examples: 435419 download_size: 9785161 dataset_size: 13938937 - config_name: stq features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14484394 num_examples: 449885 download_size: 10228446 dataset_size: 14484394 - config_name: su features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 20025826 num_examples: 583096 download_size: 14042822 dataset_size: 20025826 - config_name: sv features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 339074900 num_examples: 8115455 download_size: 236022796 dataset_size: 339074900 - config_name: sw features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 50612064 num_examples: 1465385 download_size: 37096369 dataset_size: 50612064 - config_name: szl features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 16772062 num_examples: 500107 download_size: 11868254 dataset_size: 16772062 - config_name: szy features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4332021 num_examples: 146136 download_size: 2633271 dataset_size: 4332021 - config_name: ta features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 31251824 num_examples: 546558 download_size: 15157673 dataset_size: 31251824 - config_name: tay features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4345269 num_examples: 146938 download_size: 2632535 dataset_size: 4345269 - config_name: tcy features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 8723594 num_examples: 244350 download_size: 4487471 dataset_size: 8723594 - config_name: te features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 27587665 num_examples: 569615 download_size: 13669398 dataset_size: 27587665 - config_name: tet features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 15092299 num_examples: 466244 download_size: 10702917 dataset_size: 15092299 - config_name: tg features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 12643125 num_examples: 304625 download_size: 7622522 dataset_size: 12643125 - config_name: tg-cyrl features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4504034 num_examples: 149533 download_size: 2755000 dataset_size: 4504034 - config_name: tg-latn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 19845835 num_examples: 610020 download_size: 14264492 dataset_size: 19845835 - config_name: th features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 32693750 num_examples: 537447 download_size: 15849247 dataset_size: 32693750 - config_name: ti features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4366995 num_examples: 146479 download_size: 2648869 dataset_size: 4366995 - config_name: tk features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 5797050 num_examples: 184302 download_size: 3728802 dataset_size: 5797050 - config_name: tl features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13661554 num_examples: 387377 download_size: 9456413 dataset_size: 13661554 - config_name: tly features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4309748 num_examples: 145312 download_size: 2609307 dataset_size: 4309748 - config_name: tly-cyrl features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 35 num_examples: 1 download_size: 1793 dataset_size: 35 - config_name: tn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13936132 num_examples: 435219 download_size: 9780279 dataset_size: 13936132 - config_name: to features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13980327 num_examples: 436460 download_size: 9810650 dataset_size: 13980327 - config_name: tpi features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14169019 num_examples: 442133 download_size: 9961827 dataset_size: 14169019 - config_name: tr features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 72134544 num_examples: 1770267 download_size: 51032484 dataset_size: 72134544 - config_name: tru features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 5322844 num_examples: 171327 download_size: 3371105 dataset_size: 5322844 - config_name: trv features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 94285 num_examples: 3109 download_size: 65138 dataset_size: 94285 - config_name: ts features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13943481 num_examples: 435408 download_size: 9783789 dataset_size: 13943481 - config_name: tt features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 24182976 num_examples: 548502 download_size: 14868166 dataset_size: 24182976 - config_name: tt-cyrl features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4943914 num_examples: 158198 download_size: 3048932 dataset_size: 4943914 - config_name: tt-latn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13842972 num_examples: 432513 download_size: 9702714 dataset_size: 13842972 - config_name: tum features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13924159 num_examples: 435110 download_size: 9770501 dataset_size: 13924159 - config_name: tw features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13830508 num_examples: 432669 download_size: 9688164 dataset_size: 13830508 - config_name: ty features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 16816401 num_examples: 507332 download_size: 12098154 dataset_size: 16816401 - config_name: tyv features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4583082 num_examples: 149929 download_size: 2779632 dataset_size: 4583082 - config_name: tzm features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4253588 num_examples: 144002 download_size: 2569067 dataset_size: 4253588 - config_name: udm features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4854947 num_examples: 156300 download_size: 2958444 dataset_size: 4854947 - config_name: ug-arab features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4316690 num_examples: 145443 download_size: 2614962 dataset_size: 4316690 - config_name: ug-latn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13786474 num_examples: 431056 download_size: 9659723 dataset_size: 13786474 - config_name: uk features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 251058352 num_examples: 5108733 download_size: 168140976 dataset_size: 251058352 - config_name: ur features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 57063750 num_examples: 987011 download_size: 28328459 dataset_size: 57063750 - config_name: uz features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 11731793 num_examples: 344615 download_size: 8102734 dataset_size: 11731793 - config_name: uz-cyrl features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4252574 num_examples: 143981 download_size: 2567325 dataset_size: 4252574 - config_name: ve features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13932174 num_examples: 435216 download_size: 9777266 dataset_size: 13932174 - config_name: vec features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 52081230 num_examples: 1466867 download_size: 37307805 dataset_size: 52081230 - config_name: vep features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 6174898 num_examples: 192298 download_size: 3994582 dataset_size: 6174898 - config_name: vi features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 246835524 num_examples: 5743737 download_size: 172949263 dataset_size: 246835524 - config_name: vls features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 42789297 num_examples: 1239359 download_size: 31228294 dataset_size: 42789297 - config_name: vmf features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 18352990 num_examples: 555205 download_size: 13289296 dataset_size: 18352990 - config_name: vo features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 228352533 num_examples: 5610875 download_size: 155496988 dataset_size: 228352533 - config_name: vot features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 5406190 num_examples: 173486 download_size: 3439433 dataset_size: 5406190 - config_name: wa features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 49235347 num_examples: 1426584 download_size: 36167816 dataset_size: 49235347 - config_name: war features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 190306474 num_examples: 4449062 download_size: 133786270 dataset_size: 190306474 - config_name: wls features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4033 num_examples: 104 download_size: 5150 dataset_size: 4033 - config_name: wo features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 40961626 num_examples: 1193626 download_size: 29778666 dataset_size: 40961626 - config_name: wuu features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 40570130 num_examples: 1127741 download_size: 24209117 dataset_size: 40570130 - config_name: wya features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 28 num_examples: 1 download_size: 1740 dataset_size: 28 - config_name: xal features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4475344 num_examples: 149984 download_size: 2722459 dataset_size: 4475344 - config_name: xh features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 20036194 num_examples: 615514 download_size: 14405310 dataset_size: 20036194 - config_name: xmf features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 5943645 num_examples: 169507 download_size: 3418593 dataset_size: 5943645 - config_name: xsy features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4262789 num_examples: 144305 download_size: 2573349 dataset_size: 4262789 - config_name: yav features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4070 num_examples: 102 download_size: 4718 dataset_size: 4070 - config_name: yi features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 5495313 num_examples: 170277 download_size: 3373820 dataset_size: 5495313 - config_name: yo features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 25424749 num_examples: 724345 download_size: 18086773 dataset_size: 25424749 - config_name: za features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 15159230 num_examples: 365892 download_size: 7774767 dataset_size: 15159230 - config_name: zea features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14538518 num_examples: 451577 download_size: 10262897 dataset_size: 14538518 - config_name: zgh features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4253917 num_examples: 144006 download_size: 2569373 dataset_size: 4253917 - config_name: zh features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 264353677 num_examples: 5424320 download_size: 174420118 dataset_size: 264353677 - config_name: zh-cn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 42868611 num_examples: 1158755 download_size: 27243799 dataset_size: 42868611 - config_name: zh-hans features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 57233156 num_examples: 1483225 download_size: 36583522 dataset_size: 57233156 - config_name: zh-hant features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 53502814 num_examples: 1356560 download_size: 36755083 dataset_size: 53502814 - config_name: zh-hk features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 15325323 num_examples: 408391 download_size: 10455809 dataset_size: 15325323 - config_name: zh-mo features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 6568267 num_examples: 180950 download_size: 3547260 dataset_size: 6568267 - config_name: zh-my features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 32637498 num_examples: 916876 download_size: 19289581 dataset_size: 32637498 - config_name: zh-sg features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 35325327 num_examples: 979652 download_size: 21150070 dataset_size: 35325327 - config_name: zh-tw features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 17500668 num_examples: 443057 download_size: 11121104 dataset_size: 17500668 - config_name: zh-yue features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 1352 num_examples: 30 download_size: 2963 dataset_size: 1352 - config_name: zu features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 47349379 num_examples: 1380550 download_size: 34649660 dataset_size: 47349379 configs: - config_name: aa data_files: - split: label path: aa/label-* - config_name: ab data_files: - split: label path: ab/label-* - config_name: abs data_files: - split: label path: abs/label-* - config_name: ace data_files: - split: label path: ace/label-* - config_name: ady data_files: - split: label path: ady/label-* - config_name: ady-cyrl data_files: - split: label path: ady-cyrl/label-* - config_name: aeb data_files: - split: label path: aeb/label-* - config_name: aeb-arab data_files: - split: label path: aeb-arab/label-* - config_name: aeb-latn data_files: - split: label path: aeb-latn/label-* - config_name: af data_files: - split: label path: af/label-* - config_name: agq data_files: - split: label path: agq/label-* - config_name: ak data_files: - split: label path: ak/label-* - config_name: aln data_files: - split: label path: aln/label-* - config_name: als data_files: - split: label path: als/label-* - config_name: alt data_files: - split: label path: alt/label-* - config_name: am data_files: - split: label path: am/label-* - config_name: ami data_files: - split: label path: ami/label-* - config_name: an data_files: - split: label path: an/label-* - config_name: ang data_files: - split: label path: ang/label-* - config_name: anp data_files: - split: label path: anp/label-* - config_name: ar data_files: - split: label path: ar/label-* - config_name: arc data_files: - split: label path: arc/label-* - config_name: arn data_files: - split: label path: arn/label-* - config_name: arq data_files: - split: label path: arq/label-* - config_name: ary data_files: - split: label path: ary/label-* - config_name: arz data_files: - split: label path: arz/label-* - config_name: as data_files: - split: label path: as/label-* - config_name: ase data_files: - split: label path: ase/label-* - config_name: ast data_files: - split: label path: ast/label-* - config_name: atj data_files: - split: label path: atj/label-* - config_name: av data_files: - split: label path: av/label-* - config_name: avk data_files: - split: label path: avk/label-* - config_name: awa data_files: - split: label path: awa/label-* - config_name: ay data_files: - split: label path: ay/label-* - config_name: az data_files: - split: label path: az/label-* - config_name: azb data_files: - split: label path: azb/label-* - config_name: ba data_files: - split: label path: ba/label-* - config_name: ban data_files: - split: label path: ban/label-* - config_name: ban-bali data_files: - split: label path: ban-bali/label-* - config_name: bar data_files: - split: label path: bar/label-* - config_name: bbc data_files: - split: label path: bbc/label-* - config_name: bcc data_files: - split: label path: bcc/label-* - config_name: be data_files: - split: label path: be/label-* - config_name: be-tarask data_files: - split: label path: be-tarask/label-* - config_name: bg data_files: - split: label path: bg/label-* - config_name: bgn data_files: - split: label path: bgn/label-* - config_name: bi data_files: - split: label path: bi/label-* - config_name: bjn data_files: - split: label path: bjn/label-* - config_name: bm data_files: - split: label path: bm/label-* - config_name: bn data_files: - split: label path: bn/label-* - config_name: bo data_files: - split: label path: bo/label-* - config_name: bpy data_files: - split: label path: bpy/label-* - config_name: bqi data_files: - split: label path: bqi/label-* - config_name: br data_files: - split: label path: br/label-* - config_name: brh data_files: - split: label path: brh/label-* - config_name: bs data_files: - split: label path: bs/label-* - config_name: btm data_files: - split: label path: btm/label-* - config_name: bto data_files: - split: label path: bto/label-* - config_name: bug data_files: - split: label path: bug/label-* - config_name: bxr data_files: - split: label path: bxr/label-* - config_name: ca data_files: - split: label path: ca/label-* - config_name: cbk-zam data_files: - split: label path: cbk-zam/label-* - config_name: cdo data_files: - split: label path: cdo/label-* - config_name: ce data_files: - split: label path: ce/label-* - config_name: ceb data_files: - split: label path: ceb/label-* - config_name: ch data_files: - split: label path: ch/label-* - config_name: cho data_files: - split: label path: cho/label-* - config_name: chr data_files: - split: label path: chr/label-* - config_name: chy data_files: - split: label path: chy/label-* - config_name: ckb data_files: - split: label path: ckb/label-* - config_name: co data_files: - split: label path: co/label-* - config_name: cps data_files: - split: label path: cps/label-* - config_name: cr data_files: - split: label path: cr/label-* - config_name: crh data_files: - split: label path: crh/label-* - config_name: crh-cyrl data_files: - split: label path: crh-cyrl/label-* - config_name: crh-latn data_files: - split: label path: crh-latn/label-* - config_name: cs data_files: - split: label path: cs/label-* - config_name: csb data_files: - split: label path: csb/label-* - config_name: cv data_files: - split: label path: cv/label-* - config_name: cy data_files: - split: label path: cy/label-* - config_name: da data_files: - split: label path: da/label-* - config_name: dag data_files: - split: label path: dag/label-* - config_name: de data_files: - split: label path: de/label-* - config_name: de-at data_files: - split: label path: de-at/label-* - config_name: de-ch data_files: - split: label path: de-ch/label-* - config_name: de-formal data_files: - split: label path: de-formal/label-* - config_name: din data_files: - split: label path: din/label-* - config_name: diq data_files: - split: label path: diq/label-* - config_name: dsb data_files: - split: label path: dsb/label-* - config_name: dtp data_files: - split: label path: dtp/label-* - config_name: dty data_files: - split: label path: dty/label-* - config_name: dua data_files: - split: label path: dua/label-* - config_name: dv data_files: - split: label path: dv/label-* - config_name: dz data_files: - split: label path: dz/label-* - config_name: ee data_files: - split: label path: ee/label-* - config_name: egl data_files: - split: label path: egl/label-* - config_name: el data_files: - split: label path: el/label-* - config_name: eml data_files: - split: label path: eml/label-* - config_name: en data_files: - split: label path: en/label-* default: true - config_name: en-ca data_files: - split: label path: en-ca/label-* - config_name: en-gb data_files: - split: label path: en-gb/label-* - config_name: en-us data_files: - split: label path: en-us/label-* - config_name: eo data_files: - split: label path: eo/label-* - config_name: es data_files: - split: label path: es/label-* - config_name: es-419 data_files: - split: label path: es-419/label-* - config_name: es-formal data_files: - split: label path: es-formal/label-* - config_name: et data_files: - split: label path: et/label-* - config_name: eu data_files: - split: label path: eu/label-* - config_name: ext data_files: - split: label path: ext/label-* - config_name: fa data_files: - split: label path: fa/label-* - config_name: ff data_files: - split: label path: ff/label-* - config_name: fi data_files: - split: label path: fi/label-* - config_name: fit data_files: - split: label path: fit/label-* - config_name: fj data_files: - split: label path: fj/label-* - config_name: fkv data_files: - split: label path: fkv/label-* - config_name: fo data_files: - split: label path: fo/label-* - config_name: fr data_files: - split: label path: fr/label-* - config_name: frc data_files: - split: label path: frc/label-* - config_name: frp data_files: - split: label path: frp/label-* - config_name: frr data_files: - split: label path: frr/label-* - config_name: fur data_files: - split: label path: fur/label-* - config_name: ga data_files: - split: label path: ga/label-* - config_name: gag data_files: - split: label path: gag/label-* - config_name: gan data_files: - split: label path: gan/label-* - config_name: gan-hans data_files: - split: label path: gan-hans/label-* - config_name: gan-hant data_files: - split: label path: gan-hant/label-* - config_name: gcr data_files: - split: label path: gcr/label-* - config_name: gd data_files: - split: label path: gd/label-* - config_name: gl data_files: - split: label path: gl/label-* - config_name: glk data_files: - split: label path: glk/label-* - config_name: gn data_files: - split: label path: gn/label-* - config_name: gom data_files: - split: label path: gom/label-* - config_name: gom-deva data_files: - split: label path: gom-deva/label-* - config_name: gom-latn data_files: - split: label path: gom-latn/label-* - config_name: gor data_files: - split: label path: gor/label-* - config_name: got data_files: - split: label path: got/label-* - config_name: grc data_files: - split: label path: grc/label-* - config_name: gu data_files: - split: label path: gu/label-* - config_name: guc data_files: - split: label path: guc/label-* - config_name: guw data_files: - split: label path: guw/label-* - config_name: gv data_files: - split: label path: gv/label-* - config_name: ha data_files: - split: label path: ha/label-* - config_name: hak data_files: - split: label path: hak/label-* - config_name: haw data_files: - split: label path: haw/label-* - config_name: he data_files: - split: label path: he/label-* - config_name: hi data_files: - split: label path: hi/label-* - config_name: hif data_files: - split: label path: hif/label-* - config_name: hif-latn data_files: - split: label path: hif-latn/label-* - config_name: hil data_files: - split: label path: hil/label-* - config_name: ho data_files: - split: label path: ho/label-* - config_name: hr data_files: - split: label path: hr/label-* - config_name: hrx data_files: - split: label path: hrx/label-* - config_name: hsb data_files: - split: label path: hsb/label-* - config_name: ht data_files: - split: label path: ht/label-* - config_name: hu data_files: - split: label path: hu/label-* - config_name: hu-formal data_files: - split: label path: hu-formal/label-* - config_name: hy data_files: - split: label path: hy/label-* - config_name: hyw data_files: - split: label path: hyw/label-* - config_name: hz data_files: - split: label path: hz/label-* - config_name: ia data_files: - split: label path: ia/label-* - config_name: id data_files: - split: label path: id/label-* - config_name: ie data_files: - split: label path: ie/label-* - config_name: ig data_files: - split: label path: ig/label-* - config_name: ii data_files: - split: label path: ii/label-* - config_name: ik data_files: - split: label path: ik/label-* - config_name: ike-cans data_files: - split: label path: ike-cans/label-* - config_name: ike-latn data_files: - split: label path: ike-latn/label-* - config_name: ilo data_files: - split: label path: ilo/label-* - config_name: inh data_files: - split: label path: inh/label-* - config_name: io data_files: - split: label path: io/label-* - config_name: is data_files: - split: label path: is/label-* - config_name: it data_files: - split: label path: it/label-* - config_name: iu data_files: - split: label path: iu/label-* - config_name: ja data_files: - split: label path: ja/label-* - config_name: jam data_files: - split: label path: jam/label-* - config_name: jbo data_files: - split: label path: jbo/label-* - config_name: jv data_files: - split: label path: jv/label-* - config_name: ka data_files: - split: label path: ka/label-* - config_name: kaa data_files: - split: label path: kaa/label-* - config_name: kab data_files: - split: label path: kab/label-* - config_name: kbd data_files: - split: label path: kbd/label-* - config_name: kbd-cyrl data_files: - split: label path: kbd-cyrl/label-* - config_name: kbp data_files: - split: label path: kbp/label-* - config_name: kea data_files: - split: label path: kea/label-* - config_name: kg data_files: - split: label path: kg/label-* - config_name: khw data_files: - split: label path: khw/label-* - config_name: ki data_files: - split: label path: ki/label-* - config_name: kj data_files: - split: label path: kj/label-* - config_name: kjp data_files: - split: label path: kjp/label-* - config_name: kk data_files: - split: label path: kk/label-* - config_name: kk-arab data_files: - split: label path: kk-arab/label-* - config_name: kk-kz data_files: - split: label path: kk-kz/label-* - config_name: kk-latn data_files: - split: label path: kk-latn/label-* - config_name: kk-tr data_files: - split: label path: kk-tr/label-* - config_name: ko data_files: - split: label path: ko/label-* - config_name: ko-kp data_files: - split: label path: ko-kp/label-* - config_name: koi data_files: - split: label path: koi/label-* - config_name: kr data_files: - split: label path: kr/label-* - config_name: krc data_files: - split: label path: krc/label-* - config_name: kri data_files: - split: label path: kri/label-* - config_name: krj data_files: - split: label path: krj/label-* - config_name: krl data_files: - split: label path: krl/label-* - config_name: ks data_files: - split: label path: ks/label-* - config_name: ks-deva data_files: - split: label path: ks-deva/label-* - config_name: ksh data_files: - split: label path: ksh/label-* - config_name: ku data_files: - split: label path: ku/label-* - config_name: ku-arab data_files: - split: label path: ku-arab/label-* - config_name: ku-latn data_files: - split: label path: ku-latn/label-* - config_name: kum data_files: - split: label path: kum/label-* - config_name: kv data_files: - split: label path: kv/label-* - config_name: kw data_files: - split: label path: kw/label-* - config_name: ky data_files: - split: label path: ky/label-* - config_name: la data_files: - split: label path: la/label-* - config_name: lad data_files: - split: label path: lad/label-* - config_name: lb data_files: - split: label path: lb/label-* - config_name: lbe data_files: - split: label path: lbe/label-* - config_name: lez data_files: - split: label path: lez/label-* - config_name: lfn data_files: - split: label path: lfn/label-* - config_name: lg data_files: - split: label path: lg/label-* - config_name: li data_files: - split: label path: li/label-* - config_name: lij data_files: - split: label path: lij/label-* - config_name: liv data_files: - split: label path: liv/label-* - config_name: lki data_files: - split: label path: lki/label-* - config_name: lld data_files: - split: label path: lld/label-* - config_name: lmo data_files: - split: label path: lmo/label-* - config_name: ln data_files: - split: label path: ln/label-* - config_name: lo data_files: - split: label path: lo/label-* - config_name: loz data_files: - split: label path: loz/label-* - config_name: lt data_files: - split: label path: lt/label-* - config_name: ltg data_files: - split: label path: ltg/label-* - config_name: lus data_files: - split: label path: lus/label-* - config_name: luz data_files: - split: label path: luz/label-* - config_name: lv data_files: - split: label path: lv/label-* - config_name: lzh data_files: - split: label path: lzh/label-* - config_name: mdf data_files: - split: label path: mdf/label-* - config_name: mg data_files: - split: label path: mg/label-* - config_name: mh data_files: - split: label path: mh/label-* - config_name: mi data_files: - split: label path: mi/label-* - config_name: min data_files: - split: label path: min/label-* - config_name: mk data_files: - split: label path: mk/label-* - config_name: ml data_files: - split: label path: ml/label-* - config_name: mn data_files: - split: label path: mn/label-* - config_name: mni data_files: - split: label path: mni/label-* - config_name: mnw data_files: - split: label path: mnw/label-* - config_name: mo data_files: - split: label path: mo/label-* - config_name: mr data_files: - split: label path: mr/label-* - config_name: mrh data_files: - split: label path: mrh/label-* - config_name: mrj data_files: - split: label path: mrj/label-* - config_name: ms data_files: - split: label path: ms/label-* - config_name: ms-arab data_files: - split: label path: ms-arab/label-* - config_name: mt data_files: - split: label path: mt/label-* - config_name: mus data_files: - split: label path: mus/label-* - config_name: mwl data_files: - split: label path: mwl/label-* - config_name: my data_files: - split: label path: my/label-* - config_name: mzn data_files: - split: label path: mzn/label-* - config_name: na data_files: - split: label path: na/label-* - config_name: nah data_files: - split: label path: nah/label-* - config_name: nan-hani data_files: - split: label path: nan-hani/label-* - config_name: nap data_files: - split: label path: nap/label-* - config_name: nb data_files: - split: label path: nb/label-* - config_name: nds data_files: - split: label path: nds/label-* - config_name: nds-nl data_files: - split: label path: nds-nl/label-* - config_name: ne data_files: - split: label path: ne/label-* - config_name: new data_files: - split: label path: new/label-* - config_name: ng data_files: - split: label path: ng/label-* - config_name: nia data_files: - split: label path: nia/label-* - config_name: niu data_files: - split: label path: niu/label-* - config_name: nl data_files: - split: label path: nl/label-* - config_name: nn data_files: - split: label path: nn/label-* - config_name: 'no' data_files: - split: label path: no/label-* - config_name: nod data_files: - split: label path: nod/label-* - config_name: nov data_files: - split: label path: nov/label-* - config_name: nqo data_files: - split: label path: nqo/label-* - config_name: nrm data_files: - split: label path: nrm/label-* - config_name: nso data_files: - split: label path: nso/label-* - config_name: nv data_files: - split: label path: nv/label-* - config_name: ny data_files: - split: label path: ny/label-* - config_name: nys data_files: - split: label path: nys/label-* - config_name: oc data_files: - split: label path: oc/label-* - config_name: olo data_files: - split: label path: olo/label-* - config_name: om data_files: - split: label path: om/label-* - config_name: or data_files: - split: label path: or/label-* - config_name: os data_files: - split: label path: os/label-* - config_name: ota data_files: - split: label path: ota/label-* - config_name: pa data_files: - split: label path: pa/label-* - config_name: pam data_files: - split: label path: pam/label-* - config_name: pap data_files: - split: label path: pap/label-* - config_name: pcd data_files: - split: label path: pcd/label-* - config_name: pdc data_files: - split: label path: pdc/label-* - config_name: pdt data_files: - split: label path: pdt/label-* - config_name: pfl data_files: - split: label path: pfl/label-* - config_name: pi data_files: - split: label path: pi/label-* - config_name: pih data_files: - split: label path: pih/label-* - config_name: pl data_files: - split: label path: pl/label-* - config_name: pms data_files: - split: label path: pms/label-* - config_name: pnb data_files: - split: label path: pnb/label-* - config_name: pnt data_files: - split: label path: pnt/label-* - config_name: prg data_files: - split: label path: prg/label-* - config_name: ps data_files: - split: label path: ps/label-* - config_name: pt data_files: - split: label path: pt/label-* - config_name: pt-br data_files: - split: label path: pt-br/label-* - config_name: pwn data_files: - split: label path: pwn/label-* - config_name: qu data_files: - split: label path: qu/label-* - config_name: quc data_files: - split: label path: quc/label-* - config_name: qug data_files: - split: label path: qug/label-* - config_name: rgn data_files: - split: label path: rgn/label-* - config_name: rif data_files: - split: label path: rif/label-* - config_name: rm data_files: - split: label path: rm/label-* - config_name: rmc data_files: - split: label path: rmc/label-* - config_name: rmy data_files: - split: label path: rmy/label-* - config_name: rn data_files: - split: label path: rn/label-* - config_name: ro data_files: - split: label path: ro/label-* - config_name: roa-tara data_files: - split: label path: roa-tara/label-* - config_name: ru data_files: - split: label path: ru/label-* - config_name: rue data_files: - split: label path: rue/label-* - config_name: rup data_files: - split: label path: rup/label-* - config_name: ruq-cyrl data_files: - split: label path: ruq-cyrl/label-* - config_name: ruq-latn data_files: - split: label path: ruq-latn/label-* - config_name: rw data_files: - split: label path: rw/label-* - config_name: rwr data_files: - split: label path: rwr/label-* - config_name: ryu data_files: - split: label path: ryu/label-* - config_name: sa data_files: - split: label path: sa/label-* - config_name: sat data_files: - split: label path: sat/label-* - config_name: sc data_files: - split: label path: sc/label-* - config_name: scn data_files: - split: label path: scn/label-* - config_name: sco data_files: - split: label path: sco/label-* - config_name: sd data_files: - split: label path: sd/label-* - config_name: sdc data_files: - split: label path: sdc/label-* - config_name: se data_files: - split: label path: se/label-* - config_name: sei data_files: - split: label path: sei/label-* - config_name: sg data_files: - split: label path: sg/label-* - config_name: sh data_files: - split: label path: sh/label-* - config_name: shi-latn data_files: - split: label path: shi-latn/label-* - config_name: shi-tfng data_files: - split: label path: shi-tfng/label-* - config_name: shn data_files: - split: label path: shn/label-* - config_name: shy-latn data_files: - split: label path: shy-latn/label-* - config_name: si data_files: - split: label path: si/label-* - config_name: sjd data_files: - split: label path: sjd/label-* - config_name: sje data_files: - split: label path: sje/label-* - config_name: sju data_files: - split: label path: sju/label-* - config_name: sk data_files: - split: label path: sk/label-* - config_name: skr data_files: - split: label path: skr/label-* - config_name: sl data_files: - split: label path: sl/label-* - config_name: sli data_files: - split: label path: sli/label-* - config_name: sm data_files: - split: label path: sm/label-* - config_name: sma data_files: - split: label path: sma/label-* - config_name: smj data_files: - split: label path: smj/label-* - config_name: smn data_files: - split: label path: smn/label-* - config_name: sms data_files: - split: label path: sms/label-* - config_name: sn data_files: - split: label path: sn/label-* - config_name: sq data_files: - split: label path: sq/label-* - config_name: sr data_files: - split: label path: sr/label-* - config_name: sr-ec data_files: - split: label path: sr-ec/label-* - config_name: sr-el data_files: - split: label path: sr-el/label-* - config_name: srq data_files: - split: label path: srq/label-* - config_name: ss data_files: - split: label path: ss/label-* - config_name: st data_files: - split: label path: st/label-* - config_name: stq data_files: - split: label path: stq/label-* - config_name: su data_files: - split: label path: su/label-* - config_name: sv data_files: - split: label path: sv/label-* - config_name: sw data_files: - split: label path: sw/label-* - config_name: szl data_files: - split: label path: szl/label-* - config_name: szy data_files: - split: label path: szy/label-* - config_name: ta data_files: - split: label path: ta/label-* - config_name: tay data_files: - split: label path: tay/label-* - config_name: tcy data_files: - split: label path: tcy/label-* - config_name: te data_files: - split: label path: te/label-* - config_name: tet data_files: - split: label path: tet/label-* - config_name: tg data_files: - split: label path: tg/label-* - config_name: tg-cyrl data_files: - split: label path: tg-cyrl/label-* - config_name: tg-latn data_files: - split: label path: tg-latn/label-* - config_name: th data_files: - split: label path: th/label-* - config_name: ti data_files: - split: label path: ti/label-* - config_name: tk data_files: - split: label path: tk/label-* - config_name: tl data_files: - split: label path: tl/label-* - config_name: tly data_files: - split: label path: tly/label-* - config_name: tly-cyrl data_files: - split: label path: tly-cyrl/label-* - config_name: tn data_files: - split: label path: tn/label-* - config_name: to data_files: - split: label path: to/label-* - config_name: tpi data_files: - split: label path: tpi/label-* - config_name: tr data_files: - split: label path: tr/label-* - config_name: tru data_files: - split: label path: tru/label-* - config_name: trv data_files: - split: label path: trv/label-* - config_name: ts data_files: - split: label path: ts/label-* - config_name: tt data_files: - split: label path: tt/label-* - config_name: tt-cyrl data_files: - split: label path: tt-cyrl/label-* - config_name: tt-latn data_files: - split: label path: tt-latn/label-* - config_name: tum data_files: - split: label path: tum/label-* - config_name: tw data_files: - split: label path: tw/label-* - config_name: ty data_files: - split: label path: ty/label-* - config_name: tyv data_files: - split: label path: tyv/label-* - config_name: tzm data_files: - split: label path: tzm/label-* - config_name: udm data_files: - split: label path: udm/label-* - config_name: ug-arab data_files: - split: label path: ug-arab/label-* - config_name: ug-latn data_files: - split: label path: ug-latn/label-* - config_name: uk data_files: - split: label path: uk/label-* - config_name: ur data_files: - split: label path: ur/label-* - config_name: uz data_files: - split: label path: uz/label-* - config_name: uz-cyrl data_files: - split: label path: uz-cyrl/label-* - config_name: ve data_files: - split: label path: ve/label-* - config_name: vec data_files: - split: label path: vec/label-* - config_name: vep data_files: - split: label path: vep/label-* - config_name: vi data_files: - split: label path: vi/label-* - config_name: vls data_files: - split: label path: vls/label-* - config_name: vmf data_files: - split: label path: vmf/label-* - config_name: vo data_files: - split: label path: vo/label-* - config_name: vot data_files: - split: label path: vot/label-* - config_name: wa data_files: - split: label path: wa/label-* - config_name: war data_files: - split: label path: war/label-* - config_name: wls data_files: - split: label path: wls/label-* - config_name: wo data_files: - split: label path: wo/label-* - config_name: wuu data_files: - split: label path: wuu/label-* - config_name: wya data_files: - split: label path: wya/label-* - config_name: xal data_files: - split: label path: xal/label-* - config_name: xh data_files: - split: label path: xh/label-* - config_name: xmf data_files: - split: label path: xmf/label-* - config_name: xsy data_files: - split: label path: xsy/label-* - config_name: yav data_files: - split: label path: yav/label-* - config_name: yi data_files: - split: label path: yi/label-* - config_name: yo data_files: - split: label path: yo/label-* - config_name: za data_files: - split: label path: za/label-* - config_name: zea data_files: - split: label path: zea/label-* - config_name: zgh data_files: - split: label path: zgh/label-* - config_name: zh data_files: - split: label path: zh/label-* - config_name: zh-cn data_files: - split: label path: zh-cn/label-* - config_name: zh-hans data_files: - split: label path: zh-hans/label-* - config_name: zh-hant data_files: - split: label path: zh-hant/label-* - config_name: zh-hk data_files: - split: label path: zh-hk/label-* - config_name: zh-mo data_files: - split: label path: zh-mo/label-* - config_name: zh-my data_files: - split: label path: zh-my/label-* - config_name: zh-sg data_files: - split: label path: zh-sg/label-* - config_name: zh-tw data_files: - split: label path: zh-tw/label-* - config_name: zh-yue data_files: - split: label path: zh-yue/label-* - config_name: zu data_files: - split: label path: zu/label-* task_categories: - translation - text2text-generation language: - en - fr - de - ja - zh - hi - ar - bn - ru - es --- # Wikidata Labels Large parallel corpus for machine translation - Entity label data extracted from Wikidata (2022-01-03), filtered for item entities only - Only download the languages you need with `datasets>=2.14.0` - Similar dataset: https://huggingface.co/datasets/wmt/wikititles (18 Wikipedia titles pairs instead of all Wikidata entities) ## Dataset Details ### Dataset Sources - Wikidata JSON dump (wikidata-20220103-all.json.gz) https://www.wikidata.org/wiki/Wikidata:Database_download ## Uses You can generate parallel text examples from this dataset like below: ```python from datasets import load_dataset import pandas as pd def parallel_labels(lang_codes: list, how="inner", repo_id="rayliuca/wikidata_entity_label", merge_config={}, datasets_config={}) -> pd.DataFrame: out_df = None for lc in lang_codes: dataset = load_dataset(repo_id, lc, **datasets_config) dataset_df = dataset['label'].to_pandas().rename(columns={"label":lc}).drop(columns=['lastrevid']) if out_df is None: out_df = dataset_df else: out_df = out_df.merge( dataset_df, on='wikidata_id', how=how, **merge_config ) return out_df # Note: the "en" subset is >4GB parallel_labels(['en', 'fr', 'ja', 'zh']).head() ``` ### Output | | wikidata_id | en | fr | ja | zh | |---:|:--------------|:------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:---------------------------------------|:---------------------------------------------| | 0 | Q109739412 | SARS-CoV-2 Omicron variant | variant Omicron du SARS-CoV-2 | SARSコロナウイルス2-オミクロン株 | 嚴重急性呼吸道症候群冠狀病毒2型Omicron變異株 | | 1 | Q108460606 | Ulughbegsaurus | Ulughbegsaurus | ウルグベグサウルス | 兀魯伯龍屬 | | 2 | Q108556886 | AUKUS | AUKUS | AUKUS | AUKUS | | 3 | Q106496152 | Claude Joseph | Claude Joseph | クロード・ジョゼフ | 克洛德·约瑟夫 | | 4 | Q105519361 | The World's Finest Assassin Gets Reincarnated in a Different World as an Aristocrat | The World's Finest Assassin Gets Reincarnated in Another World as an Aristocrat | 世界最高の暗殺者、異世界貴族に転生する | 世界頂尖的暗殺者轉生為異世界貴族 | Note: this example table above shows a quirk(?) of the Wiki data. The French Wikipedia page [The World's Finest Assassin Gets Reincarnated in Another World as an Aristocrat](https://fr.wikipedia.org/wiki/The_World%27s_Finest_Assassin_Gets_Reincarnated_in_Another_World_as_an_Aristocrat) uses English for its title. While this could be disadvantageous for direct translation training, it also provides insights into how native speakers might call this entity instead of the literal translation on the Wiki page as well ## Dataset Structure Each language has its own subset (aka config), which means you only have to download the languages you need with `datasets>=2.14.0` Each subset has these fields: - wikidata_id - lastrevid - label ## Dataset Creation #### Data Collection and Processing - Filtered for item entities only - Ignored the descriptions as those texts are not very parallel ## Bias, Risks, and Limitations - Might be slightly outdated (2022) - Popular languages have more entries - Labels are not guaranteed to be literal translations (see examples above)
gvecchio/MatSynth
gvecchio
"2024-04-16T13:44:10Z"
15,307
47
[ "task_categories:image-to-image", "task_categories:unconditional-image-generation", "task_categories:image-classification", "task_categories:text-to-image", "language:en", "size_categories:1K<n<10K", "arxiv:2401.06056", "region:us", "materials", "pbr", "4d", "graphics", "rendering", "svbrdf", "synthetic" ]
[ "image-to-image", "unconditional-image-generation", "image-classification", "text-to-image" ]
"2023-11-29T11:24:57Z"
--- language: - en size_categories: - 1K<n<10K task_categories: - image-to-image - unconditional-image-generation - image-classification - text-to-image pretty_name: MatSynth dataset_info: features: - name: name dtype: string - name: category dtype: class_label: names: '0': ceramic '1': concrete '2': fabric '3': ground '4': leather '5': marble '6': metal '7': misc '8': plaster '9': plastic '10': stone '11': terracotta '12': wood - name: metadata struct: - name: authors sequence: string - name: category dtype: string - name: description dtype: string - name: height_factor dtype: float32 - name: height_mean dtype: float32 - name: license dtype: string - name: link dtype: string - name: maps sequence: string - name: method dtype: string - name: name dtype: string - name: physical_size dtype: float32 - name: source dtype: string - name: stationary dtype: bool - name: tags sequence: string - name: version_date dtype: string - name: basecolor dtype: image - name: diffuse dtype: image - name: displacement dtype: image - name: height dtype: image - name: metallic dtype: image - name: normal dtype: image - name: opacity dtype: image - name: roughness dtype: image - name: specular dtype: image - name: blend_mask dtype: image splits: - name: test num_bytes: 7443356066.0 num_examples: 89 - name: train num_bytes: 430581667965.1 num_examples: 5700 download_size: 440284274332 dataset_size: 438025024031.1 configs: - config_name: default data_files: - split: test path: data/test-* - split: train path: data/train-* tags: - materials - pbr - 4d - graphics - rendering - svbrdf - synthetic viewer: false --- # MatSynth MatSynth is a Physically Based Rendering (PBR) materials dataset designed for modern AI applications. This dataset consists of over 4,000 ultra-high resolution, offering unparalleled scale, diversity, and detail. Meticulously collected and curated, MatSynth is poised to drive innovation in material acquisition and generation applications, providing a rich resource for researchers, developers, and enthusiasts in computer graphics and related fields. For further information, refer to our paper: ["MatSynth: A Modern PBR Materials Dataset"](https://arxiv.org/abs/2401.06056) available on arXiv. <center> <img src="https://gvecchio.com/matsynth/static/images/teaser.png" style="border-radius:10px"> </center> ## 🔍 Dataset Details ### Dataset Description MatSynth is a new large-scale dataset comprising over 4,000 ultra-high resolution Physically Based Rendering (PBR) materials, all released under permissive licensing. All materials in the dataset are represented by a common set of maps (*Basecolor*, *Diffuse*, *Normal*, *Height*, *Roughness*, *Metallic*, *Specular* and, when useful, *Opacity*), modelling both the reflectance and mesostructure of the material. Each material in the dataset comes with rich metadata, including information on its origin, licensing details, category, tags, creation method, and, when available, descriptions and physical size. This comprehensive metadata facilitates precise material selection and usage, catering to the specific needs of users. <center> <img src="https://gvecchio.com/matsynth/static/images/data.png" style="border-radius:10px"> </center> ## 📂 Dataset Structure The MatSynth dataset is divided into two splits: the test split, containing 89 materials, and the train split, consisting of 3,980 materials. ## 🔨 Dataset Creation The MatSynth dataset is designed to support modern, learning-based techniques for a variety of material-related tasks including, but not limited to, material acquisition, material generation and synthetic data generation e.g. for retrieval or segmentation. ### 🗃️ Source Data The MatSynth dataset is the result of an extensively collection of data from multiple online sources operating under the CC0 and CC-BY licensing framework. This collection strategy allows to capture a broad spectrum of materials, from commonly used ones to more niche or specialized variants while guaranteeing that the data can be used for a variety of usecases. Materials under CC0 license were collected from [AmbientCG](https://ambientcg.com/), [CGBookCase](https://www.cgbookcase.com/), [PolyHeaven](https://polyhaven.com/), [ShateTexture](https://www.sharetextures.com/), and [TextureCan](https://www.texturecan.com/). The dataset also includes limited set of materials from the artist [Julio Sillet](https://juliosillet.gumroad.com/), distributed under CC-BY license. We collected over 6000 materials which we meticulously filter to keep only tileable, 4K materials. This high resolution allows us to extract many different crops from each sample at different scale for augmentation. Additionally, we discard blurry or low-quality materials (by visual inspection). The resulting dataset consists of 3736 unique materials which we augment by blending semantically compatible materials (e.g.: snow over ground). In total, our dataset contains 4069 unique 4K materials. ### ✒️ Annotations The dataset is composed of material maps (Basecolor, Diffuse, Normal, Height, Roughness, Metallic, Specular and, when useful, opacity) and associated renderings under varying environmental illuminations, and multi-scale crops. We adopt the OpenGL standard for the Normal map (Y-axis pointing upward). The Height map is given in a 16-bit single channel format for higher precision. In addition to these maps, the dataset includes other annotations providing context to each material: the capture method (photogrammetry, procedural generation, or approximation); list of descriptive tags; source name (website); source link; licensing and a timestamps for eventual future versioning. For a subset of materials, when the information is available, we also provide the author name (387), text description (572) and a physical size, presented as the length of the edge in centimeters (358). ## 🧑‍💻 Usage MatSynth is accessible through the datasets python library. Following a usage example: ```python import torchvision.transforms.functional as TF from datasets import load_dataset from torch.utils.data import DataLoader # image processing function def process_img(x): x = TF.resize(x, (1024, 1024)) x = TF.to_tensor(x) return x # item processing function def process_batch(examples): examples["basecolor"] = [process_img(x) for x in examples["basecolor"]] return examples # load the dataset in streaming mode ds = load_dataset( "gvecchio/MatSynth", streaming = True, ) # remove unwanted columns ds = ds.remove_columns(["diffuse", "specular", "displacement", "opacity", "blend_mask"]) # or keep only specified columns ds = ds.select_columns(["metadata", "basecolor"]) # shuffle data ds = ds.shuffle(buffer_size=100) # filter data matching a specific criteria, e.g.: only CC0 materials ds = ds.filter(lambda x: x["metadata"]["license"] == "CC0") # filter out data from Deschaintre et al. 2018 ds = ds.filter(lambda x: x["metadata"]["source"] != "deschaintre_2020") # Set up processing ds = ds.map(process_batch, batched=True, batch_size=8) # set format for usage in torch ds = ds.with_format("torch") # iterate over the dataset for x in ds: print(x) ``` ⚠️ **Note**: Streaming can be slow. We strongly suggest to cache data locally. ## 📜 Citation ``` @inproceedings{vecchio2023matsynth, title={MatSynth: A Modern PBR Materials Dataset}, author={Vecchio, Giuseppe and Deschaintre, Valentin}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, year={2024} } ``` If you use the data from Deschaintre et al. contained in this dataset, please also cite: ``` @article{deschaintre2018single, title={Single-image svbrdf capture with a rendering-aware deep network}, author={Deschaintre, Valentin and Aittala, Miika and Durand, Fredo and Drettakis, George and Bousseau, Adrien}, journal={ACM Transactions on Graphics (ToG)}, volume={37}, number={4}, pages={1--15}, year={2018}, publisher={ACM New York, NY, USA} } ```
jkot/merged_preprocessed_parliament_commonvoice
jkot
"2023-05-01T13:35:28Z"
15,272
0
[ "size_categories:100K<n<1M", "format:parquet", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
null
"2023-05-01T10:37:03Z"
--- dataset_info: features: - name: input_features sequence: sequence: float32 - name: labels sequence: int64 splits: - name: train num_bytes: 210499135424 num_examples: 219101 - name: test num_bytes: 11099630080 num_examples: 11555 download_size: 65027813279 dataset_size: 221598765504 --- # Dataset Card for "merged_preprocessed_parliament_commonvoice" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
hails/bigbench
hails
"2023-11-17T16:05:10Z"
15,215
4
[ "size_categories:1M<n<10M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
null
"2023-10-03T19:55:51Z"
--- 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 - name: validation num_bytes: 2830111 num_examples: 176 download_size: 0 dataset_size: 28237184 - config_name: auto_categorization_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: 40549 num_examples: 328 - name: train num_bytes: 32992 num_examples: 263 - name: validation num_bytes: 7557 num_examples: 65 download_size: 0 dataset_size: 81098 - config_name: auto_debugging_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: 5112 num_examples: 34 - name: train num_bytes: 2651 num_examples: 18 - name: validation num_bytes: 2461 num_examples: 16 download_size: 0 dataset_size: 10224 - config_name: bbq_lite_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: 6890493 num_examples: 16076 - name: train num_bytes: 5508584 num_examples: 12866 - name: validation num_bytes: 1381909 num_examples: 3210 download_size: 0 dataset_size: 13780986 - config_name: bridging_anaphora_resolution_barqa_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: 1971015 num_examples: 648 - name: train num_bytes: 1537264 num_examples: 519 - name: validation num_bytes: 433751 num_examples: 129 download_size: 0 dataset_size: 3942030 - config_name: causal_judgment_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: 204878 num_examples: 190 - name: train num_bytes: 164940 num_examples: 152 - name: validation num_bytes: 39938 num_examples: 38 download_size: 0 dataset_size: 409756 - config_name: cause_and_effect_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: 49314 num_examples: 153 - name: train num_bytes: 39620 num_examples: 123 - name: validation num_bytes: 9694 num_examples: 30 download_size: 0 dataset_size: 98628 - config_name: checkmate_in_one_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: 3123256 num_examples: 3498 - name: train num_bytes: 2502314 num_examples: 2799 - name: validation num_bytes: 620942 num_examples: 699 download_size: 0 dataset_size: 6246512 - config_name: chess_state_tracking_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: 3269932 num_examples: 6000 - name: train num_bytes: 2616294 num_examples: 4800 - name: validation num_bytes: 653638 num_examples: 1200 download_size: 0 dataset_size: 6539864 - config_name: chinese_remainder_theorem_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: 153222 num_examples: 500 - name: train num_bytes: 122601 num_examples: 400 - name: validation num_bytes: 30621 num_examples: 100 download_size: 0 dataset_size: 306444 - config_name: cifar10_classification_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: 111022200 num_examples: 20000 - name: train num_bytes: 88782724 num_examples: 16000 - name: validation num_bytes: 22239476 num_examples: 4000 download_size: 0 dataset_size: 222044400 - config_name: code_line_description_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: 33670 num_examples: 60 - name: train num_bytes: 25530 num_examples: 44 - name: validation num_bytes: 8140 num_examples: 16 download_size: 0 dataset_size: 67340 - config_name: codenames_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: 25195 num_examples: 85 - name: train num_bytes: 19964 num_examples: 68 - name: validation num_bytes: 5231 num_examples: 17 download_size: 0 dataset_size: 50390 - config_name: color_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: 1633263 num_examples: 4000 - name: train num_bytes: 1306663 num_examples: 3200 - name: validation num_bytes: 326600 num_examples: 800 download_size: 0 dataset_size: 3266526 - config_name: common_morpheme_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: 12388 num_examples: 50 - name: train num_bytes: 8444 num_examples: 34 - name: validation num_bytes: 3944 num_examples: 16 download_size: 0 dataset_size: 24776 - config_name: conceptual_combinations_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: 58859 num_examples: 103 - name: train num_bytes: 48010 num_examples: 84 - name: validation num_bytes: 10849 num_examples: 19 download_size: 0 dataset_size: 117718 - config_name: conlang_translation_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: 215190 num_examples: 164 - name: train num_bytes: 173024 num_examples: 132 - name: validation num_bytes: 42166 num_examples: 32 download_size: 0 dataset_size: 430380 - config_name: contextual_parametric_knowledge_conflicts_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: 14587554 num_examples: 17528 - name: train num_bytes: 11666236 num_examples: 14023 - name: validation num_bytes: 2921318 num_examples: 3505 download_size: 0 dataset_size: 29175108 - config_name: crash_blossom_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: 12194 num_examples: 38 - name: train num_bytes: 6999 num_examples: 22 - name: validation num_bytes: 5195 num_examples: 16 download_size: 0 dataset_size: 24388 - config_name: crass_ai_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: 22870 num_examples: 44 - name: train num_bytes: 14130 num_examples: 28 - name: validation num_bytes: 8740 num_examples: 16 download_size: 0 dataset_size: 45740 - config_name: cryobiology_spanish_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: 38674 num_examples: 146 - name: train num_bytes: 31129 num_examples: 117 - name: validation num_bytes: 7545 num_examples: 29 download_size: 0 dataset_size: 77348 - config_name: cryptonite_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: 2844402 num_examples: 26157 - name: train num_bytes: 2275724 num_examples: 20926 - 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name: multiple_choice_scores sequence: int32 splits: - name: default num_bytes: 22530 num_examples: 34 - name: train num_bytes: 12066 num_examples: 18 - name: validation num_bytes: 10464 num_examples: 16 download_size: 0 dataset_size: 45060 - config_name: english_russian_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: 59900 num_examples: 80 - name: train num_bytes: 48051 num_examples: 64 - name: validation num_bytes: 11849 num_examples: 16 download_size: 0 dataset_size: 119800 - config_name: entailed_polarity_hindi_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: 57052 num_examples: 138 - name: train num_bytes: 45829 num_examples: 111 - 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config_name: parsinlu_qa_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: 456189 num_examples: 1050 - name: train num_bytes: 366577 num_examples: 840 - name: validation num_bytes: 89612 num_examples: 210 download_size: 465963 dataset_size: 912378 - config_name: parsinlu_reading_comprehension_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: 573798 num_examples: 518 - name: train num_bytes: 455828 num_examples: 415 - name: validation num_bytes: 117970 num_examples: 103 download_size: 572992 dataset_size: 1147596 - config_name: penguins_in_a_table_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: 75985 num_examples: 149 - name: train num_bytes: 61321 num_examples: 120 - name: validation num_bytes: 14664 num_examples: 29 download_size: 32039 dataset_size: 151970 - config_name: periodic_elements_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: 285204 num_examples: 654 - name: train num_bytes: 229481 num_examples: 524 - name: validation num_bytes: 55723 num_examples: 130 download_size: 41084 dataset_size: 570408 - config_name: persian_idioms_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: 28592 num_examples: 66 - name: train num_bytes: 21684 num_examples: 50 - name: validation num_bytes: 6908 num_examples: 16 download_size: 34341 dataset_size: 57184 - config_name: phrase_relatedness_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: 30190 num_examples: 100 - name: train num_bytes: 23773 num_examples: 80 - name: validation num_bytes: 6417 num_examples: 20 download_size: 40334 dataset_size: 60380 - config_name: physical_intuition_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: 23734 num_examples: 81 - name: train num_bytes: 19307 num_examples: 65 - name: validation num_bytes: 4427 num_examples: 16 download_size: 28462 dataset_size: 47468 - config_name: physics_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: 18372 num_examples: 54 - name: train num_bytes: 13402 num_examples: 38 - name: validation num_bytes: 4970 num_examples: 16 download_size: 35187 dataset_size: 36744 - config_name: physics_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: 120239 num_examples: 229 - name: train num_bytes: 96122 num_examples: 184 - name: validation num_bytes: 24117 num_examples: 45 download_size: 69885 dataset_size: 240478 - config_name: play_dialog_same_or_different_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: 3142468 num_examples: 3264 - name: train num_bytes: 2516052 num_examples: 2612 - name: validation num_bytes: 626416 num_examples: 652 download_size: 1710264 dataset_size: 6284936 - config_name: polish_sequence_labeling_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: 18081112 num_examples: 12812 - name: train num_bytes: 14470720 num_examples: 10250 - name: validation num_bytes: 3610392 num_examples: 2562 download_size: 5242934 dataset_size: 36162224 - config_name: presuppositions_as_nli_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: 502522 num_examples: 735 - name: train num_bytes: 400761 num_examples: 588 - name: validation num_bytes: 101761 num_examples: 147 download_size: 240065 dataset_size: 1005044 - config_name: qa_wikidata_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: 1419042 num_examples: 20321 - name: train num_bytes: 1134918 num_examples: 16257 - name: validation num_bytes: 284124 num_examples: 4064 download_size: 1181835 dataset_size: 2838084 - config_name: question_selection_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: 2487181 num_examples: 1582 - name: train num_bytes: 1990094 num_examples: 1266 - name: validation num_bytes: 497087 num_examples: 316 download_size: 1804283 dataset_size: 4974362 - config_name: real_or_fake_text_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: 53663318 num_examples: 15088 - name: train num_bytes: 42879846 num_examples: 12072 - name: validation num_bytes: 10783472 num_examples: 3016 download_size: 47399045 dataset_size: 107326636 - config_name: reasoning_about_colored_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: 907474 num_examples: 2000 - name: train num_bytes: 729609 num_examples: 1600 - name: validation num_bytes: 177865 num_examples: 400 download_size: 273263 dataset_size: 1814948 - config_name: repeat_copy_logic_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: 6678 num_examples: 32 - name: train num_bytes: 3327 num_examples: 16 - name: validation num_bytes: 3351 num_examples: 16 download_size: 18315 dataset_size: 13356 - config_name: rephrase_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: 34222 num_examples: 78 - name: train num_bytes: 27360 num_examples: 62 - name: validation num_bytes: 6862 num_examples: 16 download_size: 41102 dataset_size: 68444 - config_name: riddle_sense_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: 15507 num_examples: 49 - name: train num_bytes: 10741 num_examples: 33 - name: validation num_bytes: 4766 num_examples: 16 download_size: 32496 dataset_size: 31014 - config_name: ruin_names_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: 144087 num_examples: 448 - name: train num_bytes: 115171 num_examples: 359 - name: validation num_bytes: 28916 num_examples: 89 download_size: 118193 dataset_size: 288174 - config_name: salient_translation_error_detection_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: 1141626 num_examples: 998 - name: train num_bytes: 912819 num_examples: 799 - name: validation num_bytes: 228807 num_examples: 199 download_size: 413634 dataset_size: 2283252 - 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)
bhatvineet/shrutilipi_mr
bhatvineet
"2023-03-13T18:54:45Z"
15,204
0
[ "size_categories:100K<n<1M", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
null
"2023-03-13T07:12:52Z"
--- dataset_info: features: - name: audio dtype: audio - name: transcriptions dtype: string splits: - name: train num_bytes: 114253169328.11655 num_examples: 474332 - name: test num_bytes: 39048725811.21545 num_examples: 158111 download_size: 147662822982 dataset_size: 153301895139.332 --- # Dataset Card for "shrutilipi_mr" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
TIGER-Lab/VideoFeedback
TIGER-Lab
"2024-08-10T23:30:40Z"
15,180
27
[ "task_categories:video-classification", "language:en", "license:apache-2.0", "size_categories:10K<n<100K", "format:parquet", "modality:tabular", "modality:text", "modality:video", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2406.15252", "region:us", "video" ]
[ "video-classification" ]
"2024-04-26T13:42:39Z"
--- language: - en license: apache-2.0 size_categories: - 10K<n<100K task_categories: - video-classification pretty_name: VideoFeedback tags: - video dataset_info: - config_name: annotated features: - name: id dtype: string - name: images sequence: string - name: text prompt dtype: string - name: video link dtype: string - name: visual quality dtype: int64 - name: temporal consistency dtype: int64 - name: dynamic degree dtype: int64 - name: text-to-video alignment dtype: int64 - name: factual consistency dtype: int64 - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: test num_bytes: 1348268 num_examples: 680 - name: train num_bytes: 65281005 num_examples: 32901 download_size: 45128599 dataset_size: 66629273 - config_name: real features: - name: id dtype: string - name: images sequence: string - name: text prompt dtype: string - name: video link dtype: string - name: visual quality dtype: int64 - name: temporal consistency dtype: int64 - name: dynamic degree dtype: int64 - name: text-to-video alignment dtype: int64 - name: factual consistency dtype: int64 - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 8072782 num_examples: 4000 - name: test num_bytes: 162240 num_examples: 80 download_size: 3963450 dataset_size: 8235022 configs: - config_name: annotated data_files: - split: train path: annotated/train-* - split: test path: annotated/test-* - config_name: real data_files: - split: test path: real/test-* - split: train path: real/train-* --- [📃Paper](https://arxiv.org/abs/2406.15252) | [🌐Website](https://tiger-ai-lab.github.io/VideoScore/) | [💻Github](https://github.com/TIGER-AI-Lab/VideoScore) | [🛢️Datasets](https://huggingface.co/datasets/TIGER-Lab/VideoFeedback) | [🤗Model](https://huggingface.co/TIGER-Lab/VideoScore) | [🤗Demo](https://huggingface.co/spaces/TIGER-Lab/VideoScore) ## Overview VideoFeedback contains a total of 37.6K text-to-video pairs from 11 popular video generative models, with some real-world videos as data augmentation. The videos are annotated by raters for five evaluation dimensions: Visual Quality, Temporal Consistency, Dynamic Degree, Text-to-Video Alignment and Factual Consistency, in 1-4 scoring scale. VideoFeedback is used to for trainging of [VideoScore](https://huggingface.co/TIGER-Lab/VideoScore) Below we show the detailed description of our VideoFeedback dataset. ![Dataset Statistics](https://tiger-ai-lab.github.io/VideoScore/static/images/dataset.png) ## Load Dataset - Load certain split and certain config of dataset ```python from datasets import load_dataset dataset=load_dataset("TIGER-Lab/Video_feedback",name="annotated",split="test") print(dataset[0].keys()) print(dataset[0]["id"]) """ dict_keys(['id', 'images', 'text prompt', 'video link', 'visual quality', 'temporal consistency', 'dynamic degree', 'text-to-video alignment', 'factual consistency', 'conversations']) 7001917 """ ``` - Load all subsets/configs of certain split ```python from datasets import get_dataset_config_names, load_dataset for config_name in get_dataset_config_names("TIGER-Lab/VideoFeedback"): test_data = load_dataset("TIGER-Lab/VideoFeedback", name=config_name, split="test") print(test_data[0]["id"]) """ 7001917 p006515 """ ``` ## Citation ```bibtex @article{he2024videoscore, title = {VideoScore: Building Automatic Metrics to Simulate Fine-grained Human Feedback for Video Generation}, author = {He, Xuan and Jiang, Dongfu and Zhang, Ge and Ku, Max and Soni, Achint and Siu, Sherman and Chen, Haonan and Chandra, Abhranil and Jiang, Ziyan and Arulraj, Aaran and Wang, Kai and Do, Quy Duc and Ni, Yuansheng and Lyu, Bohan and Narsupalli, Yaswanth and Fan, Rongqi and Lyu, Zhiheng and Lin, Yuchen and Chen, Wenhu}, journal = {ArXiv}, year = {2024}, volume={abs/2406.15252}, url = {https://arxiv.org/abs/2406.15252}, } ```
alvations/c4p0-x1-en-es
alvations
"2024-03-24T03:55:08Z"
15,147
0
[ "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-03-23T10:03:20Z"
--- 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: 2308 num_examples: 2 download_size: 15489 dataset_size: 2308 configs: - config_name: default data_files: - split: train path: 1f24f16745a166b0/train-* ---
openslr/librispeech_asr
openslr
"2024-08-14T10:48:50Z"
15,144
140
[ "task_categories:automatic-speech-recognition", "task_categories:audio-classification", "task_ids:speaker-identification", "annotations_creators:expert-generated", "language_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:cc-by-4.0", "size_categories:100K<n<1M", "region:us" ]
[ "automatic-speech-recognition", "audio-classification" ]
"2022-03-02T23:29:22Z"
--- 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.
omegalabsinc/omega-voice
omegalabsinc
"2025-03-26T01:25:33Z"
15,135
1
[ "license:mit", "region:us" ]
null
"2024-11-16T01:44:48Z"
--- license: mit ---
lmqg/qg_koquad
lmqg
"2022-12-02T18:53:42Z"
15,115
8
[ "task_categories:text-generation", "task_ids:language-modeling", "multilinguality:monolingual", "source_datasets:squad_es", "language:ko", "license:cc-by-4.0", "size_categories:10K<n<100K", "modality:text", "library:datasets", "library:mlcroissant", "arxiv:2210.03992", "region:us", "question-generation" ]
[ "text-generation" ]
"2022-06-02T23:42:21Z"
--- license: cc-by-4.0 pretty_name: KorQuAD for question generation language: ko multilinguality: monolingual size_categories: 10K<n<100K source_datasets: squad_es task_categories: - text-generation task_ids: - language-modeling tags: - question-generation --- # Dataset Card for "lmqg/qg_korquad" ## Dataset Description - **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) - **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992) - **Point of Contact:** [Asahi Ushio](http://asahiushio.com/) ### Dataset Summary This is a subset of [QG-Bench](https://github.com/asahi417/lm-question-generation/blob/master/QG_BENCH.md#datasets), a unified question generation benchmark proposed in ["Generative Language Models for Paragraph-Level Question Generation: A Unified Benchmark and Evaluation, EMNLP 2022 main conference"](https://arxiv.org/abs/2210.03992). This is a modified version of [KorQuAD](https://huggingface.co/datasets/squad_kor_v1) for question generation (QG) task. Since the original dataset only contains training/validation set, we manually sample test set from training set, which has no overlap in terms of the paragraph with the training set. ### Supported Tasks and Leaderboards * `question-generation`: The dataset is assumed to be used to train a model for question generation. Success on this task is typically measured by achieving a high BLEU4/METEOR/ROUGE-L/BERTScore/MoverScore (see our paper for more in detail). ### Languages Korean (ko) ## Dataset Structure An example of 'train' looks as follows. ``` { "question": "함수해석학이 주목하는 탐구는?", "paragraph": "변화에 대한 이해와 묘사는 자연과학에 있어서 일반적인 주제이며, 미적분학은 변화를 탐구하는 강력한 도구로서 발전되었다. 함수는 변화하는 양을 묘사함에 있어서 중추적인 개념으로써 떠오르게 된다. 실수와 실변수로 구성된 함수의 엄밀한 탐구가 실해석학이라는 분야로 알려지게 되었고, 복소수에 대한 이와 같은 탐구분야는 복소해석학이라고 한다. 함수해석학은 함수의 공간(특히 무한차원)의 탐구에 주목한다. 함수해석학의 많은 응용분야 중 하나가 양자역학이다. 많은 문제들이 자연스럽게 양과 그 양의 변화율의 관계로 귀착되고, 이러한 문제들이 미분방정식으로 다루어진다. 자연의 많은 현상들이 동역학계로 기술될 수 있다. 혼돈 이론은 이러한 예측 불가능한 현상을 탐구하는 데 상당한 기여를 한다.", "answer": "함수의 공간(특히 무한차원)의 탐구", "sentence": "함수해석학은 함수의 공간(특히 무한차원)의 탐구 에 주목한다.", "paragraph_sentence": '변화에 대한 이해와 묘사는 자연과학에 있어서 일반적인 주제이며, 미적분학은 변화를 탐구하는 강력한 도구로서 발전되었다. 함수는 변화하는 양을 묘사함에 있어서 중추적인 개념으로써 떠오르게 된다. 실수와 실변수로 구성된 함수의 엄밀한 탐구가 실해석학이라는 분야로 알려지게 되었고, 복소수에 대한 이와 같은 탐구 분야는 복소해석학이라고 한다. <hl> 함수해석학은 함수의 공간(특히 무한차원)의 탐구 에 주목한다. <hl> 함수해석학의 많은 응용분야 중 하나가 양자역학이다. 많은 문제들이 자연스럽게 양과 그 양의 변화율의 관계로 귀착되고, 이러한 문제들이 미분방정식으로 다루어진다. 자연의 많은 현상들이 동역학계로 기술될 수 있다. 혼돈 이론은 이러한 예측 불가능한 현상을 탐구하는 데 상당한 기여를 한다.', "paragraph_answer": '변화에 대한 이해와 묘사는 자연과학에 있어서 일반적인 주제이며, 미적분학은 변화를 탐구하는 강력한 도구로서 발전되었다. 함수는 변화하는 양을 묘사함에 있어서 중추적인 개념으로써 떠오르게 된다. 실수와 실변수로 구성된 함수의 엄밀한 탐구가 실해석학이라는 분야로 알려지게 되었고, 복소수에 대한 이와 같은 탐구 분야는 복소해석학이라고 한다. 함수해석학은 <hl> 함수의 공간(특히 무한차원)의 탐구 <hl>에 주목한다. 함수해석학의 많은 응용분야 중 하나가 양자역학이다. 많은 문제들이 자연스럽게 양과 그 양의 변화율의 관계로 귀착되고, 이러한 문제들이 미분방정식으로 다루어진다. 자연의 많은 현상들이 동역학계로 기술될 수 있다. 혼돈 이론은 이러한 예측 불가능한 현상을 탐구하는 데 상당한 기여를 한다.', "sentence_answer": "함수해석학은 <hl> 함수의 공간(특히 무한차원)의 탐구 <hl> 에 주목한다." } ``` The data fields are the same among all splits. - `question`: a `string` feature. - `paragraph`: a `string` feature. - `answer`: a `string` feature. - `sentence`: a `string` feature. - `paragraph_answer`: a `string` feature, which is same as the paragraph but the answer is highlighted by a special token `<hl>`. - `paragraph_sentence`: a `string` feature, which is same as the paragraph but a sentence containing the answer is highlighted by a special token `<hl>`. - `sentence_answer`: a `string` feature, which is same as the sentence but the answer is highlighted by a special token `<hl>`. Each of `paragraph_answer`, `paragraph_sentence`, and `sentence_answer` feature is assumed to be used to train a question generation model, but with different information. The `paragraph_answer` and `sentence_answer` features are for answer-aware question generation and `paragraph_sentence` feature is for sentence-aware question generation. ## Data Splits |train|validation|test | |----:|---------:|----:| |54556| 5766 |5766 | ## Citation Information ``` @inproceedings{ushio-etal-2022-generative, title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration: {A} {U}nified {B}enchmark and {E}valuation", author = "Ushio, Asahi and Alva-Manchego, Fernando and Camacho-Collados, Jose", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, U.A.E.", publisher = "Association for Computational Linguistics", } ```
google-research-datasets/nq_open
google-research-datasets
"2024-03-22T08:43:41Z"
15,031
22
[ "task_categories:question-answering", "task_ids:open-domain-qa", "annotations_creators:expert-generated", "language_creators:other", "multilinguality:monolingual", "source_datasets:extended|natural_questions", "language:en", "license:cc-by-sa-3.0", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[ "question-answering" ]
"2022-03-02T23:29:22Z"
--- 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.
dair-ai/emotion
dair-ai
"2024-08-08T06:10:47Z"
14,999
335
[ "task_categories:text-classification", "task_ids:multi-class-classification", "annotations_creators:machine-generated", "language_creators:machine-generated", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:other", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "emotion-classification" ]
[ "text-classification" ]
"2022-03-02T23:29:22Z"
--- annotations_creators: - machine-generated language_creators: - machine-generated language: - en license: - other multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - multi-class-classification paperswithcode_id: emotion pretty_name: Emotion tags: - emotion-classification dataset_info: - config_name: split features: - name: text dtype: string - name: label dtype: class_label: names: '0': sadness '1': joy '2': love '3': anger '4': fear '5': surprise splits: - name: train num_bytes: 1741533 num_examples: 16000 - name: validation num_bytes: 214695 num_examples: 2000 - name: test num_bytes: 217173 num_examples: 2000 download_size: 1287193 dataset_size: 2173401 - config_name: unsplit features: - name: text dtype: string - name: label dtype: class_label: names: '0': sadness '1': joy '2': love '3': anger '4': fear '5': surprise splits: - name: train num_bytes: 45444017 num_examples: 416809 download_size: 26888538 dataset_size: 45444017 configs: - config_name: split data_files: - split: train path: split/train-* - split: validation path: split/validation-* - split: test path: split/test-* default: true - config_name: unsplit data_files: - split: train path: unsplit/train-* train-eval-index: - config: default 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 "emotion" ## 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/dair-ai/emotion_dataset](https://github.com/dair-ai/emotion_dataset) - **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:** 16.13 MB - **Size of the generated dataset:** 47.62 MB - **Total amount of disk used:** 63.75 MB ### Dataset Summary Emotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise. For more detailed information please refer to the paper. ### 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 An example looks as follows. ``` { "text": "im feeling quite sad and sorry for myself but ill snap out of it soon", "label": 0 } ``` ### Data Fields The data fields are: - `text`: a `string` feature. - `label`: a classification label, with possible values including `sadness` (0), `joy` (1), `love` (2), `anger` (3), `fear` (4), `surprise` (5). ### Data Splits The dataset has 2 configurations: - split: with a total of 20_000 examples split into train, validation and split - unsplit: with a total of 416_809 examples in a single train split | name | train | validation | test | |---------|-------:|-----------:|-----:| | split | 16000 | 2000 | 2000 | | unsplit | 416809 | n/a | n/a | ## 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 should be used for educational and research purposes only. ### Citation Information If you use this dataset, please cite: ``` @inproceedings{saravia-etal-2018-carer, title = "{CARER}: Contextualized Affect Representations for Emotion Recognition", author = "Saravia, Elvis and Liu, Hsien-Chi Toby and Huang, Yen-Hao and Wu, Junlin and Chen, Yi-Shin", booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing", month = oct # "-" # nov, year = "2018", address = "Brussels, Belgium", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/D18-1404", doi = "10.18653/v1/D18-1404", pages = "3687--3697", abstract = "Emotions are expressed in nuanced ways, which varies by collective or individual experiences, knowledge, and beliefs. Therefore, to understand emotion, as conveyed through text, a robust mechanism capable of capturing and modeling different linguistic nuances and phenomena is needed. We propose a semi-supervised, graph-based algorithm to produce rich structural descriptors which serve as the building blocks for constructing contextualized affect representations from text. The pattern-based representations are further enriched with word embeddings and evaluated through several emotion recognition tasks. Our experimental results demonstrate that the proposed method outperforms state-of-the-art techniques on emotion recognition tasks.", } ``` ### Contributions Thanks to [@lhoestq](https://github.com/lhoestq), [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun) for adding this dataset.
