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hssd/hssd-hab | hssd | "2025-02-14T02:19:58Z" | 12,363 | 35 | [
"language:en",
"license:cc-by-nc-4.0",
"region:us",
"3D scenes",
"Embodied AI"
] | null | "2023-06-04T18:59:50Z" | ---
language:
- en
pretty_name: HSSD
tags:
- 3D scenes
- Embodied AI
license: cc-by-nc-4.0
extra_gated_heading: "Acknowledge license to accept the repository"
extra_gated_prompt: "You agree to use this dataset under the [CC BY-NC 4.0 license](https://creativecommons.org/licenses/by-nc/4.0/) terms"
viewer: false
---
HSSD: Habitat Synthetic Scenes Dataset
==================================
The [Habitat Synthetic Scenes Dataset (HSSD)](https://3dlg-hcvc.github.io/hssd/) is a human-authored 3D scene dataset that more closely mirrors real scenes than prior datasets.
Our dataset represents real interiors and contains a diverse set of 211 scenes and more than 18000 models of real-world objects.
<img src="https://i.imgur.com/XEkLxNs.png" width=50%>
This repository provides a Habitat consumption-ready compressed version of HSSD.
See [this repository](https://huggingface.co/datasets/hssd/hssd-models) for corresponding uncompressed assets.
## Dataset Structure
```
├── objects
│ ├── */*.glb
│ ├── */*.collider.glb
│ ├── */*.filteredSupportSurface(.ply|.glb)
│ ├── */*.object_config.json
├── stages
│ ├── *.glb
│ ├── *.stage_config.json
├── scenes
│ ├── *.scene_instance.json
├── scenes_uncluttered
│ ├── *.scene_instance.json
├── scenes_articulated
│ ├── *.scene_instance.json
├── scene_filter_files
│ ├── *.rec_filter.json
├── metadata
│ ├── *.csv
│ ├── *.json
├── semantics
│ ├── hssd-hab_semantic_lexicon.json
│ ├── scenes
| ├── *.semantic_config.json
├── urdf
│ ├── <model_name>
| ├── *.glb
| ├── *.urdf
| ├── *.ao_config.json
└── hssd-hab.scene_dataset_config.json
└── hssd-hab-uncluttered.scene_dataset_config.json
└── hssd-hab-articulated.scene_dataset_config.json
```
- `hssd-hab.scene_dataset_config.json`: This SceneDataset config file aggregates the assets and metadata necessary to fully describe the set of stages, objects, and scenes constituting the dataset.
- `objects`: 3D models representing distinct objects that are used to compose scenes. Contains configuration files, render assets, collider assets, and Receptacle mesh assets.
- `stages`: A stage in Habitat is the set of static mesh components which make up the backdrop of a scene (e.g. floor, walls, stairs, etc.).
- `scenes`: A scene is a single 3D world composed of a static stage and a variable number of objects.
- `scene_filter_files`: These .rec_filter.json files contain mappings of Receptacle instance unique_names to active or filtered sets based on their locations and accessibility within the scene. They also contain a "within_set" defining Receptacles which can only be accessed when the parent Furniture object's "default_link" is in the "open" state.
- `metadata`: The metadata directory contains several csv and json files which provide semantic mappings for objects in the dataset as well as rational mappings from regions to the types of clutter objects typically found in them to support procedural generation.
- `semantics`: Primarily defines instance semantics for the scenes. *.semantic_config.json files contain the region annotations for each scene.
- `urdf`: The urdf directory contains the articulated furniture assets, each contained in its own sub-directory named after the source asset. The .urdf files define the articulation properties. Each .glb file is either a render asset or Receptacle mesh connected to a rigid link. The .ao_config.json file contains habitat-specific metadata such as markersets and Receptacle definitions.
### Rearrange-ready assets:
Supporting Habitat 3.0 embodied rearrangement tasks with updated colliders, adjusted and de-cluttered scene contents, receptacle meshes, and receptacle filter files. See [aihabitat.org/habitat3/](aihabitat.org/habitat3/) for more details.
- `hssd-hab-uncluttered.scene_dataset_config.json`: This SceneDataset config file aggregates adds the adjusted and uncluttered scenes for rearrangement tasks.
- `scenes_uncluttered`: Contains the adjusted scene instance configuration files.
- `scene_filter_files`: A scene filter file organizes available Receptacle instances in a scene into active and inactive groups based on simualtion heuristics and manual edits. It is consumed by the RearrangeEpisodeGenerator to construct valid RearrangeEpisodeDatasets.
### Articulated scenes and assets:
Introduced in `v0.3.0`, the `hssd-hab-articulated.scene_dataset_config.json` SceneDataset provides 202 fully articulated HSSD scenes ready for use within the AI Habitat simulation ecosystem. Note that only 161 are publicly available on this repo. The remainder and their unique assets are reserved as an internal test set.
To enable more realistic indoor object manipulation, articulated 3D furniture models such as drawers, cabinets, and appliances were added to replace rigid assets. These models were converted from rigid source assets in HSSD and swapped into the scenes.
Furniture is annotated with a set of Receptacles (surfaces which support small object placement such as shelves and drawers) and can be opened and closed by the agents. Receptacles are further filtered contextually in each scene to ensure that the active set is accessible to the agents.
Additional annotations include point or marker sets for each furniture, region annotations, and semantic classification of objects.
## Getting Started
To load HSSD scenes into the Habitat simulator, you can start by installing [habitat-sim](https://github.com/facebookresearch/habitat-sim) using instructions specified [here](https://github.com/facebookresearch/habitat-sim#installation).
Once installed, you can run the interactive Habitat viewer to load a scene:
```
habitat-viewer --dataset /path/to/hssd-hab/hssd-hab.scene_dataset_config.json -- 102344280
# or ./build/viewer if compiling from source
```
You can find more information about using the interactive viewer [here](https://github.com/facebookresearch/habitat-sim#testing:~:text=path/to/data/-,Interactive%20testing,-%3A%20Use%20the%20interactive).
Habitat-Sim is typically used with [Habitat-Lab](https://github.com/facebookresearch/habitat-lab), a modular high-level library for end-to-end experiments in embodied AI.
To define embodied AI tasks (e.g. navigation, instruction following, question answering), train agents, and benchmark their performance using standard metrics, you can download habitat-lab using the instructions provided [here](https://github.com/facebookresearch/habitat-lab#installation).
## Changelog
- `v0.3.0`: **Articulated Scenes and PARTNR support**
- This major version update adds a large set of changes to support the introduction of 202 articulated HSSD scenes and the [PARTNR benchmark](https://github.com/facebookresearch/partnr-planner).
- Includes improvements to stage texture/geometry and object collision shapes and receptacles.
- Adds:
- 2000+ articulated assets in the urdf/ directory representing and replacing rigid furniture objects. Annotated with Receptacles and semantics.
- 202 new articulated scenes with rigid objects replaced by AOs. These are uncluttered and often significantly altered from originals to accommodate the new assets.
- Note that test scenes and assets are removed before migration to this repo.
- Receptacle filter files for new scenes annotating accessible Receptacles and "within" Receptacles (those which require opening an articulated link for access).
- Note that only one link per AO is configured with an active Receptacle. This is based on logic in PARTNR and habitat-lab (default_link).
- Region volume semantic annotations to all scenes
- Semantic lexicon file with updated classes
- Metadata files mapping object semantics and common-sense object->region sets for PARTNR
- `v0.2.5`: **Rearrange-ready HSSD**
- Note: this is a checkpoint. Known issues exist and continued polish is ongoing.
- Adds Receptacle meshes describing support surfaces for small objects (e.g. table or shelf surfaces).
- Adds collider meshes (.collider.glb) for assets with Receptacle meshes to support simulation.
- Adds new scenes 'scenes_uncluttered' and new SceneDataset 'hssd-hab-uncluttered' containing adjusted and de-cluttered versions of the scenes for use in embodied rearrangement tasks.
- Adds 'scene_filter_files' which sort Receptacles in each scene into active and inactive groups for RearrangeEpisode generation.
- `v0.2.4`:
- Recompresses several object GLBs to preserve PBR material status.
- Adds CSV with object metadata and semantic lexicon files for Habitat.
- Adds train/val scene splits file.
- `v0.2.3`: First release.
|
neashton/ahmedml | neashton | "2025-02-15T15:42:44Z" | 12,327 | 1 | [
"license:cc-by-sa-4.0",
"size_categories:n<1K",
"format:imagefolder",
"modality:image",
"library:datasets",
"library:mlcroissant",
"arxiv:2407.20801",
"region:us"
] | null | "2025-02-10T11:19:24Z" | ---
license: cc-by-sa-4.0
---
AhmedML: High-Fidelity Computational Fluid Dynamics dataset for incompressible, low-speed bluff body aerodynamics
-------
Contact:
----------
Neil Ashton (NVIDIA) - [email protected]
website:
----------
https://caemldatasets.org
Summary:
-------
This dataset contains 500 different geometric variations of the Ahmed Car Body - a simplified car-like shape that exhibits many of the flow topologies that are present on bluff bodies such as road vehicles. The dataset contains a wide range of geometries that exhibit fundamental flow physics such as geometry and pressure-induced flow separation of flows as well as 3D vortical structures. Each variation of the Ahmed car body were run using a time-accurate hybrid Reynolds-Averaged Navier-Stokes (RANS) - Large-Eddy Simulation (LES) turbulence modelling approach using the open-source CFD code OpenFOAM. The dataset contains both surface boundary, 3D volume, geometry STL and forces/moments in open-source formats (.vtu,.vtp).
CFD Solver:
----------
All cases were run using the open-source finite-volume code OpenFOAM v2212. Each case was run transiently for approximately 80 convective time units (CTU) on meshes of approximately 20M cells. Please see the paper for full details on the code and validation:
How to cite this dataset:
----------------
In order to cite the use of this dataset please cite the paper below which contains full details on the dataset. It can be found here: https://arxiv.org/abs/2407.20801
@article{ashton2024ahmed,
title = {{AhmedML: High-Fidelity Computational Fluid Dynamics dataset for incompressible, low-speed bluff body aerodynamics}},
year = {2024},
journal = {arxiv.org},
author = {Ashton, Neil and Maddix, Danielle and Gundry, Samuel and Shabestari, Parisa}
}
Files:
-------
Each folder (e.g run_1,run_2...run_"i" etc) corresponds to a different geometry that contains the following files where "i" is the run number:
* ahmed_i.stl : geometry stl (~5mb):
* geo_parameters_1.csv (missing run 500): parameters that define the geometry
* boundary_i.vtp : Boundary VTP (~500mb)
* volume_i.vtu : Volume field VTU (~5GB)
* force_mom_i.csv : forces (Cd,Cl) time-averaged with constant reference area
* force_mom_varref_i.csv : forces (Cd,Cl) time-averaged with varying reference area
* slices : folder containing .vtp slices in x,y,z that contain flow-field variables
* images : (folder) that contains images of the following variables (CpT, UxMean) for slices of the domain in the X,Y & Z locations.
In addition we provide:
* force_mom_all.csv : run, cd,cl for all runs in a single file
* force_mom_varref_all.csv : run, cd,cl for all runs in a single file with varying reference area
* geo_parameters_all.csv : all the geometry parameters for each run inside a single file
* ahmedml.slvs : SolveSpace input file to create the parametric geometries
* stl : folder containing stl files that were used as inputs to the OpenFOAM process
* openfoam-casesetup.tgz : complete OpenFOAM setup that can be used to extend or reproduce the dataset
* validation : folder containing full outputs from all four mesh levels that were used to validate the methodology
Acknowledgements
-----------
* OpenFOAM solver and workflow development by Neil Ashton (Amazon Web Services, now NVIDIA)
* Geometry parameterization by Samuel Gundry (Amazon Web Services) and Parisa Shabestari (Amazon Web Services)
* Guidance on dataset preparation for ML by Danielle Madix (Amazon Web Services)
* Simulation runs, HPC setup and dataset preparation by Neil Ashton (Amazon Web Services, now NVIDIA)
License
----
This dataset is provided under the CC BY SA 4.0 license, please see LICENSE.txt for full license text.
version history:
---------------
* 15/02/2025 - files uploaded to HuggingFace
* 12/11/2024 - added validation folder that contains the full output from all four mesh levels that were used to validate the methodology used.
* 04/08/2024 - updates to the file description and arxiv paper
* 05/06/2024 - global forces/geo added for all runs
* 01/05/2024 - force/moments corrected (prior version had incorrect Cs data)
* 18/04/2024 - draft version produced
|
Tuxifan/UbuntuIRC | Tuxifan | "2023-06-04T15:35:31Z" | 12,259 | 0 | [
"task_categories:text-generation",
"license:cc0-1.0",
"size_categories:1M<n<10M",
"format:text",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us"
] | [
"text-generation"
] | "2023-06-02T22:48:40Z" | ---
license: cc0-1.0
task_categories:
- text-generation
pretty_name: Ubuntu IRC channels
---
Completely uncurated collection of IRC logs from the Ubuntu IRC channels |
wendlerc/RenderedText | wendlerc | "2023-07-12T09:28:10Z" | 12,246 | 41 | [
"task_categories:text-to-image",
"task_categories:image-to-text",
"language:en",
"size_categories:10M<n<100M",
"format:webdataset",
"modality:image",
"modality:text",
"library:datasets",
"library:webdataset",
"library:mlcroissant",
"region:us",
"OCR",
"blender",
"LAION",
"Stability"
] | [
"text-to-image",
"image-to-text"
] | "2023-06-26T11:26:16Z" | ---
task_categories:
- text-to-image
- image-to-text
language:
- en
tags:
- OCR
- blender
- LAION
- Stability
size_categories:
- 10M<n<100M
---
*This dataset has been created by Stability AI and LAION.*
This dataset contains 12 million 1024x1024 images of handwritten text written on a digital 3D sheet of paper generated using Blender geometry nodes and rendered using Blender Cycles. The text has varying font size, color, and rotation, and the paper was rendered under random lighting conditions.
Note that, the first 10 million examples are in the root folder of this dataset repository and the remaining 2 million are in ./remaining (due to the constraint on number of files per directory).
It was generated with the script https://github.com/GbotHQ/ocr-dataset-rendering/, which utilizes:
- ~8000 fonts from https://www.urbanfonts.com/free-fonts.htm and https://www.fontspace.com/
- 643 CC0 HDRIs from https://polyhaven.com/
- 1837 CC0 PRB materials from https://ambientcg.com/
- random sentences sampled from https://huggingface.co/datasets/ChristophSchuhmann/wikipedia-en-nov22-1-sentence-level and https://huggingface.co/datasets/ChristophSchuhmann/1-sentence-level-gutenberg-en_arxiv_pubmed_soda
to generate example images as shown below.
The dataset contains both line-level, as well as character level annotations for each example. The annotations are stored in the accompanying json files and are of the following form:
```
{
'ocr_annotation':
{'bounding_boxes': [[[145.0, 370.0], [788.0, 353.0], [827.0, 633.0], [182.0, 669.0]]],
'text': ['Joe.'],
'bb_relative': [[[0.1416015625, 0.361328125], [0.76953125, 0.3447265625], [0.8076171875, 0.6181640625], [0.177734375, 0.6533203125]]],
'char': ['J', 'o', 'e', '.'],
'char_idx': [0, 1, 2, 3],
'bb_character_level': [[[145.0, 370.0], [346.0, 365.0], [382.0, 651.0], [181.0, 662.0]], [[375.0, 438.0], [557.0, 431.0], [585.0, 640.0], [402.0, 650.0]], [[578.0, 440.0], [744.0, 434.0], [771.0, 629.0], [604.0, 638.0]], [[778.0, 591.0], [821.0, 589.0], [827.0, 633.0], [784.0, 635.0]]],
'font_path': '/fsx/home-wendlerc/blender-dataset/assets/fonts/fontcollection/HelloScribbles-axapm.ttf',
'font_color': [17, 25, 231],
'text_rotation_angle': 7},
'width':1024,
'height':1024,
}
```
Browse a few more examples here: https://colab.research.google.com/drive/1o0rZhtY9aeurzNrAbu6nJypULSIIcf1v?authuser=1 |
MMInstruction/ArxivCap | MMInstruction | "2024-10-03T03:17:00Z" | 12,238 | 50 | [
"task_categories:image-to-text",
"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:2403.00231",
"region:us",
"arxiv",
"multi-modal"
] | [
"image-to-text"
] | "2023-12-01T15:47:54Z" | ---
license: cc-by-4.0
task_categories:
- image-to-text
language:
- en
pretty_name: ArxivCap
size_categories:
- 1M<n<10M
tags:
- arxiv
- multi-modal
---
# Dataset Card for ArxivCap
## Table of Contents
- [Dataset Card for ArxivCap](#dataset-card-for-arxivcap)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Curation Process](#curation-process)
- [Dataset Structure](#dataset-structure)
- [Data Loading](#data-loading)
- [Data Fields](#data-fields)
- [Data Instances](#data-instances)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Paper:** [Multimodal ArXiv](https://arxiv.org/abs/2403.00231)
- **Point of Contact:** [email protected]
- **HomePage**: https://mm-arxiv.github.io/
### Data Instances
<details>
<summary>Example-1 of single (image, caption) pairs</summary>
"......" stands for omitted parts.
```
{
'src': 'arXiv_src_2112_060/2112.08947',
'meta':
{
'meta_from_kaggle':
{
'journey': '',
'license': 'http://arxiv.org/licenses/nonexclusive-distrib/1.0/',
'categories': 'cs.ET'
},
'meta_from_s2':
{
'citationCount': 8,
'influentialCitationCount': 0,
'publicationTypes': ['JournalArticle']
}
},
'arxiv_id': '2112.08947',
'title': 'Computational metrics and parameters of an injection-locked large area semiconductor laser for neural network computing',
'abstract': 'Artificial neural networks have become a staple computing technique in many fields. Yet, they present fundamental differences with classical computing hardware in the way they process information. Photonic implementations of neural network architectures potentially offer fundamental advantages over their electronic counterparts in terms of speed, processing parallelism, scalability and energy efficiency. Scalable and high performance photonic neural networks (PNNs) have been demonstrated, yet they remain scarce. In this work, we study the performance of such a scalable, fully parallel and autonomous PNN based on a large area vertical-cavity surface-emitting laser\n(LA-VCSEL). We show how the performance varies with different physical parameters, namely, injection wavelength, injection power, and bias current. Furthermore, we link these physical parameters to the general computational measures of consistency and dimensionality. We present a general method of gauging dimensionality in high dimensional nonlinear systems subject to noise, which could be applied to many systems in the context of neuromorphic computing. Our work will inform future implementations of spatially multiplexed VCSEL PNNs.\n',
'caption_images':
[
{
'caption': '(a) Working principle of the LA-VCSEL spatially multiplexed reservoir. (b) Input information $\\mathbf{u}$ and the subsequent LA-VCSEL response for 3-bit binary headers. The graph shows the target output $y^{\\text{target}}$ (yellow) for classifying header 001 and different reservoir outputs $y^{\\text{out}}$ of decreasing mean square error (MSE) (red, blue and green). (c) Schematic illustration of the error landscape, showing the MSE as a function of the output weights configuration. The outlined (red, blue and green) Boolean matrices correspond to the output weights giving the output from (b). (d) Representative performance of the PNN on a 6-bit header recognition task.',
'cil_pairs':
[
{
'sub_caption': '',
'image_file': 'arXiv_src_2112_060/2112.08947_0.jpg',
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2016x1063 at 0x7F098E288040>,
'image_ocr': ['(a)', 'LA-VCSEL', 'DMDa', 'DMD', 'MMF', 'DET', 'Win', 'xt', 'Spatial positions', 'Output', 'Input', 'Wint', 'Carrier diffusion', 'Cavity diffraction', 'Reservoir', '(d)50', '6bit HR', 'Error(MSE)', '830', '001', '000', '001', '100', '001', '111', 'ER', 'S', '10', '0', 'Configuration DMD.', '0', '1000', 'Input examples', 'Learning epochs']
}
]
}
......
]
}
```
</details>
<details>
<summary>Example-2 of multiple images and subcaptions</summary>
"......" stands for omitted parts.
```
{
'src': 'arXiv_src_0309_001/quant-ph0309051',
'meta':
{
'meta_from_kaggle': {'journey': '', 'license': '', 'categories': 'quant-ph'},
'meta_from_s2': {'citationCount': 9, 'influentialCitationCount': 1, 'publicationTypes': ['JournalArticle']}
},
'arxiv_id': 'quant-ph/0309051',
'title': 'Implementing a Quantum Algorithm with Exchange-Coupled Quantum Dots: a Feasibility study.',
'abstract': '\nWe present Monte Carlo wavefunction simulations for quantum computations employing an exchange-coupled array of quantum dots. Employing a combination of experimentally and theoretically available parameters, we find that gate fidelities greater than 98 \\% may be obtained with current experimental and technological capabilities. Application to an encoded 3 qubit\n(nine physical qubits) Deutsch-Josza computation indicates that the algorithmic fidelity is more a question of the total time to implement the gates than of the physical complexity of those gates.\n',
'caption_images':
[
......
{
'caption': 'Representation of analytic sequence of local transformations that transform the 19-exchange sequence $U_{cnot}^{exchange}$ from Ref. {divincenzo00} into the true CNOT in the computational basis. The exchange gates and times corresponding to the elementary local transformations are then obtained using the quaternion representation of the desired $SU(2)$ unitaries (see Appendix <ref> for details).',
'cil_pairs':
[
{
'sub_caption': 'A single qubit gate ($\\frac{\\sqrt{3}}{2}-\\frac{i}{2}\\sigma_y$) acting on the second logical qubit diagonalizes the 19-gate exchange sequence. The resulting diagonal 4-by-4 matrix is then converted into the C-PHASE by $\\sigma_z$-rotations acting on both the first and the second qubit, with angles $\\phi=0.612497$ and $\\theta=-0.547580$, respectively. These values are determined from the analytic solutions to a linear equation system with 3 unknowns: $\\phi$, $\\theta$ and a global phase. See Appendix <ref> for details as to how these parameters were obtained.',
'image_file': 'arXiv_src_0309_001/quant-ph0309051_4.jpg',
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2016x493 at 0x7F102471EF70>,
'image_ocr': ['Exch,', '7', 'C', '2', '+', '2', '2', 'CNOT', '2', '2', 'PHASE']
},
{
'sub_caption': 'The C-PHASE gate can be transformed into the CNOT gate by acting with Hadamard gates on the second qubit before and after the C-PHASE gate.',
'image_file': 'arXiv_src_0309_001/quant-ph0309051_5.jpg',
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2016x411 at 0x7F102471EDC0>,
'image_ocr': ['C', '2', 'PHASE']
}
]
},
......
]
}
```
</details>
### Dataset Summary
The ArxivCap dataset consists of 6.4 million images and 3.9 million captions with 193 million words from 570k academic papers accompanied with abstracts and titles. (papers before **June 2023**)
### Curation Process
Refer to our paper for the curation and filter process.
## Dataset Structure
### Data Loading
```python
from datasets import load_dataset
dataset = load_dataset("MMInstruction/ArxivCap")
dataset["train"] # list of dictionaries
```
---
```bash
# for quick download in linux
set -e
sudo apt-get install git-lfs -y
git clone https://huggingface.co/datasets/MMInstruction/ArxivCap
cd ArxivCap/data
```
```python
# then you can load the parquet files in python use something like
data = load_dataset(
"parquet",
data_files="/path/to/parquet/arXiv_src_9912_001.parquet"
)
```
### Data Fields
One record refers to one paper:
- src: **String**. "\<Arxiv Tar File Name>/\<Folder Name in Tar File>"e.g. "arXiv_src_2112_060/2112.08947"
- arxiv_id: **String**. Arxiv id of the paper, e.g. "2112.08947"
- title: **String**. Title of the paper.
- abstract: **String**. Abstract of the paper.
- meta:
- meta_from_kaggle: refers to [arXiv Dataset](https://www.kaggle.com/datasets/Cornell-University/arxiv)
- journey: **String**. Information about the journal the paper was published in.
- licence: **String**. License for the paper.
- categories: **String**. Categories / tags in the ArXiv system.
- meta_from_s2: refers to [SEMANTIC SCHOLAR](https://api.semanticscholar.org/api-docs/#tag/Paper-Data/operation/get_graph_get_paper)
- citationCount: **Integer**. Total number of citations S2 has found for this paper
- influentialCitationCount: **Integer**. Refers [here](https://www.semanticscholar.org/faq#influential-citations)
- publicationTypes: **List[String]**. Journal Article, Conference, Review, etc.
- caption_images:
- caption: **String**. Main caption.
- cil_pairs:
- sub_caption: **String**. Subcaption for the image.
- image_file: **String**. Unique file name for the image.
- image: **PIL.Image.Image**. A PIL.Image.Image object containing the image.
- image_ocr: **List[String]**. OCR result for the image using [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)
```python
import datasets
features = datasets.Features(
{
"src": datasets.Value("string"),
"arxiv_id": datasets.Value("string"),
"title": datasets.Value("string"),
"abstract": datasets.Value("string"),
"meta": {
"meta_from_kaggle": {
"journey": datasets.Value("string"),
"license": datasets.Value("string"),
"categories": datasets.Value("string"),
},
"meta_from_s2": {
"citationCount": datasets.Value("int32"),
"influentialCitationCount": datasets.Value("int32"),
"publicationTypes": [datasets.Value("string")],
}
},
"caption_images": [{
"caption": datasets.Value("string"),
"cil_pairs": [{
"sub_caption": datasets.Value("string"),
"image_file": datasets.Value("string"),
"image": datasets.Image(),
"image_ocr": [datasets.Value("string")],
}]
}]
}
)
```
## Additional Information
### Licensing Information
ArxivCap is released under [CC BY-NC-SA 4.0](http://creativecommons.org/licenses/by-nc-sa/4.0/).
### Citation Information
```
@inproceedings{li-etal-2024-multimodal-arxiv,
title = "Multimodal {A}r{X}iv: A Dataset for Improving Scientific Comprehension of Large Vision-Language Models",
author = "Li, Lei and
Wang, Yuqi and
Xu, Runxin and
Wang, Peiyi and
Feng, Xiachong and
Kong, Lingpeng and
Liu, Qi",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.775",
doi = "10.18653/v1/2024.acl-long.775",
pages = "14369--14387"
}
``` |
DFKI-SLT/argmicro | DFKI-SLT | "2025-03-10T15:29:52Z" | 12,178 | 0 | [
"language:en",
"language:de",
"license:cc-by-nc-sa-4.0",
"size_categories:n<1K",
"region:us"
] | null | "2023-08-08T16:17:53Z" | ---
license: cc-by-nc-sa-4.0
language:
- en
- de
pretty_name: argmicro
size_categories:
- n<1K
---
# Dataset Card for "argmicro"
### Dataset Summary
The arg-microtexts corpus features 112 short argumentative texts. All texts were originally written in German and have been professionally translated to English.
Based on Freeman’s theory of the macro-structure of arguments ([1991](https://api.pageplace.de/preview/DT0400.9783110875843_A19822678/preview-9783110875843_A19822678.pdf); [2011](https://link.springer.com/book/10.1007/978-94-007-0357-5)) and Toulmin ([2003](https://www.cambridge.org/core/books/uses-of-argument/26CF801BC12004587B66778297D5567C))'s diagramming techniques, ArgMicro consists of `pro` (proponent) and `opp` (opponent) components and six types of relations: `seg` (segment), `add` (addition), `exa` (example), `reb` (rebut), `sup` (support), and `und` (undercut). It also introduced segment-based spans, which also contain non-argumentative parts, in order to cover the whole text.
### Supported Tasks and Leaderboards
- **Tasks:** Structure Prediction, Relation Identification, Central Claim Identification, Role Classification, Function Classification
- **Leaderboards:** \[More Information Needed\]
### Languages
German, with English translation (by a professional translator).
## Dataset Structure
### Data Instances
- **Size of downloaded dataset files:** 2.89 MB
```
{
"id": "micro_b001",
"topic_id": "waste_separation",
"stance": 1,
"text": "Yes, it's annoying and cumbersome to separate your rubbish properly all the time. Three different bin bags stink away in the kitchen and have to be sorted into different wheelie bins. But still Germany produces way too much rubbish and too many resources are lost when what actually should be separated and recycled is burnt. We Berliners should take the chance and become pioneers in waste separation!",
"edus": {
"id": ["e1", "e2", "e3", "e4", "e5"],
"start": [0, 82, 184, 232, 326],
"end": [81, 183, 231, 325, 402]
},
"adus": {
"id": ["a1", "a2", "a3", "a4", "a5"],
"type": [0, 0, 1, 1, 1]
},
"edges": {
"id": ["c1", "c10", "c2", "c3", "c4", "c6", "c7", "c8", "c9"],
"src": ["a1", "e5", "a2", "a3", "a4", "e1", "e2", "e3", "e4"],
"trg": ["a5", "a5", "a1", "c1", "c3", "a1", "a2", "a3", "a4"],
"type": [4, 0, 1, 5, 3, 0, 0, 0, 0]
}
}
```
### Data Fields
- `id`: the instance `id` of the document, a `string` feature
- `topic_id`: the topic of the document, a `string` feature (see [list of topics](https://huggingface.co/datasets/DFKI-SLT/argmicro/blob/main/topics_triggers.md))
- `stance`: the index of stance on the topic, an `int` feature (see [stance labels](https://huggingface.co/datasets/DFKI-SLT/argmicro/blob/main/argmicro.py#L35))
- `text`: the text content of the document, a `string` feature
- `edus`: elementary discourse units; a segmented span of text (see the authors' further [explanation](https://github.com/peldszus/arg-microtexts/blob/master/corpus/arggraph.dtd#L17-L20))
- `id`: the instance `id` of EDUs, a list of `string` feature
- `start`: the indices indicating the inclusive start of the spans, a list of `int` feature
- `end`: the indices indicating the exclusive end of the spans, a list of `int` feature
- `adus`: argumentative discourse units; argumentatively relevant claims built on EDUs (see the authors' further [explanation](https://github.com/peldszus/arg-microtexts/blob/master/corpus/arggraph.dtd#L22-L28))
- `id`: the instance `id` of ADUs, a list of `string` feature
- `type`: the indices indicating the ADU type, a list of `int` feature (see [type list](https://huggingface.co/datasets/DFKI-SLT/argmicro/blob/main/argmicro.py#L36))
- `edges`: the relations between `adus` or `adus` and other `edges` (see the authors' further [explanation](https://github.com/peldszus/arg-microtexts/blob/master/corpus/arggraph.dtd#L39-L47))
- `id`: the instance `id` of edges, a list of `string` feature
- `src`: the `id` of `adus` indicating the source element in a relation, a list of `string` feature
- `trg`: the `id` of `adus` or `edges` indicating the target element in a relation, a list of `string` feature
- `type`: the indices indicating the edge type, a list of `int` feature (see [type list](https://huggingface.co/datasets/DFKI-SLT/argmicro/blob/main/argmicro.py#L37))
### Data Splits
| | train |
| -------------------------------------- | ----: |
| No. of instances | 112 |
| No. of sentences/instance (on average) | 5.1 |
### Data Labels
#### Stance
| Stance | Count | Percentage |
| ----------- | ----: | ---------: |
| `pro` | 46 | 41.1 % |
| `con` | 42 | 37.5 % |
| `unclear` | 1 | 0.9 % |
| `UNDEFINED` | 23 | 20.5 % |
- `pro`: yes, in favour of the proposed issue
- `con`: no, against the proposed issue
- `unclear`: the position of the author is unclear
- `UNDEFINED`: no stance label assigned
See [stances types](https://github.com/peldszus/arg-microtexts/blob/master/corpus/arggraph.dtd#L74-L83).
#### ADUs
| ADUs | Count | Percentage |
| ----- | ----: | ---------: |
| `pro` | 451 | 78.3 % |
| `opp` | 125 | 21.7 % |
- `pro`: proponent, who presents and defends his claims
- `opp`: opponent, who critically questions the proponent in a regimented fashion (Peldszus, 2015, p.5)
#### Relations
| Relations | Count | Percentage |
| -------------- | ----: | ---------: |
| support: `sup` | 281 | 55.2 % |
| support: `exa` | 9 | 1.8 % |
| attack: `und` | 65 | 12.8 % |
| attack: `reb` | 110 | 21.6 % |
| other: `joint` | 44 | 8.6 % |
- `sup`: support (ADU->ADU)
- `exa`: support by example (ADU->ADU)
- `add`: additional source, for combined/convergent arguments with multiple premises, i.e., linked support, convergent support, serial support (ADU->ADU)
- `reb`: rebutting attack (ADU->ADU)
- definition: "targeting another node and thereby challenging its acceptability"
- `und`: undercutting attack (ADU->Edge)
- definition: "targeting an edge and thereby challenging the acceptability of the inference from the source to the target node"
([P&S, 2016](https://github.com/peldszus/arg-microtexts/blob/master/corpus/arggraph.dtd); [EN annotation guideline](https://www.ling.uni-potsdam.de/~stede/Papers/ArgGuidelinesEnglish.pdf))
- `joint`: combines text segments if one does not express a complete proposition on its own, or if the author divides a clause/sentence into parts, using punctuation
See other corpus statistics in Peldszus (2015), Section 5.
#### Example
(Peldszus & Stede, 2015, p. 940, Figure 1)
## Dataset Creation
This section is composed of information and excerpts provided in Peldszus ([2015](https://peldszus.github.io/files/eca2015-preprint.pdf)).