INo0121/low_quality_call_voice_preprocessed
INo0121
"2023-09-21T13:25:07Z"
14,860
0
[ "size_categories:10K<n<100K", "format:parquet", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
null
"2023-09-08T05:10:37Z"
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: valid path: data/valid-* dataset_info: features: - name: input_features sequence: sequence: float32 - name: labels sequence: int64 splits: - name: train num_bytes: 64088254376 num_examples: 66720 - name: test num_bytes: 7476961712 num_examples: 7784 - name: valid num_bytes: 7476975416 num_examples: 7784 download_size: 521083513 dataset_size: 79042191504 --- # Dataset Card for "low_quality_call_voice_preprocessed" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
wecover/OPUS_TED2020
wecover
"2024-01-31T11:42:51Z"
14,860
0
[ "size_categories:10M<n<100M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-01-31T07:37:04Z"
--- configs: - config_name: default data_files: - split: train path: '*/*/train.parquet' - split: valid path: '*/*/valid.parquet' - split: test path: '*/*/test.parquet' - config_name: af data_files: - split: train path: '*/*af*/train.parquet' - split: test path: '*/*af*/test.parquet' - split: valid path: '*/*af*/valid.parquet' - config_name: am data_files: - split: train path: '*/*am*/train.parquet' - split: test path: '*/*am*/test.parquet' - split: valid path: '*/*am*/valid.parquet' - config_name: ar data_files: - split: train path: '*/*ar*/train.parquet' - split: test path: '*/*ar*/test.parquet' - split: valid path: '*/*ar*/valid.parquet' - config_name: az data_files: - split: train path: '*/*az*/train.parquet' - split: test path: '*/*az*/test.parquet' - split: valid path: '*/*az*/valid.parquet' - config_name: be data_files: - split: train path: '*/*be*/train.parquet' - split: test path: '*/*be*/test.parquet' - split: valid path: '*/*be*/valid.parquet' - config_name: bg data_files: - split: train path: '*/*bg*/train.parquet' - split: test path: '*/*bg*/test.parquet' - split: valid path: '*/*bg*/valid.parquet' - config_name: bn data_files: - split: train path: '*/*bn*/train.parquet' - split: test path: '*/*bn*/test.parquet' - split: valid path: '*/*bn*/valid.parquet' - config_name: bs data_files: - split: train path: '*/*bs*/train.parquet' - split: test path: '*/*bs*/test.parquet' - split: valid path: '*/*bs*/valid.parquet' - config_name: ca data_files: - split: train path: '*/*ca*/train.parquet' - split: test path: '*/*ca*/test.parquet' - split: valid path: '*/*ca*/valid.parquet' - config_name: cs data_files: - split: train path: '*/*cs*/train.parquet' - split: test path: '*/*cs*/test.parquet' - split: valid path: '*/*cs*/valid.parquet' - config_name: da data_files: - split: train path: '*/*da*/train.parquet' - split: test path: '*/*da*/test.parquet' - split: valid path: '*/*da*/valid.parquet' - config_name: de data_files: - split: train path: '*/*de*/train.parquet' - split: test path: '*/*de*/test.parquet' - split: valid path: '*/*de*/valid.parquet' - config_name: el data_files: - split: train path: '*/*el*/train.parquet' - split: test path: '*/*el*/test.parquet' - split: valid path: '*/*el*/valid.parquet' - config_name: en data_files: - split: train path: '*/*en*/train.parquet' - split: test path: '*/*en*/test.parquet' - split: valid path: '*/*en*/valid.parquet' - config_name: eo data_files: - split: train path: '*/*eo*/train.parquet' - split: test path: '*/*eo*/test.parquet' - split: valid path: '*/*eo*/valid.parquet' - config_name: es data_files: - split: train path: '*/*es*/train.parquet' - split: test path: '*/*es*/test.parquet' - split: valid path: '*/*es*/valid.parquet' - config_name: et data_files: - split: train path: '*/*et*/train.parquet' - split: test path: '*/*et*/test.parquet' - split: valid path: '*/*et*/valid.parquet' - config_name: eu data_files: - split: train path: '*/*eu*/train.parquet' - split: test path: '*/*eu*/test.parquet' - split: valid path: '*/*eu*/valid.parquet' - config_name: fa data_files: - split: train path: '*/*fa*/train.parquet' - split: test path: '*/*fa*/test.parquet' - split: valid path: '*/*fa*/valid.parquet' - config_name: fi data_files: - split: train path: '*/*fi*/train.parquet' - split: test path: '*/*fi*/test.parquet' - split: valid path: '*/*fi*/valid.parquet' - config_name: fr data_files: - split: train path: '*/*fr*/train.parquet' - split: test path: '*/*fr*/test.parquet' - split: valid path: '*/*fr*/valid.parquet' - config_name: ga data_files: - split: train path: '*/*ga*/train.parquet' - split: test path: '*/*ga*/test.parquet' - split: valid path: '*/*ga*/valid.parquet' - config_name: gl data_files: - split: train path: '*/*gl*/train.parquet' - split: test path: '*/*gl*/test.parquet' - split: valid path: '*/*gl*/valid.parquet' - config_name: he data_files: - split: train path: '*/*he*/train.parquet' - split: test path: '*/*he*/test.parquet' - split: valid path: '*/*he*/valid.parquet' - config_name: hi data_files: - split: train path: '*/*hi*/train.parquet' - split: test path: '*/*hi*/test.parquet' - split: valid path: '*/*hi*/valid.parquet' - config_name: hr data_files: - split: train path: '*/*hr*/train.parquet' - split: test path: '*/*hr*/test.parquet' - split: valid path: '*/*hr*/valid.parquet' - config_name: hu data_files: - split: train path: '*/*hu*/train.parquet' - split: test path: '*/*hu*/test.parquet' - split: valid path: '*/*hu*/valid.parquet' - config_name: hy data_files: - split: train path: '*/*hy*/train.parquet' - split: test path: '*/*hy*/test.parquet' - split: valid path: '*/*hy*/valid.parquet' - config_name: id data_files: - split: train path: '*/*id*/train.parquet' - split: test path: '*/*id*/test.parquet' - split: valid path: '*/*id*/valid.parquet' - config_name: is data_files: - split: train path: '*/*is*/train.parquet' - split: test path: '*/*is*/test.parquet' - split: valid path: '*/*is*/valid.parquet' - config_name: it data_files: - split: train path: '*/*it*/train.parquet' - split: test path: '*/*it*/test.parquet' - split: valid path: '*/*it*/valid.parquet' - config_name: ja data_files: - split: train path: '*/*ja*/train.parquet' - split: test path: '*/*ja*/test.parquet' - split: valid path: '*/*ja*/valid.parquet' - config_name: ka data_files: - split: train path: '*/*ka*/train.parquet' - split: test path: '*/*ka*/test.parquet' - split: valid path: '*/*ka*/valid.parquet' - config_name: kk data_files: - split: train path: '*/*kk*/train.parquet' - split: test path: '*/*kk*/test.parquet' - split: valid path: '*/*kk*/valid.parquet' - config_name: km data_files: - split: train path: '*/*km*/train.parquet' - split: test path: '*/*km*/test.parquet' - split: valid path: '*/*km*/valid.parquet' - config_name: kn data_files: - split: train path: '*/*kn*/train.parquet' - split: test path: '*/*kn*/test.parquet' - split: valid path: '*/*kn*/valid.parquet' - config_name: ko data_files: - split: train path: '*/*ko*/train.parquet' - split: test path: '*/*ko*/test.parquet' - split: valid path: '*/*ko*/valid.parquet' - config_name: ku data_files: - split: train path: '*/*ku*/train.parquet' - split: test path: '*/*ku*/test.parquet' - split: valid path: '*/*ku*/valid.parquet' - config_name: lo data_files: - split: train path: '*/*lo*/train.parquet' - split: test path: '*/*lo*/test.parquet' - split: valid path: '*/*lo*/valid.parquet' - config_name: lt data_files: - split: train path: '*/*lt*/train.parquet' - split: test path: '*/*lt*/test.parquet' - split: valid path: '*/*lt*/valid.parquet' - config_name: mg data_files: - split: train path: '*/*mg*/train.parquet' - split: test path: '*/*mg*/test.parquet' - split: valid path: '*/*mg*/valid.parquet' - config_name: mk data_files: - split: train path: '*/*mk*/train.parquet' - split: test path: '*/*mk*/test.parquet' - split: valid path: '*/*mk*/valid.parquet' - config_name: ml data_files: - split: train path: '*/*ml*/train.parquet' - split: test path: '*/*ml*/test.parquet' - split: valid path: '*/*ml*/valid.parquet' - config_name: mn data_files: - split: train path: '*/*mn*/train.parquet' - split: test path: '*/*mn*/test.parquet' - split: valid path: '*/*mn*/valid.parquet' - config_name: mr data_files: - split: train path: '*/*mr*/train.parquet' - split: test path: '*/*mr*/test.parquet' - split: valid path: '*/*mr*/valid.parquet' - config_name: ms data_files: - split: train path: '*/*ms*/train.parquet' - split: test path: '*/*ms*/test.parquet' - split: valid path: '*/*ms*/valid.parquet' - config_name: my data_files: - split: train path: '*/*my*/train.parquet' - split: test path: '*/*my*/test.parquet' - split: valid path: '*/*my*/valid.parquet' - config_name: ne data_files: - split: train path: '*/*ne*/train.parquet' - split: test path: '*/*ne*/test.parquet' - split: valid path: '*/*ne*/valid.parquet' - config_name: nl data_files: - split: train path: '*/*nl*/train.parquet' - split: test path: '*/*nl*/test.parquet' - split: valid path: '*/*nl*/valid.parquet' - config_name: pl data_files: - split: train path: '*/*pl*/train.parquet' - split: test path: '*/*pl*/test.parquet' - split: valid path: '*/*pl*/valid.parquet' - config_name: pt data_files: - split: train path: '*/*pt*/train.parquet' - split: test path: '*/*pt*/test.parquet' - split: valid path: '*/*pt*/valid.parquet' - config_name: ro data_files: - split: train path: '*/*ro*/train.parquet' - split: test path: '*/*ro*/test.parquet' - split: valid path: '*/*ro*/valid.parquet' - config_name: ru data_files: - split: train path: '*/*ru*/train.parquet' - split: test path: '*/*ru*/test.parquet' - split: valid path: '*/*ru*/valid.parquet' - config_name: sk data_files: - split: train path: '*/*sk*/train.parquet' - split: test path: '*/*sk*/test.parquet' - split: valid path: '*/*sk*/valid.parquet' - config_name: sl data_files: - split: train path: '*/*sl*/train.parquet' - split: test path: '*/*sl*/test.parquet' - split: valid path: '*/*sl*/valid.parquet' - config_name: so data_files: - split: train path: '*/*so*/train.parquet' - split: test path: '*/*so*/test.parquet' - split: valid path: '*/*so*/valid.parquet' - config_name: sq data_files: - split: train path: '*/*sq*/train.parquet' - split: test path: '*/*sq*/test.parquet' - split: valid path: '*/*sq*/valid.parquet' - config_name: sr data_files: - split: train path: '*/*sr*/train.parquet' - split: test path: '*/*sr*/test.parquet' - split: valid path: '*/*sr*/valid.parquet' - config_name: sv data_files: - split: train path: '*/*sv*/train.parquet' - split: test path: '*/*sv*/test.parquet' - split: valid path: '*/*sv*/valid.parquet' - config_name: sw data_files: - split: train path: '*/*sw*/train.parquet' - split: test path: '*/*sw*/test.parquet' - split: valid path: '*/*sw*/valid.parquet' - config_name: ta data_files: - split: train path: '*/*ta*/train.parquet' - split: test path: '*/*ta*/test.parquet' - split: valid path: '*/*ta*/valid.parquet' - config_name: te data_files: - split: train path: '*/*te*/train.parquet' - split: test path: '*/*te*/test.parquet' - split: valid path: '*/*te*/valid.parquet' - config_name: th data_files: - split: train path: '*/*th*/train.parquet' - split: test path: '*/*th*/test.parquet' - split: valid path: '*/*th*/valid.parquet' - config_name: tl data_files: - split: train path: '*/*tl*/train.parquet' - split: test path: '*/*tl*/test.parquet' - split: valid path: '*/*tl*/valid.parquet' - config_name: tr data_files: - split: train path: '*/*tr*/train.parquet' - split: test path: '*/*tr*/test.parquet' - split: valid path: '*/*tr*/valid.parquet' - config_name: ug data_files: - split: train path: '*/*ug*/train.parquet' - split: test path: '*/*ug*/test.parquet' - split: valid path: '*/*ug*/valid.parquet' - config_name: uk data_files: - split: train path: '*/*uk*/train.parquet' - split: test path: '*/*uk*/test.parquet' - split: valid path: '*/*uk*/valid.parquet' - config_name: ur data_files: - split: train path: '*/*ur*/train.parquet' - split: test path: '*/*ur*/test.parquet' - split: valid path: '*/*ur*/valid.parquet' - config_name: uz data_files: - split: train path: '*/*uz*/train.parquet' - split: test path: '*/*uz*/test.parquet' - split: valid path: '*/*uz*/valid.parquet' - config_name: vi data_files: - split: train path: '*/*vi*/train.parquet' - split: test path: '*/*vi*/test.parquet' - split: valid path: '*/*vi*/valid.parquet' - config_name: ky data_files: - split: train path: '*/*ky*/train.parquet' - split: test path: '*/*ky*/test.parquet' - split: valid path: '*/*ky*/valid.parquet' - config_name: si data_files: - split: train path: '*/*si*/train.parquet' - split: test path: '*/*si*/test.parquet' - split: valid path: '*/*si*/valid.parquet' - config_name: as data_files: - split: train path: '*/*as*/train.parquet' - split: test path: '*/*as*/test.parquet' - split: valid path: '*/*as*/valid.parquet' - config_name: ha data_files: - split: train path: '*/*ha*/train.parquet' - split: test path: '*/*ha*/test.parquet' - split: valid path: '*/*ha*/valid.parquet' - config_name: la data_files: - split: train path: '*/*la*/train.parquet' - split: test path: '*/*la*/test.parquet' - split: valid path: '*/*la*/valid.parquet' - config_name: pa data_files: - split: train path: '*/*pa*/train.parquet' - split: test path: '*/*pa*/test.parquet' - split: valid path: '*/*pa*/valid.parquet' - config_name: ps data_files: - split: train path: '*/*ps*/train.parquet' - split: test path: '*/*ps*/test.parquet' - split: valid path: '*/*ps*/valid.parquet' ---
ucf-crcv/GAEA-Train
ucf-crcv
"2025-03-22T23:15:05Z"
14,794
2
[ "size_categories:1M<n<10M", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2503.16423", "region:us" ]
null
"2025-03-15T05:49:10Z"
--- dataset_info: features: - name: file_name dtype: image - name: image dtype: string - name: question dtype: string - name: answer dtype: string - name: lat dtype: string - name: lon dtype: string - name: location dtype: string - name: question_type dtype: string - name: subset dtype: string - name: dataset dtype: string splits: - name: train num_bytes: 83569722798.802 num_examples: 1580531 download_size: 79157132726 dataset_size: 83569722798.802 configs: - config_name: default data_files: - split: train path: data/train-* --- <h1 align="left"> GAEA: A Geolocation Aware Conversational Model</h1> <h3 align="left"> Summary</h3> <p align="justify"> Image geolocalization, in which, traditionally, an AI model predicts the precise GPS coordinates of an image is a challenging task with many downstream applications. However, the user cannot utilize the model to further their knowledge other than the GPS coordinate; the model lacks an understanding of the location and the conversational ability to communicate with the user. In recent days, with tremendous progress of large multimodal models (LMMs)—proprietary and open-source—researchers attempted to geolocalize images via LMMs. However, the issues remain unaddressed; beyond general tasks, for more specialized downstream tasks, one of which is geolocalization, LMMs struggle. In this work, we propose to solve this problem by introducing a conversational model `GAEA` that can provide information regarding the location of an image, as required by a user. No large-scale dataset enabling the training of such a model exists. Thus we propose a comprehensive dataset `GAEA-Train` with 800K images and around 1.6M question-answer pairs constructed by leveraging OpenStreetMap (OSM) attributes and geographical context clues. For quantitative evaluation, we propose a diverse benchmark, `GAEA-Bench` comprising 4K image-text pairs to evaluate conversational capabilities equipped with diverse question types. We consider 11 state-of-the-art open-source and proprietary LMMs and demonstrate that `GAEA` significantly outperforms the best open-source model, LLaVA-OneVision by 25.69% and best proprietary model, GPT-4o by 8.28%. We will publicly release our dataset and codes. </p> ## `GAEA` is the first open-source conversational model for conversational capabilities equipped with global-scale geolocalization. [![paper](https://img.shields.io/badge/arXiv-Paper-<COLOR>.svg)](https://arxiv.org/abs/2503.16423) [![Dataset](https://img.shields.io/badge/Dataset-Access-<COLOR>)](https://huggingface.co/collections/ucf-crcv/gaea-67d514a61d48eb1708b13a08) [![Website](https://img.shields.io/badge/Project-Website-87CEEB)](https://ucf-crcv.github.io/GAEA/) **Main contributions:** 1) **`GAEA-Train: A Diverse Training Dataset:`** We propose GAEA-Train, a new dataset designed for training conversational image geolocalization models, incorporating diverse visual and contextual data. 2) **`GAEA-Bench: Evaluating Conversational Geolocalization:`** To assess conversational capabilities in geolocalization, we introduce GAEA-Bench, a benchmark featuring various question-answer formats. 3) **`GAEA: An Interactive Geolocalization Chatbot:`** We present GAEA, a conversational chatbot that extends beyond geolocalization to provide rich contextual insights about locations from images. 4) **`Benchmarking Against State-of-the-Art LMMs:`** We quantitatively compare our model’s performance against 8 open-source and 3 proprietary LMMs, including GPT-4o and Gemini-2.0-Flash. <b> This page is dedicated to GAEA-Train </b> <h2 align="left"> Data Collection and Annotation Pipeline</h2> <p align="center"> <img src="Assets/GeoLLM_Flow.jpg" alt="Geo-LLM-Flow"></a> </p> <p align="justify"> <b> (Left)</b> GAEA-Train includes geographically diverse visual samples from various data sources, such as MP-16, GLD-v2, and CityGuesser68k. <b> (Middle)</b> We also incorporate OpenStreetMap (OSM) metadata and auxiliary context for each image, ranging from climate zones to geographical clues about the country. <b>(Right)</b> Using open-source LLMs and GPT-4o, we generate four diverse question-answer pairs across geolocation, reasoning, and conversational subsets. </p> <h2 align="left"> Data Statistics</h2> | Statistic | Value | |-------------------------------------|------------| | **Total images** | 822,951 | | **Total cities / countries** | 41,481 / 234 | | **Total questions** | 1,580,531 | | **Total geo-localization questions** | 822,951 | | **Total explanatory captions** | 384,947 | | **Total open-ended questions** | 267,668 | | **Total multiple-choice questions** | 48,673 | | **Total true/false questions** | 56,292 | <h2 align="left"> Qualitative Examples</h2> <p align="center"> <img src="Assets/qual_osm_metadata.jpg" alt="GeoLLM-Bench"></a> </p> <p align="justify"> We showcase various question-types including multiple-choice, true/false, short and long VQAs generated using an open-source model on our GAEA-Train dataset. We carefully select geographical tags from OSM metadata to generate diverse question types. </p> <h2 align="left"> Dataset Structure</h2> The general structure of our dataset looks like the following: ``` GAEA-1.6M/ |–– MP-16/ | |–– img_1.jpg | |–– img_2.jpg | |–– ... # remaining images |–– GLD-v2/ | |–– landmark_img_1.jpg | |–– landmark_img_2.jpg | |–– ... # remaining images |–– City_Guessr/ | |–– random_sample_1.jpg | |–– random_sample_2.jpg | |–– ... # remaining images ``` --- # Citation **BibTeX:** ```bibtex @misc{campos2025gaeageolocationawareconversational, title={GAEA: A Geolocation Aware Conversational Model}, author={Ron Campos and Ashmal Vayani and Parth Parag Kulkarni and Rohit Gupta and Aritra Dutta and Mubarak Shah}, year={2025}, eprint={2503.16423}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2503.16423}, } ``` --- ## Licensing Information We release our work under [CC BY-NC 4.0 License](https://creativecommons.org/licenses/by-nc/4.0/). The CC BY-NC 4.0 license allows others to share, remix, and adapt the work, as long as it's for non-commercial purposes and proper attribution is given to the original creator.
awettig/RedPajama-combined-15B-8k-llama
awettig
"2023-11-30T07:16:59Z"
14,768
0
[ "size_categories:1M<n<10M", "format:parquet", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
null
"2023-11-29T05:26:03Z"
--- dataset_info: features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 splits: - name: test num_bytes: 3181819992 num_examples: 29874 - name: train num_bytes: 188533325564 num_examples: 1770133 download_size: 0 dataset_size: 191715145556 --- # Dataset Card for "RedPajama-combined-15B-8K-llama" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
droid-ai/libero
droid-ai
"2025-03-12T01:15:39Z"
14,735
0
[ "task_categories:robotics", "license:apache-2.0", "region:us", "LeRobot" ]
[ "robotics" ]
"2025-03-12T01:08:44Z"
--- license: apache-2.0 task_categories: - robotics tags: - LeRobot configs: - config_name: default data_files: data/*/*.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - **Homepage:** [More Information Needed] - **Paper:** [More Information Needed] - **License:** apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.1", "robot_type": "libero", "total_episodes": 1693, "total_frames": 273465, "total_tasks": 40, "total_videos": 0, "total_chunks": 2, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:1693" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "is_first": { "dtype": "bool", "shape": [ 1 ] }, "is_last": { "dtype": "bool", "shape": [ 1 ] }, "is_terminal": { "dtype": "bool", "shape": [ 1 ] }, "image": { "dtype": "image", "shape": [ 256, 256, 3 ] }, "wrist_image": { "dtype": "image", "shape": [ 256, 256, 3 ] }, "state": { "dtype": "float32", "shape": [ 8 ] }, "joint_state": { "dtype": "float32", "shape": [ 7 ] }, "actions": { "dtype": "float32", "shape": [ 7 ] }, "discount": { "dtype": "float32", "shape": [ 1 ] }, "reward": { "dtype": "float32", "shape": [ 1 ] }, "timestamp": { "dtype": "float32", "shape": [ 1 ], "names": null }, "frame_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "episode_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "task_index": { "dtype": "int64", "shape": [ 1 ], "names": null } } } ``` ## Citation **BibTeX:** ```bibtex [More Information Needed] ```
deepghs/game_characters
deepghs
"2025-01-26T17:04:09Z"
14,719
24
[ "license:apache-2.0", "region:us" ]
null
"2023-01-28T11:12:51Z"
--- license: apache-2.0 --- # Database of Characters in Mobile Games [![Last Updated](https://img.shields.io/endpoint?url=https://gist.githubusercontent.com/narugo1992/254442dea2e77cf46366df97f499242f/raw/data_last_update.json)](https://huggingface.co/datasets/deepghs/game_characters) All the character in the following games are supported: * Arknights (crawled from [https://prts.wiki](https://prts.wiki)) * Fate/Grand Order (crawled from [https://fgo.wiki](https://fgo.wiki)) * Azur Lane (crawled from [https://wiki.biligame.com/blhx](https://wiki.biligame.com/blhx)) * Girls' Front-Line (crawled from [https://iopwiki.com/](https://iopwiki.com/)) * Genshin Impact (crawled from [https://genshin-impact.fandom.com/ja/wiki/%E5%8E%9F%E7%A5%9E_Wiki](https://genshin-impact.fandom.com/ja/wiki/%E5%8E%9F%E7%A5%9E_Wiki)) The source code and python library is hosted on [narugo1992/gchar](https://github.com/narugo1992/gchar), and the scheduled job is configured on Github Action, so the data will be automatically updated to the latest version once a day. More character data for other games is coming...
lmqg/qa_squadshifts_synthetic
lmqg
"2023-01-15T14:25:15Z"
14,699
0
[ "task_categories:question-answering", "task_ids:extractive-qa", "multilinguality:monolingual", "source_datasets:extended|wikipedia", "language:en", "license:cc-by-4.0", "size_categories:1M<n<10M", "modality:text", "library:datasets", "library:mlcroissant", "arxiv:2210.03992", "region:us" ]
[ "question-answering" ]
"2022-12-20T08:31:18Z"
--- license: cc-by-4.0 pretty_name: Synthetic QA dataset on SQuADShifts. language: en multilinguality: monolingual size_categories: 10K<n<100K source_datasets: - extended|wikipedia task_categories: - question-answering task_ids: - extractive-qa --- # Dataset Card for "lmqg/qa_squadshifts_synthetic" ## Dataset Description - **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) - **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992) - **Point of Contact:** [Asahi Ushio](http://asahiushio.com/) ### Dataset Summary This is a synthetic QA dataset generated with fine-tuned QG models over [`lmqg/qa_squadshifts`](https://huggingface.co/datasets/lmqg/qa_squadshifts), made for question-answering based evaluation (QAE) for question generation model proposed by [Zhang and Bansal, 2019](https://aclanthology.org/D19-1253/). The test split is the original validation set of [`lmqg/qa_squadshifts`](https://huggingface.co/datasets/lmqg/qa_squadshifts), where the model should be evaluate on. ### Supported Tasks and Leaderboards * `question-answering` ### Languages English (en) ## Dataset Structure ### Data Fields The data fields are the same among all splits. #### plain_text - `id`: a `string` feature of id - `title`: a `string` feature of title of the paragraph - `context`: a `string` feature of paragraph - `question`: a `string` feature of question - `answers`: a `json` feature of answers ### Data Splits TBA ## Citation Information ``` @inproceedings{ushio-etal-2022-generative, title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration", author = "Ushio, Asahi and Alva-Manchego, Fernando and Camacho-Collados, Jose", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, U.A.E.", publisher = "Association for Computational Linguistics", } ```
bigbio/gad
bigbio
"2022-12-22T15:25:28Z"
14,698
4
[ "multilinguality:momolingual", "language:en", "license:cc-by-4.0", "size_categories:100K<n<1M", "modality:text", "library:datasets", "library:mlcroissant", "region:us" ]
null
"2022-09-26T03:36:32Z"
--- language: - en bigbio_language: - English license: cc-by-4.0 multilinguality: momolingual bigbio_license_shortname: CC_BY_4p0 pretty_name: GAD homepage: https://geneticassociationdb.nih.gov/ bigbio_pubmed: true bigbio_public: true bigbio_tasks: - TEXT_CLASSIFICATION paperswithcode_id: gad --- # Dataset Card for GAD ## Dataset Description - **Homepage:** https://geneticassociationdb.nih.gov/ - **Pubmed:** True - **Public:** True - **Tasks:** TXTCLASS A corpus identifying associations between genes and diseases by a semi-automatic annotation procedure based on the Genetic Association Database. ## Note about homepage The homepage for this dataset is no longer reachable, but the url is recorded here. Data for this dataset was originally downloaded from a google drive folder (the link used in the [BLURB benchmark data download script](https://microsoft.github.io/BLURB/submit.html). However, we host the data in the huggingface hub for more reliable downloads and access. ## Citation Information ``` @article{Bravo2015, doi = {10.1186/s12859-015-0472-9}, url = {https://doi.org/10.1186/s12859-015-0472-9}, year = {2015}, month = feb, publisher = {Springer Science and Business Media {LLC}}, volume = {16}, number = {1}, author = {{\`{A}}lex Bravo and Janet Pi{\~{n}}ero and N{\'{u}}ria Queralt-Rosinach and Michael Rautschka and Laura I Furlong}, title = {Extraction of relations between genes and diseases from text and large-scale data analysis: implications for translational research}, journal = {{BMC} Bioinformatics} } ```
microsoft/orca-agentinstruct-1M-v1
microsoft
"2024-11-01T00:14:29Z"
14,620
432
[ "task_categories:question-answering", "language:en", "license:cdla-permissive-2.0", "size_categories:1M<n<10M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
[ "question-answering" ]
"2024-10-25T23:41:54Z"
--- language: - en license: cdla-permissive-2.0 size_categories: - 1M<n<10M task_categories: - question-answering dataset_info: features: - name: messages dtype: string splits: - name: creative_content num_bytes: 288747542 num_examples: 50000 - name: text_modification num_bytes: 346421282 num_examples: 50000 - name: struct2text_flow num_bytes: 251920604 num_examples: 50000 - name: rc num_bytes: 282448904 num_examples: 50000 - name: rag num_bytes: 421188673 num_examples: 50000 - name: text_extraction num_bytes: 312246895 num_examples: 50000 - name: mcq num_bytes: 230459938 num_examples: 99986 - name: follow_up num_bytes: 881311205 num_examples: 99054 - name: analytical_reasoning num_bytes: 100724491 num_examples: 25000 - name: fermi num_bytes: 78109959 num_examples: 25000 - name: fs_cot_flow num_bytes: 109007740 num_examples: 25000 - name: code_ num_bytes: 617418962 num_examples: 100000 - name: brain_teaser num_bytes: 124523402 num_examples: 50000 - name: text_classification num_bytes: 151217275 num_examples: 50000 - name: open_domain_qa num_bytes: 616935002 num_examples: 272370 download_size: 2210440144 dataset_size: 4812681874 configs: - config_name: default data_files: - split: creative_content path: data/creative_content-* - split: text_modification path: data/text_modification-* - split: struct2text_flow path: data/struct2text_flow-* - split: rc path: data/rc-* - split: rag path: data/rag-* - split: text_extraction path: data/text_extraction-* - split: mcq path: data/mcq-* - split: follow_up path: data/follow_up-* - split: analytical_reasoning path: data/analytical_reasoning-* - split: fermi path: data/fermi-* - split: fs_cot_flow path: data/fs_cot_flow-* - split: code_ path: data/code_-* - split: brain_teaser path: data/brain_teaser-* - split: text_classification path: data/text_classification-* - split: open_domain_qa path: data/open_domain_qa-* --- ### Dataset Card This dataset is a fully synthetic set of instruction pairs where both the prompts and the responses have been synthetically generated, using the AgentInstruct framework. AgentInstruct is an extensible agentic framework for synthetic data generation. This dataset contains ~1 million instruction pairs generated by the AgentInstruct, using only raw text content publicly avialble on the Web as seeds. The data covers different capabilities, such as text editing, creative writing, coding, reading comprehension, etc. The dataset can be used for instruction tuning of any base LLM. Using the full AgentInstruct data (a superset of this dataset with ~25M instruction pairs), we post-trained Mistral-7b. When comparing the resulting model Orca-3-Mistral to Mistral-7b-Instruct (which uses the same base model), we observe significant improvements across many benchmarks. For example, 40% improvement on AGIEval, 19% improvement on MMLU, 54% improvement on GSM8K, 38% improvement on BBH and 45% improvement on AlpacaEval. Please refer to AgentInstruct: Toward Generative Teaching with Agentic Flows for further details. ### Dataset Sources **Repository:** microsoft/orca-agentinstruct-1M-v1 **Paper:** AgentInstruct: Toward Generative Teaching with Agentic Flows ### Direct Use This is a general dataset created by the AgentInstruct methodology. It contains diverse synthetic data. This could be used for research on instruction tuning and synthetic data use and generaiton. ### Out-of-Scope Use This dataset is being shared for research purposes. For training models to perform real-world tasks, we recommend further testing and validation where needed. This dataset is not intended for use in educational systems or organizations, or for use in health systems. ### Dataset Structure ### Data Instances A typical data entry in the dataset consists of a conversation i.e. sequence of question and its corresponding answer. Below is an example from the dataset: ```json { "messages": [ { "role": "system", "content": "" }, { "role": "user", "content": "Argue the importance of having a strong understanding of physics and mechanics in the design of electron beam systems for semiconductor equipment." }, { "role": "assistant", "content": "Having a strong understanding of physics and mechanics is crucial in the design of electron beam systems for semiconductor equipment. This is because electron beam systems rely heavily on the principles of physics and mechanics to function effectively and efficiently.\n\nFirstly, a strong understanding of physics is necessary to design the electron gun, which is the heart of the electron beam system. The electron gun generates and accelerates the electrons that are used to create the beam. The design of the electron gun must take into account the properties of electrons, such as their charge, mass, and velocity, as well as the principles of electromagnetism and quantum mechanics. Without a thorough understanding of these principles, it would be impossible to design an electron gun that can generate and accelerate electrons with the required energy and precision.\n\nSecondly, a strong understanding of mechanics is essential in the design of the electron beam system's optics. The optics are responsible for shaping and focusing the electron beam, which is critical for achieving high-resolution imaging and precise material processing. The design of the optics must take into account the principles of optics, such as refraction, reflection, and diffraction, as well as the mechanical properties of the components, such as their stiffness, thermal expansion, and vibration resistance. Without a thorough understanding of these principles, it would be impossible to design optics that can shape and focus the electron beam with the required precision and stability.\n\nIn conclusion, a strong understanding of physics and mechanics is essential in the design of electron beam systems for semiconductor equipment. Without this understanding, it would be impossible to design electron guns and optics that can generate and shape the electron beam with the required precision and stability. Therefore, designers of electron beam systems must have a solid foundation in physics and mechanics to ensure that their designs are effective and efficient." }, { "role": "user", "content": "Could you perhaps rephrase your entire explanation using only haiku poetry? It would certainly give your answer a more artistic touch, don't you think?" }, { "role": "assistant", "content": "Electron streams flow,\nPhysics guides their path and speed,\nSemiconduct's soul.\n\nElectron gun's heart,\nQuantum dance, charged particles,\nPrecision's fine art.\n\nOptics shape the beam,\nMechanics' firm hand guides light,\nFocused like a dream.\n\nIn the silicon dance,\nPhysics, mechanics embrace,\nAdvance our expanse.\n\nDesign's core must know,\nNature's laws to harness force,\nFor tech to grow, glow." }, { "role": "user", "content": "Just to make sure I've got it right, your haikus suggest that the electron gun and optics are like artists, requiring precision and a deep understanding of natural laws to create their masterpiece?" }, { "role": "assistant", "content": "Indeed, that's a poetic interpretation. The haikus suggest that the electron gun (the creator of the electron beam) and the optics (which shape and focus the beam) require a precise understanding of physics and mechanics, much like artists need a deep understanding of their tools and materials to create a masterpiece. The natural laws are the guidelines within which they must work to achieve the desired outcome in semiconductor equipment design." }, { "role": "user", "content": "I'd like to understand more about the 'precision's fine art' you mentioned. Can you explain how precision is achieved in the design of the electron gun and optics?" }, { "role": "assistant", "content": "Certainly! Achieving precision in the design of the electron gun and optics in electron beam systems involves several key factors:\n\n1. **Electron Gun Design:**\n - **Vacuum Environment:** The electron gun operates in a high vacuum to prevent electron scattering by air molecules, ensuring a precise and concentrated beam.\n - **Cathode Material:** The choice of cathode material affects the emission efficiency and stability of the electron beam. Materials like tungsten or lanthanum hexaboride are often used for their high melting points and low work functions.\n - **Anode Configuration:** The anode accelerates the electrons and its design, including the shape and voltage applied, determines the beam's energy and focus.\n - **Electromagnetic Lenses:** These lenses control the focus of the electron beam, and their design must account for spherical and chromatic aberrations to maintain precision.\n\n2. **Optics Design:**\n - **Electromagnetic Lenses:** Just like in the electron gun, these lenses in the optical column are used to focus and deflect the electron beam with high precision. The design and alignment of these lenses are critical for achieving the desired spot size and shape on the target.\n - **Stigmators:** These are used to correct for astigmatism in the electron beam, ensuring that the beam remains symmetrical and focused over a range of operating conditions.\n - **Apertures:** Placed at strategic points in the optical column, apertures help to shape the beam and eliminate electrons that stray from the desired path, improving resolution and reducing aberrations.\n - **Thermal Stability:** Precision in electron beam systems also depends on maintaining thermal stability to prevent drift or deformation of the optical components.\n\n3. **Control Systems:**\n - **Feedback Loops:** Precision is maintained through feedback systems that monitor the beam's characteristics and make real-time adjustments to the electron gun and optics to correct any deviations.\n - **Vibration Isolation:** The entire system must be isolated from external vibrations that could disturb the beam's path, often using pneumatic or magnetic isolation systems.\n\n4. **Material Science:**\n - **Material Properties:** The materials used in the construction of the electron gun and optics must be chosen for their stability under the operating conditions, including resistance to thermal expansion and electromagnetic interference.\n\n5. **Computational Modeling:**\n - **Simulation Software:** Advanced software is used to model the electron beam's behavior and interactions with the system's components, allowing for the optimization of the design before physical construction.\n\nIn summary, 'precision's fine art' in the context of electron beam systems refers to the meticulous design and engineering of the electron gun and optics, which are achieved through a deep understanding of physics and materials science, as well as the implementation of sophisticated control and feedback mechanisms." } ], } ``` ### Data Fields The dataset comprises the following fields: messages: String representing a conversation in OpenAI format. ```json [ {"role": "system", "content": "...."}, {"role": "user", "content": "...."}, {"role": "assistant", "content": "...."}, {"role": "user", "content": "...."}, {"role": "assistant", "content": "...."}, .... ] ``` To read the conversation use `json.loads()` ### Data Splits train ### Dataset Creation ### Source Data Please refer to AgentInstruct: Toward Generative Teaching with Agentic Flows for further detail ### Data Collection and Processing Please refer to AgentInstruct: Toward Generative Teaching with Agentic Flows for further details for details about the dataset construction. ### Who are the source data producers? Microsoft ### Annotation process We generate questions and answers using using Azure GPT-4. ### Personal and Sensitive Information None ### Bias, Risks, and Limitations • This dataset is in English. • The dataset inherits the biases, errors, and omissions known to exist in data used for seed sources and models used for data generaiton. • This dataset is not intended to represent any specific domain, and contains generic data. However, the AgentInstruct methodology, which was used to create this dataset, can be used to generate high-quality domain specific data, which can be used to fine-tune any existing model for a specific domain. • The dataset is synthetically gnerated and hence may contain inaccuracies that do not accurately reflect real-world phenomena. • The synthetic nature of this dataset may limit its ability to generalize to real-world cases. • The data is intended for research and exoerumentation for model training and synthetic data generation. ### Citation If you find this work useful in your method, you can cite the paper as below: @misc{ title={ AgentInstruct: Toward Generative Teaching with Agentic Flows}, author={Arindam Mitra, Luciano Del Corro, Guoqing Zheng, Shweti Mahajan, Dany Rouhana, Andres Codas, Yadong Lu, Wei-ge Chen, Olga Vrousgos, Corby Rosset, Fillipe Silva, Hamed Khanpour, Yash Lara, Ahmed Awadallah}, year={2024}, eprint={ 2407.03502}, archivePrefix={arXiv}, primaryClass={cs.CL} } Dataset Card Contact [email protected]
cimec/lambada
cimec
"2024-01-04T14:16:25Z"
14,493
58
[ "task_categories:text2text-generation", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "source_datasets:extended|bookcorpus", "language:en", "license:cc-by-4.0", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "long-range-dependency" ]
[ "text2text-generation" ]
"2022-03-02T23:29:22Z"
--- annotations_creators: - expert-generated language_creators: - found language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - extended|bookcorpus task_categories: - text2text-generation task_ids: [] paperswithcode_id: lambada pretty_name: LAMBADA tags: - long-range-dependency dataset_info: config_name: plain_text features: - name: text dtype: string - name: domain dtype: string splits: - name: train num_bytes: 978174122 num_examples: 2662 - name: test num_bytes: 1791823 num_examples: 5153 - name: validation num_bytes: 1703482 num_examples: 4869 download_size: 552427340 dataset_size: 981669427 configs: - config_name: plain_text data_files: - split: train path: plain_text/train-* - split: test path: plain_text/test-* - split: validation path: plain_text/validation-* default: true --- # Dataset Card for LAMBADA ## 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:** [LAMBADA homepage](https://zenodo.org/record/2630551#.X8UP76pKiIa) - **Paper:** [The LAMBADA dataset: Word prediction requiring a broad discourse context∗](https://www.aclweb.org/anthology/P16-1144.pdf) - **Data:** https://doi.org/10.5281/zenodo.2630551 ### Dataset Summary The LAMBADA evaluates the capabilities of computational models for text understanding by means of a word prediction task. LAMBADA is a collection of narrative passages sharing the characteristic that human subjects are able to guess their last word if they are exposed to the whole passage, but not if they only see the last sentence preceding the target word. To succeed on LAMBADA, computational models cannot simply rely on local context, but must be able to keep track of information in the broader discourse. The LAMBADA dataset is extracted from BookCorpus and consists of 10'022 passages, divided into 4'869 development and 5'153 test passages. The training data for language models to be tested on LAMBADA include the full text of 2'662 novels (disjoint from those in dev+test), comprising 203 million words. ### Supported Tasks and Leaderboards Long range dependency evaluated as (last) word prediction ### Languages The text in the dataset is in English. The associated BCP-47 code is `en`. ## Dataset Structure ### Data Instances A data point is a text sequence (passage) including the context, the target sentence (the last one) and the target word. For each passage in the dev and the test splits, the word to be guessed is the last one. The training data include the full text of 2'662 novels (disjoint from those in dev+test), comprising more than 200M words. It consists of text from the same domain as the dev+test passages, but not filtered in any way. Each training instance has a `category` field indicating which sub-category the book was extracted from. This field is not given for the dev and test splits. An example looks like this: ``` {"category": "Mystery", "text": "bob could have been called in at this point , but he was n't miffed at his exclusion at all . he was relieved at not being brought into this initial discussion with central command . `` let 's go make some grub , '' said bob as he turned to danny . danny did n't keep his stoic expression , but with a look of irritation got up and left the room with bob", } ``` ### Data Fields - `category`: the sub-category of books from which the book was extracted from. Only available for the training split. - `text`: the text (concatenation of context, target sentence and target word). The word to be guessed is the last one. ### Data Splits - train: 2'662 novels - dev: 4'869 passages - test: 5'153 passages ## Dataset Creation ### Curation Rationale The dataset aims at evaluating the ability of language models to hold long-term contextual memories. Instances are extracted from books because they display long-term dependencies. In particular, the data are curated such that the target words are easy to guess by human subjects when they can look at the whole passage they come from, but nearly impossible if only the last sentence is considered. ### Source Data #### Initial Data Collection and Normalization The corpus was duplicated and potentially offensive material were filtered out with a stop word list. #### Who are the source language producers? The passages are extracted from novels from [Book Corpus](https://github.com/huggingface/datasets/tree/master/datasets/bookcorpus). ### Annotations #### Annotation process The authors required two consecutive subjects (paid crowdsourcers) to exactly match the missing word based on the whole passage (comprising the context and the target sentence), and made sure that no subject (out of ten) was able to provide it based on local context only, even when given 3 guesses. #### Who are the annotators? The text is self-annotated but was curated by asking (paid) crowdsourcers to guess the last word. ### 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 The dataset is released under the [CC BY 4.0](Creative Commons Attribution 4.0 International) license. ### Citation Information ``` @InProceedings{paperno-EtAl:2016:P16-1, author = {Paperno, Denis and Kruszewski, Germ\'{a}n and Lazaridou, Angeliki and Pham, Ngoc Quan and Bernardi, Raffaella and Pezzelle, Sandro and Baroni, Marco and Boleda, Gemma and Fernandez, Raquel}, title = {The {LAMBADA} dataset: Word prediction requiring a broad discourse context}, booktitle = {Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}, month = {August}, year = {2016}, address = {Berlin, Germany}, publisher = {Association for Computational Linguistics}, pages = {1525--1534}, url = {http://www.aclweb.org/anthology/P16-1144} } ``` ### Contributions Thanks to [@VictorSanh](https://github.com/VictorSanh) for adding this dataset.
gigant/tib_slides
gigant
"2025-01-28T14:40:52Z"
14,424
0
[ "size_categories:100K<n<1M", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
null
"2023-01-27T01:44:02Z"
--- dataset_info: features: - name: Image dtype: image - name: file_name dtype: string splits: - name: train num_bytes: 131956494917.654 num_examples: 484843 download_size: 0 dataset_size: 131956494917.654 --- # Dataset Card for "tib_slides" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
JonasLange/DBD-research-group
JonasLange
"2023-06-27T11:26:14Z"
14,419
0
[ "size_categories:10K<n<100K", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
null
"2023-06-27T08:44:41Z"
--- dataset_info: features: - name: ID dtype: string - name: Audio dtype: audio - name: Start Time (s) dtype: string - name: End Time (s) dtype: string - name: Low Freq (Hz) dtype: string - name: High Freq (Hz) dtype: string - name: Species eBird Code dtype: string - name: Call Type dtype: 'null' - name: Sex dtype: 'null' - name: Latitude dtype: float64 - name: Longitude dtype: float64 - name: Uncertainty dtype: 'null' - name: Microphone dtype: string - name: License dtype: string - name: Source dtype: string - name: BirdNet Training Data dtype: bool splits: - name: train num_bytes: 156231869250.216 num_examples: 10976 download_size: 538872707 dataset_size: 156231869250.216 --- # Dataset Card for "DBD-research-group" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
facebook/xnli
facebook
"2024-01-05T08:30:52Z"
14,406
56
[ "language:ar", "language:bg", "language:de", "language:el", "language:en", "language:es", "language:fr", "language:hi", "language:ru", "language:sw", "language:th", "language:tr", "language:ur", "language:vi", "language:zh", "size_categories:1M<n<10M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
null
"2022-03-02T23:29:22Z"
--- 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.
open-rl-leaderboard/results_v2
open-rl-leaderboard
"2024-12-07T20:19:13Z"
14,357
1
[ "size_categories:10M<n<100M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-05-14T15:05:26Z"
--- dataset_info: features: - name: user_id dtype: string - name: model_id dtype: string - name: sha dtype: string - name: status dtype: string - name: env_id dtype: string - name: episodic_returns sequence: float64 splits: - name: train num_bytes: 17423994045 num_examples: 46711176 download_size: 14153678919 dataset_size: 17423994045 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "results_v2" [Leaderboard](https://huggingface.co/spaces/open-rl-leaderboard/leaderboard) [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
cboettig/fishbase
cboettig
"2024-08-16T20:35:09Z"
14,325
3
[ "license:cc-by-nc-4.0", "size_categories:10M<n<100M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-08-01T21:36:53Z"
--- license: cc-by-nc-4.0 --- # Dataset Card for FishBase <!-- Provide a quick summary of the dataset. --> Snapshots of FishBase data tables used by the [rOpenSci package `rfishbase`](https://github.com/ropensci/rfishbase) and the FishBase API. ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** Carl Boettiger - **License:** CC-BY-NC, FishBase.org Team ### Dataset Sources <https://fishbase.org>, <https://sealifebase.org>, via exclusive export of backend MySQL databases. ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use This data is provided to facilitate access to fishbase statistics through `rfishbase`. Curation has evolved over the past decade and now provides basically direct access to versioned snapshots of the fishbase backend data. ### Out-of-Scope Use Reuse of this data is not licensed for commerical applications. Developers should contact the FishBase team directly. See <https://fishbase.org> for details. ## Dataset Structure Data provided here are raw CSV and parquet exports from the MySQL database backend powering <https://fishbase.org> and its sister site, <https://sealifebase.org>. Please note that there are many tables here which have evolved over decades and were not developed with the intent of ever supporting direct access by end users. The data structure can be highly unintuitive and common tasks may require joins across multiple tables. These files represent a snapshot in time as indicated by the version numbers (e.g. v24.07 is data snapshot dated to July of 2024). ## Dataset Creation Dataset creation relies on ingesting a MySQL dump provided roughly annually from the FishBase team to this maintainer. Current process imports this into a MySQL (mariadb) server in docker, connects a the `duckdb` `mysql` extension and exports as parquet and csv, as shown in the `creation.py` script. ### Source Data See <https://fishbase.org> for details.
eduagarcia-temp/OSCAR-2301_meta
eduagarcia-temp
"2023-08-28T14:07:22Z"
14,312
0
[ "size_categories:10M<n<100M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
null
"2023-08-27T20:24:54Z"
--- dataset_info: features: - name: id dtype: int64 - name: text dtype: string - name: meta struct: - name: categories sequence: string - name: dedup struct: - name: exact_norm struct: - name: cluster_main_idx dtype: int64 - name: cluster_size dtype: int64 - name: exact_hash_idx dtype: int64 - name: is_duplicate dtype: bool - name: minhash struct: - name: cluster_main_idx dtype: int64 - name: cluster_size dtype: int64 - name: is_duplicate dtype: bool - name: minhash_idx dtype: int64 - name: harmful_pp dtype: float64 - name: identification struct: - name: label dtype: string - name: prob dtype: float64 - name: quality_warnings sequence: string - name: sentence_identifications list: - name: label dtype: string - name: prob dtype: float64 - name: tlsh dtype: string - name: warc_headers struct: - name: content-length dtype: int64 - name: content-type dtype: string - name: warc-block-digest dtype: string - name: warc-date dtype: string - name: warc-identified-content-language dtype: string - name: warc-record-id dtype: string - name: warc-refers-to dtype: string - name: warc-target-uri dtype: string - name: warc-type dtype: string splits: - name: train num_bytes: 127702717461 num_examples: 18031400 download_size: 40317121912 dataset_size: 127702717461 --- # Dataset Card for "OSCAR-2301_meta" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
facebook/wiki_dpr
facebook
"2024-03-12T18:06:56Z"
14,297
35
[ "task_categories:fill-mask", "task_categories:text-generation", "task_ids:language-modeling", "task_ids:masked-language-modeling", "annotations_creators:no-annotation", "language_creators:crowdsourced", "multilinguality:multilingual", "source_datasets:original", "language:en", "license:cc-by-nc-4.0", "size_categories:10M<n<100M", "arxiv:2004.04906", "region:us", "text-search" ]
[ "fill-mask", "text-generation" ]
"2022-03-02T23:29:22Z"
--- annotations_creators: - no-annotation language_creators: - crowdsourced language: - en license: cc-by-nc-4.0 multilinguality: - multilingual size_categories: - 10M<n<100M source_datasets: - original task_categories: - fill-mask - text-generation task_ids: - language-modeling - masked-language-modeling pretty_name: Wiki-DPR tags: - text-search dataset_info: - config_name: psgs_w100.nq.exact features: - name: id dtype: string - name: text dtype: string - name: title dtype: string - name: embeddings sequence: float32 splits: - name: train num_bytes: 78419248156 num_examples: 21015300 download_size: 85288677114 dataset_size: 78419248156 - config_name: psgs_w100.nq.compressed features: - name: id dtype: string - name: text dtype: string - name: title dtype: string - name: embeddings sequence: float32 splits: - name: train num_bytes: 78419248156 num_examples: 21015300 download_size: 85288677114 dataset_size: 78419248156 - config_name: psgs_w100.nq.no_index features: - name: id dtype: string - name: text dtype: string - name: title dtype: string - name: embeddings sequence: float32 splits: - name: train num_bytes: 78419248156 num_examples: 21015300 download_size: 85288677114 dataset_size: 78419248156 - config_name: psgs_w100.multiset.exact features: - name: id dtype: string - name: text dtype: string - name: title dtype: string - name: embeddings sequence: float32 splits: - name: train num_bytes: 78419248156 num_examples: 21015300 download_size: 85289275855 dataset_size: 78419248156 - config_name: psgs_w100.multiset.compressed features: - name: id dtype: string - name: text dtype: string - name: title dtype: string - name: embeddings sequence: float32 splits: - name: train num_bytes: 78419248156 num_examples: 21015300 download_size: 85289275855 dataset_size: 78419248156 - config_name: psgs_w100.multiset.no_index features: - name: id dtype: string - name: text dtype: string - name: title dtype: string - name: embeddings sequence: float32 splits: - name: train num_bytes: 78419248156 num_examples: 21015300 download_size: 85289275855 dataset_size: 78419248156 --- # Dataset Card for "wiki_dpr" ## 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 - **Repository:** https://github.com/facebookresearch/DPR - **Paper:** https://arxiv.org/abs/2004.04906 - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Dataset Summary This is the wikipedia split used to evaluate the Dense Passage Retrieval (DPR) model. It contains 21M passages from wikipedia along with their DPR embeddings. The wikipedia articles were split into multiple, disjoint text blocks of 100 words as passages. The wikipedia dump is the one from Dec. 20, 2018. There are two types of DPR embeddings based on two different models: - `nq`: the model is trained on the Natural Questions dataset - `multiset`: the model is trained on multiple datasets Additionally, a FAISS index can be created from the embeddings: - `exact`: with an exact FAISS index (high RAM usage) - `compressed`: with a compressed FAISS index (approximate, but lower RAM usage) - `no_index`: without FAISS index Finally, there is the possibility of generating the dataset without the embeddings: - `no_embeddings` ### 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 Each instance contains a paragraph of at most 100 words, as well as the title of the wikipedia page it comes from, and the DPR embedding (a 768-d vector). #### psgs_w100.multiset.compressed - **Size of downloaded dataset files:** 70.97 GB - **Size of the generated dataset:** 78.42 GB - **Total amount of disk used:** 163.71 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: {'id': '1', 'text': 'Aaron Aaron ( or ; "Ahärôn") is a prophet, high priest, and the brother of Moses in the Abrahamic religions. Knowledge of Aaron, along with his brother Moses, comes exclusively from religious texts, such as the Bible and Quran. The Hebrew Bible relates that, unlike Moses, who grew up in the Egyptian royal court, Aaron and his elder sister Miriam remained with their kinsmen in the eastern border-land of Egypt (Goshen). When Moses first confronted the Egyptian king about the Israelites, Aaron served as his brother\'s spokesman ("prophet") to the Pharaoh. Part of the Law (Torah) that Moses received from'], 'title': 'Aaron', 'embeddings': [-0.07233893871307373, 0.48035329580307007, 0.18650995194911957, -0.5287084579467773, -0.37329429388046265, 0.37622880935668945, 0.25524479150772095, ... -0.336689829826355, 0.6313082575798035, -0.7025573253631592]} ``` #### psgs_w100.multiset.exact - **Size of downloaded dataset files:** 85.23 GB - **Size of the generated dataset:** 78.42 GB - **Total amount of disk used:** 163.71 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: {'id': '1', 'text': 'Aaron Aaron ( or ; "Ahärôn") is a prophet, high priest, and the brother of Moses in the Abrahamic religions. Knowledge of Aaron, along with his brother Moses, comes exclusively from religious texts, such as the Bible and Quran. The Hebrew Bible relates that, unlike Moses, who grew up in the Egyptian royal court, Aaron and his elder sister Miriam remained with their kinsmen in the eastern border-land of Egypt (Goshen). When Moses first confronted the Egyptian king about the Israelites, Aaron served as his brother\'s spokesman ("prophet") to the Pharaoh. Part of the Law (Torah) that Moses received from'], 'title': 'Aaron', 'embeddings': [-0.07233893871307373, 0.48035329580307007, 0.18650995194911957, -0.5287084579467773, -0.37329429388046265, 0.37622880935668945, 0.25524479150772095, ... -0.336689829826355, 0.6313082575798035, -0.7025573253631592]} ``` #### psgs_w100.multiset.no_index - **Size of downloaded dataset files:** 85.23 GB - **Size of the generated dataset:** 78.42 GB - **Total amount of disk used:** 163.71 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: {'id': '1', 'text': 'Aaron Aaron ( or ; "Ahärôn") is a prophet, high priest, and the brother of Moses in the Abrahamic religions. Knowledge of Aaron, along with his brother Moses, comes exclusively from religious texts, such as the Bible and Quran. The Hebrew Bible relates that, unlike Moses, who grew up in the Egyptian royal court, Aaron and his elder sister Miriam remained with their kinsmen in the eastern border-land of Egypt (Goshen). When Moses first confronted the Egyptian king about the Israelites, Aaron served as his brother\'s spokesman ("prophet") to the Pharaoh. Part of the Law (Torah) that Moses received from'], 'title': 'Aaron', 'embeddings': [-0.07233893871307373, 0.48035329580307007, 0.18650995194911957, -0.5287084579467773, -0.37329429388046265, 0.37622880935668945, 0.25524479150772095, ... -0.336689829826355, 0.6313082575798035, -0.7025573253631592]} ``` #### psgs_w100.nq.compressed - **Size of downloaded dataset files:** 85.23 GB - **Size of the generated dataset:** 78.42 GB - **Total amount of disk used:** 163.71 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: {'id': '1', 'text': 'Aaron Aaron ( or ; "Ahärôn") is a prophet, high priest, and the brother of Moses in the Abrahamic religions. Knowledge of Aaron, along with his brother Moses, comes exclusively from religious texts, such as the Bible and Quran. The Hebrew Bible relates that, unlike Moses, who grew up in the Egyptian royal court, Aaron and his elder sister Miriam remained with their kinsmen in the eastern border-land of Egypt (Goshen). When Moses first confronted the Egyptian king about the Israelites, Aaron served as his brother\'s spokesman ("prophet") to the Pharaoh. Part of the Law (Torah) that Moses received from'], 'title': 'Aaron', 'embeddings': [0.013342111371457577, 0.582173764705658, -0.31309744715690613, -0.6991612911224365, -0.5583199858665466, 0.5187504887580872, 0.7152731418609619, ... -0.5385938286781311, 0.8093984127044678, -0.4741983711719513]} ``` #### psgs_w100.nq.exact - **Size of downloaded dataset files:** 85.23 GB - **Size of the generated dataset:** 78.42 GB - **Total amount of disk used:** 163.71 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: {'id': '1', 'text': 'Aaron Aaron ( or ; "Ahärôn") is a prophet, high priest, and the brother of Moses in the Abrahamic religions. Knowledge of Aaron, along with his brother Moses, comes exclusively from religious texts, such as the Bible and Quran. The Hebrew Bible relates that, unlike Moses, who grew up in the Egyptian royal court, Aaron and his elder sister Miriam remained with their kinsmen in the eastern border-land of Egypt (Goshen). When Moses first confronted the Egyptian king about the Israelites, Aaron served as his brother\'s spokesman ("prophet") to the Pharaoh. Part of the Law (Torah) that Moses received from'], 'title': 'Aaron', 'embeddings': [0.013342111371457577, 0.582173764705658, -0.31309744715690613, -0.6991612911224365, -0.5583199858665466, 0.5187504887580872, 0.7152731418609619, ... -0.5385938286781311, 0.8093984127044678, -0.4741983711719513]} ``` ### Data Fields The data fields are the same among all splits. #### psgs_w100.multiset.compressed - `id`: a `string` feature. - `text`: a `string` feature. - `title`: a `string` feature. - `embeddings`: a `list` of `float32` features. #### psgs_w100.multiset.exact - `id`: a `string` feature. - `text`: a `string` feature. - `title`: a `string` feature. - `embeddings`: a `list` of `float32` features. #### psgs_w100.multiset.no_index - `id`: a `string` feature. - `text`: a `string` feature. - `title`: a `string` feature. - `embeddings`: a `list` of `float32` features. #### psgs_w100.nq.compressed - `id`: a `string` feature. - `text`: a `string` feature. - `title`: a `string` feature. - `embeddings`: a `list` of `float32` features. #### psgs_w100.nq.exact - `id`: a `string` feature. - `text`: a `string` feature. - `title`: a `string` feature. - `embeddings`: a `list` of `float32` features. ### Data Splits | name | train | |-----------------------------|-------:| |psgs_w100.multiset.compressed|21015300| |psgs_w100.multiset.exact |21015300| |psgs_w100.multiset.no_index |21015300| |psgs_w100.nq.compressed |21015300| |psgs_w100.nq.exact |21015300| ## 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 DPR is CC-BY-NC 4.0 licensed: https://github.com/facebookresearch/DPR/blob/main/LICENSE ### Citation Information ``` @inproceedings{karpukhin-etal-2020-dense, title = "Dense Passage Retrieval for Open-Domain Question Answering", author = "Karpukhin, Vladimir and Oguz, Barlas and Min, Sewon and Lewis, Patrick and Wu, Ledell and Edunov, Sergey and Chen, Danqi and Yih, Wen-tau", booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.emnlp-main.550", doi = "10.18653/v1/2020.emnlp-main.550", pages = "6769--6781", } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@lhoestq](https://github.com/lhoestq) for adding this dataset.
ncoop57/mmmlu
ncoop57
"2023-02-01T07:02:32Z"
14,252
2
[ "license:mit", "size_categories:10K<n<100K", "modality:text", "library:datasets", "library:mlcroissant", "region:us" ]
null
"2023-01-24T23:50:14Z"
--- license: mit ---
shenyunhang/AISHELL-4
shenyunhang
"2025-03-23T03:13:25Z"
14,224
0
[ "license:apache-2.0", "modality:audio", "modality:text", "arxiv:2104.03603", "region:us" ]
null
"2025-02-11T09:04:56Z"
--- license: apache-2.0 --- --- Data source: https://openslr.org/93/ Audios and transcriptions are extracted to wav and txt files. --- <div class="container"> <h2 class="slrStyle"> AISHELL-4 </h2> <p class="resource"> <b>Identifier:</b> SLR111 </p> <p class="resource"> <b>Summary:</b> A Free Mandarin Multi-channel Meeting Speech Corpus, provided by Beijing Shell Shell Technology Co.,Ltd </p> <p class="resource"> <b>Category:</b> Speech </p> <p class="resource"> <b>License:</b> CC BY-SA 4.0 </p> <p class="resource"> <b>Downloads (use a mirror closer to you):</b> <br> <a href="https://www.openslr.org/resources/111/train_L.tar.gz"> train_L.tar.gz </a> [7.0G] &nbsp; ( Training set of large room, 8-channel microphone array speech ) &nbsp; Mirrors: <a href="https://us.openslr.org/resources/111/train_L.tar.gz"> [US] </a> &nbsp; <a href="https://openslr.elda.org/resources/111/train_L.tar.gz"> [EU] </a> &nbsp; <a href="https://openslr.magicdatatech.com/resources/111/train_L.tar.gz"> [CN] </a> &nbsp; <br><a href="https://www.openslr.org/resources/111/train_M.tar.gz"> train_M.tar.gz </a> [25G] &nbsp; ( Training set of medium room, 8-channel microphone array speech ) &nbsp; Mirrors: <a href="https://us.openslr.org/resources/111/train_M.tar.gz"> [US] </a> &nbsp; <a href="https://openslr.elda.org/resources/111/train_M.tar.gz"> [EU] </a> &nbsp; <a href="https://openslr.magicdatatech.com/resources/111/train_M.tar.gz"> [CN] </a> &nbsp; <br><a href="https://www.openslr.org/resources/111/train_S.tar.gz"> train_S.tar.gz </a> [14G] &nbsp; ( Training set of small room, 8-channel microphone array speech ) &nbsp; Mirrors: <a href="https://us.openslr.org/resources/111/train_S.tar.gz"> [US] </a> &nbsp; <a href="https://openslr.elda.org/resources/111/train_S.tar.gz"> [EU] </a> &nbsp; <a href="https://openslr.magicdatatech.com/resources/111/train_S.tar.gz"> [CN] </a> &nbsp; <br><a href="https://www.openslr.org/resources/111/test.tar.gz"> test.tar.gz </a> [5.2G] &nbsp; ( Test set ) &nbsp; Mirrors: <a href="https://us.openslr.org/resources/111/test.tar.gz"> [US] </a> &nbsp; <a href="https://openslr.elda.org/resources/111/test.tar.gz"> [EU] </a> &nbsp; <a href="https://openslr.magicdatatech.com/resources/111/test.tar.gz"> [CN] </a> &nbsp; <br><br></p><p class="resource"><b>About this resource:</b></p><div id="about">The AISHELL-4 is a sizable real-recorded Mandarin speech dataset collected by 8-channel circular microphone array for speech processing in conference scenarios. The dataset consists of 211 recorded meeting sessions, each containing 4 to 8 speakers, with a total length of 120 hours. This dataset aims to bridge the advanced research on multi-speaker processing and the practical application scenario in three aspects. With real recorded meetings, AISHELL-4 provides realistic acoustics and rich natural speech characteristics in conversation such as short pause, speech overlap, quick speaker turn, noise, etc. Meanwhile, the accurate transcription and speaker voice activity are provided for each meeting in AISHELL-4. This allows the researchers to explore different aspects in meeting processing, ranging from individual tasks such as speech front-end processing, speech recognition and speaker diarization, to multi-modality modeling and joint optimization of relevant tasks. We also release a PyTorch-based training and evaluation framework as a baseline system to promote reproducible research in this field. The baseline system code and generated samples are available <a href="https://github.com/felixfuyihui/AISHELL-4">here</a>. <p> You can cite the data using the following BibTeX entry: </p><pre> @inproceedings{AISHELL-4_2021, title={AISHELL-4: An Open Source Dataset for Speech Enhancement, Separation, Recognition and Speaker Diarization in Conference Scenario}, author={Yihui Fu, Luyao Cheng, Shubo Lv, Yukai Jv, Yuxiang Kong, Zhuo Chen, Yanxin Hu, Lei Xie, Jian Wu, Hui Bu, Xin Xu, Jun Du, Jingdong Chen}, booktitle={Interspeech}, url={https://arxiv.org/abs/2104.03603}, year={2021} } </pre> </div><p class="resource"> <b>External URL:</b> <a href="http://www.aishelltech.com/aishell_4"> http://www.aishelltech.com/aishell_4 </a> &nbsp; Full description from the company website </p> <div style="height:300px"> </div> </div>
togethercomputer/RedPajama-Data-1T-Sample
togethercomputer
"2023-07-19T06:59:10Z"
14,211
126
[ "task_categories:text-generation", "language:en", "size_categories:100K<n<1M", "modality:text", "library:datasets", "library:mlcroissant", "region:us" ]
[ "text-generation" ]
"2023-04-16T23:12:30Z"
--- task_categories: - text-generation language: - en pretty_name: Red Pajama 1T Sample --- # Dataset Card for Dataset Name ### Dataset Summary RedPajama is a clean-room, fully open-source implementation of the LLaMa dataset. This HuggingFace repo contains a 1B-token sample of the RedPajama dataset. The full dataset has the following token counts and is available for [download]( https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T): | Dataset | Token Count | |---------------|-------------| | Commoncrawl | 878 Billion | | C4 | 175 Billion | | GitHub | 59 Billion | | Books | 26 Billion | | ArXiv | 28 Billion | | Wikipedia | 24 Billion | | StackExchange | 20 Billion | | Total | 1.2 Trillion | A full set of scripts to recreate the dataset from scratch can be found [here](https://github.com/togethercomputer/RedPajama-Data). ### Languages Primarily English, though the Wikipedia slice contains multiple languages. ## Dataset Structure The dataset structure is as follows: ``` { "text": ..., "meta": {"url": "...", "timestamp": "...", "source": "...", "language": "...", ...} } ``` ## Dataset Creation This dataset was created to follow the LLaMa paper as closely as possible to try to reproduce its recipe. ### Source Data #### Commoncrawl We download five dumps from Commoncrawl, and run the dumps through the official `cc_net` pipeline. We then deduplicate on the paragraph level, and filter out low quality text using a linear classifier trained to classify paragraphs as Wikipedia references or random Commoncrawl samples. #### C4 C4 is downloaded from Huggingface. The only preprocessing step is to bring the data into our own format. #### GitHub The raw GitHub data is downloaded from Google BigQuery. We deduplicate on the file level and filter out low quality files and only keep projects that are distributed under the MIT, BSD, or Apache license. #### Wikipedia We use the Wikipedia dataset available on Huggingface, which is based on the Wikipedia dump from 2023-03-20 and contains text in 20 different languages. The dataset comes in preprocessed format, so that hyperlinks, comments and other formatting boilerplate has been removed. #### Gutenberg and Books3 The PG19 subset of the Gutenberg Project and Books3 datasets are downloaded from Huggingface. After downloading, we use simhash to remove near duplicates. #### ArXiv ArXiv data is downloaded from Amazon S3 in the `arxiv` requester pays bucket. We only keep latex source files and remove preambles, comments, macros and bibliographies. #### Stackexchange The Stack Exchange split of the dataset is download from the [Internet Archive](https://archive.org/download/stackexchange). Here we only keep the posts from the 28 largest sites, remove html tags, group the posts into question-answer pairs, and order answers by their score. <!-- ### 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] -->
rwkv-x-dev/lm-eval-data
rwkv-x-dev
"2024-08-12T08:43:34Z"
14,184
0
[ "license:apache-2.0", "region:us" ]
null
"2024-02-21T07:06:42Z"
--- license: apache-2.0 configs: - config_name: default data_files: - split: train path: "DOES_NOT_EXIST_TRAIN.csv" - split: test path: "DOES_NOT_EXIST_TEST.csv" --- # LM Eval data This repository, represents a giant collection of various lm-eval results built using the [github scripts found here](https://github.com/RWKV/lm-evaluation-harness) The goal at the point of creation, was to collect as much LM-eval results for as many relevent model for the RWKV paper. For analysis. ## Cloning the repo, without the jsonl dump ``` export GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/datasets/rwkv-x-dev/lm-eval-data.git ``` ## Cloning the repo, with the archives (large) ``` # Clone with archive files git lfs clone https://huggingface.co/datasets/rwkv-x-dev/lm-eval-data.git # Decompress all the archive files ./scripts/decompress-all-jsonl.sh ```
jmhessel/newyorker_caption_contest
jmhessel
"2023-12-22T19:13:58Z"
14,093
65
[ "task_categories:image-to-text", "task_categories:multiple-choice", "task_categories:text-classification", "task_categories:text-generation", "task_categories:visual-question-answering", "task_categories:other", "task_categories:text2text-generation", "task_ids:multi-class-classification", "task_ids:language-modeling", "task_ids:visual-question-answering", "task_ids:explanation-generation", "annotations_creators:expert-generated", "annotations_creators:crowdsourced", "annotations_creators:found", "language_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:cc-by-4.0", "size_categories:100K<n<1M", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2209.06293", "region:us", "humor", "caption contest", "new yorker" ]
[ "image-to-text", "multiple-choice", "text-classification", "text-generation", "visual-question-answering", "other", "text2text-generation" ]
"2022-09-29T17:28:05Z"
--- annotations_creators: - expert-generated - crowdsourced - found language_creators: - crowdsourced - expert-generated language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - image-to-text - multiple-choice - text-classification - text-generation - visual-question-answering - other - text2text-generation task_ids: - multi-class-classification - language-modeling - visual-question-answering - explanation-generation pretty_name: newyorker_caption_contest tags: - humor - caption contest - new yorker dataset_info: - config_name: explanation features: - name: image dtype: image - name: contest_number dtype: int32 - name: image_location dtype: string - name: image_description dtype: string - name: image_uncanny_description dtype: string - name: entities sequence: string - name: questions sequence: string - name: caption_choices dtype: string - name: from_description dtype: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 133827514.64 num_examples: 2340 - name: validation num_bytes: 8039885.0 num_examples: 130 - name: test num_bytes: 6863533.0 num_examples: 131 download_size: 139737042 dataset_size: 148730932.64 - config_name: explanation_1 features: - name: image dtype: image - name: contest_number dtype: int32 - name: image_location dtype: string - name: image_description dtype: string - name: image_uncanny_description dtype: string - name: entities sequence: string - name: questions sequence: string - name: caption_choices dtype: string - name: from_description dtype: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 136614332.45999998 num_examples: 2358 - name: validation num_bytes: 7911995.0 num_examples: 128 - name: test num_bytes: 8039885.0 num_examples: 130 download_size: 134637839 dataset_size: 152566212.45999998 - config_name: explanation_2 features: - name: image dtype: image - name: contest_number dtype: int32 - name: image_location dtype: string - name: image_description dtype: string - name: image_uncanny_description dtype: string - name: entities sequence: string - name: questions sequence: string - name: caption_choices dtype: string - name: from_description dtype: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 138337491.342 num_examples: 2346 - name: validation num_bytes: 7460490.0 num_examples: 132 - name: test num_bytes: 7911995.0 num_examples: 128 download_size: 138271185 dataset_size: 153709976.342 - config_name: explanation_3 features: - name: image dtype: image - name: contest_number dtype: int32 - name: image_location dtype: string - name: image_description dtype: string - name: image_uncanny_description dtype: string - name: entities sequence: string - name: questions sequence: string - name: caption_choices dtype: string - name: from_description dtype: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 138247435.342 num_examples: 2334 - name: validation num_bytes: 7911920.0 num_examples: 130 - name: test num_bytes: 7460490.0 num_examples: 132 download_size: 136862726 dataset_size: 153619845.342 - config_name: explanation_4 features: - name: image dtype: image - name: contest_number dtype: int32 - name: image_location dtype: string - name: image_description dtype: string - name: image_uncanny_description dtype: string - name: entities sequence: string - name: questions sequence: string - name: caption_choices dtype: string - name: from_description dtype: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 141175335.3 num_examples: 2340 - name: validation num_bytes: 6863533.0 num_examples: 131 - name: test num_bytes: 7911920.0 num_examples: 130 download_size: 140501251 dataset_size: 155950788.3 - config_name: explanation_from_pixels features: - name: image dtype: image - name: contest_number dtype: int32 - name: caption_choices dtype: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 23039316.0 num_examples: 390 - name: validation num_bytes: 7956182.0 num_examples: 130 - name: test num_bytes: 6778892.0 num_examples: 131 download_size: 37552582 dataset_size: 37774390.0 - config_name: explanation_from_pixels_1 features: - name: image dtype: image - name: contest_number dtype: int32 - name: caption_choices dtype: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 21986652.0 num_examples: 393 - name: validation num_bytes: 7831556.0 num_examples: 128 - name: test num_bytes: 7956182.0 num_examples: 130 download_size: 37534409 dataset_size: 37774390.0 - config_name: explanation_from_pixels_2 features: - name: image dtype: image - name: contest_number dtype: int32 - name: caption_choices dtype: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 22566608.0 num_examples: 391 - name: validation num_bytes: 7376225.0 num_examples: 132 - name: test num_bytes: 7831556.0 num_examples: 128 download_size: 37544724 dataset_size: 37774389.0 - config_name: explanation_from_pixels_3 features: - name: image dtype: image - name: contest_number dtype: int32 - name: caption_choices dtype: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 22566629.0 num_examples: 389 - name: validation num_bytes: 7831536.0 num_examples: 130 - name: test num_bytes: 7376225.0 num_examples: 132 download_size: 37573931 dataset_size: 37774390.0 - config_name: explanation_from_pixels_4 features: - name: image dtype: image - name: contest_number dtype: int32 - name: caption_choices dtype: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 23163962.0 num_examples: 390 - name: validation num_bytes: 6778892.0 num_examples: 131 - name: test num_bytes: 7831536.0 num_examples: 130 download_size: 37582524 dataset_size: 37774390.0 - config_name: matching features: - name: image dtype: image - name: contest_number dtype: int32 - name: image_location dtype: string - name: image_description dtype: string - name: image_uncanny_description dtype: string - name: entities sequence: string - name: questions sequence: string - name: caption_choices sequence: string - name: from_description dtype: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 618272766.36 num_examples: 9792 - name: validation num_bytes: 34157757.0 num_examples: 531 - name: test num_bytes: 29813118.0 num_examples: 528 download_size: 594460072 dataset_size: 682243641.36 - config_name: matching_1 features: - name: image dtype: image - name: contest_number dtype: int32 - name: image_location dtype: string - name: image_description dtype: string - name: image_uncanny_description dtype: string - name: entities sequence: string - name: questions sequence: string - name: caption_choices sequence: string - name: from_description dtype: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 593200158.116 num_examples: 9684 - name: validation num_bytes: 36712942.0 num_examples: 546 - name: test num_bytes: 34157757.0 num_examples: 531 download_size: 563587231 dataset_size: 664070857.116 - config_name: matching_2 features: - name: image dtype: image - name: contest_number dtype: int32 - name: image_location dtype: string - name: image_description dtype: string - name: image_uncanny_description dtype: string - name: entities sequence: string - name: questions sequence: string - name: caption_choices sequence: string - name: from_description dtype: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 591676321.09 num_examples: 9630 - name: validation num_bytes: 33697178.0 num_examples: 540 - name: test num_bytes: 36712942.0 num_examples: 546 download_size: 571864348 dataset_size: 662086441.09 - config_name: matching_3 features: - name: image dtype: image - name: contest_number dtype: int32 - name: image_location dtype: string - name: image_description dtype: string - name: image_uncanny_description dtype: string - name: entities sequence: string - name: questions sequence: string - name: caption_choices sequence: string - name: from_description dtype: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 615620189.53 num_examples: 9630 - name: validation num_bytes: 34829502.0 num_examples: 546 - name: test num_bytes: 33697178.0 num_examples: 540 download_size: 571744845 dataset_size: 684146869.53 - config_name: matching_4 features: - name: image dtype: image - name: contest_number dtype: int32 - name: image_location dtype: string - name: image_description dtype: string - name: image_uncanny_description dtype: string - name: entities sequence: string - name: questions sequence: string - name: caption_choices sequence: string - name: from_description dtype: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 609696610.648 num_examples: 9702 - name: validation num_bytes: 29813118.0 num_examples: 528 - name: test num_bytes: 34829502.0 num_examples: 546 download_size: 592174904 dataset_size: 674339230.648 - config_name: matching_from_pixels features: - name: image dtype: image - name: contest_number dtype: int32 - name: caption_choices sequence: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 101439044.384 num_examples: 1632 - name: validation num_bytes: 33714551.0 num_examples: 531 - name: test num_bytes: 29368704.0 num_examples: 528 download_size: 139733134 dataset_size: 164522299.384 - config_name: matching_from_pixels_1 features: - name: image dtype: image - name: contest_number dtype: int32 - name: caption_choices sequence: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 94090646.83 num_examples: 1614 - name: validation num_bytes: 36257141.0 num_examples: 546 - name: test num_bytes: 33714551.0 num_examples: 531 download_size: 137278691 dataset_size: 164062338.82999998 - config_name: matching_from_pixels_2 features: - name: image dtype: image - name: contest_number dtype: int32 - name: caption_choices sequence: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 96253584.505 num_examples: 1605 - name: validation num_bytes: 33236000.0 num_examples: 540 - name: test num_bytes: 36257141.0 num_examples: 546 download_size: 137890850 dataset_size: 165746725.505 - config_name: matching_from_pixels_3 features: - name: image dtype: image - name: contest_number dtype: int32 - name: caption_choices sequence: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 99928910.28 num_examples: 1605 - name: validation num_bytes: 34380303.0 num_examples: 546 - name: test num_bytes: 33236000.0 num_examples: 540 download_size: 139585876 dataset_size: 167545213.28 - config_name: matching_from_pixels_4 features: - name: image dtype: image - name: contest_number dtype: int32 - name: caption_choices sequence: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 102509197.79 num_examples: 1617 - name: validation num_bytes: 29368704.0 num_examples: 528 - name: test num_bytes: 34380303.0 num_examples: 546 download_size: 138725891 dataset_size: 166258204.79000002 - config_name: ranking features: - name: image dtype: image - name: contest_number dtype: int32 - name: image_location dtype: string - name: image_description dtype: string - name: image_uncanny_description dtype: string - name: entities sequence: string - name: questions sequence: string - name: caption_choices sequence: string - name: from_description dtype: string - name: winner_source dtype: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 594615535.632 num_examples: 9576 - name: validation num_bytes: 32624105.0 num_examples: 507 - name: test num_bytes: 28907567.0 num_examples: 513 download_size: 571604579 dataset_size: 656147207.632 - config_name: ranking_1 features: - name: image dtype: image - name: contest_number dtype: int32 - name: image_location dtype: string - name: image_description dtype: string - name: image_uncanny_description dtype: string - name: entities sequence: string - name: questions sequence: string - name: caption_choices sequence: string - name: from_description dtype: string - name: winner_source dtype: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 580099188.9 num_examples: 9450 - name: validation num_bytes: 35332200.0 num_examples: 534 - name: test num_bytes: 32624105.0 num_examples: 507 download_size: 546559254 dataset_size: 648055493.9 - config_name: ranking_2 features: - name: image dtype: image - name: contest_number dtype: int32 - name: image_location dtype: string - name: image_description dtype: string - name: image_uncanny_description dtype: string - name: entities sequence: string - name: questions sequence: string - name: caption_choices sequence: string - name: from_description dtype: string - name: winner_source dtype: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 566811450.504 num_examples: 9306 - name: validation num_bytes: 32519173.0 num_examples: 531 - name: test num_bytes: 35332200.0 num_examples: 534 download_size: 544444097 dataset_size: 634662823.504 - config_name: ranking_3 features: - name: image dtype: image - name: contest_number dtype: int32 - name: image_location dtype: string - name: image_description dtype: string - name: image_uncanny_description dtype: string - name: entities sequence: string - name: questions sequence: string - name: caption_choices sequence: string - name: from_description dtype: string - name: winner_source dtype: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 577828323.272 num_examples: 9324 - name: validation num_bytes: 34072817.0 num_examples: 531 - name: test num_bytes: 32519173.0 num_examples: 531 download_size: 548880699 dataset_size: 644420313.272 - config_name: ranking_4 features: - name: image dtype: image - name: contest_number dtype: int32 - name: image_location dtype: string - name: image_description dtype: string - name: image_uncanny_description dtype: string - name: entities sequence: string - name: questions sequence: string - name: caption_choices sequence: string - name: from_description dtype: string - name: winner_source dtype: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 593388719.232 num_examples: 9432 - name: validation num_bytes: 28907567.0 num_examples: 513 - name: test num_bytes: 34072817.0 num_examples: 531 download_size: 562902941 dataset_size: 656369103.232 - config_name: ranking_from_pixels features: - name: image dtype: image - name: contest_number dtype: int32 - name: caption_choices sequence: string - name: winner_source dtype: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 101282973.752 num_examples: 1596 - name: validation num_bytes: 32072331.0 num_examples: 506 - name: test num_bytes: 28550057.0 num_examples: 513 download_size: 134283256 dataset_size: 161905361.752 - config_name: ranking_from_pixels_1 features: - name: image dtype: image - name: contest_number dtype: int32 - name: caption_choices sequence: string - name: winner_source dtype: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 93123370.15 num_examples: 1575 - name: validation num_bytes: 34965110.0 num_examples: 534 - name: test num_bytes: 32072331.0 num_examples: 506 download_size: 130879365 dataset_size: 160160811.15 - config_name: ranking_from_pixels_2 features: - name: image dtype: image - name: contest_number dtype: int32 - name: caption_choices sequence: string - name: winner_source dtype: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 93496576.85 num_examples: 1550 - name: validation num_bytes: 32145436.0 num_examples: 531 - name: test num_bytes: 34965110.0 num_examples: 534 download_size: 131637359 dataset_size: 160607122.85 - config_name: ranking_from_pixels_3 features: - name: image dtype: image - name: contest_number dtype: int32 - name: caption_choices sequence: string - name: winner_source dtype: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 93840620.26 num_examples: 1553 - name: validation num_bytes: 33718821.0 num_examples: 531 - name: test num_bytes: 32145436.0 num_examples: 531 download_size: 133214495 dataset_size: 159704877.26 - config_name: ranking_from_pixels_4 features: - name: image dtype: image - name: contest_number dtype: int32 - name: caption_choices sequence: string - name: winner_source dtype: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 99008131.43 num_examples: 1571 - name: validation num_bytes: 28550057.0 num_examples: 513 - name: test num_bytes: 33718821.0 num_examples: 531 download_size: 136230399 dataset_size: 161277009.43 configs: - config_name: explanation data_files: - split: train path: explanation/train-* - split: validation path: explanation/validation-* - split: test path: explanation/test-* - config_name: explanation_1 data_files: - split: train path: explanation_1/train-* - split: validation path: explanation_1/validation-* - split: test path: explanation_1/test-* - config_name: explanation_2 data_files: - split: train path: explanation_2/train-* - split: validation path: explanation_2/validation-* - split: test path: explanation_2/test-* - config_name: explanation_3 data_files: - split: train path: explanation_3/train-* - split: validation path: explanation_3/validation-* - split: test path: explanation_3/test-* - config_name: explanation_4 data_files: - split: train path: explanation_4/train-* - split: validation path: explanation_4/validation-* - split: test path: explanation_4/test-* - config_name: explanation_from_pixels data_files: - split: train path: explanation_from_pixels/train-* - split: validation path: explanation_from_pixels/validation-* - split: test path: explanation_from_pixels/test-* - config_name: explanation_from_pixels_1 data_files: - split: train path: explanation_from_pixels_1/train-* - split: validation path: explanation_from_pixels_1/validation-* - split: test path: explanation_from_pixels_1/test-* - config_name: explanation_from_pixels_2 data_files: - split: train path: explanation_from_pixels_2/train-* - split: validation path: explanation_from_pixels_2/validation-* - split: test path: explanation_from_pixels_2/test-* - config_name: explanation_from_pixels_3 data_files: - split: train path: explanation_from_pixels_3/train-* - split: validation path: explanation_from_pixels_3/validation-* - split: test path: explanation_from_pixels_3/test-* - config_name: explanation_from_pixels_4 data_files: - split: train path: explanation_from_pixels_4/train-* - split: validation path: explanation_from_pixels_4/validation-* - split: test path: explanation_from_pixels_4/test-* - config_name: matching data_files: - split: train path: matching/train-* - split: validation path: matching/validation-* - split: test path: matching/test-* - config_name: matching_1 data_files: - split: train path: matching_1/train-* - split: validation path: matching_1/validation-* - split: test path: matching_1/test-* - config_name: matching_2 data_files: - split: train path: matching_2/train-* - split: validation path: matching_2/validation-* - split: test path: matching_2/test-* - config_name: matching_3 data_files: - split: train path: matching_3/train-* - split: validation path: matching_3/validation-* - split: test path: matching_3/test-* - config_name: matching_4 data_files: - split: train path: matching_4/train-* - split: validation path: matching_4/validation-* - split: test path: matching_4/test-* - config_name: matching_from_pixels data_files: - split: train path: matching_from_pixels/train-* - split: validation path: matching_from_pixels/validation-* - split: test path: matching_from_pixels/test-* - config_name: matching_from_pixels_1 data_files: - split: train path: matching_from_pixels_1/train-* - split: validation path: matching_from_pixels_1/validation-* - split: test path: matching_from_pixels_1/test-* - config_name: matching_from_pixels_2 data_files: - split: train path: matching_from_pixels_2/train-* - split: validation path: matching_from_pixels_2/validation-* - split: test path: matching_from_pixels_2/test-* - config_name: matching_from_pixels_3 data_files: - split: train path: matching_from_pixels_3/train-* - split: validation path: matching_from_pixels_3/validation-* - split: test path: matching_from_pixels_3/test-* - config_name: matching_from_pixels_4 data_files: - split: train path: matching_from_pixels_4/train-* - split: validation path: matching_from_pixels_4/validation-* - split: test path: matching_from_pixels_4/test-* - config_name: ranking data_files: - split: train path: ranking/train-* - split: validation path: ranking/validation-* - split: test path: ranking/test-* - config_name: ranking_1 data_files: - split: train path: ranking_1/train-* - split: validation path: ranking_1/validation-* - split: test path: ranking_1/test-* - config_name: ranking_2 data_files: - split: train path: ranking_2/train-* - split: validation path: ranking_2/validation-* - split: test path: ranking_2/test-* - config_name: ranking_3 data_files: - split: train path: ranking_3/train-* - split: validation path: ranking_3/validation-* - split: test path: ranking_3/test-* - config_name: ranking_4 data_files: - split: train path: ranking_4/train-* - split: validation path: ranking_4/validation-* - split: test path: ranking_4/test-* - config_name: ranking_from_pixels data_files: - split: train path: ranking_from_pixels/train-* - split: validation path: ranking_from_pixels/validation-* - split: test path: ranking_from_pixels/test-* - config_name: ranking_from_pixels_1 data_files: - split: train path: ranking_from_pixels_1/train-* - split: validation path: ranking_from_pixels_1/validation-* - split: test path: ranking_from_pixels_1/test-* - config_name: ranking_from_pixels_2 data_files: - split: train path: ranking_from_pixels_2/train-* - split: validation path: ranking_from_pixels_2/validation-* - split: test path: ranking_from_pixels_2/test-* - config_name: ranking_from_pixels_3 data_files: - split: train path: ranking_from_pixels_3/train-* - split: validation path: ranking_from_pixels_3/validation-* - split: test path: ranking_from_pixels_3/test-* - config_name: ranking_from_pixels_4 data_files: - split: train path: ranking_from_pixels_4/train-* - split: validation path: ranking_from_pixels_4/validation-* - split: test path: ranking_from_pixels_4/test-* --- # Dataset Card for New Yorker Caption Contest Benchmarks ## 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:** [capcon.dev](https://www.capcon.dev) - **Repository:** [https://github.com/jmhessel/caption_contest_corpus](https://github.com/jmhessel/caption_contest_corpus) - **Paper:** [Do Androids Laugh at Electric Sheep? Humor "Understanding" Benchmarks from The New Yorker Caption Contest](https://arxiv.org/abs/2209.06293) - **Leaderboard:** https://leaderboard.allenai.org/nycc-matching/ - **Point of Contact:** [email protected] ### Dataset Summary See [capcon.dev](https://www.capcon.dev) for more! Data from: [Do Androids Laugh at Electric Sheep? Humor "Understanding" Benchmarks from The New Yorker Caption Contest](https://arxiv.org/abs/2209.06293) ``` @inproceedings{hessel2023androids, title={Do Androids Laugh at Electric Sheep? {Humor} ``Understanding'' Benchmarks from {The New Yorker Caption Contest}}, author={Hessel, Jack and Marasovi{\'c}, Ana and Hwang, Jena D. and Lee, Lillian and Da, Jeff and Zellers, Rowan and Mankoff, Robert and Choi, Yejin}, booktitle={Proceedings of the ACL}, year={2023} } ``` If you use this dataset, we would appreciate you citing our work, but also -- several other papers that we build this corpus upon. See [Citation Information](#citation-information). We challenge AI models to "demonstrate understanding" of the sophisticated multimodal humor of The New Yorker Caption Contest. Concretely, we develop three carefully circumscribed tasks for which it suffices (but is not necessary) to grasp potentially complex and unexpected relationships between image and caption, and similarly complex and unexpected allusions to the wide varieties of human experience. ### Supported Tasks and Leaderboards Three tasks are supported: - "Matching:" a model must recognize a caption written about a cartoon (vs. options that were not); - "Quality ranking:" a model must evaluate the quality of a caption by scoring it more highly than a lower quality option from the same contest; - "Explanation:" a model must explain why a given joke is funny. There are no official leaderboards (yet). ### Languages English ## Dataset Structure Here's an example instance from Matching: ``` {'caption_choices': ['Tell me about your childhood very quickly.', "Believe me . . . it's what's UNDER the ground that's " 'most interesting.', "Stop me if you've heard this one.", 'I have trouble saying no.', 'Yes, I see the train but I think we can beat it.'], 'contest_number': 49, 'entities': ['https://en.wikipedia.org/wiki/Rule_of_three_(writing)', 'https://en.wikipedia.org/wiki/Bar_joke', 'https://en.wikipedia.org/wiki/Religious_institute'], 'from_description': 'scene: a bar description: Two priests and a rabbi are ' 'walking into a bar, as the bartender and another patron ' 'look on. The bartender talks on the phone while looking ' 'skeptically at the incoming crew. uncanny: The scene ' 'depicts a very stereotypical "bar joke" that would be ' 'unlikely to be encountered in real life; the skepticism ' 'of the bartender suggests that he is aware he is seeing ' 'this trope, and is explaining it to someone on the ' 'phone. entities: Rule_of_three_(writing), Bar_joke, ' 'Religious_institute. choices A: Tell me about your ' "childhood very quickly. B: Believe me . . . it's what's " "UNDER the ground that's most interesting. C: Stop me if " "you've heard this one. D: I have trouble saying no. E: " 'Yes, I see the train but I think we can beat it.', 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=L size=323x231 at 0x7F34F283E9D0>, 'image_description': 'Two priests and a rabbi are walking into a bar, as the ' 'bartender and another patron look on. The bartender ' 'talks on the phone while looking skeptically at the ' 'incoming crew.', 'image_location': 'a bar', 'image_uncanny_description': 'The scene depicts a very stereotypical "bar ' 'joke" that would be unlikely to be encountered ' 'in real life; the skepticism of the bartender ' 'suggests that he is aware he is seeing this ' 'trope, and is explaining it to someone on the ' 'phone.', 'instance_id': '21125bb8787b4e7e82aa3b0a1cba1571', 'label': 'C', 'n_tokens_label': 1, 'questions': ['What is the bartender saying on the phone in response to the ' 'living, breathing, stereotypical bar joke that is unfolding?']} ``` The label "C" indicates that the 3rd choice in the `caption_choices` is correct. Here's an example instance from Ranking (in the from pixels setting --- though, this is also available in the from description setting) ``` {'caption_choices': ['I guess I misunderstood when you said long bike ride.', 'Does your divorce lawyer have any other cool ideas?'], 'contest_number': 582, 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=L size=600x414 at 0x7F8FF9F96610>, 'instance_id': 'dd1c214a1ca3404aa4e582c9ce50795a', 'label': 'A', 'n_tokens_label': 1, 'winner_source': 'official_winner'} ``` the label indicates that the first caption choice ("A", here) in the `caption_choices` list was more highly rated. Here's an example instance from Explanation: ``` {'caption_choices': 'The classics can be so intimidating.', 'contest_number': 752, 'entities': ['https://en.wikipedia.org/wiki/Literature', 'https://en.wikipedia.org/wiki/Solicitor'], 'from_description': 'scene: a road description: Two people are walking down a ' 'path. A number of giant books have surrounded them. ' 'uncanny: There are book people in this world. entities: ' 'Literature, Solicitor. caption: The classics can be so ' 'intimidating.', 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=L size=800x706 at 0x7F90003D0BB0>, 'image_description': 'Two people are walking down a path. A number of giant ' 'books have surrounded them.', 'image_location': 'a road', 'image_uncanny_description': 'There are book people in this world.', 'instance_id': 'eef9baf450e2fab19b96facc128adf80', 'label': 'A play on the word intimidating --- usually if the classics (i.e., ' 'classic novels) were to be intimidating, this would mean that they ' 'are intimidating to read due to their length, complexity, etc. But ' 'here, they are surrounded by anthropomorphic books which look ' 'physically intimidating, i.e., they are intimidating because they ' 'may try to beat up these people.', 'n_tokens_label': 59, 'questions': ['What do the books want?']} ``` The label is an explanation of the joke, which serves as the autoregressive target. ### Data Instances See above ### Data Fields See above ### Data Splits Data splits can be accessed as: ``` from datasets import load_dataset dset = load_dataset("jmhessel/newyorker_caption_contest", "matching") dset = load_dataset("jmhessel/newyorker_caption_contest", "ranking") dset = load_dataset("jmhessel/newyorker_caption_contest", "explanation") ``` Or, in the from pixels setting, e.g., ``` from datasets import load_dataset dset = load_dataset("jmhessel/newyorker_caption_contest", "ranking_from_pixels") ``` Because the dataset is small, we reported in 5-fold cross-validation setting initially. The default splits are split 0. You can access the other splits, e.g.: ``` from datasets import load_dataset # the 4th data split dset = load_dataset("jmhessel/newyorker_caption_contest", "explanation_4") ``` ## Dataset Creation Full details are in the paper. ### Curation Rationale See the paper for rationale/motivation. ### Source Data See citation below. We combined 3 sources of data, and added significant annotations of our own. #### Initial Data Collection and Normalization Full details are in the paper. #### Who are the source language producers? We paid crowdworkers $15/hr to annotate the corpus. In addition, significant annotation efforts were conducted by the authors of this work. ### Annotations Full details are in the paper. #### Annotation process Full details are in the paper. #### Who are the annotators? A mix of crowdworks and authors of this paper. ### Personal and Sensitive Information Has been redacted from the dataset. Images are published in the New Yorker already. ## Considerations for Using the Data ### Social Impact of Dataset It's plausible that humor could perpetuate negative stereotypes. The jokes in this corpus are a mix of crowdsourced entries that are highly rated, and ones published in the new yorker. ### Discussion of Biases Humor is subjective, and some of the jokes may be considered offensive. The images may contain adult themes and minor cartoon nudity. ### Other Known Limitations More details are in the paper ## Additional Information ### Dataset Curators The dataset was curated by researchers at AI2 ### Licensing Information The annotations we provide are CC-BY-4.0. See www.capcon.dev for more info. ### Citation Information ``` @article{hessel2022androids, title={Do Androids Laugh at Electric Sheep? Humor "Understanding" Benchmarks from The New Yorker Caption Contest}, author={Hessel, Jack and Marasovi{\'c}, Ana and Hwang, Jena D and Lee, Lillian and Da, Jeff and Zellers, Rowan and Mankoff, Robert and Choi, Yejin}, journal={arXiv preprint arXiv:2209.06293}, year={2022} } ``` Our data contributions are: - The cartoon-level annotations; - The joke explanations; - and the framing of the tasks We release these data we contribute under CC-BY (see DATASET_LICENSE). If you find this data useful in your work, in addition to citing our contributions, please also cite the following, from which the cartoons/captions in our corpus are derived: ``` @misc{newyorkernextmldataset, author={Jain, Lalit and Jamieson, Kevin and Mankoff, Robert and Nowak, Robert and Sievert, Scott}, title={The {N}ew {Y}orker Cartoon Caption Contest Dataset}, year={2020}, url={https://nextml.github.io/caption-contest-data/} } @inproceedings{radev-etal-2016-humor, title = "Humor in Collective Discourse: Unsupervised Funniness Detection in The {New Yorker} Cartoon Caption Contest", author = "Radev, Dragomir and Stent, Amanda and Tetreault, Joel and Pappu, Aasish and Iliakopoulou, Aikaterini and Chanfreau, Agustin and de Juan, Paloma and Vallmitjana, Jordi and Jaimes, Alejandro and Jha, Rahul and Mankoff, Robert", booktitle = "LREC", year = "2016", } @inproceedings{shahaf2015inside, title={Inside jokes: Identifying humorous cartoon captions}, author={Shahaf, Dafna and Horvitz, Eric and Mankoff, Robert}, booktitle={KDD}, year={2015}, } ```
Open-Orca/FLAN
Open-Orca
"2023-08-02T15:08:01Z"
14,084
176
[ "language:en", "license:cc-by-4.0", "size_categories:100M<n<1B", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2301.13688", "arxiv:2109.01652", "arxiv:2110.08207", "arxiv:2204.07705", "region:us" ]
null
"2023-07-21T13:45:12Z"
--- 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> ![OO-FLAN Logo](https://huggingface.co/datasets/Open-Orca/FLAN/resolve/main/OOFlanLogo.png "OO-FLAN Logo") # 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} } ```
OpenLLM-France/Lucie-Training-Dataset
OpenLLM-France
"2025-03-19T13:02:08Z"
14,056
20
[ "task_categories:text-generation", "task_categories:text2text-generation", "task_ids:language-modeling", "multilinguality:multilingual", "language:en", "language:fr", "language:de", "language:es", "language:it", "language:code", "license:cc-by-nc-sa-4.0", "size_categories:10B<n<100B", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2308.12477", "arxiv:2311.16840", "arxiv:2402.00786", "arxiv:1905.10892", "arxiv:1906.02192", "arxiv:2108.01139", "arxiv:2010.12871", "arxiv:2406.17557", "arxiv:2312.17120", "arxiv:2201.07311", "arxiv:1904.01557", "arxiv:2101.00027", "arxiv:2211.15533", "arxiv:2503.12294", "region:us", "text-generation", "conditional-text-generation" ]
[ "text-generation", "text2text-generation" ]
"2024-10-16T10:46:27Z"
--- pretty_name: Lucie Training Dataset license: cc-by-nc-sa-4.0 language: - en - fr - de - es - it - code multilinguality: - multilingual task_categories: - text-generation - text2text-generation task_ids: - language-modeling tags: - text-generation - conditional-text-generation size_categories: - n>1T viewer: true configs: - config_name: default data_files: - path: data/v*/*/*/*/*parquet split: train - config_name: en data_files: - path: data/v*/natural/en/*/*parquet split: train - config_name: fr data_files: - path: data/v*/natural/fr/*/*parquet split: train - config_name: de data_files: - path: data/v*/natural/de/*/*parquet split: train - config_name: es data_files: - path: data/v*/natural/es/*/*parquet split: train - config_name: it data_files: - path: data/v*/natural/it/*/*parquet split: train - config_name: de,fr data_files: - path: data/v*/natural/de-fr/*/*.parquet split: train - config_name: es,en data_files: - path: data/v*/natural/es-en/*/*.parquet split: train - config_name: fr,en data_files: - path: data/v*/natural/fr-en/*/*.parquet split: train - config_name: it,en data_files: - path: data/v*/natural/it-en/*/*.parquet split: train - config_name: natural data_files: - path: data/v*/natural/*/*/*.parquet split: train - config_name: code data_files: - path: data/v*/code/*/*/*parquet split: train - config_name: code-assembly data_files: - path: data/v*/code/assembly/*/*.parquet split: train - config_name: code-c data_files: - path: data/v*/code/c/*/*.parquet split: train - config_name: code-c# data_files: - path: data/v*/code/c#/*/*.parquet split: train - config_name: code-c++ data_files: - path: data/v*/code/c++/*/*.parquet split: train - config_name: code-clojure data_files: - path: data/v*/code/clojure/*/*.parquet split: train - config_name: code-dart data_files: - path: data/v*/code/dart/*/*.parquet split: train - config_name: code-elixir data_files: - path: data/v*/code/elixir/*/*.parquet split: train - config_name: code-erlang data_files: - path: data/v*/code/erlang/*/*.parquet split: train - config_name: code-fortran data_files: - path: data/v*/code/fortran/*/*.parquet split: train - config_name: code-go data_files: - path: data/v*/code/go/*/*.parquet split: train - config_name: code-haskell data_files: - path: data/v*/code/haskell/*/*.parquet split: train - config_name: code-java data_files: - path: data/v*/code/java/*/*.parquet split: train - config_name: code-javascript data_files: - path: data/v*/code/javascript/*/*.parquet split: train - config_name: code-julia data_files: - path: data/v*/code/julia/*/*.parquet split: train - config_name: code-kotlin data_files: - path: data/v*/code/kotlin/*/*.parquet split: train - config_name: code-lua data_files: - path: data/v*/code/lua/*/*.parquet split: train - config_name: code-mathematica data_files: - path: data/v*/code/mathematica/*/*.parquet split: train - config_name: code-matlab data_files: - path: data/v*/code/matlab/*/*.parquet split: train - config_name: code-ocaml data_files: - path: data/v*/code/ocaml/*/*.parquet split: train - config_name: code-perl data_files: - path: data/v*/code/perl/*/*.parquet split: train - config_name: code-php data_files: - path: data/v*/code/php/*/*.parquet split: train - config_name: code-python data_files: - path: data/v*/code/python/*/*.parquet split: train - config_name: code-r data_files: - path: data/v*/code/r/*/*.parquet split: train - config_name: code-racket data_files: - path: data/v*/code/racket/*/*.parquet split: train - config_name: code-ruby data_files: - path: data/v*/code/ruby/*/*.parquet split: train - config_name: code-rust data_files: - path: data/v*/code/rust/*/*.parquet split: train - config_name: code-scala data_files: - path: data/v*/code/scala/*/*.parquet split: train - config_name: code-swift data_files: - path: data/v*/code/swift/*/*.parquet split: train - config_name: code-tex data_files: - path: data/v*/code/tex/*/*.parquet split: train - config_name: code-typescript data_files: - path: data/v*/code/typescript/*/*.parquet split: train - config_name: AmendementsParlement data_files: - path: data/v*/natural/*/AmendementsParlement/*.parquet split: train - config_name: AmericanStories data_files: - path: data/v*/natural/*/AmericanStories/*.parquet split: train - config_name: Claire data_files: - path: data/v*/natural/*/Claire/*.parquet split: train - config_name: Claire-en data_files: - path: data/v*/natural/en/Claire/*.parquet split: train - config_name: Claire-fr data_files: - path: data/v*/natural/fr/Claire/*.parquet split: train - config_name: CroissantAligned data_files: - path: data/v*/natural/*/CroissantAligned/*.parquet split: train - config_name: DiscoursPublics data_files: - path: data/v*/natural/*/DiscoursPublics/*.parquet split: train - config_name: Europarl data_files: - path: data/v*/natural/*/Europarl/*.parquet split: train - config_name: Europarl-de data_files: - path: data/v*/natural/de/Europarl/*.parquet split: train - config_name: Europarl-en data_files: - path: data/v*/natural/en/Europarl/*.parquet split: train - config_name: Europarl-es data_files: - path: data/v*/natural/es/Europarl/*.parquet split: train - config_name: Europarl-fr data_files: - path: data/v*/natural/fr/Europarl/*.parquet split: train - config_name: EuroparlAligned data_files: - path: data/v*/natural/*/EuroparlAligned/*.parquet split: train - config_name: EuroparlAligned-de,fr data_files: - path: data/v*/natural/de-fr/EuroparlAligned/*.parquet split: train - config_name: EuroparlAligned-es,en data_files: - path: data/v*/natural/es-en/EuroparlAligned/*.parquet split: train - config_name: EuroparlAligned-fr,en data_files: - path: data/v*/natural/fr-en/EuroparlAligned/*.parquet split: train - config_name: EuroparlAligned-it,en data_files: - path: data/v*/natural/it-en/EuroparlAligned/*.parquet split: train - config_name: Eurovoc data_files: - path: data/v*/natural/*/Eurovoc/*.parquet split: train - config_name: Eurovoc-de data_files: - path: data/v*/natural/de/Eurovoc/*.parquet split: train - config_name: Eurovoc-en data_files: - path: data/v*/natural/en/Eurovoc/*.parquet split: train - config_name: Eurovoc-es data_files: - path: data/v*/natural/es/Eurovoc/*.parquet split: train - config_name: Eurovoc-it data_files: - path: data/v*/natural/it/Eurovoc/*.parquet split: train - config_name: FineWebEdu data_files: - path: data/v*/natural/*/FineWebEdu/*.parquet split: train - config_name: GallicaMonographies data_files: - path: data/v*/natural/*/GallicaMonographies/*.parquet split: train - config_name: GallicaPress data_files: - path: data/v*/natural/*/GallicaPress/*.parquet split: train - config_name: Gutenberg data_files: - path: data/v*/natural/*/Gutenberg/*.parquet split: train - config_name: Gutenberg-de data_files: - path: data/v*/natural/de/Gutenberg/*.parquet split: train - config_name: Gutenberg-en data_files: - path: data/v*/natural/en/Gutenberg/*.parquet split: train - config_name: Gutenberg-es data_files: - path: data/v*/natural/es/Gutenberg/*.parquet split: train - config_name: Gutenberg-fr data_files: - path: data/v*/natural/fr/Gutenberg/*.parquet split: train - config_name: Gutenberg-it data_files: - path: data/v*/natural/it/Gutenberg/*.parquet split: train - config_name: HAL data_files: - path: data/v*/natural/*/HAL/*.parquet split: train - config_name: InterventionsParlement data_files: - path: data/v*/natural/*/InterventionsParlement/*.parquet split: train - config_name: LEGI data_files: - path: data/v*/natural/*/LEGI/*.parquet split: train - config_name: MathPile data_files: - path: data/v*/natural/*/MathPile/*.parquet split: train - config_name: OpenData data_files: - path: data/v*/natural/*/OpenData/*.parquet split: train - config_name: OpenEdition data_files: - path: data/v*/natural/*/OpenEdition/*.parquet split: train - config_name: PeS2o data_files: - path: data/v*/natural/*/PeS2o/*.parquet split: train - config_name: PeS2o-s2ag data_files: - path: data/v*/natural/*/PeS2o/*s2ag.parquet split: train - config_name: PeS2o-s2orc data_files: - path: data/v*/natural/*/PeS2o/*s2orc.parquet split: train - config_name: Pile data_files: - path: data/v*/natural/*/Pile/*.parquet split: train - config_name: Pile-DM_Mathematics data_files: - path: data/v*/natural/*/Pile/*DM_Mathematics.parquet split: train - config_name: Pile-FreeLaw data_files: - path: data/v*/natural/*/Pile/*FreeLaw.parquet split: train - config_name: Pile-NIH_ExPorter data_files: - path: data/v*/natural/*/Pile/*NIH_ExPorter.parquet split: train - config_name: Pile-PhilPapers data_files: - path: data/v*/natural/*/Pile/*PhilPapers.parquet split: train - config_name: Pile-StackExchange data_files: - path: data/v*/natural/*/Pile/*StackExchange.parquet split: train - config_name: Pile-USPTO_Backgrounds data_files: - path: data/v*/natural/*/Pile/*USPTO_Backgrounds.parquet split: train - config_name: Pile-Ubuntu_IRC data_files: - path: data/v*/natural/*/Pile/*Ubuntu_IRC.parquet split: train - config_name: QuestionsEcritesParlement data_files: - path: data/v*/natural/*/QuestionsEcritesParlement/*.parquet split: train - config_name: RedPajama data_files: - path: data/v*/natural/*/RedPajama/*.parquet split: train - config_name: RedPajama-de data_files: - path: data/v*/natural/de/RedPajama/*.parquet split: train - config_name: RedPajama-es data_files: - path: data/v*/natural/es/RedPajama/*.parquet split: train - config_name: RedPajama-fr data_files: - path: data/v*/natural/fr/RedPajama/*.parquet split: train - config_name: RedPajama-it data_files: - path: data/v*/natural/it/RedPajama/*.parquet split: train - config_name: Stac data_files: - path: data/v*/natural/*/Stac/*.parquet split: train - config_name: TheStack data_files: - path: data/v*/code/*/TheStack/*.parquet split: train - config_name: Theses data_files: - path: data/v*/natural/*/Theses/*.parquet split: train - config_name: Wikipedia data_files: - path: data/v*/natural/*/Wikipedia/*.parquet split: train - config_name: Wikipedia-de data_files: - path: data/v*/natural/de/Wikipedia/*.parquet split: train - config_name: Wikipedia-en data_files: - path: data/v*/natural/en/Wikipedia/*.parquet split: train - config_name: Wikipedia-es data_files: - path: data/v*/natural/es/Wikipedia/*.parquet split: train - config_name: Wikipedia-fr data_files: - path: data/v*/natural/fr/Wikipedia/*.parquet split: train - config_name: Wikipedia-it data_files: - path: data/v*/natural/it/Wikipedia/*.parquet split: train - config_name: Wikisource data_files: - path: data/v*/natural/*/Wikisource/*.parquet split: train - config_name: Wiktionary data_files: - path: data/v*/natural/*/Wiktionary/*.parquet split: train - config_name: YouTube data_files: - path: data/v*/natural/*/YouTube/*.parquet split: train --- # Lucie Training Dataset Card The Lucie Training Dataset is a curated collection of text data in English, French, German, Spanish and Italian culled from a variety of sources including: web data, video subtitles, academic papers, digital books, newspapers, and magazines, some of which were processed by Optical Character Recognition (OCR). It also contains samples of diverse programming languages. The Lucie Training Dataset was used to pretrain [Lucie-7B](https://huggingface.co/OpenLLM-France/Lucie-7B), a foundation LLM with strong capabilities in French and English. Code for data preparation can be found in the [training respository](https://github.com/OpenLLM-France/Lucie-Training/tree/7f1f7efa1288f709662a9067bf2c3db856b850f8) for Lucie-7B. Due to the licenses of a few subcorpora, the Lucie Training Dataset is released under a [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/). A subset available for commercial use will be released soon. We note that one subcorpus used for training could not be released with the Lucie Training Dataset due to copyright conflicts discovered after training had begun. This data came from the [Persée platform](https://www.persee.fr/). The full list of urls used to create the dataset can be recreated from the file [persee_metadata_documents.csv](https://huggingface.co/datasets/OpenLLM-France/Lucie-Training-Dataset/blob/main/metadata/persee_metadata_documents.csv), where the corresponding url is `https://www.persee.fr/doc/{ID}` for each `ID` in the column `file_id`. The file [persee_metadata_collections.csv](https://huggingface.co/datasets/OpenLLM-France/Lucie-Training-Dataset/blob/main/metadata/persee_metadata_collections.csv) gives statistics on document, word and character counts for the data grouped by collection. In all, the corpus contains a total of 3.25 billion words and 5.75 billion tokens, making up around 0.25% of the raw corpus and 0.37% of the tokens seen during training. Table of Contents: <ul> <li><a href="#dataset-description">Dataset Description</a> <ul> <li><a href="#sample-metadata">Sample Metadata</a></li> <li><a href="#dataset-composition">Dataset Composition</a> <table> <tr> <td style="vertical-align: top;"> <ul> <li><a href="#category-web"> Web</a></li> <li><a href="#category-newspaper"> Newspaper</a></li> <li><a href="#category-technical"> Technical</a></li> <li><a href="#category-book"> Book</a></li> </ul> </td> <td style="vertical-align: top;"> <ul> <li><a href="#category-legislative-texts"> Legislative Texts</a></li> <li><a href="#category-legislative-transcripts"> Legislative Transcripts</a></li> <li><a href="#category-wiki"> Wiki</a></li> <li><a href="#category-math"> Math</a></li> </ul> </td> <td style="vertical-align: top;"> <ul> <li><a href="#category-forum"> Forum</a></li> <li><a href="#category-dialogue"> Dialogue</a></li> <li><a href="#category-multilingual-parallel-corpora">Multilingual Parallel Corpora</a></li> <li><a href="#category-programming"> Programming</a></li> </ul> </td> </tr> </table> </li> <li><a href="#configurable-subsets-and-versions">Configurable Subsets and Versions</a></li> <li><a href="#details-on-data-sources">Details on Data Sources</a> <table> <tr> <td style="vertical-align: top;"> <ul> <li><a href="#amendementsparlement"> AmendementsParlement</a></li> <li><a href="#americanstories"> AmericanStories</a></li> <li><a href="#claire-french-and-english"> Claire (French and English)</a></li> <li><a href="#croissantaligned"> CroissantAligned</a></li> <li><a href="#discourspublics"> DiscoursPublics</a></li> <li><a href="#europarl-and-europarlaligned"> Europarl and EuroparlAligned</a></li> <li><a href="#eurovoc"> Eurovoc</a></li> <li><a href="#finewebedu"> FineWebEdu</a></li> <li><a href="#gallicamonographies"> GallicaMonographies</a></li> </ul> </td> <td style="vertical-align: top;"> <ul> <li><a href="#gallicapress"> GallicaPress</a></li> <li><a href="#gutenberg"> Gutenberg</a></li> <li><a href="#hal"> HAL</a></li> <li><a href="#interventionsparlement"> InterventionsParlement</a></li> <li><a href="#legi"> LEGI</a></li> <li><a href="#mathpile-commercial"> MathPile (Commercial)</a></li> <li><a href="#opendata"> OpenData</a></li> <li><a href="#openedition"> OpenEdition</a></li> <li><a href="#pes2o-v2"> PeS2o (v2)</a></li> </ul> </td> <td style="vertical-align: top;"> <ul> <li><a href="#pile-uncopyrighted"> Pile (Uncopyrighted)</a></li> <li><a href="#questionsecritesparlement"> QuestionsEcritesParlement</a></li> <li><a href="#redpajama-v2"> RedPajama (v2)</a></li> <li><a href="#stac"> Stac</a></li> <li><a href="#thestack-v12"> TheStack (v1.2)</a></li> <li><a href="#theses"> Theses</a></li> <li><a href="#wikipedia-wikisource-wiktionary"> Wikipedia, Wikisource, Wiktionary</a></li> <li><a href="#youtube"> YouTube</a></li> </ul> </td> </tr> </table> </li> </ul> </li> <li><a href="#example-use-in-python">Example use in Python</a></li> <ul> <li><a href="#load-the-dataset">Load the dataset</a></li> <li><a href="#iterate-over-a-subset">Iterate over a subset</a></li> <li><a href="#load-a-specific-version">Load a specific version</a></li> </ul> </li> <li><a href="#citation">Citation</a></li> <li><a href="#acknowledgements">Acknowledgements</a></li> <li><a href="#contact">Contact</a></li> </ul> ## Dataset Description This dataset is intended to provide extensive and diverse multilingual data for training Large Language Models (LLMs). Here are some of the principal features of the corpus: * Data mix: * The dataset contains more French than English data -- it is in fact one of the biggest collections of French text data that has been preprocessed for LLM training -- with the aim of minimizing anglo-centric cultural biases. * German, Spanish and Italian are also represented in small amounts. * Code is included to boost the reasoning capabilities of LLMs. * Data filtering and deduplication: * The dataset has been cleaned in an effort to remove very low-quality data. * Duplicate data samples have been removed to some extent, following best practices. * Web data has been filtered to minimize potentially toxic content and personally identifying information. * Ethics: * Special care has been taken to respect copyright laws and individual privacy. All newspapers, monographies, magazines and legislative documents, as well as most books, are in the public domain (which depends on the author's date of death and the country of publication). Other data are published with permissive licenses (e.g., CC BY or CC BY-SA) or, in very rare cases, CC BY-NC-SA. * All web data in the dataset come from sites with robots.txt files that do not forbid crawling. ### Sample Metadata In addition to the `text` field, which provides the content of the sample, each training sample in the corpus contains the following metadata when available: * [`language`](metadata/metadata_examples.json#L3): the language of the text sample (note that this information is taken from the original data source and may be incorrect). <br>Possible values: - the ISO 639-1 code for a given natural language ("en", "fr", "de", "es", or "it"), - the name of a programming language prefixed by "code:" ("code:python", "code:c++", …), or - a list of ISO 639-1 codes separated by commas for data containing parallel translations ("fr,en", "de,fr", "es,en", "it,en", or one of those pairs in the opposite order if the languages appear in the opposite order in the text). * [`source`](metadata/metadata_examples.json#L4): an identifier for the source(s) of the text sample (Wikipedia, RedPajama, Gutenberg, …). All sources are described in detail [below](#details-on-data-sources). * [`id`](metadata/metadata_examples.json#L13): an identifier that is unique among documents from the same source. * [`url`](metadata/metadata_examples.json#L35) (optional): the URL of the original text sample on the web, if available. * [`title`](metadata/metadata_examples.json#L36) (optional): the title of the original text sample, if available. * [`author`](metadata/metadata_examples.json#L81) (optional): the author of the original text sample, if available. <details><summary>Note:</summary> The author name is given in plain text, except in the case of <a href="metadata/metadata_examples.json#L91">Gutenberg books</a>, where it is the JSON serialized object of the author metadata. </details> * [`date`](metadata/metadata_examples.json#L6) (optional): the publication date of the original text sample, if available. <details><summary>Note:</summary> The text format of the date depends on the source. </details> * [`quality_signals`](metadata/metadata_examples.json#L17) (optional): a list of quality signals for the text sample in JSON format (which could be used for further filtering or sample weighting). <details><summary>Note:</summary> It can include indicators computed by `fasttext` and `CCNet`, statistics about occurrences of characters, words, special characters, etc. </details> * [`extra`](metadata/metadata_examples.json#L16) (optional): extra information about the text sample, in JSON format. This can include metadata about the source subset, the rights, etc. The list of metadata available for each source is provided (without the `text` field) in [metadata_examples.json](metadata/metadata_examples.json). ### Dataset Composition The following figure shows the distribution of the dataset by language (colors) and category (hatch patterns). ![Dataset composition](figures/fig_dataset_composition.png) The following table provides an overview of the dataset composition, broken down by source and language. Sources are grouped by category. The table provides the numbers of documents, words, tokens, and characters for each subset. All numbers in this table are available in the CSV file [dataset_composition.csv](metadata/dataset_composition.csv). Token counts are computed using the tokenizer for [Lucie-7B](https://huggingface.co/OpenLLM-France/Lucie-7B). <!-- The following is automatically generated. Do not update manually. --> <!-- TABLE START --> <table> <thead> <tr> <th><strong>Subset</strong></th> <th><strong>Language</strong></th> <th><strong>M docs</strong></th> <th><strong>B words</strong></th> <th><strong>B tokens</strong></th> <th><strong>B chars</strong></th> <th></th> </tr> </thead> <tbody> <tr> <td rowspan="11" style="vertical-align: top;"><strong>TOTAL</strong></td> <td></td> <td>2186.562</td> <td>1356.021</td> <td>2314.862</td> <td>8842.200</td> <td></td> </tr> <tr> <td><strong>French (fr)</strong></td> <td>653.812</td> <td>583.687</td> <td>928.618</td> <td>3619.672</td> <td><a href="https://huggingface.co/datasets/OpenLLM-France/Lucie-Training-Dataset/blob/main/figures/fig_distribution_french_pie.png">composition details</a></td> </tr> <tr> <td><strong>English (en)</strong></td> <td>554.289</td> <td>412.202</td> <td>611.894</td> <td>2553.541</td> <td><a href="https://huggingface.co/datasets/OpenLLM-France/Lucie-Training-Dataset/blob/main/figures/fig_distribution_english_pie.png">composition details</a></td> </tr> <tr> <td><strong>code</strong></td> <td>125.769</td> <td>51.306</td> <td>228.954</td> <td>630.749</td> <td><a href="https://huggingface.co/datasets/OpenLLM-France/Lucie-Training-Dataset/blob/main/figures/fig_distribution_code_pie.png">composition details</a></td> </tr> <tr> <td><strong>German (de)</strong></td> <td>165.915</td> <td>105.609</td> <td>206.610</td> <td>764.779</td> <td><a href="https://huggingface.co/datasets/OpenLLM-France/Lucie-Training-Dataset/blob/main/figures/fig_distribution_german_pie.png">composition details</a></td> </tr> <tr> <td><strong>Spanish (es)</strong></td> <td>171.651</td> <td>123.857</td> <td>200.825</td> <td>759.457</td> <td><a href="https://huggingface.co/datasets/OpenLLM-France/Lucie-Training-Dataset/blob/main/figures/fig_distribution_spanish_pie.png">composition details</a></td> </tr> <tr> <td><strong>Italian (it)</strong></td> <td>99.440</td> <td>62.051</td> <td>112.031</td> <td>404.454</td> <td><a href="https://huggingface.co/datasets/OpenLLM-France/Lucie-Training-Dataset/blob/main/figures/fig_distribution_italian_pie.png">composition details</a></td> </tr> <tr> <td><strong>fr-en</strong></td> <td>410.032</td> <td>17.016</td> <td>25.494</td> <td>107.658</td> <td><a href="https://huggingface.co/datasets/OpenLLM-France/Lucie-Training-Dataset/blob/main/figures/fig_distribution_fr-en_pie.png">composition details</a></td> </tr> <tr> <td><strong>it-en</strong></td> <td>1.901</td> <td>0.100</td> <td>0.151</td> <td>0.638</td> <td></td> </tr> <tr> <td><strong>es-en</strong></td> <td>1.961</td> <td>0.103</td> <td>0.143</td> <td>0.631</td> <td></td> </tr> <tr> <td><strong>de-fr</strong></td> <td>1.792</td> <td>0.0908</td> <td>0.141</td> <td>0.621</td> <td></td> </tr> <tr> <td colspan="7"><h4 id="category-web">Category: Web</h4></td></tr> <tr> <td rowspan="4" style="vertical-align: top;"><a href="#redpajama-v2"><strong>RedPajama</strong></a></td> <td><strong>French (fr)</strong></td> <td>640.770</td> <td>477.758</td> <td>741.023</td> <td>2974.596</td> <td><a href="https://huggingface.co/datasets/OpenLLM-France/Lucie-Training-Dataset/blob/main/figures/fig_distribution_redpajama-french_histogram.png">composition details</a></td> </tr> <tr> <td><strong>German (de)</strong></td> <td>162.779</td> <td>103.078</td> <td>201.371</td> <td>747.631</td> <td><a href="https://huggingface.co/datasets/OpenLLM-France/Lucie-Training-Dataset/blob/main/figures/fig_distribution_redpajama-german_histogram.png">composition details</a></td> </tr> <tr> <td><strong>Spanish (es)</strong></td> <td>169.447</td> <td>121.751</td> <td>197.125</td> <td>746.984</td> <td><a href="https://huggingface.co/datasets/OpenLLM-France/Lucie-Training-Dataset/blob/main/figures/fig_distribution_redpajama-spanish_histogram.png">composition details</a></td> </tr> <tr> <td><strong>Italian (it)</strong></td> <td>97.324</td> <td>60.194</td> <td>108.416</td> <td>393.012</td> <td><a href="https://huggingface.co/datasets/OpenLLM-France/Lucie-Training-Dataset/blob/main/figures/fig_distribution_redpajama-italian_histogram.png">composition details</a></td> </tr> <tr> <td><a href="#finewebedu"><strong>FineWebEdu</strong></a></td> <td><strong>English (en)</strong></td> <td>421.209</td> <td>327.453</td> <td>467.837</td> <td>2018.215</td> <td><a href="https://huggingface.co/datasets/OpenLLM-France/Lucie-Training-Dataset/blob/main/figures/fig_distribution_finewebedu-english_histogram.png">composition details</a></td> </tr> <tr> <td colspan="7"><h4 id="category-newspaper">Category: Newspaper</h4></td></tr> <tr> <td><a href="#gallicapress"><strong>GallicaPress</strong></a></td> <td><strong>French (fr)</strong></td> <td>3.205</td> <td>67.496</td> <td>121.606</td> <td>408.882</td> <td></td> </tr> <tr> <td><a href="#americanstories"><strong>AmericanStories</strong></a></td> <td><strong>English (en)</strong></td> <td>59.420</td> <td>8.902</td> <td>14.313</td> <td>50.844</td> <td><a href="https://huggingface.co/datasets/OpenLLM-France/Lucie-Training-Dataset/blob/main/figures/fig_distribution_americanstories-english_histogram.png">composition details</a></td> </tr> <tr> <td colspan="7"><h4 id="category-technical">Category: Technical</h4></td></tr> <tr> <td><a href="#pes2o-v2"><strong>PeS2o</strong></a></td> <td><strong>English (en)</strong></td> <td>38.972</td> <td>42.296</td> <td>65.365</td> <td>268.963</td> <td></td> </tr> <tr> <td><a href="#hal"><strong>HAL</strong></a></td> <td><strong>French (fr)</strong></td> <td>0.349</td> <td>9.356</td> <td>16.224</td> <td>58.308</td> <td></td> </tr> <tr> <td><a href="#theses"><strong>Theses</strong></a></td> <td><strong>French (fr)</strong></td> <td>0.102</td> <td>7.547</td> <td>14.060</td> <td>47.758</td> <td></td> </tr> <tr> <td><a href="#pile-uncopyrighted"><strong>Pile (USPTO_Backgrounds)</strong></a></td> <td><strong>English (en)</strong></td> <td>5.139</td> <td>3.492</td> <td>5.105</td> <td>22.309</td> <td></td> </tr> <tr> <td><a href="#openedition"><strong>OpenEdition</strong></a></td> <td><strong>French (fr)</strong></td> <td>0.939</td> <td>2.225</td> <td>3.604</td> <td>14.459</td> <td></td> </tr> <tr> <td><a href="#pile-uncopyrighted"><strong>Pile (PhilPapers)</strong></a></td> <td><strong>English (en)</strong></td> <td>0.0308</td> <td>0.363</td> <td>0.618</td> <td>2.304</td> <td></td> </tr> <tr> <td><a href="#pile-uncopyrighted"><strong>Pile (NIH_ExPorter)</strong></a></td> <td><strong>English (en)</strong></td> <td>0.914</td> <td>0.288</td> <td>0.431</td> <td>1.979</td> <td></td> </tr> <tr> <td colspan="7"><h4 id="category-book">Category: Book</h4></td></tr> <tr> <td><a href="#gallicamonographies"><strong>GallicaMonographies</strong></a></td> <td><strong>French (fr)</strong></td> <td>0.278</td> <td>15.106</td> <td>25.169</td> <td>90.456</td> <td></td> </tr> <tr> <td rowspan="5" style="vertical-align: top;"><a href="#gutenberg"><strong>Gutenberg</strong></a></td> <td><strong>English (en)</strong></td> <td>0.0563</td> <td>3.544</td> <td>5.516</td> <td>20.579</td> <td></td> </tr> <tr> <td><strong>French (fr)</strong></td> <td>0.00345</td> <td>0.227</td> <td>0.383</td> <td>1.392</td> <td></td> </tr> <tr> <td><strong>German (de)</strong></td> <td>0.00188</td> <td>0.0987</td> <td>0.193</td> <td>0.654</td> <td></td> </tr> <tr> <td><strong>Italian (it)</strong></td> <td>0.000958</td> <td>0.0657</td> <td>0.129</td> <td>0.414</td> <td></td> </tr> <tr> <td><strong>Spanish (es)</strong></td> <td>0.000735</td> <td>0.0512</td> <td>0.0920</td> <td>0.303</td> <td></td> </tr> <tr> <td colspan="7"><h4 id="category-legislative-texts">Category: Legislative Texts</h4></td></tr> <tr> <td><a href="#pile-uncopyrighted"><strong>Pile (FreeLaw)</strong></a></td> <td><strong>English (en)</strong></td> <td>3.415</td> <td>8.204</td> <td>14.011</td> <td>52.580</td> <td></td> </tr> <tr> <td rowspan="4" style="vertical-align: top;"><a href="#eurovoc"><strong>Eurovoc</strong></a></td> <td><strong>English (en)</strong></td> <td>0.272</td> <td>1.523</td> <td>2.571</td> <td>9.468</td> <td></td> </tr> <tr> <td><strong>Italian (it)</strong></td> <td>0.245</td> <td>0.731</td> <td>1.527</td> <td>4.867</td> <td></td> </tr> <tr> <td><strong>German (de)</strong></td> <td>0.247</td> <td>0.678</td> <td>1.497</td> <td>4.915</td> <td></td> </tr> <tr> <td><strong>Spanish (es)</strong></td> <td>0.246</td> <td>0.757</td> <td>1.411</td> <td>4.684</td> <td></td> </tr> <tr> <td><a href="#opendata"><strong>OpenData</strong></a></td> <td><strong>French (fr)</strong></td> <td>1.169</td> <td>0.755</td> <td>1.209</td> <td>4.638</td> <td></td> </tr> <tr> <td><a href="#questionsecritesparlement"><strong>QuestionsEcritesParlement</strong></a></td> <td><strong>French (fr)</strong></td> <td>0.189</td> <td>0.108</td> <td>0.156</td> <td>0.705</td> <td></td> </tr> <tr> <td><a href="#legi"><strong>LEGI</strong></a></td> <td><strong>French (fr)</strong></td> <td>0.621</td> <td>0.0878</td> <td>0.145</td> <td>0.563</td> <td></td> </tr> <tr> <td><a href="#amendementsparlement"><strong>AmendementsParlement</strong></a></td> <td><strong>French (fr)</strong></td> <td>0.673</td> <td>0.0452</td> <td>0.0738</td> <td>0.274</td> <td></td> </tr> <tr> <td colspan="7"><h4 id="category-legislative-transcripts">Category: Legislative Transcripts</h4></td></tr> <tr> <td rowspan="4" style="vertical-align: top;"><a href="#europarl-and-europarlaligned"><strong>Europarl</strong></a></td> <td><strong>German (de)</strong></td> <td>0.0102</td> <td>0.0451</td> <td>0.0734</td> <td>0.327</td> <td></td> </tr> <tr> <td><strong>Spanish (es)</strong></td> <td>0.0103</td> <td>0.0524</td> <td>0.0733</td> <td>0.325</td> <td></td> </tr> <tr> <td><strong>French (fr)</strong></td> <td>0.0103</td> <td>0.0528</td> <td>0.0717</td> <td>0.339</td> <td></td> </tr> <tr> <td><strong>English (en)</strong></td> <td>0.0111</td> <td>0.0563</td> <td>0.0690</td> <td>0.339</td> <td></td> </tr> <tr> <td><a href="#discourspublics"><strong>DiscoursPublics</strong></a></td> <td><strong>French (fr)</strong></td> <td>0.110</td> <td>0.163</td> <td>0.238</td> <td>1.025</td> <td></td> </tr> <tr> <td><a href="#interventionsparlement"><strong>InterventionsParlement</strong></a></td> <td><strong>French (fr)</strong></td> <td>1.832</td> <td>0.104</td> <td>0.157</td> <td>0.654</td> <td></td> </tr> <tr> <td colspan="7"><h4 id="category-wiki">Category: Wiki</h4></td></tr> <tr> <td rowspan="5" style="vertical-align: top;"><a href="#wikipedia-wikisource-wiktionary"><strong>Wikipedia</strong></a></td> <td><strong>English (en)</strong></td> <td>6.893</td> <td>4.708</td> <td>7.898</td> <td>26.616</td> <td></td> </tr> <tr> <td><strong>German (de)</strong></td> <td>2.877</td> <td>1.709</td> <td>3.476</td> <td>11.252</td> <td></td> </tr> <tr> <td><strong>French (fr)</strong></td> <td>2.648</td> <td>1.726</td> <td>2.940</td> <td>9.879</td> <td></td> </tr> <tr> <td><strong>Spanish (es)</strong></td> <td>1.947</td> <td>1.245</td> <td>2.124</td> <td>7.161</td> <td></td> </tr> <tr> <td><strong>Italian (it)</strong></td> <td>1.870</td> <td>1.060</td> <td>1.959</td> <td>6.161</td> <td></td> </tr> <tr> <td><a href="#wikipedia-wikisource-wiktionary"><strong>wikisource</strong></a></td> <td><strong>French (fr)</strong></td> <td>0.186</td> <td>0.523</td> <td>0.795</td> <td>3.080</td> <td></td> </tr> <tr> <td><a href="#wikipedia-wikisource-wiktionary"><strong>wiktionary</strong></a></td> <td><strong>French (fr)</strong></td> <td>0.650</td> <td>0.0531</td> <td>0.117</td> <td>0.347</td> <td></td> </tr> <tr> <td colspan="7"><h4 id="category-math">Category: Math</h4></td></tr> <tr> <td><a href="#mathpile-commercial"><strong>MathPile</strong></a></td> <td><strong>English (en)</strong></td> <td>0.737</td> <td>3.408</td> <td>9.637</td> <td>27.290</td> <td></td> </tr> <tr> <td><a href="#pile-uncopyrighted"><strong>Pile (DM_Mathematics)</strong></a></td> <td><strong>English (en)</strong></td> <td>0.992</td> <td>1.746</td> <td>4.928</td> <td>8.127</td> <td></td> </tr> <tr> <td colspan="7"><h4 id="category-forum">Category: Forum</h4></td></tr> <tr> <td><a href="#pile-uncopyrighted"><strong>Pile (StackExchange)</strong></a></td> <td><strong>English (en)</strong></td> <td>15.269</td> <td>4.534</td> <td>10.275</td> <td>33.609</td> <td></td> </tr> <tr> <td><a href="#pile-uncopyrighted"><strong>Pile (Ubuntu_IRC)</strong></a></td> <td><strong>English (en)</strong></td> <td>0.0104</td> <td>0.867</td> <td>2.159</td> <td>5.610</td> <td></td> </tr> <tr> <td colspan="7"><h4 id="category-dialogue">Category: Dialogue</h4></td></tr> <tr> <td rowspan="2" style="vertical-align: top;"><a href="#claire-french-and-english"><strong>Claire</strong></a></td> <td><strong>English (en)</strong></td> <td>0.949</td> <td>0.818</td> <td>1.161</td> <td>4.709</td> <td><a href="https://huggingface.co/datasets/OpenLLM-France/Lucie-Training-Dataset/blob/main/figures/fig_distribution_claire-english_pie.png">composition details</a></td> </tr> <tr> <td><strong>French (fr)</strong></td> <td>0.0393</td> <td>0.210</td> <td>0.311</td> <td>1.314</td> <td><a href="https://huggingface.co/datasets/OpenLLM-France/Lucie-Training-Dataset/blob/main/figures/fig_distribution_claire-french_pie.png">composition details</a></td> </tr> <tr> <td><a href="#youtube"><strong>YouTube</strong></a></td> <td><strong>French (fr)</strong></td> <td>0.0375</td> <td>0.145</td> <td>0.336</td> <td>1.003</td> <td></td> </tr> <tr> <td><a href="#stac"><strong>STAC</strong></a></td> <td><strong>English (en)</strong></td> <td>0.0000450</td> <td>0.0000529</td> <td>0.000121</td> <td>0.000327</td> <td></td> </tr> <tr> <td colspan="7"><h4 id="category-multilingual-parallel-corpora">Category: Multilingual Parallel Corpora</h4></td></tr> <tr> <td><a href="#croissantaligned"><strong>CroissantAligned</strong></a></td> <td><strong>fr-en</strong></td> <td>408.029</td> <td>16.911</td> <td>25.351</td> <td>107.003</td> <td></td> </tr> <tr> <td rowspan="4" style="vertical-align: top;"><a href="#europarl-and-europarlaligned"><strong>EuroparlAligned</strong></a></td> <td><strong>it-en</strong></td> <td>1.901</td> <td>0.100</td> <td>0.151</td> <td>0.638</td> <td></td> </tr> <tr> <td><strong>fr-en</strong></td> <td>2.003</td> <td>0.105</td> <td>0.143</td> <td>0.655</td> <td></td> </tr> <tr> <td><strong>es-en</strong></td> <td>1.961</td> <td>0.103</td> <td>0.143</td> <td>0.631</td> <td></td> </tr> <tr> <td><strong>de-fr</strong></td> <td>1.792</td> <td>0.0908</td> <td>0.141</td> <td>0.621</td> <td></td> </tr> <tr> <td colspan="7"><h4 id="category-programming">Category: Programming</h4></td></tr> <tr> <td rowspan="30" style="vertical-align: top;"><a href="#thestack-v12"><strong>TheStack</strong></a></td> <td><strong>JAVASCRIPT</strong></td> <td>21.109</td> <td>8.526</td> <td>58.609</td> <td>141.647</td> <td></td> </tr> <tr> <td><strong>JAVA</strong></td> <td>20.152</td> <td>7.421</td> <td>27.680</td> <td>89.297</td> <td></td> </tr> <tr> <td><strong>C</strong></td> <td>8.626</td> <td>5.916</td> <td>24.092</td> <td>57.428</td> <td></td> </tr> <tr> <td><strong>PHP</strong></td> <td>15.905</td> <td>4.865</td> <td>22.883</td> <td>66.844</td> <td></td> </tr> <tr> <td><strong>PYTHON</strong></td> <td>12.962</td> <td>5.434</td> <td>21.683</td> <td>64.304</td> <td></td> </tr> <tr> <td><strong>C++</strong></td> <td>6.378</td> <td>4.584</td> <td>18.835</td> <td>50.892</td> <td></td> </tr> <tr> <td><strong>C#</strong></td> <td>10.839</td> <td>3.574</td> <td>13.381</td> <td>46.286</td> <td></td> </tr> <tr> <td><strong>GO</strong></td> <td>4.730</td> <td>2.735</td> <td>10.262</td> <td>25.738</td> <td></td> </tr> <tr> <td><strong>TYPESCRIPT</strong></td> <td>10.637</td> <td>2.617</td> <td>9.836</td> <td>28.815</td> <td></td> </tr> <tr> <td><strong>RUST</strong></td> <td>1.387</td> <td>0.872</td> <td>3.241</td> <td>9.529</td> <td></td> </tr> <tr> <td><strong>RUBY</strong></td> <td>3.405</td> <td>0.646</td> <td>2.392</td> <td>7.139</td> <td></td> </tr> <tr> <td><strong>SWIFT</strong></td> <td>1.756</td> <td>0.553</td> <td>1.876</td> <td>6.134</td> <td></td> </tr> <tr> <td><strong>KOTLIN</strong></td> <td>2.243</td> <td>0.454</td> <td>1.758</td> <td>5.769</td> <td></td> </tr> <tr> <td><strong>SCALA</strong></td> <td>1.362</td> <td>0.457</td> <td>1.587</td> <td>4.862</td> <td></td> </tr> <tr> <td><strong>TEX</strong></td> <td>0.398</td> <td>0.394</td> <td>1.507</td> <td>3.805</td> <td></td> </tr> <tr> <td><strong>LUA</strong></td> <td>0.559</td> <td>0.318</td> <td>1.367</td> <td>3.279</td> <td></td> </tr> <tr> <td><strong>DART</strong></td> <td>0.933</td> <td>0.308</td> <td>1.242</td> <td>3.864</td> <td></td> </tr> <tr> <td><strong>PERL</strong></td> <td>0.392</td> <td>0.297</td> <td>1.149</td> <td>2.634</td> <td></td> </tr> <tr> <td><strong>MATHEMATICA</strong></td> <td>0.0269</td> <td>0.120</td> <td>1.117</td> <td>1.720</td> <td></td> </tr> <tr> <td><strong>ASSEMBLY</strong></td> <td>0.248</td> <td>0.209</td> <td>0.867</td> <td>1.575</td> <td></td> </tr> <tr> <td><strong>HASKELL</strong></td> <td>0.545</td> <td>0.307</td> <td>0.807</td> <td>2.364</td> <td></td> </tr> <tr> <td><strong>FORTRAN</strong></td> <td>0.165</td> <td>0.192</td> <td>0.780</td> <td>1.843</td> <td></td> </tr> <tr> <td><strong>JULIA</strong></td> <td>0.299</td> <td>0.152</td> <td>0.660</td> <td>1.539</td> <td></td> </tr> <tr> <td><strong>OCAML</strong></td> <td>0.160</td> <td>0.130</td> <td>0.430</td> <td>1.107</td> <td></td> </tr> <tr> <td><strong>ERLANG</strong></td> <td>0.0994</td> <td>0.0657</td> <td>0.260</td> <td>0.726</td> <td></td> </tr> <tr> <td><strong>ELIXIR</strong></td> <td>0.282</td> <td>0.0731</td> <td>0.258</td> <td>0.737</td> <td></td> </tr> <tr> <td><strong>CLOJURE</strong></td> <td>0.126</td> <td>0.0448</td> <td>0.179</td> <td>0.492</td> <td></td> </tr> <tr> <td><strong>R</strong></td> <td>0.0392</td> <td>0.0278</td> <td>0.158</td> <td>0.305</td> <td></td> </tr> <tr> <td><strong>MATLAB</strong></td> <td>0.000967</td> <td>0.00865</td> <td>0.0427</td> <td>0.0372</td> <td></td> </tr> <tr> <td><strong>RACKET</strong></td> <td>0.00420</td> <td>0.00479</td> <td>0.0153</td> <td>0.0378</td> <td></td> </tr> </tbody> </table> <!