### Curation Rationale
"Argumentation can, for theoretical purposes, be studied on the basis of carefully constructed examples that illustrate specific phenomena...\[We\] address this need by making a resource publicly available that is designed to fill a particular gap." (pp. 2-3)
### Source Data
23 texts were written by the authors as a “proof of concept” for the idea. These texts also have been used as examples in teaching and testing argumentation analysis with students.
90 texts have been collected in a controlled text generation experiment, where normal competent language users wrote short texts of controlled linguistic and rhetoric complexity.
#### Initial Data Collection and Normalization
"Our contribution is a collection of 112 “microtexts” that have been written in response to trigger questions, mostly in the form of “Should one do X”. The texts are short but at the same time “complete” in that they provide a standpoint and a justification, by necessity in a fairly dense form." (p.2)
"The probands were asked to first gather a list with the pros and cons of the trigger question, then take stance for one side and argue for it on the basis of their reflection in a short argumentative text. Each text was to fulfill three requirements: It should be about five segments long; all segments should be argumentatively relevant, either formulating the main claim of the text, supporting the main claim or another segment, or attacking the main claim or another segment. Also, the probands were asked that at least one possible objection to the claim should be considered in the text. Finally, the text should be written in such a way that it would be understandable without having its trigger question as a headline." (p.3)
"\[A\]ll texts have been corrected for spelling and grammar errors...Their segmentation was corrected when necessary...some modifications in the remaining segments to maintain text coherence, which we made as minimal as possible." (p.4)
"We thus constrained the translation to preserve the segmentation of the text on the one hand (effectively ruling out phrasal translations of clause-type segments) and to preserve its linearization on the other hand (disallowing changes to the order of appearance of arguments)." (p.5)
#### Who are the source language producers?
The texts with ids b001-b064 and k001-k031 have been collected in a controlled text generation experiment from 23 subjects discussing various controversial issues from a fixed list. All probands were native speakers of
German, of varying age, education and profession.
The texts with ids d01-d23 have been written by Andreas Peldszus, the author.
### Annotations
#### Annotation process
All texts are annotated with argumentation structures, following the scheme proposed in Peldszus & Stede ([2013](https://www.ling.uni-potsdam.de/~peldszus/ijcini2013-preprint.pdf)). For inter-annotator-agreement scores see Peldszus (2014). The (German) annotation guidelines are published in Peldszus, Warzecha, Stede (2016). See the annotation guidelines ([de](https://www.ling.uni-potsdam.de/~stede/Papers/ArgGuidelinesGerman.pdf), [en](https://www.ling.uni-potsdam.de/~stede/Papers/ArgGuidelinesEnglish.pdf)), and the [annotation schemes](https://github.com/peldszus/arg-microtexts/blob/master/corpus/arggraph.dtd).
"\[T\]he markup of argumentation structures in the full corpus was done by one expert annotator. All annotations have been checked, controversial instances have been discussed in a reconciliation phase by two or more expert annotators...The annotation of the corpus was originally done manually on paper. In follow-up annotations, we used GraPAT ([Sonntag & Stede, 2014](http://www.lrec-conf.org/proceedings/lrec2014/pdf/824_Paper.pdf))." (p.7)
#### Who are the annotators?
\[More Information Needed\]
### Personal and Sensitive Information
\[More Information Needed\]
## Considerations for Using the Data
### Social Impact of Dataset
"Automatic argumentation recognition has many possible applications, including improving document summarization (Teufel and Moens, 2002), retrieval capabilities of legal databases (Palau and Moens, 2011), opinion mining for commercial purposes, or also as a tool for assessing public
opinion on political questions.
"...\[W\]e suggest there is yet one resource missing that could facilitate the development of automatic argumentation recognition systems: Short texts with explicit argumentation, little argumentatively irrelevant material, less rhetorical gimmicks (or even deception), in clean written language."
(Peldszus, [2014](https://aclanthology.org/W14-2112.pdf), p. 88)
### Discussion of Biases
\[More Information Needed\]
### Other Known Limitations
\[More Information Needed\]
## Additional Information
### Dataset Curators
\[More Information Needed\]
### Licensing Information
The arg-microtexts corpus is released under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. (see [license agreement](https://creativecommons.org/licenses/by-nc-sa/4.0/))
### Citation Information
```
@inproceedings{peldszus2015annotated,
title={An annotated corpus of argumentative microtexts},
author={Peldszus, Andreas and Stede, Manfred},
booktitle={Argumentation and Reasoned Action: Proceedings of the 1st European Conference on Argumentation, Lisbon},
volume={2},
pages={801--815},
year={2015}
}
```
```
@inproceedings{peldszus2014towards,
title={Towards segment-based recognition of argumentation structure in short texts},
author={Peldszus, Andreas},
booktitle={Proceedings of the First Workshop on Argumentation Mining},
pages={88--97},
year={2014}
}
```
### Contributions
Thanks to [@idalr](https://github.com/idalr) for adding this dataset.
|
facebook/voxpopuli | facebook | "2022-10-14T13:43:12Z" | 12,164 | 107 | [
"task_categories:automatic-speech-recognition",
"multilinguality:multilingual",
"language:en",
"language:de",
"language:fr",
"language:es",
"language:pl",
"language:it",
"language:ro",
"language:hu",
"language:cs",
"language:nl",
"language:fi",
"language:hr",
"language:sk",
"language:sl",
"language:et",
"language:lt",
"license:cc0-1.0",
"license:other",
"size_categories:100K<n<1M",
"modality:audio",
"modality:text",
"library:datasets",
"library:mlcroissant",
"arxiv:2101.00390",
"region:us"
] | [
"automatic-speech-recognition"
] | "2022-05-10T14:42:49Z" | ---
annotations_creators: []
language:
- en
- de
- fr
- es
- pl
- it
- ro
- hu
- cs
- nl
- fi
- hr
- sk
- sl
- et
- lt
language_creators: []
license:
- cc0-1.0
- other
multilinguality:
- multilingual
pretty_name: VoxPopuli
size_categories: []
source_datasets: []
tags: []
task_categories:
- automatic-speech-recognition
task_ids: []
---
# Dataset Card for Voxpopuli
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/facebookresearch/voxpopuli
- **Repository:** https://github.com/facebookresearch/voxpopuli
- **Paper:** https://arxiv.org/abs/2101.00390
- **Point of Contact:** [[email protected]](mailto:[email protected]), [[email protected]](mailto:[email protected]), [[email protected]](mailto:[email protected])
### Dataset Summary
VoxPopuli is a large-scale multilingual speech corpus for representation learning, semi-supervised learning and interpretation.
The raw data is collected from 2009-2020 [European Parliament event recordings](https://multimedia.europarl.europa.eu/en/home). We acknowledge the European Parliament for creating and sharing these materials.
This implementation contains transcribed speech data for 18 languages.
It also contains 29 hours of transcribed speech data of non-native English intended for research in ASR for accented speech (15 L2 accents)
### Example usage
VoxPopuli contains labelled data for 18 languages. To load a specific language pass its name as a config name:
```python
from datasets import load_dataset
voxpopuli_croatian = load_dataset("facebook/voxpopuli", "hr")
```
To load all the languages in a single dataset use "multilang" config name:
```python
voxpopuli_all = load_dataset("facebook/voxpopuli", "multilang")
```
To load a specific set of languages, use "multilang" config name and pass a list of required languages to `languages` parameter:
```python
voxpopuli_slavic = load_dataset("facebook/voxpopuli", "multilang", languages=["hr", "sk", "sl", "cs", "pl"])
```
To load accented English data, use "en_accented" config name:
```python
voxpopuli_accented = load_dataset("facebook/voxpopuli", "en_accented")
```
**Note that L2 English subset contains only `test` split.**
### Supported Tasks and Leaderboards
* automatic-speech-recognition: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER).
Accented English subset can also be used for research in ASR for accented speech (15 L2 accents)
### Languages
VoxPopuli contains labelled (transcribed) data for 18 languages:
| Language | Code | Transcribed Hours | Transcribed Speakers | Transcribed Tokens |
|:---:|:---:|:---:|:---:|:---:|
| English | En | 543 | 1313 | 4.8M |
| German | De | 282 | 531 | 2.3M |
| French | Fr | 211 | 534 | 2.1M |
| Spanish | Es | 166 | 305 | 1.6M |
| Polish | Pl | 111 | 282 | 802K |
| Italian | It | 91 | 306 | 757K |
| Romanian | Ro | 89 | 164 | 739K |
| Hungarian | Hu | 63 | 143 | 431K |
| Czech | Cs | 62 | 138 | 461K |
| Dutch | Nl | 53 | 221 | 488K |
| Finnish | Fi | 27 | 84 | 160K |
| Croatian | Hr | 43 | 83 | 337K |
| Slovak | Sk | 35 | 96 | 270K |
| Slovene | Sl | 10 | 45 | 76K |
| Estonian | Et | 3 | 29 | 18K |
| Lithuanian | Lt | 2 | 21 | 10K |
| Total | | 1791 | 4295 | 15M |
Accented speech transcribed data has 15 various L2 accents:
| Accent | Code | Transcribed Hours | Transcribed Speakers |
|:---:|:---:|:---:|:---:|
| Dutch | en_nl | 3.52 | 45 |
| German | en_de | 3.52 | 84 |
| Czech | en_cs | 3.30 | 26 |
| Polish | en_pl | 3.23 | 33 |
| French | en_fr | 2.56 | 27 |
| Hungarian | en_hu | 2.33 | 23 |
| Finnish | en_fi | 2.18 | 20 |
| Romanian | en_ro | 1.85 | 27 |
| Slovak | en_sk | 1.46 | 17 |
| Spanish | en_es | 1.42 | 18 |
| Italian | en_it | 1.11 | 15 |
| Estonian | en_et | 1.08 | 6 |
| Lithuanian | en_lt | 0.65 | 7 |
| Croatian | en_hr | 0.42 | 9 |
| Slovene | en_sl | 0.25 | 7 |
## Dataset Structure
### Data Instances
```python
{
'audio_id': '20180206-0900-PLENARY-15-hr_20180206-16:10:06_5',
'language': 11, # "hr"
'audio': {
'path': '/home/polina/.cache/huggingface/datasets/downloads/extracted/44aedc80bb053f67f957a5f68e23509e9b181cc9e30c8030f110daaedf9c510e/train_part_0/20180206-0900-PLENARY-15-hr_20180206-16:10:06_5.wav',
'array': array([-0.01434326, -0.01055908, 0.00106812, ..., 0.00646973], dtype=float32),
'sampling_rate': 16000
},
'raw_text': '',
'normalized_text': 'poast genitalnog sakaenja ena u europi tek je jedna od manifestacija takve tetne politike.',
'gender': 'female',
'speaker_id': '119431',
'is_gold_transcript': True,
'accent': 'None'
}
```
### Data Fields
* `audio_id` (string) - id of audio segment
* `language` (datasets.ClassLabel) - numerical id of audio segment
* `audio` (datasets.Audio) - a dictionary containing the path to the audio, the decoded audio array, and the sampling rate. In non-streaming mode (default), the path points to the locally extracted audio. In streaming mode, the path is the relative path of an audio inside its archive (as files are not downloaded and extracted locally).
* `raw_text` (string) - original (orthographic) audio segment text
* `normalized_text` (string) - normalized audio segment transcription
* `gender` (string) - gender of speaker
* `speaker_id` (string) - id of speaker
* `is_gold_transcript` (bool) - ?
* `accent` (string) - type of accent, for example "en_lt", if applicable, else "None".
### Data Splits
All configs (languages) except for accented English contain data in three splits: train, validation and test. Accented English `en_accented` config contains only test split.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
The raw data is collected from 2009-2020 [European Parliament event recordings](https://multimedia.europarl.europa.eu/en/home)
#### Initial Data Collection and Normalization
The VoxPopuli transcribed set comes from aligning the full-event source speech audio with the transcripts for plenary sessions. Official timestamps
are available for locating speeches by speaker in the full session, but they are frequently inaccurate, resulting in truncation of the speech or mixture
of fragments from the preceding or the succeeding speeches. To calibrate the original timestamps,
we perform speaker diarization (SD) on the full-session audio using pyannote.audio (Bredin et al.2020) and adopt the nearest SD timestamps (by L1 distance to the original ones) instead for segmentation.
Full-session audios are segmented into speech paragraphs by speaker, each of which has a transcript available.
The speech paragraphs have an average duration of 197 seconds, which leads to significant. We hence further segment these paragraphs into utterances with a
maximum duration of 20 seconds. We leverage speech recognition (ASR) systems to force-align speech paragraphs to the given transcripts.
The ASR systems are TDS models (Hannun et al., 2019) trained with ASG criterion (Collobert et al., 2016) on audio tracks from in-house deidentified video data.
The resulting utterance segments may have incorrect transcriptions due to incomplete raw transcripts or inaccurate ASR force-alignment.
We use the predictions from the same ASR systems as references and filter the candidate segments by a maximum threshold of 20% character error rate(CER).
#### Who are the source language producers?
Speakers are participants of the European Parliament events, many of them are EU officials.
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
Gender speakers distribution is imbalanced, percentage of female speakers is mostly lower than 50% across languages, with the minimum of 15% for the Lithuanian language data.
VoxPopuli includes all available speeches from the 2009-2020 EP events without any selections on the topics or speakers.
The speech contents represent the standpoints of the speakers in the EP events, many of which are EU officials.
### Other Known Limitations
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
The dataset is distributet under CC0 license, see also [European Parliament's legal notice](https://www.europarl.europa.eu/legal-notice/en/) for the raw data.
### Citation Information
Please cite this paper:
```bibtex
@inproceedings{wang-etal-2021-voxpopuli,
title = "{V}ox{P}opuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation",
author = "Wang, Changhan and
Riviere, Morgane and
Lee, Ann and
Wu, Anne and
Talnikar, Chaitanya and
Haziza, Daniel and
Williamson, Mary and
Pino, Juan and
Dupoux, Emmanuel",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.80",
pages = "993--1003",
}
```
### Contributions
Thanks to [@polinaeterna](https://github.com/polinaeterna) for adding this dataset.
|
songlab/TraitGym | songlab | "2025-03-25T19:09:05Z" | 12,145 | 6 | [
"license:mit",
"size_categories:10M<n<100M",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"dna",
"variant-effect-prediction",
"biology",
"genomics"
] | null | "2025-01-26T23:37:15Z" | ---
license: mit
tags:
- dna
- variant-effect-prediction
- biology
- genomics
configs:
- config_name: "mendelian_traits"
data_files:
- split: test
path: "mendelian_traits_matched_9/test.parquet"
- config_name: "complex_traits"
data_files:
- split: test
path: "complex_traits_matched_9/test.parquet"
- config_name: "mendelian_traits_full"
data_files:
- split: test
path: "mendelian_traits_all/test.parquet"
- config_name: "complex_traits_full"
data_files:
- split: test
path: "complex_traits_all/test.parquet"
---
# 🧬 TraitGym
[Benchmarking DNA Sequence Models for Causal Regulatory Variant Prediction in Human Genetics](https://www.biorxiv.org/content/10.1101/2025.02.11.637758v1)
🏆 Leaderboard: https://huggingface.co/spaces/songlab/TraitGym-leaderboard
## ⚡️ Quick start
- Load a dataset
```python
from datasets import load_dataset
dataset = load_dataset("songlab/TraitGym", "mendelian_traits", split="test")
```
- Example notebook to run variant effect prediction with a gLM, runs in 5 min on Google Colab: `TraitGym.ipynb` [](https://colab.research.google.com/github/songlab-cal/TraitGym/blob/main/TraitGym.ipynb)
## 🤗 Resources (https://huggingface.co/datasets/songlab/TraitGym)
- Datasets: `{dataset}/test.parquet`
- Subsets: `{dataset}/subset/{subset}.parquet`
- Features: `{dataset}/features/{features}.parquet`
- Predictions: `{dataset}/preds/{subset}/{model}.parquet`
- Metrics: `{dataset}/{metric}/{subset}/{model}.csv`
`dataset` examples (`load_dataset` config name):
- `mendelian_traits_matched_9` (`mendelian_traits`)
- `complex_traits_matched_9` (`complex_traits`)
- `mendelian_traits_all` (`mendelian_traits_full`)
- `complex_traits_all` (`complex_traits_full`)
`subset` examples:
- `all` (default)
- `3_prime_UTR_variant`
- `disease`
- `BMI`
`features` examples:
- `GPN-MSA_LLR`
- `GPN-MSA_InnerProducts`
- `Borzoi_L2`
`model` examples:
- `GPN-MSA_LLR.minus.score`
- `GPN-MSA.LogisticRegression.chrom`
- `CADD+GPN-MSA+Borzoi.LogisticRegression.chrom`
`metric` examples:
- `AUPRC_by_chrom_weighted_average` (main metric)
- `AUPRC`
## 💻 Code (https://github.com/songlab-cal/TraitGym)
- Tries to follow [recommended Snakemake structure](https://snakemake.readthedocs.io/en/stable/snakefiles/deployment.html)
- GPN-Promoter code is in [the main GPN repo](https://github.com/songlab-cal/gpn)
### Installation
First, clone the repo and `cd` into it.