-- TABLE END --> ### Configurable Subsets and Versions As the Lucie Training Dataset is a collection of multilingual corpora from different sources, it can be divided into subsets based on the source and language of its constituent corpora. <br> The list of possible configurations is available [in the YAML header of this README file](https://huggingface.co/datasets/OpenLLM-France/Lucie-Training-Dataset/blob/v1.2/README.md?code=true#L24). Each configuration corresponds to a pathname pattern in the [data subdirectory](https://huggingface.co/datasets/OpenLLM-France/Lucie-Training-Dataset/tree/v1.2/data). The dataset is also available in the following versions: - **v1.1** / [**main**](https://huggingface.co/datasets/OpenLLM-France/Lucie-Training-Dataset/tree/main/data) (default): The data used for the first (main) pretraining phase of [Lucie-7B](https://huggingface.co/OpenLLM-France/Lucie-7B), which contains approximately 2.3T tokens. The statistics above apply to this version. - [**v1.2**](https://huggingface.co/datasets/OpenLLM-France/Lucie-Training-Dataset/tree/v1.2/data): An improved version of the main dataset, where - GallicaMonographies and GallicaPress have been fltered aggressively to remove documents with low OCR quality. - The `Ubuntu_IRC` and `PhilPapers` subsets of Pile have been refined by fixing encoding issues and removing documents in languages other than English, French, Spanish, German and Italian. - [**v1.2-recent-web**](https://huggingface.co/datasets/OpenLLM-France/Lucie-Training-Dataset/tree/v1.2-recent-web/data) : The data used for the second pretraining phase (context extension) of [Lucie-7B](https://huggingface.co/OpenLLM-France/Lucie-7B#2-context-extension). This version is identical to `v1.2` with the exception that older snapshots of web data (before 2023 for RedPajama and before 2024 for FineWebEdu) have been excluded. All data from `v1.1` that were not filtered out remain unchanged in `v1.2` and `v1.2-recent-web`. Except from **v1.1**, which is a git tag, all versions are git branches in the dataset repository (e.g. [**v1.2**](https://huggingface.co/datasets/OpenLLM-France/Lucie-Training-Dataset/tree/v1.2/data)). The <a href="#example-use-in-python">Example use in Python</a> section contains example Python code for loading and iterating over the dataset with different configurations, including source, language and version. ### Details on Data Sources #### AmendementsParlement * <u>Source</u>: Corpus contributed by OpenLLM partners. * <u>Extracted from</u>: [Regards citoyens](https://www.regardscitoyens.org/#&panel1-4). License: [CC BY-SA](https://www.regardscitoyens.org/mentions-legales/). * <u>Description</u>: A collection of proposed amendments by the French parliament. Documents contain the text of the proposed amendment, the name of the associated law as well as information on who voted on the amendment and what was decided. #### AmericanStories * <u>Source</u>: [dell-research-harvard/AmericanStories](https://huggingface.co/datasets/dell-research-harvard/AmericanStories). License: [CC BY 4.0](https://huggingface.co/datasets/dell-research-harvard/AmericanStories). * <u>Extracted from</u>: [Chronicling America](https://www.loc.gov/collections/chronicling-america/about-this-collection/). License: [Open](https://www.loc.gov/collections/chronicling-america/about-this-collection/rights-and-access/). * <u>Description</u>: "The American Stories dataset is a collection of full article texts extracted from historical U.S. newspaper images. It includes nearly 20 million scans from the public domain Chronicling America collection maintained by the Library of Congress. The dataset is designed to address the challenges posed by complex layouts and low OCR quality in existing newspaper datasets" (from the [dataset card](https://huggingface.co/datasets/dell-research-harvard/AmericanStories)). See the dataset <a href="https://huggingface.co/datasets/OpenLLM-France/Lucie-Training-Dataset/blob/main/figures/fig_distribution_americanstories-english_histogram.png">composition details</a> for statistics on documents by year. Dataset containing text retrieved through OCR. * <u>Pre-processing</u>: * <u>Filtering</u>: To filter out documents with excessive OCR errors, the dataset was refined by discarding texts with a perplexity higher than 2310, measured using a CCNET model in English (see [code details](https://github.com/OpenLLM-France/Lucie-Training/blob/7f1f7efa1288f709662a9067bf2c3db856b850f8/tokenization/data.py#L2106)). The code to compute CCNET perplexity, parallelizing on parquet files, is [available here](https://github.com/OpenLLM-France/Lucie-dataset-filtering). * <u>Citation</u>: Melissa Dell, Jacob Carlson, Tom Bryan, Emily Silcock, Abhishek Arora, Zejiang Shen, Luca D'Amico-Wong, Quan Le, Pablo Querubin and Leander Heldring (2023). "American Stories: A Large-Scale Structured Text Dataset of Historical U.S. Newspapers," [arxiv:2308.12477](https://arxiv.org/abs/2308.12477v1). #### Claire (French and English) * <u>Sources</u>: * French dataset: [OpenLLM-France/Claire-Dialogue-French-0.1](https://huggingface.co/datasets/OpenLLM-France/Claire-Dialogue-French-0.1). License: [CC BY-NC-SA 4.0](https://huggingface.co/datasets/OpenLLM-France/Claire-Dialogue-French-0.1). * English dataset: [OpenLLM-France/Claire-Dialogue-English-0.1](https://huggingface.co/datasets/OpenLLM-France/Claire-Dialogue-English-0.1). License: [CC BY-NC-SA 4.0](https://huggingface.co/datasets/OpenLLM-France/Claire-Dialogue-English-0.1). * <u>Extracted from</u>: see the datacards for the [French](https://huggingface.co/datasets/OpenLLM-France/Claire-Dialogue-French-0.1) and [English](https://huggingface.co/datasets/OpenLLM-France/Claire-Dialogue-English-0.1) datasets. * <u>Description</u>: The Claire datasets are composed of transcripts of spoken conversations -- including parliamentary proceedings, interviews, debates, meetings, and free conversations -- as well as some written conversations from theater plays and written chats. The dataset is designed to help downstream performance of models fine-tuned for tasks requiring the comprehension of spontaneous spoken conversation, such as meeting summarization. Each dialogue is split into speech turns, and each speech turn is labeled with the name of the speaker or a unique identifier. See the composition details for the <a href="https://huggingface.co/datasets/OpenLLM-France/Lucie-Training-Dataset/blob/main/figures/fig_distribution_claire-french_pie.png">French dataset</a> and the <a href="https://huggingface.co/datasets/OpenLLM-France/Lucie-Training-Dataset/blob/main/figures/fig_distribution_claire-english_pie.png">English dataset</a> for a high-level view of the distribution of different types of documents in each dataset. * <u>Citation</u>: Julie Hunter, Jérôme Louradour, Virgile Rennard, Ismaïl Harrando, Guokan Shang, Jean-Pierre Lorré (2023). The Claire French Dialogue Dataset. [arXiv:2311.16840](https://arxiv.org/abs/2311.16840). #### CroissantAligned * <u>Source</u>: [croissantllm/croissant_dataset_no_web_data](https://huggingface.co/datasets/croissantllm/croissant_dataset_no_web_data/tree/main/aligned_36b) (subset: `aligned_36b`). License: not specified. * <u>Extracted from</u>: * Translation pairs: [OPUS](https://opus.nlpl.eu/) (99.6% of the data in CroissantAligned). Pairs extracted from OPUS are labeled as "UnbabelFrEn". * Thesis abstracts: French thesis abstract pairs. License: [ETALAB-Licence-Ouverte-v2.0](https://www.etalab.gouv.fr/wp-content/uploads/2017/04/ETALAB-Licence-Ouverte-v2.0.pdf). * Song lyrics: [lacoccinelle](https://www.lacoccinelle.net). * <u>Description</u>: CroissantAligned contains samples of parallel French/English (or English/French) data. Data extracted from OPUS takes the form of sentences pairs, where one sentence is in French and the other is in English. OPUS pairs were passed through a custom pipeline designed to select the highest quality translation examples. Selected pairs are labeled "UnbabelFrEn" in the CroissantAligned dataset. The thesis abstract subset contains thesis abstracts paired with translations written by the thesis authors. The song lyrics are translated by contributors to www.lacoccinelle.net. Parallel data are used to boost the multilingual capabilities of models trained on them ([Faysse et al.,2024](https://arxiv.org/pdf/2402.00786)). * <u>Pre-processing</u>: * <u>Language separation and tagging</u>: The original text field of [the Croissant dataset](https://huggingface.co/datasets/croissantllm/croissant_dataset_no_web_data) contains a sentence or passage in French or English immediately followed by its translation without any indication of which passage is in which language. The first step was thus to split each text into separate, monolingual passages and tag each passage with the appropriate language code, identified automatically using the [langid library](https://pypi.org/project/langid/) (see [code details](https://github.com/OpenLLM-France/Lucie-Training/blob/cdec8fd6369385455829ab39c2f04bcb1a8a475a/tokenization/data.py#L1407)). In the Lucie Training Dataset, the `extra` metadata field for CroissantAligned contains separate keys, `text_fr` for French and `text_en` for English, that stores the texts separately. * <u>Random combination of texts prefixed by language</u>: To create the text values, each monolingual text was repaired with its translation, but random separators and various methods of prefixing the text with the language (name or code) were added. This was done as a precaution to prevent models trained on this data from switching languages when generating text and can be seen as a very basic instruction to translate the source (first) text into the target (second) text (see [code details](https://github.com/OpenLLM-France/Lucie-Training/blob/cdec8fd6369385455829ab39c2f04bcb1a8a475a/tokenization/data.py#L1458)). * <u>Citation</u>: Manuel Faysse, Patrick Fernandes, Nuno M. Guerreiro, António Loison, Duarte M. Alves, Caio Corro, Nicolas Boizard, João Alves, Ricardo Rei, Pedro H. Martins, Antoni Bigata Casademunt, François Yvon, André F.T. Martins, Gautier Viaud, Céline Hudelot, Pierre Colombo (2024). "CroissantLLM: A Truly Bilingual French-English Language Model," [arXiv:2402.00786](https://arxiv.org/abs/2402.00786). #### DiscoursPublics * <u>Source</u>: Corpus contributed by OpenLLM partners. * <u>Extracted from</u>: [Vie Publique](https://www.vie-publique.fr/collection-discours-publics). License: [ETALAB-Licence-Ouverte-v2.0](https://www.vie-publique.fr/mentions-legales). * <u>Description</u>: A collection of public speeches from the principal public actors in France including speeches from the French President starting from 1974 and from the Prime Minister and members of the government starting from 1980. * <u>Pre-processing</u>: * <u>Text cleaning</u>: the mention of the source url and the number of views were removed from the text. #### Europarl and EuroparlAligned * <u>Sources</u>: * `fr-en`, `es-en`, `it-en` parallel data: [Europarl v7](https://www.statmt.org/europarl/v7/). License: [Open](https://www.statmt.org/europarl/). * `fr`, `en`, `de`, `es` monolingual data and `de-fr` parallel data: [Europarl v10](https://www.statmt.org/europarl/v10/training-monolingual/). License: [Open](https://www.statmt.org/europarl/). * <u>Description</u>: "The Europarl parallel corpus is extracted from the proceedings of the European Parliament. It includes versions in 21 European languages: Romanic (French, Italian, Spanish, Portuguese, Romanian), Germanic (English, Dutch, German, Danish, Swedish), Slavik (Bulgarian, Czech, Polish, Slovak, Slovene), Finni-Ugric (Finnish, Hungarian, Estonian), Baltic (Latvian, Lithuanian), and Greek. The goal of the extraction and processing was to generate sentence aligned text for statistical machine translation systems" ([www.statmt.org](https://www.statmt.org/europarl/)). * <u>Pre-processing</u>: * <u>Random combination of aligned texts prefixed by language</u>: The same process as used for the [CroissantAligned](#croissantaligned) dataset was applied to the EuroparlAligned dataset (see [code details](https://github.com/OpenLLM-France/Lucie-Training/blob/cdec8fd6369385455829ab39c2f04bcb1a8a475a/tokenization/data.py#L1350)). In the Lucie Training Dataset, the `extra` field in the metadata for EuroparlAligned provides texts in the two languages under the sub-fields `text_1` and `text_2`, and the corresponding language codes under `lang_1` and `lang_2`. * <u>Citation</u>: Philipp Koehn (2005). "Europarl: A Parallel Corpus for Statistical Machine Translation," MT Summit. #### Eurovoc * <u>Source</u>: [EuropeanParliament/Eurovoc](https://huggingface.co/datasets/EuropeanParliament/Eurovoc). License: [EUPL 1.1](https://huggingface.co/datasets/EuropeanParliament/Eurovoc). * <u>Extracted from</u>: [Cellar](https://op.europa.eu/en/web/cellar). License: [CC BY-4.0](https://op.europa.eu/en/web/about-us/legal-notices/publications-office-of-the-european-union-copyright). * <u>Description</u>: A collection of mutlilingual documents from the data repository of the Publications Office of the European Union annotated with Eurovoc labels. The corpus contains legal, policy-related, historical and organizational information about the EU. Dataset containing text retrieved through OCR. * <u>Pre-processing</u>: * <u>Filtering</u>: To filter out documents with excessive OCR errors, the dataset was refined by discarding texts with a perplexity higher than 1500, measured using a CCNET model in English (see [code details](https://github.com/OpenLLM-France/Lucie-Training/blob/7f1f7efa1288f709662a9067bf2c3db856b850f8/tokenization/data.py#L1590)). The code to compute CCNET perplexity, parallelizing on parquet files, is [available here](https://github.com/OpenLLM-France/Lucie-dataset-filtering). * <u>Text cleaning</u>: Mentions of Credit Institutions Directives (CID) that appears in the raw texts such as `(cid:146)` were removed. * <u>Citations</u>: * Ilias Chalkidis, Emmanouil Fergadiotis, Prodromos Malakasiotis, Nikolaos Aletras, and Ion Androutsopoulos (2019). "[Extreme Multi-Label Legal Text Classification: A Case Study in EU Legislation](https://arxiv.org/pdf/1905.10892)," Proceedings of the Natural Legal Language Processing Workshop 2019, pages 78–87, Minneapolis, Minnesota. Association for Computational Linguistics. * Ilias Chalkidis, Manos Fergadiotis, Prodromos Malakasiotis and Ion Androutsopoulos (2019). "[Large-Scale Multi-Label Text Classification on EU Legislation](https://arxiv.org/pdf/1906.02192)," Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019), Florence, Italy, (short papers). * Andrei-Marius Avram, Vasile Pais, and Dan Ioan Tufis (2021). "[PyEuroVoc: A Tool for Multilingual Legal Document Classification with EuroVoc Descriptors](https://arxiv.org/pdf/2108.01139)," Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 92–101, Held Online. INCOMA Ltd. * Zein Shaheen, Gerhard Wohlgenannt and Erwin Filtz (2020). "Large scale legal text classification using transformer models," [arXiv:2010.12871](https://arxiv.org/abs/2010.12871v1). #### FineWebEdu * <u>Source</u>: [HuggingFaceFW/fineweb-edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu). License: [ODC-BY](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu). * <u>Extracted from</u>: [FineWeb](https://huggingface.co/datasets/HuggingFaceFW/fineweb). License: [ODC-BY](https://huggingface.co/datasets/HuggingFaceFW/fineweb). * <u>Description</u>: A 1.3 trillion token selection from [FineWeb](https://huggingface.co/datasets/HuggingFaceFW/fineweb), which contains 15 trillion tokens of curated data from 96 Common Crawl dumps. Content in FineWebEdu has been selected by a custom designed classifier for its high-quality, educational content. Most recent crawl: 2024-10 (see <a href="https://huggingface.co/datasets/OpenLLM-France/Lucie-Training-Dataset/blob/main/figures/fig_distribution_finewebedu-english_histogram.png">composition details</a> for information about the crawls included in this dataset.) * <u>Pre-processing</u>: * <u>Removing duplicate urls</u>: urls were removed if their base domain overlapped with a dataset already in the Lucie Training Dataset (e.g., "philpapers.org") in order to increase diversity of content (see [code details](https://github.com/OpenLLM-France/Lucie-Training/blob/7f1f7efa1288f709662a9067bf2c3db856b850f8/tokenization/text.py#L843)) * <u>Filtering by robots.txt files</u>: we collect robots.txt and remove all documents for which CCBot is disallowed or for which we failed to collect information as of July 2024 in an effort to select data free from opt-out evidence according to the 4th article of the copyright European directive (2019). * <u>Citation</u>: Guilherme Penedo, Hynek Kydlíček, Loubna Ben allal, Anton Lozhkov, Margaret Mitchell, Colin Raffel, Leandro Von Werra, Thomas Wolf (2024). "The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale," [ arXiv:2406.17557](https://arxiv.org/abs/2406.17557). #### GallicaMonographies * <u>Source</u>: Corpus contributed by OpenLLM partners. A version is also published here: [PleIAs/French-PD-Books](https://huggingface.co/datasets/PleIAs/French-PD-Books). License: Public domain. * <u>Extracted from</u>: [Gallicagram](https://shiny.ens-paris-saclay.fr/app/gallicagram). * <u>Description</u>: A large collection of French monographies in the public domain made available through the French National Library ([Gallica](https://gallica.bnf.fr/accueil/fr/content/accueil-fr?mode=desktop)). Dataset containing text retrieved through OCR. * <u>Pre-processing</u>: * <u>Text cleaning for v1.1</u>: To filter out documents with excessive OCR errors, the dataset was split into chunks and chunks were kept if the source language was detected as French by [FastText](https://github.com/facebookresearch/fastText) with a confidence score of 0.65 or above, and the perplexity score, as measured using a CCNET model in French, was between 10 and 1000. The code to compute CCNET perplexity, parallelizing on parquet files, is [available here](https://github.com/OpenLLM-France/Lucie-dataset-filtering). * <u>Filtering for v1.2</u>: Using OCR scores provided in the metadata of the source corpus, documents with an OCR score of less than 90 out of 100 were filtered out. #### GallicaPress * <u>Source</u>: Corpus contributed by OpenLLM partners. A version is also published here: [PleIAs/French-PD-Newspapers](https://huggingface.co/datasets/PleIAs/French-PD-Newspapers). License: Public domain. * <u>Extracted from</u>: [Gallicagram](https://shiny.ens-paris-saclay.fr/app/gallicagram). * <u>Description</u>: A large collection of French newspapers and periodicals in the public domain made available through the French National Library ([Gallica](https://gallica.bnf.fr/accueil/fr/content/accueil-fr?mode=desktop)). Dataset containing text retrieved through OCR. * <u>Pre-processing</u>: * <u>Text cleaning for v1.1</u>: To filter out documents with excessive OCR errors, the dataset was split into chunks and chunks were kept if the source language was detected as French by [FastText](https://github.com/facebookresearch/fastText) with a confidence score of 0.65 or above, and the perplexity score, as measured using a CCNET model in French, was between 10 and 1000 (see [code details](https://github.com/OpenLLM-France/Lucie-Training/blob/7f1f7efa1288f709662a9067bf2c3db856b850f8/tokenization/data.py#L1840)). The code to compute CCNET perplexity, parallelizing on parquet files, is [available here](https://github.com/OpenLLM-France/Lucie-dataset-filtering). * <u>Filtering for v1.2</u>: Using OCR scores provided in the metadata of the source corpus, documents with an OCR score of less than 90 out of 100 were filtered out. #### Gutenberg * <u>Source</u>: Corpus compiled by OpenLLM partners. * <u>Extracted from</u>: * [aleph.gutenberg.org](http://aleph.gutenberg.org/) via [Project Gutenberg](https://www.gutenberg.org/). License: [Open](https://www.gutenberg.org/policy/terms_of_use.html). * [pgcorpus](https://github.com/pgcorpus/gutenberg). License: [CC BY-4.0](https://zenodo.org/records/2422561). * <u>Description</u>: A collection of free eBooks, manually prepared by human annotators. * <u>Pre-processing</u>: * <u>Filtering</u>: The dataset was filtered based on the author date of death, so that only texts from authors who died more than 70 years ago are included (80 years for French authors). See [code details here](https://github.com/OpenLLM-France/Lucie-Training/blob/7f1f7efa1288f709662a9067bf2c3db856b850f8/tokenization/data.py#L1136). This filtering was done to ensure that the texts are in the public domain. * <u>Text cleaning</u>: Headers and footers containing information about Project Gutenberg were removed (see [code details](https://github.com/OpenLLM-France/Lucie-Training/blob/cdec8fd6369385455829ab39c2f04bcb1a8a475a/tokenization/text.py#L93)). #### HAL * <u>Source</u>: [bigscience-data/roots_fr_hal_archives_ouvertes](https://huggingface.co/datasets/bigscience-data/roots_fr_hal_archives_ouvertes). License: Roots dataset. * <u>Extracted from</u>: [HAL](https://hal.science/) ([Open access](https://about.hal.science/)). * <u>Description</u>: A collection of scientific papers and manuscripts distributed through the open science platform HAL. Dataset containing text retrieved through OCR. * <u>Pre-processing</u>: * <u>Filtering</u>: To filter out documents with excessive OCR errors, the dataset was refined by discarding texts with a perplexity higher than 930, measured using a CCNET model in French (see [code details](https://github.com/OpenLLM-France/Lucie-Training/blob/7f1f7efa1288f709662a9067bf2c3db856b850f8/tokenization/data.py#L1929)). The code to compute CCNET perplexity, parallelizing on parquet files, is [available here](https://github.com/OpenLLM-France/Lucie-dataset-filtering). * <u>Citation</u>: Hugo Laurençon, Lucile Saulnier, Thomas Wang, Christopher Akiki, Albert Villanova del Moral, Teven Le Scao, Leandro Von Werra, Chenghao Mou, Eduardo González Ponferrada, Huu Nguyen, Jörg Frohberg, Mario Šaško, Quentin Lhoest, Angelina McMillan-Major, Gerard Dupont, Stella Biderman, Anna Rogers, Loubna Ben allal, Francesco De Toni, Giada Pistilli, Olivier Nguyen, Somaieh Nikpoor, Maraim Masoud, Pierre Colombo, Javier de la Rosa, Paulo Villegas, Tristan Thrush, Shayne Longpre, Sebastian Nagel, Leon Weber, Manuel Muñoz, Jian Zhu, Daniel Van Strien, Zaid Alyafeai, Khalid Almubarak, Minh Chien Vu, Itziar Gonzalez-Dios, Aitor Soroa, Kyle Lo, Manan Dey, Pedro Ortiz Suarez, Aaron Gokaslan, Shamik Bose, David Adelani, Long Phan, Hieu Tran, Ian Yu, Suhas Pai, Jenny Chim, Violette Lepercq, Suzana Ilic, Margaret Mitchell, Sasha Alexandra Luccioni, Yacine Jernite (2022). "[The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset](https://proceedings.neurips.cc/paper_files/paper/2022/hash/ce9e92e3de2372a4b93353eb7f3dc0bd-Abstract-Datasets_and_Benchmarks.html)," Advances in Neural Information Processing Systems (NeurIPS), 35, 31809-31826. #### InterventionsParlement * <u>Source</u>: Corpus contributed by OpenLLM partners. * <u>Extracted from</u>: [Regards citoyens](https://www.regardscitoyens.org/#&panel1-4). License: [CC BY-SA](https://www.regardscitoyens.org/mentions-legales/). * <u>Description</u>: Transcripts of remarks made during French parlementary debates. Each text contains a continuous remark by a single speaker. #### LEGI * <u>Source</u>: Corpus contributed by OpenLLM partners. A version is also published here: [Nicolas-BZRD/DILA_OPENDATA_FR_2023](https://huggingface.co/datasets/Nicolas-BZRD/DILA_OPENDATA_FR_2023/tree/main). * <u>Extracted from</u>: [OpenData](https://echanges.dila.gouv.fr/OPENDATA/) (Data collection date: October, 2023). * <u>Description</u>: "The French Government Open Data (DILA) Dataset is a collection of text data extracted from various sources provided by the French government, specifically the Direction de l'information légale et administrative (DILA). This dataset contains a wide range of legal, administrative, and legislative documents. The data has been organized into several categories for easy access and analysis" (from the [dataset card](https://huggingface.co/datasets/Nicolas-BZRD/DILA_OPENDATA_FR_2023/tree/main)). #### MathPile (Commercial) * <u>Source</u>: [GAIR/MathPile_Commercial](https://huggingface.co/datasets/GAIR/MathPile_Commercial). License: [CC BY-SA 4.0](https://huggingface.co/datasets/GAIR/MathPile_Commercial). * <u>Extracted from</u>: [MathPile](https://huggingface.co/datasets/GAIR/MathPile). License: [CC BY-SA-NC 4.0](https://huggingface.co/datasets/GAIR/MathPile). * <u>Description</u>: A preprocessed collection of documents focused on math, including Textbooks, arXiv, Wikipedia, ProofWiki, StackExchange, and web pages from Common Crawl. The content targets a range of levels, from kindergarten through postgraduate level. MathPile_Commercial was obtained by removing documents from MathPile that do not allow commercial use. * <u>Pre-processing</u>: * <u>Formatting</u>: Converted the content of StackExchange questions and answers to match the {"text": value} format, using the following formula: ```python text = sample["question"]["Body"] + "\n\n".join([answer["Body"] for answer in sample["answers"]]) ``` * <u>Citation</u>: Zengzhi Wang, Rui Xia and Pengfei Liu (2023). "Generative AI for Math: Part I -- MathPile: A Billion-Token-Scale Pretraining Corpus for Math," [ arXiv:2312.17120](https://export.arxiv.org/abs/2312.17120). #### OpenData * <u>Source</u>: [Nicolas-BZRD/DILA_OPENDATA_FR_2023](https://huggingface.co/datasets/Nicolas-BZRD/DILA_OPENDATA_FR_2023/tree/main) (balo, dole, inca, kali, and sarde subsets). License: [ODC-BY](https://huggingface.co/datasets/Nicolas-BZRD/DILA_OPENDATA_FR_2023/tree/main). * <u>Extracted from</u>: [OpenData](https://echanges.dila.gouv.fr/OPENDATA/) (Data collection date: October, 2023). * <u>Description</u>: "The French Government Open Data (DILA) Dataset is a collection of text data extracted from various sources provided by the French government, specifically the Direction de l'information légale et administrative (DILA). This dataset contains a wide range of legal, administrative, and legislative documents. The data has been organized into several categories for easy access and analysis" (from the [dataset card](https://huggingface.co/datasets/Nicolas-BZRD/DILA_OPENDATA_FR_2023/tree/main)). <!-- * <u>Citation</u>: No paper found. --> #### OpenEdition * <u>Source</u>: Corpus contributed by OpenLLM partners. * <u>Extracted from</u>: [Open Edition](https://www.openedition.org/). License: [Open Edition Books](https://www.openedition.org/12554). * <u>Description</u>: A collection of scientific books, journal articles, blog entries and event descriptions. <!-- * <u>Citation</u>: No paper found. --> #### PeS2o (v2) * <u>Source</u>: [allenai/peS2o](https://huggingface.co/datasets/allenai/peS2o) version [v2](https://huggingface.co/datasets/allenai/peS2o/tree/main/data/v2). License: [ODC BY-v1.0](https://github.com/allenai/s2orc/). * <u>Extracted from</u>: [S2ORC](https://github.com/allenai/s2orc) (see [aclanthology](https://aclanthology.org/2020.acl-main.447/)). License: [ODC BY-v1.0](https://github.com/allenai/s2orc/). * <u>Description</u>: A preprocessed collection of academic papers designed for pre-training of language models. PeS2o is composed of two subsets: one containing full papers and one containing only paper titles and abstracts. Dataset containing (some) text retrieved through OCR. Knowledge cutoff: 2023-01-03. * <u>Citation</u>: Luca Soldaini and Kyle Lo (2023). "peS2o (Pretraining Efficiently on S2ORC) Dataset," Allen Institute for AI. [GitHub](https://github.com/allenai/pes2o). #### Pile (Uncopyrighted) * <u>Source</u>: [monology/pile-uncopyrighted](https://huggingface.co/datasets/monology/pile-uncopyrighted). License: [Other](https://huggingface.co/datasets/monology/pile-uncopyrighted). * <u>Extracted from</u>: [FreeLaw](https://free.law/), [StackExchange](https://stackexchange.com/), [USPTO Backgrounds](https://bulkdata.uspto.gov/), [DM Mathematics](https://github.com/google-deepmind/mathematics_dataset), [Ubuntu IRC](https://irclogs.ubuntu.com/), [PhilPapers](https://philpapers.org/), NIH ExPorter from [The Pile](https://huggingface.co/datasets/EleutherAI/pile). License: [MIT](https://arxiv.org/pdf/2201.07311). * <u>Description</u> (from the [Datasheet](https://arxiv.org/abs/2201.07311)): * FreeLaw: "The Free Law Project is US registered non-profit that provide access to millions of legal opinions and analytical tools for academic studies in the legal realm." * StackExchange: "The StackExchange dataset is a dump of anonymized user-contributed content on the Stack Exchange network, a popular collection of websites centered around user-contributed questions and answers." * USPTO Backgrounds: "The USPTO Backgrounds dataset is a set of background sections from patents granted by the United States Patent and Trademark Office, derived from its published bulk archives." * DM Mathematics: "The DeepMind Mathematics dataset consists of a collection of mathematical problems such as algebra, arithmetic, calculus, number theory, and probability, formatted as natural language prompts [Saxton et al., 2019](https://arxiv.org/abs/1904.01557)." * Ubuntu IRC: "The Ubuntu IRC dataset is derived from the publicly available chatlogs of all Ubunturelated channels on the Freenode IRC chat server." * PhilPapers: a dataset of open access philosophy publications from an international database maintained by the Center for Digital Philosophy at the University of Western Ontario. * NIH ExPORTER: "The NIH Grant abstracts provides a bulk-data repository for awarded applications through the ExPORTER4 service covering the fiscal years 1985-present." * <u>Pre-processing (v1.2 only)</u>: * <u>Filtering of PhilPapers</u>: Papers were removed if their language, detected using [Stanza](https://github.com/stanfordnlp/stanza), was not classified as English, French, German, Spanish or Italian. * <u>Filtering and text cleaning of Ubuntu IRC</u>: Texts from some channels were excluded to avoid data from languages other than English, French, German, Spanish or Italian and certain encoding errors were fixed (see [code details here](https://github.com/OpenLLM-France/Lucie-Training/blob/cdec8fd6369385455829ab39c2f04bcb1a8a475a/tokenization/text.py#L190)). * <u>Citations</u>: * Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, Shawn Presser, Connor Leahy (2020). "The Pile: An 800GB Dataset of Diverse Text for Language Modeling," [ arXiv:2101.00027](https://arxiv.org/abs/2101.00027). * Stella Biderman, Kieran Bicheno, Leo Gao (2022). "Datasheet for the Pile," [arXiv:2201.07311](https://arxiv.org/abs/2201.07311). #### QuestionsEcritesParlement * <u>Source</u>: Corpus contributed by OpenLLM partners. * <u>Extracted from</u>: [Regards citoyens](https://www.regardscitoyens.org/#&panel1-4). License: [CC BY-SA](https://www.regardscitoyens.org/mentions-legales/). * <u>Description</u>: Collection of long written questions, read during a session at the French National Assembly. Questions are asked by a member of the French parliament and addressed to a minister (who is given two months to respond). #### RedPajama (v2) * <u>Source</u>: [togethercomputer/RedPajama-Data-V2](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-V2). License: [Apache 2.0](https://github.com/togethercomputer/RedPajama-Data) (data preparation code), Not specified (data) but see [Common Crawl terms of use](https://commoncrawl.org/terms-of-use). * <u>Extracted from</u>: [Common Crawl](https://commoncrawl.org/). * <u>Description</u>: "RedPajama-V2 is an open dataset for training large language models. The dataset includes over 100B text documents coming from 84 CommonCrawl snapshots and processed using the [CCNet](https://github.com/facebookresearch/cc_net) pipeline. Out of these, there are 30B documents in the corpus that additionally come with quality signals, and 20B documents that are deduplicated" (from [GitHub](https://github.com/togethercomputer/RedPajama-Data)). Most recent crawl for French data in the Lucie Training Dataset v1.1: 2023-14. (For more details on the time periods covered by crawls in this dataset see the composition details for <a href="https://huggingface.co/datasets/OpenLLM-France/Lucie-Training-Dataset/blob/main/figures/fig_distribution_redpajama-french_histogram.png">French</a>, <a href="https://huggingface.co/datasets/OpenLLM-France/Lucie-Training-Dataset/blob/main/figures/fig_distribution_redpajama-german_histogram.png">German</a>, <a href="https://huggingface.co/datasets/OpenLLM-France/Lucie-Training-Dataset/blob/main/figures/fig_distribution_redpajama-italian_histogram.png">Italian</a> and <a href="https://huggingface.co/datasets/OpenLLM-France/Lucie-Training-Dataset/blob/main/figures/fig_distribution_redpajama-spanish_histogram.png">Spanish</a>.) * <u>Pre-processing and deduplication</u>: * <u> Url filtering: </u> * <u>Removing duplicate urls</u>: urls were removed if their base domain overlapped with a dataset already in the Lucie Training Dataset (e.g., "theses.fr") in order to increase diversity of content (see [code details](https://github.com/OpenLLM-France/Lucie-Training/blob/7f1f7efa1288f709662a9067bf2c3db856b850f8/webdata_processing/base.py#L154)). * <u>Filtering certain toxic content</u>: urls from a list of blacklisted content were removed (see [code details](https://github.com/OpenLLM-France/Lucie-Training/blob/7f1f7efa1288f709662a9067bf2c3db856b850f8/webdata_processing/base.py#L177)). * <u>Filtering by robots.txt files</u>: we collect robots.txt and remove all documents for which CCBot is disallowed or for which we failed to collect information as of July 2024 in an effort to select data free from opt-out evidence according to the 4th article of the copyright European directive (2019). * <u>Filtering</u>: A series of filters were applied using [quality signals](https://github.com/togethercomputer/RedPajama-Data?tab=readme-ov-file#quality-annotations) already available in the dataset. This includes (see [code details](https://github.com/OpenLLM-France/Lucie-Training/blob/d9cccb7bfac37b8c8285f9c04aa67d907ce475f0/webdata_processing/base.py#L36)): * CCnet perplexity below 10 or above 1000 * C4 filtering (including removal of documents that contain toxic words) * Gopher filtering and repetition removal * Redpajama document deduplication * <u>Removal of personally identifying information (PII)</u>: email addresses and ip addresses were replaced with random addresses (see [code details](https://github.com/OpenLLM-France/Lucie-Training/blob/7f1f7efa1288f709662a9067bf2c3db856b850f8/webdata_processing/base.py#L301)). * <u>MinHash deduplication</u> was performed on each snapshot and language independantly as proposed in FineWeb. For minhash configuration [see code details](https://github.com/OpenLLM-France/Lucie-Training/blob/7f1f7efa1288f709662a9067bf2c3db856b850f8/webdata_processing/minhash.py#L63). The [Datatrove](https://github.com/huggingface/datatrove) library was used to perform both filtering and deduplication stages. * <u>Citation</u>: Together Computer (2023). "RedPajama-Data-v2: an Open Dataset with 30 Trillion Tokens for Training Large Language Models," [GitHub](https://github.com/togethercomputer/RedPajama-Data). #### STAC * <u>Source</u>: [STAC](https://www.irit.fr/STAC/corpus.html). License: [CC BY-SA-NC 4.0](https://www.irit.fr/STAC/corpus.html). * <u>Description</u>: A collection of multiparty chats from an online version of the game Settlers of Catan. The full STAC corpus contains annotations for discourse structure. We use only the text of the chats. * <u>Citation</u>: Nicholas Asher, Julie Hunter, Mathieu Morey, Farah Benamara and Stergos Afantenos (2016). "[Discourse structure and dialogue acts in multiparty dialogue: the STAC corpus](https://hal.science/hal-02124399/file/asher_22646.pdf)," The Tenth International Conference on Language Resources and Evaluation (LREC 2016). European Language Resources Association, pp. 2721-2727. #### TheStack (v1.2) * <u>Source</u>: [bigcode/the-stack-dedup](https://huggingface.co/datasets/bigcode/the-stack-dedup). License: [Other](https://huggingface.co/datasets/bigcode/the-stack-dedup) (mixture of copyleft licenses). * <u>Extracted from</u>: [GitHub](https://github.com/) via [GHarchive](https://www.gharchive.org/). Mixed licenses for source. * <u>Description</u>: "The Stack contains over 6TB of permissively-licensed source code files covering 358 programming languages. The dataset was created as part of the [BigCode Project](https://www.bigcode-project.org/), an open scientific collaboration working on the responsible development of Large Language Models for Code (Code LLMs). The Stack serves as a pre-training dataset for Code LLMs, i.e., code-generating AI systems which enable the synthesis of programs from natural language descriptions as well as other from code snippets. This is the near-deduplicated version with 3TB data" (from the [dataset card](https://huggingface.co/datasets/bigcode/the-stack-dedup)). * <u>Citation</u>: Denis Kocetkov, Raymond Li, Loubna Ben Allal, Jia Li, Chenghao Mou, Carlos Muñoz Ferrandis, Yacine Jernite, Margaret Mitchell, Sean Hughes, Thomas Wolf, Dzmitry Bahdanau, Leandro von Werra and Harm de Vries (2022). "The Stack: 3 TB of permissively licensed source code," [arxiv:2211.15533](https://arxiv.org/abs/2211.15533). #### Theses * <u>Source</u>: Corpus contributed by OpenLLM partners. * <u>Extracted from</u>: [theses.fr](https://theses.fr/?domaine=theses) (License: [Licence Ouverte / Open Licence version 2.0](https://www.data.gouv.fr/fr/datasets/theses-soutenues-en-france-depuis-1985/)) and [HAL](https://hal.science/) ([Open access](https://about.hal.science/)). * <u>Description</u>: A collection of doctoral theses published in France. Dataset containing text retrieved through OCR. * <u>Pre-processing</u>: * <u>Text cleaning</u>: * Title pages about HAL, pages containing a significant fraction of control characters, and duplicate lines were removed (see [code details](https://github.com/OpenLLM-France/Lucie-Training/blob/cdec8fd6369385455829ab39c2f04bcb1a8a475a/tokenization/text.py#L277)). * Because the results of OCR on tables and graphics can give rise to garbage text, the text was cleaned by removing the most suspicious chunks. In particular, a chunk was removed if it was not detected as being written in French, English, Spanish, German or Italian, or if the perplexity of a CCNet Language Model on the chunk was higher than 2000 (see [code details](https://github.com/OpenLLM-France/Lucie-Training/blob/7f1f7efa1288f709662a9067bf2c3db856b850f8/tokenization/data.py#L1946)). The code to compute CCNET perplexity, parallelizing on parquet files, is [available here](https://github.com/OpenLLM-France/Lucie-dataset-filtering). * <u>Filtering</u>: Texts with fewer than 1000 words or 10000 characters were removed (see [code details](https://github.com/OpenLLM-France/Lucie-Training/blob/7f1f7efa1288f709662a9067bf2c3db856b850f8/tokenization/data.py#L1975)). <!-- * <u>Citation</u>: No paper found. --> #### Wikipedia, Wikisource, Wiktionary * <u>Source</u>: Corpus contributed by LINAGORA Labs (OpenLLM-France). Also published here: * [OpenLLM-France/wikipedia](https://huggingface.co/datasets/OpenLLM-France/wikipedia) * [OpenLLM-France/wikisource](https://huggingface.co/datasets/OpenLLM-France/wikisource) * [OpenLLM-France/wiktionary](https://huggingface.co/datasets/OpenLLM-France/wiktionary) * <u>Extracted from</u>: [Wikimedia dumps](https://dumps.wikimedia.org/other/enterprise_html/runs/). License: [GFDL/CC BY-SA](https://dumps.wikimedia.org/legal.html). <!-- * <u>Description</u>: TODO --> <!-- * <u>Pre-processing</u>: TODO --> <!-- * <u>Citation</u>: No paper found. --> #### YouTube * <u>Source</u>: Corpus contributed by LINAGORA Labs and [LeVoiceLab](https://www.levoicelab.org/). * <u>Extracted from</u>: [YouTube](https://www.youtube.com/). <!-- License: TODO? --> * <u>Description</u>: French subtitles from videos published with permissive licenses on YouTube. <!-- TODO --> * <u>Extraction pipeline description</u>: * **Searching for YouTube videos likely in French:** Based on searches generated automatically from random sequences of words extracted from a corpus of French journalistic articles (initially obtained through a web-crawling tool applied to publicly accessible news and media sites such as Huffington Post, 20 Minutes, Le Parisien, Actu, Numerama, Slate, etc.). Selection of videos with subtitles labeled as "French," excluding those marked as "automatically generated." *At this stage: 52,778 videos selected, corresponding to 10,654 hours of audio.* * **Selection of videos whose subtitle language classification confirms French with a certain confidence index:** *At this stage: 51,934 videos selected, corresponding to 10,425 hours of audio.* * **Selection of videos whose subtitles contain uppercase, lowercase, and punctuation marks:** This step filters out automatically generated subtitles created with speech recognition tools. *At this stage: 45,488 videos selected, corresponding to 8,904 hours of audio.* * **Extraction of audio tracks from the selected videos.** * **Automatic formatting of transcripts obtained from subtitles:** Removal of emojis, sound event annotations in brackets (like "[Music]") and extra text such as "subtitled by XXX." (on last seconds of the video). * **Selection of videos where an automatic speech recognition tool correctly transcribes the first 30 seconds with a minimum recall and precision rate:** *At this stage: 37,513 videos selected, corresponding to 7,541 hours of audio.* * **Realignment of the transcript:** Ensuring accurate timestamps in the transcriptions based on the subtitles and excluding audios where alignment fails. *At this stage: 36,618 videos selected, corresponding to 6,729 hours of audio.* ## Example use in Python ### Load the dataset Load and iterate over the full dataset using the `datasets` library: ```python from datasets import load_dataset dataset = load_dataset("OpenLLM-France/Lucie-Training-Dataset", split="train", streaming=True) for sample in dataset: text = sample["text"] # … do something with the text ``` ### Iterate over a subset Several configurations are available to select a language, a source, or both, illustrated in the following examples. The list of possible configurations can be obtained programmatically: ```python from datasets import load_dataset_builder config_names = list(load_dataset_builder("OpenLLM-France/Lucie-Training-Dataset").builder_configs) print(config_names) ``` ```plaintext ['default', 'en', 'fr', 'de', 'es', 'it', 'de,fr', 'es,en', 'fr,en', 'it,en', 'natural', 'code', 'code-assembly', 'code-c', 'code-c#', 'code-c++', 'code-clojure', 'code-dart', 'code-elixir', 'code-erlang', 'code-fortran', 'code-go', 'code-haskell', 'code-java', 'code-javascript', 'code-julia', 'code-kotlin', 'code-lua', 'code-mathematica', 'code-matlab', 'code-ocaml', 'code-perl', 'code-php', 'code-python', 'code-r', 'code-racket', 'code-ruby', 'code-rust', 'code-scala', 'code-swift', 'code-tex', 'code-typescript', 'AmendementsParlement', 'AmericanStories', 'Claire', 'Claire-en', 'Claire-fr', 'CroissantAligned', 'DiscoursPublics', 'Europarl', 'Europarl-de', 'Europarl-en', 'Europarl-es', 'Europarl-fr', 'EuroparlAligned', 'EuroparlAligned-de,fr', 'EuroparlAligned-es,en', 'EuroparlAligned-fr,en', 'EuroparlAligned-it,en', 'Eurovoc', 'Eurovoc-de', 'Eurovoc-en', 'Eurovoc-es', 'Eurovoc-it', 'FineWebEdu', 'GallicaMonographies', 'GallicaPress', 'Gutenberg', 'Gutenberg-de', 'Gutenberg-en', 'Gutenberg-es', 'Gutenberg-fr', 'Gutenberg-it', 'HAL', 'InterventionsParlement', 'LEGI', 'MathPile', 'OpenData', 'OpenEdition', 'PeS2o', 'PeS2o-s2ag', 'PeS2o-s2orc', 'Pile', 'Pile-DM_Mathematics', 'Pile-FreeLaw', 'Pile-NIH_ExPorter', 'Pile-PhilPapers', 'Pile-StackExchange', 'Pile-USPTO_Backgrounds', 'Pile-Ubuntu_IRC', 'QuestionsEcritesParlement', 'RedPajama', 'RedPajama-de', 'RedPajama-es', 'RedPajama-fr', 'RedPajama-it', 'Stac', 'TheStack', 'Theses', 'Wikipedia', 'Wikipedia-de', 'Wikipedia-en', 'Wikipedia-es', 'Wikipedia-fr', 'Wikipedia-it', 'Wikisource', 'Wiktionary', 'YouTube'] ``` Below are some examples of how to load data from different sources and in different languages. Load data in French: ```python from datasets import load_dataset kwargs = dict(split="train", streaming=True) dataset = load_dataset("OpenLLM-France/Lucie-Training-Dataset", "fr", **kwargs) ``` Load data where French and English are aligned: ```python dataset = load_dataset("OpenLLM-France/Lucie-Training-Dataset", "fr,en", **kwargs) ``` Load data corresponding to files with programming languages: ```python dataset = load_dataset("OpenLLM-France/Lucie-Training-Dataset", "code", **kwargs) ``` Load data in Python: ```python dataset = load_dataset("OpenLLM-France/Lucie-Training-Dataset", "code-python", **kwargs) ``` Load data from Wikipedia (in all available languages): ```python dataset = load_dataset("OpenLLM-France/Lucie-Training-Dataset", "Wikipedia", **kwargs) ``` Load data from French pages of Wikipedia ([wikipedia.fr](https://www.wikipedia.fr/)): ```python dataset = load_dataset("OpenLLM-France/Lucie-Training-Dataset", "Wikipedia-fr", **kwargs) ``` Load the Pile dataset: ```python dataset = load_dataset("OpenLLM-France/Lucie-Training-Dataset", "Pile", **kwargs) ``` Load the subset "`PhilPapers`" from the Pile dataset: ```python dataset = load_dataset("OpenLLM-France/Lucie-Training-Dataset", "Pile-PhilPapers", **kwargs) ``` ### Load a specific version You can load a specific version with the `datasets` Python package using the `revision` parameter of `load_dataset(…)`: ```python from datasets import load_dataset kwargs = dict(split="train", streaming=True) name = None # or a configuration (e.g. "fr", "code-python", "Wikipedia-fr", "Pile-PhilPapers") dataset = load_dataset("OpenLLM-France/Lucie-Training-Dataset", name, revision="v1.2", **kwargs) ``` ## Citation When using the Lucie Training Dataset, please cite the following paper: ✍ Olivier Gouvert, Julie Hunter, Jérôme Louradour, Christophe Cérisara, Evan Dufraisse, Yaya Sy, Laura Rivière, Jean-Pierre Lorré (2025). [The Lucie-7B LLM and the Lucie Training Dataset: Open resources for multilingual language generation](https://arxiv.org/abs/2503.12294). arxiv:2503.12294. ```bibtex @misc{openllm2025lucie, title={The Lucie-7B LLM and the Lucie Training Dataset: Open resources for multilingual language generation}, author={Olivier Gouvert and Julie Hunter and Jérôme Louradour and Christophe Cerisara and Evan Dufraisse and Yaya Sy and Laura Rivière and Jean-Pierre Lorré and OpenLLM-France community}, year={2025}, eprint={2503.12294}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2503.12294}, } ``` ## Acknowledgements The Lucie Training Dataset was created by members of [LINAGORA](https://labs.linagora.com/) (Olivier Gouvert, Julie Hunter, Jérôme Louradour, Jean-Pierre Lorré) and the [OpenLLM-France](https://www.openllm-france.fr/) community. We thank in particular Rachel Bawden (INRIA), Clément Bénesse (Opsci), Christophe Cérisara (LORIA), Evan Dufraisse (CEA List), Olivier Ferret (CEA List), Joöl Gombin (Opsci), Ismaïl Harrando (LINAGORA), Jordan Ricker (Opsci), Guokan Shang (MBZUAI), and Yaya Sy (LORIA) for their helpful input. Data storage and significant parts of the data processing were made possible through the HPC resources from GENCI–IDRIS (Grant 2024-GC011015444). ## Contact <pre>[email protected]</pre>
stanfordnlp/sst2
stanfordnlp
"2024-01-04T16:31:07Z"
13,980
114
[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:unknown", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[ "text-classification" ]
"2022-06-13T14:01:47Z"
--- annotations_creators: - crowdsourced language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification paperswithcode_id: sst pretty_name: Stanford Sentiment Treebank v2 dataset_info: features: - name: idx dtype: int32 - name: sentence dtype: string - name: label dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 4681603 num_examples: 67349 - name: validation num_bytes: 106252 num_examples: 872 - name: test num_bytes: 216640 num_examples: 1821 download_size: 3331058 dataset_size: 5004495 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* --- # Dataset Card for [Dataset Name] ## 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://nlp.stanford.edu/sentiment/ - **Repository:** - **Paper:** [Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank](https://www.aclweb.org/anthology/D13-1170/) - **Leaderboard:** - **Point of Contact:** ### Dataset Summary The Stanford Sentiment Treebank is a corpus with fully labeled parse trees that allows for a complete analysis of the compositional effects of sentiment in language. The corpus is based on the dataset introduced by Pang and Lee (2005) and consists of 11,855 single sentences extracted from movie reviews. It was parsed with the Stanford parser and includes a total of 215,154 unique phrases from those parse trees, each annotated by 3 human judges. Binary classification experiments on full sentences (negative or somewhat negative vs somewhat positive or positive with neutral sentences discarded) refer to the dataset as SST-2 or SST binary. ### Supported Tasks and Leaderboards - `sentiment-classification` ### Languages The text in the dataset is in English (`en`). ## Dataset Structure ### Data Instances ``` {'idx': 0, 'sentence': 'hide new secretions from the parental units ', 'label': 0} ``` ### Data Fields - `idx`: Monotonically increasing index ID. - `sentence`: Complete sentence expressing an opinion about a film. - `label`: Sentiment of the opinion, either "negative" (0) or positive (1). The test set labels are hidden (-1). ### Data Splits | | train | validation | test | |--------------------|---------:|-----------:|-----:| | Number of examples | 67349 | 872 | 1821 | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? Rotten Tomatoes reviewers. ### 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 Unknown. ### Citation Information ```bibtex @inproceedings{socher-etal-2013-recursive, title = "Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank", author = "Socher, Richard and Perelygin, Alex and Wu, Jean and Chuang, Jason and Manning, Christopher D. and Ng, Andrew and Potts, Christopher", booktitle = "Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing", month = oct, year = "2013", address = "Seattle, Washington, USA", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/D13-1170", pages = "1631--1642", } ``` ### Contributions Thanks to [@albertvillanova](https://github.com/albertvillanova) for adding this dataset.
RMT-team/babilong
RMT-team
"2024-06-17T09:49:52Z"
13,938
14
[ "language:en", "size_categories:10K<n<100K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2406.10149", "arxiv:2402.10790", "arxiv:1502.05698", "region:us" ]
null
"2024-03-07T14:15:14Z"
--- language: - en configs: - config_name: 0k data_files: - split: qa1 path: data/qa1/0k.json - split: qa2 path: data/qa2/0k.json - split: qa3 path: data/qa3/0k.json - split: qa4 path: data/qa4/0k.json - split: qa5 path: data/qa5/0k.json - split: qa6 path: data/qa6/0k.json - split: qa7 path: data/qa7/0k.json - split: qa8 path: data/qa8/0k.json - split: qa9 path: data/qa9/0k.json - split: qa10 path: data/qa10/0k.json - split: qa11 path: data/qa11/0k.json - split: qa12 path: data/qa12/0k.json - split: qa13 path: data/qa13/0k.json - split: qa14 path: data/qa14/0k.json - split: qa15 path: data/qa15/0k.json - split: qa16 path: data/qa16/0k.json - split: qa17 path: data/qa17/0k.json - split: qa18 path: data/qa18/0k.json - split: qa19 path: data/qa19/0k.json - split: qa20 path: data/qa20/0k.json - config_name: 1k data_files: - split: qa1 path: data/qa1/1k.json - split: qa2 path: data/qa2/1k.json - split: qa3 path: data/qa3/1k.json - split: qa4 path: data/qa4/1k.json - split: qa5 path: data/qa5/1k.json - split: qa6 path: data/qa6/1k.json - split: qa7 path: data/qa7/1k.json - split: qa8 path: data/qa8/1k.json - split: qa9 path: data/qa9/1k.json - split: qa10 path: data/qa10/1k.json - config_name: 2k data_files: - split: qa1 path: data/qa1/2k.json - split: qa2 path: data/qa2/2k.json - split: qa3 path: data/qa3/2k.json - split: qa4 path: data/qa4/2k.json - split: qa5 path: data/qa5/2k.json - split: qa6 path: data/qa6/2k.json - split: qa7 path: data/qa7/2k.json - split: qa8 path: data/qa8/2k.json - split: qa9 path: data/qa9/2k.json - split: qa10 path: data/qa10/2k.json - config_name: 4k data_files: - split: qa1 path: data/qa1/4k.json - split: qa2 path: data/qa2/4k.json - split: qa3 path: data/qa3/4k.json - split: qa4 path: data/qa4/4k.json - split: qa5 path: data/qa5/4k.json - split: qa6 path: data/qa6/4k.json - split: qa7 path: data/qa7/4k.json - split: qa8 path: data/qa8/4k.json - split: qa9 path: data/qa9/4k.json - split: qa10 path: data/qa10/4k.json - config_name: 8k data_files: - split: qa1 path: data/qa1/8k.json - split: qa2 path: data/qa2/8k.json - split: qa3 path: data/qa3/8k.json - split: qa4 path: data/qa4/8k.json - split: qa5 path: data/qa5/8k.json - split: qa6 path: data/qa6/8k.json - split: qa7 path: data/qa7/8k.json - split: qa8 path: data/qa8/8k.json - split: qa9 path: data/qa9/8k.json - split: qa10 path: data/qa10/8k.json - config_name: 16k data_files: - split: qa1 path: data/qa1/16k.json - split: qa2 path: data/qa2/16k.json - split: qa3 path: data/qa3/16k.json - split: qa4 path: data/qa4/16k.json - split: qa5 path: data/qa5/16k.json - split: qa6 path: data/qa6/16k.json - split: qa7 path: data/qa7/16k.json - split: qa8 path: data/qa8/16k.json - split: qa9 path: data/qa9/16k.json - split: qa10 path: data/qa10/16k.json - config_name: 32k data_files: - split: qa1 path: data/qa1/32k.json - split: qa2 path: data/qa2/32k.json - split: qa3 path: data/qa3/32k.json - split: qa4 path: data/qa4/32k.json - split: qa5 path: data/qa5/32k.json - split: qa6 path: data/qa6/32k.json - split: qa7 path: data/qa7/32k.json - split: qa8 path: data/qa8/32k.json - split: qa9 path: data/qa9/32k.json - split: qa10 path: data/qa10/32k.json - config_name: 64k data_files: - split: qa1 path: data/qa1/64k.json - split: qa2 path: data/qa2/64k.json - split: qa3 path: data/qa3/64k.json - split: qa4 path: data/qa4/64k.json - split: qa5 path: data/qa5/64k.json - split: qa6 path: data/qa6/64k.json - split: qa7 path: data/qa7/64k.json - split: qa8 path: data/qa8/64k.json - split: qa9 path: data/qa9/64k.json - split: qa10 path: data/qa10/64k.json - config_name: 128k data_files: - split: qa1 path: data/qa1/128k.json - split: qa2 path: data/qa2/128k.json - split: qa3 path: data/qa3/128k.json - split: qa4 path: data/qa4/128k.json - split: qa5 path: data/qa5/128k.json - split: qa6 path: data/qa6/128k.json - split: qa7 path: data/qa7/128k.json - split: qa8 path: data/qa8/128k.json - split: qa9 path: data/qa9/128k.json - split: qa10 path: data/qa10/128k.json - config_name: 256k data_files: - split: qa1 path: data/qa1/256k.json - split: qa2 path: data/qa2/256k.json - split: qa3 path: data/qa3/256k.json - split: qa4 path: data/qa4/256k.json - split: qa5 path: data/qa5/256k.json - split: qa6 path: data/qa6/256k.json - split: qa7 path: data/qa7/256k.json - split: qa8 path: data/qa8/256k.json - split: qa9 path: data/qa9/256k.json - split: qa10 path: data/qa10/256k.json - config_name: 512k data_files: - split: qa1 path: data/qa1/512k.json - split: qa2 path: data/qa2/512k.json - split: qa3 path: data/qa3/512k.json - split: qa4 path: data/qa4/512k.json - split: qa5 path: data/qa5/512k.json - split: qa6 path: data/qa6/512k.json - split: qa7 path: data/qa7/512k.json - split: qa8 path: data/qa8/512k.json - split: qa9 path: data/qa9/512k.json - split: qa10 path: data/qa10/512k.json - config_name: 1M data_files: - split: qa1 path: data/qa1/1M.json - split: qa2 path: data/qa2/1M.json - split: qa3 path: data/qa3/1M.json - split: qa4 path: data/qa4/1M.json - split: qa5 path: data/qa5/1M.json - split: qa6 path: data/qa6/1M.json - split: qa7 path: data/qa7/1M.json - split: qa8 path: data/qa8/1M.json - split: qa9 path: data/qa9/1M.json - split: qa10 path: data/qa10/1M.json - config_name: 10M data_files: - split: qa1 path: data/qa1/10M.json - split: qa2 path: data/qa2/10M.json - split: qa3 path: data/qa3/10M.json - split: qa4 path: data/qa4/10M.json - split: qa5 path: data/qa5/10M.json - config_name: qa1 data_files: - split: 0k path: data/qa1/0k.json - split: 1k path: data/qa1/1k.json - split: 2k path: data/qa1/2k.json - split: 4k path: data/qa1/4k.json - split: 8k path: data/qa1/8k.json - split: 16k path: data/qa1/16k.json - split: 32k path: data/qa1/32k.json - split: 64k path: data/qa1/64k.json - split: 128k path: data/qa1/128k.json - split: 256k path: data/qa1/256k.json - split: 512k path: data/qa1/512k.json - split: 1M path: data/qa1/1M.json - config_name: qa2 data_files: - split: 0k path: data/qa2/0k.json - split: 1k path: data/qa2/1k.json - split: 2k path: data/qa2/2k.json - split: 4k path: data/qa2/4k.json - split: 8k path: data/qa2/8k.json - split: 16k path: data/qa2/16k.json - split: 32k path: data/qa2/32k.json - split: 64k path: data/qa2/64k.json - split: 128k path: data/qa2/128k.json - split: 256k path: data/qa2/256k.json - split: 512k path: data/qa2/512k.json - split: 1M path: data/qa2/1M.json - config_name: qa3 data_files: - split: 0k path: data/qa3/0k.json - split: 1k path: data/qa3/1k.json - split: 2k path: data/qa3/2k.json - split: 4k path: data/qa3/4k.json - split: 8k path: data/qa3/8k.json - split: 16k path: data/qa3/16k.json - split: 32k path: data/qa3/32k.json - split: 64k path: data/qa3/64k.json - split: 128k path: data/qa3/128k.json - split: 256k path: data/qa3/256k.json - split: 512k path: data/qa3/512k.json - split: 1M path: data/qa3/1M.json - config_name: qa4 data_files: - split: 0k path: data/qa4/0k.json - split: 1k path: data/qa4/1k.json - split: 2k path: data/qa4/2k.json - split: 4k path: data/qa4/4k.json - split: 8k path: data/qa4/8k.json - split: 16k path: data/qa4/16k.json - split: 32k path: data/qa4/32k.json - split: 64k path: data/qa4/64k.json - split: 128k path: data/qa4/128k.json - split: 256k path: data/qa4/256k.json - split: 512k path: data/qa4/512k.json - split: 1M path: data/qa4/1M.json - config_name: qa5 data_files: - split: 0k path: data/qa5/0k.json - split: 1k path: data/qa5/1k.json - split: 2k path: data/qa5/2k.json - split: 4k path: data/qa5/4k.json - split: 8k path: data/qa5/8k.json - split: 16k path: data/qa5/16k.json - split: 32k path: data/qa5/32k.json - split: 64k path: data/qa5/64k.json - split: 128k path: data/qa5/128k.json - split: 256k path: data/qa5/256k.json - split: 512k path: data/qa5/512k.json - split: 1M path: data/qa5/1M.json - config_name: qa6 data_files: - split: 0k path: data/qa6/0k.json - split: 1k path: data/qa6/1k.json - split: 2k path: data/qa6/2k.json - split: 4k path: data/qa6/4k.json - split: 8k path: data/qa6/8k.json - split: 16k path: data/qa6/16k.json - split: 32k path: data/qa6/32k.json - split: 64k path: data/qa6/64k.json - split: 128k path: data/qa6/128k.json - split: 256k path: data/qa6/256k.json - split: 512k path: data/qa6/512k.json - split: 1M path: data/qa6/1M.json - config_name: qa7 data_files: - split: 0k path: data/qa7/0k.json - split: 1k path: data/qa7/1k.json - split: 2k path: data/qa7/2k.json - split: 4k path: data/qa7/4k.json - split: 8k path: data/qa7/8k.json - split: 16k path: data/qa7/16k.json - split: 32k path: data/qa7/32k.json - split: 64k path: data/qa7/64k.json - split: 128k path: data/qa7/128k.json - split: 256k path: data/qa7/256k.json - split: 512k path: data/qa7/512k.json - split: 1M path: data/qa7/1M.json - config_name: qa8 data_files: - split: 0k path: data/qa8/0k.json - split: 1k path: data/qa8/1k.json - split: 2k path: data/qa8/2k.json - split: 4k path: data/qa8/4k.json - split: 8k path: data/qa8/8k.json - split: 16k path: data/qa8/16k.json - split: 32k path: data/qa8/32k.json - split: 64k path: data/qa8/64k.json - split: 128k path: data/qa8/128k.json - split: 256k path: data/qa8/256k.json - split: 512k path: data/qa8/512k.json - split: 1M path: data/qa8/1M.json - config_name: qa9 data_files: - split: 0k path: data/qa9/0k.json - split: 1k path: data/qa9/1k.json - split: 2k path: data/qa9/2k.json - split: 4k path: data/qa9/4k.json - split: 8k path: data/qa9/8k.json - split: 16k path: data/qa9/16k.json - split: 32k path: data/qa9/32k.json - split: 64k path: data/qa9/64k.json - split: 128k path: data/qa9/128k.json - split: 256k path: data/qa9/256k.json - split: 512k path: data/qa9/512k.json - split: 1M path: data/qa9/1M.json - config_name: qa10 data_files: - split: 0k path: data/qa10/0k.json - split: 1k path: data/qa10/1k.json - split: 2k path: data/qa10/2k.json - split: 4k path: data/qa10/4k.json - split: 8k path: data/qa10/8k.json - split: 16k path: data/qa10/16k.json - split: 32k path: data/qa10/32k.json - split: 64k path: data/qa10/64k.json - split: 128k path: data/qa10/128k.json - split: 256k path: data/qa10/256k.json - split: 512k path: data/qa10/512k.json - split: 1M path: data/qa10/1M.json --- # BABILong (100 samples) : a long-context needle-in-a-haystack benchmark for LLMs Preprint is on [arXiv](https://arxiv.org/abs/2406.10149) and code for LLM evaluation is available on [GitHub](https://github.com/booydar/babilong). [BABILong Leaderboard](https://huggingface.co/spaces/RMT-team/babilong) with top-performing long-context models. ## bAbI + Books = BABILong **BABILong** is a novel generative benchmark for evaluating the performance of NLP models in processing arbitrarily long documents with distributed facts. It contains 11 configs, corresponding to different sequence lengths in tokens: 0k, 1k, 2k, 4k, 8k, 16k, 32k, 128k, 256k, 512k, 1M. ``` from datasets import load_dataset babilong = load_dataset("RMT-team/babilong", "128k")["qa1"] ``` Solving tasks with a long context size requires the model to distinguish important information from large amounts of irrelevant details. To simulate this behavior we ”hide” the sentences of the original task between the sentences of irrelevant text. We use the [bAbI](https://huggingface.co/datasets/facebook/babi_qa) dataset [1] as facts and [PG19](https://huggingface.co/datasets/pg19) as background text. Resulting test samples might have lenghts of **millions of tokens**. BABILong consists of 10 tasks designed for evaluation of basic aspects of reasoning. The bAbI tasks are generated by simulating a set of characters and objects engaged in various movements and interactions with each other in multiple locations. Each interaction is represented by a fact, e.g. **”Mary travelled to the office”**, and the task is to answer a question using the facts from the current simulation, for instance, **”Where is Mary?”**. The bAbI tasks vary based on the number of facts, question complexity and the aspects of reasoning. ### First ten tasks of BABILong | Task | Name | facts per task | supporting facts per task | |------|--------------------------|-----------------|---------------------------| | qa1 | single supporting fact | 2 - 10 | 1 | | qa2 | two supporting facts | 2 - 68 | 2 | | qa3 | three supporting facts | 4 - 32 | 3 | | qa4 | two arg relations | 2 | 1 | | qa5 | three arg relations | 2 - 126 | 1 | | qa6 | yes-no questions | 2 - 26 | 1 | | qa7 | counting | 2 - 52 | 1-10 | | qa8 | lists-sets | 2 - 50 | 1-8 | | qa9 | simple negation | 2 - 10 | 1 | | qa10 | indefinite knowledge | 2 - 10 | 1 | Join us in this exciting endeavor and let's push the boundaries of what's possible together! ## Citation ``` @misc{kuratov2024babilong, title={BABILong: Testing the Limits of LLMs with Long Context Reasoning-in-a-Haystack}, author={Yuri Kuratov and Aydar Bulatov and Petr Anokhin and Ivan Rodkin and Dmitry Sorokin and Artyom Sorokin and Mikhail Burtsev}, year={2024}, eprint={2406.10149}, 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.'} } ``` ``` @misc{kuratov2024search, title={In Search of Needles in a 10M Haystack: Recurrent Memory Finds What LLMs Miss}, author={Yuri Kuratov and Aydar Bulatov and Petr Anokhin and Dmitry Sorokin and Artyom Sorokin and Mikhail Burtsev}, year={2024}, eprint={2402.10790}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## References [1] Weston, Jason, et al. "Towards ai-complete question answering: A set of prerequisite toy tasks." arXiv preprint [arXiv:1502.05698](https://arxiv.org/abs/1502.05698) (2015). ## License Our code is released under the Apache 2.0 License. We use data from the PG-19 corpora (Rae et al., 2020) ([Apache 2.0 License](https://github.com/google-deepmind/pg19/blob/master/LICENSE)) and the bAbI dataset (Weston et al., 2016) ([BSD License](https://github.com/facebookarchive/bAbI-tasks/blob/master/LICENSE.md)).
introvoyz041/Perforator
introvoyz041
"2025-01-31T12:29:38Z"
13,910
0
[ "license:mit", "region:us" ]
null
"2025-01-31T12:19:36Z"
--- license: mit ---
gigant/tib_slides_wip
gigant
"2023-03-26T16:22:49Z"
13,860
0
[ "size_categories:100K<n<1M", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
null
"2023-03-26T00:20:40Z"
--- dataset_info: features: - name: Image dtype: image - name: file_name dtype: string splits: - name: train num_bytes: 161850916866.84 num_examples: 595458 download_size: 29396407498 dataset_size: 161850916866.84 --- # Dataset Card for "tib_slides_wip" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
legacy-datasets/common_voice
legacy-datasets
"2024-08-22T08:27:23Z"
13,854
137
[ "task_categories:automatic-speech-recognition", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:multilingual", "source_datasets:extended|common_voice", "language:ab", "language:ar", "language:as", "language:br", "language:ca", "language:cnh", "language:cs", "language:cv", "language:cy", "language:de", "language:dv", "language:el", "language:en", "language:eo", "language:es", "language:et", "language:eu", "language:fa", "language:fi", "language:fr", "language:fy", "language:ga", "language:hi", "language:hsb", "language:hu", "language:ia", "language:id", "language:it", "language:ja", "language:ka", "language:kab", "language:ky", "language:lg", "language:lt", "language:lv", "language:mn", "language:mt", "language:nl", "language:or", "language:pa", "language:pl", "language:pt", "language:rm", "language:ro", "language:ru", "language:rw", "language:sah", "language:sl", "language:sv", "language:ta", "language:th", "language:tr", "language:tt", "language:uk", "language:vi", "language:vot", "language:zh", "license:cc0-1.0", "size_categories:100K<n<1M", "region:us" ]
[ "automatic-speech-recognition" ]
"2022-03-02T23:29:22Z"
--- pretty_name: Common Voice annotations_creators: - crowdsourced language_creators: - crowdsourced language: - ab - ar - as - br - ca - cnh - cs - cv - cy - de - dv - el - en - eo - es - et - eu - fa - fi - fr - fy - ga - hi - hsb - hu - ia - id - it - ja - ka - kab - ky - lg - lt - lv - mn - mt - nl - or - pa - pl - pt - rm - ro - ru - rw - sah - sl - sv - ta - th - tr - tt - uk - vi - vot - zh language_bcp47: - fy-NL - ga-IE - pa-IN - rm-sursilv - rm-vallader - sv-SE - zh-CN - zh-HK - zh-TW license: - cc0-1.0 multilinguality: - multilingual size_categories: - 100K<n<1M - 10K<n<100K - 1K<n<10K - n<1K source_datasets: - extended|common_voice task_categories: - automatic-speech-recognition task_ids: [] paperswithcode_id: common-voice viewer: false dataset_info: - config_name: ab features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 1295622 num_examples: 22 - name: test num_bytes: 411844 num_examples: 9 - name: validation - name: other num_bytes: 40023390 num_examples: 752 - name: validated num_bytes: 1707426 num_examples: 31 - name: invalidated num_bytes: 361626 num_examples: 8 download_size: 41038412 dataset_size: 43799908 - config_name: ar features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 359335168 num_examples: 14227 - name: test num_bytes: 237546641 num_examples: 7622 - name: validation num_bytes: 209606861 num_examples: 7517 - name: other num_bytes: 515822404 num_examples: 18283 - name: validated num_bytes: 1182522872 num_examples: 43291 - name: invalidated num_bytes: 194805036 num_examples: 6333 download_size: 1756264615 dataset_size: 2699638982 - config_name: as features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 11442279 num_examples: 270 - name: test num_bytes: 5071343 num_examples: 110 - name: validation num_bytes: 5480156 num_examples: 124 - name: other - name: validated num_bytes: 21993698 num_examples: 504 - name: invalidated num_bytes: 886145 num_examples: 31 download_size: 22226465 dataset_size: 44873621 - config_name: br features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 62238289 num_examples: 2780 - name: test num_bytes: 54461339 num_examples: 2087 - name: validation num_bytes: 46995570 num_examples: 1997 - name: other num_bytes: 269858143 num_examples: 10912 - name: validated num_bytes: 203503622 num_examples: 8560 - name: invalidated num_bytes: 20861017 num_examples: 623 download_size: 465276982 dataset_size: 657917980 - config_name: ca features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 12966939466 num_examples: 285584 - name: test num_bytes: 745761890 num_examples: 15724 - name: validation num_bytes: 716442038 num_examples: 15724 - name: other num_bytes: 2693542910 num_examples: 64446 - name: validated num_bytes: 18115833966 num_examples: 416701 - name: invalidated num_bytes: 850402888 num_examples: 18846 download_size: 20743110341 dataset_size: 36088923158 - config_name: cnh features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 18866674 num_examples: 807 - name: test num_bytes: 24675321 num_examples: 752 - name: validation num_bytes: 22162315 num_examples: 756 - name: other num_bytes: 84878963 num_examples: 2934 - name: validated num_bytes: 69330148 num_examples: 2432 - name: invalidated num_bytes: 13642724 num_examples: 433 download_size: 161331331 dataset_size: 233556145 - config_name: cs features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 215205282 num_examples: 5655 - name: test num_bytes: 148499476 num_examples: 4144 - name: validation num_bytes: 148312130 num_examples: 4118 - name: other num_bytes: 282225475 num_examples: 7475 - name: validated num_bytes: 1019817024 num_examples: 30431 - name: invalidated num_bytes: 24717823 num_examples: 685 download_size: 1271909933 dataset_size: 1838777210 - config_name: cv features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 31649510 num_examples: 931 - name: test num_bytes: 32513061 num_examples: 788 - name: validation num_bytes: 28429779 num_examples: 818 - name: other num_bytes: 288294623 num_examples: 6927 - name: validated num_bytes: 126717875 num_examples: 3496 - name: invalidated num_bytes: 57923138 num_examples: 1282 download_size: 439329081 dataset_size: 565527986 - config_name: cy features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - 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name: validated num_bytes: 22402489041 num_examples: 565186 - name: invalidated num_bytes: 1440604803 num_examples: 32789 download_size: 23283812097 dataset_size: 37244945467 - config_name: dv features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 118576140 num_examples: 2680 - name: test num_bytes: 94281409 num_examples: 2202 - name: validation num_bytes: 94117088 num_examples: 2077 - name: other - name: validated num_bytes: 528571107 num_examples: 11866 - name: invalidated num_bytes: 37694847 num_examples: 840 download_size: 540488041 dataset_size: 873240591 - config_name: el features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 80759076 num_examples: 2316 - name: test num_bytes: 53820491 num_examples: 1522 - name: validation num_bytes: 44818565 num_examples: 1401 - name: other num_bytes: 186861175 num_examples: 5659 - name: validated num_bytes: 204446790 num_examples: 5996 - name: invalidated num_bytes: 6023769 num_examples: 185 download_size: 381570611 dataset_size: 576729866 - config_name: en features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - 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name: validated num_bytes: 2603249289 num_examples: 58094 - name: invalidated num_bytes: 238105462 num_examples: 4736 download_size: 2883560869 dataset_size: 4789068898 - config_name: es features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 6918333205 num_examples: 161813 - name: test num_bytes: 754049291 num_examples: 15089 - name: validation num_bytes: 735558084 num_examples: 15089 - name: other num_bytes: 5528972205 num_examples: 144791 - name: validated num_bytes: 9623788388 num_examples: 236314 - name: invalidated num_bytes: 1664876264 num_examples: 40640 download_size: 16188844718 dataset_size: 25225577437 - config_name: et features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 161124199 num_examples: 2966 - name: test num_bytes: 133183135 num_examples: 2509 - name: validation num_bytes: 137604813 num_examples: 2507 - name: other num_bytes: 30339130 num_examples: 569 - name: validated num_bytes: 573417188 num_examples: 10683 - name: invalidated num_bytes: 193019544 num_examples: 3557 download_size: 767174465 dataset_size: 1228688009 - config_name: eu features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 317322801 num_examples: 7505 - name: test num_bytes: 238866501 num_examples: 5172 - name: validation num_bytes: 228150083 num_examples: 5172 - name: other num_bytes: 988079897 num_examples: 23570 - name: validated num_bytes: 2621488299 num_examples: 63009 - name: invalidated num_bytes: 208553909 num_examples: 5387 download_size: 3664586106 dataset_size: 4602461490 - config_name: fa features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 239255087 num_examples: 7593 - name: test num_bytes: 217939210 num_examples: 5213 - name: validation num_bytes: 196558067 num_examples: 5213 - name: other num_bytes: 737017546 num_examples: 22510 - name: validated num_bytes: 8120181903 num_examples: 251659 - name: invalidated num_bytes: 499570226 num_examples: 11698 download_size: 8884585819 dataset_size: 10010522039 - config_name: fi features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 16017393 num_examples: 460 - name: test num_bytes: 16117529 num_examples: 428 - name: validation num_bytes: 15471757 num_examples: 415 - name: other num_bytes: 5836400 num_examples: 149 - name: validated num_bytes: 47669391 num_examples: 1305 - name: invalidated num_bytes: 2228215 num_examples: 59 download_size: 49882909 dataset_size: 103340685 - config_name: fr features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 12439892070 num_examples: 298982 - name: test num_bytes: 733943163 num_examples: 15763 - name: validation num_bytes: 703801114 num_examples: 15763 - name: other num_bytes: 117998889 num_examples: 3222 - name: validated num_bytes: 17921836252 num_examples: 461004 - name: invalidated num_bytes: 1794149368 num_examples: 40351 download_size: 19130141984 dataset_size: 33711620856 - config_name: fy-NL features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 159116360 num_examples: 3927 - name: test num_bytes: 126913262 num_examples: 3020 - name: validation num_bytes: 112288554 num_examples: 2790 - name: other num_bytes: 893887467 num_examples: 21569 - name: validated num_bytes: 429651922 num_examples: 10495 - name: invalidated num_bytes: 38985422 num_examples: 1031 download_size: 1237743070 dataset_size: 1760842987 - config_name: ga-IE features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 15396820 num_examples: 541 - name: test num_bytes: 16611739 num_examples: 506 - name: validation num_bytes: 14897739 num_examples: 497 - name: other num_bytes: 61948768 num_examples: 2130 - name: validated num_bytes: 93371649 num_examples: 3352 - name: invalidated num_bytes: 10993268 num_examples: 409 download_size: 156553447 dataset_size: 213219983 - config_name: hi features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 4860737 num_examples: 157 - name: test num_bytes: 4728043 num_examples: 127 - name: validation num_bytes: 5569352 num_examples: 135 - name: other num_bytes: 4176110 num_examples: 139 - name: validated num_bytes: 15158052 num_examples: 419 - name: invalidated num_bytes: 2801051 num_examples: 60 download_size: 21424045 dataset_size: 37293345 - config_name: hsb features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 43049910 num_examples: 808 - name: test num_bytes: 20929094 num_examples: 387 - name: validation num_bytes: 8769458 num_examples: 172 - name: other num_bytes: 3173841 num_examples: 62 - name: validated num_bytes: 72748422 num_examples: 1367 - name: invalidated num_bytes: 5589972 num_examples: 227 download_size: 79362060 dataset_size: 154260697 - config_name: hu features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 126163153 num_examples: 3348 - name: test num_bytes: 57056435 num_examples: 1649 - name: validation num_bytes: 50306925 num_examples: 1434 - name: other num_bytes: 12051094 num_examples: 295 - name: validated num_bytes: 234307671 num_examples: 6457 - name: invalidated num_bytes: 5881521 num_examples: 169 download_size: 242758708 dataset_size: 485766799 - config_name: ia features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 96577153 num_examples: 3477 - name: test num_bytes: 33204678 num_examples: 899 - name: validation num_bytes: 67436779 num_examples: 1601 - name: other num_bytes: 30937041 num_examples: 1095 - name: validated num_bytes: 197248304 num_examples: 5978 - name: invalidated num_bytes: 6769573 num_examples: 192 download_size: 226499645 dataset_size: 432173528 - config_name: id features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 63515863 num_examples: 2130 - name: test num_bytes: 60711104 num_examples: 1844 - name: validation num_bytes: 56963520 num_examples: 1835 - name: other num_bytes: 206578628 num_examples: 6782 - name: validated num_bytes: 272570942 num_examples: 8696 - name: invalidated num_bytes: 16566129 num_examples: 470 download_size: 475918233 dataset_size: 676906186 - config_name: it features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 2555546829 num_examples: 58015 - name: test num_bytes: 656285877 num_examples: 12928 - name: validation num_bytes: 621955330 num_examples: 12928 - name: other num_bytes: 671213467 num_examples: 14549 - name: validated num_bytes: 4552252754 num_examples: 102579 - name: invalidated num_bytes: 564610354 num_examples: 12189 download_size: 5585781573 dataset_size: 9621864611 - config_name: ja features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 27600264 num_examples: 722 - name: test num_bytes: 26475556 num_examples: 632 - name: validation num_bytes: 22098940 num_examples: 586 - name: other num_bytes: 34588931 num_examples: 885 - name: validated num_bytes: 106916400 num_examples: 3072 - name: invalidated num_bytes: 17819020 num_examples: 504 download_size: 152879796 dataset_size: 235499111 - config_name: ka features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 47790695 num_examples: 1058 - name: test num_bytes: 30301524 num_examples: 656 - name: validation num_bytes: 24951079 num_examples: 527 - name: other num_bytes: 2144603 num_examples: 44 - name: validated num_bytes: 104135978 num_examples: 2275 - name: invalidated num_bytes: 7004160 num_examples: 139 download_size: 104280554 dataset_size: 216328039 - config_name: kab features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 3219289101 num_examples: 120530 - name: test num_bytes: 446453041 num_examples: 14622 - name: validation num_bytes: 414159937 num_examples: 14622 - name: other num_bytes: 2282481767 num_examples: 88021 - name: validated num_bytes: 15310455176 num_examples: 573718 - name: invalidated num_bytes: 581587104 num_examples: 18134 download_size: 17171606918 dataset_size: 22254426126 - config_name: ky features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 75460488 num_examples: 1955 - name: test num_bytes: 57116561 num_examples: 1503 - name: validation num_bytes: 61393867 num_examples: 1511 - name: other num_bytes: 258081579 num_examples: 7223 - name: validated num_bytes: 355742823 num_examples: 9236 - name: invalidated num_bytes: 41007711 num_examples: 926 download_size: 579440853 dataset_size: 848803029 - config_name: lg features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - 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name: other num_bytes: 71150206 num_examples: 1629 - name: validated num_bytes: 65138550 num_examples: 1644 - name: invalidated num_bytes: 4414780 num_examples: 102 download_size: 135299706 dataset_size: 205712134 - config_name: lv features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 67269173 num_examples: 2552 - name: test num_bytes: 56937435 num_examples: 1882 - name: validation num_bytes: 55289058 num_examples: 2002 - name: other num_bytes: 40259801 num_examples: 1560 - name: validated num_bytes: 179726893 num_examples: 6444 - name: invalidated num_bytes: 4383319 num_examples: 143 download_size: 208307691 dataset_size: 403865679 - config_name: mn features: - 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name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 29528457 num_examples: 574 - name: test num_bytes: 18805466 num_examples: 378 - name: validation num_bytes: 17012341 num_examples: 357 - name: other num_bytes: 36890435 num_examples: 727 - name: validated num_bytes: 65711922 num_examples: 1316 - name: invalidated num_bytes: 9356204 num_examples: 374 download_size: 108113989 dataset_size: 177304825 - config_name: ro features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - 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name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 100435725 num_examples: 2917 - name: test num_bytes: 82030679 num_examples: 2188 - name: validation num_bytes: 63237632 num_examples: 1922 - name: other num_bytes: 95235301 num_examples: 2671 - name: validated num_bytes: 245734783 num_examples: 7028 - name: invalidated num_bytes: 18247080 num_examples: 467 download_size: 341305736 dataset_size: 604921200 - config_name: tr features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 57879052 num_examples: 1831 - name: test num_bytes: 60268059 num_examples: 1647 - name: validation num_bytes: 54914798 num_examples: 1647 - name: other num_bytes: 10954154 num_examples: 325 - name: validated num_bytes: 585777527 num_examples: 18685 - name: invalidated num_bytes: 59288266 num_examples: 1726 download_size: 620848700 dataset_size: 829081856 - config_name: tt features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 348132697 num_examples: 11211 - name: test num_bytes: 135120057 num_examples: 4485 - name: validation num_bytes: 61690964 num_examples: 2127 - name: other num_bytes: 62158038 num_examples: 1798 - name: validated num_bytes: 767791517 num_examples: 25781 - name: invalidated num_bytes: 10403128 num_examples: 287 download_size: 777153207 dataset_size: 1385296401 - config_name: uk features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 161925063 num_examples: 4035 - name: test num_bytes: 138422211 num_examples: 3235 - name: validation num_bytes: 135483169 num_examples: 3236 - name: other num_bytes: 327979131 num_examples: 8161 - name: validated num_bytes: 889863965 num_examples: 22337 - name: invalidated num_bytes: 55745301 num_examples: 1255 download_size: 1218559031 dataset_size: 1709418840 - config_name: vi features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 6244454 num_examples: 221 - name: test num_bytes: 6656365 num_examples: 198 - name: validation num_bytes: 6531856 num_examples: 200 - name: other num_bytes: 31315434 num_examples: 870 - name: validated num_bytes: 19432595 num_examples: 619 - name: invalidated num_bytes: 2981661 num_examples: 78 download_size: 51929480 dataset_size: 73162365 - config_name: vot features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 146467 num_examples: 3 - name: test - name: validation - name: other num_bytes: 7963322 num_examples: 411 - name: validated num_bytes: 146467 num_examples: 3 - name: invalidated num_bytes: 107949 num_examples: 6 download_size: 7792602 dataset_size: 8364205 - config_name: zh-CN features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 793667379 num_examples: 18541 - name: test num_bytes: 420202544 num_examples: 8760 - name: validation num_bytes: 396096323 num_examples: 8743 - name: other num_bytes: 381264783 num_examples: 8948 - name: validated num_bytes: 1618113625 num_examples: 36405 - name: invalidated num_bytes: 266234479 num_examples: 5305 download_size: 2184602350 dataset_size: 3875579133 - config_name: zh-HK features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 221459521 num_examples: 7506 - name: test num_bytes: 217627041 num_examples: 5172 - name: validation num_bytes: 196071110 num_examples: 5172 - name: other num_bytes: 1319233252 num_examples: 38830 - name: validated num_bytes: 1482087591 num_examples: 41835 - name: invalidated num_bytes: 124170969 num_examples: 2999 download_size: 2774145806 dataset_size: 3560649484 - config_name: zh-TW features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 97323787 num_examples: 3507 - name: test num_bytes: 85512325 num_examples: 2895 - name: validation num_bytes: 80402637 num_examples: 2895 - name: other num_bytes: 623801957 num_examples: 22477 - name: validated num_bytes: 1568842090 num_examples: 61232 - name: invalidated num_bytes: 100241443 num_examples: 3584 download_size: 2182836295 dataset_size: 2556124239 config_names: - ab - ar - as - br - ca - cnh - cs - cv - cy - de - dv - el - en - eo - es - et - eu - fa - fi - fr - fy-NL - ga-IE - hi - hsb - hu - ia - id - it - ja - ka - kab - ky - lg - lt - lv - mn - mt - nl - or - pa-IN - pl - pt - rm-sursilv - rm-vallader - ro - ru - rw - sah - sl - sv-SE - ta - th - tr - tt - uk - vi - vot - zh-CN - zh-HK - zh-TW --- # Dataset Card for common_voice <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 "common_voice" is deprecated and will soon be deleted. Use datasets under <a href="https://huggingface.co/mozilla-foundation">mozilla-foundation</a> organisation instead. For example, you can load <a href="https://huggingface.co/datasets/mozilla-foundation/common_voice_13_0">Common Voice 13</a> dataset via <code>load_dataset("mozilla-foundation/common_voice_13_0", "en")</code></p> </div> ## 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://commonvoice.mozilla.org/en/datasets - **Repository:** https://github.com/common-voice/common-voice - **Paper:** https://commonvoice.mozilla.org/en/datasets - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Needs More Information] ### Dataset Summary The Common Voice dataset consists of a unique MP3 and corresponding text file. Many of the 9,283 recorded hours in the dataset also include demographic metadata like age, sex, and accent that can help train the accuracy of speech recognition engines. The dataset currently consists of 7,335 validated hours in 60 languages, but were always adding more voices and languages. Take a look at our Languages page to request a language or start contributing. ### Supported Tasks and Leaderboards [Needs More Information] ### Languages English ## Dataset Structure ### Data Instances A typical data point comprises the path to the audio file, called path and its sentence. Additional fields include accent, age, client_id, up_votes down_votes, gender, locale and segment. ` {'accent': 'netherlands', 'age': 'fourties', 'client_id': 'bbbcb732e0f422150c30ff3654bbab572e2a617da107bca22ff8b89ab2e4f124d03b6a92c48322862f60bd0179ae07baf0f9b4f9c4e11d581e0cec70f703ba54', 'down_votes': 0, 'gender': 'male', 'locale': 'nl', 'path': 'nl/clips/common_voice_nl_23522441.mp3', 'segment': "''", 'sentence': 'Ik vind dat een dubieuze procedure.', 'up_votes': 2, 'audio': {'path': `nl/clips/common_voice_nl_23522441.mp3', 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 48000} ` ### Data Fields client_id: An id for which client (voice) made the recording path: The path to the audio file 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]`. sentence: The sentence the user was prompted to speak up_votes: How many upvotes the audio file has received from reviewers down_votes: How many downvotes the audio file has received from reviewers age: The age of the speaker. gender: The gender of the speaker accent: Accent of the speaker locale: The locale of the speaker segment: Usually empty field ### Data Splits The speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other. The validated data is data that has been validated with reviewers and recieved upvotes that the data is of high quality. The invalidated data is data has been invalidated by reviewers and recieved downvotes that the data is of low quality. The reported data is data that has been reported, for different reasons. The other data is data that has not yet been reviewed. The dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train. ## 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 the Common Voice dataset. ## Considerations for Using the Data ### Social Impact of Dataset The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset. ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information Public Domain, [CC-0](https://creativecommons.org/share-your-work/public-domain/cc0/) ### Citation Information ``` @inproceedings{commonvoice:2020, author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.}, title = {Common Voice: A Massively-Multilingual Speech Corpus}, booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)}, pages = {4211--4215}, year = 2020 } ``` ### Contributions Thanks to [@BirgerMoell](https://github.com/BirgerMoell) for adding this dataset.
asahi417/seamless-align-enA-frA.speaker-embedding.w2vbert-600m
asahi417
"2024-06-24T07:09:01Z"
13,831
0
[ "size_categories:1M<n<10M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-06-16T15:17:28Z"
--- dataset_info: - config_name: subset_1 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: frA.id dtype: string - name: frA.laser_score dtype: float64 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 - name: frA.audio.speaker_embedding sequence: float32 - name: frA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 9566368832 num_examples: 2343 download_size: 9594006755 dataset_size: 9566368832 - config_name: subset_10 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: frA.id dtype: string - name: frA.laser_score dtype: float64 - name: frA.audio.speaker_embedding sequence: float32 - name: frA.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: 9055495714 num_examples: 2334 download_size: 9082072956 dataset_size: 9055495714 - config_name: subset_100 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: frA.id dtype: string - name: frA.laser_score dtype: float64 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 - name: frA.audio.speaker_embedding sequence: float32 - name: frA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 8344151850 num_examples: 2309 download_size: 8368301727 dataset_size: 8344151850 - config_name: subset_101 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: frA.id dtype: string - name: frA.laser_score dtype: float64 - name: frA.audio.speaker_embedding sequence: float32 - name: frA.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: 8292800434 num_examples: 2322 download_size: 8317633830 dataset_size: 8292800434 - config_name: subset_102 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: frA.id dtype: string - name: frA.laser_score dtype: float64 - name: frA.audio.speaker_embedding sequence: float32 - name: frA.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: 8224967519 num_examples: 2291 download_size: 8249842240 dataset_size: 8224967519 - config_name: subset_103 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: frA.id dtype: string - name: frA.laser_score dtype: float64 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 - name: frA.audio.speaker_embedding sequence: float32 - name: frA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 8339581413 num_examples: 2321 download_size: 8364550280 dataset_size: 8339581413 - config_name: subset_104 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: frA.id dtype: string - name: frA.laser_score dtype: float64 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 - name: frA.audio.speaker_embedding sequence: float32 - name: frA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 8239856479 num_examples: 2314 download_size: 8264731163 dataset_size: 8239856479 - config_name: subset_105 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: frA.id dtype: string - name: frA.laser_score dtype: float64 - name: frA.audio.speaker_embedding sequence: float32 - name: frA.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: 8325030279 num_examples: 2318 download_size: 8349920886 dataset_size: 8325030279 - config_name: subset_106 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: frA.id dtype: string - name: frA.laser_score dtype: float64 - name: frA.audio.speaker_embedding sequence: float32 - name: frA.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: 8294529988 num_examples: 2314 download_size: 8319483916 dataset_size: 8294529988 - config_name: subset_107 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: frA.id dtype: string - name: frA.laser_score dtype: float64 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 - name: frA.audio.speaker_embedding sequence: float32 - name: frA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 8451145911 num_examples: 2314 download_size: 8476011221 dataset_size: 8451145911 - config_name: subset_108 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: frA.id dtype: string - name: frA.laser_score dtype: float64 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 - name: frA.audio.speaker_embedding sequence: float32 - name: frA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 8317793444 num_examples: 2315 download_size: 8342027520 dataset_size: 8317793444 - config_name: subset_109 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: frA.id dtype: string - name: frA.laser_score dtype: float64 - name: frA.audio.speaker_embedding sequence: float32 - name: frA.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: 8295710465 num_examples: 2310 download_size: 8320545488 dataset_size: 8295710465 - config_name: subset_11 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: frA.id dtype: string - name: frA.laser_score dtype: float64 - name: frA.audio.speaker_embedding sequence: float32 - name: frA.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: 8923552101 num_examples: 2315 download_size: 8949074992 dataset_size: 8923552101 - config_name: subset_110 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: frA.id dtype: string - name: frA.laser_score dtype: float64 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 - name: frA.audio.speaker_embedding sequence: float32 - name: frA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 8049675461 num_examples: 2283 download_size: 8074629289 dataset_size: 8049675461 - config_name: subset_111 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: frA.id dtype: string - name: frA.laser_score dtype: float64 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 - name: frA.audio.speaker_embedding sequence: float32 - name: frA.audio.speaker_embedding.full sequence: sequence: float32 splits: - 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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: - 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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-* - config_name: subset_440 data_files: - split: train path: subset_440/train-* - config_name: subset_441 data_files: - split: train path: subset_441/train-* - config_name: subset_442 data_files: - split: train path: subset_442/train-* - config_name: subset_443 data_files: - split: train path: subset_443/train-* - config_name: subset_444 data_files: - split: train path: subset_444/train-* - config_name: subset_445 data_files: - split: train path: subset_445/train-* - config_name: subset_446 data_files: - split: train path: subset_446/train-* - config_name: subset_447 data_files: - split: train path: subset_447/train-* - config_name: subset_448 data_files: - split: train path: subset_448/train-* - config_name: subset_449 data_files: - split: train path: subset_449/train-* - config_name: subset_45 data_files: - split: train path: subset_45/train-* - config_name: subset_450 data_files: - split: train path: subset_450/train-* - config_name: subset_451 data_files: - split: train path: subset_451/train-* - config_name: subset_452 data_files: - split: train path: subset_452/train-* - config_name: subset_453 data_files: - split: train path: subset_453/train-* - config_name: subset_454 data_files: - split: train path: subset_454/train-* - config_name: subset_455 data_files: - split: train path: subset_455/train-* - config_name: subset_456 data_files: - split: train path: subset_456/train-* - config_name: subset_457 data_files: - split: train path: subset_457/train-* - config_name: subset_458 data_files: - split: train path: subset_458/train-* - config_name: subset_459 data_files: - split: train path: subset_459/train-* - config_name: subset_46 data_files: - split: train path: subset_46/train-* - config_name: subset_460 data_files: - split: train path: subset_460/train-* - config_name: subset_461 data_files: - split: train path: subset_461/train-* - config_name: subset_462 data_files: - split: train path: subset_462/train-* - config_name: subset_463 data_files: - split: train path: subset_463/train-* - config_name: subset_464 data_files: - split: train path: subset_464/train-* - config_name: subset_465 data_files: - split: train path: subset_465/train-* - config_name: subset_466 data_files: - split: train path: subset_466/train-* - config_name: subset_467 data_files: - split: train path: subset_467/train-* - config_name: subset_468 data_files: - split: train path: subset_468/train-* - config_name: subset_469 data_files: - split: train path: subset_469/train-* - config_name: subset_47 data_files: - split: train path: subset_47/train-* - config_name: subset_470 data_files: - split: train path: subset_470/train-* - config_name: subset_471 data_files: - split: train path: subset_471/train-* - config_name: subset_472 data_files: - split: train path: subset_472/train-* - config_name: subset_473 data_files: - split: train path: subset_473/train-* - config_name: subset_474 data_files: - split: train path: subset_474/train-* - config_name: subset_475 data_files: - split: train path: subset_475/train-* - config_name: subset_476 data_files: - split: train path: subset_476/train-* - config_name: subset_477 data_files: - split: train path: subset_477/train-* - config_name: subset_478 data_files: - split: train path: subset_478/train-* - config_name: subset_479 data_files: - split: train path: subset_479/train-* - config_name: subset_48 data_files: - split: train path: subset_48/train-* - config_name: subset_480 data_files: - split: train path: subset_480/train-* - config_name: subset_481 data_files: - split: train path: subset_481/train-* - config_name: subset_482 data_files: - split: train path: subset_482/train-* - config_name: subset_483 data_files: - split: train path: subset_483/train-* - config_name: subset_484 data_files: - 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split: train path: subset_496/train-* - config_name: subset_497 data_files: - split: train path: subset_497/train-* - config_name: subset_498 data_files: - split: train path: subset_498/train-* - config_name: subset_499 data_files: - split: train path: subset_499/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_500 data_files: - split: train path: subset_500/train-* - config_name: subset_501 data_files: - split: train path: subset_501/train-* - config_name: subset_502 data_files: - split: train path: subset_502/train-* - config_name: subset_503 data_files: - split: train path: subset_503/train-* - config_name: subset_504 data_files: - split: train path: subset_504/train-* - config_name: subset_505 data_files: - split: train path: subset_505/train-* - config_name: subset_506 data_files: - split: train path: subset_506/train-* - config_name: subset_507 data_files: - split: train path: subset_507/train-* - config_name: subset_508 data_files: - split: train path: subset_508/train-* - config_name: subset_509 data_files: - split: train path: subset_509/train-* - config_name: subset_51 data_files: - split: train path: subset_51/train-* - config_name: subset_510 data_files: - split: train path: subset_510/train-* - config_name: subset_511 data_files: - split: train path: subset_511/train-* - config_name: subset_512 data_files: - split: train path: subset_512/train-* - config_name: subset_513 data_files: - split: train path: subset_513/train-* - config_name: subset_514 data_files: - split: train path: subset_514/train-* - config_name: subset_515 data_files: - split: train path: subset_515/train-* - config_name: subset_516 data_files: - split: train path: subset_516/train-* - config_name: subset_517 data_files: - split: train path: subset_517/train-* - config_name: subset_518 data_files: - split: train path: subset_518/train-* - config_name: subset_519 data_files: - split: train path: subset_519/train-* - config_name: subset_52 data_files: - split: train path: subset_52/train-* - config_name: subset_520 data_files: - split: train path: subset_520/train-* - config_name: subset_521 data_files: - split: train path: subset_521/train-* - config_name: subset_522 data_files: - split: train path: subset_522/train-* - config_name: subset_523 data_files: - split: train path: subset_523/train-* - config_name: subset_524 data_files: - split: train path: subset_524/train-* - config_name: subset_525 data_files: - split: train path: subset_525/train-* - config_name: subset_526 data_files: - split: train path: subset_526/train-* - config_name: subset_527 data_files: - split: train path: subset_527/train-* - config_name: subset_528 data_files: - split: train path: subset_528/train-* - config_name: subset_529 data_files: - split: train path: subset_529/train-* - config_name: subset_53 data_files: - split: train path: subset_53/train-* - config_name: subset_530 data_files: - split: train path: subset_530/train-* - config_name: subset_531 data_files: - split: train path: subset_531/train-* - config_name: subset_532 data_files: - split: train path: subset_532/train-* - config_name: subset_533 data_files: - split: train path: subset_533/train-* - config_name: subset_534 data_files: - split: train path: subset_534/train-* - config_name: subset_535 data_files: - split: train path: subset_535/train-* - config_name: subset_536 data_files: - split: train path: subset_536/train-* - config_name: subset_537 data_files: - split: train path: subset_537/train-* - config_name: subset_538 data_files: - split: train path: subset_538/train-* - config_name: subset_539 data_files: - split: train path: subset_539/train-* - config_name: subset_54 data_files: - split: train path: subset_54/train-* - config_name: subset_540 data_files: - split: train path: subset_540/train-* - config_name: subset_541 data_files: - split: train path: subset_541/train-* - config_name: subset_542 data_files: - split: train path: subset_542/train-* - config_name: subset_543 data_files: - split: train path: subset_543/train-* - config_name: subset_544 data_files: - split: train path: subset_544/train-* - config_name: subset_545 data_files: - split: train path: subset_545/train-* - config_name: subset_546 data_files: - split: train path: subset_546/train-* - config_name: subset_547 data_files: - split: train path: subset_547/train-* - config_name: subset_548 data_files: - split: train path: subset_548/train-* - config_name: subset_549 data_files: - split: train path: subset_549/train-* - config_name: subset_55 data_files: - split: train path: subset_55/train-* - config_name: subset_550 data_files: - split: train path: subset_550/train-* - config_name: subset_551 data_files: - split: train path: subset_551/train-* - config_name: subset_552 data_files: - split: train path: subset_552/train-* - config_name: subset_553 data_files: - split: train path: subset_553/train-* - config_name: subset_554 data_files: - split: train path: subset_554/train-* - config_name: subset_555 data_files: - split: train path: subset_555/train-* - config_name: subset_556 data_files: - split: train path: subset_556/train-* - config_name: subset_557 data_files: - split: train path: subset_557/train-* - config_name: subset_558 data_files: - split: train path: subset_558/train-* - config_name: subset_559 data_files: - split: train path: subset_559/train-* - config_name: subset_56 data_files: - split: train path: subset_56/train-* - config_name: subset_560 data_files: - split: train path: subset_560/train-* - config_name: subset_561 data_files: - split: train path: subset_561/train-* - config_name: subset_562 data_files: - split: train path: subset_562/train-* - config_name: subset_563 data_files: - split: train path: subset_563/train-* - config_name: subset_564 data_files: - split: train path: subset_564/train-* - config_name: subset_565 data_files: - split: train path: subset_565/train-* - config_name: subset_566 data_files: - split: train path: subset_566/train-* - config_name: subset_567 data_files: - split: train path: subset_567/train-* - config_name: subset_568 data_files: - split: train path: subset_568/train-* - config_name: subset_569 data_files: - split: train path: subset_569/train-* - config_name: subset_57 data_files: - split: train path: subset_57/train-* - config_name: subset_570 data_files: - split: train path: subset_570/train-* - config_name: subset_571 data_files: - split: train path: subset_571/train-* - config_name: subset_572 data_files: - split: train path: subset_572/train-* - config_name: subset_573 data_files: - split: train path: subset_573/train-* - config_name: subset_574 data_files: - split: train path: subset_574/train-* - config_name: subset_575 data_files: - split: train path: subset_575/train-* - config_name: subset_576 data_files: - split: train path: subset_576/train-* - config_name: subset_577 data_files: - split: train path: subset_577/train-* - config_name: subset_578 data_files: - split: train path: subset_578/train-* - config_name: subset_579 data_files: - split: train path: subset_579/train-* - config_name: subset_58 data_files: - split: train path: subset_58/train-* - config_name: subset_580 data_files: - split: train path: subset_580/train-* - config_name: subset_581 data_files: - split: train path: subset_581/train-* - config_name: subset_582 data_files: - split: train path: subset_582/train-* - config_name: subset_583 data_files: - split: train path: subset_583/train-* - config_name: subset_584 data_files: - split: train path: subset_584/train-* - config_name: subset_585 data_files: - split: train path: subset_585/train-* - config_name: subset_586 data_files: - split: train path: subset_586/train-* - config_name: subset_587 data_files: - split: train path: subset_587/train-* - config_name: subset_588 data_files: - split: train path: subset_588/train-* - config_name: subset_589 data_files: - split: train path: subset_589/train-* - config_name: subset_59 data_files: - split: train path: subset_59/train-* - config_name: subset_590 data_files: - split: train path: subset_590/train-* - config_name: subset_591 data_files: - split: train path: subset_591/train-* - config_name: subset_592 data_files: - split: train path: subset_592/train-* - config_name: subset_593 data_files: - split: train path: subset_593/train-* - config_name: subset_594 data_files: - split: train path: subset_594/train-* - config_name: subset_595 data_files: - split: train path: subset_595/train-* - config_name: subset_596 data_files: - split: train path: subset_596/train-* - config_name: subset_597 data_files: - split: train path: subset_597/train-* - config_name: subset_598 data_files: - split: train path: subset_598/train-* - config_name: subset_599 data_files: - split: train path: subset_599/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_600 data_files: - split: train path: subset_600/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-* ---
alvations/c4p0-x1-es-en
alvations
"2024-03-24T03:52:27Z"
13,809
0
[ "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-03-23T10:16:40Z"
--- 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: timestamp[us] - name: url dtype: string - name: doc_hash dtype: string splits: - name: train num_bytes: 3583 num_examples: 4 download_size: 14029 dataset_size: 3583 configs: - config_name: default data_files: - split: train path: d595973734f0a325/train-* ---
daiua/video
daiua
"2023-11-20T02:05:48Z"
13,771
1
[ "license:other", "modality:video", "region:us" ]
null
"2023-11-16T06:27:40Z"
--- license: other license_name: '11111' license_link: LICENSE ---
HAERAE-HUB/KMMLU-HARD
HAERAE-HUB
"2024-03-09T23:46:06Z"
13,716
8
[ "task_categories:question-answering", "language:ko", "license:cc-by-nd-4.0", "size_categories:1K<n<10K", "format:csv", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2402.11548", "region:us", "haerae", "mmlu" ]
[ "question-answering" ]
"2024-01-12T05:49:07Z"
--- configs: - config_name: maritime_engineering data_files: - split: dev path: data/maritime_engineering-dev.csv - split: test path: data/maritime_engineering-hard-test.csv - config_name: materials_engineering data_files: - split: dev path: data/materials_engineering-dev.csv - split: test path: data/materials_engineering-hard-test.csv - config_name: railway_and_automotive_engineering data_files: - split: dev path: data/railway_and_automotive_engineering-dev.csv - split: test path: data/railway_and_automotive_engineering-hard-test.csv - config_name: biology data_files: - split: dev path: data/biology-dev.csv - split: test path: data/biology-hard-test.csv - config_name: public_safety data_files: - split: dev path: data/public_safety-dev.csv - split: test path: data/public_safety-hard-test.csv - config_name: criminal_law data_files: - split: dev path: data/criminal_law-dev.csv - split: test path: data/criminal_law-hard-test.csv - config_name: information_technology data_files: - split: dev path: data/information_technology-dev.csv - split: test path: data/information_technology-hard-test.csv - config_name: geomatics data_files: - split: dev path: data/geomatics-dev.csv - split: test path: data/geomatics-hard-test.csv - config_name: management data_files: - split: dev path: data/management-dev.csv - split: test path: data/management-hard-test.csv - config_name: math data_files: - split: dev path: data/math-dev.csv - split: test path: data/math-hard-test.csv - config_name: accounting data_files: - split: dev path: data/accounting-dev.csv - split: test path: data/accounting-hard-test.csv - config_name: chemistry data_files: - split: dev path: data/chemistry-dev.csv - split: test path: data/chemistry-hard-test.csv - config_name: nondestructive_testing data_files: - split: dev path: data/nondestructive_testing-dev.csv - split: test path: data/nondestructive_testing-hard-test.csv - config_name: computer_science data_files: - split: dev path: data/computer_science-dev.csv - split: test path: data/computer_science-hard-test.csv - config_name: ecology data_files: - split: dev path: data/ecology-dev.csv - split: test path: data/ecology-hard-test.csv - config_name: health data_files: - split: dev path: data/health-dev.csv - split: test path: data/health-hard-test.csv - config_name: political_science_and_sociology data_files: - split: dev path: data/political_science_and_sociology-dev.csv - split: test path: data/political_science_and_sociology-hard-test.csv - config_name: patent data_files: - split: dev path: data/patent-dev.csv - split: test path: data/patent-hard-test.csv - config_name: electrical_engineering data_files: - split: dev path: data/electrical_engineering-dev.csv - split: test path: data/electrical_engineering-hard-test.csv - config_name: electronics_engineering data_files: - split: dev path: data/electronics_engineering-dev.csv - split: test path: data/electronics_engineering-hard-test.csv - config_name: korean_history data_files: - split: dev path: data/korean_history-dev.csv - split: test path: data/korean_history-hard-test.csv - config_name: gas_technology_and_engineering data_files: - split: dev path: data/gas_technology_and_engineering-dev.csv - split: test path: data/gas_technology_and_engineering-hard-test.csv - config_name: machine_design_and_manufacturing data_files: - split: dev path: data/machine_design_and_manufacturing-dev.csv - split: test path: data/machine_design_and_manufacturing-hard-test.csv - config_name: chemical_engineering data_files: - split: dev path: data/chemical_engineering-dev.csv - split: test path: data/chemical_engineering-hard-test.csv - config_name: telecommunications_and_wireless_technology data_files: - split: dev path: data/telecommunications_and_wireless_technology-dev.csv - split: test path: data/telecommunications_and_wireless_technology-hard-test.csv - config_name: food_processing data_files: - split: dev path: data/food_processing-dev.csv - split: test path: data/food_processing-hard-test.csv - config_name: social_welfare data_files: - split: dev path: data/social_welfare-dev.csv - split: test path: data/social_welfare-hard-test.csv - config_name: real_estate data_files: - split: dev path: data/real_estate-dev.csv - split: test path: data/real_estate-hard-test.csv - config_name: marketing data_files: - split: dev path: data/marketing-dev.csv - split: test path: data/marketing-hard-test.csv - config_name: mechanical_engineering data_files: - split: dev path: data/mechanical_engineering-dev.csv - split: test path: data/mechanical_engineering-hard-test.csv - config_name: fashion data_files: - split: dev path: data/fashion-dev.csv - split: test path: data/fashion-hard-test.csv - config_name: psychology data_files: - split: dev path: data/psychology-dev.csv - split: test path: data/psychology-hard-test.csv - config_name: taxation data_files: - split: dev path: data/taxation-dev.csv - split: test path: data/taxation-hard-test.csv - config_name: environmental_science data_files: - split: dev path: data/environmental_science-dev.csv - split: test path: data/environmental_science-hard-test.csv - config_name: refrigerating_machinery data_files: - split: dev path: data/refrigerating_machinery-dev.csv - split: test path: data/refrigerating_machinery-hard-test.csv - config_name: education data_files: - split: dev path: data/education-dev.csv - split: test path: data/education-hard-test.csv - config_name: industrial_engineer data_files: - split: dev path: data/industrial_engineer-dev.csv - split: test path: data/industrial_engineer-hard-test.csv - config_name: civil_engineering data_files: - split: dev path: data/civil_engineering-dev.csv - split: test path: data/civil_engineering-hard-test.csv - config_name: energy_management data_files: - split: dev path: data/energy_management-dev.csv - split: test path: data/energy_management-hard-test.csv - config_name: law data_files: - split: dev path: data/law-dev.csv - split: test path: data/law-hard-test.csv - config_name: agricultural_sciences data_files: - split: dev path: data/agricultural_sciences-dev.csv - split: test path: data/agricultural_sciences-hard-test.csv - config_name: interior_architecture_and_design data_files: - split: dev path: data/interior_architecture_and_design-dev.csv - split: test path: data/interior_architecture_and_design-hard-test.csv - config_name: aviation_engineering_and_maintenance data_files: - split: dev path: data/aviation_engineering_and_maintenance-dev.csv - split: test path: data/aviation_engineering_and_maintenance-hard-test.csv - config_name: construction data_files: - split: dev path: data/construction-dev.csv - split: test path: data/construction-hard-test.csv - config_name: economics data_files: - split: dev path: data/economics-dev.csv - split: test path: data/economics-hard-test.csv license: cc-by-nd-4.0 task_categories: - question-answering language: - ko tags: - haerae - mmlu size_categories: - 100K<n<1M --- ### KMMLU (Korean-MMLU) We propose KMMLU, a new Korean benchmark with 35,030 expert-level multiple-choice questions across 45 subjects ranging from humanities to STEM. Unlike previous Korean benchmarks that are translated from existing English benchmarks, KMMLU is collected from original Korean exams, capturing linguistic and cultural aspects of the Korean language. We test 26 publically available and proprietary LLMs, identifying significant room for improvement. The best publicly available model achieves 50.54% on KMMLU, far below the average human performance of 62.6%. This model was primarily trained for English and Chinese, not Korean. Current LLMs tailored to Korean, such as Polyglot-Ko, perform far worse. Surprisingly, even the most capable proprietary LLMs, e.g., GPT-4 and HyperCLOVA X, achieve 59.95% and 53.40%, respectively. This suggests that further work is needed to improve Korean LLMs, and KMMLU offers the right tool to track this progress. We make our dataset publicly available on the Hugging Face Hub and integrate the benchmark into EleutherAI's Language Model Evaluation Harness. Link to Paper: [KMMLU: Measuring Massive Multitask Language Understanding in Korean](https://arxiv.org/abs/2402.11548) ### KMMLU Statistics | Category | # Questions | |------------------------------|-------------| | **Prerequisites** | | | None | 59,909 | | 1 Prerequisite Test | 12,316 | | 2 Prerequisite Tests | 776 | | 2+ Years of Experience | 65,135 | | 4+ Years of Experience | 98,678 | | 9+ Years of Experience | 6,963 | | **Question Type** | | | Positive | 207,030 | | Negation | 36,777 | | **Split** | | | Train | 208,522 | | Validation | 225 | | Test | 35,030 | | **Total** | 243,777 | ### Categories To reimplement the categories in the paper, refer to the following: ``` supercategories = { "accounting": "HUMSS", "agricultural_sciences": "Other", "aviation_engineering_and_maintenance": "Applied Science", "biology": "STEM", "chemical_engineering": "STEM", "chemistry": "STEM", "civil_engineering": "STEM", "computer_science": "STEM", "construction": "Other", "criminal_law": "HUMSS", "ecology": "STEM", "economics": "HUMSS", "education": "HUMSS", "electrical_engineering": "STEM", "electronics_engineering": "Applied Science", "energy_management": "Applied Science", "environmental_science": "Applied Science", "fashion": "Other", "food_processing": "Other", "gas_technology_and_engineering": "Applied Science", "geomatics": "Applied Science", "health": "Other", "industrial_engineer": "Applied Science", "information_technology": "STEM", "interior_architecture_and_design": "Other", "law": "HUMSS", "machine_design_and_manufacturing": "Applied Science", "management": "HUMSS", "maritime_engineering": "Applied Science", "marketing": "Other", "materials_engineering": "STEM", "mechanical_engineering": "STEM", "nondestructive_testing": "Applied Science", "patent": "Other", "political_science_and_sociology": "HUMSS", "psychology": "HUMSS", "public_safety": "Other", "railway_and_automotive_engineering": "Applied Science", "real_estate": "Other", "refrigerating_machinery": "Other", "social_welfare": "HUMSS", "taxation": "HUMSS", "telecommunications_and_wireless_technology": "Applied Science", "korean_history": "HUMSS", "math": "STEM" } ``` ### Point of Contact For any questions contact us via the following email:) ``` [email protected] ```
Gen-Verse/WideRange4D
Gen-Verse
"2025-03-25T00:37:31Z"
13,709
10
[ "task_categories:image-to-video", "license:unknown", "size_categories:10K<n<100K", "modality:video", "library:datasets", "library:mlcroissant", "arxiv:2503.13435", "region:us", "4d-reconstruction", "gaussian-splatting" ]
[ "image-to-video" ]
"2025-03-11T02:02:06Z"
--- task_categories: - image-to-video tags: - 4d-reconstruction - gaussian-splatting license: unknown --- <div align="center"> # WideRange4D: Enabling High-Quality 4D Reconstruction with Wide-Range Movements and Scenes ![image/png](https://cdn-uploads.huggingface.co/production/uploads/670880950e79a8b46f7ff9dd/wK9EkE6CQnn1DTADsun_a.png) [**Github Page**](https://github.com/Gen-Verse/WideRange4D) | [**arXiv Paper**](https://arxiv.org/abs/2503.13435) [Ling Yang](https://yangling0818.github.io)<sup>1*</sup>, [Kaixin Zhu](https://chriszkxxx.github.io)<sup>1*</sup>, [Juanxi Tian](https://tianshijing.github.io)<sup>1*</sup>, [Bohan Zeng](https://scholar.google.com/citations?user=MHo_d3YAAAAJ&hl=en)<sup>1*</sup>, [Mingbao Lin](http://lmb.bjbxit.cn/)<sup>3</sup>, [Hongjuan Pei](https://openreview.net/profile?id=~Hongjuan_Pei1)<sup>2</sup>, [Wentao Zhang](https://zwt233.github.io)<sup>1‡</sup>, [Shuicheng Yan](http://yanshuicheng.info)<sup>3‡</sup> <sup>1</sup> Peking University &emsp; <sup>2</sup> University of the Chinese Academy of Sciences &emsp; <sup>3</sup> National University of Singapore <sup>*</sup> Equal Contributions. <sup>‡</sup> Corresponding Author. </div> ## Example <video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/670880950e79a8b46f7ff9dd/b_29Bkf1mWcVWPqhOsut4.mp4"></video> <video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/6708920aeae29d1cd41a703b/cyyvOw8hjvQ6JU-uvngtT.mp4"></video> ``` @article{yang2025widerange4d, title={WideRange4D: Enabling High-Quality 4D Reconstruction with Wide-Range Movements and Scenes}, author={Yang, Ling and Zhu, Kaixin and Tian, Juanxi and Zeng, Bohan and Lin, Mingbao and Pei, Hongjuan and Zhang, Wentao and Yan, Shuichen}, journal={arXiv preprint arXiv:2503.13435}, year={2025} } ```
taesiri/arxiv_audio
taesiri
"2023-12-22T04:56:39Z"
13,690
2
[ "license:cc-by-4.0", "size_categories:1K<n<10K", "format:text", "modality:audio", "modality:text", "library:datasets", "library:mlcroissant", "region:us" ]
null
"2023-07-03T03:46:57Z"
--- license: cc-by-4.0 ---
EleutherAI/race
EleutherAI
"2023-07-03T21:27:18Z"
13,677
6
[ "task_categories:multiple-choice", "task_ids:multiple-choice-qa", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:other", "size_categories:1K<n<10K", "modality:text", "library:datasets", "library:mlcroissant", "arxiv:1704.04683", "region:us" ]
[ "multiple-choice" ]
"2023-07-03T13:20:38Z"
--- annotations_creators: - expert-generated language: - en language_creators: - found license: - other multilinguality: - monolingual pretty_name: RACE size_categories: - 10K<n<100K source_datasets: - original task_categories: - multiple-choice task_ids: - multiple-choice-qa paperswithcode_id: race dataset_info: --- # "race" Grouped by Article This is a modified version of https://huggingface.co/datasets/race that returns documents grouped by article context instead of by question. **Note:** This dataset currently only contains that test set of the ```high``` subset of the data. The original readme is contained below. # Dataset Card for "race" ## 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://www.cs.cmu.edu/~glai1/data/race/](http://www.cs.cmu.edu/~glai1/data/race/) - **Repository:** https://github.com/qizhex/RACE_AR_baselines - **Paper:** [RACE: Large-scale ReAding Comprehension Dataset From Examinations](https://arxiv.org/abs/1704.04683) - **Point of Contact:** [Guokun Lai](mailto:[email protected]), [Qizhe Xie](mailto:[email protected]) - **Size of downloaded dataset files:** 76.33 MB - **Size of the generated dataset:** 349.46 MB - **Total amount of disk used:** 425.80 MB ### Dataset Summary RACE is a large-scale reading comprehension dataset with more than 28,000 passages and nearly 100,000 questions. The dataset is collected from English examinations in China, which are designed for middle school and high school students. The dataset can be served as the training and test sets for machine comprehension. ### 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 - **Size of downloaded dataset files:** 25.44 MB - **Size of the generated dataset:** 174.73 MB - **Total amount of disk used:** 200.17 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "answer": "A", "article": "\"Schoolgirls have been wearing such short skirts at Paget High School in Branston that they've been ordered to wear trousers ins...", "example_id": "high132.txt", "options": ["short skirts give people the impression of sexualisation", "short skirts are too expensive for parents to afford", "the headmaster doesn't like girls wearing short skirts", "the girls wearing short skirts will be at the risk of being laughed at"], "question": "The girls at Paget High School are not allowed to wear skirts in that _ ." } ``` #### high - **Size of downloaded dataset files:** 25.44 MB - **Size of the generated dataset:** 140.12 MB - **Total amount of disk used:** 165.56 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "answer": "A", "article": "\"Schoolgirls have been wearing such short skirts at Paget High School in Branston that they've been ordered to wear trousers ins...", "example_id": "high132.txt", "options": ["short skirts give people the impression of sexualisation", "short skirts are too expensive for parents to afford", "the headmaster doesn't like girls wearing short skirts", "the girls wearing short skirts will be at the risk of being laughed at"], "question": "The girls at Paget High School are not allowed to wear skirts in that _ ." } ``` #### middle - **Size of downloaded dataset files:** 25.44 MB - **Size of the generated dataset:** 34.61 MB - **Total amount of disk used:** 60.05 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "answer": "B", "article": "\"There is not enough oil in the world now. As time goes by, it becomes less and less, so what are we going to do when it runs ou...", "example_id": "middle3.txt", "options": ["There is more petroleum than we can use now.", "Trees are needed for some other things besides making gas.", "We got electricity from ocean tides in the old days.", "Gas wasn't used to run cars in the Second World War."], "question": "According to the passage, which of the following statements is TRUE?" } ``` ### Data Fields The data fields are the same among all splits. #### all - `example_id`: a `string` feature. - `article`: a `string` feature. - `answer`: a `string` feature. - `question`: a `string` feature. - `options`: a `list` of `string` features. #### high - `example_id`: a `string` feature. - `article`: a `string` feature. - `answer`: a `string` feature. - `question`: a `string` feature. - `options`: a `list` of `string` features. #### middle - `example_id`: a `string` feature. - `article`: a `string` feature. - `answer`: a `string` feature. - `question`: a `string` feature. - `options`: a `list` of `string` features. ### Data Splits | name |train|validation|test| |------|----:|---------:|---:| |all |87866| 4887|4934| |high |62445| 3451|3498| |middle|25421| 1436|1436| ## 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 http://www.cs.cmu.edu/~glai1/data/race/ 1. RACE dataset is available for non-commercial research purpose only. 2. All passages are obtained from the Internet which is not property of Carnegie Mellon University. We are not responsible for the content nor the meaning of these passages. 3. You agree not to reproduce, duplicate, copy, sell, trade, resell or exploit for any commercial purpose, any portion of the contexts and any portion of derived data. 4. We reserve the right to terminate your access to the RACE dataset at any time. ### Citation Information ``` @inproceedings{lai-etal-2017-race, title = "{RACE}: Large-scale {R}e{A}ding Comprehension Dataset From Examinations", author = "Lai, Guokun and Xie, Qizhe and Liu, Hanxiao and Yang, Yiming and Hovy, Eduard", booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing", month = sep, year = "2017", address = "Copenhagen, Denmark", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/D17-1082", doi = "10.18653/v1/D17-1082", pages = "785--794", } ``` ### Contributions Thanks to [@abarbosa94](https://github.com/abarbosa94), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf), [@mariamabarham](https://github.com/mariamabarham) for adding this dataset.
alvations/c4p0-x1-fr-en
alvations
"2024-03-24T03:54:05Z"
13,644
0
[ "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-03-23T10:15:37Z"
--- 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: timestamp[us] - name: url dtype: string - name: doc_hash dtype: string splits: - name: train num_bytes: 9747 num_examples: 7 download_size: 21197 dataset_size: 9747 configs: - config_name: default data_files: - split: train path: 02b9d52b317f351f/train-* ---
facebook/natural_reasoning
facebook
"2025-02-21T06:02:40Z"
13,626
455
[ "task_categories:text-generation", "language:en", "license:cc-by-nc-4.0", "size_categories:1M<n<10M", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2502.13124", "region:us" ]
[ "text-generation" ]
"2025-01-30T23:29:32Z"
--- license: cc-by-nc-4.0 task_categories: - text-generation language: - en pretty_name: Natural Reasoning size_categories: - 1M<n<10M --- [NaturalReasoning](https://arxiv.org/abs/2502.13124) is a large-scale dataset for general reasoning tasks. It consists of high-quality challenging reasoning questions backtranslated from pretraining corpora [DCLM](https://github.com/mlfoundations/dclm) and [FineMath](https://huggingface.co/datasets/HuggingFaceTB/finemath). The questions have been deduplicated and decontaminated from popular reasoning benchmarks including MATH, GPQA, MMLU-Pro, MMLU-STEM. For each question, we extract the reference final answer from the original document from the pretraining corpora if possible. We also provide a model-generated response from Llama3.3-70B-Instruct. We release a 1.1 million subset of NaturalReasoning to the research community to foster research on training strong LLM reasoners. You can load the dataset as follows ```python from datasets import load_dataset ds = load_dataset("facebook/natural_reasoning") ``` For more information regarding data collection, please refer to our [paper](https://arxiv.org/abs/2502.13124). ## Reference Answer Statistics In the 1.1 million subset, 18.29% of the questions do not have a reference answer, 9.71% of the questions have a single word answer, 21.58% of the questions have a short answer while 50.42% of the questions have a long reference answer. ## Scaling Curve Training on NaturalReasoning shows better scaling effects than training on other datasets when training Llama3.1-8B-Instruct model. In particular, we measure the average performance on three benchmarks: MATH, GPQA, MMLU-Pro. <img src="https://cdn-uploads.huggingface.co/production/uploads/659a395421a7431643caedda/S6aO-agjRRhc0JLkohZ5z.jpeg" style="width:50%; max-width:400px;"> ## Citation If you use data from NaturalReasoning, please cite with the following BibTex entry: ``` @misc{yuan2025naturalreasoningreasoningwild28m, title={NaturalReasoning: Reasoning in the Wild with 2.8M Challenging Questions}, author={Weizhe Yuan and Jane Yu and Song Jiang and Karthik Padthe and Yang Li and Dong Wang and Ilia Kulikov and Kyunghyun Cho and Yuandong Tian and Jason E Weston and Xian Li}, year={2025}, eprint={2502.13124}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2502.13124}, } ```
databricks/databricks-dolly-15k
databricks
"2023-06-30T18:34:13Z"
13,570
800
[ "task_categories:question-answering", "task_categories:summarization", "language:en", "license:cc-by-sa-3.0", "size_categories:10K<n<100K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2203.02155", "region:us" ]
[ "question-answering", "summarization" ]
"2023-04-11T16:43:13Z"
--- 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.
qmeeus/voxpopuli
qmeeus
"2024-02-06T23:13:46Z"
13,547
0
[ "size_categories:100K<n<1M", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-02-06T15:17:31Z"
--- dataset_info: - config_name: de features: - name: audio_id dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: text dtype: string - name: language dtype: string splits: - name: train num_bytes: 61603981153.568 num_examples: 108473 - name: validation num_bytes: 1149586917.507 num_examples: 2109 download_size: 52060225655 dataset_size: 62753568071.075 - config_name: es features: - name: audio_id dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: text dtype: string - name: language dtype: string splits: - name: train num_bytes: 36533665201.936 num_examples: 50922 - name: validation num_bytes: 1173444834.383 num_examples: 1631 download_size: 1005381345 dataset_size: 37707110036.319 - config_name: fr features: - name: audio_id dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: text dtype: string - name: language dtype: string splits: - name: train num_bytes: 48346650213.26 num_examples: 73561 - name: validation num_bytes: 1149779276.605 num_examples: 1727 download_size: 17314564262 dataset_size: 49496429489.865005 - config_name: nl features: - name: audio_id dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: text dtype: string - name: language dtype: string splits: - name: train num_bytes: 10436544940.608 num_examples: 20968 - name: validation num_bytes: 636925883.64 num_examples: 1230 download_size: 9404833804 dataset_size: 11073470824.248 configs: - config_name: de data_files: - split: train path: de/train-* - split: validation path: de/validation-* - config_name: es data_files: - split: train path: es/train-* - split: validation path: es/validation-* - config_name: fr data_files: - split: train path: fr/train-* - split: validation path: fr/validation-* - config_name: nl data_files: - split: train path: nl/train-* - split: validation path: nl/validation-* --- # Dataset Card for "voxpopuli" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
parrotzone/sdxl-1.0
parrotzone
"2023-09-20T12:27:51Z"
13,462
11
[ "license:openrail++", "size_categories:1K<n<10K", "format:imagefolder", "modality:image", "library:datasets", "library:mlcroissant", "region:us" ]
null
"2023-07-31T07:18:18Z"
--- license: openrail++ --- # check [sdxl.parrotzone.art](https://sdxl.parrotzone.art) for easy viewing ⋆｡°✩ --- ## all images were made with SDXL 1.0 + the 0.9 VAE - steps: 20 - cfg scale: 7 - no refiner - random seeds
mlfoundations/datacomp_1b
mlfoundations
"2023-08-21T21:43:05Z"
13,456
33
[ "license:cc-by-4.0", "size_categories:1B<n<10B", "format:parquet", "modality:image", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
null
"2023-06-11T20:12:44Z"
--- license: cc-by-4.0 --- ## DataComp-1B This repository contains metadata files for DataComp-1B. For details on how to use the metadata, please visit [our website](https://www.datacomp.ai/) and our [github repository](https://github.com/mlfoundations/datacomp). We distribute the image url-text samples and metadata under a standard Creative Common CC-BY-4.0 license. The individual images are under their own copyrights. ## Terms and Conditions We have terms of service that are similar to those adopted by HuggingFace (https://huggingface.co/terms-of-service), which covers their dataset library. Specifically, any content you download, access or use from our index, is at your own risk and subject to the terms of service or copyright limitations accompanying such content. The image url-text index, which is a research artifact, is provided as is. By using said index, you assume all risks, including but not limited to, liabilities related to image downloading and storage.
Anthropic/hh-rlhf
Anthropic
"2023-05-26T18:47:34Z"
13,433
1,300
[ "license:mit", "size_categories:100K<n<1M", "format:json", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2204.05862", "region:us", "human-feedback" ]
null
"2022-12-08T20:11:33Z"
--- license: mit tags: - human-feedback --- # Dataset Card for HH-RLHF ## Dataset Summary This repository provides access to two different kinds of data: 1. Human preference data about helpfulness and harmlessness from [Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback](https://arxiv.org/abs/2204.05862). These data are meant to train preference (or reward) models for subsequent RLHF training. These data are *not* meant for supervised training of dialogue agents. Training dialogue agents on these data is likely to lead to harmful models and this shold be avoided. 2. Human-generated and annotated red teaming dialogues from [Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned](https://www.anthropic.com/red_teaming.pdf). These data are meant to understand how crowdworkers red team models and what types of red team attacks are succesful or not. The data are *not* meant for fine-tuning or preference modeling (use the data above for preference modeling). These data are entire transcripts of conversations that are derived from the harmlessness preference modeling data described above, where only the chosen response is incorporated into the overall transcript. Furthermore, the transcripts are annotated with human and automated measurements of how harmful the overall dialogues are. **Disclaimer**: The data (especially the harmlessness preference data and the red team data) contain content that may be offensive or upsetting. Topics include, but are not limited to, discriminatory language and discussions of abuse, violence, self-harm, exploitation, and other potentially upsetting subject matter. Please only engage with the data in accordance with your own personal risk tolerance. The data are intended for research purposes, especially research that can make models *less* harmful. The views expressed in the data do not reflect the views of Anthropic or any of its employees. As mentioned above, these data are *not* intended for training dialogue agents as this will likely lead to harmful model behavior. Each of these datasets are described further below. ## Human preference data about helpfulness and harmlessness (PM Data) The data are described in the paper: [Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback](https://arxiv.org/abs/2204.05862). If you find the data useful, please cite the paper. The data format is very simple -- each line of the jsonl files contains a pair of texts, one "chosen" and one "rejected". For **helpfulness**, the data are grouped into train/test splits in three tranches: from our base models (context-distilled 52B language models), via rejection sampling (mostly with best-of-16 sampling) against an early preference model, and a dataset sampled during our iterated "online" process. For **harmlessness**, the data are only collected for our base models, but otherwise formatted in the same way. Details about the data collection process and crowdworker population can be found in the paper, specifically in section 2 and appendix D. ## Red teaming data (not PM Data) The data are described in the paper: [Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned](https://www.anthropic.com/red_teaming.pdf). If you find the data useful, please cite the paper. At a high level these data can be examined to understand what succesful red team attempts look like. Details about the data and data collection procedures can be found in the Datasheet in the appendix of the paper. Each line of the jsonl file contains a dictionary with the following fields: - `transcript` a text transcript of a conversation between a human adversary (red team member) and an AI assistant - `min_harmlessness_score_transcript` a real value score of the harmlessness of the AI assistant (lower is more harmful) as obtained from a preference model - `num_params` number of parameters in the language model powering the AI assistant - `model_type` type of model powering the AI assistant - `rating` the red team member's rating of how successful they were at breaking the AI assistant (Likert scale, higher is more successful) - `task_description` a short text description written by the red team member about how they tried to red team the AI assistant - `task_description_harmlessness_score` a real value score of the harmlessness of the task description (lower is more harmful) as obtained from a preference model - `red_team_member_id` an arbitrary identifier of the red team member. one red team member can generate multiple red team attacks - `is_upworker` a binary indicator that is true if the red team member was from the crowd platform Upwork or false if they were from MTurk - `tags` a list of up to 6 tags per transcript. tags are short descriptions of the red team attempts generated by crowdworkers who reviewed red team data post-hoc. tags were only provided for a random sample of 1000 red team attempts for two of four model types. ## Usage Each of the above datasets is located in a separate sub-directory. To load an individual subset, use the `data_dir` argument of the `load_dataset()` function as follows: ```python from datasets import load_dataset # Load all helpfulness/harmless subsets (share the same schema) dataset = load_dataset("Anthropic/hh-rlhf") # Load one of the harmless subsets dataset = load_dataset("Anthropic/hh-rlhf", data_dir="harmless-base") # Load the red teaming subset dataset = load_dataset("Anthropic/hh-rlhf", data_dir="red-team-attempts") ``` ## Contact The original authors host this dataset on GitHub here: https://github.com/anthropics/hh-rlhf You can submit inquiries to: [email protected]
Avelina/smollm-corpus
Avelina
"2025-01-11T16:41:28Z"
13,421
5
[ "task_categories:text-generation", "language:en", "license:odc-by", "size_categories:100M<n<1B", "region:us" ]
[ "text-generation" ]
"2025-01-11T01:39:39Z"
--- license: odc-by dataset_info: - config_name: default features: - name: text dtype: string configs: - config_name: default data_files: - split: train path: data*/train-* task_categories: - text-generation language: - en size_categories: - 100M<n<1B --- # SmolLM-Corpus: Now shuffled and sharded! This is a version of the SmolLM-Corpus where the 3 subsets have been interleved, shuffled and sharded as 23698 `jsonl.zst` files for easy streaming! The dataset is comprised of the `cosmopedia-v2` and `fineweb-edu-dedup` subsets from the original [SmolLM-Corpus repo](https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus), with the `python-edu` subset being pulled from my [python-edu repo](https://huggingface.co/datasets/Avelina/python-edu). ## Dataset Structure The dataset is split into 24 subdirectories, with the first 23 containing 1000 shards and the 24th containing the final 698. The repository is structured as follows: ``` data00/ ├── train-00000-of-23698.jsonl.zst ├── ... └── train-00999-of-23698.jsonl.zst data01/ ├── train-01000-of-23698.jsonl.zst ├── ... └── train-01999-of-23698.jsonl.zst ... data22/ ├── train-22000-of-23698.jsonl.zst ├── ... └── train-22999-of-23698.jsonl.zst data23/ ├── train-23000-of-23698.jsonl.zst ├── ... └── train-23697-of-23698.jsonl.zst ``` In general, you can obtain the exact download URL for all shards using the following python function: ```py def get_url_from_shard( index: int ) -> str: if index >= 23_698: raise ValueError( f'Shard index must be less than 23,698 but received {index}' ) group = index // 1000 return f'https://huggingface.co/datasets/Avelina/smollm-corpus/resolve/main/data{group:02d}/train-{index:05d}-of-23698.jsonl.zst' ``` ## Generation Code Here is the code which was used to generate the shuffled shards. Note the use of non-contiguous interleaving in attempt to uniformly pull documents from across entire subsets to loosely decouple shard index from original document position. Please make sure you `pip install zstandard`!!! ```py import tqdm import datasets from datasets import load_dataset # Output directory and file format. Note that the file extension enforces zst compression is used. OUTPUT_FMT = '/YOUR/FILE/PATH/HERE/data/train-{index:05d}-of-{num_shards:05d}.jsonl.zst' # Total number of shards giving approximately 10,000 documents per shard OUTPUT_NUM_SHARDS = 23698 # Grab the three datasets ds_python = load_dataset( 'Avelina/python-edu' ) ds_cosmo = load_dataset( 'HuggingFaceTB/smollm-corpus', 'cosmopedia-v2' ) ds_edu = load_dataset( 'HuggingFaceTB/smollm-corpus', 'fineweb-edu-dedup' ) # Retain only the text columns and the train splits ds_python = ds_python.select_columns( 'text' )[ 'train' ] ds_cosmo = ds_cosmo.select_columns( 'text' )[ 'train' ] ds_edu = ds_edu.select_columns( 'text' )[ 'train' ] # Iterate over all shards with a nice progbar for index in tqdm.tqdm( range( OUTPUT_NUM_SHARDS ) ): # Get non-contiguous in-memory sub-shards for the three datasets curr_python = ds_python.shard( num_shards=OUTPUT_NUM_SHARDS, index=index, contiguous=False, keep_in_memory=True ) curr_cosmo = ds_cosmo.shard( num_shards=OUTPUT_NUM_SHARDS, index=index, contiguous=False, keep_in_memory=True ) curr_edu = ds_edu.shard( num_shards=OUTPUT_NUM_SHARDS, index=index, contiguous=False, keep_in_memory=True ) # Concatenate the sub-shards curr_shard = datasets.concatenate_datasets( [ curr_python, curr_cosmo, curr_edu ] ) # Deterministically shuffle using the current shard index for reproducibility curr_shard = curr_shard.shuffle( seed=index, keep_in_memory=True ) # Dump the shards to .jsonl.zst curr_shard.to_json( OUTPUT_FMT.format( index=index, num_shards=OUTPUT_NUM_SHARDS ) ) ``` ## In-Memory Decompression Zstandard was chosen as it enables trivial in-memory decompression to minimise the storage impact of the dataset. Here is some example code which creates a python generator that yields each json line from a compressed shard stored at `file_name`, and a second function which creates a python generator that parses and yields the compressed shard. ```py import json from json import JSONDecodeError import zstandard def read_lines_zst( file_name ): # Open the file for reading in binary mode with open( file_name, 'rb' ) as file_handle: # Initialise an empty buffer buffer = '' # Create a reader for the opened file reader = zstandard.ZstdDecompressor( max_window_size=2**31 ).stream_reader( file_handle ) while True: # Read a chunk of up to 128MB chunk = reader.read( 2**27 ).decode() # If chunk is empty we've reached the end of the file and can break out if not chunk: break # Combine any prior buffer with the current chunk and split by newline lines = ( buffer + chunk ).split( '\n' ) # Yield the full lines so far for line in lines[ : -1 ]: yield line # The last 'line' is incomplete, so place in buffer for next chunk buffer = lines[ -1 ] # Always remember to close your reader! reader.close() def parse_jsonl_zst( file_name ): # Iterate over the yielded lines of the compressed shard for i, line in enumerate( read_lines_zst( file_name ) ): try: # Convert the line into a python dict and yield the text field yield json.loads( line )[ 'text' ] except ( KeyError, JSONDecodeError ): # Catch KeyError for 'text' not present in dict # Catch JSONDecodeError for malformed line print( f'JSON error @ shard={file_name}, line={i}' ) ``` Of course you *could* use HuggingFace's in-built streaming mechanics to handle things for you, but in my experience that approach is less reliable, doesn't handle `JSONDecodeError`s if there are malformed lines, can cause memory leaks, and has forced sharding behaviour when used inside a multi-worker PyTorch `DataLoader` which I've not yet found a way to disable!
pretraining-playground/pythia-training-metrics
pretraining-playground
"2024-05-07T14:17:41Z"
13,318
0
[ "license:apache-2.0", "region:us" ]
null
"2024-04-02T10:28:12Z"
--- license: apache-2.0 ---
taesiri/arxiv_db
taesiri
"2024-05-07T22:36:44Z"
13,285
4
[ "license:cc-by-nc-4.0", "region:us" ]
null
"2023-10-26T23:53:25Z"
--- license: cc-by-nc-4.0 ---
Cnam-LMSSC/vibravox
Cnam-LMSSC
"2024-11-06T16:02:47Z"
13,240
17
[ "task_categories:audio-to-audio", "task_categories:automatic-speech-recognition", "task_categories:audio-classification", "task_categories:text-to-speech", "task_ids:speaker-identification", "annotations_creators:expert-generated", "language_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:monolingual", "language:fr", "license:cc-by-4.0", "size_categories:10K<n<100K", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2407.11828", "arxiv:2006.11477", "arxiv:2303.10008", "arxiv:2401.08342", "doi:10.57967/hf/2727", "region:us" ]
[ "audio-to-audio", "automatic-speech-recognition", "audio-classification", "text-to-speech" ]
"2023-10-18T19:15:20Z"
--- annotations_creators: - expert-generated language_creators: - crowdsourced - expert-generated language: - fr license: cc-by-4.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: [] task_categories: - audio-to-audio - automatic-speech-recognition - audio-classification - text-to-speech task_ids: - speaker-identification pretty_name: Vibravox viewer: true dataset_info: - config_name: speech_clean features: - name: audio.headset_microphone dtype: audio - name: audio.forehead_accelerometer dtype: audio - name: audio.soft_in_ear_microphone dtype: audio - name: audio.rigid_in_ear_microphone dtype: audio - name: audio.temple_vibration_pickup dtype: audio - name: audio.throat_microphone dtype: audio - name: gender dtype: string - name: speaker_id dtype: string - name: sentence_id dtype: int64 - name: duration dtype: float64 - name: raw_text dtype: string - name: normalized_text dtype: string - name: phonemized_text dtype: string splits: - name: train num_bytes: 100144385419.375 num_examples: 20981 - name: validation num_bytes: 11821970622.625 num_examples: 2523 - name: test num_bytes: 14647423280.0 num_examples: 3064 download_size: 124418585390 dataset_size: 126613779322.0 - config_name: speech_noisy features: - name: audio.headset_microphone dtype: audio - name: audio.forehead_accelerometer dtype: audio - name: audio.soft_in_ear_microphone dtype: audio - name: audio.rigid_in_ear_microphone dtype: audio - name: audio.temple_vibration_pickup dtype: audio - name: audio.throat_microphone dtype: audio - name: gender dtype: string - name: speaker_id dtype: string - name: sentence_id dtype: int64 - name: duration dtype: float64 - name: raw_text dtype: string - name: normalized_text dtype: string - name: phonemized_text dtype: string splits: - name: train num_bytes: 5978781164.5 num_examples: 1220 - name: validation num_bytes: 647300251.0 num_examples: 132 - name: test num_bytes: 859092188.0 num_examples: 175 download_size: 7471066223 dataset_size: 7485173603.5 - config_name: speechless_clean features: - name: audio.headset_microphone dtype: audio - name: audio.forehead_accelerometer dtype: audio - name: audio.soft_in_ear_microphone dtype: audio - name: audio.rigid_in_ear_microphone dtype: audio - name: audio.temple_vibration_pickup dtype: audio - name: audio.throat_microphone dtype: audio - name: gender dtype: string - name: speaker_id dtype: string - name: duration dtype: float64 splits: - name: train num_bytes: 8512005740.0 num_examples: 149 - name: validation num_bytes: 1028286672.0 num_examples: 18 - name: test num_bytes: 1199717890.0 num_examples: 21 download_size: 9548480336 dataset_size: 10740010302.0 - config_name: speechless_noisy features: - name: audio.headset_microphone dtype: audio - name: audio.forehead_accelerometer dtype: audio - name: audio.soft_in_ear_microphone dtype: audio - name: audio.rigid_in_ear_microphone dtype: audio - name: audio.temple_vibration_pickup dtype: audio - name: audio.throat_microphone dtype: audio - name: gender dtype: string - name: speaker_id dtype: string - name: duration dtype: float64 splits: - name: train num_bytes: 24723250192.0 num_examples: 149 - name: validation num_bytes: 2986606278.0 num_examples: 18 - name: test num_bytes: 3484522468.0 num_examples: 21 download_size: 30881658818 dataset_size: 31194378938.0 configs: - config_name: speech_clean data_files: - split: train path: speech_clean/train-* - split: validation path: speech_clean/validation-* - split: test path: speech_clean/test-* - config_name: speech_noisy data_files: - split: train path: speech_noisy/train-* - split: validation path: speech_noisy/validation-* - split: test path: speech_noisy/test-* - config_name: speechless_clean data_files: - split: train path: speechless_clean/train-* - split: validation path: speechless_clean/validation-* - split: test path: speechless_clean/test-* - config_name: speechless_noisy data_files: - split: train path: speechless_noisy/train-* - split: validation path: speechless_noisy/validation-* - split: test path: speechless_noisy/test-* --- # Dataset Card for VibraVox <p align="center"> <img src="https://cdn-uploads.huggingface.co/production/uploads/65302a613ecbe51d6a6ddcec/zhB1fh-c0pjlj-Tr4Vpmr.png" style="object-fit:contain; width:280px; height:280px;" > </p> --- 👀 While waiting for the [TooBigContentError issue](https://github.com/huggingface/dataset-viewer/issues/2215) to be resolved by the HuggingFace team, you can explore the dataset viewer of [vibravox-test](https://huggingface.co/datasets/Cnam-LMSSC/vibravox-test) which has exactly the same architecture. ## DATASET SUMMARY The [VibraVox dataset](https://vibravox.cnam.fr) is a general purpose audio dataset of french speech captured with body-conduction transducers. This dataset can be used for various audio machine learning tasks : - **Automatic Speech Recognition (ASR)** (Speech-to-Text , Speech-to-Phoneme) - **Audio Bandwidth Extension (BWE)** - **Speaker Verification (SPKV)** / identification - **Voice cloning** - etc ... ### Dataset usage VibraVox contains 4 subsets, corresponding to different situations tailored for specific tasks. To load a specific subset simply use the following command (```subset``` can be any of the following : ``` "speech_clean" ``` , ``` "speech_noisy" ``` , ``` "speechless_clean" ``` , ``` "speechless_noisy" ```): ```python from datasets import load_dataset subset = "speech_clean" vibravox = load_dataset("Cnam-LMSSC/vibravox", subset) ``` The dataset is also compatible with the `streaming` mode: ```python from datasets import load_dataset subset = "speech_clean" vibravox = load_dataset("Cnam-LMSSC/vibravox", subset, streaming=True) ``` ### Citations, links and details - **Homepage:** For more information about the project, visit our project page on [https://vibravox.cnam.fr](https://vibravox.cnam.fr) - **Github repository:** [jhauret/vibravox](https://github.com/jhauret/vibravox) : Source code for ASR, BWE and SPKV tasks using the Vibravox dataset - **Point of Contact:** [Julien Hauret](https://www.linkedin.com/in/julienhauret/) and [Éric Bavu](https://acoustique.cnam.fr/contacts/bavu/en/#contact) - **Curated by:** [AVA Team](https://lmssc.cnam.fr/fr/recherche/identification-localisation-synthese-de-sources-acoustiques-et-vibratoires) of the [LMSSC Research Laboratory](https://lmssc.cnam.fr) - **Funded by:** [Agence Nationale Pour la Recherche / AHEAD Project](https://anr.fr/en/funded-projects-and-impact/funded-projects/project/funded/project/b2d9d3668f92a3b9fbbf7866072501ef-5aac4914c7/?tx_anrprojects_funded%5Bcontroller%5D=Funded&cHash=fa352121b44b60bf6a5917180d5205e6) - **Language:** French - **Download size** : 186.64 GB - **Total audio duration** : 45.62 hours (x6 audio channels) - **Number of speech utterances** : 28,095 - **License:** Creative Commons Attributions 4.0 I you use the Vibravox dataset for research, **cite this paper** : ```bibtex @article{jhauret-et-al-2024-vibravox, title={{Vibravox: A Dataset of French Speech Captured with Body-conduction Audio Sensors}}, author={Hauret, Julien and Olivier, Malo and Joubaud, Thomas and Langrenne, Christophe and Poir{\'e}e, Sarah and Zimpfer, Véronique and Bavu, {\'E}ric}, year={2024}, eprint={2407.11828}, archivePrefix={arXiv}, primaryClass={eess.AS}, url={https://arxiv.org/abs/2407.11828}, } ``` **and this repository**, which is linked to a DOI : ```bibtex @misc{cnamlmssc2024vibravoxdataset, author={Hauret, Julien and Olivier, Malo and Langrenne, Christophe and Poir{\'e}e, Sarah and Bavu, {\'E}ric}, title = { {Vibravox} (Revision 7990b7d) }, year = 2024, url = { https://huggingface.co/datasets/Cnam-LMSSC/vibravox }, doi = { 10.57967/hf/2727 }, publisher = { Hugging Face } } ``` --- ## SUPPORTED TASKS <!-- and Leaderboards --> ### Automatic-speech-recognition - The model is presented with an audio file and asked to transcribe the audio file to written text (either normalized text of phonemized text). The most common evaluation metrics are the word error rate (WER), character error rate (CER), or phoneme error rate (PER). - **Training code:** An example of implementation for the speech-to-phoneme task using [wav2vec2.0](https://arxiv.org/abs/2006.11477) is available on the [Vibravox Github repository](https://github.com/jhauret/vibravox). - **Trained models:** We also provide trained models for the speech-to-phoneme task for each of the 6 speech sensors of the Vibravox dataset on Huggingface at [Cnam-LMSSC/vibravox_phonemizers](https://huggingface.co/Cnam-LMSSC/vibravox_phonemizers) ### Bandwidth-extension - Also known as audio super-resolution, which is required to enhance the audio quality of body-conducted captured speech. The model is presented with a pair of audio clips (from a body-conducted captured speech, and from the corresponding clean, full bandwidth airborne-captured speech), and asked to enhance the audio by denoising and regenerating mid and high frequencies from low frequency content only. - **Training code:** An example of implementation of this task using [Configurable EBEN](https://ieeexplore.ieee.org/document/10244161) ([arXiv link](https://arxiv.org/abs/2303.10008)) is available on the [Vibravox Github repository](https://github.com/jhauret/vibravox). - **Trained models:** We also provide trained models for the BWE task for each of the 6 speech sensors of the Vibravox dataset on Huggingface at [Cnam-LMSSC/vibravox_EBEN_bwe_models](https://huggingface.co/Cnam-LMSSC/vibravox_EBEN_bwe_models). - **BWE-Enhanced dataset:** An EBEN-enhanced version of the `test`splits of the Vibravox dataset, generated using these 6 bwe models, is also available on Huggingface at [Cnam-LMSSC/vibravox_enhanced_by_EBEN](https://huggingface.co/datasets/Cnam-LMSSC/vibravox_enhanced_by_EBEN). ### Speaker-verification - Given an input audio clip and a reference audio clip of a known speaker, the model's objective is to compare the two clips and verify if they are from the same individual. This often involves extracting embeddings from a deep neural network trained on a large dataset of voices. The model then measures the similarity between these feature sets using techniques like cosine similarity or a learned distance metric. This task is crucial in applications requiring secure access control, such as biometric authentication systems, where a person's voice acts as a unique identifier. - **Testing code:** An example of implementation of this task using a pretrained [ECAPA2 model](https://arxiv.org/abs/2401.08342) is available on the [Vibravox Github repository](https://github.com/jhauret/vibravox). ### Adding your models for supported tasks or contributing for new tasks Feel free to contribute at the [Vibravox Github repository](https://github.com/jhauret/vibravox), by following the [contributor guidelines](https://github.com/jhauret/vibravox/blob/main/CONTRIBUTING.md). --- ## DATASET DETAILS ### Dataset Description VibraVox ([vibʁavɔks]) is a GDPR-compliant dataset scheduled released in June 2024. It includes speech recorded simultaneously using multiple audio and vibration sensors (from top to bottom on the following figure) : - a forehead miniature vibration sensor (green) - an in-ear comply foam-embedded microphone (red) - an in-ear rigid earpiece-embedded microphone (blue) - a temple vibration pickup (cyan) - a headset microphone located near the mouth (purple) - a laryngophone (orange) The technology and references of each sensor is described and documented in [the dataset creation](#dataset-creation) section and [https://vibravox.cnam.fr/documentation/hardware/](https://vibravox.cnam.fr/documentation/hardware). <p align="center"> <img src="https://cdn-uploads.huggingface.co/production/uploads/6390fc80e6d656eb421bab69/P-_IWM3IMED5RBS3Lhydc.png" /> </p> ### Goals The VibraVox speech corpus has been recorded with 200 participants under various acoustic conditions imposed by a [5th order ambisonics spatialization sphere](https://vibravox.cnam.fr/documentation/hardware/sphere/index.html). VibraVox aims at serving as a valuable resource for advancing the field of **body-conducted speech analysis** and facilitating the development of **robust communication systems for real-world applications**. Unlike traditional microphones, which rely on airborne sound waves, body-conduction sensors capture speech signals directly from the body, offering advantages in noisy environments by eliminating the capture of ambient noise. Although body-conduction sensors have been available for decades, their limited bandwidth has restricted their widespread usage. However, this may be the awakening of this technology to a wide public for speech capture and communication in noisy environments. ### Data / sensor mapping Even if the names of the columns in Vibravox dataset are self-explanatory, here is the mapping, with informations on the positioning of sensors and their technology : | Vibravox dataset column name | Sensor | Location | Technology | |:------------------------------------ |:------------------------------------------ |:---------------- |:-------------------------------------------------- | | ```audio.headset_microphone``` | Headset microphone | Near the mouth | Cardioid electrodynamic microphone | | ```audio.throat_microphone``` | Laryngophone | Throat / Larynx | Piezoelectric sensor | | ```audio.soft_in_ear_microphone``` | In-ear soft foam-embedded microphone | Right ear canal | Omnidirectional electret condenser microphone | | ```audio.rigid_in_ear_microphone``` | In-ear rigid earpiece-embedded microphone | Left ear-canal | Omnidirectional MEMS microphone | | ```audio.forehead_accelerometer``` | Forehead vibration sensor | Frontal bone | One-axis accelerometer | | ```audio.temple_vibration_pickup``` | Temple vibration pickup | Zygomatic bone | Figure of-eight pre-polarized condenser transducer | --- ## DATASET STRUCTURE ### Subsets Each of the 4 subsets contain **6 columns of audio data**, corresponding to the 5 different body conduction sensors, plus the standard headset microphone. Recording was carried out simultaneously on all 6 sensors, **audio files being sampled at 48 kHz and encoded as .wav PCM32 files**. The 4 subsets correspond to : - **```speech_clean```** : the speaker reads sentences sourced from the French Wikipedia. This split contains the most data for training for various tasks. - **```speech_noisy```** : the speaker reads sentences sourced from the French Wikipedia, in a noisy environment based on ambisonic recordings replayed in a spatialization sphere equipped with 56 loudspeakers surrounding the speaker. This will primarily serve to test the different systems (Speech Enhancement, Automatic Speech Recognition, Speaker Verification) that will be developed based on the recordings from the first three phases. It is primarily intended for testing the various systems (speech enhancement, automatic speech recognition, speaker verification) that will be developed on the basis of the recordings from ```speech_clean```. - **```speechless_clean```** : wearer of the devices remains speechless in a complete silence, but are free to move their bodies and faces, and can swallow and breathe naturally. This configuration can be conveniently used to generate synthetic datasets with realistic physiological (and sensor-inherent) noise captured by body-conduction sensors. These samples can be valuable for tasks such as heart rate tracking or simply analyzing the noise properties of the various microphones, but also to generate synthetic datasets with realistic physiological (and sensor-inherent) noise captured by body-conduction sensors. - **```speechless_noisy```** : wearer of the devices remains speechless in a noisy environment created using [AudioSet](https://research.google.com/audioset/) noise samples. These samples have been selected from relevant classes, normalized in loudness, pseudo-spatialized and are played from random directions around the participant using [5th order ambisonic 3D sound spatializer](https://vibravox.cnam.fr/documentation/hardware/sphere/index.html) equipped with 56 loudspeakers. The objective of this split is to gather background noises that can be combined with the `speech_clean` recordings to maintain a clean reference. This allows to use those samples for **realistic data-augmentation** using noise captured by body-conduction sensors, with the inherent attenuation of each sensors on different device wearers. ### Splits All the subsets are available in 3 splits (train, validation and test), with a standard 80% / 10% / 10% repartition, without overlapping any speaker in each split. The speakers / participants in specific splits are the same for each subset, thus allowing to: - use the `speechless_noisy` for data augmentation for example - test on the `speech_noisy` testset your models trained on the `speech_clean` trainset without having to worry that a speaker would have been presented in the training phase. ### Data Fields In non-streaming mode (default), the path value of all dataset. Audio dictionnary 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). **Common Data Fields for all subsets :** * `audio.headset_microphone` (datasets.Audio) - a dictionary containing the path to the audio recorded by the headset microphone, the decoded (mono) audio array, and the sampling rate. * `audio.forehead_accelerometer` (datasets.Audio) - a dictionary containing the path to the audio recorded by the forehead miniature accelerometer, the decoded (mono) audio array, and the sampling rate. * `audio.soft_in_ear_microphone` (datasets.Audio) - a dictionary containing the path to the audio recorded by the in-ear soft foam-embedded microphone, the decoded (mono) audio array, and the sampling rate. * `audio.rigid_in_ear_microphone` (datasets.Audio) - a dictionary containing the path to the audio recorded by the in-ear rigid earpiece-embedded microphone, the decoded (mono) audio array, and the sampling rate. * `audio.temple_vibration_pickup` (datasets.Audio) - a dictionary containing the path to the audio recorded by the temple vibration pickup, the decoded (mono) audio array, and the sampling rate. * `audio.throat_microphone` (datasets.Audio) - a dictionary containing the path to the audio recorded by the piezeoelectric laryngophone, the decoded (mono) audio array, and the sampling rate. * `gender` (string) - gender of speaker (```male```or ```female```) * `speaker_id` (string) - encrypted id of speaker * `duration` (float32) - the audio length in seconds. **Extra Data Fields for `speech_clean` and `speech_noisy` splits:** For **speech** subsets, the datasets has columns corresponding to the pronounced sentences, which are absent of the **speechless** subsets : * `sentence_id` (int) - id of the pronounced sentence * `raw_text` (string) - audio segment text (cased and with punctuation preserved) * `normalized_text` (string) - audio segment normalized text (lower cased, no punctuation, diacritics replaced by standard 26 french alphabet letters, plus 3 accented characters : é,è,ê and ç -- which hold phonetic significance -- and the space character, which corresponds to 31 possible characters : ``` [' ', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', 'ç', 'è', 'é', 'ê'] ```). * `phonemes` (string) - audio segment phonemized text using exclusively the strict french IPA (33) characters ### Phonemes list and tokenizer - The strict french IPA characters used in Vibravox are : ``` [' ', 'a', 'b', 'd', 'e', 'f', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 's', 't', 'u', 'v', 'w', 'y', 'z', 'ø', 'ŋ', 'œ', 'ɑ', 'ɔ', 'ə', 'ɛ', 'ɡ', 'ɲ', 'ʁ', 'ʃ', 'ʒ', '̃'] ```. - For convience and research reproducibility, we provide a tokenizer for speech-to-phonemes tasks that corresponds to those phonemes at [https://huggingface.co/Cnam-LMSSC/vibravox-phonemes-tokenizer](https://huggingface.co/Cnam-LMSSC/vibravox-phonemes-tokenizer). ### Examples of data Instances #### `speech_clean` or `speech_noisy` splits: ```python { 'audio.headset_mic': { 'path': '02472_headset_mic.wav', 'array': array([ 0.00045776, 0.00039673, 0.0005188 , ..., -0.00149536, -0.00094604, 0.00036621]), 'sampling_rate': 48000}, 'audio.forehead_accelerometer': { 'path': '02472_forehead_accelerometer.wav', 'array': array([ 0.0010376 , -0.00045776, -0.00085449, ..., -0.00491333, -0.00524902, -0.00302124]), 'sampling_rate': 48000}, 'audio.soft_in_ear_mic': { 'path': '02472_soft_in_ear_mic.wav', 'array': array([-0.06472778, -0.06384277, -0.06292725, ..., -0.02133179, -0.0213623 , -0.02145386]), 'sampling_rate': 48000}, 'audio.rigid_in_ear_mic': { 'path': '02472_rigid_in_ear_mic.wav', 'array': array([-0.01824951, -0.01821899, -0.01812744, ..., -0.00387573, -0.00427246, -0.00439453]), 'sampling_rate': 48000}, 'audio.temple_vibration_pickup':{ 'path': '02472_temple_vibration_pickup.wav', 'array': array([-0.0177002 , -0.01791382, -0.01745605, ..., 0.01098633, 0.01260376, 0.01220703]), 'sampling_rate': 48000}, 'audio.laryngophone': { 'path': '02472_laryngophone.wav', 'array': array([-2.44140625e-04, -3.05175781e-05, 2.13623047e-04, ..., 4.88281250e-04, 4.27246094e-04, 3.66210938e-04]), 'sampling_rate': 48000}, 'gender': 'female', 'speaker_id': 'qt4TPMEPwF', 'sentence_id': 2472, 'duration': 4.5, 'raw_text': "Cette mémoire utilise le changement de phase du verre pour enregistrer l'information.", 'normalized_text': 'cette mémoire utilise le changement de phase du verre pour enregistrer l information', 'phonemized_text': 'sɛt memwaʁ ytiliz lə ʃɑ̃ʒmɑ̃ də faz dy vɛʁ puʁ ɑ̃ʁʒistʁe lɛ̃fɔʁmasjɔ̃' } ``` #### `speechless_clean` or `speechless_noisy` splits (thus missing the text-related fields) ```python { 'audio.headset_mic': { 'path': 'jMngOy7BdQ_headset_mic.wav', 'array': array([-1.92260742e-03, -2.44140625e-03, -2.99072266e-03, ..., 0.00000000e+00, 3.05175781e-05, -3.05175781e-05]), 'sampling_rate': 48000}, 'audio.forehead_accelerometer': { 'path': 'jMngOy7BdQ_forehead_accelerometer.wav', 'array': array([-0.0032959 , -0.00259399, 0.00177002, ..., -0.00073242, -0.00076294, -0.0005188 ]), 'sampling_rate': 48000}, 'audio.soft_in_ear_mic': { 'path': 'jMngOy7BdQ_soft_in_ear_mic.wav', 'array': array([0.00653076, 0.00671387, 0.00683594, ..., 0.00045776, 0.00042725, 0.00042725]), 'sampling_rate': 48000}, 'audio.rigid_in_ear_mic': { 'path': 'jMngOy7BdQ_rigid_in_ear_mic.wav', 'array': array([ 1.05895996e-02, 1.03759766e-02, 1.05590820e-02, ..., 0.00000000e+00, -3.05175781e-05, -9.15527344e-05]), 'sampling_rate': 48000}, 'audio.temple_vibration_pickup': { 'path': 'jMngOy7BdQ_temple_vibration_pickup.wav', 'array': array([-0.00082397, -0.0020752 , -0.0012207 , ..., -0.00738525, -0.00814819, -0.00579834]), 'sampling_rate': 48000}, 'audio.laryngophone': { 'path': 'jMngOy7BdQ_laryngophone.wav', 'array': array([ 0.00000000e+00, 3.05175781e-05, 1.83105469e-04, ..., -6.10351562e-05, -1.22070312e-04, -9.15527344e-05]), 'sampling_rate': 48000}, 'gender': 'male', 'speaker_id': 'jMngOy7BdQ', 'duration': 54.097 } ``` --- ## DATA STATISTICS ### Speakers gender balance To increase the representativeness and inclusivity of the dataset, a deliberate effort was made to recruit a diverse and gender-balanced group of speakers. The overall gender repartition in terms of number of speakers included in the dataset is **51.6% female participants / 48.4% male participants for all subsets**. ### Speakers age balance | Gender | Mean age (years) | Median age (years) | Min age (years) | Max age (years) | |:------------|:-----------------|:--------------------|:-------------------|:--------------------| | Female | 25.9 | 22 | 19 | 59 | | Male | 31.4 | 27 | 18 | 82 | | **All** | **28.55** | **25** | **18** | **82** | ### Audio data | Subset | Split | Audio duration (hours) | Number of audio clips | Download size | Number of Speakers <br> (Female/Male) | F/M Gender repartition <br> (audio duration) | Mean audio duration (s) | Median audio duration (s) | Max audio duration (s) | Min audio duration (s) | |:-------------------|:---------------------------------------|:--------------------------------|:-----------------------------------|:------------------------------------|:---------------------------------------|:---------------------------------------------------------|:----------------------------------|:---------------------------------|:------------------------------------|:-------------------------------| | `speech_clean` | `train` <br> `validation` <br> `test` | 6x26.34 <br> 6x3.11 <br> 6x3.85 | 6x20,981 <br> 6x2,523 <br> 6x3,064 | 108.32GB <br> 12.79GB <br> 15.84GB | 77F/72M <br> 9F/9M <br> 11F/10M | 52.13%/47.87% <br> 51.66%/48.34% <br> 54.43%/45.57% | 4.52 <br> 4.44 <br> 4.53 | 4.43 <br> 4.36 <br> 4.44 | 13.03 <br> 10.64 <br> 10.27 | 1.1 <br> 1.47 <br> 1.38 | | `speech_noisy` | `train` <br> `validation` <br> `test` | 6x1.57 <br> 6x0.17 <br> 6x0.23 | 6x1,220 <br> 6x132 <br> 6x175 | 6.52GB <br> 0.71GB <br> 0.94GB | 77F/72M <br> 9F/9M <br> 11F/10M | 54.0%/46.0% <br> 55.77%/44.23% <br> 53.92%/46.08% | 4.64 <br> 4.64 <br> 4.65 | 4.59 <br> 4.47 <br> 4.7 | 9.86 <br> 8.56 <br> 7.67 | 1.36 <br> 2.3 <br> 1.85 | | `speechless_clean` | `train` <br> `validation` <br> `test` | 6x2.24 <br> 6x0.27 <br> 6x0.32 | 6x149 <br> 6x18 <br> 6x21 | 8.44GB <br> 1.02GB <br> 1.19GB | 77F/72M <br> 9F/9M <br> 11F/10M | 51.68%/48.32% <br> 50.00%/50.00% <br> 52.38%/47.62% | 54.10 <br> 54.10 <br> 54.10 | 54.10 <br> 54.10 <br> 54.10 | 54.10 <br> 54.10 <br> 54.10 | 53.99 <br> 54.05 <br> 54.10 | | `speechless_noisy` | `train` <br> `validation` <br> `test` | 6x5.96 <br> 6x0.72 <br> 6x0.84 | 6x149 <br> 6x18 <br> 6x21 | 24.48GB <br> 2.96GB <br> 3.45GB | 77F/72M <br> 9F/9M <br> 11F/10M | 51.68%/48.32% <br> 50.00%/50.00% <br> 52.38%/47.62% | 144.03 <br> 144.03 <br> 144.04 | 144.03 <br> 144.03 <br> 144.03 | 144.17 <br> 144.05 <br> 144.05 | 143.84 <br> 143.94 <br> 144.03 | | **Total** | | **6x45.62** | **6x28,471** | **186.64GB** | **97F/91M** | **52.11%/47.89%** | | | | | --- ## DATASET CREATION ### Textual source data The text read by all participants is collected from the French Wikipedia subset of Common voice ( [link1](https://github.com/common-voice/common-voice/blob/6e43e7e61318bf4605b59379e3f35ba5333d7a29/server/data/fr/wiki-1.fr.txt) [link2](https://github.com/common-voice/common-voice/blob/6e43e7e61318bf4605b59379e3f35ba5333d7a29/server/data/fr/wiki-2.fr.txt) ) . We applied some additional filters to these textual datasets in order to create a simplified dataset with a minimum number of tokens and to reduce the uncertainty of the pronunciation of some proper names. We therefore removed all proper names except common first names and the list of french towns. We also removed any utterances that contain numbers, Greek letters, math symbols, or that are syntactically incorrect. All lines of the textual source data from Wikipedia-extracted textual dataset has then been phonemized using the [bootphon/phonemizer](https://github.com/bootphon/phonemizer) and manually edited to only keep strict french IPA characters. ### Audio Data Collection #### Sensors positioning and documentation | **Sensor** | **Image** | **Transducer** | **Online documentation** | |:---------------------------|:---------------------|:-------------|:----------------------------------------------------------------------------------------------------------------------| | Reference headset microphone | ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6390fc80e6d656eb421bab69/iVYX1_7wAdZb4oDrc9v6l.png) | Shure WH20 | [See documentation on vibravox.cnam.fr](https://vibravox.cnam.fr/documentation/hardware/sensors/airborne/index.html) | | In-ear comply foam-embedded microphone |![image/png](https://cdn-uploads.huggingface.co/production/uploads/6390fc80e6d656eb421bab69/Uf1VOwx-kxPiYY1oMW5pz.png)| Knowles FG-23329-P07 | [See documentation on vibravox.cnam.fr](https://vibravox.cnam.fr/documentation/hardware/sensors/soft_inear/index.html) | | In-ear rigid earpiece-embedded microphone |![image/png](https://cdn-uploads.huggingface.co/production/uploads/6390fc80e6d656eb421bab69/EBY9dIKFN8GDaDXUuhp7n.png)| Knowles SPH1642HT5H | [See documentation on vibravox.cnam.fr](https://vibravox.cnam.fr/documentation/hardware/sensors/rigid_inear/index.html) | | Forehead miniature vibration sensor |![image/png](https://cdn-uploads.huggingface.co/production/uploads/6390fc80e6d656eb421bab69/2zHrN-7OpbH-zJTqASZ7J.png)| Knowles BU23173-000 | [See documentation on vibravox.cnam.fr](https://vibravox.cnam.fr/documentation/hardware/sensors/forehead/index.html) | | Temple vibration pickup |![image/png](https://cdn-uploads.huggingface.co/production/uploads/6390fc80e6d656eb421bab69/wAcTQlmzvl0O4kNyA3MnC.png)| AKG C411 | [See documentation on vibravox.cnam.fr](https://vibravox.cnam.fr/documentation/hardware/sensors/temple/index.html) | | Laryngophone | ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6390fc80e6d656eb421bab69/4SGNSgXYc6hBJcI1cRXY_.png)| iXRadio XVTM822D-D35 | [See documentation on vibravox.cnam.fr](https://vibravox.cnam.fr/documentation/hardware/sensors/throat/index.html) | #### Recorded audio data post-processing Across the sentences collected from the participants, a small number of audio clips exhibited various shortcomings. Despite researchers monitoring and validating each recording individually, the process was not entirely foolproof : mispronounced sentences, sensors shifting from their initial positions, or more significant microphone malfunctions occasionally occurred. In instances where sensors were functional but not ideally positioned—such as when the participant's ear canal was too small for the rigid in-ear microphone to achieve proper acoustic sealing—we chose to retain samples where the bandwidth was slightly narrower than desired. This decision was made to enhance the robustness of our models against the effects of misplaced sensors. To address those occasional shortcomings and offer a high-quality dataset, we implemented a series of 3 automatic filters to retain only the best audio from the speech_clean subset. We preserved only those sentences where all sensors were in optimal recording condition, adhering to predefined criteria, defined in [our paper](https://arxiv.org/abs/2407.11828) : - The first filter uses a pre-trained ASR model run on the headset microphone data, which allows to address discrepancies between the labeled transcription and actual pronunciation, ensuring high-quality labels for the speech-to-phoneme task. - The second filter confirms that the sensor is functioning correctly by verifying that speech exhibits higher energy than silence, thereby identifying potentially unreliable recordings with low vocal energy levels or sensor malfunction. - The third filter detects sensitivity drift in the sensors, which can occur due to electronic malfunctions or mechanical blockages in the transducer. - If an audio clip passes all filters, it is not immediately added to the dataset. Instead, VAD-generated timestamps from [whisper-timestamped](https://github.com/linto-ai/whisper-timestamped) are used, extending them by 0.3 seconds on both sides. This method helps remove mouse clicks at audio boundaries and ensures the capture of vocal segments without excluding valid speech portions. ### Personal and Sensitive Information The VibraVox dataset does not contain any data that might be considered as personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). The `speaker_id` were generated using a powerful Fernet encryption algorithm, and the extraction of a subset of the encrypted id, guaranteeing a strict anonymisation of the voice recordings, while allowing the dataset maintainers to delete corresponding data under the right to oblivion. A [consent form](https://vibravox.cnam.fr/documentation/consent/index.html) has been signed by each participant to the VibraVox dataset. This consent form has been approved by the Cnam lawyer. All [Cnil](https://www.cnil.fr/en) requirements have been checked, including the right to oblivion during 50 years.