Second, install the dependencies:
```bash
conda env create -f workflow/envs/general.yaml
conda activate TraitGym
```
Optionally, download precomputed datasets and predictions (6.7G):
```bash
mkdir -p results/dataset
huggingface-cli download songlab/TraitGym --repo-type dataset --local-dir results/dataset/
```
### Running
To compute a specific result, specify its path:
```bash
snakemake --cores all <path>
```
Example paths (these are already computed):
```bash
# zero-shot LLR
results/dataset/complex_traits_matched_9/AUPRC_by_chrom_weighted_average/all/GPN-MSA_absLLR.plus.score.csv
# logistic regression/linear probing
results/dataset/complex_traits_matched_9/AUPRC_by_chrom_weighted_average/all/GPN-MSA.LogisticRegression.chrom.csv
```
We recommend the following:
```bash
# Snakemake sometimes gets confused about which files it needs to rerun and this forces
# not to rerun any existing file
snakemake --cores all <path> --touch
# to output an execution plan
snakemake --cores all <path> --dry-run
```
To evaluate your own set of model features, place a dataframe of shape `n_variants,n_features` in `results/dataset/{dataset}/features/{features}.parquet`.
For zero-shot evaluation of column `{feature}` and sign `{sign}` (`plus` or `minus`), you would invoke:
```bash
snakemake --cores all results/dataset/{dataset}/{metric}/all/{features}.{sign}.{feature}.csv
```
To train and evaluate a logistic regression model, you would invoke:
```bash
snakemake --cores all results/dataset/{dataset}/{metric}/all/{feature_set}.LogisticRegression.chrom.csv
```
where `{feature_set}` should first be defined in `feature_sets` in `config/config.yaml` (this allows combining features defined in different files).
## Citation
[Link to paper](https://www.biorxiv.org/content/10.1101/2025.02.11.637758v2)
```bibtex
@article{traitgym,
title={Benchmarking DNA Sequence Models for Causal Regulatory Variant Prediction in Human Genetics},
author={Benegas, Gonzalo and Eraslan, G{\"o}kcen and Song, Yun S},
journal={bioRxiv},
pages={2025--02},
year={2025},
publisher={Cold Spring Harbor Laboratory}
}
``` |
luulinh90s/chm-corr-prj-giang | luulinh90s | "2024-07-06T14:42:17Z" | 12,113 | 0 | [
"license:mit",
"size_categories:n<1K",
"format:json",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2023-10-03T01:26:35Z" | ---
license: mit
---
|
fka/awesome-chatgpt-prompts | fka | "2025-01-06T00:02:53Z" | 12,111 | 7,643 | [
"task_categories:question-answering",
"license:cc0-1.0",
"size_categories:n<1K",
"format:csv",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"ChatGPT"
] | [
"question-answering"
] | "2022-12-13T23:47:45Z" | ---
license: cc0-1.0
tags:
- ChatGPT
task_categories:
- question-answering
size_categories:
- 100K<n<1M
---
<p align="center"><h1>🧠 Awesome ChatGPT Prompts [CSV dataset]</h1></p>
This is a Dataset Repository of **Awesome ChatGPT Prompts**
**[View All Prompts on GitHub](https://github.com/f/awesome-chatgpt-prompts)**
# License
CC-0 |
fleaven/Retargeted_AMASS_for_robotics | fleaven | "2025-02-21T14:16:52Z" | 12,084 | 5 | [
"task_categories:robotics",
"language:en",
"license:cc-by-4.0",
"size_categories:10K<n<100K",
"region:us",
"AMASS",
"Retarget",
"Robotics",
"Humanoid"
] | [
"robotics"
] | "2025-01-25T04:25:24Z" | ---
license: cc-by-4.0
task_categories:
- robotics
language:
- en
tags:
- AMASS
- Retarget
- Robotics
- Humanoid
pretty_name: Retargeted AMASS for Robotics
size_categories:
- 10K<n<100K
---
# Retargeted AMASS for Robotics
## Project Overview
This project aims to retarget motion data from the AMASS dataset to various robot models and open-source the retargeted data to facilitate research and applications in robotics and human-robot interaction. AMASS (Archive of Motion Capture as Surface Shapes) is a high-quality human motion capture dataset, and the SMPL-X model is a powerful tool for generating realistic human motion data.
By adapting the motion data from AMASS to different robot models, we hope to provide a more diverse and accessible motion dataset for robot training and human-robot interaction.
## Dataset Content
This open-source project includes the following:
1. **Retargeted Motions**: Motion files retargeted from AMASS to various robot models.
- **Unitree G1**:
<iframe src="//player.bilibili.com/player.html?bvid=BV1zd6iYkEZ2&page=1&high_quality=1&danmaku=0" allowfullscreen="allowfullscreen" width="100%" height="500" scrolling="no" frameborder="0" sandbox="allow-top-navigation allow-same-origin allow-forms allow-scripts"></iframe>
The retargeted motions for the Unitree G1 robot are generated based on the official open-source model provided by Unitree.
https://github.com/unitreerobotics/unitree_ros/blob/master/robots/g1_description/g1_29dof_rev_1_0.xml
The joint positions comply with the constraints defined in the XML file.
data shape：[-1,36]
0:3 root world position
3:7 root quaternion rotation, order: xyzw
7:36 joint positions
joint order:
```txt
left_hip_pitch_joint
left_hip_roll_joint
left_hip_yaw_joint
left_knee_joint
left_ankle_pitch_joint
left_ankle_roll_joint
right_hip_pitch_joint
right_hip_roll_joint
right_hip_yaw_joint
right_knee_joint
right_ankle_pitch_joint
right_ankle_roll_joint
waist_yaw_joint
waist_roll_joint
waist_pitch_joint
left_shoulder_pitch_joint
left_shoulder_roll_joint
left_shoulder_yaw_joint
left_elbow_joint
left_wrist_roll_joint
left_wrist_pitch_joint
left_wrist_yaw_joint
right_shoulder_pitch_joint
right_shoulder_roll_joint
right_shoulder_yaw_joint
right_elbow_joint
right_wrist_roll_joint
right_wrist_pitch_joint
right_wrist_yaw_joint
```
- **Others**: Future Updates
2. **Usage Examples**: Code examples on how to use the retargeted data.
./g1/visualize.py
3. **License Files**: Original license information for each sub-dataset within AMASS.
## License
The retargeted data in this project is derived from the AMASS dataset and therefore adheres to the original license terms of AMASS. Each sub-dataset within AMASS may have different licenses, so please ensure compliance with the following requirements when using the data:
- **Propagate Original Licenses**: When using or distributing the retargeted data, you must include and comply with the original licenses of the sub-datasets within AMASS.
- **Attribution Requirements**: Properly cite this work and the original authors and sources of the AMASS dataset and its sub-datasets.
For detailed license information, please refer to the `LICENSE` file in this project.
## Acknowledgments
This project is built on the AMASS dataset and the SMPL-X model. Special thanks to the research team at the Max Planck Institute for Intelligent Systems for providing this valuable resource.
## Citation
If you use the data or code from this project, please cite this work and relevant papers for AMASS and SMPL-X:
```bibtex
@misc{Retargeted_AMASS_R,
title={Retargeted AMASS for Robotics},
author={Kun Zhao},
url={https://huggingface.co/datasets/fleaven/Retargeted_AMASS_for_robotics}
}
@inproceedings{AMASS2019,
title={AMASS: Archive of Motion Capture as Surface Shapes},
author={Mahmood, Naureen and Ghorbani, Nima and Troje, Nikolaus F. and Pons-Moll, Gerard and Black, Michael J.},
booktitle={International Conference on Computer Vision (ICCV)},
year={2019}
}
@inproceedings{SMPL-X2019,
title={Expressive Body Capture: 3D Hands, Face, and Body from a Single Image},
author={Pavlakos, Georgios and Choutas, Vasileios and Ghorbani, Nima and Bolkart, Timo and Osman, Ahmed A. A. and Tzionas, Dimitrios and Black, Michael J.},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2019}
}
```
## Contact
For any questions or suggestions, please contact:
- **Kun Zhao**: [email protected]
For more information, follow my Xiaohongshu and Bilibili:
https://www.xiaohongshu.com/user/profile/60cdc5360000000001007e33
https://space.bilibili.com/678369952 |
haonan-li/cmmlu | haonan-li | "2023-07-13T10:19:29Z" | 12,054 | 66 | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"language:zh",
"license:cc-by-nc-4.0",
"size_categories:10K<n<100K",
"modality:text",
"library:datasets",
"library:mlcroissant",
"arxiv:2306.09212",
"region:us",
"chinese",
"llm",
"evaluation"
] | [
"multiple-choice",
"question-answering"
] | "2023-06-25T16:37:44Z" | ---
license: cc-by-nc-4.0
task_categories:
- multiple-choice
- question-answering
language:
- zh
tags:
- chinese
- llm
- evaluation
pretty_name: CMMLU
size_categories:
- 10K<n<100K
---
# CMMLU: Measuring massive multitask language understanding in Chinese
- **Homepage:** [https://github.com/haonan-li/CMMLU](https://github.com/haonan-li/CMMLU)
- **Repository:** [https://huggingface.co/datasets/haonan-li/cmmlu](https://huggingface.co/datasets/haonan-li/cmmlu)
- **Paper:** [CMMLU: Measuring Chinese Massive Multitask Language Understanding](https://arxiv.org/abs/2306.09212).
## Table of Contents
- [Introduction](#introduction)
- [Leaderboard](#leaderboard)
- [Data](#data)
- [Citation](#citation)
- [License](#license)
## Introduction
CMMLU is a comprehensive Chinese assessment suite specifically designed to evaluate the advanced knowledge and reasoning abilities of LLMs within the Chinese language and cultural context.
CMMLU covers a wide range of subjects, comprising 67 topics that span from elementary to advanced professional levels. It includes subjects that require computational expertise, such as physics and mathematics, as well as disciplines within humanities and social sciences.
Many of these tasks are not easily translatable from other languages due to their specific contextual nuances and wording.
Furthermore, numerous tasks within CMMLU have answers that are specific to China and may not be universally applicable or considered correct in other regions or languages.
## Leaderboard
Latest leaderboard is in our [github](https://github.com/haonan-li/CMMLU).
## Data
We provide development and test dataset for each of 67 subjects, with 5 questions in development set and 100+ quesitons in test set.
Each question in the dataset is a multiple-choice questions with 4 choices and only one choice as the correct answer.
Here are two examples:
```
题目：同一物种的两类细胞各产生一种分泌蛋白，组成这两种蛋白质的各种氨基酸含量相同，但排列顺序不同。其原因是参与这两种蛋白质合成的：
A. tRNA种类不同
B. 同一密码子所决定的氨基酸不同
C. mRNA碱基序列不同
D. 核糖体成分不同
答案是：C
```
```
题目：某种植物病毒V是通过稻飞虱吸食水稻汁液在水稻间传播的。稻田中青蛙数量的增加可减少该病毒在水稻间的传播。下列叙述正确的是：
A. 青蛙与稻飞虱是捕食关系
B. 水稻和病毒V是互利共生关系
C. 病毒V与青蛙是寄生关系
D. 水稻与青蛙是竞争关系
答案是：
```
#### Load data
```python
from datasets import load_dataset
cmmlu=load_dataset(r"haonan-li/cmmlu", 'agronomy')
print(cmmlu['test'][0])
```
#### Load all data at once
```python
task_list = ['agronomy', 'anatomy', 'ancient_chinese', 'arts', 'astronomy', 'business_ethics', 'chinese_civil_service_exam', 'chinese_driving_rule', 'chinese_food_culture', 'chinese_foreign_policy', 'chinese_history', 'chinese_literature',
'chinese_teacher_qualification', 'clinical_knowledge', 'college_actuarial_science', 'college_education', 'college_engineering_hydrology', 'college_law', 'college_mathematics', 'college_medical_statistics', 'college_medicine', 'computer_science',
'computer_security', 'conceptual_physics', 'construction_project_management', 'economics', 'education', 'electrical_engineering', 'elementary_chinese', 'elementary_commonsense', 'elementary_information_and_technology', 'elementary_mathematics',
'ethnology', 'food_science', 'genetics', 'global_facts', 'high_school_biology', 'high_school_chemistry', 'high_school_geography', 'high_school_mathematics', 'high_school_physics', 'high_school_politics', 'human_sexuality',
'international_law', 'journalism', 'jurisprudence', 'legal_and_moral_basis', 'logical', 'machine_learning', 'management', 'marketing', 'marxist_theory', 'modern_chinese', 'nutrition', 'philosophy', 'professional_accounting', 'professional_law',
'professional_medicine', 'professional_psychology', 'public_relations', 'security_study', 'sociology', 'sports_science', 'traditional_chinese_medicine', 'virology', 'world_history', 'world_religions']
from datasets import load_dataset
cmmlu = {k: load_dataset(r"haonan-li/cmmlu", k) for k in task_list}
```
## Citation
```
@misc{li2023cmmlu,
title={CMMLU: Measuring massive multitask language understanding in Chinese},
author={Haonan Li and Yixuan Zhang and Fajri Koto and Yifei Yang and Hai Zhao and Yeyun Gong and Nan Duan and Timothy Baldwin},
year={2023},
eprint={2306.09212},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## License
The CMMLU dataset is licensed under a
[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-nc-sa/4.0/).
|
japanese-asr/whisper_transcriptions.reazon_speech_all | japanese-asr | "2024-09-14T08:02:36Z" | 12,025 | 2 | [
"size_categories:10M<n<100M",
"format:parquet",
"modality:audio",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-09-07T13:00:19Z" | ---
dataset_info:
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dtype:
audio:
sampling_rate: 16000
- name: transcription
dtype: string
- name: transcription/en_gpt3.5
dtype: string
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sequence: int64
- name: whisper_transcription/en_gpt3.5
sequence: int64
splits:
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download_size: 11943682535
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path: subset_9.9/train-*
---
|
criteo/CriteoPrivateAd | criteo | "2025-02-26T15:18:35Z" | 11,943 | 2 | [
"task_categories:tabular-classification",
"task_categories:tabular-regression",
"license:cc-by-sa-4.0",
"size_categories:10M<n<100M",
"arxiv:2502.12103",
"arxiv:2201.13123",
"region:us",
"criteo",
"advertising"
] | [
"tabular-classification",
"tabular-regression"
] | "2025-02-18T14:35:40Z" | ---
license: cc-by-sa-4.0
size_categories:
- 10M<n<100M
task_categories:
- tabular-classification
- tabular-regression
tags:
- criteo
- advertising
---
# Dataset Documentation
## Private Bidding Optimisation {#private-conversion-optimisation}
The advertising industry lacks a common benchmark to assess the privacy
/ utility trade-off in private advertising systems. To fill this gap, we
are open-sourcing CriteoPrivateAd, the largest real-world anonymised
bidding dataset, in terms of number of features. This dataset enables
engineers and researchers to:
- assess the impact of removing cross-domain user signals,
highlighting the effects of third-party cookie deprecation;
- design and test private bidding optimisation approaches using
contextual signals and user features;
- evaluate the relevancy of answers provided by aggregation APIs for
bidding model learning.
## Summary
This dataset is released by Criteo to foster research and industrial
innovation on privacy-preserving machine learning applied to a major
advertising use-case, namely bid optimisation under user signal loss /
obfuscation.
This use-case is inspired by challenges both browser vendors and AdTech
companies are facing due to third-party cookie deprecation, such as
ensuring a viable cookie-less advertising business via a pragmatic
performance / privacy trade-off. In particular, we are expecting to see
improvements of Google Chrome Privacy Sandbox and Microsoft Ad Selection
APIs via offline benchmarks based on this dataset.
The dataset contains an anonymised log aiming to mimic production
performance of AdTech bidding engines, so that offline results based on
this dataset could be taken as ground truth to improve online
advertising performance under privacy constraints. Features are grouped
into several groups depending on their nature, envisioned privacy
constraints and availability at inference time.
Based on this dataset, the intended objective is to implement privacy
constraints (e.g. by aggregating labels or by adding differential
privacy to features and/or labels) and then learn click and conversion
(e.g. sales) prediction models.
The associated paper is available [here](https://arxiv.org/abs/2502.12103)
As a leading AdTech company that drives commerce outcomes for media
owners and marketers, Criteo is committed to evaluating proposals that
might affect the way we will perform attribution, reporting and campaign
optimisation in the future. Criteo has already participated in testing
and providing feedback on browser proposals such as the Privacy Sandbox
one; see all our [Medium articles](https://techblog.criteo.com) Back in 2021, we also
organised a public challenge aiming to assess bidding performance when
learning on aggregated data: our learnings are available [here](https://arxiv.org/abs/2201.13123).
## Dataset Description
A precise description of the dataset and each column is available in [the
companion paper](https://arxiv.org/abs/2502.12103)
This dataset represents a 100M anonymised sample of 30 days of Criteo
live data retrieved from third-party cookie traffic on Chrome. Each line corresponds to one impression (a banner)
that was displayed to a user. It is partionned by day (`day_int`) to facilitate exploration, model seeding and train/validation/test split.
For each impression, we are providing:
- campaign x publisher x (user x day) granularity with respective ids, to match Chrome Privacy Sandbox scenarios and both
display and user-level privacy.
- 4 labels (click, click leading to a landing on an advertiser
website, click leading to a visit on an advertiser website -
i.e. landing followed by one advertiser event, number of sales
attributed to the clicked display).
- more than 100 features grouped in 5 buckets with respect to their
logging and inference constraints in Protected Audience API from
Chrome Privacy Sandbox (note that these buckets are generic enough
to cover other private advertising frameworks as we are mainly
providing a split between ad campaign features, single-domain &
cross-domain user features, and contextual features) :
- Features available in the key-value server with 12-bit logging
constraint (i.e. derived from current version of modelingSignals
and standing for single-domain user features).
- Features available in the key-value server with no logging
constraint (i.e. derived from Interest Group name / renderURL).
- Features available in browser with 12-bit constraint
(i.e. cross-domain features available in generateBid).
- Features from contextual call with no logging constraint
(i.e. contextual features).
- Features not available (i.e. cross-device and cross-domain
ones).
- `day_int` enabling (1) splitting the log into training, validation
and testing sets; (2) performing relevant model seeding.
- Information about conversion delay to simulate the way Privacy Sandbox APIs are working.
- `time_between_request_timestamp_and_post_display_event` (column name
in clear): time delta (in minutes) between the request timestamp and the
click or sale event. All displays are considered starting the day of
the event at 00:00 to avoid providing complete timelines.
- We include a display order from 1 to K for display on the same day
for the same user.
The displays-per-user histograms can be deduced from event_per_user_contribution.csv. It is useful to build importance sampling ratios and user-level DP, as it is detailed in the companion paper.
## Metrics
The metrics best suited to the click and conversion estimation problems
are:
- the log-likelihood (LLH), and preferably a rescaled version named LLH-CompVN defined
as the relative log-likelihood uplift compared to the naive model
always predicting the average label in the training dataset;
- calibration, defined as the ratio between the sum of the predictions
and the sum of the validation labels. It must be close to 1 for a
bidding application;
We would like to point out that conventional classification measures
such as area under the curve (AUC) are less relevant for comparing
auction models.
The click-through rate is higher than the one encountered in real-world
advertising systems on the open internet. To design realistic bidding
applications, one must use a weighted loss for validation. We defer the
interested readers to the [associated companion paper](https://arxiv.org/abs/2502.12103) for more details
## Baselines
The Training period has been fixed to 1->25 and Validation period to 26->30. The chosen loss is the LLH-CompVN with weighting as defined above. The Sales | Display is a product of the Landed Click | Display and the Sales | Landed Click.
| Task/CTR | 0.1% | 0.5% | 1% |
|-------------------------|-------|-------|-------|
| Landed Click \| Display | 0.170 | 0.186 | 0.234 |
| Sales \| Landed Click | 0.218 | 0.218 | 0.218 |
| Sales \| Display | 0.171 | 0.187 | 0.237 |
Note that our baseline results might be difficult to achieve because of the anonymisation of the dataset.
## License
The data is released under the license. You are free to
Share and Adapt this data provided that you respect the Attribution and
ShareAlike conditions. Please read carefully the full license before
using.
## Citation
If you use the dataset in your research please cite it using the
following Bibtex excerpt:
```bibtex
@misc{sebbar2025criteoprivateadrealworldbiddingdataset,
title={CriteoPrivateAd: A Real-World Bidding Dataset to Design Private Advertising Systems},
author={Mehdi Sebbar and Corentin Odic and Mathieu Léchine and Aloïs Bissuel and
Nicolas Chrysanthos and Anthony D'Amato and Alexandre Gilotte and
Fabian Höring and Sarah Nogueira and Maxime Vono},
year={2025},
eprint={2502.12103},
archivePrefix={arXiv},
primaryClass={cs.CR},
url={https://arxiv.org/abs/2502.12103},
}
```
## Acknowledgment
We would like to thank:
- Google Chrome Privacy Sandbox team, especially Charlie Harrison,
for feedbacks on the usefulness of this dataset.
- W3C PATCG group, notably for their public data requests to foster
work on the future of attribution and reporting.
- Criteo stakeholders who took part of this dataset release: Anthony
D'Amato, Mathieu Léchine, Mehdi Sebbar, Corentin Odic, Maxime Vono,
Camille Jandot, Fatma Moalla, Nicolas Chrysanthos, Romain Lerallut,
Alexandre Gilotte, Aloïs Bissuel, Lionel Basdevant, Henry Jantet. |
huggingface/release-assets | huggingface | "2024-09-26T12:48:50Z" | 11,936 | 1 | [
"license:mit",
"size_categories:n<1K",
"format:imagefolder",
"modality:image",
"library:datasets",
"library:mlcroissant",
"region:us"
] | null | "2024-09-25T10:32:15Z" | ---
license: mit
---
|
distil-whisper/librispeech_long | distil-whisper | "2023-11-02T14:22:54Z" | 11,930 | 2 | [
"size_categories:n<1K",
"format:parquet",
"modality:audio",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2023-11-02T14:22:51Z" | ---
dataset_info:
config_name: clean
features:
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dtype: audio
splits:
- name: validation
num_bytes: 1998609.0
num_examples: 1
download_size: 1984721
dataset_size: 1998609.0
configs:
- config_name: clean
data_files:
- split: validation
path: clean/validation-*
---
# Dataset Card for "librispeech_long"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ericphann/video-game-super-resolution | ericphann | "2025-03-14T13:36:19Z" | 11,925 | 0 | [
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:imagefolder",
"modality:image",
"library:datasets",
"library:mlcroissant",
"region:us"
] | null | "2025-03-09T02:59:54Z" | ---
license: apache-2.0
---
|
GEM/xwikis | GEM | "2023-02-22T13:05:19Z" | 11,911 | 3 | [
"task_categories:summarization",
"annotations_creators:found",
"language_creators:unknown",
"multilinguality:unknown",
"source_datasets:original",
"language:de",
"language:en",
"language:fr",
"language:cs",
"license:cc-by-sa-4.0",
"arxiv:2202.09583",
"region:us"
] | [
"summarization"
] | "2022-03-14T15:31:48Z" | ---
annotations_creators:
- found
language_creators:
- unknown
language:
- de
- en
- fr
- cs
license:
- cc-by-sa-4.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- summarization
task_ids: []
pretty_name: xwikis
---
# Dataset Card for GEM/xwikis
## Dataset Description
- **Homepage:** https://github.com/lauhaide/clads
- **Repository:** [Needs More Information]
- **Paper:** https://arxiv.org/abs/2202.09583
- **Leaderboard:** N/A
- **Point of Contact:** Laura Perez-Beltrachini
### Link to Main Data Card
You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/xwikis).
### Dataset Summary
The XWikis Corpus provides datasets with different language pairs and directions for cross-lingual and multi-lingual abstractive document summarisation.
You can load the dataset via:
```
import datasets
data = datasets.load_dataset('GEM/xwikis')
```
The data loader can be found [here](https://huggingface.co/datasets/GEM/xwikis).
#### website
[Github](https://github.com/lauhaide/clads)
#### paper
https://arxiv.org/abs/2202.09583
#### authors
Laura Perez-Beltrachini (University of Edinburgh)
## Dataset Overview
### Where to find the Data and its Documentation
#### Webpage
<!-- info: What is the webpage for the dataset (if it exists)? -->
<!-- scope: telescope -->
[Github](https://github.com/lauhaide/clads)
#### Paper
<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
<!-- scope: telescope -->
https://arxiv.org/abs/2202.09583
#### BibTex
<!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. -->
<!-- scope: microscope -->
```
@InProceedings{clads-emnlp,
author = "Laura Perez-Beltrachini and Mirella Lapata",
title = "Models and Datasets for Cross-Lingual Summarisation",
booktitle = "Proceedings of The 2021 Conference on Empirical Methods in Natural Language Processing ",
year = "2021",
address = "Punta Cana, Dominican Republic",
}
```
#### Contact Name
<!-- quick -->
<!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
Laura Perez-Beltrachini
#### Contact Email
<!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
[email protected]
#### Has a Leaderboard?
<!-- info: Does the dataset have an active leaderboard? -->
<!-- scope: telescope -->
no
### Languages and Intended Use
#### Multilingual?
<!-- quick -->
<!-- info: Is the dataset multilingual? -->
<!-- scope: telescope -->
yes
#### Covered Languages
<!-- quick -->
<!-- info: What languages/dialects are covered in the dataset? -->
<!-- scope: telescope -->
`German`, `English`, `French`, `Czech`, `Chinese`
#### License
<!-- quick -->
<!-- info: What is the license of the dataset? -->
<!-- scope: telescope -->
cc-by-sa-4.0: Creative Commons Attribution Share Alike 4.0 International
#### Intended Use
<!-- info: What is the intended use of the dataset? -->
<!-- scope: microscope -->
Cross-lingual and Multi-lingual single long input document abstractive summarisation.
#### Primary Task
<!-- info: What primary task does the dataset support? -->
<!-- scope: telescope -->
Summarization
#### Communicative Goal
<!-- quick -->
<!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. -->
<!-- scope: periscope -->
Entity descriptive summarisation, that is, generate a summary that conveys the most salient facts of a document related to a given entity.
### Credit
#### Curation Organization Type(s)
<!-- info: In what kind of organization did the dataset curation happen? -->
<!-- scope: telescope -->
`academic`
#### Dataset Creators
<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
<!-- scope: microscope -->
Laura Perez-Beltrachini (University of Edinburgh)
#### Who added the Dataset to GEM?
<!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. -->
<!-- scope: microscope -->
Laura Perez-Beltrachini (University of Edinburgh) and Ronald Cardenas (University of Edinburgh)
### Dataset Structure
#### Data Splits
<!-- info: Describe and name the splits in the dataset if there are more than one. -->
<!-- scope: periscope -->
For each language pair and direction there exists a train/valid/test split.
The test split is a sample of size 7k from the intersection of titles existing in the four languages (cs,fr,en,de).
Train/valid are randomly split.
## Dataset in GEM
### Rationale for Inclusion in GEM
#### Similar Datasets
<!-- info: Do other datasets for the high level task exist? -->
<!-- scope: telescope -->
no
### GEM-Specific Curation
#### Modificatied for GEM?
<!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? -->
<!-- scope: telescope -->
no
#### Additional Splits?
<!-- info: Does GEM provide additional splits to the dataset? -->
<!-- scope: telescope -->
no
### Getting Started with the Task
## Previous Results
### Previous Results
#### Measured Model Abilities
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: telescope -->
- identification of entity salient information
- translation
- multi-linguality
- cross-lingual transfer, zero-shot, few-shot
#### Metrics
<!-- info: What metrics are typically used for this task? -->
<!-- scope: periscope -->
`ROUGE`
#### Previous results available?
<!-- info: Are previous results available? -->
<!-- scope: telescope -->
yes
#### Other Evaluation Approaches
<!-- info: What evaluation approaches have others used? -->
<!-- scope: periscope -->
ROUGE-1/2/L
## Dataset Curation
### Original Curation
#### Sourced from Different Sources
<!-- info: Is the dataset aggregated from different data sources? -->
<!-- scope: telescope -->
no
### Language Data
#### How was Language Data Obtained?
<!-- info: How was the language data obtained? -->
<!-- scope: telescope -->
`Found`
#### Where was it found?
<!-- info: If found, where from? -->
<!-- scope: telescope -->
`Single website`
#### Data Validation
<!-- info: Was the text validated by a different worker or a data curator? -->
<!-- scope: telescope -->
other
#### Was Data Filtered?
<!-- info: Were text instances selected or filtered? -->
<!-- scope: telescope -->
not filtered
### Structured Annotations
#### Additional Annotations?
<!-- quick -->
<!-- info: Does the dataset have additional annotations for each instance? -->
<!-- scope: telescope -->
found
#### Annotation Service?
<!-- info: Was an annotation service used? -->
<!-- scope: telescope -->
no
#### Annotation Values
<!-- info: Purpose and values for each annotation -->
<!-- scope: microscope -->
The input documents have section structure information.
#### Any Quality Control?
<!-- info: Quality control measures? -->
<!-- scope: telescope -->
validated by another rater
#### Quality Control Details
<!-- info: Describe the quality control measures that were taken. -->
<!-- scope: microscope -->
Bilingual annotators assessed the content overlap of source document and target summaries.
### Consent
#### Any Consent Policy?
<!-- info: Was there a consent policy involved when gathering the data? -->
<!-- scope: telescope -->
no
### Private Identifying Information (PII)
#### Contains PII?
<!-- quick -->
<!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? -->
<!-- scope: telescope -->
no PII
### Maintenance
#### Any Maintenance Plan?
<!-- info: Does the original dataset have a maintenance plan? -->
<!-- scope: telescope -->
no
## Broader Social Context
### Previous Work on the Social Impact of the Dataset
#### Usage of Models based on the Data
<!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? -->
<!-- scope: telescope -->
no
### Impact on Under-Served Communities
#### Addresses needs of underserved Communities?
<!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). -->
<!-- scope: telescope -->
no
### Discussion of Biases
#### Any Documented Social Biases?
<!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. -->
<!-- scope: telescope -->
no
## Considerations for Using the Data
### PII Risks and Liability
### Licenses
#### Copyright Restrictions on the Dataset
<!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? -->
<!-- scope: periscope -->
`public domain`
#### Copyright Restrictions on the Language Data
<!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? -->
<!-- scope: periscope -->
`public domain`
### Known Technical Limitations
|
asahi417/seamless-align-enA-zhA.speaker-embedding.hubert-xl | asahi417 | "2024-06-16T12:04:50Z" | 11,899 | 0 | [
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] | null | "2024-06-12T09:01:20Z" | ---
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---
|
vikhyatk/docmatix-single | vikhyatk | "2024-07-19T02:31:20Z" | 11,890 | 6 | [
"size_categories:100K<n<1M",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-07-18T23:35:08Z" | ---
dataset_info:
features:
- name: images
sequence: image
- name: texts
list:
- name: user
dtype: string
- name: assistant
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 244951255658.16818
num_examples: 565009
download_size: 145422811605
dataset_size: 244951255658.16818
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
[Docmatix](https://huggingface.co/datasets/HuggingFaceM4/Docmatix), but with multi-image samples filtered out. |
MMInstruction/M3IT | MMInstruction | "2023-11-24T08:23:25Z" | 11,887 | 125 | [
"task_categories:image-to-text",
"task_categories:image-classification",
"language:en",
"language:zh",
"license:other",
"size_categories:1M<n<10M",
"region:us"
] | [
"image-to-text",
"image-classification"
] | "2023-05-04T01:43:31Z" | ---
license: other
task_categories:
- image-to-text
- image-classification
size_categories:
- 1M<n<10M
language:
- en
- zh
---
# Dataset Card for M3IT
Project Page: [M3IT](https://m3-it.github.io/)
## Dataset Description
- **Homepage: https://huggingface.co/datasets/MMInstruction/M3IT**
- **Repository: https://huggingface.co/datasets/MMInstruction/M3IT**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Languages
English and Chinese. 80 translated version can be found at [M3IT-80](https://huggingface.co/datasets/MMInstruction/M3IT-80).
## Dataset Statistics
Our dataset compiles diverse tasks of classical vision-language tasks, including captioning,
visual question answering~(VQA), visual conditioned generation, reasoning and classification.