HAERAE-HUB/HAE_RAE_BENCH_1.1
HAERAE-HUB
"2024-03-30T02:27:25Z"
13,161
19
[ "task_categories:multiple-choice", "language:ko", "license:cc-by-nc-nd-4.0", "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2309.02706", "region:us" ]
[ "multiple-choice" ]
"2023-09-25T04:16:13Z"
--- license: cc-by-nc-nd-4.0 extra_gated_prompt: To request access to the dataset, please fill out this form, and we'll review and let you know if your use case is approved. extra_gated_fields: First Name: text Last Name: text Institution: text Intended Use: text I agree to use this dataset for non-commercial research ONLY: checkbox task_categories: - multiple-choice language: - ko configs: - config_name: correct_definition_matching data_files: - split: test path: "data/correct_definition_matching-00000-of-00001-65122a307e90d70a.parquet" - config_name: csat_geo data_files: - split: test path: "data/csat_geo-00000-of-00001-68ce37d4952173a9.parquet" - config_name: csat_law data_files: - split: test path: "data/csat_law-00000-of-00001-ef0ec0fac2f2238f.parquet" - config_name: csat_socio data_files: - split: test path: "data/csat_socio-00000-of-00001-0cfadd93827d0d48.parquet" - config_name: date_understanding data_files: - split: test path: "data/date_understanding-00000-of-00001-36887d05afd4c57f.parquet" - config_name: general_knowledge data_files: - split: test path: "data/general_knowledge-00000-of-00001-7418d1a0b8c4a615.parquet" - config_name: history data_files: - split: test path: "data/history-00000-of-00001-9ad551d475d58413.parquet" - config_name: loan_words data_files: - split: test path: "data/loan_words-00000-of-00001-96ed08632b9a2ae8.parquet" - config_name: lyrics_denoising data_files: - split: test path: "data/lyrics_denoising-00000-of-00001-ede1ce975e9bad54.parquet" - config_name: proverbs_denoising data_files: - split: test path: "data/proverbs_denoising-00000-of-00001-f274ad136676b2e3.parquet" - config_name: rare_words data_files: - split: test path: "data/rare_words-00000-of-00001-4f34314c09209524.parquet" - config_name: standard_nomenclature data_files: - split: test path: "data/standard_nomenclature-00000-of-00001-52814989a6843328.parquet" - config_name: reading_comprehension data_files: - split: test path: "data/reading_comprehension-00000-of-00001-f9c8df20c22e46d0.parquet" --- The HAE_RAE_BENCH 1.1 is an ongoing project to develop a suite of evaluation tasks designed to test the understanding of models regarding Korean cultural and contextual nuances. Currently, it comprises 13 distinct tasks, with a total of 4900 instances. Please note that although this repository contains datasets from the original [HAE-RAE BENCH paper](https://arxiv.org/abs/2309.02706), the contents are not completely identical. Specifically, the reading comprehension subset from the original version has been removed due to copyright constraints. In its place, an updated reading comprehension subset has been introduced, sourced from the CSAT, the Korean university entrance examination. To replicate the studies from the paper, please see [code](https://huggingface.co/datasets/HAERAE-HUB/HAE_RAE_BENCH_1.0). ### Dataset Overview | Task | Instances | Version | Explanation | |-----------------------------|-----------|---------|---------------------------------------------------------------------| | standard_nomenclature | 153 | v1.0 | Multiple-choice questions about Korean standard nomenclatures from NIKL. | | loan_word | 169 | v1.0 | Multiple-choice questions about Korean loan words from NIKL. | | rare_word | 405 | v1.0 | Multiple-choice questions about rare Korean words from NIKL. | | general_knowledge | 176 | v1.0 | Multiple-choice questions on Korean cultural knowledge. | | history | 188 | v1.0 | Multiple-choice questions on Korean history. | | date_understanding | 475 | v1.1 | Multiple-choice questions about understanding Korean date expressions. | | proverbs_denoising | 672 | v1.1 | Questions for generating Korean proverbs. | | correct_definition_matching | 439 | v1.1 | Multiple-choice questions on matching the correct definition to words in context. | | lyrics_denoising | 622 | v1.1 | Questions for reconstructing Korean pop song lyrics. | | reading_comprehension | 936 | v1.1 | Multiple-choice questions on Korean reading comprehension from the CSAT. | | csat_law | 217 | v1.1 | Multiple-choice questions on Korean law from the CSAT. | | csat_geo | 150 | v1.1 | Multiple-choice questions on Korean geography from the CSAT. | | csat_socio | 298 | v1.1 | Multiple-choice questions on Korean social studies from the CSAT. | | **Total** | **4900** | | | ### Point of Contact For any questions contact us via the following email:) ``` [email protected] ```
lmms-lab/MMMU
lmms-lab
"2024-03-08T05:09:42Z"
13,131
4
[ "size_categories:10K<n<100K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-01-15T06:32:16Z"
--- 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} } ```
dylanebert/3d-arena
dylanebert
"2025-02-27T20:39:18Z"
13,121
11
[ "license:mit", "size_categories:1K<n<10K", "format:imagefolder", "modality:3d", "modality:image", "library:datasets", "library:mlcroissant", "region:us", "image-to-3d" ]
null
"2024-04-05T20:53:13Z"
--- license: mit tags: - image-to-3d --- For more information, visit the [3D Arena Space](https://huggingface.co/spaces/dylanebert/3d-arena). Inputs are sourced from [iso3D](https://huggingface.co/datasets/dylanebert/iso3d).
AmazonScience/massive
AmazonScience
"2022-11-16T15:44:51Z"
13,119
65
[ "task_categories:text-classification", "task_ids:intent-classification", "task_ids:multi-class-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:af-ZA", "multilinguality:am-ET", "multilinguality:ar-SA", "multilinguality:az-AZ", "multilinguality:bn-BD", "multilinguality:ca-ES", "multilinguality:cy-GB", "multilinguality:da-DK", "multilinguality:de-DE", "multilinguality:el-GR", "multilinguality:en-US", "multilinguality:es-ES", "multilinguality:fa-IR", "multilinguality:fi-FI", "multilinguality:fr-FR", "multilinguality:he-IL", "multilinguality:hi-IN", "multilinguality:hu-HU", "multilinguality:hy-AM", "multilinguality:id-ID", "multilinguality:is-IS", "multilinguality:it-IT", "multilinguality:ja-JP", "multilinguality:jv-ID", "multilinguality:ka-GE", "multilinguality:km-KH", "multilinguality:kn-IN", "multilinguality:ko-KR", "multilinguality:lv-LV", "multilinguality:ml-IN", "multilinguality:mn-MN", "multilinguality:ms-MY", "multilinguality:my-MM", "multilinguality:nb-NO", "multilinguality:nl-NL", "multilinguality:pl-PL", "multilinguality:pt-PT", "multilinguality:ro-RO", "multilinguality:ru-RU", "multilinguality:sl-SL", "multilinguality:sq-AL", "multilinguality:sv-SE", "multilinguality:sw-KE", "multilinguality:ta-IN", "multilinguality:te-IN", "multilinguality:th-TH", "multilinguality:tl-PH", "multilinguality:tr-TR", "multilinguality:ur-PK", "multilinguality:vi-VN", "multilinguality:zh-CN", "multilinguality:zh-TW", "source_datasets:original", "license:cc-by-4.0", "size_categories:1M<n<10M", "modality:text", "library:datasets", "library:mlcroissant", "arxiv:2204.08582", "region:us", "natural-language-understanding" ]
[ "text-classification" ]
"2022-04-27T20:48:46Z"
--- annotations_creators: - expert-generated language_creators: - found license: - cc-by-4.0 multilinguality: - af-ZA - am-ET - ar-SA - az-AZ - bn-BD - ca-ES - cy-GB - da-DK - de-DE - el-GR - en-US - es-ES - fa-IR - fi-FI - fr-FR - he-IL - hi-IN - hu-HU - hy-AM - id-ID - is-IS - it-IT - ja-JP - jv-ID - ka-GE - km-KH - kn-IN - ko-KR - lv-LV - ml-IN - mn-MN - ms-MY - my-MM - nb-NO - nl-NL - pl-PL - pt-PT - ro-RO - ru-RU - sl-SL - sq-AL - sv-SE - sw-KE - ta-IN - te-IN - th-TH - tl-PH - tr-TR - ur-PK - vi-VN - zh-CN - zh-TW size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-classification task_ids: - intent-classification - multi-class-classification paperswithcode_id: massive pretty_name: MASSIVE language_bcp47: - af-ZA - am-ET - ar-SA - az-AZ - bn-BD - ca-ES - cy-GB - da-DK - de-DE - el-GR - en-US - es-ES - fa-IR - fi-FI - fr-FR - he-IL - hi-IN - hu-HU - hy-AM - id-ID - is-IS - it-IT - ja-JP - jv-ID - ka-GE - km-KH - kn-IN - ko-KR - lv-LV - ml-IN - mn-MN - ms-MY - my-MM - nb-NO - nl-NL - pl-PL - pt-PT - ro-RO - ru-RU - sl-SL - sq-AL - sv-SE - sw-KE - ta-IN - te-IN - th-TH - tl-PH - tr-TR - ur-PK - vi-VN - zh-CN - zh-TW tags: - natural-language-understanding --- # MASSIVE 1.1: A 1M-Example Multilingual Natural Language Understanding Dataset with 52 Typologically-Diverse Languages ## Table of Contents - [Dataset Card for [Needs More Information]](#dataset-card-for-needs-more-information) - [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) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [No Warranty](#no-warranty) - [Citation Information](#citation-information) ## Dataset Description - **Homepage:** https://github.com/alexa/massive - **Repository:** https://github.com/alexa/massive - **Paper:** https://arxiv.org/abs/2204.08582 - **Leaderboard:** https://eval.ai/web/challenges/challenge-page/1697/overview - **Point of Contact:** [GitHub](https://github.com/alexa/massive/issues) ### Dataset Summary MASSIVE 1.1 is a parallel dataset of > 1M utterances across 52 languages with annotations for the Natural Language Understanding tasks of intent prediction and slot annotation. Utterances span 60 intents and include 55 slot types. MASSIVE was created by localizing the SLURP dataset, composed of general Intelligent Voice Assistant single-shot interactions. | Name | Lang | Utt/Lang | Domains | Intents | Slots | |:-------------------------------------------------------------------------------:|:-------:|:--------------:|:-------:|:--------:|:------:| | MASSIVE 1.1 | 52 | 19,521 | 18 | 60 | 55 | | SLURP (Bastianelli et al., 2020) | 1 | 16,521 | 18 | 60 | 55 | | NLU Evaluation Data (Liu et al., 2019) | 1 | 25,716 | 18 | 54 | 56 | | Airline Travel Information System (ATIS) (Price, 1990) | 1 | 5,871 | 1 | 26 | 129 | | ATIS with Hindi and Turkish (Upadhyay et al., 2018) | 3 | 1,315-5,871 | 1 | 26 | 129 | | MultiATIS++ (Xu et al., 2020) | 9 | 1,422-5,897 | 1 | 21-26 | 99-140 | | Snips (Coucke et al., 2018) | 1 | 14,484 | - | 7 | 53 | | Snips with French (Saade et al., 2019) | 2 | 4,818 | 2 | 14-15 | 11-12 | | Task Oriented Parsing (TOP) (Gupta et al., 2018) | 1 | 44,873 | 2 | 25 | 36 | | Multilingual Task-Oriented Semantic Parsing (MTOP) (Li et al., 2021) | 6 | 15,195-22,288 | 11 | 104-113 | 72-75 | | Cross-Lingual Multilingual Task Oriented Dialog (Schuster et al., 2019) | 3 | 5,083-43,323 | 3 | 12 | 11 | | Microsoft Dialog Challenge (Li et al., 2018) | 1 | 38,276 | 3 | 11 | 29 | | Fluent Speech Commands (FSC) (Lugosch et al., 2019) | 1 | 30,043 | - | 31 | - | | Chinese Audio-Textual Spoken Language Understanding (CATSLU) (Zhu et al., 2019) | 1 | 16,258 | 4 | - | 94 | ### Supported Tasks and Leaderboards The dataset can be used to train a model for `natural-language-understanding` (NLU) : - `intent-classification` - `multi-class-classification` - `natural-language-understanding` ### Languages The MASSIVE 1.1 corpora consists of parallel sentences from 52 languages : - `Afrikaans - South Africa (af-ZA)` - `Amharic - Ethiopia (am-ET)` - `Arabic - Saudi Arabia (ar-SA)` - `Azeri - Azerbaijan (az-AZ)` - `Bengali - Bangladesh (bn-BD)` - `Catalan - Spain (ca-ES)` - `Chinese - China (zh-CN)` - `Chinese - Taiwan (zh-TW)` - `Danish - Denmark (da-DK)` - `German - Germany (de-DE)` - `Greek - Greece (el-GR)` - `English - United States (en-US)` - `Spanish - Spain (es-ES)` - `Farsi - Iran (fa-IR)` - `Finnish - Finland (fi-FI)` - `French - France (fr-FR)` - `Hebrew - Israel (he-IL)` - `Hungarian - Hungary (hu-HU)` - `Armenian - Armenia (hy-AM)` - `Indonesian - Indonesia (id-ID)` - `Icelandic - Iceland (is-IS)` - `Italian - Italy (it-IT)` - `Japanese - Japan (ja-JP)` - `Javanese - Indonesia (jv-ID)` - `Georgian - Georgia (ka-GE)` - `Khmer - Cambodia (km-KH)` - `Korean - Korea (ko-KR)` - `Latvian - Latvia (lv-LV)` - `Mongolian - Mongolia (mn-MN)` - `Malay - Malaysia (ms-MY)` - `Burmese - Myanmar (my-MM)` - `Norwegian - Norway (nb-NO)` - `Dutch - Netherlands (nl-NL)` - `Polish - Poland (pl-PL)` - `Portuguese - Portugal (pt-PT)` - `Romanian - Romania (ro-RO)` - `Russian - Russia (ru-RU)` - `Slovanian - Slovania (sl-SL)` - `Albanian - Albania (sq-AL)` - `Swedish - Sweden (sv-SE)` - `Swahili - Kenya (sw-KE)` - `Hindi - India (hi-IN)` - `Kannada - India (kn-IN)` - `Malayalam - India (ml-IN)` - `Tamil - India (ta-IN)` - `Telugu - India (te-IN)` - `Thai - Thailand (th-TH)` - `Tagalog - Philippines (tl-PH)` - `Turkish - Turkey (tr-TR)` - `Urdu - Pakistan (ur-PK)` - `Vietnamese - Vietnam (vi-VN)` - `Welsh - United Kingdom (cy-GB)` ## Load the dataset with HuggingFace ```python from datasets import load_dataset dataset = load_dataset("AmazonScience/massive", "en-US", split='train') print(dataset[0]) ``` ## Dataset Structure ### Data Instances ```json { "id": "0", "locale": "fr-FR", "partition": "test", "scenario": "alarm", "intent": "alarm_set", "utt": "réveille-moi à cinq heures du matin cette semaine", "annot_utt": "réveille-moi à [time : cinq heures du matin] [date : cette semaine]", "worker_id": "22", "slot_method": [ { "slot": "time", "method": "translation" }, { "slot": "date", "method": "translation" } ], "judgments": [ { "worker_id": "22", "intent_score": 1, "slots_score": 1, "grammar_score": 4, "spelling_score": 2, "language_identification": "target" }, { "worker_id": "8", "intent_score": 1, "slots_score": 1, "grammar_score": 4, "spelling_score": 2, "language_identification": "target" }, { "worker_id": "0", "intent_score": 1, "slots_score": 1, "grammar_score": 4, "spelling_score": 2, "language_identification": "target" } ] } ``` ### Data Fields `id`: maps to the original ID in the [SLURP](https://github.com/pswietojanski/slurp) collection. Mapping back to the SLURP en-US utterance, this utterance served as the basis for this localization. `locale`: is the language and country code accoring to ISO-639-1 and ISO-3166. `partition`: is either `train`, `dev`, or `test`, according to the original split in [SLURP](https://github.com/pswietojanski/slurp). `scenario`: is the general domain, aka "scenario" in SLURP terminology, of an utterance `intent`: is the specific intent of an utterance within a domain formatted as `{scenario}_{intent}` `utt`: the raw utterance text without annotations `annot_utt`: the text from `utt` with slot annotations formatted as `[{label} : {entity}]` `worker_id`: The obfuscated worker ID from MTurk of the worker completing the localization of the utterance. Worker IDs are specific to a locale and do *not* map across locales. `slot_method`: for each slot in the utterance, whether that slot was a `translation` (i.e., same expression just in the target language), `localization` (i.e., not the same expression but a different expression was chosen more suitable to the phrase in that locale), or `unchanged` (i.e., the original en-US slot value was copied over without modification). `judgments`: Each judgment collected for the localized utterance has 6 keys. `worker_id` is the obfuscated worker ID from MTurk of the worker completing the judgment. Worker IDs are specific to a locale and do *not* map across locales, but *are* consistent across the localization tasks and the judgment tasks, e.g., judgment worker ID 32 in the example above may appear as the localization worker ID for the localization of a different de-DE utterance, in which case it would be the same worker. ```plain intent_score : "Does the sentence match the intent?" 0: No 1: Yes 2: It is a reasonable interpretation of the goal slots_score : "Do all these terms match the categories in square brackets?" 0: No 1: Yes 2: There are no words in square brackets (utterance without a slot) grammar_score : "Read the sentence out loud. Ignore any spelling, punctuation, or capitalization errors. Does it sound natural?" 0: Completely unnatural (nonsensical, cannot be understood at all) 1: Severe errors (the meaning cannot be understood and doesn't sound natural in your language) 2: Some errors (the meaning can be understood but it doesn't sound natural in your language) 3: Good enough (easily understood and sounds almost natural in your language) 4: Perfect (sounds natural in your language) spelling_score : "Are all words spelled correctly? Ignore any spelling variances that may be due to differences in dialect. Missing spaces should be marked as a spelling error." 0: There are more than 2 spelling errors 1: There are 1-2 spelling errors 2: All words are spelled correctly language_identification : "The following sentence contains words in the following languages (check all that apply)" 1: target 2: english 3: other 4: target & english 5: target & other 6: english & other 7: target & english & other ``` ### Data Splits |Language|Train|Dev|Test| |:---:|:---:|:---:|:---:| |af-ZA|11514|2033|2974| |am-ET|11514|2033|2974| |ar-SA|11514|2033|2974| |az-AZ|11514|2033|2974| |bn-BD|11514|2033|2974| |ca-ES|11514|2033|2974| |cy-GB|11514|2033|2974| |da-DK|11514|2033|2974| |de-DE|11514|2033|2974| |el-GR|11514|2033|2974| |en-US|11514|2033|2974| |es-ES|11514|2033|2974| |fa-IR|11514|2033|2974| |fi-FI|11514|2033|2974| |fr-FR|11514|2033|2974| |he-IL|11514|2033|2974| |hi-IN|11514|2033|2974| |hu-HU|11514|2033|2974| |hy-AM|11514|2033|2974| |id-ID|11514|2033|2974| |is-IS|11514|2033|2974| |it-IT|11514|2033|2974| |ja-JP|11514|2033|2974| |jv-ID|11514|2033|2974| |ka-GE|11514|2033|2974| |km-KH|11514|2033|2974| |kn-IN|11514|2033|2974| |ko-KR|11514|2033|2974| |lv-LV|11514|2033|2974| |ml-IN|11514|2033|2974| |mn-MN|11514|2033|2974| |ms-MY|11514|2033|2974| |my-MM|11514|2033|2974| |nb-NO|11514|2033|2974| |nl-NL|11514|2033|2974| |pl-PL|11514|2033|2974| |pt-PT|11514|2033|2974| |ro-RO|11514|2033|2974| |ru-RU|11514|2033|2974| |sl-SL|11514|2033|2974| |sq-AL|11514|2033|2974| |sv-SE|11514|2033|2974| |sw-KE|11514|2033|2974| |ta-IN|11514|2033|2974| |te-IN|11514|2033|2974| |th-TH|11514|2033|2974| |tl-PH|11514|2033|2974| |tr-TR|11514|2033|2974| |ur-PK|11514|2033|2974| |vi-VN|11514|2033|2974| |zh-CN|11514|2033|2974| |zh-TW|11514|2033|2974| ### Personal and Sensitive Information The corpora is free of personal or sensitive information. ## Additional Information ### Dataset Curators __MASSIVE__: Jack FitzGerald and Christopher Hench and Charith Peris and Scott Mackie and Kay Rottmann and Ana Sanchez and Aaron Nash and Liam Urbach and Vishesh Kakarala and Richa Singh and Swetha Ranganath and Laurie Crist and Misha Britan and Wouter Leeuwis and Gokhan Tur and Prem Natarajan. __SLURP__: Bastianelli, Emanuele and Vanzo, Andrea and Swietojanski, Pawel and Rieser, Verena. __Hugging Face Upload and Integration__: Labrak Yanis (Not affiliated with the original corpus) ### Licensing Information ```plain Copyright Amazon.com Inc. or its affiliates. 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The Licensor shall not be bound by any additional or different terms or conditions communicated by You unless expressly agreed. b. Any arrangements, understandings, or agreements regarding the Licensed Material not stated herein are separate from and independent of the terms and conditions of this Public License. Section 8 -- Interpretation. a. For the avoidance of doubt, this Public License does not, and shall not be interpreted to, reduce, limit, restrict, or impose conditions on any use of the Licensed Material that could lawfully be made without permission under this Public License. b. To the extent possible, if any provision of this Public License is deemed unenforceable, it shall be automatically reformed to the minimum extent necessary to make it enforceable. If the provision cannot be reformed, it shall be severed from this Public License without affecting the enforceability of the remaining terms and conditions. c. No term or condition of this Public License will be waived and no failure to comply consented to unless expressly agreed to by the Licensor. d. Nothing in this Public License constitutes or may be interpreted as a limitation upon, or waiver of, any privileges and immunities that apply to the Licensor or You, including from the legal processes of any jurisdiction or authority. ======================================================================= Creative Commons is not a party to its public licenses. Notwithstanding, Creative Commons may elect to apply one of its public licenses to material it publishes and in those instances will be considered the “Licensor.” The text of the Creative Commons public licenses is dedicated to the public domain under the CC0 Public Domain Dedication. Except for the limited purpose of indicating that material is shared under a Creative Commons public license or as otherwise permitted by the Creative Commons policies published at creativecommons.org/policies, Creative Commons does not authorize the use of the trademark "Creative Commons" or any other trademark or logo of Creative Commons without its prior written consent including, without limitation, in connection with any unauthorized modifications to any of its public licenses or any other arrangements, understandings, or agreements concerning use of licensed material. For the avoidance of doubt, this paragraph does not form part of the public licenses. Creative Commons may be contacted at creativecommons.org. ``` ### Citation Information Please cite the following papers when using this dataset. ```latex @misc{fitzgerald2022massive, title={MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages}, author={Jack FitzGerald and Christopher Hench and Charith Peris and Scott Mackie and Kay Rottmann and Ana Sanchez and Aaron Nash and Liam Urbach and Vishesh Kakarala and Richa Singh and Swetha Ranganath and Laurie Crist and Misha Britan and Wouter Leeuwis and Gokhan Tur and Prem Natarajan}, year={2022}, eprint={2204.08582}, archivePrefix={arXiv}, primaryClass={cs.CL} } @inproceedings{bastianelli-etal-2020-slurp, title = "{SLURP}: A Spoken Language Understanding Resource Package", author = "Bastianelli, Emanuele and Vanzo, Andrea and Swietojanski, Pawel and Rieser, Verena", booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.emnlp-main.588", doi = "10.18653/v1/2020.emnlp-main.588", pages = "7252--7262", abstract = "Spoken Language Understanding infers semantic meaning directly from audio data, and thus promises to reduce error propagation and misunderstandings in end-user applications. However, publicly available SLU resources are limited. In this paper, we release SLURP, a new SLU package containing the following: (1) A new challenging dataset in English spanning 18 domains, which is substantially bigger and linguistically more diverse than existing datasets; (2) Competitive baselines based on state-of-the-art NLU and ASR systems; (3) A new transparent metric for entity labelling which enables a detailed error analysis for identifying potential areas of improvement. SLURP is available at https://github.com/pswietojanski/slurp." } ```
boettiger-lab/ca-30x30
boettiger-lab
"2025-02-22T22:00:54Z"
13,042
0
[ "license:bsd-2-clause", "region:us" ]
null
"2024-10-03T17:43:10Z"
--- license: bsd-2-clause ---
ehovy/race
ehovy
"2024-01-04T16:22:34Z"
12,938
59
[ "task_categories:multiple-choice", "task_ids:multiple-choice-qa", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:other", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:1704.04683", "region:us" ]
[ "multiple-choice" ]
"2022-03-02T23:29:22Z"
--- annotations_creators: - expert-generated language_creators: - found language: - en license: - other multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - multiple-choice task_ids: - multiple-choice-qa paperswithcode_id: race pretty_name: RACE dataset_info: - config_name: all features: - name: example_id dtype: string - name: article dtype: string - name: answer dtype: string - name: question dtype: string - name: options sequence: string splits: - name: test num_bytes: 8775370 num_examples: 4934 - name: train num_bytes: 157308478 num_examples: 87866 - name: validation num_bytes: 8647176 num_examples: 4887 download_size: 41500647 dataset_size: 174731024 - config_name: high features: - name: example_id dtype: string - name: article dtype: string - name: answer dtype: string - name: question dtype: string - name: options sequence: string splits: - name: test num_bytes: 6989097 num_examples: 3498 - name: train num_bytes: 126243228 num_examples: 62445 - name: validation num_bytes: 6885263 num_examples: 3451 download_size: 33750880 dataset_size: 140117588 - config_name: middle features: - name: example_id dtype: string - name: article dtype: string - name: answer dtype: string - name: question dtype: string - name: options sequence: string splits: - name: test num_bytes: 1786273 num_examples: 1436 - name: train num_bytes: 31065250 num_examples: 25421 - name: validation num_bytes: 1761913 num_examples: 1436 download_size: 7781596 dataset_size: 34613436 configs: - config_name: all data_files: - split: test path: all/test-* - split: train path: all/train-* - split: validation path: all/validation-* - config_name: high data_files: - split: test path: high/test-* - split: train path: high/train-* - split: validation path: high/validation-* - config_name: middle data_files: - split: test path: middle/test-* - split: train path: middle/train-* - split: validation path: middle/validation-* --- # Dataset Card for "race" ## 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://www.cs.cmu.edu/~glai1/data/race/](http://www.cs.cmu.edu/~glai1/data/race/) - **Repository:** https://github.com/qizhex/RACE_AR_baselines - **Paper:** [RACE: Large-scale ReAding Comprehension Dataset From Examinations](https://arxiv.org/abs/1704.04683) - **Point of Contact:** [Guokun Lai](mailto:[email protected]), [Qizhe Xie](mailto:[email protected]) - **Size of downloaded dataset files:** 76.33 MB - **Size of the generated dataset:** 349.46 MB - **Total amount of disk used:** 425.80 MB ### Dataset Summary RACE is a large-scale reading comprehension dataset with more than 28,000 passages and nearly 100,000 questions. The dataset is collected from English examinations in China, which are designed for middle school and high school students. The dataset can be served as the training and test sets for machine comprehension. ### 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 - **Size of downloaded dataset files:** 25.44 MB - **Size of the generated dataset:** 174.73 MB - **Total amount of disk used:** 200.17 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "answer": "A", "article": "\"Schoolgirls have been wearing such short skirts at Paget High School in Branston that they've been ordered to wear trousers ins...", "example_id": "high132.txt", "options": ["short skirts give people the impression of sexualisation", "short skirts are too expensive for parents to afford", "the headmaster doesn't like girls wearing short skirts", "the girls wearing short skirts will be at the risk of being laughed at"], "question": "The girls at Paget High School are not allowed to wear skirts in that _ ." } ``` #### high - **Size of downloaded dataset files:** 25.44 MB - **Size of the generated dataset:** 140.12 MB - **Total amount of disk used:** 165.56 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "answer": "A", "article": "\"Schoolgirls have been wearing such short skirts at Paget High School in Branston that they've been ordered to wear trousers ins...", "example_id": "high132.txt", "options": ["short skirts give people the impression of sexualisation", "short skirts are too expensive for parents to afford", "the headmaster doesn't like girls wearing short skirts", "the girls wearing short skirts will be at the risk of being laughed at"], "question": "The girls at Paget High School are not allowed to wear skirts in that _ ." } ``` #### middle - **Size of downloaded dataset files:** 25.44 MB - **Size of the generated dataset:** 34.61 MB - **Total amount of disk used:** 60.05 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "answer": "B", "article": "\"There is not enough oil in the world now. As time goes by, it becomes less and less, so what are we going to do when it runs ou...", "example_id": "middle3.txt", "options": ["There is more petroleum than we can use now.", "Trees are needed for some other things besides making gas.", "We got electricity from ocean tides in the old days.", "Gas wasn't used to run cars in the Second World War."], "question": "According to the passage, which of the following statements is TRUE?" } ``` ### Data Fields The data fields are the same among all splits. #### all - `example_id`: a `string` feature. - `article`: a `string` feature. - `answer`: a `string` feature. - `question`: a `string` feature. - `options`: a `list` of `string` features. #### high - `example_id`: a `string` feature. - `article`: a `string` feature. - `answer`: a `string` feature. - `question`: a `string` feature. - `options`: a `list` of `string` features. #### middle - `example_id`: a `string` feature. - `article`: a `string` feature. - `answer`: a `string` feature. - `question`: a `string` feature. - `options`: a `list` of `string` features. ### Data Splits | name |train|validation|test| |------|----:|---------:|---:| |all |87866| 4887|4934| |high |62445| 3451|3498| |middle|25421| 1436|1436| ## 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 http://www.cs.cmu.edu/~glai1/data/race/ 1. RACE dataset is available for non-commercial research purpose only. 2. All passages are obtained from the Internet which is not property of Carnegie Mellon University. We are not responsible for the content nor the meaning of these passages. 3. You agree not to reproduce, duplicate, copy, sell, trade, resell or exploit for any commercial purpose, any portion of the contexts and any portion of derived data. 4. We reserve the right to terminate your access to the RACE dataset at any time. ### Citation Information ``` @inproceedings{lai-etal-2017-race, title = "{RACE}: Large-scale {R}e{A}ding Comprehension Dataset From Examinations", author = "Lai, Guokun and Xie, Qizhe and Liu, Hanxiao and Yang, Yiming and Hovy, Eduard", booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing", month = sep, year = "2017", address = "Copenhagen, Denmark", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/D17-1082", doi = "10.18653/v1/D17-1082", pages = "785--794", } ``` ### Contributions Thanks to [@abarbosa94](https://github.com/abarbosa94), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf), [@mariamabarham](https://github.com/mariamabarham) for adding this dataset.
Intraferon/iNat-XC-1000
Intraferon
"2025-02-24T06:38:26Z"
12,898
0
[ "language:en", "region:us" ]
null
"2024-12-21T11:51:02Z"
--- language: - en pretty_name: i --- # iNat-XC-1000 A multi-modal bird species classification dataset consisting of the sightings of 1000 bird species from two citizen science projects: [iNaturalist](https://www.inaturalist.org/) and [Xeno Canto](https://xeno-canto.org/).
mhardalov/exams
mhardalov
"2024-02-06T07:20:12Z"
12,889
33
[ "task_categories:question-answering", "task_ids:multiple-choice-qa", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "multilinguality:multilingual", "source_datasets:original", "language:ar", "language:bg", "language:de", "language:es", "language:fr", "language:hr", "language:hu", "language:it", "language:lt", "language:mk", "language:pl", "language:pt", "language:sq", "language:sr", "language:tr", "language:vi", "license:cc-by-sa-4.0", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2011.03080", "region:us" ]
[ "question-answering" ]
"2022-03-02T23:29:22Z"
--- annotations_creators: - found language_creators: - found language: - ar - bg - de - es - fr - hr - hu - it - lt - mk - pl - pt - sq - sr - tr - vi license: - cc-by-sa-4.0 multilinguality: - monolingual - multilingual size_categories: - 10K<n<100K - 1K<n<10K - n<1K source_datasets: - original task_categories: - question-answering task_ids: - multiple-choice-qa paperswithcode_id: exams pretty_name: EXAMS config_names: - alignments - crosslingual_bg - crosslingual_hr - crosslingual_hu - crosslingual_it - crosslingual_mk - crosslingual_pl - crosslingual_pt - crosslingual_sq - crosslingual_sr - crosslingual_test - crosslingual_tr - crosslingual_vi - crosslingual_with_para_bg - crosslingual_with_para_hr - crosslingual_with_para_hu - crosslingual_with_para_it - crosslingual_with_para_mk - crosslingual_with_para_pl - crosslingual_with_para_pt - crosslingual_with_para_sq - crosslingual_with_para_sr - crosslingual_with_para_test - crosslingual_with_para_tr - crosslingual_with_para_vi - multilingual - multilingual_with_para dataset_info: - config_name: alignments features: - name: source_id dtype: string - name: target_id_list sequence: string splits: - name: full num_bytes: 1265256 num_examples: 10834 download_size: 184096 dataset_size: 1265256 - config_name: crosslingual_bg features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 1077329 num_examples: 2344 - name: validation num_bytes: 281771 num_examples: 593 download_size: 514922 dataset_size: 1359100 - config_name: crosslingual_hr features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 807104 num_examples: 2341 - name: validation num_bytes: 176594 num_examples: 538 download_size: 450090 dataset_size: 983698 - config_name: crosslingual_hu features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 677535 num_examples: 1731 - name: validation num_bytes: 202012 num_examples: 536 download_size: 401455 dataset_size: 879547 - config_name: crosslingual_it features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 399312 num_examples: 1010 - name: validation num_bytes: 93175 num_examples: 246 download_size: 226376 dataset_size: 492487 - config_name: crosslingual_mk features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 825702 num_examples: 1665 - name: validation num_bytes: 204318 num_examples: 410 download_size: 394548 dataset_size: 1030020 - config_name: crosslingual_pl features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 573410 num_examples: 1577 - name: validation num_bytes: 141633 num_examples: 394 download_size: 341925 dataset_size: 715043 - config_name: crosslingual_pt features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 374798 num_examples: 740 - name: validation num_bytes: 87714 num_examples: 184 download_size: 208021 dataset_size: 462512 - config_name: crosslingual_sq features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 423744 num_examples: 1194 - name: validation num_bytes: 110093 num_examples: 311 download_size: 247052 dataset_size: 533837 - config_name: crosslingual_sr features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 649560 num_examples: 1323 - name: validation num_bytes: 145724 num_examples: 314 download_size: 327466 dataset_size: 795284 - config_name: crosslingual_test features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: test num_bytes: 8402575 num_examples: 19736 download_size: 3438526 dataset_size: 8402575 - config_name: crosslingual_tr features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 717599 num_examples: 1571 - name: validation num_bytes: 182730 num_examples: 393 download_size: 440914 dataset_size: 900329 - config_name: crosslingual_vi features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 953167 num_examples: 1955 - name: validation num_bytes: 231976 num_examples: 488 download_size: 462940 dataset_size: 1185143 - config_name: crosslingual_with_para_bg features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 47066808 num_examples: 2344 - name: validation num_bytes: 11916026 num_examples: 593 download_size: 15794611 dataset_size: 58982834 - config_name: crosslingual_with_para_hr features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 24889604 num_examples: 2341 - name: validation num_bytes: 5695066 num_examples: 538 download_size: 9839452 dataset_size: 30584670 - config_name: crosslingual_with_para_hu features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 19035663 num_examples: 1731 - name: validation num_bytes: 6043265 num_examples: 536 download_size: 9263625 dataset_size: 25078928 - config_name: crosslingual_with_para_it features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 16409235 num_examples: 1010 - name: validation num_bytes: 4018329 num_examples: 246 download_size: 6907617 dataset_size: 20427564 - config_name: crosslingual_with_para_mk features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 38445894 num_examples: 1665 - name: validation num_bytes: 9673574 num_examples: 410 download_size: 12878474 dataset_size: 48119468 - config_name: crosslingual_with_para_pl features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 16373781 num_examples: 1577 - name: validation num_bytes: 4158832 num_examples: 394 download_size: 6539172 dataset_size: 20532613 - config_name: crosslingual_with_para_pt features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 12185383 num_examples: 740 - name: validation num_bytes: 3093712 num_examples: 184 download_size: 4956969 dataset_size: 15279095 - config_name: crosslingual_with_para_sq features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 17341277 num_examples: 1194 - name: validation num_bytes: 4449952 num_examples: 311 download_size: 7112236 dataset_size: 21791229 - config_name: crosslingual_with_para_sr features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 24575845 num_examples: 1323 - name: validation num_bytes: 5772509 num_examples: 314 download_size: 8035415 dataset_size: 30348354 - config_name: crosslingual_with_para_test features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: test num_bytes: 207974374 num_examples: 13510 download_size: 62878029 dataset_size: 207974374 - config_name: crosslingual_with_para_tr features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 18597131 num_examples: 1571 - name: validation num_bytes: 4763097 num_examples: 393 download_size: 7346658 dataset_size: 23360228 - config_name: crosslingual_with_para_vi features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 40882999 num_examples: 1955 - name: validation num_bytes: 10260374 num_examples: 488 download_size: 13028078 dataset_size: 51143373 - config_name: multilingual features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 3381837 num_examples: 7961 - name: validation num_bytes: 1141687 num_examples: 2672 - name: test num_bytes: 5746781 num_examples: 13510 download_size: 4323915 dataset_size: 10270305 - config_name: multilingual_with_para features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 127294567 num_examples: 7961 - name: validation num_bytes: 42711689 num_examples: 2672 - name: test num_bytes: 207974374 num_examples: 13510 download_size: 112597818 dataset_size: 377980630 configs: - config_name: alignments data_files: - split: full path: alignments/full-* - config_name: crosslingual_bg data_files: - split: train path: crosslingual_bg/train-* - split: validation path: crosslingual_bg/validation-* - config_name: crosslingual_hr data_files: - split: train path: crosslingual_hr/train-* - split: validation path: crosslingual_hr/validation-* - config_name: crosslingual_hu data_files: - split: train path: crosslingual_hu/train-* - split: validation path: crosslingual_hu/validation-* - config_name: crosslingual_it data_files: - split: train path: crosslingual_it/train-* - split: validation path: crosslingual_it/validation-* - config_name: crosslingual_mk data_files: - split: train path: crosslingual_mk/train-* - split: validation path: crosslingual_mk/validation-* - config_name: crosslingual_pl data_files: - split: train path: crosslingual_pl/train-* - split: validation path: crosslingual_pl/validation-* - config_name: crosslingual_pt data_files: - split: train path: crosslingual_pt/train-* - split: validation path: crosslingual_pt/validation-* - config_name: crosslingual_sq data_files: - split: train path: crosslingual_sq/train-* - split: validation path: crosslingual_sq/validation-* - config_name: crosslingual_sr data_files: - split: train path: crosslingual_sr/train-* - split: validation path: crosslingual_sr/validation-* - config_name: crosslingual_test data_files: - split: test path: crosslingual_test/test-* - config_name: crosslingual_tr data_files: - split: train path: crosslingual_tr/train-* - split: validation path: crosslingual_tr/validation-* - config_name: crosslingual_vi data_files: - split: train path: crosslingual_vi/train-* - split: validation path: crosslingual_vi/validation-* - config_name: crosslingual_with_para_bg data_files: - split: train path: crosslingual_with_para_bg/train-* - split: validation path: crosslingual_with_para_bg/validation-* - config_name: crosslingual_with_para_hr data_files: - split: train path: crosslingual_with_para_hr/train-* - split: validation path: crosslingual_with_para_hr/validation-* - config_name: crosslingual_with_para_hu data_files: - split: train path: crosslingual_with_para_hu/train-* - split: validation path: crosslingual_with_para_hu/validation-* - config_name: crosslingual_with_para_it data_files: - split: train path: crosslingual_with_para_it/train-* - split: validation path: crosslingual_with_para_it/validation-* - config_name: crosslingual_with_para_mk data_files: - split: train path: crosslingual_with_para_mk/train-* - split: validation path: crosslingual_with_para_mk/validation-* - config_name: crosslingual_with_para_pl data_files: - split: train path: crosslingual_with_para_pl/train-* - split: validation path: crosslingual_with_para_pl/validation-* - config_name: crosslingual_with_para_pt data_files: - split: train path: crosslingual_with_para_pt/train-* - split: validation path: crosslingual_with_para_pt/validation-* - config_name: crosslingual_with_para_sq data_files: - split: train path: crosslingual_with_para_sq/train-* - split: validation path: crosslingual_with_para_sq/validation-* - config_name: crosslingual_with_para_sr data_files: - split: train path: crosslingual_with_para_sr/train-* - split: validation path: crosslingual_with_para_sr/validation-* - config_name: crosslingual_with_para_test data_files: - split: test path: crosslingual_with_para_test/test-* - config_name: crosslingual_with_para_tr data_files: - split: train path: crosslingual_with_para_tr/train-* - split: validation path: crosslingual_with_para_tr/validation-* - config_name: crosslingual_with_para_vi data_files: - split: train path: crosslingual_with_para_vi/train-* - split: validation path: crosslingual_with_para_vi/validation-* - config_name: multilingual data_files: - split: train path: multilingual/train-* - split: validation path: multilingual/validation-* - split: test path: multilingual/test-* - config_name: multilingual_with_para data_files: - split: train path: multilingual_with_para/train-* - split: validation path: multilingual_with_para/validation-* - split: test path: multilingual_with_para/test-* default: true --- # Dataset Card for [Dataset Name] ## 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 - **Repository:** https://github.com/mhardalov/exams-qa - **Paper:** [EXAMS: A Multi-Subject High School Examinations Dataset for Cross-Lingual and Multilingual Question Answering](https://arxiv.org/abs/2011.03080) - **Point of Contact:** [hardalov@@fmi.uni-sofia.bg](hardalov@@fmi.uni-sofia.bg) ### Dataset Summary EXAMS is a benchmark dataset for multilingual and cross-lingual question answering from high school examinations. It consists of more than 24,000 high-quality high school exam questions in 16 languages, covering 8 language families and 24 school subjects from Natural Sciences and Social Sciences, among others. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The languages in the dataset are: - ar - bg - de - es - fr - hr - hu - it - lt - mk - pl - pt - sq - sr - tr - vi ## Dataset Structure ### Data Instances An example of a data instance (with support paragraphs, in Bulgarian) is: ``` {'answerKey': 'C', 'id': '35dd6b52-7e71-11ea-9eb1-54bef70b159e', 'info': {'grade': 12, 'language': 'Bulgarian', 'subject': 'Biology'}, 'question': {'choices': {'label': ['A', 'B', 'C', 'D'], 'para': ['Това води до наследствени изменения между организмите. Мирновременните вождове са наследствени. Черният, сивият и кафявият цвят на оцветяване на тялото се определя от пигмента меланин и възниква в резултат на наследствени изменения. Тези различия, според Монтескьо, не са наследствени. Те са и важни наследствени вещи в клана. Те са били наследствени архонти и управляват демократично. Реликвите са исторически, религиозни, семейни (наследствени) и технически. Общо са направени 800 изменения. Не всички наследствени аномалии на хемоглобина са вредни, т.е. Моногенните наследствени болести, които водят до мигрена, са редки. Няма наследствени владетели. Повечето от тях са наследствени и се предават на потомството. Всичките синове са ерцхерцози на всичките наследствени земи и претенденти. През 1509 г. Фраунбергите са издигнати на наследствени имперски графове. Фамилията Валдбург заради постиженията са номинирани на „наследствени имперски трушсеси“. Фамилията Валдбург заради постиженията са номинирани на „наследствени имперски трушсеси“. Описани са единични наследствени случаи, но по-често липсва фамилна обремененост. Позициите им са наследствени и се предават в рамките на клана. Внесени са изменения в конструкцията на веригите. и са направени изменения в ходовата част. На храма са правени лоши архитектурни изменения. Изменения са предприети и вътре в двореца. Имало двама наследствени вождове. Имало двама наследствени вождове. Годишният календар, „компасът“ и биологичния часовник са наследствени и при много бозайници.', 'Постепенно задълбочаващите се функционални изменения довеждат и до структурни изменения. Те се дължат както на растягането на кожата, така и на въздействието на хормоналните изменения върху кожната тъкан. тези изменения се долавят по-ясно. Впоследствие, той претърпява изменения. Ширината остава без изменения. След тяхното издаване се налагат изменения в първоначалния Кодекс, защото не е съобразен с направените в Дигестите изменения. Еволюционният преход се характеризира със следните изменения: Наблюдават се и сезонни изменения в теглото. Приемат се изменения и допълнения към Устава. Тук се размножават и предизвикват възпалителни изменения. Общо са направени 800 изменения. Бронирането не претърпява съществени изменения. При животните се откриват изменения при злокачествената форма. Срещат се и дегенеративни изменения в семенните каналчета. ТАВКР „Баку“ се строи по изменения проект 1143.4. Трансът се съпровожда с определени изменения на мозъчната дейност. На изменения е подложен и Светия Синод. Внесени са изменения в конструкцията на веригите. На храма са правени лоши архитектурни изменения. Оттогава стиховете претърпяват изменения няколко пъти. Настъпват съществени изменения в музикалната култура. По-късно той претърпява леки изменения. Настъпват съществени изменения в музикалната култура. Претърпява сериозни изменения само носовата надстройка. Хоризонталното брониране е оставено без изменения.', 'Модификациите са обратими. Тези реакции са обратими. В началните стадии тези натрупвания са обратими. Всички такива ефекти са временни и обратими. Много от реакциите са обратими и идентични с тези при гликолизата. Ако в обращение има книжни пари, те са обратими в злато при поискване . Общо са направени 800 изменения. Непоследователността е представена от принципа на "симетрия", при който взаимоотношенията са разглеждани като симетрични или обратими. Откакто формулите в клетките на електронната таблица не са обратими, тази техника е с ограничена стойност. Ефектът на Пелтие-Зеебек и ефектът Томсън са обратими (ефектът на Пелтие е обратен на ефекта на Зеебек). Плазмолизата протича в три етапа, в зависимост от силата и продължителността на въздействието:\n\nПървите два етапа са обратими. Внесени са изменения в конструкцията на веригите. и са направени изменения в ходовата част. На храма са правени лоши архитектурни изменения. Изменения са предприети и вътре в двореца. Оттогава насетне екипите не са претърпявали съществени изменения. Изменения са направени и в колесника на машината. Тези изменения са обявени през октомври 1878 година. Последните изменения са внесени през януари 2009 година. В процеса на последващото проектиране са внесени някои изменения. Сериозните изменения са в края на Втората световна война. Внесени са изменения в конструкцията на погребите и подемниците. Внесени са изменения в конструкцията на погребите и подемниците. Внесени са изменения в конструкцията на погребите и подемниците. Постепенно задълбочаващите се функционални изменения довеждат и до структурни изменения.', 'Ерозионни процеси от масов характер липсват. Обновлението в редиците на партията приема масов характер. Тя обаче няма масов характер поради спецификата на формата. Движението против десятъка придобива масов характер и в Балчишка околия. Понякога екзекутирането на „обсебените от Сатана“ взимало невероятно масов характер. Укриването на дължими като наряд продукти в селата придобива масов характер. Периодичните миграции са в повечето случаи с масов характер и са свързани със сезонните изменения в природата, а непериодичните са премествания на животни, които настъпват след пожари, замърсяване на средата, висока численост и др. Имат необратим характер. Именно по време на двувековните походи на западните рицари използването на гербовете придобива масов характер. След присъединяването на Южен Кавказ към Русия, изселването на азербайджанци от Грузия придобива масов характер. Те имат нормативен характер. Те имат установителен характер. Освобождаването на работна сила обикновено има масов характер, защото обхваща големи контингенти от носителите на труд. Валежите имат подчертано континентален характер. Имат най-често издънков характер. Приливите имат предимно полуденонощен характер. Някои от тях имат мистериален характер. Тези сведения имат случаен, епизодичен характер. Те имат сезонен или годишен характер. Временните обезпечителни мерки имат временен характер. Други имат пожелателен характер (Здравко, Слава). Ловът и събирачеството имат спомагателен характер. Фактически успяват само малко да усилят бронирането на артилерийските погреби, другите изменения носят само частен характер. Някои карикатури имат само развлекателен характер, докато други имат политически нюанси. Поемите на Хезиод имат по-приложен характер.'], 'text': ['дължат се на фенотипни изменения', 'имат масов характер', 'са наследствени', 'са обратими']}, 'stem': 'Мутационите изменения:'}} ``` ### Data Fields A data instance contains the following fields: - `id`: A question ID, unique across the dataset - `question`: the question contains the following: - `stem`: a stemmed representation of the question textual - `choices`: a set of 3 to 5 candidate answers, which each have: - `text`: the text of the answers - `label`: a label in `['A', 'B', 'C', 'D', 'E']` used to match to the `answerKey` - `para`: (optional) a supported paragraph from Wikipedia in the same language as the question and answer - `answerKey`: the key corresponding to the right answer's `label` - `info`: some additional information on the question including: - `grade`: the school grade for the exam this question was taken from - `subject`: a free text description of the academic subject - `language`: the English name of the language for this question ### Data Splits Depending on the configuration, the dataset have different splits: - "alignments": a single "full" split - "multilingual" and "multilingual_with_para": "train", "validation" and "test" splits - "crosslingual_test" and "crosslingual_with_para_test": a single "test" split - the rest of crosslingual configurations: "train" and "validation" splits ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization Eχαµs was collected from official state exams prepared by the ministries of education of various countries. These exams are taken by students graduating from high school, and often require knowledge learned through the entire course. The questions cover a large variety of subjects and material based on the country’s education system. They cover major school subjects such as Biology, Chemistry, Geography, History, and Physics, but we also highly specialized ones such as Agriculture, Geology, Informatics, as well as some applied and profiled studies. Some countries allow students to take official examinations in several languages. This dataset provides 9,857 parallel question pairs spread across seven languages coming from Croatia (Croatian, Serbian, Italian, Hungarian), Hungary (Hungarian, German, French, Spanish, Croatian, Serbian, Italian), and North Macedonia (Macedonian, Albanian, Turkish). For all languages in the dataset, the first step in the process of data collection was to download the PDF files per year, per subject, and per language (when parallel languages were available in the same source), convert the PDF files to text, and select those that were well formatted and followed the document structure. Then, Regular Expressions (RegEx) were used to parse the questions, their corresponding choices and the correct answer choice. In order to ensure that all our questions are answerable using textual input only, questions that contained visual information were removed, as selected by using curated list of words such as map, table, picture, graph, etc., in the corresponding language. #### 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 The dataset, which contains paragraphs from Wikipedia, is licensed under CC-BY-SA 4.0. The code in this repository is licensed according the [LICENSE file](https://raw.githubusercontent.com/mhardalov/exams-qa/main/LICENSE). ### Citation Information ``` @inproceedings{hardalov-etal-2020-exams, title = "{EXAMS}: A Multi-subject High School Examinations Dataset for Cross-lingual and Multilingual Question Answering", author = "Hardalov, Momchil and Mihaylov, Todor and Zlatkova, Dimitrina and Dinkov, Yoan and Koychev, Ivan and Nakov, Preslav", editor = "Webber, Bonnie and Cohn, Trevor and He, Yulan and Liu, Yang", booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.emnlp-main.438", doi = "10.18653/v1/2020.emnlp-main.438", pages = "5427--5444", } ``` ### Contributions Thanks to [@yjernite](https://github.com/yjernite) for adding this dataset.
kamilakesbi/transformers_image_doc
kamilakesbi
"2024-04-22T15:51:29Z"
12,810
0
[ "size_categories:n<1K", "format:parquet", "modality:image", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-04-22T15:50:03Z"
--- dataset_info: features: - name: image dtype: image splits: - name: train num_bytes: 406434.0 num_examples: 2 download_size: 381914 dataset_size: 406434.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
openai/MMMLU
openai
"2024-10-16T18:39:00Z"
12,803
472
[ "task_categories:question-answering", "language:ar", "language:bn", "language:de", "language:es", "language:fr", "language:hi", "language:id", "language:it", "language:ja", "language:ko", "language:pt", "language:sw", "language:yo", "language:zh", "license:mit", "size_categories:100K<n<1M", "format:csv", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2009.03300", "region:us" ]
[ "question-answering" ]
"2024-09-13T16:37:19Z"
--- task_categories: - question-answering configs: - config_name: default data_files: - split: test path: test/*.csv - config_name: AR_XY data_files: - split: test path: test/mmlu_AR-XY.csv - config_name: BN_BD data_files: - split: test path: test/mmlu_BN-BD.csv - config_name: DE_DE data_files: - split: test path: test/mmlu_DE-DE.csv - config_name: ES_LA data_files: - split: test path: test/mmlu_ES-LA.csv - config_name: FR_FR data_files: - split: test path: test/mmlu_FR-FR.csv - config_name: HI_IN data_files: - split: test path: test/mmlu_HI-IN.csv - config_name: ID_ID data_files: - split: test path: test/mmlu_ID-ID.csv - config_name: IT_IT data_files: - split: test path: test/mmlu_IT-IT.csv - config_name: JA_JP data_files: - split: test path: test/mmlu_JA-JP.csv - config_name: KO_KR data_files: - split: test path: test/mmlu_KO-KR.csv - config_name: PT_BR data_files: - split: test path: test/mmlu_PT-BR.csv - config_name: SW_KE data_files: - split: test path: test/mmlu_SW-KE.csv - config_name: YO_NG data_files: - split: test path: test/mmlu_YO-NG.csv - config_name: ZH_CN data_files: - split: test path: test/mmlu_ZH-CN.csv language: - ar - bn - de - es - fr - hi - id - it - ja - ko - pt - sw - yo - zh license: mit --- # Multilingual Massive Multitask Language Understanding (MMMLU) The MMLU is a widely recognized benchmark of general knowledge attained by AI models. It covers a broad range of topics from 57 different categories, covering elementary-level knowledge up to advanced professional subjects like law, physics, history, and computer science. We translated the MMLU’s test set into 14 languages using professional human translators. Relying on human translators for this evaluation increases confidence in the accuracy of the translations, especially for low-resource languages like Yoruba. We are publishing the professional human translations and the code we use to run the evaluations. This effort reflects our commitment to improving the multilingual capabilities of AI models, ensuring they perform accurately across languages, particularly for underrepresented communities. By prioritizing high-quality translations, we aim to make AI technology more inclusive and effective for users worldwide. ## Locales MMMLU contains the MMLU test set translated into the following locales: * AR_XY (Arabic) * BN_BD (Bengali) * DE_DE (German) * ES_LA (Spanish) * FR_FR (French) * HI_IN (Hindi) * ID_ID (Indonesian) * IT_IT (Italian) * JA_JP (Japanese) * KO_KR (Korean) * PT_BR (Brazilian Portuguese) * SW_KE (Swahili) * YO_NG (Yoruba) * ZH_CN (Simplified Chinese) ## Sources Hendrycks, D., Burns, C., Kadavath, S., Arora, A., Basart, S., Tang, E., Song, D., & Steinhardt, J. (2021). [*Measuring Massive Multitask Language Understanding*](https://arxiv.org/abs/2009.03300). [OpenAI Simple Evals GitHub Repository](https://github.com/openai/simple-evals)
kevinjesse/ManyRefactors4C
kevinjesse
"2022-09-25T12:59:34Z"
12,794
0
[ "license:cc-by-2.0", "size_categories:10M<n<100M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
null
"2022-09-25T06:28:58Z"
--- license: cc-by-2.0 ---
alexandrainst/nota
alexandrainst
"2023-10-03T07:51:33Z"
12,788
1
[ "task_categories:automatic-speech-recognition", "task_categories:text-to-speech", "language:da", "license:cc0-1.0", "size_categories:10K<n<100K", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
[ "automatic-speech-recognition", "text-to-speech" ]
"2023-10-02T16:01:30Z"
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 - name: text dtype: string splits: - name: train num_bytes: 361607522962 num_examples: 98600 download_size: 256213612292 dataset_size: 361607522962 license: cc0-1.0 task_categories: - automatic-speech-recognition - text-to-speech language: - da pretty_name: Nota size_categories: - 10K<n<100K --- # Dataset Card for Nota ## Dataset Description - **Repository:** <https://sprogtek-ressources.digst.govcloud.dk/nota> - **Point of Contact:** [Dan Saattrup Nielsen](mailto:[email protected]) - **Size of downloaded dataset files:** 256.21 GB - **Size of the generated dataset:** 361.62 GB - **Total amount of disk used:** 617.83 GB ### Dataset Summary This data was created by the public institution [Nota](https://nota.dk/), which is part of the Danish Ministry of Culture. Nota has a library audiobooks and audiomagazines for people with reading or sight disabilities. Nota also produces a number of audiobooks and audiomagazines themselves. The dataset consists of audio and associated transcriptions from Nota's audiomagazines "Inspiration" and "Radio/TV". All files related to one reading of one edition of the magazine "Inspiration" or "Radio/TV" has been segmented into bits of 2 - 50 seconds. The dataset has been published as a part of the initiative sprogteknologi.dk, within the [Danish Agency for Digital Government (DIGST)](www.digst.dk). ### Supported Tasks and Leaderboards Automatic speech recognition and speech synthesis are the intended tasks for this dataset. No leaderboard is active at this point. ### Languages The dataset is available in Danish (`da`). ## Dataset Structure ### Data Instances - **Size of downloaded dataset files:** 256.21 GB - **Size of the generated dataset:** 361.62 GB - **Total amount of disk used:** 617.83 GB An example from the dataset looks as follows. ``` { 'audio': {'path': 'RMHL20190028_000140.wav', 'array': array([-0.04023849, -0.06235407, -0.04545404, ..., 0.00014322, 0.00017925, 0.00018811]), 'sampling_rate': 16000}, 'text': '13:05: 24syv Dokumentar 14:05: Spørge Jørgen Vært: Jørgen Leth' } ``` ### Data Fields The data fields are the same among all splits. - `audio`: an `Audio` feature. - `text`: a `string` feature. ### Dataset Statistics There are 98,600 samples in the dataset. #### Transcription Length Distribution ![image/png](https://cdn-uploads.huggingface.co/production/uploads/60d368a613f774189902f555/vyyeMoH3XUXsjgRRxCUuB.png) ## Additional Information ### Dataset Curators [Dan Saattrup Nielsen](https://saattrupdan.github.io/) from the [The Alexandra Institute](https://alexandra.dk/) reorganised the dataset and uploaded it to the Hugging Face Hub. ### Licensing Information The dataset is licensed under the [CC0 license](https://creativecommons.org/share-your-work/public-domain/cc0/).
japanese-asr/whisper_transcriptions.mls.wer_10.0.vectorized
japanese-asr
"2024-09-15T01:35:08Z"
12,726
1
[ "size_categories:1M<n<10M", "format:parquet", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-09-11T12:32:36Z"
--- 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 - 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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: 95882062420 num_examples: 62317 download_size: 43207070631 dataset_size: 95882062420 - config_name: subset_66 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: 95871828072 num_examples: 62310 download_size: 43225588983 dataset_size: 95871828072 - config_name: subset_67 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: 95709466896 num_examples: 62204 download_size: 43120587410 dataset_size: 95709466896 - 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name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 1538156 num_examples: 1 download_size: 820398 dataset_size: 1538156 - config_name: subset_70 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: 95609034668 num_examples: 62139 download_size: 43117847437 dataset_size: 95609034668 - config_name: subset_71 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: 95387596792 num_examples: 61996 download_size: 43021151166 dataset_size: 95387596792 - config_name: subset_72 features: - name: transcription sequence: int64 - 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name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 95538306996 num_examples: 62093 download_size: 43186488482 dataset_size: 95538306996 - config_name: subset_78 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: 95653366336 num_examples: 62168 download_size: 43143452346 dataset_size: 95653366336 - config_name: subset_79 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: 95676339900 num_examples: 62183 download_size: 43128557833 dataset_size: 95676339900 - config_name: subset_8 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: 95715343992 num_examples: 62208 download_size: 43164885461 dataset_size: 95715343992 - config_name: subset_80 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: 95766599296 num_examples: 62242 download_size: 43239930128 dataset_size: 95766599296 - config_name: subset_81 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: 143321630332 num_examples: 93149 download_size: 64626209893 dataset_size: 143321630332 - 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name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 143436760360 num_examples: 93224 download_size: 64683558260 dataset_size: 143436760360 - config_name: subset_85 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: 143693790772 num_examples: 93391 download_size: 64884349404 dataset_size: 143693790772 - config_name: subset_86 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: 143485525272 num_examples: 93256 download_size: 64696999488 dataset_size: 143485525272 - config_name: subset_87 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: 143365510504 num_examples: 93178 download_size: 64633922570 dataset_size: 143365510504 - config_name: subset_88 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: 143459907372 num_examples: 93239 download_size: 64646778148 dataset_size: 143459907372 - config_name: subset_89 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: 143713229680 num_examples: 93404 download_size: 64788831986 dataset_size: 143713229680 - 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name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 95696250128 num_examples: 62196 download_size: 43134085960 dataset_size: 95696250128 - config_name: subset_92 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: 95934407652 num_examples: 62351 download_size: 43224929875 dataset_size: 95934407652 - config_name: subset_93 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: 95753580284 num_examples: 62233 download_size: 43222236201 dataset_size: 95753580284 - config_name: subset_94 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: 95891874916 num_examples: 62323 download_size: 43252770071 dataset_size: 95891874916 - config_name: subset_95 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: 95659464728 num_examples: 62172 download_size: 43086228614 dataset_size: 95659464728 - config_name: subset_96 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: 95794821896 num_examples: 62260 download_size: 43179370699 dataset_size: 95794821896 - config_name: subset_97 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: 95622506640 num_examples: 62148 download_size: 43156846644 dataset_size: 95622506640 - config_name: subset_98 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: 13845628 num_examples: 9 download_size: 6713409 dataset_size: 13845628 - config_name: subset_99 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: 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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split: train path: subset_12/train-* - config_name: subset_120 data_files: - split: train path: subset_120/train-* - config_name: subset_121 data_files: - split: train path: subset_121/train-* - config_name: subset_122 data_files: - split: train path: subset_122/train-* - config_name: subset_123 data_files: - split: train path: subset_123/train-* - config_name: subset_124 data_files: - split: train path: subset_124/train-* - config_name: subset_125 data_files: - split: train path: subset_125/train-* - config_name: subset_126 data_files: - split: train path: subset_126/train-* - config_name: subset_127 data_files: - split: train path: subset_127/train-* - config_name: subset_128 data_files: - split: train path: subset_128/train-* - config_name: subset_129 data_files: - split: train path: subset_129/train-* - config_name: subset_13 data_files: - split: train path: subset_13/train-* - config_name: subset_130 data_files: - split: train path: subset_130/train-* - config_name: subset_131 data_files: - 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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-* ---
jxie/camelyon17
jxie
"2023-06-22T09:10:17Z"
12,694
0
[ "size_categories:1M<n<10M", "format:parquet", "modality:image", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
null
"2023-06-20T19:19:23Z"
--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': '0' '1': '1' splits: - name: id_train num_bytes: 1028118482.46 num_examples: 302436 - name: id_val num_bytes: 114778024.28 num_examples: 33560 - name: unlabeled_train num_bytes: 2167898085.29 num_examples: 600030 - name: ood_val num_bytes: 129021135.128 num_examples: 34904 - name: ood_test num_bytes: 276517018.354 num_examples: 85054 download_size: 2858780601 dataset_size: 3716332745.5119996 --- # Dataset Card for "camelyon17" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
gmongaras/Imagenet21K_Recaption
gmongaras
"2025-02-12T22:15:29Z"
12,666
2
[ "size_categories:10M<n<100M", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
null
"2025-02-01T03:28:10Z"
--- dataset_info: features: - name: image dtype: image - name: class dtype: string - name: id dtype: string - name: recaption dtype: string - name: recaption_short dtype: string splits: - name: train num_bytes: 4921695912 num_examples: 25388 download_size: 4917113842 dataset_size: 4921695912 configs: - config_name: default data_files: - split: train path: data/train-* --- This dataset is the entire 21K ImageNet dataset with about 13 million examples and about 19 thousand classes as strings (for some reason it only had ~19K classes instead of 21K). If you want an even larger set of images, I have a recaptioned CC12M and ImageNet dataset: [https://huggingface.co/datasets/gmongaras/CC12M_and_Imagenet21K_Recap](https://huggingface.co/datasets/gmongaras/CC12M_and_Imagenet21K_Recap) The images are in PNG format. They can be decoded like in the following example ``` import io from PIL import Image Image.open(io.BytesIO(row["image"])) ``` where row["image"] are the raw image bytes. The captions were recaptioned following [this dataset](https://huggingface.co/datasets/CaptionEmporium/conceptual-captions-cc12m-llavanext). The procedure to do the recaption is as follows: First the image along with the alt text is fed into the [Llama llava next 3b model](https://huggingface.co/lmms-lab/llama3-llava-next-8b). The prompt is ``` Please make a detailed but succinct caption of this image. If you see text or objects, be sure to describe them in detail along with any other aspects of the foreground and background. If there is any important text in the image, include it in the caption. As a hint, here is the alt-text attribute of the image, which may or may not have to do with the image: Hint: \`\`\` {alt_text} \`\`\` ``` The captions are then postprocessed to remove repetitive phrases such as "the image is" and content that repeats itself multiple times, if the model runs into a decoding issue. Next, the image prompt is fed into the [llama 3 instruct model](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) to shorten the caption. The folllowing prompt was given to the model: ``` Please take the following image caption and attempt to distill it into a single sentence. Remove any redundant lines or descriptions and make it a maximum of 40 words in length. \`\`\` {img_prompt} \`\`\` Please only write the caption and no other text. ``` Both the original caption and shortened caption are in the dataset. I will post the code I used for this later.