### Instruction Statistics
| Task | #Instructions |
|---------------------------|---------------|
| Image Captioning | 52 |
| Classification | 113 |
| Visual Question Answering | 95 |
| Knowledgeable Visual QA | 40 |
| Reasoning | 60 |
| Generation | 40 |
| Total | 400 |
### Task Statistics
| Task | Description | #Train | #Val | #Test |
|---------------------------|-----------------------------------------------------------------|---------|---------|---------|
| Image Captioning | Given an image, write a description for the image. | 679,087 | 41,462 | 27,499 |
| Classification | Given an image, classify the image into pre-defined categories. | 238,303 | 100,069 | 21,206 |
| Visual Question Answering | Given an image, answer a question relevant to the image. | 177,633 | 46,314 | 10,828 |
| Knowledgeable Visual QA | Given an image, answer the question requires outside knowledge. | 39,981 | 11,682 | 5,477 |
| Reasoning | Given an image, conduct reasoning over the images. | 99,372 | 11,500 | 10,000 |
| Generation | Given an image, make compositions with certain requirements. | 145,000 | 11,315 | 17,350 |
| Chinese | CAP, CLS, VQA, and GEN tasks in Chinese. | 192,076 | 77,306 | 4,100 |
| Video | CAP, CLS, and VQA tasks on video-language datasets. | 20,868 | 7,542 | 9,294 |
| Multi-lingual | Translated tasks in 80 languages | 0 | 240,000 | 184,000 |
### Detailed Dataset Statistics
| Task | Dataset | #Train | #Val | #Test |
|---------------------------|------------------------------|---------|--------|--------|
| Image Captioning | `coco` | 566,747 | 25,010 | 25,010 |
| | `textcap` | 97,765 | 13,965 | 0 |
| | `image-paragraph-captioning` | 14,575 | 2,487 | 2,489 |
| Classification | `coco-goi` | 30,000 | 2,000 | 0 |
| | `coco-text` | 118,312 | 27,550 | 0 |
| | `imagenet` | 30,000 | 50,000 | 0 |
| | `coco-itm` | 30,000 | 5,000 | 5,000 |
| | `snli-ve` | 20,000 | 14,339 | 14,740 |
| | `mocheg` | 4,991 | 180 | 466 |
| | `iqa` | 5,000 | 1,000 | 1,000 |
| Visual Question Answering | `vqa-v2` | 30,000 | 30,000 | 0 |
| | `shapes` | 13,568 | 1,024 | 1,024 |
| | `docvqa` | 39,463 | 5,349 | 0 |
| | `ocr-vqa` | 11,414 | 4,940 | 0 |
| | `st-vqa` | 26,074 | 0 | 4,070 |
| | `text-vqa` | 27,113 | 0 | 5,734 |
| | `gqa` | 30,001 | 5,001 | 0 |
| Knowledgeable Visual QA | `okvqa` | 9,009 | 5,046 | 0 |
| | `a-okvqa` | 17,056 | 1,145 | 0 |
| | `science-qa` | 12,726 | 4,241 | 4,241 |
| | `viquae` | 1,190 | 1,250 | 1,236 |
| Reasoning | `clevr` | 30,000 | 2,000 | 0 |
| | `nlvr` | 29,372 | 2,000 | 0 |
| | `vcr` | 25,000 | 5,000 | 5,000 |
| | `visual-mrc` | 15,000 | 2,500 | 5,000 |
| | `winoground` | 0 | 0 | 800 |
| Generation | `vist` | 5,000 | 4,315 | 4,350 |
| | `visual-dialog` | 50,000 | 1,000 | 1,000 |
| | `multi30k` | 90,000 | 6,000 | 12,000 |
| Chinese | `fm-iqa` | 164,735 | 75,206 | 0 |
| | `coco-cn` | 18,341 | 1,000 | 1,000 |
| | `flickr8k-cn` | 6,000 | 1,000 | 1,000 |
| | `chinese-food` | 0 | 0 | 1,100 |
| | `mmchat` | 3,000 | 1,000 | 1,000 |
| Video | `ss` | 2,000 | 2,000 | 2,000 |
| | `ivqa` | 5,994 | 2,000 | 2,000 |
| | `msvd-qa` | 1,161 | 245 | 504 |
| | `activitynet-qa` | 3,200 | 1,800 | 800 |
| | `msrvtt` | 6,513 | 497 | 2,990 |
| | `msrvtt-qa` | 2,000 | 1,000 | 1,000 |
## Dataset Structure
### HuggingFace Login (Optional)
```python
# OR run huggingface-cli login
from huggingface_hub import login
hf_token = "hf_xxx" # TODO: set a valid HuggingFace access token for loading datasets/models
login(token=hf_token)
```
### Data Loading
```python
from datasets import load_dataset
ds_name = "coco" # change the dataset name here
dataset = load_dataset("MMInstruction/M3IT", ds_name)
```
### Data Splits
```python
from datasets import load_dataset
ds_name = "coco" # change the dataset name here
dataset = load_dataset("MMInstruction/M3IT", ds_name)
train_set = dataset["train"]
validation_set = dataset["validation"]
test_set = dataset["test"]
```
### Data Instances
```python
from datasets import load_dataset
from io import BytesIO
from base64 import b64decode
from PIL import Image
ds_name = "coco" # change the dataset name here
dataset = load_dataset("MMInstruction/M3IT", ds_name)
train_set = dataset["train"]
for train_instance in train_set:
instruction = train_instance["instruction"] # str
inputs = train_instance["inputs"] # str
outputs = train_instance["outputs"] # str
image_base64_str_list = train_instance["image_base64_str"] # str (base64)
image_0 = Image.open(BytesIO(b64decode(image_base64_str_list[0])))
```
### Data Fields
```python
import datasets
features = datasets.Features(
{
"instruction": datasets.Value("string"),
"inputs": datasets.Value("string"),
"image_base64_str": [datasets.Value("string")],
"outputs": datasets.Value("string"),
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
| Task | Dataset [Citation] | Source |
|---------------------------|----------------------------------|------------------------------------------------------------------------------------|
| Image Captioning | `coco` [1] | [Source](https://cocodataset.org/#home) |
| | `textcap` [2] | [Source](https://textvqa.org/textcaps/) |
| | `image-paragraph-captioning` [3] | [Source](https://cs.stanford.edu/people/ranjaykrishna/im2p/index.html) |
| Classification | `coco-goi` [1] | [Source](https://cocodataset.org/#home) |
| | `coco-text` [4] | [Source](https://bgshih.github.io/cocotext/) |
| | `imagenet` [5] | [Source](https://www.image-net.org/) |
| | `coco-itm` [1] | [Source](https://cocodataset.org/#home) |
| | `snli-ve` [6] | [Source](https://github.com/necla-ml/SNLI-VE) |
| | `mocheg` [7] | [Source](https://github.com/VT-NLP/Mocheg) |
| | `iqa` [8] | [Source](https://github.com/icbcbicc/IQA-Dataset) |
| Visual Question Answering | `vqa-v2` [9] | [Source](https://visualqa.org/) |
| | `shapes` [10] | [Source](https://github.com/ronghanghu/n2nmn) |
| | `docvqa` [11] | [Source](https://www.docvqa.org/) |
| | `ocr-vqa` [12] | [Source](https://ocr-vqa.github.io/) |
| | `st-vqa` [13] | [Source](https://rrc.cvc.uab.es/?ch=11) |
| | `text-vqa` [14] | [Source](https://textvqa.org/) |
| | `gqa` [15] | [Source](https://cs.stanford.edu/people/dorarad/gqa/about.html) |
| Knowledgeable Visual QA | `okvqa` [16] | [Source](https://okvqa.allenai.org/) |
| | `a-okvqa` [17] | [Source](https://allenai.org/project/a-okvqa/home) |
| | `science-qa` [18] | [Source](https://scienceqa.github.io/) |
| | `viquae` [19] | [Source](https://github.com/PaulLerner/ViQuAE) |
| Reasoning | `clevr` [20] | [Source](https://cs.stanford.edu/people/jcjohns/clevr/) |
| | `nlvr` [21] | [Source](https://lil.nlp.cornell.edu/nlvr/) |
| | `vcr` [22] | [Source](https://visualcommonsense.com/) |
| | `visual-mrc` [23] | [Source](https://github.com/nttmdlab-nlp/VisualMRC) |
| | `winoground` [24] | [Source](https://huggingface.co/datasets/facebook/winoground) |
| Generation | `vist` [25] | [Source](https://visionandlanguage.net/VIST/) |
| | `visual-dialog` [26] | [Source](https://visualdialog.org/) |
| | `multi30k` [27] | [Source](https://github.com/multi30k/dataset) |
| Chinese | `fm-iqa` [28] | [Source](https://paperswithcode.com/dataset/fm-iqa) |
| | `coco-cn` [29] | [Source](https://github.com/li-xirong/coco-cn) |
| | `flickr8k-cn` [30] | [Source](https://github.com/li-xirong/flickr8kcn) |
| | `chinese-food` [31] | [Source](https://sites.google.com/view/chinesefoodnet) |
| | `mmchat` [32] | [Source](https://github.com/silverriver/MMChat) |
| Video | `ss` [33] | [Source](https://developer.qualcomm.com/software/ai-datasets/something-something) |
| | `ivqa` [34] | [Source](https://antoyang.github.io/just-ask.html) |
| | `msvd-qa` [35] | [Source](https://paperswithcode.com/dataset/msvd) |
| | `activitynet-qa` [36] | [Source](https://github.com/MILVLG/activitynet-qa) |
| | `msrvtt` [35] | [Source](https://paperswithcode.com/dataset/msr-vtt) |
| | `msrvtt-qa` [37] | [Source](https://paperswithcode.com/sota/visual-question-answering-on-msrvtt-qa-1) |
### Annotations
#### Annotation process
To build high-quality multimodal instruction datasets,
we rewrite various datasets into multimodal-to-text dialog format.
The annotation process includes four steps:
- (1) **Stage I: Instruction Writing**: writing instructions for each task;
- (2) **Stage II: Data Format Unification**: structuring images and texts into a unified schema;
- (3) **Stage III: Quality Check**: checking the overall dataset quality;
- (4) **Stage IV: Key Datasets Translation**: building multilingual sets.
#### Who are the annotators?
Eight authors of this work are employed as human annotators,
each of whom is a graduate student familiar with relevant literature.
## Additional Information
### Licensing Information
The content of original dataset follows their original license.
We suggest that for the task with Unknown/Custom license, the user can check the original project or contact the dataset owner for detailed license information.
Our annotated instruction data is licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).
### Citation Information
```bibtex
@article{li2023m3it,
title={M$^3$IT: A Large-Scale Dataset towards Multi-Modal Multilingual Instruction Tuning},
author={Lei Li and Yuwei Yin and Shicheng Li and Liang Chen and Peiyi Wang and Shuhuai Ren and Mukai Li and Yazheng Yang and Jingjing Xu and Xu Sun and Lingpeng Kong and Qi Liu},
journal={arXiv preprint arXiv:2306.04387},
year={2023}
}
```
### Contributions
M3IT is an open-source, large-scale Multi-modal, Multilingual Instruction Tuning dataset,
designed to enable the development of general-purpose multi-modal agents.
## References
- [1] Microsoft COCO: Common Objects in Context
- [2] TextCaps: a dataset for image captioning with reading comprehension
- [3] A Hierarchical Approach for Generating Descriptive Image Paragraphs
- [4] COCO-Text: Dataset and benchmark for text detection and recognition in natural images
- [5] Imagenet large scale visual recognition challenge
- [6] E-ViL: A Dataset and Benchmark for Natural Language Explanations in Vision-Language Tasks
- [7] End-to-End Multimodal Fact-Checking and Explanation Generation: A Challenging Dataset and Models
- [8] Quantifying visual image quality: A Bayesian view
- [9] Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering
- [10] Neural Module Networks
- [11] DocVQA: A dataset for vqa on document images
- [12] OCR-VQA: Visual Question Answering by Reading Text in Images
- [13] Scene Text Visual Question Answering
- [14] Towards VQA Models That Can Read
- [15] GQA: A new dataset for real-world visual reasoning and compositional question answering
- [16] OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge
- [17] A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge
- [18] Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering
- [19] ViQuAE: a dataset for knowledge-based visual question answering about named entities
- [20] CLEVR: A diagnostic dataset for compositional language and elementary visual reasoning
- [21] A Corpus of Natural Language for Visual Reasoning
- [22] From recognition to cognition: Visual Commonsense Reasoning
- [23] VisualMRC: Machine reading comprehension on document images
- [24] WinoGround: Probing vision and language models for visio-linguistic compositionality
- [25] Visual Storytelling
- [26] Visual Dialog
- [27] Multi30k: Multilingual english-german image descriptions
- [28] Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question
- [29] COCO-CN for cross-lingual image tagging, captioning, and retrieval
- [30] Adding Chinese Captions to Images
- [31] ChineseFoodNet: A large-scale image dataset for chinese food recognition
- [32] MMChat: Multi-Modal Chat Dataset on Social Media
- [33] The "Something Something" Video Database for Learning and Evaluating Visual Common Sense
- [34] Just Ask: Learning to answer questions from millions of narrated videos
- [35] Video Question Answering via Gradually Refined Attention over Appearance and Motion
- [36] ActivityNet-qa: A dataset for understanding complex web videos via question answering
- [37] MSR-VTT: A large video description dataset for bridging video and language |
laion/LAION-Audio-300M | laion | "2025-01-10T21:33:57Z" | 11,881 | 26 | [
"license:apache-2.0",
"size_categories:100M<n<1B",
"format:webdataset",
"modality:audio",
"modality:text",
"library:datasets",
"library:webdataset",
"library:mlcroissant",
"region:us"
] | null | "2024-12-29T09:50:41Z" | ---
license: apache-2.0
---
|
mii-llm/requests | mii-llm | "2025-03-20T06:29:43Z" | 11,851 | 0 | [
"license:apache-2.0",
"region:us"
] | null | "2024-05-13T18:05:34Z" | ---
license: apache-2.0
---
|
AI4Math/MathVista | AI4Math | "2024-02-11T23:09:05Z" | 11,755 | 141 | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:visual-question-answering",
"task_categories:text-classification",
"task_ids:multiple-choice-qa",
"task_ids:closed-domain-qa",
"task_ids:open-domain-qa",
"task_ids:visual-question-answering",
"task_ids:multi-class-classification",
"annotations_creators:expert-generated",
"annotations_creators:found",
"language_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"language:zh",
"language:fa",
"license:cc-by-sa-4.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2310.02255",
"region:us",
"multi-modal-qa",
"math-qa",
"figure-qa",
"geometry-qa",
"math-word-problem",
"textbook-qa",
"vqa",
"arithmetic-reasoning",
"statistical-reasoning",
"algebraic-reasoning",
"geometry-reasoning",
"numeric-common-sense",
"scientific-reasoning",
"logical-reasoning",
"geometry-diagram",
"synthetic-scene",
"chart",
"plot",
"scientific-figure",
"table",
"function-plot",
"abstract-scene",
"puzzle-test",
"document-image",
"medical-image",
"mathematics",
"science",
"chemistry",
"biology",
"physics",
"engineering",
"natural-science"
] | [
"multiple-choice",
"question-answering",
"visual-question-answering",
"text-classification"
] | "2023-10-15T17:49:10Z" | ---
annotations_creators:
- expert-generated
- found
language_creators:
- expert-generated
- found
language:
- en
- zh
- fa
license: cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- multiple-choice
- question-answering
- visual-question-answering
- text-classification
task_ids:
- multiple-choice-qa
- closed-domain-qa
- open-domain-qa
- visual-question-answering
- multi-class-classification
paperswithcode_id: mathvista
pretty_name: MathVista
tags:
- multi-modal-qa
- math-qa
- figure-qa
- geometry-qa
- math-word-problem
- textbook-qa
- vqa
- arithmetic-reasoning
- statistical-reasoning
- algebraic-reasoning
- geometry-reasoning
- numeric-common-sense
- scientific-reasoning
- logical-reasoning
- geometry-diagram
- synthetic-scene
- chart
- plot
- scientific-figure
- table
- function-plot
- abstract-scene
- puzzle-test
- document-image
- medical-image
- mathematics
- science
- chemistry
- biology
- physics
- engineering
- natural-science
configs:
- config_name: default
data_files:
- split: testmini
path: data/testmini-*
- split: test
path: data/test-*
dataset_info:
features:
- name: pid
dtype: string
- name: question
dtype: string
- name: image
dtype: string
- name: decoded_image
dtype: image
- name: choices
sequence: string
- name: unit
dtype: string
- name: precision
dtype: float64
- name: answer
dtype: string
- name: question_type
dtype: string
- name: answer_type
dtype: string
- name: metadata
struct:
- name: category
dtype: string
- name: context
dtype: string
- name: grade
dtype: string
- name: img_height
dtype: int64
- name: img_width
dtype: int64
- name: language
dtype: string
- name: skills
sequence: string
- name: source
dtype: string
- name: split
dtype: string
- name: task
dtype: string
- name: query
dtype: string
splits:
- name: testmini
num_bytes: 142635198.0
num_examples: 1000
- name: test
num_bytes: 648291350.22
num_examples: 5141
download_size: 885819490
dataset_size: 790926548.22
---
# Dataset Card for MathVista
- [Dataset Description](https://huggingface.co/datasets/AI4Math/MathVista/blob/main/README.md#dataset-description)
- [Paper Information](https://huggingface.co/datasets/AI4Math/MathVista/blob/main/README.md#paper-information)
- [Dataset Examples](https://huggingface.co/datasets/AI4Math/MathVista/blob/main/README.md#dataset-examples)
- [Leaderboard](https://huggingface.co/datasets/AI4Math/MathVista/blob/main/README.md#leaderboard)
- [Dataset Usage](https://huggingface.co/datasets/AI4Math/MathVista/blob/main/README.md#dataset-usage)
- [Data Downloading](https://huggingface.co/datasets/AI4Math/MathVista/blob/main/README.md#data-downloading)
- [Data Format](https://huggingface.co/datasets/AI4Math/MathVista/blob/main/README.md#data-format)
- [Data Visualization](https://huggingface.co/datasets/AI4Math/MathVista/blob/main/README.md#data-visualization)
- [Data Source](https://huggingface.co/datasets/AI4Math/MathVista/blob/main/README.md#data-source)
- [Automatic Evaluation](https://huggingface.co/datasets/AI4Math/MathVista/blob/main/README.md#automatic-evaluation)
- [License](https://huggingface.co/datasets/AI4Math/MathVista/blob/main/README.md#license)
- [Citation](https://huggingface.co/datasets/AI4Math/MathVista/blob/main/README.md#citation)
## Dataset Description
**MathVista** is a consolidated Mathematical reasoning benchmark within Visual contexts. It consists of **three newly created datasets, IQTest, FunctionQA, and PaperQA**, which address the missing visual domains and are tailored to evaluate logical reasoning on puzzle test figures, algebraic reasoning over functional plots, and scientific reasoning with academic paper figures, respectively. It also incorporates **9 MathQA datasets** and **19 VQA datasets** from the literature, which significantly enrich the diversity and complexity of visual perception and mathematical reasoning challenges within our benchmark. In total, **MathVista** includes **6,141 examples** collected from **31 different datasets**.