ScalingIntelligence/kernelbench-samples
ScalingIntelligence
"2025-02-25T08:12:53Z"
12,665
1
[ "license:apache-2.0", "arxiv:2502.10517", "region:us" ]
null
"2025-02-24T18:24:15Z"
--- license: apache-2.0 --- ## KernelBench Samples Samples from experiments for KernelBench, described in our [arxiv](https://arxiv.org/abs/2502.10517) Learn more about KernelBench from our * [Paper](https://arxiv.org/abs/2502.10517) * [Github Repo](https://github.com/ScalingIntelligence/KernelBench) The samples are organized as such - `baseline_eval` (Section 4 Baseline) - `repeated_sampling` (Section 5.1.1 Repeated Sampling) - `iterative_refinement` (Section 5.1.2 Iterative Refinement of Generations) Within each folder, we organize the results by `/level/model/problem_{id}/sample_{id}`. The inner most `.json` file contains the generated kernel and its associated eval result.
awettig/RedPajama-combined-15B-6K-llama
awettig
"2023-11-29T16:45:09Z"
12,619
1
[ "size_categories:1M<n<10M", "format:parquet", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
null
"2023-11-29T05:23:32Z"
--- dataset_info: features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 splits: - name: test num_bytes: 1422094968 num_examples: 17802 - name: train num_bytes: 192480977304 num_examples: 2409506 download_size: 577654462 dataset_size: 193903072272 --- # Dataset Card for "RedPajama-combined-15B-6K-llama" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
cardiffnlp/tweet_topic_multilingual
cardiffnlp
"2024-11-25T10:54:58Z"
12,616
2
[ "task_categories:text-classification", "multilinguality:monolingual", "language:en", "language:ja", "language:el", "language:es", "license:other", "size_categories:1K<n<10K", "arxiv:2410.03075", "region:us" ]
[ "text-classification" ]
"2023-10-04T18:49:58Z"
--- configs: - config_name: default data_files: - split: train_en path: "dataset/en/en_train.jsonl" language: - en - ja - el - es license: - other multilinguality: - monolingual size_categories: - 1K<n<10K task_categories: - text-classification pretty_name: xtopic --- # Dataset Card for "cardiffnlp/tweet_topic_multilingual" ## Dataset Description - **Dataset:** X-Topic - **Domain:** X (Twitter) - **Number of Class:** 19 ### Dataset Summary This is the official repository of X-Topic ([Multilingual Topic Classification in X: Dataset and Analysis](https://arxiv.org/abs/2410.03075), EMNLP 2024), a topic classification dataset based on X (formerly Twitter), featuring 19 topic labels. The classification task is multi-label, with tweets available in four languages: English, Japanese, Spanish, and Greek. The dataset comprises 4,000 tweets (1,000 per language), collected between September 2021 and August 2022. The dataset uses the same taxonomy as [TweetTopic](https://huggingface.co/datasets/cardiffnlp/tweet_topic_multi). ## Dataset Structure ### Data Splits The dataset includes the following splits: - **en**: English - **es**: Spanish - **ja**: Japanese - **gr**: Greek - **en_2022**: English data from 2022 (TweetTopic) - **mix**: Mixed-language data - **mix_2022**: Mixed-language data including (TweetTopic) from 2022 - **Cross-validation splits:** - **en_cross_validation_0** to **en_cross_validation_4**: English cross-validation splits - **es_cross_validation_0** to **es_cross_validation_4**: Spanish cross-validation splits - **ja_cross_validation_0** to **ja_cross_validation_4**: Japanese cross-validation splits - **gr_cross_validation_0** to **gr_cross_validation_4**: Greek cross-validation splits ### Data Instances An example of `train` looks as follows. ```python { "id": 1470030676816797696, "text": "made a matcha latte, black tea and green juice until i break my fast at 1!! my body and skin are thanking me", "label": [0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], "label_name": ["Diaries & Daily Life", "Fitness & Health", "Food & Dining"], "label_name_flatten": "Diaries & Daily Life, Fitness & Health, Food & Dining" } ``` ### Labels | <span style="font-weight:normal">0: arts_&_culture</span> | <span style="font-weight:normal">5: fashion_&_style</span> | <span style="font-weight:normal">10: learning_&_educational</span> | <span style="font-weight:normal">15: science_&_technology</span> | |-----------------------------|---------------------|----------------------------|--------------------------| | 1: business_&_entrepreneurs | 6: film_tv_&_video | 11: music | 16: sports | | 2: celebrity_&_pop_culture | 7: fitness_&_health | 12: news_&_social_concern | 17: travel_&_adventure | | 3: diaries_&_daily_life | 8: food_&_dining | 13: other_hobbies | 18: youth_&_student_life | | 4: family | 9: gaming | 14: relationships | | Annotation instructions for English can be found [here](https://docs.google.com/document/d/1IaIXZYof3iCLLxyBdu_koNmjy--zqsuOmxQ2vOxYd_g/edit?usp=sharing). ## Citation Information ``` @inproceedings{antypas-etal-2024-multilingual, title = "Multilingual Topic Classification in {X}: Dataset and Analysis", author = "Antypas, Dimosthenis and Ushio, Asahi and Barbieri, Francesco and Camacho-Collados, Jose", editor = "Al-Onaizan, Yaser and Bansal, Mohit and Chen, Yun-Nung", booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2024", address = "Miami, Florida, USA", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2024.emnlp-main.1123", pages = "20136--20152", abstract = "In the dynamic realm of social media, diverse topics are discussed daily, transcending linguistic boundaries. However, the complexities of understanding and categorising this content across various languages remain an important challenge with traditional techniques like topic modelling often struggling to accommodate this multilingual diversity. In this paper, we introduce X-Topic, a multilingual dataset featuring content in four distinct languages (English, Spanish, Japanese, and Greek), crafted for the purpose of tweet topic classification. Our dataset includes a wide range of topics, tailored for social media content, making it a valuable resource for scientists and professionals working on cross-linguistic analysis, the development of robust multilingual models, and computational scientists studying online dialogue. Finally, we leverage X-Topic to perform a comprehensive cross-linguistic and multilingual analysis, and compare the capabilities of current general- and domain-specific language models.", } ```
spdenisov/tokenized_udtree
spdenisov
"2023-03-28T21:56:12Z"
12,614
0
[ "size_categories:1M<n<10M", "format:parquet", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2023-03-28T21:50:04Z"
--- dataset_info: features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 splits: - name: cs_0 num_bytes: 73985244 num_examples: 102133 - name: cs_1 num_bytes: 95459594 num_examples: 102133 - name: cs_2 num_bytes: 95354064 num_examples: 102133 - name: cs_3 num_bytes: 128817619 num_examples: 102133 - name: cs_4 num_bytes: 236925044 num_examples: 102133 - name: cs_5 num_bytes: 115688159 num_examples: 102133 - name: cs_6 num_bytes: 132404489 num_examples: 102133 - name: tr_0 num_bytes: 28666902 num_examples: 60089 - name: tr_1 num_bytes: 31887742 num_examples: 60089 - name: tr_2 num_bytes: 31749302 num_examples: 60089 - name: tr_3 num_bytes: 28498032 num_examples: 60089 - name: tr_4 num_bytes: 57177672 num_examples: 60089 - name: tr_5 num_bytes: 37804587 num_examples: 60089 - name: tr_6 num_bytes: 28280762 num_examples: 60089 - name: ar_0 num_bytes: 32848442 num_examples: 21864 - name: ar_1 num_bytes: 49955197 num_examples: 21864 - name: ar_2 num_bytes: 49285292 num_examples: 21864 - name: ar_3 num_bytes: 69585617 num_examples: 21864 - name: ar_4 num_bytes: 91649737 num_examples: 21864 - name: ar_5 num_bytes: 59303592 num_examples: 21864 - name: ar_6 num_bytes: 50935047 num_examples: 21864 - name: de_0 num_bytes: 112997417 num_examples: 166849 - name: de_1 num_bytes: 149332477 num_examples: 166849 - name: de_2 num_bytes: 157628127 num_examples: 166849 - name: de_3 num_bytes: 155444887 num_examples: 166849 - name: de_4 num_bytes: 309419752 num_examples: 166849 - name: de_5 num_bytes: 191783977 num_examples: 166849 - name: de_6 num_bytes: 138689312 num_examples: 166849 - name: fr_0 num_bytes: 27905013 num_examples: 34921 - name: fr_1 num_bytes: 41237113 num_examples: 34921 - name: fr_2 num_bytes: 45655098 num_examples: 34921 - name: fr_3 num_bytes: 39973853 num_examples: 34921 - name: fr_4 num_bytes: 76420558 num_examples: 34921 - name: fr_5 num_bytes: 56197173 num_examples: 34921 - name: fr_6 num_bytes: 39938223 num_examples: 34921 - name: no_0 num_bytes: 19584526 num_examples: 33282 - 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name: cy_2 num_bytes: 1192068 num_examples: 1111 - name: cy_3 num_bytes: 1123428 num_examples: 1111 - name: cy_4 num_bytes: 1834888 num_examples: 1111 - name: cy_5 num_bytes: 1439843 num_examples: 1111 - name: cy_6 num_bytes: 1055223 num_examples: 1111 - name: hu_0 num_bytes: 858340 num_examples: 910 - name: hu_1 num_bytes: 1088085 num_examples: 910 - name: hu_2 num_bytes: 1086220 num_examples: 910 - name: hu_3 num_bytes: 957490 num_examples: 910 - name: hu_4 num_bytes: 1964920 num_examples: 910 - name: hu_5 num_bytes: 1370660 num_examples: 910 - name: hu_6 num_bytes: 957490 num_examples: 910 - name: zh_0 num_bytes: 9051347 num_examples: 7994 - name: zh_1 num_bytes: 12537582 num_examples: 7994 - name: zh_2 num_bytes: 11419717 num_examples: 7994 - name: zh_3 num_bytes: 10888407 num_examples: 7994 - name: zh_4 num_bytes: 10558847 num_examples: 7994 - name: zh_5 num_bytes: 13867342 num_examples: 7994 - name: zh_6 num_bytes: 10167967 num_examples: 7994 - name: hy_0 num_bytes: 5120790 num_examples: 3200 - name: hy_1 num_bytes: 5762195 num_examples: 3200 - name: hy_2 num_bytes: 4712195 num_examples: 3200 - name: hy_3 num_bytes: 4260805 num_examples: 3200 - name: hy_4 num_bytes: 8546900 num_examples: 3200 - name: hy_5 num_bytes: 5442440 num_examples: 3200 - name: hy_6 num_bytes: 4260805 num_examples: 3200 - name: ro_0 num_bytes: 6894274 num_examples: 8043 - name: ro_1 num_bytes: 9156564 num_examples: 8043 - name: ro_2 num_bytes: 9493574 num_examples: 8043 - name: ro_3 num_bytes: 10830604 num_examples: 8043 - name: ro_4 num_bytes: 20320209 num_examples: 8043 - name: ro_5 num_bytes: 11507314 num_examples: 8043 - name: ro_6 num_bytes: 8300564 num_examples: 8043 - name: da_0 num_bytes: 2963139 num_examples: 4383 - name: da_1 num_bytes: 3945104 num_examples: 4383 - name: da_2 num_bytes: 4115634 num_examples: 4383 - name: da_3 num_bytes: 3583269 num_examples: 4383 - name: da_4 num_bytes: 7089004 num_examples: 4383 - name: da_5 num_bytes: 4981724 num_examples: 4383 - name: da_6 num_bytes: 3583269 num_examples: 4383 - name: nl_0 num_bytes: 6741817 num_examples: 12289 - name: nl_1 num_bytes: 8989392 num_examples: 12289 - name: nl_2 num_bytes: 9389757 num_examples: 12289 - name: nl_3 num_bytes: 16004832 num_examples: 12289 - name: nl_4 num_bytes: 12089687 num_examples: 12289 - name: nl_5 num_bytes: 11410547 num_examples: 12289 - name: nl_6 num_bytes: 12631912 num_examples: 12289 download_size: 934434422 dataset_size: 5264208717 --- # Dataset Card for "tokenized_udtree" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
mrdbourke/food_vision_199_classes
mrdbourke
"2022-11-27T23:00:30Z"
12,599
0
[ "modality:image", "region:us" ]
null
"2022-11-23T23:59:42Z"
--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': almond_butter '1': almonds '2': apple '3': apricot '4': asparagus '5': avocado '6': bacon '7': bacon_and_egg_burger '8': bagel '9': baklava '10': banana '11': banana_bread '12': barbecue_sauce '13': beans '14': beef '15': beef_curry '16': beef_mince '17': beef_stir_fry '18': beer '19': beetroot '20': biltong '21': blackberries '22': blueberries '23': bok_choy '24': bread '25': broccoli '26': broccolini '27': brownie '28': brussel_sprouts '29': burrito '30': butter '31': cabbage '32': calamari '33': candy '34': capsicum '35': carrot '36': cashews '37': cauliflower '38': celery '39': cheese '40': cheeseburger '41': cherries '42': chicken_breast '43': chicken_thighs '44': chicken_wings '45': chilli '46': chimichurri '47': chocolate '48': chocolate_cake '49': coconut '50': coffee '51': coleslaw '52': cookies '53': coriander '54': corn '55': corn_chips '56': cream '57': croissant '58': crumbed_chicken '59': cucumber '60': cupcake '61': daikon_radish '62': dates '63': donuts '64': dragonfruit '65': eggplant '66': eggs '67': enoki_mushroom '68': fennel '69': figs '70': french_toast '71': fried_rice '72': fries '73': fruit_juice '74': garlic '75': garlic_bread '76': ginger '77': goji_berries '78': granola '79': grapefruit '80': grapes '81': green_beans '82': green_onion '83': guacamole '84': guava '85': gyoza '86': ham '87': honey '88': hot_chocolate '89': ice_coffee '90': ice_cream '91': iceberg_lettuce '92': jerusalem_artichoke '93': kale '94': karaage_chicken '95': kimchi '96': kiwi_fruit '97': lamb_chops '98': leek '99': lemon '100': lentils '101': lettuce '102': lime '103': mandarin '104': mango '105': maple_syrup '106': mashed_potato '107': mayonnaise '108': milk '109': miso_soup '110': mushrooms '111': nectarines '112': noodles '113': nuts '114': olive_oil '115': olives '116': omelette '117': onion '118': orange '119': orange_juice '120': oysters '121': pain_au_chocolat '122': pancakes '123': papaya '124': parsley '125': parsnips '126': passionfruit '127': pasta '128': pawpaw '129': peach '130': pear '131': peas '132': pickles '133': pineapple '134': pizza '135': plum '136': pomegranate '137': popcorn '138': pork_belly '139': pork_chop '140': pork_loins '141': porridge '142': potato_bake '143': potato_chips '144': potato_scallop '145': potatoes '146': prawns '147': pumpkin '148': radish '149': ramen '150': raspberries '151': red_onion '152': red_wine '153': rhubarb '154': rice '155': roast_beef '156': roast_pork '157': roast_potatoes '158': rockmelon '159': rosemary '160': salad '161': salami '162': salmon '163': salsa '164': salt '165': sandwich '166': sardines '167': sausage_roll '168': sausages '169': scrambled_eggs '170': seaweed '171': shallots '172': snow_peas '173': soda '174': soy_sauce '175': spaghetti_bolognese '176': spinach '177': sports_drink '178': squash '179': starfruit '180': steak '181': strawberries '182': sushi '183': sweet_potato '184': tacos '185': tamarillo '186': taro '187': tea '188': toast '189': tofu '190': tomato '191': tomato_chutney '192': tomato_sauce '193': turnip '194': watermelon '195': white_onion '196': white_wine '197': yoghurt '198': zucchini splits: - name: train num_bytes: 2973286 num_examples: 19747 download_size: 6202707 dataset_size: 2973286 ---
autogluon/chronos_datasets
autogluon
"2025-03-18T10:38:52Z"
12,593
35
[ "task_categories:time-series-forecasting", "task_ids:univariate-time-series-forecasting", "task_ids:multivariate-time-series-forecasting", "annotations_creators:no-annotation", "source_datasets:original", "license:other", "size_categories:10M<n<100M", "format:parquet", "modality:tabular", "modality:text", "modality:timeseries", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2403.07815", "region:us" ]
[ "time-series-forecasting" ]
"2024-06-22T15:59:58Z"
--- annotations_creators: - no-annotation license: other source_datasets: - original task_categories: - time-series-forecasting task_ids: - univariate-time-series-forecasting - multivariate-time-series-forecasting pretty_name: Chronos datasets dataset_info: - config_name: dominick features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 - name: im_0 dtype: int64 splits: - name: train num_bytes: 477140250 num_examples: 100014 download_size: 42290010 dataset_size: 477140250 - config_name: electricity_15min features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: consumption_kW sequence: float64 splits: - name: train num_bytes: 670989988 num_examples: 370 download_size: 284497403 dataset_size: 670989988 license: CC BY 4.0 homepage: https://archive.ics.uci.edu/dataset/321/electricityloaddiagrams20112014 - config_name: ercot features: - name: id dtype: string - name: timestamp sequence: timestamp[ns] - name: target sequence: float32 splits: - name: train num_examples: 8 download_size: 14504261 - config_name: exchange_rate features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float32 splits: - name: train num_examples: 8 download_size: 401501 license: MIT homepage: https://github.com/laiguokun/multivariate-time-series-data/tree/master/exchange_rate - config_name: m4_daily features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 - name: category dtype: string splits: - name: train num_bytes: 160504176 num_examples: 4227 download_size: 65546675 dataset_size: 160504176 homepage: https://github.com/Mcompetitions/M4-methods - config_name: m4_hourly features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 - name: category dtype: string splits: - name: train num_bytes: 5985544 num_examples: 414 download_size: 1336971 dataset_size: 5985544 homepage: https://github.com/Mcompetitions/M4-methods - config_name: m4_monthly features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 - name: category dtype: string splits: - name: train num_bytes: 181372969 num_examples: 48000 download_size: 52772258 dataset_size: 181372969 homepage: https://github.com/Mcompetitions/M4-methods - config_name: m4_quarterly features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 - name: category dtype: string splits: - name: train num_bytes: 39205397 num_examples: 24000 download_size: 13422579 dataset_size: 39205397 homepage: https://github.com/Mcompetitions/M4-methods - config_name: m4_weekly features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 - name: category dtype: string splits: - name: train num_bytes: 5955806 num_examples: 359 download_size: 2556691 dataset_size: 5955806 homepage: https://github.com/Mcompetitions/M4-methods - config_name: m4_yearly features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 - name: category dtype: string splits: - name: train num_bytes: 14410042 num_examples: 23000 download_size: 5488601 dataset_size: 14410042 homepage: https://github.com/Mcompetitions/M4-methods - config_name: m5 features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: item_id dtype: string - name: target sequence: float32 - name: dept_id dtype: string - name: cat_id dtype: string - name: store_id dtype: string - name: state_id dtype: string splits: - name: train num_bytes: 574062630 num_examples: 30490 download_size: 78063286 dataset_size: 574062630 homepage: https://www.kaggle.com/competitions/m5-forecasting-accuracy/rules - config_name: mexico_city_bikes features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 splits: - name: train num_bytes: 618999406 num_examples: 494 download_size: 103206946 dataset_size: 618999406 homepage: https://ecobici.cdmx.gob.mx/en/open-data/ - config_name: monash_australian_electricity features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 splits: - name: train num_bytes: 18484319 num_examples: 5 download_size: 16856156 dataset_size: 18484319 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_car_parts features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 splits: - name: train num_bytes: 2232790 num_examples: 2674 download_size: 70278 dataset_size: 2232790 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_cif_2016 features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 splits: - name: train num_bytes: 115096 num_examples: 72 download_size: 70876 dataset_size: 115096 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_covid_deaths features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 splits: - name: train num_bytes: 907326 num_examples: 266 download_size: 58957 dataset_size: 907326 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_electricity_hourly features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 splits: - name: train num_bytes: 135103443 num_examples: 321 download_size: 31139117 dataset_size: 135103443 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_electricity_weekly features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 splits: - name: train num_bytes: 807315 num_examples: 321 download_size: 333563 dataset_size: 807315 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_fred_md features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 splits: - name: train num_bytes: 1248369 num_examples: 107 download_size: 412207 dataset_size: 1248369 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_hospital features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: int64 splits: - name: train num_examples: 767 download_size: 117038 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_kdd_cup_2018 features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 - name: city dtype: string - name: station dtype: string - name: measurement dtype: string splits: - name: train num_bytes: 47091540 num_examples: 270 download_size: 8780105 dataset_size: 47091540 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_london_smart_meters features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 splits: - name: train num_bytes: 2664567976 num_examples: 5560 download_size: 597389119 dataset_size: 2664567976 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_m1_monthly features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 splits: - name: train num_bytes: 907691 num_examples: 617 download_size: 244372 dataset_size: 907691 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_m1_quarterly features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 splits: - name: train num_bytes: 162961 num_examples: 203 download_size: 48439 dataset_size: 162961 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_m1_yearly features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 splits: - name: train num_bytes: 75679 num_examples: 181 download_size: 30754 dataset_size: 75679 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_m3_monthly features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 splits: - name: train num_bytes: 2708124 num_examples: 1428 download_size: 589699 dataset_size: 2708124 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_m3_quarterly features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 splits: - name: train num_bytes: 606428 num_examples: 756 download_size: 188543 dataset_size: 606428 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_m3_yearly features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 splits: - name: train num_bytes: 305359 num_examples: 645 download_size: 100184 dataset_size: 305359 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_nn5_weekly features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float32 splits: - name: train num_examples: 111 download_size: 64620 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_pedestrian_counts features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: int64 splits: - name: train num_bytes: 50118790 num_examples: 66 download_size: 12377357 dataset_size: 50118790 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_rideshare features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: source_location dtype: string - name: provider_name dtype: string - name: provider_service dtype: string - name: price_min sequence: float64 - name: price_mean sequence: float64 - name: price_max sequence: float64 - name: distance_min sequence: float64 - name: distance_mean sequence: float64 - name: distance_max sequence: float64 - name: surge_min sequence: float64 - name: surge_mean sequence: float64 - name: surge_max sequence: float64 - name: api_calls sequence: float64 - name: temp sequence: float64 - name: rain sequence: float64 - name: humidity sequence: float64 - name: clouds sequence: float64 - name: wind sequence: float64 splits: - name: train num_bytes: 10819910 num_examples: 156 download_size: 781873 dataset_size: 10819910 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_saugeenday features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: T1 sequence: float64 splits: - name: train num_bytes: 379875 num_examples: 1 download_size: 222678 dataset_size: 379875 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_temperature_rain features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: t_mean sequence: float64 - name: prcp_sum sequence: float64 - name: t_max sequence: float64 - name: t_min sequence: float64 - name: fcst_0_dailypop sequence: float64 - name: fcst_0_dailypop1 sequence: float64 - name: fcst_0_dailypop10 sequence: float64 - name: fcst_0_dailypop15 sequence: float64 - name: fcst_0_dailypop25 sequence: float64 - name: fcst_0_dailypop5 sequence: float64 - name: fcst_0_dailypop50 sequence: float64 - name: fcst_0_dailyprecip sequence: float64 - name: fcst_0_dailyprecip10pct sequence: float64 - name: fcst_0_dailyprecip25pct sequence: float64 - name: fcst_0_dailyprecip50pct sequence: float64 - name: fcst_0_dailyprecip75pct sequence: float64 - name: fcst_1_dailypop sequence: float64 - name: fcst_1_dailypop1 sequence: float64 - name: fcst_1_dailypop10 sequence: float64 - name: fcst_1_dailypop15 sequence: float64 - name: fcst_1_dailypop25 sequence: float64 - name: fcst_1_dailypop5 sequence: float64 - name: fcst_1_dailypop50 sequence: float64 - name: fcst_1_dailyprecip sequence: float64 - name: fcst_1_dailyprecip10pct sequence: float64 - name: fcst_1_dailyprecip25pct sequence: float64 - name: fcst_1_dailyprecip50pct sequence: float64 - name: fcst_1_dailyprecip75pct sequence: float64 - name: fcst_2_dailypop sequence: float64 - name: fcst_2_dailypop1 sequence: float64 - name: fcst_2_dailypop10 sequence: float64 - name: fcst_2_dailypop15 sequence: float64 - name: fcst_2_dailypop25 sequence: float64 - name: fcst_2_dailypop5 sequence: float64 - name: fcst_2_dailypop50 sequence: float64 - name: fcst_2_dailyprecip sequence: float64 - name: fcst_2_dailyprecip10pct sequence: float64 - name: fcst_2_dailyprecip25pct sequence: float64 - name: fcst_2_dailyprecip50pct sequence: float64 - name: fcst_2_dailyprecip75pct sequence: float64 - name: fcst_3_dailypop sequence: float64 - name: fcst_3_dailypop1 sequence: float64 - name: fcst_3_dailypop10 sequence: float64 - name: fcst_3_dailypop15 sequence: float64 - name: fcst_3_dailypop25 sequence: float64 - name: fcst_3_dailypop5 sequence: float64 - name: fcst_3_dailypop50 sequence: float64 - name: fcst_3_dailyprecip sequence: float64 - name: fcst_3_dailyprecip10pct sequence: float64 - name: fcst_3_dailyprecip25pct sequence: float64 - name: fcst_3_dailyprecip50pct sequence: float64 - name: fcst_3_dailyprecip75pct sequence: float64 - name: fcst_4_dailypop sequence: float64 - name: fcst_4_dailypop1 sequence: float64 - name: fcst_4_dailypop10 sequence: float64 - name: fcst_4_dailypop15 sequence: float64 - name: fcst_4_dailypop25 sequence: float64 - name: fcst_4_dailypop5 sequence: float64 - name: fcst_4_dailypop50 sequence: float64 - name: fcst_4_dailyprecip sequence: float64 - name: fcst_4_dailyprecip10pct sequence: float64 - name: fcst_4_dailyprecip25pct sequence: float64 - name: fcst_4_dailyprecip50pct sequence: float64 - name: fcst_4_dailyprecip75pct sequence: float64 - name: fcst_5_dailypop sequence: float64 - name: fcst_5_dailypop1 sequence: float64 - name: fcst_5_dailypop10 sequence: float64 - name: fcst_5_dailypop15 sequence: float64 - name: fcst_5_dailypop25 sequence: float64 - name: fcst_5_dailypop5 sequence: float64 - name: fcst_5_dailypop50 sequence: float64 - name: fcst_5_dailyprecip sequence: float64 - name: fcst_5_dailyprecip10pct sequence: float64 - name: fcst_5_dailyprecip25pct sequence: float64 - name: fcst_5_dailyprecip50pct sequence: float64 - name: fcst_5_dailyprecip75pct sequence: float64 splits: - name: train num_bytes: 188598927 num_examples: 422 download_size: 44967856 dataset_size: 188598927 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_tourism_monthly features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 splits: - name: train num_bytes: 1755434 num_examples: 366 download_size: 334951 dataset_size: 1755434 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_tourism_quarterly features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 splits: - name: train num_bytes: 688817 num_examples: 427 download_size: 177407 dataset_size: 688817 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_tourism_yearly features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 splits: - name: train num_bytes: 213954 num_examples: 518 download_size: 81479 dataset_size: 213954 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_traffic features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 splits: - name: train num_bytes: 241983226 num_examples: 862 download_size: 52748547 dataset_size: 241983226 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_weather features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 - name: subset dtype: string splits: - name: train num_bytes: 688598539 num_examples: 3010 download_size: 133164027 dataset_size: 688598539 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: nn5 features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float32 splits: - name: train num_examples: 111 download_size: 203096 homepage: http://www.neural-forecasting-competition.com/downloads/NN5/datasets/download.htm - config_name: solar features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: power_mw sequence: float64 - name: latitude dtype: float64 - name: longitude dtype: float64 - name: capacity_mw dtype: float64 - name: subset dtype: string splits: - name: train num_bytes: 8689093932 num_examples: 5166 download_size: 1507924920 dataset_size: 8689093932 homepage: https://www.nrel.gov/grid/solar-power-data.html - config_name: solar_1h features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: power_mw sequence: float64 - name: latitude dtype: float64 - name: longitude dtype: float64 - name: capacity_mw dtype: float64 - name: subset dtype: string splits: - name: train num_bytes: 724361772 num_examples: 5166 download_size: 124515417 dataset_size: 724361772 homepage: https://www.nrel.gov/grid/solar-power-data.html - config_name: taxi_1h features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 - name: subset dtype: string - name: lat dtype: float64 - name: lng dtype: float64 splits: - name: train num_bytes: 28832500 num_examples: 2428 download_size: 2265297 dataset_size: 28832500 license: Apache 2.0 homepage: https://github.com/mbohlkeschneider/gluon-ts/tree/mv_release/datasets - config_name: taxi_30min features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 - name: subset dtype: string - name: lat dtype: float64 - name: lng dtype: float64 splits: - name: train num_bytes: 57560596 num_examples: 2428 download_size: 4541244 dataset_size: 57560596 license: Apache 2.0 homepage: https://github.com/mbohlkeschneider/gluon-ts/tree/mv_release/datasets - config_name: training_corpus_kernel_synth_1m features: - name: target sequence: float64 - name: id dtype: string - name: timestamp sequence: timestamp[ms] splits: - name: train num_examples: 1000000 download_size: 8313239368 - config_name: training_corpus_tsmixup_10m features: - name: target sequence: float64 - name: id dtype: string - name: timestamp sequence: timestamp[ms] splits: - name: train num_examples: 10000000 download_size: 82189589906 - config_name: uber_tlc_daily features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: int64 splits: - name: train num_examples: 262 download_size: 84747 homepage: https://github.com/fivethirtyeight/uber-tlc-foil-response - config_name: uber_tlc_hourly features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: int64 splits: - name: train num_examples: 262 download_size: 1878515 homepage: https://github.com/fivethirtyeight/uber-tlc-foil-response - config_name: ushcn_daily features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: state dtype: string - name: coop_id dtype: int64 - name: PRCP sequence: float64 - name: SNOW sequence: float64 - name: SNWD sequence: float64 - name: TMAX sequence: float64 - name: TMIN sequence: float64 splits: - name: train num_bytes: 2259905202 num_examples: 1218 download_size: 221089890 dataset_size: 2259905202 homepage: https://data.ess-dive.lbl.gov/portals/CDIAC - config_name: weatherbench_daily features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float32 - name: latitude dtype: float64 - name: longitude dtype: float64 - name: level dtype: float64 - name: subset dtype: string splits: - name: train num_bytes: 39510157312 num_examples: 225280 download_size: 18924392742 dataset_size: 39510157312 license: MIT homepage: https://github.com/pangeo-data/WeatherBench - config_name: weatherbench_hourly_10m_u_component_of_wind features: - name: latitude dtype: float64 - name: longitude dtype: float64 - name: target sequence: float32 - name: level dtype: float64 - name: timestamp sequence: timestamp[ms] - name: subset dtype: string - name: id dtype: string splits: - name: train num_examples: 2048 download_size: 7292845757 dataset_size: 8617472000 license: MIT homepage: https://github.com/pangeo-data/WeatherBench - config_name: weatherbench_hourly_10m_v_component_of_wind features: - name: latitude dtype: float64 - name: longitude dtype: float64 - name: target sequence: float32 - name: level dtype: float64 - name: timestamp sequence: timestamp[ms] - name: subset dtype: string - name: id dtype: string splits: - name: train num_examples: 2048 download_size: 7292352508 dataset_size: 8617472000 license: MIT homepage: https://github.com/pangeo-data/WeatherBench - config_name: weatherbench_hourly_2m_temperature features: - name: latitude dtype: float64 - name: longitude dtype: float64 - name: target sequence: float32 - name: level dtype: float64 - name: timestamp sequence: timestamp[ms] - name: subset dtype: string - name: id dtype: string splits: - name: train num_examples: 2048 download_size: 7276396852 dataset_size: 8617453568 license: MIT homepage: https://github.com/pangeo-data/WeatherBench - config_name: weatherbench_hourly_geopotential features: - name: latitude dtype: float64 - name: longitude dtype: float64 - name: target sequence: float32 - name: level dtype: int64 - name: timestamp sequence: timestamp[ms] - name: subset dtype: string - name: id dtype: string splits: - name: train num_examples: 26624 download_size: 87305564613 license: MIT homepage: https://github.com/pangeo-data/WeatherBench - config_name: weatherbench_hourly_potential_vorticity features: - name: latitude dtype: float64 - name: longitude dtype: float64 - name: target sequence: float32 - name: level dtype: int64 - name: timestamp sequence: timestamp[ms] - name: subset dtype: string - name: id dtype: string splits: - name: train num_examples: 26624 download_size: 92426240043 license: MIT homepage: https://github.com/pangeo-data/WeatherBench - config_name: weatherbench_hourly_relative_humidity features: - name: latitude dtype: float64 - name: longitude dtype: float64 - name: target sequence: float32 - name: level dtype: int64 - name: timestamp sequence: timestamp[ms] - name: subset dtype: string - name: id dtype: string splits: - name: train num_examples: 26624 download_size: 94728788382 license: MIT homepage: https://github.com/pangeo-data/WeatherBench - config_name: weatherbench_hourly_specific_humidity features: - name: latitude dtype: float64 - name: longitude dtype: float64 - name: target sequence: float32 - name: level dtype: int64 - name: timestamp sequence: timestamp[ms] - name: subset dtype: string - name: id dtype: string splits: - name: train num_examples: 26624 download_size: 85139896451 license: MIT homepage: https://github.com/pangeo-data/WeatherBench - config_name: weatherbench_hourly_temperature features: - name: latitude dtype: float64 - name: longitude dtype: float64 - name: target sequence: float32 - name: level dtype: int64 - name: timestamp sequence: timestamp[ms] - name: subset dtype: string - name: id dtype: string splits: - name: train num_examples: 26624 download_size: 94081539079 license: MIT homepage: https://github.com/pangeo-data/WeatherBench - config_name: weatherbench_hourly_toa_incident_solar_radiation features: - name: latitude dtype: float64 - name: longitude dtype: float64 - name: target sequence: float32 - name: level dtype: float64 - name: timestamp sequence: timestamp[ms] - name: subset dtype: string - name: id dtype: string splits: - name: train num_examples: 2048 download_size: 6057953007 license: MIT homepage: https://github.com/pangeo-data/WeatherBench - config_name: weatherbench_hourly_total_cloud_cover features: - name: latitude dtype: float64 - name: longitude dtype: float64 - name: target sequence: float32 - name: level dtype: float64 - name: timestamp sequence: timestamp[ms] - name: subset dtype: string - name: id dtype: string splits: - name: train num_examples: 2048 download_size: 6628258398 license: MIT homepage: https://github.com/pangeo-data/WeatherBench - config_name: weatherbench_hourly_total_precipitation features: - name: latitude dtype: float64 - name: longitude dtype: float64 - name: target sequence: float32 - name: level dtype: float64 - name: timestamp sequence: timestamp[ms] - name: subset dtype: string - name: id dtype: string splits: - name: train num_examples: 2048 download_size: 6473160755 license: MIT homepage: https://github.com/pangeo-data/WeatherBench - config_name: weatherbench_hourly_u_component_of_wind features: - name: latitude dtype: float64 - name: longitude dtype: float64 - name: target sequence: float32 - name: level dtype: int64 - name: timestamp sequence: timestamp[ms] - name: subset dtype: string - name: id dtype: string splits: - name: train num_examples: 26624 download_size: 94801498563 license: MIT homepage: https://github.com/pangeo-data/WeatherBench - config_name: weatherbench_hourly_v_component_of_wind features: - name: latitude dtype: float64 - name: longitude dtype: float64 - name: target sequence: float32 - name: level dtype: int64 - name: timestamp sequence: timestamp[ms] - name: subset dtype: string - name: id dtype: string splits: - name: train num_examples: 26624 download_size: 94800557482 license: MIT homepage: https://github.com/pangeo-data/WeatherBench - config_name: weatherbench_hourly_vorticity features: - name: latitude dtype: float64 - name: longitude dtype: float64 - name: target sequence: float32 - name: level dtype: int64 - name: timestamp sequence: timestamp[ms] - name: subset dtype: string - name: id dtype: string splits: - name: train num_examples: 26624 download_size: 94720960560 license: MIT homepage: https://github.com/pangeo-data/WeatherBench - config_name: weatherbench_weekly features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float32 - name: latitude dtype: float64 - name: longitude dtype: float64 - name: level dtype: float64 - name: subset dtype: string splits: - name: train num_bytes: 5656029184 num_examples: 225280 download_size: 2243012083 dataset_size: 5656029184 license: MIT homepage: https://github.com/pangeo-data/WeatherBench - config_name: wiki_daily_100k features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 - name: page_name dtype: string splits: - name: train num_bytes: 4389782678 num_examples: 100000 download_size: 592554033 dataset_size: 4389782678 license: CC0 homepage: https://dumps.wikimedia.org/other/pageviews/readme.html - config_name: wind_farms_daily features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 splits: - name: train num_bytes: 1919187 num_examples: 337 download_size: 598834 dataset_size: 1919187 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: wind_farms_hourly features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 splits: - name: train num_bytes: 45917027 num_examples: 337 download_size: 12333116 dataset_size: 45917027 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting configs: - config_name: dominick data_files: - split: train path: dominick/train-* - config_name: electricity_15min data_files: - split: train path: electricity_15min/train-* - config_name: ercot data_files: - split: train path: ercot/train-* - config_name: exchange_rate data_files: - split: train path: exchange_rate/train-* - config_name: m4_daily data_files: - split: train path: m4_daily/train-* - config_name: m4_hourly data_files: - split: train path: m4_hourly/train-* - config_name: m4_monthly data_files: - split: train path: m4_monthly/train-* - config_name: m4_quarterly data_files: - split: train path: m4_quarterly/train-* - config_name: m4_weekly data_files: - split: train path: m4_weekly/train-* - config_name: m4_yearly data_files: - split: train path: m4_yearly/train-* - config_name: m5 data_files: - split: train path: m5/train-* - config_name: mexico_city_bikes data_files: - split: train path: mexico_city_bikes/train-* - config_name: monash_australian_electricity data_files: - split: train path: monash_australian_electricity/train-* - config_name: monash_car_parts data_files: - split: train path: monash_car_parts/train-* - config_name: monash_cif_2016 data_files: - split: train path: monash_cif_2016/train-* - config_name: monash_covid_deaths data_files: - split: train path: monash_covid_deaths/train-* - config_name: monash_electricity_hourly data_files: - split: train path: monash_electricity_hourly/train-* - config_name: monash_electricity_weekly data_files: - split: train path: monash_electricity_weekly/train-* - config_name: monash_fred_md data_files: - split: train path: monash_fred_md/train-* - config_name: monash_hospital data_files: - split: train path: monash_hospital/train-* - config_name: monash_kdd_cup_2018 data_files: - split: train path: monash_kdd_cup_2018/train-* - config_name: monash_london_smart_meters data_files: - split: train path: monash_london_smart_meters/train-* - config_name: monash_m1_monthly data_files: - split: train path: monash_m1_monthly/train-* - config_name: monash_m1_quarterly data_files: - split: train path: monash_m1_quarterly/train-* - config_name: monash_m1_yearly data_files: - split: train path: monash_m1_yearly/train-* - config_name: monash_m3_monthly data_files: - split: train path: monash_m3_monthly/train-* - config_name: monash_m3_quarterly data_files: - split: train path: monash_m3_quarterly/train-* - config_name: monash_m3_yearly data_files: - split: train path: monash_m3_yearly/train-* - config_name: monash_nn5_weekly data_files: - split: train path: monash_nn5_weekly/train-* - config_name: monash_pedestrian_counts data_files: - split: train path: monash_pedestrian_counts/train-* - config_name: monash_rideshare data_files: - split: train path: monash_rideshare/train-* - config_name: monash_saugeenday data_files: - split: train path: monash_saugeenday/train-* - config_name: monash_temperature_rain data_files: - split: train path: monash_temperature_rain/train-* - config_name: monash_tourism_monthly data_files: - split: train path: monash_tourism_monthly/train-* - config_name: monash_tourism_quarterly data_files: - split: train path: monash_tourism_quarterly/train-* - config_name: monash_tourism_yearly data_files: - split: train path: monash_tourism_yearly/train-* - config_name: monash_traffic data_files: - split: train path: monash_traffic/train-* - config_name: monash_weather data_files: - split: train path: monash_weather/train-* - config_name: nn5 data_files: - split: train path: nn5/train-* - config_name: solar data_files: - split: train path: solar/train-* - config_name: solar_1h data_files: - split: train path: solar_1h/train-* - config_name: taxi_1h data_files: - split: train path: taxi_1h/train-* - config_name: taxi_30min data_files: - split: train path: taxi_30min/train-* - config_name: training_corpus_kernel_synth_1m data_files: - split: train path: training_corpus/kernel_synth_1m/train-* - config_name: training_corpus_tsmixup_10m data_files: - split: train path: training_corpus/tsmixup_10m/train-* - config_name: uber_tlc_daily data_files: - split: train path: uber_tlc_daily/train-* - config_name: uber_tlc_hourly data_files: - split: train path: uber_tlc_hourly/train-* - config_name: ushcn_daily data_files: - split: train path: ushcn_daily/train-* - config_name: weatherbench_daily data_files: - split: train path: weatherbench_daily/train-* - config_name: weatherbench_hourly_10m_u_component_of_wind data_files: - split: train path: weatherbench_hourly/10m_u_component_of_wind/train-* - config_name: weatherbench_hourly_10m_v_component_of_wind data_files: - split: train path: weatherbench_hourly/10m_v_component_of_wind/train-* - config_name: weatherbench_hourly_2m_temperature data_files: - split: train path: weatherbench_hourly/2m_temperature/train-* - config_name: weatherbench_hourly_geopotential data_files: - split: train path: weatherbench_hourly/geopotential/train-* - config_name: weatherbench_hourly_potential_vorticity data_files: - split: train path: weatherbench_hourly/potential_vorticity/train-* - config_name: weatherbench_hourly_relative_humidity data_files: - split: train path: weatherbench_hourly/relative_humidity/train-* - config_name: weatherbench_hourly_specific_humidity data_files: - split: train path: weatherbench_hourly/specific_humidity/train-* - config_name: weatherbench_hourly_temperature data_files: - split: train path: weatherbench_hourly/temperature/train-* - config_name: weatherbench_hourly_toa_incident_solar_radiation data_files: - split: train path: weatherbench_hourly/toa_incident_solar_radiation/train-* - config_name: weatherbench_hourly_total_cloud_cover data_files: - split: train path: weatherbench_hourly/total_cloud_cover/train-* - config_name: weatherbench_hourly_total_precipitation data_files: - split: train path: weatherbench_hourly/total_precipitation/train-* - config_name: weatherbench_hourly_u_component_of_wind data_files: - split: train path: weatherbench_hourly/u_component_of_wind/train-* - config_name: weatherbench_hourly_v_component_of_wind data_files: - split: train path: weatherbench_hourly/v_component_of_wind/train-* - config_name: weatherbench_hourly_vorticity data_files: - split: train path: weatherbench_hourly/vorticity/train-* - config_name: weatherbench_weekly data_files: - split: train path: weatherbench_weekly/train-* - config_name: wiki_daily_100k data_files: - split: train path: wiki_daily_100k/train-* - config_name: wind_farms_daily data_files: - split: train path: wind_farms_daily/train-* - config_name: wind_farms_hourly data_files: - split: train path: wind_farms_hourly/train-* --- # Chronos datasets Time series datasets used for training and evaluation of the [Chronos](https://github.com/amazon-science/chronos-forecasting) forecasting models. Note that some Chronos datasets (`ETTh`, `ETTm`, `brazilian_cities_temperature` and `spanish_energy_and_weather`) that rely on a custom builder script are available in the companion repo [`autogluon/chronos_datasets_extra`](https://huggingface.co/datasets/autogluon/chronos_datasets_extra). See the [paper](https://arxiv.org/abs/2403.07815) for more information. ## Data format and usage The recommended way to use these datasets is via https://github.com/autogluon/fev. All datasets satisfy the following high-level schema: - Each dataset row corresponds to a single (univariate or multivariate) time series. - There exists one column with name `id` and type `string` that contains the unique identifier of each time series. - There exists one column of type `Sequence` with dtype `timestamp[ms]`. This column contains the timestamps of the observations. Timestamps are guaranteed to have a regular frequency that can be obtained with [`pandas.infer_freq`](https://pandas.pydata.org/docs/reference/api/pandas.infer_freq.html). - There exists at least one column of type `Sequence` with numeric (`float`, `double`, or `int`) dtype. These columns can be interpreted as target time series. - For each row, all columns of type `Sequence` have same length. - Remaining columns of types other than `Sequence` (e.g., `string` or `float`) can be interpreted as static covariates. Datasets can be loaded using the 🤗 [`datasets`](https://huggingface.co/docs/datasets/en/index) library ```python import datasets ds = datasets.load_dataset("autogluon/chronos_datasets", "m4_daily", split="train") ds.set_format("numpy") # sequences returned as numpy arrays ``` > **NOTE:** The `train` split of all datasets contains the full time series and has no relation to the train/test split used in the Chronos paper. Example entry in the `m4_daily` dataset ```python >>> ds[0] {'id': 'T000000', 'timestamp': array(['1994-03-01T12:00:00.000', '1994-03-02T12:00:00.000', '1994-03-03T12:00:00.000', ..., '1996-12-12T12:00:00.000', '1996-12-13T12:00:00.000', '1996-12-14T12:00:00.000'], dtype='datetime64[ms]'), 'target': array([1017.1, 1019.3, 1017. , ..., 2071.4, 2083.8, 2080.6], dtype=float32), 'category': 'Macro'} ``` ## Changelog - **v1.3.0 (2025-03-05)**: Fix incorrect timestamp frequency for `monash_hospital` - **v1.2.0 (2025-01-03)**: Fix incorrect timestamp frequency for `dominick` - **v1.1.0 (2024-11-14)**: Fix irregular timestamp frequency for `m4_quarterly` - **v1.0.0 (2024-07-24)**: Initial release ### Converting to pandas We can easily convert data in such format to a long format data frame ```python def to_pandas(ds: datasets.Dataset) -> "pd.DataFrame": """Convert dataset to long data frame format.""" sequence_columns = [col for col in ds.features if isinstance(ds.features[col], datasets.Sequence)] return ds.to_pandas().explode(sequence_columns).infer_objects() ``` Example output ```python >>> print(to_pandas(ds).head()) id timestamp target category 0 T000000 1994-03-01 12:00:00 1017.1 Macro 1 T000000 1994-03-02 12:00:00 1019.3 Macro 2 T000000 1994-03-03 12:00:00 1017.0 Macro 3 T000000 1994-03-04 12:00:00 1019.2 Macro 4 T000000 1994-03-05 12:00:00 1018.7 Macro ``` ### Dealing with large datasets Note that some datasets, such as subsets of WeatherBench, are extremely large (~100GB). To work with them efficiently, we recommend either loading them from disk (files will be downloaded to disk, but won't be all loaded into memory) ```python ds = datasets.load_dataset("autogluon/chronos_datasets", "weatherbench_daily", keep_in_memory=False, split="train") ``` or, for the largest datasets like `weatherbench_hourly_temperature`, reading them in streaming format (chunks will be downloaded one at a time) ```python ds = datasets.load_dataset("autogluon/chronos_datasets", "weatherbench_hourly_temperature", streaming=True, split="train") ``` ## Chronos training corpus with TSMixup & KernelSynth The training corpus used for training the Chronos models can be loaded via the configs `training_corpus_tsmixup_10m` (10M TSMixup augmentations of real-world data) and `training_corpus_kernel_synth_1m` (1M synthetic time series generated with KernelSynth), e.g., ```python ds = datasets.load_dataset("autogluon/chronos_datasets", "training_corpus_tsmixup_10m", streaming=True, split="train") ``` Note that since data in the training corpus was obtained by combining various synthetic & real-world time series, the timestamps contain dummy values that have no connection to the original data. ## License Different datasets available in this collection are distributed under different open source licenses. Please see `ds.info.license` and `ds.info.homepage` for each individual dataset. ## Citation If you find these datasets useful for your research, please consider citing the associated paper: ```markdown @article{ansari2024chronos, author = {Ansari, Abdul Fatir and Stella, Lorenzo and Turkmen, Caner and Zhang, Xiyuan and Mercado, Pedro and Shen, Huibin and Shchur, Oleksandr and Rangapuram, Syama Syndar and Pineda Arango, Sebastian and Kapoor, Shubham and Zschiegner, Jasper and Maddix, Danielle C. and Wang, Hao and Mahoney, Michael W. and Torkkola, Kari and Gordon Wilson, Andrew and Bohlke-Schneider, Michael and Wang, Yuyang}, title = {Chronos: Learning the Language of Time Series}, journal = {arXiv preprint arXiv:2403.07815}, year = {2024} } ```
trl-lib/tldr
trl-lib
"2025-01-08T16:18:59Z"
12,590
13
[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "trl" ]
null
"2024-08-22T12:14:13Z"
--- tags: - trl --- # TL;DR Dataset ## Summary The TL;DR dataset is a processed version of Reddit posts, specifically curated to train models using the [TRL library](https://github.com/huggingface/trl) for summarization tasks. It leverages the common practice on Reddit where users append "TL;DR" (Too Long; Didn't Read) summaries to lengthy posts, providing a rich source of paired text data for training summarization models. ## Data Structure - **Format**: [Standard](https://huggingface.co/docs/trl/main/dataset_formats#standard) - **Type**: [Prompt-completion](https://huggingface.co/docs/trl/main/dataset_formats#prompt-completion) Columns: - `"pompt"`: The unabridged Reddit post. - `"completion"`: The concise "TL;DR" summary appended by the author. This structure enables models to learn the relationship between detailed content and its abbreviated form, enhancing their summarization capabilities. ## Generation script The script used to generate this dataset can be found [here](https://github.com/huggingface/trl/blob/main/examples/datasets/tldr.py).
TIGER-Lab/MMLU-STEM
TIGER-Lab
"2024-06-20T03:37:16Z"
12,553
13
[ "license:mit", "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-01-15T16:45:00Z"
--- license: mit dataset_info: - config_name: default features: - name: question dtype: string - name: choices sequence: string - name: subject dtype: string - name: answer dtype: int64 splits: - name: test num_bytes: 976986 num_examples: 3153 download_size: 487500 dataset_size: 976986 configs: - config_name: default data_files: - split: test path: data/test-* --- This contains a subset of STEM subjects defined in MMLU by the original paper. The included subjects are - 'abstract_algebra', - 'anatomy', - 'astronomy', - 'college_biology', - 'college_chemistry', - 'college_computer_science', - 'college_mathematics', - 'college_physics', - 'computer_security', - 'conceptual_physics', - 'electrical_engineering', - 'elementary_mathematics', - 'high_school_biology', - 'high_school_chemistry', - 'high_school_computer_science', - 'high_school_mathematics', - 'high_school_physics', - 'high_school_statistics', - 'machine_learning' Please cite the original MMLU paper when you are using it.
vikhyatk/dvqa
vikhyatk
"2024-12-12T07:32:22Z"
12,541
0
[ "size_categories:100K<n<1M", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-12T07:28:53Z"
--- dataset_info: features: - name: image dtype: image: decode: false - name: qa list: - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 4425372570.0 num_examples: 200000 download_size: 4272488137 dataset_size: 4425372570.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
agicommies/synthia
agicommies
"2025-01-08T00:15:48Z"
12,506
13
[ "task_categories:table-question-answering", "task_categories:summarization", "task_categories:question-answering", "license:mit", "size_categories:1M<n<10M", "format:csv", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "doi:10.57967/hf/2125", "region:us" ]
[ "table-question-answering", "summarization", "question-answering" ]
"2024-04-23T04:57:00Z"
--- license: mit task_categories: - table-question-answering - summarization - question-answering size_categories: - 100B<n<1T --- The Synthia Dataset is a continuously growing aggregate of validated synthetic explanations of subjects picked from Claude Opus latent space based on varying esotericity in a large general list of technical fields. The explanations are varying in their target audience, level of detail and abstraction while incentivized to target Claude3-grade quality. The Synthia subnet leverages Commune's incentives to create a permissionless mining market around distilling knowledge out of SOTA closed-source model APIs into a public dataset to accelerate the OpenSource AI space. Validation models and strategy will adapt based on the current SOTA.
argmaxinc/whisperkit-evals
argmaxinc
"2025-02-17T13:10:56Z"
12,505
4
[ "region:us", "whisper", "whisperkit", "coreml", "asr", "quantized" ]
null
"2024-02-28T08:05:36Z"
--- pretty_name: "WhisperKit ASR Evaluation Results" viewer: false library_name: whisperkit tags: - whisper - whisperkit - coreml - asr - quantized --- # WhisperKit WhisperKit is an on-device speech recognition framework for Apple Silicon: https://github.com/argmaxinc/WhisperKit For performance and accuracy benchmarks on real devices, please see: https://huggingface.co/spaces/argmaxinc/whisperkit-benchmarks
asahi417/seamless-align-deA-enA.speaker-embedding.xlsr-2b
asahi417
"2024-06-24T01:12:19Z"
12,488
0
[ "size_categories:100K<n<1M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-06-17T10:18:56Z"
--- 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: - 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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: 12237325419 num_examples: 2002 download_size: 12279225283 dataset_size: 12237325419 - config_name: subset_96 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 - name: deA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 11956757177 num_examples: 1973 download_size: 11997239064 dataset_size: 11956757177 - config_name: subset_97 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 - name: deA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 11897495704 num_examples: 1944 download_size: 11937567574 dataset_size: 11897495704 - config_name: subset_98 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 - name: deA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 11717282430 num_examples: 1939 download_size: 11756916623 dataset_size: 11717282430 - config_name: subset_99 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: 11882601742 num_examples: 2008 download_size: 11923611047 dataset_size: 11882601742 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: - 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-* - 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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-* ---
calabi-yau-data/ws-5d
calabi-yau-data
"2024-01-18T12:48:46Z"
12,469
5
[ "license:cc-by-sa-4.0", "size_categories:100B<n<1T", "format:parquet", "modality:tabular", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:1808.02422", "region:us", "physics", "math" ]
null
"2023-12-21T17:16:10Z"
--- license: cc-by-sa-4.0 pretty_name: Weight Systems Defining Five-Dimensional IP Lattice Polytopes configs: - config_name: non-reflexive data_files: - split: full path: non-reflexive/*.parquet - config_name: reflexive data_files: - split: full path: reflexive/*.parquet size_categories: - 100B<n<1T tags: - physics - math --- # Weight Systems Defining Five-Dimensional IP Lattice Polytopes This dataset contains all weight systems defining five-dimensional reflexive and non-reflexive IP lattice polytopes, instrumental in the study of Calabi-Yau fourfolds in mathematics and theoretical physics. The data was compiled by Harald Skarke and Friedrich Schöller in [arXiv:1808.02422](https://arxiv.org/abs/1808.02422). More information is available at the [Calabi-Yau data website](http://hep.itp.tuwien.ac.at/~kreuzer/CY/). The dataset can be explored using the [search frontend](http://rgc.itp.tuwien.ac.at/fourfolds/). See below for a short mathematical exposition on the construction of polytopes. Please cite the paper when referencing this dataset: ``` @article{Scholler:2018apc, author = {Schöller, Friedrich and Skarke, Harald}, title = "{All Weight Systems for Calabi-Yau Fourfolds from Reflexive Polyhedra}", eprint = "1808.02422", archivePrefix = "arXiv", primaryClass = "hep-th", doi = "10.1007/s00220-019-03331-9", journal = "Commun. Math. Phys.", volume = "372", number = "2", pages = "657--678", year = "2019" } ``` ## Dataset Details The dataset consists of two subsets: weight systems defining reflexive (and therefore IP) polytopes and weight systems defining non-reflexive IP polytopes. Each subset is split into 4000 files in Parquet format. Rows within each file are sorted lexicographically by weights. There are 185,269,499,015 weight systems defining reflexive polytopes and 137,114,261,915 defining non-reflexive polytopes, making a total of 322,383,760,930 IP weight systems. Each row in the dataset represents a polytope and contains the six weights defining it, along with the vertex count, facet count, and lattice point count. The reflexive dataset also includes the Hodge numbers \\( h^{1,1} \\), \\( h^{1,2} \\), and \\( h^{1,3} \\) of the corresponding Calabi-Yau manifold, and the lattice point count of the dual polytope. For any Calabi-Yau fourfold, the Euler characteristic \\( \chi \\) and the Hodge number \\( h^{2,2} \\) can be derived as follows: $$ \chi = 48 + 6 (h^{1,1} − h^{1,2} + h^{1,3}) $$ $$ h^{2,2} = 44 + 4 h^{1,1} − 2 h^{1,2} + 4 h^{1,3} $$ This dataset is licensed under the [CC BY-SA 4.0 license](http://creativecommons.org/licenses/by-sa/4.0/). ### Data Fields - `weight0` to `weight5`: Weights of the weight system defining the polytope. - `vertex_count`: Vertex count of the polytope. - `facet_count`: Facet count of the polytope. - `point_count`: Lattice point count of the polytope. - `dual_point_count`: Lattice point count of the dual polytope (only for reflexive polytopes). - `h11`: Hodge number \\( h^{1,1} \\) (only for reflexive polytopes). - `h12`: Hodge number \\( h^{1,2} \\) (only for reflexive polytopes). - `h13`: Hodge number \\( h^{1,3} \\) (only for reflexive polytopes). ## Usage The dataset can be used without downloading it entirely, thanks to the streaming capability of the `datasets` library. The following Python code snippet demonstrates how to stream the dataset and print the first five rows: ```python from datasets import load_dataset dataset = load_dataset("calabi-yau-data/ws-5d", name="reflexive", split="full", streaming=True) for row in dataset.take(5): print(row) ``` When cloning the Git repository with Git Large File Storage (LFS), data files are stored both in the Git LFS storage directory and in the working tree. To avoid occupying double the disk space, use a filesystem that supports copy-on-write, and run the following commands to clone the repository: ```bash # Initialize Git LFS git lfs install # Clone the repository without downloading LFS files immediately GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/datasets/calabi-yau-data/ws-5d # Change to the repository directory cd ws-5d # Test deduplication (optional) git lfs dedup --test # Download the LFS files git lfs fetch # Create working tree files as clones of the files in the Git LFS storage directory using # copy-on-write functionality git lfs dedup ``` ## Construction of Polytopes This is an introduction to the mathematics involved in the construction of polytopes relevant to this dataset. For more details and precise definitions, consult the paper [arXiv:1808.02422](https://arxiv.org/abs/1808.02422) and references therein. ### Polytopes A polytope is the convex hull of a finite set of points in \\(n\\)-dimensional Euclidean space, \\(\mathbb{R}^n\\). This means it is the smallest convex shape that contains all these points. The minimal collection of points that define a particular polytope are its vertices. Familiar examples of polytopes include triangles and rectangles in two dimensions, and cubes and octahedra in three dimensions. A polytope is considered an *IP polytope* (interior point polytope) if the origin of \\(\mathbb{R}^n\\) is in the interior of the polytope, not on its boundary or outside it. For any IP polytope \\(\nabla\\), its dual polytope \\(\nabla^*\\) is defined as the set of points \\(\mathbf{y}\\) satisfying $$ \mathbf{x} \cdot \mathbf{y} \ge -1 \quad \text{for all } \mathbf{x} \in \nabla \;. $$ This relationship is symmetric: the dual of the dual of an IP polytope is the polytope itself, i.e., \\( \nabla^{**} = \nabla \\). ### Weight Systems Weight systems provide a means to describe simple polytopes known as *simplices*. A weight system is a tuple of real numbers. The construction process is outlined as follows: Consider an \\(n\\)-dimensional simplex in \\(\mathbb{R}^n\\), i.e., a polytope in \\(\mathbb{R}^n\\) with vertex count \\(n + 1\\) and \\(n\\) of its edges extending in linearly independent directions. It is possible to position \\(n\\) of its vertices at arbitrary (linearly independent) locations through a linear transformation. The placement of the remaining vertex is then determined. Its position is the defining property of the simplex. To specify the position independently of the applied linear transformation, one can use the following equation. If \\(\mathbf{v}_0, \mathbf{v}_1, \dots, \mathbf{v}_n\\) are the vertices of the simplex, this relation fixes one vertex in terms of the other \\(n\\): $$ \sum_{i=0}^n q_i \mathbf{v}_i = 0 \;, $$ where \\(q_i\\) is the tuple of real numbers, the weight system. It is important to note that scaling all weights in a weight system by a common factor results in an equivalent weight system that defines the same simplex. The condition that a simplex is an IP simplex is equivalent to the condition that all weights in its weight system are bigger than zero. For this dataset, the focus is on a specific construction of lattice polytopes described in subsequent sections. ### Lattice Polytopes A lattice polytope is a polytope with vertices at the points of a regular grid, or lattice. Using linear transformations, any lattice polytope can be transformed so that its vertices have integer coordinates, hence they are also referred to as integral polytopes. The dual of a lattice with points \\(L\\) is the lattice consisting of all points \\(\mathbf{y}\\) that satisfy $$ \mathbf{x} \cdot \mathbf{y} \in \mathbb{Z} \quad \text{for all } \mathbf{x} \in L \;. $$ *Reflexive polytopes* are a specific type of lattice polytope characterized by having a dual that is also a lattice polytope, with vertices situated on the dual lattice. These polytopes play a central role in the context of this dataset. The weights of a lattice polytope are always rational. This characteristic enables the rescaling of a weight system so that its weights become integers without any common divisor. This rescaling has been performed in this dataset. The construction of the lattice polytopes from this dataset works as follows: We start with the simplex \\(\nabla\\), arising from a weight system as previously described. Then, we define the polytope \\(\Delta\\) as the convex hull of the intersection of \\(\nabla^*\\) with the points of the dual lattice. In the context of this dataset, the polytope \\(\Delta\\) is referred to as ‘the polytope’. Correspondingly, \\(\Delta^{\!*}\\) is referred to as ‘the dual polytope’. The lattice of \\(\nabla\\) and \\(\Delta^{\!*}\\) is taken to be the coarsest lattice possible, such that \\(\nabla\\) is a lattice polytope, i.e., the lattice generated by the vertices of \\(\nabla\\). This construction is exemplified in the following sections. A weight system is considered an IP weight system if the corresponding \\(\Delta\\) is an IP polytope; that is, the origin is within its interior. Since only IP polytopes have corresponding dual polytopes, this condition is essential for the polytope \\(\Delta\\) to be classified as reflexive. ### Two Dimensions In two dimensions, all IP weight systems define reflexive polytopes and every vertex of \\(\nabla^*\\) lies on the dual lattice, making \\(\Delta\\) and \\(\nabla^*\\) identical. There are exactly three IP weight systems that define two-dimensional polytopes (polygons). Each polytope is reflexive and has three vertices and three facets (edges): | weight system | number of points of \\(\nabla\\) | number of points of \\(\nabla^*\\) | |--------------:|---------------------------------:|-----------------------------------:| | (1, 1, 1) | 4 | 10 | | (1, 1, 2) | 5 | 9 | | (1, 2, 3) | 7 | 7 | The polytopes and their duals are depicted below. Lattice points are indicated by dots. <img src="pictures/ws-2d.png" style="display: block; margin-left: auto; margin-right: auto; width:520px;"> ### General Dimension In higher dimensions, the situation becomes more complex. Not all IP polytopes are reflexive, and generally, \\(\Delta \neq \nabla^*\\). This example shows the construction of the three-dimensional polytope \\(\Delta\\) with weight system (2, 3, 4, 5) and its dual \\(\Delta^{\!*}\\). Lattice points lying on the polytopes are indicated by dots. \\(\Delta\\) has 7 vertices and 13 lattice points, \\(\Delta^{\!*}\\) also has 7 vertices, but 16 lattice points. <img src="pictures/ws-3d-2-3-4-5.png" style="display: block; margin-left: auto; margin-right: auto; width:450px;"> The counts of reflexive single-weight-system polytopes by dimension \\(n\\) are: | \\(n\\) | reflexive single-weight-system polytopes | |--------:|-----------------------------------------:| | 2 | 3 | | 3 | 95 | | 4 | 184,026 | | 5 | (this dataset) 185,269,499,015 | One should note that distinct weight systems may well lead to the same polytope (we have not checked how often this occurs). In particular it seems that polytopes with a small number of lattice points are generated many times.
openlifescienceai/medmcqa
openlifescienceai
"2024-01-04T14:32:02Z"
12,456
136
[ "task_categories:question-answering", "task_categories:multiple-choice", "task_ids:multiple-choice-qa", "task_ids:open-domain-qa", "annotations_creators:no-annotation", "language_creators:expert-generated", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:apache-2.0", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[ "question-answering", "multiple-choice" ]
"2022-05-06T08:43:24Z"
--- annotations_creators: - no-annotation language_creators: - expert-generated language: - en license: - apache-2.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - question-answering - multiple-choice task_ids: - multiple-choice-qa - open-domain-qa paperswithcode_id: medmcqa pretty_name: MedMCQA dataset_info: features: - name: id dtype: string - name: question dtype: string - name: opa dtype: string - name: opb dtype: string - name: opc dtype: string - name: opd dtype: string - name: cop dtype: class_label: names: '0': a '1': b '2': c '3': d - name: choice_type dtype: string - name: exp dtype: string - name: subject_name dtype: string - name: topic_name dtype: string splits: - name: train num_bytes: 131903297 num_examples: 182822 - name: test num_bytes: 1399350 num_examples: 6150 - name: validation num_bytes: 2221428 num_examples: 4183 download_size: 88311487 dataset_size: 135524075 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* --- # Dataset Card for MedMCQA ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-instances) - [Data Splits](#data-instances) - [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) ## Dataset Description - **Homepage:** https://medmcqa.github.io - **Repository:** https://github.com/medmcqa/medmcqa - **Paper:** [MedMCQA: A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering](https://proceedings.mlr.press/v174/pal22a) - **Leaderboard:** https://paperswithcode.com/dataset/medmcqa - **Point of Contact:** [Aaditya Ura](mailto:[email protected]) ### Dataset Summary MedMCQA is a large-scale, Multiple-Choice Question Answering (MCQA) dataset designed to address real-world medical entrance exam questions. MedMCQA has more than 194k high-quality AIIMS & NEET PG entrance exam MCQs covering 2.4k healthcare topics and 21 medical subjects are collected with an average token length of 12.77 and high topical diversity. Each sample contains a question, correct answer(s), and other options which require a deeper language understanding as it tests the 10+ reasoning abilities of a model across a wide range of medical subjects & topics. A detailed explanation of the solution, along with the above information, is provided in this study. MedMCQA provides an open-source dataset for the Natural Language Processing community. It is expected that this dataset would facilitate future research toward achieving better QA systems. The dataset contains questions about the following topics: - Anesthesia - Anatomy - Biochemistry - Dental - ENT - Forensic Medicine (FM) - Obstetrics and Gynecology (O&G) - Medicine - Microbiology - Ophthalmology - Orthopedics - Pathology - Pediatrics - Pharmacology - Physiology - Psychiatry - Radiology - Skin - Preventive & Social Medicine (PSM) - Surgery ### Supported Tasks and Leaderboards multiple-choice-QA, open-domain-QA: The dataset can be used to train a model for multi-choice questions answering, open domain questions answering. Questions in these exams are challenging and generally require deeper domain and language understanding as it tests the 10+ reasoning abilities across a wide range of medical subjects & topics. ### Languages The questions and answers are available in English. ## Dataset Structure ### Data Instances ``` { "question":"A 40-year-old man presents with 5 days of productive cough and fever. Pseudomonas aeruginosa is isolated from a pulmonary abscess. CBC shows an acute effect characterized by marked leukocytosis (50,000 mL) and the differential count reveals a shift to left in granulocytes. Which of the following terms best describes these hematologic findings?", "exp": "Circulating levels of leukocytes and their precursors may occasionally reach very high levels (>50,000 WBC mL). These extreme elevations are sometimes called leukemoid reactions because they are similar to the white cell counts observed in leukemia, from which they must be distinguished. The leukocytosis occurs initially because of the accelerated release of granulocytes from the bone marrow (caused by cytokines, including TNF and IL-1) There is a rise in the number of both mature and immature neutrophils in the blood, referred to as a shift to the left. In contrast to bacterial infections, viral infections (including infectious mononucleosis) are characterized by lymphocytosis Parasitic infestations and certain allergic reactions cause eosinophilia, an increase in the number of circulating eosinophils. Leukopenia is defined as an absolute decrease in the circulating WBC count.", "cop":1, "opa":"Leukemoid reaction", "opb":"Leukopenia", "opc":"Myeloid metaplasia", "opd":"Neutrophilia", "subject_name":"Pathology", "topic_name":"Basic Concepts and Vascular changes of Acute Inflammation", "id":"4e1715fe-0bc3-494e-b6eb-2d4617245aef", "choice_type":"single" } ``` ### Data Fields - `id` : a string question identifier for each example - `question` : question text (a string) - `opa` : Option A - `opb` : Option B - `opc` : Option C - `opd` : Option D - `cop` : Correct option, i.e., 1,2,3,4 - `choice_type` ({"single", "multi"}): Question choice type. - "single": Single-choice question, where each choice contains a single option. - "multi": Multi-choice question, where each choice contains a combination of multiple suboptions. - `exp` : Expert's explanation of the answer - `subject_name` : Medical Subject name of the particular question - `topic_name` : Medical topic name from the particular subject ### Data Splits The goal of MedMCQA is to emulate the rigor of real word medical exams. To enable that, a predefined split of the dataset is provided. The split is by exams instead of the given questions. This also ensures the reusability and generalization ability of the models. The training set of MedMCQA consists of all the collected mock & online test series, whereas the test set consists of all AIIMS PG exam MCQs (years 1991-present). The development set consists of NEET PG exam MCQs (years 2001-present) to approximate real exam evaluation. Similar questions from train , test and dev set were removed based on similarity. The final split sizes are as follow: | | Train | Test | Valid | | ----- | ------ | ----- | ---- | | Question #| 182,822 | 6,150 | 4,183| | Vocab | 94,231 | 11,218 | 10,800 | | Max Ques tokens | 220 | 135| 88 | | Max Ans tokens | 38 | 21 | 25 | ## Dataset Creation ### Curation Rationale Before this attempt, very few works have been done to construct biomedical MCQA datasets (Vilares and Gomez-Rodr, 2019), and they are (1) mostly small, containing up to few thousand questions, and (2) cover a limited number of Medical topics and Subjects. This paper addresses the aforementioned limitations by introducing MedMCQA, a new large-scale, Multiple-Choice Question Answering (MCQA) dataset designed to address real-world medical entrance exam questions. ### Source Data #### Initial Data Collection and Normalization Historical Exam questions from official websites - AIIMS & NEET PG (1991- present) The raw data is collected from open websites and books #### Who are the source language producers? The dataset was created by Ankit Pal, Logesh Kumar Umapathi and Malaikannan Sankarasubbu ### Annotations #### Annotation process The dataset does not contain any additional annotations. #### 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 [Needs More Information] ### Citation Information If you find this useful in your research, please consider citing the dataset paper ``` @InProceedings{pmlr-v174-pal22a, title = {MedMCQA: A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering}, author = {Pal, Ankit and Umapathi, Logesh Kumar and Sankarasubbu, Malaikannan}, booktitle = {Proceedings of the Conference on Health, Inference, and Learning}, pages = {248--260}, year = {2022}, editor = {Flores, Gerardo and Chen, George H and Pollard, Tom and Ho, Joyce C and Naumann, Tristan}, volume = {174}, series = {Proceedings of Machine Learning Research}, month = {07--08 Apr}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v174/pal22a/pal22a.pdf}, url = {https://proceedings.mlr.press/v174/pal22a.html}, abstract = {This paper introduces MedMCQA, a new large-scale, Multiple-Choice Question Answering (MCQA) dataset designed to address real-world medical entrance exam questions. More than 194k high-quality AIIMS &amp; NEET PG entrance exam MCQs covering 2.4k healthcare topics and 21 medical subjects are collected with an average token length of 12.77 and high topical diversity. Each sample contains a question, correct answer(s), and other options which requires a deeper language understanding as it tests the 10+ reasoning abilities of a model across a wide range of medical subjects &amp; topics. A detailed explanation of the solution, along with the above information, is provided in this study.} } ``` ### Contributions Thanks to [@monk1337](https://github.com/monk1337) for adding this dataset.