## Paper Information
- Paper: https://arxiv.org/abs/2310.02255
- Code: https://github.com/lupantech/MathVista
- Project: https://mathvista.github.io/
- Visualization: https://mathvista.github.io/#visualization
- Leaderboard: https://mathvista.github.io/#leaderboard
## Dataset Examples
Examples of our newly annotated datasets: IQTest, FunctionQA, and PaperQA:
<img src="https://raw.githubusercontent.com/lupantech/MathVista/main/assets/our_new_3_datasets.png" style="zoom:40%;" />
<details>
<summary>🔍 Click to expand/collapse more examples</summary>
Examples of seven mathematical reasoning skills:
1. Arithmetic Reasoning
<img src="https://raw.githubusercontent.com/lupantech/MathVista/main/assets/skills/ari.png" style="zoom:40%;" />
2. Statistical Reasoning
<img src="https://raw.githubusercontent.com/lupantech/MathVista/main/assets/skills/sta.png" style="zoom:40%;" />
3. Algebraic Reasoning
<img src="https://raw.githubusercontent.com/lupantech/MathVista/main/assets/skills/alg.png" style="zoom:40%;" />
4. Geometry Reasoning
<img src="https://raw.githubusercontent.com/lupantech/MathVista/main/assets/skills/geo.png" style="zoom:40%;" />
5. Numeric common sense
<img src="https://raw.githubusercontent.com/lupantech/MathVista/main/assets/skills/num.png" style="zoom:40%;" />
6. Scientific Reasoning
<img src="https://raw.githubusercontent.com/lupantech/MathVista/main/assets/skills/sci.png" style="zoom:40%;" />
7. Logical Reasoning
<img src="https://raw.githubusercontent.com/lupantech/MathVista/main/assets/skills/log.png" style="zoom:40%;" />
</details>
## Leaderboard
🏆 The leaderboard for the *testmini* set (1,000 examples) is available [here](https://mathvista.github.io/#leaderboard).
🏆 The leaderboard for the *test* set (5,141 examples) and the automatic evaluation on [CodaLab](https://codalab.org/) are under construction.
## Dataset Usage
### Data Downloading
All the data examples were divided into two subsets: *testmini* and *test*.
- **testmini**: 1,000 examples used for model development, validation, or for those with limited computing resources.
- **test**: 5,141 examples for standard evaluation. Notably, the answer labels for test will NOT be publicly released.
You can download this dataset by the following command (make sure that you have installed [Huggingface Datasets](https://huggingface.co/docs/datasets/quickstart)):
```python
from datasets import load_dataset
dataset = load_dataset("AI4Math/MathVista")
```
Here are some examples of how to access the downloaded dataset:
```python
# print the first example on the testmini set
print(dataset["testmini"][0])
print(dataset["testmini"][0]['pid']) # print the problem id
print(dataset["testmini"][0]['question']) # print the question text
print(dataset["testmini"][0]['query']) # print the query text
print(dataset["testmini"][0]['image']) # print the image path
print(dataset["testmini"][0]['answer']) # print the answer
dataset["testmini"][0]['decoded_image'] # display the image
# print the first example on the test set
print(dataset["test"][0])
```
### Data Format
The dataset is provided in json format and contains the following attributes:
```json
{
"question": [string] The question text,
"image": [string] A file path pointing to the associated image,
"choices": [list] Choice options for multiple-choice problems. For free-form problems, this could be a 'none' value,
"unit": [string] The unit associated with the answer, e.g., "m^2", "years". If no unit is relevant, it can be a 'none' value,
"precision": [integer] The number of decimal places the answer should be rounded to,
"answer": [string] The correct answer for the problem,
"question_type": [string] The type of question: "multi_choice" or "free_form",
"answer_type": [string] The format of the answer: "text", "integer", "float", or "list",
"pid": [string] Problem ID, e.g., "1",
"metadata": {
"split": [string] Data split: "testmini" or "test",
"language": [string] Question language: "English", "Chinese", or "Persian",
"img_width": [integer] The width of the associated image in pixels,
"img_height": [integer] The height of the associated image in pixels,
"source": [string] The source dataset from which the problem was taken,
"category": [string] The category of the problem: "math-targeted-vqa" or "general-vqa",
"task": [string] The task of the problem, e.g., "geometry problem solving",
"context": [string] The visual context type of the associated image,
"grade": [string] The grade level of the problem, e.g., "high school",
"skills": [list] A list of mathematical reasoning skills that the problem tests
},
"query": [string] the query text used as input (prompt) for the evaluation model
}
```
### Data Visualization
🎰 You can explore the dataset in an interactive way [here](https://mathvista.github.io/#visualization).
<details>
<summary>Click to expand/collapse the visualization page screeshot.</summary>
<img src="https://raw.githubusercontent.com/lupantech/MathVista/main/assets/data_visualizer.png" style="zoom:40%;" />
</details>
### Data Source
The **MathVista** dataset is derived from three newly collected datasets: IQTest, FunctionQA, and Paper, as well as 28 other source datasets. Details can be found in the [source.json](https://huggingface.co/datasets/AI4Math/MathVista/blob/main/source.json) file. All these source datasets have been preprocessed and labeled for evaluation purposes.
### Automatic Evaluation
🔔 To automatically evaluate a model on the dataset, please refer to our GitHub repository [here](https://github.com/lupantech/MathVista/tree/main).
## License
The new contributions to our dataset are distributed under the [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) license, including
- The creation of three datasets: IQTest, FunctionQA, and Paper;
- The filtering and cleaning of source datasets;
- The standard formalization of instances for evaluation purposes;
- The annotations of metadata.
The copyright of the images and the questions belongs to the original authors, and the source of every image and original question can be found in the `metadata` field and in the [source.json](https://huggingface.co/datasets/AI4Math/MathVista/blob/main/source.json) file. Alongside this license, the following conditions apply:
- **Purpose:** The dataset was primarily designed for use as a test set.
- **Commercial Use:** The dataset can be used commercially as a test set, but using it as a training set is prohibited. By accessing or using this dataset, you acknowledge and agree to abide by these terms in conjunction with the [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) license.
## Citation
If you use the **MathVista** dataset in your work, please kindly cite the paper using this BibTeX:
```
@inproceedings{lu2024mathvista,
author = {Lu, Pan and Bansal, Hritik and Xia, Tony and Liu, Jiacheng and Li, Chunyuan and Hajishirzi, Hannaneh and Cheng, Hao and Chang, Kai-Wei and Galley, Michel and Gao, Jianfeng},
title = {MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts},
booktitle = {International Conference on Learning Representations (ICLR)},
year = {2024}
}
``` |
nyu-visionx/Cambrian-10M | nyu-visionx | "2024-07-08T04:34:51Z" | 11,755 | 108 | [
"task_categories:visual-question-answering",
"task_categories:question-answering",
"language:en",
"license:apache-2.0",
"size_categories:1M<n<10M",
"arxiv:2406.16860",
"region:us"
] | [
"visual-question-answering",
"question-answering"
] | "2024-05-30T03:27:31Z" | ---
task_categories:
- visual-question-answering
- question-answering
language:
- en
size_categories:
- 1M<n<10M
license: apache-2.0
---
# Cambrian-10M Dataset
**Please see paper & website for more information:**
- https://cambrian-mllm.github.io/
- https://arxiv.org/abs/2406.16860
## Overview
Cambrian-10M is a comprehensive dataset designed for instruction tuning, particularly in multimodal settings involving visual interaction data. The dataset is crafted to address the scarcity of high-quality multimodal instruction-tuning data and to maintain the language abilities of multimodal large language models (LLMs).
## Data Collection
### Multimodal Data Sources
Unlike language data, multimodal instruction-tuning data is much rarer and harder to collect. To address this, we leverage existing multimodal benchmarks and datasets involving visual interaction data, such as Visual Question Answering (VQA) and Optical Character Recognition (OCR) data. This approach helps mitigate the catastrophic forgetting commonly observed when fine-tuning multimodal LLMs.
### Language-Only Instruction-Following Data
To ensure the preservation of language capabilities, we also collect a small volume of high-quality language-only instruction-following data from the community.
### Targeted Internet Data Collection Engine
We introduce a data engine designed to create large-scale, reliable, high-quality knowledge-based multimodal instruction tuning data. The engine works as follows:
1. **Field and Subfield Selection**: The engine selects a target field and subfield, such as “Physics”.
2. **Topic Identification**: An LLM like GPT-4 identifies topics within the field (e.g., “Newton’s Laws”).
3. **Reliable Source Search**: The engine searches reliable sources like Wikipedia for each topic.
4. **Text-Image Association Extraction**: The parser extracts image-caption-text tuples from the sources.
5. **Q&A Pair Generation**: The caption-text is fed to an LLM, such as GPT-3.5, to generate instruction-type Q&A pairs about the image.
These Q&A pairs, along with the images, form our VQA dataset.
### GPT Rewriting
We also incorporate recent MLLMs such as GPT-4v and GPT-4o to generate extended responses and free-form instruction tuning data. To play with gpt generated data, use
[gpt4v_77k](https://huggingface.co/datasets/nyu-visionx/Cambrian-10M/resolve/main/jsons/gpt4v_77k.jsonl), Curated [gpt4o_60k](https://huggingface.co/datasets/nyu-visionx/Cambrian-10M/resolve/main/jsons/gpt4o_60k.jsonl)
- [gpt4v_77k](https://huggingface.co/datasets/nyu-visionx/Cambrian-10M/resolve/main/jsons/gpt4v_77k.jsonl) contains more extended responses from Cambrian-10M.
- [gpt4o_60k](https://huggingface.co/datasets/nyu-visionx/Cambrian-10M/resolve/main/jsons/gpt4o_60k.jsonl) contains more creative data in visual interactions.
## Cambrian-10M Composition
The Cambrian-10M dataset consists of approximately 9.784 million data points, offering a diverse range of data for various research applications. The composition of the dataset is visualized in Fig. 9.
## Cambrian-7M
We make an initial effort to study data curation. In particular, we find the following data ratio to perform most optimally
- **Language**: 21.00%
- **General**: 34.52%
- **OCR**: 27.22%
- **Counting**: 8.71%
- **Math**: 7.20%
- **Code**: 0.87%
- **Science**: 0.88%
## Getting Started with Cambrian Data
Before you start, ensure you have sufficient storage space to download and process the data.
Cambrian-10M contains a total of 10 million images collected from previous datasets, an internet data engine, and GPT-generated instruction tuning data. Follow these steps to get started:
1. **Download the Data Repository**
Download the data repository. Note that due to Hugging Face policy constraints, the data folder is archived into tar files. We also split the `allava` and `data_engine` data into smaller tar files because they exceed the 50 GB size limit.
2. **Merge Tar Files**
To explore the Cambrian-10M dataset, first merge the different parts of `allava` and `data_engine` together:
```bash
python merge_tars.py
```
3. **Extract Tar Files**
Then, extract all the tar files into the current directory:
```bash
python extract.py
```
4. **Training with Cambrian**
You can train with the raw [Cambrian10M](https://huggingface.co/datasets/nyu-visionx/Cambrian-10M/resolve/main/jsons/Cambrian10M.jsonl), Curated [Cambrian7M](https://huggingface.co/datasets/nyu-visionx/Cambrian-10M/resolve/main/jsons/Cambrian7M.jsonl). We recommend using
the Curated [Cambrian7M with system prompt](https://huggingface.co/datasets/nyu-visionx/Cambrian-10M/blob/main/jsons/Cambrian7M_withsystemprompt.jsonl) that also alleviates 'answer machine' problem. |
ArmelR/the-pile-splitted | ArmelR | "2023-09-06T09:53:16Z" | 11,739 | 22 | [
"size_categories:10M<n<100M",
"format:arrow",
"modality:text",
"library:datasets",
"library:mlcroissant",
"arxiv:2101.00027",
"arxiv:2201.07311",
"region:us"
] | null | "2023-07-30T14:21:26Z" | ---
configs:
- config_name: all
data_files:
- split: train
path:
- "data/ArXiv/train/*.arrow"
- "data/BookCorpus2/train/*.arrow"
- "data/Books3/train/*.arrow"
- "data/DM Mathematics/train/*.arrow"
- "data/Enron Emails/train/*.arrow"
- "data/EuroParl/train/*.arrow"
- "data/FreeLaw/train/*.arrow"
- "data/Github/train/*.arrow"
- "data/Gutenberg (PG-19)/train/*.arrow"
- "data/HackerNews/train/*.arrow"
- "data/NIH ExPorter/train/*.arrow"
- "data/OpenSubtitles/train/*.arrow"
- "data/OpenWebText2/train/*.arrow"
- "data/PhilPapers/train/*.arrow"
- "data/Pile-CC/train/*.arrow"
- "data/PubMed Abstracts/train/*.arrow"
- "data/PubMed Central/train/*.arrow"
- "data/StackExchange/train/*.arrow"
- "data/UPSTO Backgrounds/train/*.arrow"
- "data/Ubuntu IRC/train/*.arrow"
- "data/Wikipedia (en)/train/*.arrow"
- "data/YoutubeSubtitles/train/*.arrow"
- split: test
path:
- "data/ArXiv/test/*.arrow"
- "data/BookCorpus2/test/*.arrow"
- "data/Books3/test/*.arrow"
- "data/DM Mathematics/test/*.arrow"
- "data/Enron Emails/test/*.arrow"
- "data/EuroParl/test/*.arrow"
- "data/FreeLaw/test/*.arrow"
- "data/Github/test/*.arrow"
- "data/Gutenberg (PG-19)/test/*.arrow"
- "data/HackerNews/test/*.arrow"
- "data/NIH ExPorter/test/*.arrow"
- "data/OpenSubtitles/test/*.arrow"
- "data/OpenWebText2/test/*.arrow"
- "data/PhilPapers/test/*.arrow"
- "data/Pile-CC/test/*.arrow"
- "data/PubMed Abstracts/test/*.arrow"
- "data/PubMed Central/test/*.arrow"
- "data/StackExchange/test/*.arrow"
- "data/UPSTO Backgrounds/test/*.arrow"
- "data/Ubuntu IRC/test/*.arrow"
- "data/Wikipedia (en)/test/*.arrow"
- "data/YoutubeSubtitles/test/*.arrow"
default: true
- config_name: ArXiv
data_files:
- split: train
path: "data/ArXiv/train/*.arrow"
- split: test
path: "data/ArXiv/test/*.arrow"
- config_name: BookCorpus2
data_files:
- split: train
path: "data/BookCorpus2/train/*.arrow"
- split: test
path: "data/BookCorpus2/test/*.arrow"
- config_name: Books3
data_files:
- split: train
path: "data/Books3/train/*.arrow"
- split: test
path: "data/Books3/test/*.arrow"
- config_name: DM Mathematics
data_files:
- split: train
path: "data/DM Mathematics/train/*.arrow"
- split: test
path: "data/DM Mathematics/test/*.arrow"
- config_name: Enron Emails
data_files:
- split: train
path: "data/Enron Emails/train/*.arrow"
- split: test
path: "data/Enron Emails/test/*.arrow"
- config_name: EuroParl
data_files:
- split: train
path: "data/EuroParl/train/*.arrow"
- split: test
path: "data/EuroParl/test/*.arrow"
- config_name: FreeLaw
data_files:
- split: train
path: "data/FreeLaw/train/*.arrow"
- split: test
path: "data/FreeLaw/test/*.arrow"
- config_name: Github
data_files:
- split: train
path: "data/Github/train/*.arrow"
- split: test
path: "data/Github/test/*.arrow"
- config_name: Gutenberg (PG-19)
data_files:
- split: train
path: "data/Gutenberg (PG-19)/train/*.arrow"
- split: test
path: "data/Gutenberg (PG-19)/test/*.arrow"
- config_name: HackerNews
data_files:
- split: train
path: "data/HackerNews/train/*.arrow"
- split: test
path: "data/HackerNews/test/*.arrow"
- config_name: NIH ExPorter
data_files:
- split: train
path: "data/NIH ExPorter/train/*.arrow"
- split: test
path: "data/NIH ExPorter/test/*.arrow"
- config_name: OpenSubtitles
data_files:
- split: train
path: "data/OpenSubtitles/train/*.arrow"
- split: test
path: "data/OpenSubtitles/test/*.arrow"
- config_name: OpenWebText2
data_files:
- split: train
path: "data/OpenWebText2/train/*.arrow"
- split: test
path: "data/OpenWebText2/test/*.arrow"
- config_name: PhilPapers
data_files:
- split: train
path: "data/PhilPapers/train/*.arrow"
- split: test
path: "data/PhilPapers/test/*.arrow"
- config_name: Pile-CC
data_files:
- split: train
path: "data/Pile-CC/train/*.arrow"
- split: test
path: "data/Pile-CC/test/*.arrow"
- config_name: PubMed Abstracts
data_files:
- split: train
path: "data/PubMed Abstracts/train/*.arrow"
- split: test
path: "data/PubMed Abstracts/test/*.arrow"
- config_name: PubMed Central
data_files:
- split: train
path: "data/PubMed Central/train/*.arrow"
- split: test
path: "data/PubMed Central/test/*.arrow"
- config_name: StackExchange
data_files:
- split: train
path: "data/StackExchange/train/*.arrow"
- split: test
path: "data/StackExchange/test/*.arrow"
- config_name: UPSTO Backgrounds
data_files:
- split: train
path: "data/UPSTO Backgrounds/train/*.arrow"
- split: test
path: "data/UPSTO Backgrounds/test/*.arrow"
- config_name: Ubuntu IRC
data_files:
- split: train
path: "data/Ubuntu IRC/train/*.arrow"
- split: test
path: "data/Ubuntu IRC/test/*.arrow"
- config_name: Wikipedia (en)
data_files:
- split: train
path: "data/Wikipedia (en)/train/*.arrow"
- split: test
path: "data/Wikipedia (en)/test/*.arrow"
- config_name: YoutubeSubtitles
data_files:
- split: train
path: "data/YoutubeSubtitles/train/*.arrow"
- split: test
path: "data/YoutubeSubtitles/test/*.arrow"
---
# Dataset description
[The pile](https://arxiv.org/abs/2101.00027) is an 800GB dataset of english text
designed by EleutherAI to train large-scale language models. The original version of
the dataset can be found [here](https://huggingface.co/datasets/EleutherAI/pile).