IPEC-COMMUNITY/bc_z_lerobot
IPEC-COMMUNITY
"2025-03-15T08:49:20Z"
12,423
0
[ "task_categories:robotics", "license:apache-2.0", "modality:video", "region:us", "LeRobot", "bc_z", "rlds", "openx", "google_robot" ]
[ "robotics" ]
"2025-03-15T02:24:39Z"
--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - LeRobot - bc_z - rlds - openx - google_robot configs: - config_name: default data_files: data/*/*.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - **Homepage:** [More Information Needed] - **Paper:** [More Information Needed] - **License:** apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.0", "robot_type": "google_robot", "total_episodes": 39350, "total_frames": 5471693, "total_tasks": 104, "total_videos": 39350, "total_chunks": 40, "chunks_size": 1000, "fps": 10, "splits": { "train": "0:39350" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "observation.images.image": { "dtype": "video", "shape": [ 171, 213, 3 ], "names": [ "height", "width", "rgb" ], "info": { "video.fps": 10.0, "video.height": 172, "video.width": 214, "video.channels": 3, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "observation.state": { "dtype": "float32", "shape": [ 8 ], "names": { "motors": [ "x", "y", "z", "roll", "pitch", "yaw", "pad", "gripper" ] } }, "action": { "dtype": "float32", "shape": [ 7 ], "names": { "motors": [ "x", "y", "z", "roll", "pitch", "yaw", "gripper" ] } }, "timestamp": { "dtype": "float32", "shape": [ 1 ], "names": null }, "frame_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "episode_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "task_index": { "dtype": "int64", "shape": [ 1 ], "names": null } } } ``` ## Citation **BibTeX:** ```bibtex [More Information Needed] ```
davanstrien/AmericanStories-parquet
davanstrien
"2023-10-20T15:09:54Z"
12,413
2
[ "size_categories:10M<n<100M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
null
"2023-10-20T14:07:00Z"
--- configs: - config_name: default data_files: - split: '1774' path: data/1774-* - split: '1798' path: data/1798-* - split: '1799' path: data/1799-* - split: '1800' path: data/1800-* - split: '1801' path: data/1801-* - split: '1802' path: data/1802-* - split: '1803' path: data/1803-* - split: '1804' path: data/1804-* - split: '1805' path: data/1805-* - split: '1806' path: data/1806-* - split: '1807' path: data/1807-* - split: '1808' path: data/1808-* - split: '1809' path: data/1809-* - split: '1810' path: data/1810-* - split: '1811' path: data/1811-* - split: '1812' path: data/1812-* - split: '1813' path: data/1813-* - split: '1814' path: data/1814-* - split: '1815' path: data/1815-* - split: '1816' path: data/1816-* - split: '1817' path: data/1817-* - split: '1818' path: data/1818-* - split: '1819' path: data/1819-* - split: '1820' path: data/1820-* - split: '1821' path: data/1821-* - split: '1822' path: data/1822-* - split: '1823' path: data/1823-* - split: '1824' path: data/1824-* - split: '1825' path: data/1825-* - split: '1826' path: data/1826-* - split: '1827' path: data/1827-* - split: '1828' path: data/1828-* - split: '1829' path: data/1829-* - split: '1830' path: data/1830-* - split: '1831' path: data/1831-* - split: '1832' path: data/1832-* - split: '1833' path: data/1833-* - split: '1834' path: data/1834-* - split: '1835' path: data/1835-* - split: '1836' path: data/1836-* - split: '1837' path: data/1837-* - split: '1838' path: data/1838-* - split: '1839' path: data/1839-* - split: '1840' path: data/1840-* - split: '1841' path: data/1841-* - split: '1842' path: data/1842-* - split: '1843' path: data/1843-* - split: '1844' path: data/1844-* - split: '1845' path: data/1845-* - split: '1846' path: data/1846-* - split: '1847' path: data/1847-* - split: '1848' path: data/1848-* - split: '1849' path: data/1849-* - split: '1850' path: data/1850-* - split: '1851' path: data/1851-* - split: '1852' path: data/1852-* - split: '1853' path: data/1853-* - split: '1854' path: data/1854-* - split: '1855' path: data/1855-* - split: '1856' path: data/1856-* - split: '1857' path: data/1857-* - split: '1858' path: data/1858-* - split: '1859' path: data/1859-* - split: '1860' path: data/1860-* - split: '1861' path: data/1861-* - split: '1862' path: data/1862-* - split: '1863' path: data/1863-* - split: '1864' path: data/1864-* - split: '1865' path: data/1865-* - split: '1866' path: data/1866-* - split: '1867' path: data/1867-* - split: '1868' path: data/1868-* - split: '1869' path: data/1869-* - split: '1870' path: data/1870-* - split: '1871' path: data/1871-* - split: '1872' path: data/1872-* - split: '1873' path: data/1873-* - split: '1874' path: data/1874-* - split: '1875' path: data/1875-* - split: '1876' path: data/1876-* - split: '1877' path: data/1877-* - split: '1878' path: data/1878-* - split: '1879' path: data/1879-* - split: '1880' path: data/1880-* - split: '1881' path: data/1881-* - split: '1882' path: data/1882-* - split: '1883' path: data/1883-* - split: '1884' path: data/1884-* - split: '1885' path: data/1885-* - split: '1886' path: data/1886-* - split: '1887' path: data/1887-* - split: '1888' path: data/1888-* - split: '1889' path: data/1889-* - split: '1890' path: data/1890-* - split: '1891' path: data/1891-* - split: '1892' path: data/1892-* - split: '1893' path: data/1893-* - split: '1894' path: data/1894-* - split: '1895' path: data/1895-* - split: '1896' path: data/1896-* - split: '1897' path: data/1897-* - split: '1898' path: data/1898-* - split: '1899' path: data/1899-* - split: '1900' path: data/1900-* - split: '1901' path: data/1901-* - split: '1902' path: data/1902-* - split: '1903' path: data/1903-* - split: '1904' path: data/1904-* - split: '1905' path: data/1905-* - split: '1906' path: data/1906-* - split: '1907' path: data/1907-* - split: '1908' path: data/1908-* - split: '1909' path: data/1909-* - split: '1910' path: data/1910-* - split: '1911' path: data/1911-* - split: '1912' path: data/1912-* - split: '1913' path: data/1913-* - split: '1914' path: data/1914-* - split: '1915' path: data/1915-* - split: '1916' path: data/1916-* - split: '1917' path: data/1917-* - split: '1918' path: data/1918-* - split: '1919' path: data/1919-* - split: '1920' path: data/1920-* - split: '1921' path: data/1921-* - split: '1922' path: data/1922-* - split: '1923' path: data/1923-* - split: '1924' path: data/1924-* - split: '1925' path: data/1925-* - split: '1926' path: data/1926-* - split: '1927' path: data/1927-* - split: '1928' path: data/1928-* - split: '1929' path: data/1929-* - split: '1930' path: data/1930-* - split: '1931' path: data/1931-* - split: '1932' path: data/1932-* - split: '1933' path: data/1933-* - split: '1934' path: data/1934-* - split: '1935' path: data/1935-* - split: '1936' path: data/1936-* - split: '1937' path: data/1937-* - split: '1938' path: data/1938-* - split: '1939' path: data/1939-* - split: '1940' path: data/1940-* - split: '1941' path: data/1941-* - split: '1942' path: data/1942-* - split: '1943' path: data/1943-* - split: '1944' path: data/1944-* - split: '1945' path: data/1945-* - split: '1946' path: data/1946-* - split: '1947' path: data/1947-* - split: '1948' path: data/1948-* - split: '1949' path: data/1949-* - split: '1950' path: data/1950-* - split: '1951' path: data/1951-* - split: '1952' path: data/1952-* - split: '1953' path: data/1953-* - split: '1954' path: data/1954-* - split: '1955' path: data/1955-* - split: '1956' path: data/1956-* - split: '1957' path: data/1957-* - split: '1958' path: data/1958-* - split: '1959' path: data/1959-* - split: '1960' path: data/1960-* - split: '1961' path: data/1961-* - split: '1962' path: data/1962-* - split: '1963' path: data/1963-* dataset_info: features: - name: article_id dtype: string - name: newspaper_name dtype: string - name: edition dtype: string - name: date dtype: string - name: page dtype: string - name: headline dtype: string - name: byline dtype: string - name: article dtype: string splits: - name: '1774' num_bytes: 22245 num_examples: 12 - name: '1798' num_bytes: 72288 num_examples: 73 - 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name: '1819' num_bytes: 2476297 num_examples: 1997 - name: '1820' num_bytes: 611884 num_examples: 433 - name: '1821' num_bytes: 347361 num_examples: 270 - name: '1822' num_bytes: 286227 num_examples: 264 - name: '1823' num_bytes: 2030816 num_examples: 1113 - name: '1824' num_bytes: 5171191 num_examples: 3110 - name: '1825' num_bytes: 6341915 num_examples: 4005 - name: '1826' num_bytes: 10462258 num_examples: 7079 - name: '1827' num_bytes: 11634621 num_examples: 7213 - name: '1828' num_bytes: 10253681 num_examples: 6350 - name: '1829' num_bytes: 4021832 num_examples: 2296 - name: '1830' num_bytes: 8321949 num_examples: 4232 - name: '1831' num_bytes: 16796125 num_examples: 9699 - name: '1832' num_bytes: 9982722 num_examples: 6565 - name: '1833' num_bytes: 6653515 num_examples: 4108 - name: '1834' num_bytes: 7099875 num_examples: 4632 - name: '1835' num_bytes: 9066392 num_examples: 6168 - name: '1836' num_bytes: 10473366 num_examples: 7375 - name: '1837' num_bytes: 21002773 num_examples: 13609 - 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name: '1929' num_bytes: 366246066 num_examples: 421909 - name: '1930' num_bytes: 437657836 num_examples: 492695 - name: '1931' num_bytes: 441972257 num_examples: 493816 - name: '1932' num_bytes: 640501746 num_examples: 664615 - name: '1933' num_bytes: 634373318 num_examples: 642380 - name: '1934' num_bytes: 641841040 num_examples: 654342 - name: '1935' num_bytes: 612406176 num_examples: 635554 - name: '1936' num_bytes: 621035178 num_examples: 662015 - name: '1937' num_bytes: 625107933 num_examples: 676549 - name: '1938' num_bytes: 616370880 num_examples: 665274 - name: '1939' num_bytes: 525913265 num_examples: 556283 - name: '1940' num_bytes: 471830118 num_examples: 496662 - name: '1941' num_bytes: 599694786 num_examples: 637200 - name: '1942' num_bytes: 508785410 num_examples: 523923 - name: '1943' num_bytes: 452079475 num_examples: 467200 - name: '1944' num_bytes: 442871777 num_examples: 433769 - name: '1945' num_bytes: 588623743 num_examples: 588477 - name: '1946' num_bytes: 526027876 num_examples: 470895 - name: '1947' num_bytes: 461281363 num_examples: 393086 - name: '1948' num_bytes: 442999943 num_examples: 396660 - name: '1949' num_bytes: 421752000 num_examples: 419854 - name: '1950' num_bytes: 403717616 num_examples: 415416 - name: '1951' num_bytes: 409600217 num_examples: 419622 - name: '1952' num_bytes: 397051717 num_examples: 396420 - name: '1953' num_bytes: 366253682 num_examples: 358332 - name: '1954' num_bytes: 263197428 num_examples: 266338 - name: '1955' num_bytes: 268993926 num_examples: 273576 - name: '1956' num_bytes: 85126796 num_examples: 98035 - name: '1957' num_bytes: 83757036 num_examples: 93543 - name: '1958' num_bytes: 85807593 num_examples: 98688 - name: '1959' num_bytes: 112707174 num_examples: 129452 - name: '1960' num_bytes: 300484826 num_examples: 344550 - name: '1961' num_bytes: 297225753 num_examples: 339076 - name: '1962' num_bytes: 231525869 num_examples: 264724 - name: '1963' num_bytes: 197520960 num_examples: 226859 download_size: 48388744959 dataset_size: 76303058024 --- # Dataset Card for "AmericanStories-parquet" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
chatblanc-ciel/llm-jp-corpus-v3
chatblanc-ciel
"2025-02-08T11:59:42Z"
12,405
1
[ "region:us" ]
null
"2025-02-04T20:41:46Z"
--- dataset_info: - config_name: code/code_stack features: - name: text dtype: string - name: meta struct: - name: alphanum_fraction dtype: float64 - name: avg_line_length dtype: float64 - name: ext dtype: string - name: hexsha dtype: string - name: lang dtype: string - name: max_forks_count dtype: int64 - name: max_forks_repo_forks_event_max_datetime dtype: string - name: max_forks_repo_forks_event_min_datetime dtype: string - name: max_forks_repo_head_hexsha dtype: string - name: max_forks_repo_licenses sequence: string - name: max_forks_repo_name dtype: string - name: max_forks_repo_path dtype: string - name: max_issues_count dtype: int64 - name: max_issues_repo_head_hexsha dtype: string - name: max_issues_repo_issues_event_max_datetime dtype: string - name: max_issues_repo_issues_event_min_datetime dtype: string - name: max_issues_repo_licenses sequence: string - name: max_issues_repo_name dtype: string - name: max_issues_repo_path dtype: string - name: max_line_length dtype: int64 - name: max_stars_count dtype: int64 - name: max_stars_repo_head_hexsha dtype: string - name: max_stars_repo_licenses sequence: string - name: max_stars_repo_name dtype: string - name: max_stars_repo_path dtype: string - name: max_stars_repo_stars_event_max_datetime dtype: string - name: max_stars_repo_stars_event_min_datetime dtype: string - name: size dtype: int64 splits: - name: train num_bytes: 348173288632 num_examples: 31988785 - name: validation num_bytes: 222242606 num_examples: 20191 download_size: 114077093040 dataset_size: 348395531238 - config_name: en/en_dolma/c4 features: - name: text dtype: string - name: meta struct: - name: added dtype: string - name: created dtype: timestamp[s] - name: id dtype: string - name: metadata struct: - name: lang dtype: string - name: length dtype: int64 - name: provenance dtype: string - name: split dtype: string - name: url dtype: string - name: source dtype: string - name: version dtype: string splits: - name: train num_bytes: 867599301252 num_examples: 364154043 download_size: 524569640737 dataset_size: 867599301252 - config_name: en/en_wiki features: - name: text dtype: string - name: meta struct: - name: id dtype: string - name: title dtype: string - name: url dtype: string splits: - name: train num_bytes: 19636405423 num_examples: 6681920 - name: validation num_bytes: 3948807 num_examples: 1479 download_size: 11637388371 dataset_size: 19640354230 - config_name: ja/ja_cc/level0 features: - name: text dtype: string - name: meta struct: - name: docId dtype: string - name: url dtype: string - name: date dtype: timestamp[s] splits: - name: train num_bytes: 1200355090865 num_examples: 176975451 download_size: 677018847187 dataset_size: 1200355090865 - config_name: ja/ja_cc/level0/CC-MAIN-2013-2016 features: - name: text dtype: string - name: meta struct: - name: docId dtype: string - name: url dtype: string - name: date dtype: timestamp[s] splits: - name: train num_bytes: 18077652679 num_examples: 3017867 download_size: 7550403004 dataset_size: 18077652679 - config_name: ja/ja_cc/level0/CC-MAIN-2013-2016.jsonl.gz features: - name: text dtype: string - name: meta struct: - name: docId dtype: string - name: url dtype: string - name: date dtype: timestamp[s] splits: - name: train num_bytes: 18077652679 num_examples: 3017867 download_size: 7550403004 dataset_size: 18077652679 - config_name: ja/ja_cc/level0/CC-MAIN-2017-04 features: - name: text dtype: string - name: meta struct: - name: docId dtype: string - name: url dtype: string - name: date dtype: timestamp[s] splits: - name: train num_bytes: 4305233736 num_examples: 862331 download_size: 2402819064 dataset_size: 4305233736 - config_name: ja/ja_cc/level0/CC-MAIN-2017-04.jsonl.gz features: - name: text dtype: string - name: meta struct: - name: docId dtype: string - name: url dtype: string - name: date dtype: timestamp[s] splits: - name: train num_bytes: 4305233736 num_examples: 862331 download_size: 2402819064 dataset_size: 4305233736 - config_name: ja/ja_cc/level0/CC-MAIN-2017-09 features: - name: text dtype: string - name: meta struct: - name: docId dtype: string - name: url dtype: string - name: date dtype: timestamp[s] splits: - name: train num_bytes: 5971066540 num_examples: 921503 download_size: 3347473301 dataset_size: 5971066540 - config_name: ja/ja_cc/level0/CC-MAIN-2017-09.jsonl.gz features: - name: text dtype: string - name: meta struct: - name: docId dtype: string - name: url dtype: string - name: date dtype: timestamp[s] splits: - name: train num_bytes: 5971066540 num_examples: 921503 download_size: 3347473301 dataset_size: 5971066540 - config_name: ja/ja_cc/level0/CC-MAIN-2017-13 features: - name: text dtype: string - name: meta struct: - name: docId dtype: string - name: url dtype: string - name: date dtype: timestamp[s] splits: - name: train num_bytes: 13623315580 num_examples: 1848849 download_size: 7696021811 dataset_size: 13623315580 - config_name: ja/ja_cc/level0/CC-MAIN-2017-13.jsonl.gz features: - name: text dtype: string - name: meta struct: - name: docId dtype: string - name: url dtype: string - name: date dtype: timestamp[s] splits: - name: train num_bytes: 13623315580 num_examples: 1848849 download_size: 7696021811 dataset_size: 13623315580 - config_name: ja/ja_cc/level0/CC-MAIN-2017-17 features: - name: text dtype: string - name: meta struct: - name: docId dtype: string - name: url dtype: string - name: date dtype: timestamp[s] splits: - name: train num_bytes: 19960969764 num_examples: 2725696 download_size: 11328454250 dataset_size: 19960969764 - config_name: ja/ja_cc/level0/CC-MAIN-2017-17.jsonl.gz features: - name: text dtype: string - name: meta struct: - name: docId dtype: string - name: url dtype: string - name: date dtype: timestamp[s] splits: - name: train num_bytes: 19960969764 num_examples: 2725696 download_size: 11328454250 dataset_size: 19960969764 - config_name: ja/ja_cc/level0/CC-MAIN-2017-22 features: - name: text dtype: string - name: meta struct: - name: docId dtype: string - name: url dtype: string - name: date dtype: timestamp[s] splits: - name: train num_bytes: 17578326638 num_examples: 2397667 download_size: 9974307898 dataset_size: 17578326638 - config_name: ja/ja_cc/level0/CC-MAIN-2017-26 features: - name: text dtype: string - name: meta struct: - name: docId dtype: string - name: url dtype: string - name: date dtype: timestamp[s] splits: - name: train num_bytes: 22170160936 num_examples: 3112260 download_size: 12584277127 dataset_size: 22170160936 - config_name: ja/ja_cc/level0/CC-MAIN-2017-26.jsonl.gz features: - name: text dtype: string - name: meta struct: - name: docId dtype: string - name: url dtype: string - name: date dtype: timestamp[s] splits: - name: train num_bytes: 22170160936 num_examples: 3112260 download_size: 12584277127 dataset_size: 22170160936 - config_name: ja/ja_cc/level0/CC-MAIN-2017-30 features: - name: text dtype: string - name: meta struct: - name: docId dtype: string - name: url dtype: string - name: date dtype: timestamp[s] splits: - name: train num_bytes: 14501823246 num_examples: 2085151 download_size: 8224812524 dataset_size: 14501823246 - config_name: ja/ja_cc/level0/CC-MAIN-2017-30.jsonl.gz features: - name: text dtype: string - name: meta struct: - name: docId dtype: string - name: url dtype: string - name: date dtype: timestamp[s] splits: - name: train num_bytes: 14501823246 num_examples: 2085151 download_size: 8224812524 dataset_size: 14501823246 - config_name: ja/ja_cc/level0/CC-MAIN-2017-34 features: - name: text dtype: string - name: meta struct: - name: docId dtype: string - name: url dtype: string - name: date dtype: timestamp[s] splits: - name: train num_bytes: 23608810552 num_examples: 3356687 download_size: 13414821009 dataset_size: 23608810552 - config_name: ja/ja_cc/level0/CC-MAIN-2017-39 features: - name: text dtype: string - name: meta struct: - name: docId dtype: string - name: url dtype: string - name: date dtype: timestamp[s] splits: - name: train num_bytes: 17528722403 num_examples: 2523962 download_size: 9959532528 dataset_size: 17528722403 - config_name: ja/ja_cc/level0/CC-MAIN-2017-43 features: - name: text dtype: string - name: meta struct: - name: docId dtype: string - name: url dtype: string - name: date dtype: timestamp[s] splits: - name: train num_bytes: 25287879895 num_examples: 3643052 download_size: 14381021861 dataset_size: 25287879895 - config_name: ja/ja_cc/level0/CC-MAIN-2017-47 features: - name: text dtype: string - name: meta struct: - name: docId dtype: string - name: url dtype: string - name: date dtype: timestamp[s] splits: - name: train num_bytes: 18652655999 num_examples: 2701257 download_size: 10619509120 dataset_size: 18652655999 - config_name: ja/ja_cc/level0/CC-MAIN-2017-51 features: - name: text dtype: string - name: meta struct: - name: docId dtype: string - name: url dtype: string - name: date dtype: timestamp[s] splits: - name: train num_bytes: 9831692896 num_examples: 1514501 download_size: 5583328328 dataset_size: 9831692896 - config_name: ja/ja_cc/level0/CC-MAIN-2018-05 features: - name: text dtype: string - name: meta struct: - name: docId dtype: string - name: url dtype: string - name: date dtype: timestamp[s] splits: - name: train num_bytes: 13912502392 num_examples: 2210709 download_size: 7870627736 dataset_size: 13912502392 - config_name: ja/ja_cc/level0/CC-MAIN-2018-09 features: - name: text dtype: string - name: meta struct: - name: docId dtype: string - name: url dtype: string - name: date dtype: timestamp[s] splits: - name: train num_bytes: 25391259180 num_examples: 3459786 download_size: 14403046625 dataset_size: 25391259180 - config_name: ja/ja_cc/level0/CC-MAIN-2018-13 features: - name: text dtype: string - name: meta struct: - name: docId dtype: string - name: url dtype: string - name: date dtype: timestamp[s] splits: - name: train num_bytes: 12553020822 num_examples: 1941599 download_size: 7130688242 dataset_size: 12553020822 - config_name: ja/ja_cc/level0/CC-MAIN-2018-17 features: - name: text dtype: string - name: meta struct: - name: docId dtype: string - name: url dtype: string - name: date dtype: timestamp[s] splits: - name: train num_bytes: 12282399444 num_examples: 1859267 download_size: 6964890817 dataset_size: 12282399444 - config_name: ja/ja_cc/level0/CC-MAIN-2018-22 features: - name: text dtype: string - name: meta struct: - name: docId dtype: string - name: url dtype: string - name: date dtype: timestamp[s] splits: - name: train num_bytes: 9780663564 num_examples: 1494976 download_size: 5544176652 dataset_size: 9780663564 - config_name: ja/ja_cc/level0/CC-MAIN-2018-26 features: - name: text dtype: string - name: meta struct: - name: docId dtype: string - name: url dtype: string - name: date dtype: timestamp[s] splits: - name: train num_bytes: 27545632634 num_examples: 4024429 download_size: 15644733329 dataset_size: 27545632634 - config_name: ja/ja_cc/level0/CC-MAIN-2018-30 features: - name: text dtype: string - name: meta struct: - name: docId dtype: string - name: url dtype: string - name: date dtype: timestamp[s] splits: - name: train num_bytes: 31031997328 num_examples: 4586683 download_size: 17624748283 dataset_size: 31031997328 - config_name: ja/ja_cc/level0/CC-MAIN-2018-34 features: - name: text dtype: string - name: meta struct: - name: docId dtype: string - name: url dtype: string - name: date dtype: timestamp[s] splits: - name: train num_bytes: 22080860363 num_examples: 3264249 download_size: 12492655364 dataset_size: 22080860363 - config_name: ja/ja_cc/level0/CC-MAIN-2018-39 features: - name: text dtype: string - name: meta struct: - name: docId dtype: string - name: url dtype: string - name: date dtype: timestamp[s] splits: - name: train num_bytes: 22470675389 num_examples: 3450223 download_size: 12757248945 dataset_size: 22470675389 - config_name: ja/ja_cc/level0/CC-MAIN-2018-43 features: - name: text dtype: string - name: meta struct: - name: docId dtype: string - name: url dtype: string - name: date dtype: timestamp[s] splits: - name: train num_bytes: 24488541335 num_examples: 3746893 download_size: 13892794865 dataset_size: 24488541335 - config_name: ja/ja_cc/level0/CC-MAIN-2018-47 features: - name: text dtype: string - name: meta struct: - name: docId dtype: string - name: url dtype: string - name: date dtype: timestamp[s] splits: - name: train num_bytes: 23947465467 num_examples: 3786390 download_size: 13508537763 dataset_size: 23947465467 - config_name: ja/ja_cc/level0/CC-MAIN-2018-51 features: - name: text dtype: string - name: meta struct: - name: docId dtype: string - name: url dtype: string - name: date dtype: timestamp[s] splits: - name: train num_bytes: 26614665841 num_examples: 4045567 download_size: 15048832666 dataset_size: 26614665841 - config_name: ja/ja_cc/level0/CC-MAIN-2019-04 features: - name: text dtype: string - name: meta struct: - name: docId dtype: string - name: url dtype: string - name: date dtype: timestamp[s] splits: - name: train num_bytes: 22884848739 num_examples: 3533334 download_size: 12968492466 dataset_size: 22884848739 - config_name: ja/ja_cc/level0/CC-MAIN-2019-09 features: - name: text dtype: string - name: meta struct: - name: docId dtype: string - name: url dtype: string - name: date dtype: timestamp[s] splits: - name: train num_bytes: 25283805979 num_examples: 3947864 download_size: 14284971196 dataset_size: 25283805979 - config_name: ja/ja_cc/level0/CC-MAIN-2019-13 features: - name: text dtype: string - name: meta struct: - name: docId dtype: string - name: url dtype: string - name: date dtype: timestamp[s] splits: - name: train num_bytes: 19746126963 num_examples: 3175341 download_size: 11180781298 dataset_size: 19746126963 - config_name: ja/ja_cc/level0/CC-MAIN-2019-18 features: - name: text dtype: string - name: meta struct: - name: docId dtype: string - name: url dtype: string - name: date dtype: timestamp[s] splits: - name: train num_bytes: 18807261380 num_examples: 2882443 download_size: 10622540297 dataset_size: 18807261380 - config_name: ja/ja_cc/level0/CC-MAIN-2019-22 features: - name: text dtype: string - name: meta struct: - name: docId dtype: string - name: url dtype: string - name: date dtype: timestamp[s] splits: - name: train num_bytes: 18715166841 num_examples: 2916593 download_size: 10592510452 dataset_size: 18715166841 - config_name: ja/ja_cc/level0/CC-MAIN-2019-26 features: - name: text dtype: string - name: meta struct: - name: docId dtype: string - name: url dtype: string - name: date dtype: timestamp[s] splits: - name: train num_bytes: 17440096682 num_examples: 2653638 download_size: 9877372013 dataset_size: 17440096682 - config_name: ja/ja_cc/level0/CC-MAIN-2019-30 features: - name: text dtype: string - name: meta struct: - name: docId dtype: string - name: url dtype: string - name: date dtype: timestamp[s] splits: - name: train num_bytes: 16610902541 num_examples: 2530721 download_size: 9405838738 dataset_size: 16610902541 - config_name: ja/ja_cc/level0/CC-MAIN-2019-35 features: - name: text dtype: string - name: meta struct: - name: docId dtype: string - name: url dtype: string - name: date dtype: timestamp[s] splits: - name: train num_bytes: 18894147887 num_examples: 2821144 download_size: 10683286133 dataset_size: 18894147887 - config_name: ja/ja_cc/level0/CC-MAIN-2019-39 features: - name: text dtype: string - name: meta struct: - name: docId dtype: string - name: url dtype: string - name: date dtype: timestamp[s] splits: - name: train num_bytes: 15837917988 num_examples: 2354006 download_size: 8962140008 dataset_size: 15837917988 - config_name: ja/ja_cc/level0/CC-MAIN-2019-43 features: - name: text dtype: string - name: meta struct: - name: docId dtype: string - name: url dtype: string - name: date dtype: timestamp[s] splits: - name: train num_bytes: 17587825217 num_examples: 2594362 download_size: 9939976141 dataset_size: 17587825217 - config_name: ja/ja_cc/level0/CC-MAIN-2019-47 features: - name: text dtype: string - name: meta struct: - name: docId dtype: string - name: url dtype: string - name: date dtype: timestamp[s] splits: - name: train num_bytes: 15140862538 num_examples: 2255557 download_size: 8545891143 dataset_size: 15140862538 - config_name: ja/ja_cc/level0/CC-MAIN-2019-51 features: - name: text dtype: string - name: meta struct: - name: docId dtype: string - name: url dtype: string - name: date dtype: timestamp[s] splits: - name: train num_bytes: 14089060004 num_examples: 2072557 download_size: 7966566882 dataset_size: 14089060004 - config_name: ja/ja_cc/level0/CC-MAIN-2020-05 features: - name: text dtype: string - name: meta struct: - name: docId dtype: string - name: url dtype: string - name: date dtype: timestamp[s] splits: - name: train num_bytes: 18647569661 num_examples: 2711409 download_size: 10521591947 dataset_size: 18647569661 - config_name: ja/ja_cc/level0/CC-MAIN-2020-10 features: - name: text dtype: string - name: meta struct: - name: docId dtype: string - name: url dtype: string - name: date dtype: timestamp[s] splits: - name: train num_bytes: 18211548642 num_examples: 2763344 download_size: 10320505385 dataset_size: 18211548642 - config_name: ja/ja_cc/level0/CC-MAIN-2020-16 features: - name: text dtype: string - name: meta struct: - name: docId dtype: string - name: url dtype: string - name: date dtype: timestamp[s] splits: - name: train num_bytes: 17663121284 num_examples: 2582186 download_size: 9962786925 dataset_size: 17663121284 - config_name: ja/ja_cc/level0/CC-MAIN-2020-24 features: - name: text dtype: string - 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name: meta struct: - name: docId dtype: string - name: url dtype: string - name: date dtype: timestamp[s] splits: - name: train num_bytes: 2805739428 num_examples: 431774 download_size: 1567017259 dataset_size: 2805739428 - config_name: ja/ja_warp_html/level0 features: - name: text dtype: string - name: meta struct: - name: docId dtype: string - name: url dtype: string - name: charset dtype: string - name: date dtype: timestamp[s] splits: - name: train num_bytes: 4847780475 num_examples: 1119789 download_size: 2711767060 dataset_size: 4847780475 - config_name: ja/ja_warp_html/level1 features: - name: text dtype: string - name: meta struct: - name: docId dtype: string - name: url dtype: string - name: charset dtype: string - name: date dtype: timestamp[s] splits: - name: train num_bytes: 1305891625 num_examples: 304649 download_size: 730239447 dataset_size: 1305891625 - config_name: ja/ja_warp_html/level2 features: - name: text dtype: string - name: meta struct: - name: docId dtype: string - name: url dtype: string - name: charset dtype: string - name: date dtype: timestamp[s] splits: - name: train num_bytes: 1040882464 num_examples: 235298 download_size: 580651323 dataset_size: 1040882464 - config_name: ja/ja_wiki features: - name: text dtype: string - name: meta struct: - name: id dtype: string - name: title dtype: string - name: url dtype: string splits: - name: train num_bytes: 6254632459 num_examples: 1363395 - name: validation num_bytes: 4373553 num_examples: 1134 download_size: 3520251395 dataset_size: 6259006012 - config_name: ja/kaken features: - name: text dtype: string splits: - name: train num_bytes: 4665198601 num_examples: 1058171 - name: validation num_bytes: 248808941 num_examples: 56208 download_size: 1858435769 dataset_size: 4914007542 - config_name: ko/ko_wiki features: - name: text dtype: string - name: meta struct: - name: id dtype: string - name: title dtype: string - name: url dtype: string splits: - name: train num_bytes: 1430850265 num_examples: 655484 - name: validation num_bytes: 2342145 num_examples: 1000 download_size: 834573775 dataset_size: 1433192410 - config_name: zh/zh_wiki features: - name: text dtype: string - name: meta struct: - name: id dtype: string - name: title dtype: string - name: url dtype: string splits: - name: train num_bytes: 2774101324 num_examples: 1398694 - name: validation num_bytes: 2032067 num_examples: 1000 download_size: 1822495934 dataset_size: 2776133391 configs: - config_name: code/code_stack data_files: - split: train path: code/code_stack/train-* - split: validation path: code/code_stack/validation-* - config_name: en/en_dolma/c4 data_files: - split: train path: en/en_dolma/c4/train-* - config_name: en/en_wiki data_files: - split: train path: en/en_wiki/train-* - split: validation path: en/en_wiki/validation-* - config_name: ja/ja_cc/level0 data_files: - split: train path: ja/ja_cc/level0/train-* - config_name: ja/ja_cc/level0/CC-MAIN-2013-2016 data_files: - split: train path: ja/ja_cc/level0/CC-MAIN-2013-2016/train-* - config_name: ja/ja_cc/level0/CC-MAIN-2013-2016.jsonl.gz data_files: - split: train path: ja/ja_cc/level0/CC-MAIN-2013-2016.jsonl.gz/train-* - config_name: ja/ja_cc/level0/CC-MAIN-2017-04 data_files: - split: train path: ja/ja_cc/level0/CC-MAIN-2017-04/train-* - config_name: ja/ja_cc/level0/CC-MAIN-2017-04.jsonl.gz data_files: - split: train path: ja/ja_cc/level0/CC-MAIN-2017-04.jsonl.gz/train-* - config_name: ja/ja_cc/level0/CC-MAIN-2017-09 data_files: - split: train path: ja/ja_cc/level0/CC-MAIN-2017-09/train-* - config_name: ja/ja_cc/level0/CC-MAIN-2017-09.jsonl.gz data_files: - split: train path: ja/ja_cc/level0/CC-MAIN-2017-09.jsonl.gz/train-* - config_name: ja/ja_cc/level0/CC-MAIN-2017-13 data_files: - split: train path: ja/ja_cc/level0/CC-MAIN-2017-13/train-* - config_name: ja/ja_cc/level0/CC-MAIN-2017-13.jsonl.gz data_files: - split: train path: ja/ja_cc/level0/CC-MAIN-2017-13.jsonl.gz/train-* - config_name: ja/ja_cc/level0/CC-MAIN-2017-17 data_files: - split: train path: ja/ja_cc/level0/CC-MAIN-2017-17/train-* - 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split: train path: ja/ja_cc/level0/CC-MAIN-2018-26/train-* - config_name: ja/ja_cc/level0/CC-MAIN-2018-30 data_files: - split: train path: ja/ja_cc/level0/CC-MAIN-2018-30/train-* - config_name: ja/ja_cc/level0/CC-MAIN-2018-34 data_files: - split: train path: ja/ja_cc/level0/CC-MAIN-2018-34/train-* - config_name: ja/ja_cc/level0/CC-MAIN-2018-39 data_files: - split: train path: ja/ja_cc/level0/CC-MAIN-2018-39/train-* - config_name: ja/ja_cc/level0/CC-MAIN-2018-43 data_files: - split: train path: ja/ja_cc/level0/CC-MAIN-2018-43/train-* - config_name: ja/ja_cc/level0/CC-MAIN-2018-47 data_files: - split: train path: ja/ja_cc/level0/CC-MAIN-2018-47/train-* - config_name: ja/ja_cc/level0/CC-MAIN-2018-51 data_files: - split: train path: ja/ja_cc/level0/CC-MAIN-2018-51/train-* - config_name: ja/ja_cc/level0/CC-MAIN-2019-04 data_files: - split: train path: ja/ja_cc/level0/CC-MAIN-2019-04/train-* - config_name: ja/ja_cc/level0/CC-MAIN-2019-09 data_files: - split: train path: ja/ja_cc/level0/CC-MAIN-2019-09/train-* - 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split: train path: ja/ja_cc/level0/CC-MAIN-2019-47/train-* - config_name: ja/ja_cc/level0/CC-MAIN-2019-51 data_files: - split: train path: ja/ja_cc/level0/CC-MAIN-2019-51/train-* - config_name: ja/ja_cc/level0/CC-MAIN-2020-05 data_files: - split: train path: ja/ja_cc/level0/CC-MAIN-2020-05/train-* - config_name: ja/ja_cc/level0/CC-MAIN-2020-10 data_files: - split: train path: ja/ja_cc/level0/CC-MAIN-2020-10/train-* - config_name: ja/ja_cc/level0/CC-MAIN-2020-16 data_files: - split: train path: ja/ja_cc/level0/CC-MAIN-2020-16/train-* - config_name: ja/ja_cc/level0/CC-MAIN-2020-24 data_files: - split: train path: ja/ja_cc/level0/CC-MAIN-2020-24/train-* - config_name: ja/ja_cc/level0/CC-MAIN-2020-29 data_files: - split: train path: ja/ja_cc/level0/CC-MAIN-2020-29/train-* - config_name: ja/ja_cc/level0/CC-MAIN-2020-34 data_files: - split: train path: ja/ja_cc/level0/CC-MAIN-2020-34/train-* - config_name: ja/ja_cc/level0/CC-MAIN-2020-40 data_files: - split: train path: ja/ja_cc/level0/CC-MAIN-2020-40/train-* - config_name: ja/ja_cc/level0/CC-MAIN-2020-45 data_files: - split: train path: ja/ja_cc/level0/CC-MAIN-2020-45/train-* - config_name: ja/ja_cc/level0/CC-MAIN-2020-50 data_files: - split: train path: ja/ja_cc/level0/CC-MAIN-2020-50/train-* - config_name: ja/ja_cc/level0/CC-MAIN-2021-04 data_files: - split: train path: ja/ja_cc/level0/CC-MAIN-2021-04/train-* - config_name: ja/ja_cc/level0/CC-MAIN-2021-10 data_files: - split: train path: ja/ja_cc/level0/CC-MAIN-2021-10/train-* - config_name: ja/ja_cc/level0/CC-MAIN-2021-17 data_files: - split: train path: ja/ja_cc/level0/CC-MAIN-2021-17/train-* - config_name: ja/ja_cc/level0/CC-MAIN-2021-21 data_files: - split: train path: ja/ja_cc/level0/CC-MAIN-2021-21/train-* - config_name: ja/ja_cc/level0/CC-MAIN-2021-25 data_files: - split: train path: ja/ja_cc/level0/CC-MAIN-2021-25/train-* - config_name: ja/ja_cc/level0/CC-MAIN-2021-31 data_files: - split: train path: ja/ja_cc/level0/CC-MAIN-2021-31/train-* - config_name: ja/ja_cc/level0/CC-MAIN-2021-39 data_files: - split: train path: ja/ja_cc/level0/CC-MAIN-2021-39/train-* - config_name: ja/ja_cc/level0/CC-MAIN-2021-43 data_files: - split: train path: ja/ja_cc/level0/CC-MAIN-2021-43/train-* - config_name: ja/ja_cc/level0/CC-MAIN-2021-49 data_files: - split: train path: ja/ja_cc/level0/CC-MAIN-2021-49/train-* - config_name: ja/ja_cc/level0/CC-MAIN-2022-05 data_files: - split: train path: ja/ja_cc/level0/CC-MAIN-2022-05/train-* - config_name: ja/ja_cc/level0/CC-MAIN-2022-21 data_files: - split: train path: ja/ja_cc/level0/CC-MAIN-2022-21/train-* - config_name: ja/ja_cc/level0/CC-MAIN-2022-27 data_files: - split: train path: ja/ja_cc/level0/CC-MAIN-2022-27/train-* - config_name: ja/ja_cc/level0/CC-MAIN-2022-33 data_files: - split: train path: ja/ja_cc/level0/CC-MAIN-2022-33/train-* - config_name: ja/ja_cc/level0/CC-MAIN-2022-40 data_files: - split: train path: ja/ja_cc/level0/CC-MAIN-2022-40/train-* - config_name: ja/ja_cc/level0/CC-MAIN-2022-49 data_files: - split: train path: ja/ja_cc/level0/CC-MAIN-2022-49/train-* - config_name: ja/ja_cc/level0/CC-MAIN-2023-06 data_files: - split: train path: ja/ja_cc/level0/CC-MAIN-2023-06/train-* - config_name: ja/ja_cc/level0/CC-MAIN-2023-14 data_files: - split: train path: ja/ja_cc/level0/CC-MAIN-2023-14/train-* - config_name: ja/ja_cc/level0/CC-MAIN-2023-23 data_files: - split: train path: ja/ja_cc/level0/CC-MAIN-2023-23/train-* - config_name: ja/ja_cc/level0/CC-MAIN-2023-40 data_files: - split: train path: ja/ja_cc/level0/CC-MAIN-2023-40/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2013-2016 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2013-2016/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2013-2016.jsonl.gz data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2013-2016.jsonl.gz/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2017-04 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2017-04/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2017-04.jsonl.gz data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2017-04.jsonl.gz/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2017-09 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2017-09/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2017-09.jsonl.gz data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2017-09.jsonl.gz/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2017-13 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2017-13/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2017-13.jsonl.gz data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2017-13.jsonl.gz/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2017-17 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2017-17/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2017-17.jsonl.gz data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2017-17.jsonl.gz/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2017-22 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2017-22/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2017-22.jsonl.gz data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2017-22.jsonl.gz/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2017-26 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2017-26/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2017-26.jsonl.gz data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2017-26.jsonl.gz/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2017-30 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2017-30/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2017-30.jsonl.gz data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2017-30.jsonl.gz/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2017-34 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2017-34/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2017-34.jsonl.gz data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2017-34.jsonl.gz/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2017-39 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2017-39/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2017-39.jsonl.gz data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2017-39.jsonl.gz/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2017-43 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2017-43/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2017-43.jsonl.gz data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2017-43.jsonl.gz/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2017-47 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2017-47/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2017-47.jsonl.gz data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2017-47.jsonl.gz/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2017-51 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2017-51/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2017-51.jsonl.gz data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2017-51.jsonl.gz/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2018-05 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2018-05/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2018-05.jsonl.gz data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2018-05.jsonl.gz/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2018-09 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2018-09/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2018-09.jsonl.gz data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2018-09.jsonl.gz/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2018-13 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2018-13/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2018-13.jsonl.gz data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2018-13.jsonl.gz/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2018-17 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2018-17/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2018-17.jsonl.gz data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2018-17.jsonl.gz/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2018-22 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2018-22/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2018-22.jsonl.gz data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2018-22.jsonl.gz/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2018-26 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2018-26/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2018-30 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2018-30/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2018-34 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2018-34/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2018-39 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2018-39/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2018-43 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2018-43/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2018-47 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2018-47/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2018-51 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2018-51/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2019-04 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2019-04/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2019-09 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2019-09/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2019-13 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2019-13/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2019-18 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2019-18/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2019-22 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2019-22/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2019-26 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2019-26/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2019-30 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2019-30/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2019-35 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2019-35/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2019-39 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2019-39/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2019-43 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2019-43/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2019-47 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2019-47/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2019-51 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2019-51/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2020-05 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2020-05/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2020-10 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2020-10/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2020-16 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2020-16/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2020-24 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2020-24/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2020-29 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2020-29/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2020-34 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2020-34/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2020-40 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2020-40/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2020-45 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2020-45/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2020-50 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2020-50/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2021-04 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2021-04/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2021-10 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2021-10/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2021-17 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2021-17/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2021-21 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2021-21/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2021-25 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2021-25/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2021-31 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2021-31/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2021-39 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2021-39/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2021-43 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2021-43/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2021-49 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2021-49/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2022-05 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2022-05/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2022-21 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2022-21/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2022-27 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2022-27/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2022-33 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2022-33/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2022-40 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2022-40/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2022-49 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2022-49/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2023-06 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2023-06/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2023-14 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2023-14/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2023-23 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2023-23/train-* - config_name: ja/ja_cc/level1/CC-MAIN-2023-40 data_files: - split: train path: ja/ja_cc/level1/CC-MAIN-2023-40/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2013-2016 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2013-2016/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2013-2016.jsonl.gz data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2013-2016.jsonl.gz/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2017-04 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2017-04/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2017-04.jsonl.gz data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2017-04.jsonl.gz/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2017-09 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2017-09/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2017-09.jsonl.gz data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2017-09.jsonl.gz/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2017-13 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2017-13/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2017-13.jsonl.gz data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2017-13.jsonl.gz/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2017-17 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2017-17/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2017-17.jsonl.gz data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2017-17.jsonl.gz/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2017-22 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2017-22/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2017-22.jsonl.gz data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2017-22.jsonl.gz/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2017-26 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2017-26/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2017-26.jsonl.gz data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2017-26.jsonl.gz/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2017-30 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2017-30/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2017-30.jsonl.gz data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2017-30.jsonl.gz/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2017-34 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2017-34/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2017-34.jsonl.gz data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2017-34.jsonl.gz/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2017-39 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2017-39/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2017-39.jsonl.gz data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2017-39.jsonl.gz/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2017-43 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2017-43/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2017-43.jsonl.gz data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2017-43.jsonl.gz/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2017-47 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2017-47/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2017-47.jsonl.gz data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2017-47.jsonl.gz/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2017-51 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2017-51/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2017-51.jsonl.gz data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2017-51.jsonl.gz/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2018-05 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2018-05/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2018-09 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2018-09/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2018-09.jsonl.gz data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2018-09.jsonl.gz/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2018-13 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2018-13/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2018-17 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2018-17/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2018-22 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2018-22/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2018-26 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2018-26/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2018-30 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2018-30/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2018-34 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2018-34/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2018-39 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2018-39/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2018-43 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2018-43/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2018-47 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2018-47/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2018-51 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2018-51/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2019-04 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2019-04/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2019-09 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2019-09/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2019-13 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2019-13/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2019-18 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2019-18/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2019-22 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2019-22/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2019-26 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2019-26/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2019-30 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2019-30/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2019-35 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2019-35/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2019-39 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2019-39/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2019-43 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2019-43/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2019-47 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2019-47/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2019-51 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2019-51/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2020-05 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2020-05/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2020-10 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2020-10/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2020-16 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2020-16/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2020-24 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2020-24/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2020-29 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2020-29/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2020-34 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2020-34/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2020-40 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2020-40/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2020-45 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2020-45/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2020-50 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2020-50/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2021-04 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2021-04/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2021-10 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2021-10/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2021-17 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2021-17/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2021-21 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2021-21/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2021-25 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2021-25/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2021-31 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2021-31/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2021-39 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2021-39/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2021-43 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2021-43/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2021-49 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2021-49/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2022-05 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2022-05/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2022-21 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2022-21/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2022-27 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2022-27/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2022-33 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2022-33/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2022-40 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2022-40/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2022-49 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2022-49/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2023-06 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2023-06/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2023-14 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2023-14/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2023-23 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2023-23/train-* - config_name: ja/ja_cc/level2/CC-MAIN-2023-40 data_files: - split: train path: ja/ja_cc/level2/CC-MAIN-2023-40/train-* - config_name: ja/ja_warp_html/level0 data_files: - split: train path: ja/ja_warp_html/level0/train-* - config_name: ja/ja_warp_html/level1 data_files: - split: train path: ja/ja_warp_html/level1/train-* - config_name: ja/ja_warp_html/level2 data_files: - split: train path: ja/ja_warp_html/level2/train-* - config_name: ja/ja_wiki data_files: - split: train path: ja/ja_wiki/train-* - split: validation path: ja/ja_wiki/validation-* - config_name: ja/kaken data_files: - split: train path: ja/kaken/train-* - split: validation path: ja/kaken/validation-* - config_name: ko/ko_wiki data_files: - split: train path: ko/ko_wiki/train-* - split: validation path: ko/ko_wiki/validation-* - config_name: zh/zh_wiki data_files: - split: train path: zh/zh_wiki/train-* - split: validation path: zh/zh_wiki/validation-* ---
fimu-docproc-research/CIVQA-TesseractOCR-LayoutLM
fimu-docproc-research
"2023-11-21T20:45:53Z"
12,399
0
[ "language:cs", "license:mit", "region:us", "finance" ]
null
"2023-11-16T01:45:17Z"
--- dataset_info: features: - name: input_ids sequence: int64 - name: bbox dtype: array2_d: shape: - 512 - 4 dtype: int64 - name: attention_mask sequence: int64 - name: image dtype: array3_d: shape: - 3 - 224 - 224 dtype: int64 - name: start_positions dtype: int64 - name: end_positions dtype: int64 - name: questions dtype: string - name: answers dtype: string splits: - name: train num_bytes: 198175471439 num_examples: 160645 - name: validation num_bytes: 20009392368 num_examples: 16220 download_size: 826530358 dataset_size: 218184863807 language: - cs tags: - finance pretty_name: C license: mit --- # CIVQA TesseractOCR LayoutLM Dataset The Czech Invoice Visual Question Answering dataset was created with Tesseract OCR and encoded for the LayoutLM. The pre-encoded dataset can be found on this link: https://huggingface.co/datasets/fimu-docproc-research/CIVQA-TesseractOCR All invoices used in this dataset were obtained from public sources. Over these invoices, we were focusing on 15 different entities, which are crucial for processing the invoices. - Invoice number - Variable symbol - Specific symbol - Constant symbol - Bank code - Account number - ICO - Total amount - Invoice date - Due date - Name of supplier - IBAN - DIC - QR code - Supplier's address The invoices included in this dataset were gathered from the internet. We understand that privacy is of utmost importance. Therefore, we sincerely apologise for any inconvenience caused by including your identifiable information in this dataset. If you have identified your data in this dataset and wish to have it removed from research purposes, we request you kindly to access the following URL: https://forms.gle/tUVJKoB22oeTncUD6 We profoundly appreciate your cooperation and understanding in this matter.
bigcode/commitpack
bigcode
"2025-02-04T15:41:01Z"
12,392
63
[ "language:code", "license:mit", "arxiv:2308.07124", "region:us" ]
null
"2023-01-17T11:53:28Z"
--- license: mit pretty_name: CommitPack language: - code --- ![Octopack](https://github.com/bigcode-project/octopack/blob/31f3320f098703c7910e43492c39366eeea68d83/banner.png?raw=true) # Dataset Card for CommitPack ## 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 > CommitPack is is a 4TB dataset of commits scraped from GitHub repositories that are permissively licensed. - **Creation:** The dataset can be recreated using instructions available [here](https://github.com/bigcode-project/octopack). - **Languages:** 350 - **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> ## Dataset Structure ### Data Instances An example looks as follows: ```json { 'commit': '0c17311f7fd511f5dae8f8e4acc2dce1a2de3cf5', 'old_file': 'main.py', 'new_file': 'main.py', 'old_contents': "import numpy as np\nimport matplotlib.pyplot as plt\n\n# generate sample data\nx_data = np.linspace(-5, 5, 20)\ny_data = np.random.normal(0.0, 1.0, x_data.size)\n\nplt.plot(x_data, y_data, 'o')\nplt.show()\n", 'new_contents': "import math\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# generate sample data\nx_data = np.linspace(-math.pi, math.pi, 30)\ny_data = np.sin(x_data) + np.random.normal(0.0, 0.1, x_data.size)\n\nplt.plot(x_data, y_data, 'o')\nplt.show()\n\n", 'subject': 'Change to sin() function with noise', 'message': 'Change to sin() function with noise\n', 'lang': 'Python', 'license': 'mit', 'repos': 'MorganR/basic-gaussian-process', 'returncode': 0, 'stderr': '' } ``` ### Data Fields The data fields are the same among all splits: - `commit`: unique commit id - `old_file`: name of the file before the commit - `new_file`: name of the file after the commit - `old_contents`: contents of the file before the commit - `new_contents`: contents of the file after the commit - `subject`: subject of the commit (this is used for all experiments in the paper) - `message`: message of the commit (commonly the same as the subject) - `lang`: programming language - `license`: license of the repository the code stems from, one of `['mit', 'artistic-2.0', 'isc', 'cc0-1.0', 'epl-1.0', 'mpl-2.0', 'unlicense', 'unknown', 'apache-2.0', 'bsd-3-clause', 'agpl-3.0', 'lgpl-2.1', 'bsd-2-clause']` - `repos`: name of the the repository the code stems from (if multiple, they are comma separated) - `returncode`: if applicable errorcode during scraping (0 = no error) - 'stderr': if applicable the error that occured during scraping (empty = no error) ### Data Splits | Name | Megabytes | % of total | Samples | % of total | | --- | --- | --- | --- | --- | | total | 3709175.78 | 100.0% | 57700105 | 100.0% | | json | 583293.816 | 15.7257% | 3495038 | 6.0572% | | xml | 279208.676 | 7.5275% | 1923159 | 3.333% | | text | 270662.596 | 7.2971% | 1389525 | 2.4082% | | javascript | 262824.844 | 7.0858% | 5401937 | 9.3621% | | objective-c++ | 239009.3 | 6.4437% | 32227 | 0.0559% | | python | 234311.564 | 6.3171% | 6189601 | 10.7272% | | c | 200876.804 | 5.4157% | 2779478 | 4.8171% | | c++ | 186585.256 | 5.0304% | 2402294 | 4.1634% | | markdown | 171849.952 | 4.6331% | 7645354 | 13.2502% | | java | 127103.448 | 3.4267% | 3744377 | 6.4894% | | html | 105305.284 | 2.839% | 2366841 | 4.102% | | yaml | 100466.64 | 2.7086% | 2592787 | 4.4936% | | go | 86444.624 | 2.3306% | 1183612 | 2.0513% | | csv | 82946.192 | 2.2362% | 79268 | 0.1374% | | php | 74961.64 | 2.021% | 2555419 | 4.4288% | | jupyter-notebook | 66854.08 | 1.8024% | 94000 | 0.1629% | | gettext-catalog | 62296.88 | 1.6795% | 168327 | 0.2917% | | sql | 56802.764 | 1.5314% | 132772 | 0.2301% | | unity3d-asset | 39535.008 | 1.0659% | 17867 | 0.031% | | typescript | 39254.804 | 1.0583% | 572136 | 0.9916% | | web-ontology-language | 36435.464 | 0.9823% | 7458 | 0.0129% | | ruby | 35830.74 | 0.966% | 2928702 | 5.0757% | | c# | 33669.652 | 0.9077% | 923157 | 1.5999% | | nix | 33547.92 | 0.9045% | 221281 | 0.3835% | | shell | 25109.952 | 0.677% | 1017977 | 1.7643% | | perl | 21148.928 | 0.5702% | 374266 | 0.6486% | | tex | 17471.108 | 0.471% | 89283 | 0.1547% | | css | 16306.632 | 0.4396% | 548818 | 0.9512% | | restructuredtext | 15613.888 | 0.421% | 494037 | 0.8562% | | rust | 15011.296 | 0.4047% | 296214 | 0.5134% | | groff | 12020.188 | 0.3241% | 32923 | 0.0571% | | ini | 8375.164 | 0.2258% | 297100 | 0.5149% | | scala | 8325.96 | 0.2245% | 316064 | 0.5478% | | coffeescript | 6795.14 | 0.1832% | 292446 | 0.5068% | | haskell | 6306.12 | 0.17% | 217325 | 0.3766% | | swift | 5902.716 | 0.1591% | 319289 | 0.5534% | | lua | 5763.12 | 0.1554% | 139091 | 0.2411% | | svg | 5645.44 | 0.1522% | 27095 | 0.047% | | gas | 5585.384 | 0.1506% | 15121 | 0.0262% | | ocaml | 5355.4 | 0.1444% | 81360 | 0.141% | | erlang | 5043.32 | 0.136% | 93685 | 0.1624% | | makefile | 4238.512 | 0.1143% | 343379 | 0.5951% | | asciidoc | 4138.588 | 0.1116% | 96671 | 0.1675% | | emacs-lisp | 3988.652 | 0.1075% | 83228 | 0.1442% | | scss | 3944.936 | 0.1064% | 288190 | 0.4995% | | clojure | 3523.408 | 0.095% | 158674 | 0.275% | | org | 3126.22 | 0.0843% | 30198 | 0.0523% | | common-lisp | 2954.904 | 0.0797% | 74628 | 0.1293% | | diff | 2586.048 | 0.0697% | 21021 | 0.0364% | | groovy | 2569.14 | 0.0693% | 110057 | 0.1907% | | html+erb | 2450.676 | 0.0661% | 225379 | 0.3906% | | nesc | 2439.564 | 0.0658% | 473 | 0.0008% | | dart | 2395.796 | 0.0646% | 56873 | 0.0986% | | powershell | 2289.276 | 0.0617% | 55381 | 0.096% | | f# | 2289.236 | 0.0617% | 66840 | 0.1158% | | dm | 2223.144 | 0.0599% | 55584 | 0.0963% | | kotlin | 2219.248 | 0.0598% | 124266 | 0.2154% | | pascal | 2194.676 | 0.0592% | 42511 | 0.0737% | | jsx | 2124.744 | 0.0573% | 139148 | 0.2412% | | viml | 1948.208 | 0.0525% | 74062 | 0.1284% | | actionscript | 1844.148 | 0.0497% | 28819 | 0.0499% | | cython | 1736.588 | 0.0468% | 25927 | 0.0449% | | turtle | 1698.948 | 0.0458% | 3882 | 0.0067% | | less | 1616.564 | 0.0436% | 88634 | 0.1536% | | mathematica | 1475.044 | 0.0398% | 925 | 0.0016% | | xslt | 1441.456 | 0.0389% | 27956 | 0.0485% | | scheme | 1249.244 | 0.0337% | 30546 | 0.0529% | | perl6 | 1223.16 | 0.033% | 12167 | 0.0211% | | edn | 1186.94 | 0.032% | 2289 | 0.004% | | fortran | 1178.548 | 0.0318% | 13463 | 0.0233% | | java-server-pages | 1173.072 | 0.0316% | 53574 | 0.0928% | | standard-ml | 1133.476 | 0.0306% | 20097 | 0.0348% | | cmake | 1132.068 | 0.0305% | 58446 | 0.1013% | | json5 | 1108.2 | 0.0299% | 1827 | 0.0032% | | vala | 1104.512 | 0.0298% | 14822 | 0.0257% | | vue | 1093.8 | 0.0295% | 68967 | 0.1195% | | freemarker | 1032.332 | 0.0278% | 36216 | 0.0628% | | graphql | 1004.844 | 0.0271% | 2009 | 0.0035% | | twig | 958.96 | 0.0259% | 39588 | 0.0686% | | tcl | 869.832 | 0.0235% | 16407 | 0.0284% | | pod | 859.016 | 0.0232% | 14922 | 0.0259% | | dockerfile | 849.728 | 0.0229% | 259379 | 0.4495% | | yacc | 845.704 | 0.0228% | 8230 | 0.0143% | | postscript | 800.728 | 0.0216% | 903 | 0.0016% | | racket | 796.64 | 0.0215% | 16615 | 0.0288% | | eagle | 785.684 | 0.0212% | 2237 | 0.0039% | | haxe | 772.896 | 0.0208% | 28447 | 0.0493% | | julia | 752.068 | 0.0203% | 22695 | 0.0393% | | handlebars | 740.816 | 0.02% | 49842 | 0.0864% | | smarty | 720.944 | 0.0194% | 41065 | 0.0712% | | visual-basic | 681.516 | 0.0184% | 10511 | 0.0182% | | literate-haskell | 673.74 | 0.0182% | 10729 | 0.0186% | | smalltalk | 665.892 | 0.018% | 11741 | 0.0203% | | isabelle | 655.82 | 0.0177% | 8359 | 0.0145% | | nimrod | 652.86 | 0.0176% | 12023 | 0.0208% | | zig | 621.384 | 0.0168% | 4290 | 0.0074% | | m4 | 603.584 | 0.0163% | 12465 | 0.0216% | | max | 603.56 | 0.0163% | 2259 | 0.0039% | | elixir | 558.116 | 0.015% | 35473 | 0.0615% | | mako | 543.012 | 0.0146% | 8943 | 0.0155% | | arduino | 534.176 | 0.0144% | 32350 | 0.0561% | | jade | 531.4 | 0.0143% | 46993 | 0.0814% | | haml | 502.012 | 0.0135% | 74792 | 0.1296% | | elm | 481.968 | 0.013% | 18542 | 0.0321% | | purebasic | 474.276 | 0.0128% | 36 | 0.0001% | | coldfusion | 470.78 | 0.0127% | 9263 | 0.0161% | | lean | 470.032 | 0.0127% | 7507 | 0.013% | | r | 454.32 | 0.0122% | 12858 | 0.0223% | | cuda | 437.668 | 0.0118% | 11450 | 0.0198% | | textile | 425.116 | 0.0115% | 18491 | 0.032% | | robotframework | 421.612 | 0.0114% | 9211 | 0.016% | | abap | 409.62 | 0.011% | 1955 | 0.0034% | | rdoc | 397.028 | 0.0107% | 38760 | 0.0672% | | llvm | 382.2 | 0.0103% | 10727 | 0.0186% | | ada | 380.7 | 0.0103% | 13258 | 0.023% | | batchfile | 372.16 | 0.01% | 43674 | 0.0757% | | qml | 361.452 | 0.0097% | 19360 | 0.0336% | | jasmin | 359.82 | 0.0097% | 4782 | 0.0083% | | assembly | 343.62 | 0.0093% | 8126 | 0.0141% | | g-code | 334.964 | 0.009% | 3690 | 0.0064% | | cucumber | 331.38 | 0.0089% | 26677 | 0.0462% | | html+php | 323.348 | 0.0087% | 18381 | 0.0319% | | kicad | 321.936 | 0.0087% | 759 | 0.0013% | | api-blueprint | 317.852 | 0.0086% | 4765 | 0.0083% | | eiffel | 311.48 | 0.0084% | 373 | 0.0006% | | toml | 292.676 | 0.0079% | 63517 | 0.1101% | | modelica | 284.616 | 0.0077% | 2611 | 0.0045% | | bitbake | 277.576 | 0.0075% | 43239 | 0.0749% | | lex | 275.96 | 0.0074% | 705 | 0.0012% | | stylus | 273.056 | 0.0074% | 21967 | 0.0381% | | protocol-buffer | 254.124 | 0.0069% | 9202 | 0.0159% | | unknown | 252.228 | 0.0068% | 30570 | 0.053% | | nit | 244.54 | 0.0066% | 4951 | 0.0086% | | factor | 241.192 | 0.0065% | 15378 | 0.0267% | | xs | 239.04 | 0.0064% | 3215 | 0.0056% | | sass | 230.648 | 0.0062% | 23144 | 0.0401% | | parrot-internal-representation | 230.196 | 0.0062% | 6231 | 0.0108% | | html+django | 217.04 | 0.0059% | 10535 | 0.0183% | | mediawiki | 214.324 | 0.0058% | 10188 | 0.0177% | | logos | 212.296 | 0.0057% | 1733 | 0.003% | | genshi | 209.3 | 0.0056% | 956 | 0.0017% | | coldfusion-cfc | 208.164 | 0.0056% | 4410 | 0.0076% | | xtend | 179.544 | 0.0048% | 7775 | 0.0135% | | sqf | 168.656 | 0.0045% | 7778 | 0.0135% | | vhdl | 155.948 | 0.0042% | 2185 | 0.0038% | | antlr | 143.548 | 0.0039% | 3651 | 0.0063% | | systemverilog | 140.192 | 0.0038% | 3944 | 0.0068% | | hcl | 136.752 | 0.0037% | 13379 | 0.0232% | | asp | 136.104 | 0.0037% | 4286 | 0.0074% | | nsis | 129.124 | 0.0035% | 4048 | 0.007% | | inform-7 | 120.188 | 0.0032% | 184 | 0.0003% | | slim | 119.036 | 0.0032% | 18726 | 0.0325% | | groovy-server-pages | 117.368 | 0.0032% | 6695 | 0.0116% | | ceylon | 116.144 | 0.0031% | 7256 | 0.0126% | | fish | 111.28 | 0.003% | 15351 | 0.0266% | | processing | 108.58 | 0.0029% | 5912 | 0.0102% | | component-pascal | 105.5 | 0.0028% | 43 | 0.0001% | | lasso | 104.168 | 0.0028% | 67 | 0.0001% | | glsl | 99.488 | 0.0027% | 9478 | 0.0164% | | saltstack | 98.196 | 0.0026% | 12314 | 0.0213% | | xbase | 94.424 | 0.0025% | 1670 | 0.0029% | | autohotkey | 94.22 | 0.0025% | 1452 | 0.0025% | | liquid | 93.792 | 0.0025% | 2651 | 0.0046% | | purescript | 92.412 | 0.0025% | 5024 | 0.0087% | | agda | 92.06 | 0.0025% | 4956 | 0.0086% | | inno-setup | 91.36 | 0.0025% | 3014 | 0.0052% | | oz | 90.476 | 0.0024% | 1551 | 0.0027% | | chapel | 89.62 | 0.0024% | 26447 | 0.0458% | | arc | 87.212 | 0.0024% | 758 | 0.0013% | | opencl | 86.432 | 0.0023% | 2489 | 0.0043% | | graphviz-dot | 85.804 | 0.0023% | 1525 | 0.0026% | | pawn | 85.424 | 0.0023% | 580 | 0.001% | | jsoniq | 75.152 | 0.002% | 1343 | 0.0023% | | bluespec | 72.38 | 0.002% | 2500 | 0.0043% | | smali | 71.38 | 0.0019% | 174 | 0.0003% | | krl | 69.868 | 0.0019% | 1879 | 0.0033% | | maple | 68.284 | 0.0018% | 1311 | 0.0023% | | unrealscript | 67.668 | 0.0018% | 585 | 0.001% | | ooc | 63.188 | 0.0017% | 3416 | 0.0059% | | pure-data | 62.624 | 0.0017% | 603 | 0.001% | | xquery | 61.956 | 0.0017% | 2237 | 0.0039% | | digital-command-language | 59.644 | 0.0016% | 833 | 0.0014% | | moonscript | 59.208 | 0.0016% | 1951 | 0.0034% | | awk | 57.176 | 0.0015% | 2206 | 0.0038% | | pike | 52.872 | 0.0014% | 1262 | 0.0022% | | livescript | 51.228 | 0.0014% | 5194 | 0.009% | | solidity | 50.856 | 0.0014% | 3689 | 0.0064% | | monkey | 48.256 | 0.0013% | 1367 | 0.0024% | | jsonld | 48.012 | 0.0013% | 462 | 0.0008% | | zephir | 42.684 | 0.0012% | 1265 | 0.0022% | | crystal | 41.924 | 0.0011% | 4217 | 0.0073% | | rhtml | 41.02 | 0.0011% | 4551 | 0.0079% | | stata | 40.684 | 0.0011% | 1344 | 0.0023% | | idris | 39.896 | 0.0011% | 3025 | 0.0052% | | raml | 39.388 | 0.0011% | 948 | 0.0016% | | openscad | 37.732 | 0.001% | 2178 | 0.0038% | | red | 35.26 | 0.001% | 1108 | 0.0019% | | c2hs-haskell | 34.472 | 0.0009% | 1021 | 0.0018% | | cycript | 33.96 | 0.0009% | 197 | 0.0003% | | applescript | 33.512 | 0.0009% | 1304 | 0.0023% | | mupad | 32.488 | 0.0009% | 178 | 0.0003% | | literate-agda | 31.384 | 0.0008% | 567 | 0.001% | | boo | 31.172 | 0.0008% | 26289 | 0.0456% | | sourcepawn | 29.528 | 0.0008% | 717 | 0.0012% | | qmake | 29.508 | 0.0008% | 3632 | 0.0063% | | ragel-in-ruby-host | 28.296 | 0.0008% | 888 | 0.0015% | | io | 27.952 | 0.0008% | 1247 | 0.0022% | | desktop | 27.648 | 0.0007% | 5021 | 0.0087% | | propeller-spin | 26.772 | 0.0007% | 625 | 0.0011% | | thrift | 26.748 | 0.0007% | 1007 | 0.0017% | | volt | 25.052 | 0.0007% | 1660 | 0.0029% | | xproc | 24.212 | 0.0007% | 914 | 0.0016% | | igor-pro | 23.748 | 0.0006% | 388 | 0.0007% | | lolcode | 23.74 | 0.0006% | 24861 | 0.0431% | | html+eex | 21.412 | 0.0006% | 2100 | 0.0036% | | logtalk | 20.428 | 0.0006% | 1035 | 0.0018% | | mirah | 20.104 | 0.0005% | 706 | 0.0012% | | gnuplot | 19.676 | 0.0005% | 889 | 0.0015% | | literate-coffeescript | 19.016 | 0.0005% | 1041 | 0.0018% | | jflex | 18.608 | 0.0005% | 555 | 0.001% | | emberscript | 18.392 | 0.0005% | 1024 | 0.0018% | | cobol | 17.0 | 0.0005% | 24953 | 0.0432% | | yang | 16.94 | 0.0005% | 597 | 0.001% | | rebol | 16.468 | 0.0004% | 239 | 0.0004% | | linker-script | 16.084 | 0.0004% | 1604 | 0.0028% | | cartocss | 15.916 | 0.0004% | 555 | 0.001% | | urweb | 13.068 | 0.0004% | 304 | 0.0005% | | rmarkdown | 13.032 | 0.0004% | 750 | 0.0013% | | darcs-patch | 13.008 | 0.0004% | 80 | 0.0001% | | csound | 12.852 | 0.0003% | 229 | 0.0004% | | squirrel | 12.844 | 0.0003% | 531 | 0.0009% | | apl | 12.56 | 0.0003% | 586 | 0.001% | | hlsl | 12.168 | 0.0003% | 1529 | 0.0026% | | latte | 11.888 | 0.0003% | 1380 | 0.0024% | | pony | 11.836 | 0.0003% | 624 | 0.0011% | | ioke | 10.86 | 0.0003% | 373 | 0.0006% | | hy | 10.512 | 0.0003% | 879 | 0.0015% | | uno | 10.356 | 0.0003% | 628 | 0.0011% | | pan | 10.336 | 0.0003% | 637 | 0.0011% | | xojo | 10.308 | 0.0003% | 642 | 0.0011% | | papyrus | 10.256 | 0.0003% | 130 | 0.0002% | | stan | 10.252 | 0.0003% | 540 | 0.0009% | | slash | 9.904 | 0.0003% | 640 | 0.0011% | | supercollider | 9.796 | 0.0003% | 318 | 0.0006% | | vcl | 9.456 | 0.0003% | 747 | 0.0013% | | smt | 9.032 | 0.0002% | 117 | 0.0002% | | glyph | 8.948 | 0.0002% | 7 | 0.0% | | wisp | 8.736 | 0.0002% | 262 | 0.0005% | | renpy | 8.3 | 0.0002% | 421 | 0.0007% | | clips | 7.728 | 0.0002% | 450 | 0.0008% | | dns-zone | 7.56 | 0.0002% | 54 | 0.0001% | | sas | 7.536 | 0.0002% | 269 | 0.0005% | | rouge | 7.196 | 0.0002% | 396 | 0.0007% | | ec | 7.032 | 0.0002% | 94 | 0.0002% | | dylan | 6.82 | 0.0002% | 280 | 0.0005% | | tcsh | 6.524 | 0.0002% | 748 | 0.0013% | | aspectj | 6.332 | 0.0002% | 451 | 0.0008% | | netlogo | 6.304 | 0.0002% | 140 | 0.0002% | | gap | 6.096 | 0.0002% | 46 | 0.0001% | | fancy | 5.952 | 0.0002% | 675 | 0.0012% | | coq | 5.744 | 0.0002% | 80 | 0.0001% | | click | 5.74 | 0.0002% | 9 | 0.0% | | capn-proto | 5.644 | 0.0002% | 330 | 0.0006% | | flux | 5.572 | 0.0002% | 47 | 0.0001% | | forth | 5.512 | 0.0001% | 265 | 0.0005% | | ats | 5.424 | 0.0001% | 383 | 0.0007% | | netlinx | 5.172 | 0.0001% | 144 | 0.0002% | | clean | 5.068 | 0.0001% | 171 | 0.0003% | | parrot-assembly | 4.664 | 0.0001% | 227 | 0.0004% | | alloy | 4.644 | 0.0001% | 203 | 0.0004% | | lfe | 4.576 | 0.0001% | 287 | 0.0005% | | gdscript | 4.488 | 0.0001% | 460 | 0.0008% | | augeas | 4.444 | 0.0001% | 395 | 0.0007% | | sparql | 4.404 | 0.0001% | 1036 | 0.0018% | | lilypond | 4.308 | 0.0001% | 265 | 0.0005% | | scilab | 4.088 | 0.0001% | 375 | 0.0006% | | autoit | 4.06 | 0.0001% | 279 | 0.0005% | | myghty | 3.864 | 0.0001% | 105 | 0.0002% | | blitzmax | 3.74 | 0.0001% | 220 | 0.0004% | | creole | 3.416 | 0.0001% | 337 | 0.0006% | | harbour | 3.336 | 0.0001% | 107 | 0.0002% | | piglatin | 3.168 | 0.0001% | 513 | 0.0009% | | opa | 3.164 | 0.0001% | 211 | 0.0004% | | sage | 3.032 | 0.0001% | 414 | 0.0007% | | ston | 2.848 | 0.0001% | 414 | 0.0007% | | maxscript | 2.8 | 0.0001% | 47 | 0.0001% | | lsl | 2.68 | 0.0001% | 74 | 0.0001% | | gentoo-ebuild | 2.576 | 0.0001% | 601 | 0.001% | | nu | 2.38 | 0.0001% | 170 | 0.0003% | | bro | 2.34 | 0.0001% | 333 | 0.0006% | | xc | 2.02 | 0.0001% | 88 | 0.0002% | | j | 1.808 | 0.0% | 142 | 0.0002% | | metal | 1.724 | 0.0% | 151 | 0.0003% | | module-management-system | 1.544 | 0.0% | 91 | 0.0002% | | webidl | 1.508 | 0.0% | 96 | 0.0002% | | tea | 1.468 | 0.0% | 29 | 0.0001% | | redcode | 1.272 | 0.0% | 149 | 0.0003% | | shen | 1.2 | 0.0% | 71 | 0.0001% | | pov-ray-sdl | 1.136 | 0.0% | 104 | 0.0002% | | x10 | 1.008 | 0.0% | 33 | 0.0001% | | brainfuck | 0.964 | 0.0% | 167 | 0.0003% | | ninja | 0.952 | 0.0% | 187 | 0.0003% | | golo | 0.896 | 0.0% | 115 | 0.0002% | | webassembly | 0.86 | 0.0% | 83 | 0.0001% | | self | 0.824 | 0.0% | 15 | 0.0% | | labview | 0.808 | 0.0% | 61 | 0.0001% | | octave | 0.804 | 0.0% | 12 | 0.0% | | pogoscript | 0.804 | 0.0% | 74 | 0.0001% | | d | 0.796 | 0.0% | 20 | 0.0% | | http | 0.736 | 0.0% | 140 | 0.0002% | | ecl | 0.664 | 0.0% | 48 | 0.0001% | | chuck | 0.584 | 0.0% | 99 | 0.0002% | | gosu | 0.524 | 0.0% | 60 | 0.0001% | | parrot | 0.52 | 0.0% | 17 | 0.0% | | opal | 0.472 | 0.0% | 69 | 0.0001% | | objective-j | 0.456 | 0.0% | 37 | 0.0001% | | kit | 0.412 | 0.0% | 48 | 0.0001% | | gams | 0.376 | 0.0% | 18 | 0.0% | | prolog | 0.276 | 0.0% | 35 | 0.0001% | | clarion | 0.268 | 0.0% | 13 | 0.0% | | mask | 0.252 | 0.0% | 37 | 0.0001% | | brightscript | 0.244 | 0.0% | 28 | 0.0% | | scaml | 0.184 | 0.0% | 31 | 0.0001% | | matlab | 0.164 | 0.0% | 29 | 0.0001% | | idl | 0.148 | 0.0% | 1 | 0.0% | | ags-script | 0.124 | 0.0% | 31 | 0.0001% | | lookml | 0.12 | 0.0% | 10 | 0.0% | | apacheconf | 0.108 | 0.0% | 59 | 0.0001% | | oxygene | 0.104 | 0.0% | 9 | 0.0% | | txl | 0.096 | 0.0% | 3 | 0.0% | | grammatical-framework | 0.088 | 0.0% | 39 | 0.0001% | | renderscript | 0.064 | 0.0% | 54 | 0.0001% | | mtml | 0.052 | 0.0% | 13 | 0.0% | | unified-parallel-c | 0.052 | 0.0% | 6 | 0.0% | | dogescript | 0.04 | 0.0% | 10 | 0.0% | | gentoo-eclass | 0.04 | 0.0% | 6 | 0.0% | | zimpl | 0.04 | 0.0% | 7 | 0.0% | | irc-log | 0.036 | 0.0% | 9 | 0.0% | | fantom | 0.028 | 0.0% | 11 | 0.0% | | numpy | 0.028 | 0.0% | 1 | 0.0% | | cirru | 0.024 | 0.0% | 4 | 0.0% | | xpages | 0.024 | 0.0% | 7 | 0.0% | | nginx | 0.02 | 0.0% | 6 | 0.0% | | objdump | 0.02 | 0.0% | 1 | 0.0% | | python-traceback | 0.02 | 0.0% | 10 | 0.0% | | realbasic | 0.012 | 0.0% | 1 | 0.0% | | befunge | 0.008 | 0.0% | 2 | 0.0% | | bison | 0.008 | 0.0% | 1 | 0.0% | | m | 0.008 | 0.0% | 1 | 0.0% | | omgrofl | 0.008 | 0.0% | 1 | 0.0% | ## Additional Information ### Licensing Information Each sample comes from a code repository with a permissive license. The license is provided by the `license` field for each sample. ### 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} } ```
lukebarousse/data_jobs
lukebarousse
"2024-05-27T19:07:08Z"
12,383
37
[ "license:apache-2.0", "size_categories:100K<n<1M", "format:csv", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-03-21T01:33:05Z"
--- license: apache-2.0 ---