The dataset is divided into 22 smaller high-quality datasets. For more information
each of them, please refer to [the datasheet for the pile](https://arxiv.org/abs/2201.07311).
However, the current version of the dataset, available on the Hub, is not splitted accordingly.
We had to solve this problem in order to improve the user experience when it comes to deal with
the pile via the hub.
Here is an instance of the pile
```
{
'meta': {'pile_set_name': 'Pile-CC'},
'text': 'It is done, and submitted. You can play “Survival of the Tastiest” on Android, and on the web. Playing on...'
}
```
We used the `meta` column to properly divide the dataset in subsets. Each instance `example` belongs to the subset
`domain` and `domain = example['meta']['pile_set_name']`. By doing this, we were able to create a [new version of the pile](https://huggingface.co/datasets/ArmelR/sharded-pile)
that is properly divided, each instance having a new column `domain`.
We further splitted each subset in train/test (97%/3%) to build the current dataset which the following structure
```
data
ArXiv
train
test
BookCorpus2
train
test
Books3
train
test
```
# Usage
```python
from datasets import load_dataset
dataset = load_dataset(
"ArmelR/the-pile-splitted",
subset_of_interest,
num_proc=8
)
```
Using `subset_of_interest = "default"` will load the whole dataset.
|
dominguesm/CC-MAIN-2023-23 | dominguesm | "2023-09-17T00:02:06Z" | 11,727 | 3 | [
"task_categories:text-generation",
"task_categories:fill-mask",
"language:pt",
"license:cc-by-4.0",
"size_categories:10M<n<100M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | [
"text-generation",
"fill-mask"
] | "2023-09-16T20:32:49Z" | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: text
dtype: string
- name: url
dtype: string
- name: crawl_timestamp
dtype: timestamp[ns, tz=UTC]
splits:
- name: train
num_bytes: 97584560119
num_examples: 16899389
download_size: 18490153155
dataset_size: 97584560119
license: cc-by-4.0
task_categories:
- text-generation
- fill-mask
language:
- pt
pretty_name: CC-MAIN-2023-23-PT
size_categories:
- 10B<n<100B
---
# Dataset Card for "CC-MAIN-2023-23"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
wecover/OPUS_Tatoeba | wecover | "2024-02-03T10:13:01Z" | 11,701 | 1 | [
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-01-31T07:16:25Z" | ---
configs:
- config_name: default
data_files:
- split: train
path: '*/*/train.parquet'
- split: valid
path: '*/*/valid.parquet'
- config_name: af
data_files:
- split: train
path: '*/*af*/train.parquet'
- split: valid
path: '*/*af*/valid.parquet'
- config_name: ar
data_files:
- split: train
path: '*/*ar*/train.parquet'
- split: valid
path: '*/*ar*/valid.parquet'
- config_name: ca
data_files:
- split: train
path: '*/*ca*/train.parquet'
- split: valid
path: '*/*ca*/valid.parquet'
- config_name: cs
data_files:
- split: train
path: '*/*cs*/train.parquet'
- split: valid
path: '*/*cs*/valid.parquet'
- config_name: de
data_files:
- split: train
path: '*/*de*/train.parquet'
- split: valid
path: '*/*de*/valid.parquet'
- config_name: en
data_files:
- split: train
path: '*/*en*/train.parquet'
- split: valid
path: '*/*en*/valid.parquet'
- config_name: eo
data_files:
- split: train
path: '*/*eo*/train.parquet'
- split: valid
path: '*/*eo*/valid.parquet'
- config_name: es
data_files:
- split: train
path: '*/*es*/train.parquet'
- split: valid
path: '*/*es*/valid.parquet'
- config_name: fi
data_files:
- split: train
path: '*/*fi*/train.parquet'
- split: valid
path: '*/*fi*/valid.parquet'
- config_name: fr
data_files:
- split: train
path: '*/*fr*/train.parquet'
- split: valid
path: '*/*fr*/valid.parquet'
- config_name: ga
data_files:
- split: train
path: '*/*ga*/train.parquet'
- split: valid
path: '*/*ga*/valid.parquet'
- config_name: it
data_files:
- split: train
path: '*/*it*/train.parquet'
- split: valid
path: '*/*it*/valid.parquet'
- config_name: ja
data_files:
- split: train
path: '*/*ja*/train.parquet'
- split: valid
path: '*/*ja*/valid.parquet'
- config_name: la
data_files:
- split: train
path: '*/*la*/train.parquet'
- split: valid
path: '*/*la*/valid.parquet'
- config_name: nl
data_files:
- split: train
path: '*/*nl*/train.parquet'
- split: valid
path: '*/*nl*/valid.parquet'
- config_name: pl
data_files:
- split: train
path: '*/*pl*/train.parquet'
- split: valid
path: '*/*pl*/valid.parquet'
- config_name: pt
data_files:
- split: train
path: '*/*pt*/train.parquet'
- split: valid
path: '*/*pt*/valid.parquet'
- config_name: ro
data_files:
- split: train
path: '*/*ro*/train.parquet'
- split: valid
path: '*/*ro*/valid.parquet'
- config_name: ru
data_files:
- split: train
path: '*/*ru*/train.parquet'
- split: valid
path: '*/*ru*/valid.parquet'
- config_name: sv
data_files:
- split: train
path: '*/*sv*/train.parquet'
- split: valid
path: '*/*sv*/valid.parquet'
- config_name: tr
data_files:
- split: train
path: '*/*tr*/train.parquet'
- split: valid
path: '*/*tr*/valid.parquet'
- config_name: uk
data_files:
- split: train
path: '*/*uk*/train.parquet'
- split: valid
path: '*/*uk*/valid.parquet'
- config_name: xh
data_files:
- split: train
path: '*/*xh*/train.parquet'
- split: valid
path: '*/*xh*/valid.parquet'
- config_name: yi
data_files:
- split: train
path: '*/*yi*/train.parquet'
- split: valid
path: '*/*yi*/valid.parquet'
- config_name: am
data_files:
- split: train
path: '*/*am*/train.parquet'
- split: valid
path: '*/*am*/valid.parquet'
- config_name: bg
data_files:
- split: train
path: '*/*bg*/train.parquet'
- split: valid
path: '*/*bg*/valid.parquet'
- config_name: da
data_files:
- split: train
path: '*/*da*/train.parquet'
- split: valid
path: '*/*da*/valid.parquet'
- config_name: el
data_files:
- split: train
path: '*/*el*/train.parquet'
- split: valid
path: '*/*el*/valid.parquet'
- config_name: he
data_files:
- split: train
path: '*/*he*/train.parquet'
- split: valid
path: '*/*he*/valid.parquet'
- config_name: hu
data_files:
- split: train
path: '*/*hu*/train.parquet'
- split: valid
path: '*/*hu*/valid.parquet'
- config_name: ko
data_files:
- split: train
path: '*/*ko*/train.parquet'
- split: valid
path: '*/*ko*/valid.parquet'
- config_name: ku
data_files:
- split: train
path: '*/*ku*/train.parquet'
- split: valid
path: '*/*ku*/valid.parquet'
- config_name: lt
data_files:
- split: train
path: '*/*lt*/train.parquet'
- split: valid
path: '*/*lt*/valid.parquet'
- config_name: mk
data_files:
- split: train
path: '*/*mk*/train.parquet'
- split: valid
path: '*/*mk*/valid.parquet'
- config_name: ug
data_files:
- split: train
path: '*/*ug*/train.parquet'
- split: valid
path: '*/*ug*/valid.parquet'
- config_name: ur
data_files:
- split: train
path: '*/*ur*/train.parquet'
- split: valid
path: '*/*ur*/valid.parquet'
- config_name: as
data_files:
- split: train
path: '*/*as*/train.parquet'
- split: valid
path: '*/*as*/valid.parquet'
- config_name: bn
data_files:
- split: train
path: '*/*bn*/train.parquet'
- split: valid
path: '*/*bn*/valid.parquet'
- config_name: hi
data_files:
- split: train
path: '*/*hi*/train.parquet'
- split: valid
path: '*/*hi*/valid.parquet'
- config_name: az
data_files:
- split: train
path: '*/*az*/train.parquet'
- split: valid
path: '*/*az*/valid.parquet'
- config_name: kk
data_files:
- split: train
path: '*/*kk*/train.parquet'
- split: valid
path: '*/*kk*/valid.parquet'
- config_name: be
data_files:
- split: train
path: '*/*be*/train.parquet'
- split: valid
path: '*/*be*/valid.parquet'
- config_name: et
data_files:
- split: train
path: '*/*et*/train.parquet'
- split: valid
path: '*/*et*/valid.parquet'
- config_name: sl
data_files:
- split: train
path: '*/*sl*/train.parquet'
- split: valid
path: '*/*sl*/valid.parquet'
- config_name: sr
data_files:
- split: train
path: '*/*sr*/train.parquet'
- split: valid
path: '*/*sr*/valid.parquet'
- config_name: vi
data_files:
- split: train
path: '*/*vi*/train.parquet'
- split: valid
path: '*/*vi*/valid.parquet'
- config_name: id
data_files:
- split: train
path: '*/*id*/train.parquet'
- split: valid
path: '*/*id*/valid.parquet'
- config_name: br
data_files:
- split: train
path: '*/*br*/train.parquet'
- split: valid
path: '*/*br*/valid.parquet'
- config_name: bs
data_files:
- split: train
path: '*/*bs*/train.parquet'
- split: valid
path: '*/*bs*/valid.parquet'
- config_name: hr
data_files:
- split: train
path: '*/*hr*/train.parquet'
- split: valid
path: '*/*hr*/valid.parquet'
- config_name: gl
data_files:
- split: train
path: '*/*gl*/train.parquet'
- split: valid
path: '*/*gl*/valid.parquet'
- config_name: fy
data_files:
- split: train
path: '*/*fy*/train.parquet'
- split: valid
path: '*/*fy*/valid.parquet'
- config_name: ka
data_files:
- split: train
path: '*/*ka*/train.parquet'
- split: valid
path: '*/*ka*/valid.parquet'
- config_name: tl
data_files:
- split: train
path: '*/*tl*/train.parquet'
- split: valid
path: '*/*tl*/valid.parquet'
- config_name: cy
data_files:
- split: train
path: '*/*cy*/train.parquet'
- split: valid
path: '*/*cy*/valid.parquet'
- config_name: is
data_files:
- split: train
path: '*/*is*/train.parquet'
- split: valid
path: '*/*is*/valid.parquet'
- config_name: eu
data_files:
- split: train
path: '*/*eu*/train.parquet'
- split: valid
path: '*/*eu*/valid.parquet'
- config_name: gd
data_files:
- split: train
path: '*/*gd*/train.parquet'
- split: valid
path: '*/*gd*/valid.parquet'
- config_name: ha
data_files:
- split: train
path: '*/*ha*/train.parquet'
- split: valid
path: '*/*ha*/valid.parquet'
- config_name: hy
data_files:
- split: train
path: '*/*hy*/train.parquet'
- split: valid
path: '*/*hy*/valid.parquet'
- config_name: km
data_files:
- split: train
path: '*/*km*/train.parquet'
- split: valid
path: '*/*km*/valid.parquet'
- config_name: ky
data_files:
- split: train
path: '*/*ky*/train.parquet'
- split: valid
path: '*/*ky*/valid.parquet'
- config_name: mn
data_files:
- split: train
path: '*/*mn*/train.parquet'
- split: valid
path: '*/*mn*/valid.parquet'
- config_name: mr
data_files:
- split: train
path: '*/*mr*/train.parquet'
- split: valid
path: '*/*mr*/valid.parquet'
- config_name: my
data_files:
- split: train
path: '*/*my*/train.parquet'
- split: valid
path: '*/*my*/valid.parquet'
- config_name: th
data_files:
- split: train
path: '*/*th*/train.parquet'
- split: valid
path: '*/*th*/valid.parquet'
- config_name: uz
data_files:
- split: train
path: '*/*uz*/train.parquet'
- split: valid
path: '*/*uz*/valid.parquet'
- config_name: jv
data_files:
- split: train
path: '*/*jv*/train.parquet'
- split: valid
path: '*/*jv*/valid.parquet'
- config_name: kn
data_files:
- split: train
path: '*/*kn*/train.parquet'
- split: valid
path: '*/*kn*/valid.parquet'
- config_name: lo
data_files:
- split: train
path: '*/*lo*/train.parquet'
- split: valid
path: '*/*lo*/valid.parquet'
- config_name: mg
data_files:
- split: train
path: '*/*mg*/train.parquet'
- split: valid
path: '*/*mg*/valid.parquet'
- config_name: ml
data_files:
- split: train
path: '*/*ml*/train.parquet'
- split: valid
path: '*/*ml*/valid.parquet'
- config_name: or
data_files:
- split: train
path: '*/*or*/train.parquet'
- split: valid
path: '*/*or*/valid.parquet'
- config_name: pa
data_files:
- split: train
path: '*/*pa*/train.parquet'
- split: valid
path: '*/*pa*/valid.parquet'
- config_name: ps
data_files:
- split: train
path: '*/*ps*/train.parquet'
- split: valid
path: '*/*ps*/valid.parquet'
- config_name: sa
data_files:
- split: train
path: '*/*sa*/train.parquet'
- split: valid
path: '*/*sa*/valid.parquet'
- config_name: sd
data_files:
- split: train
path: '*/*sd*/train.parquet'
- config_name: si
data_files:
- split: train
path: '*/*si*/train.parquet'
- split: valid
path: '*/*si*/valid.parquet'
- config_name: so
data_files:
- split: train
path: '*/*so*/train.parquet'
- split: valid
path: '*/*so*/valid.parquet'
- config_name: sq
data_files:
- split: train
path: '*/*sq*/train.parquet'
- split: valid
path: '*/*sq*/valid.parquet'
- config_name: su
data_files:
- split: train
path: '*/*su*/train.parquet'
- split: valid
path: '*/*su*/valid.parquet'
- config_name: ta
data_files:
- split: train
path: '*/*ta*/train.parquet'
- split: valid
path: '*/*ta*/valid.parquet'
- config_name: te
data_files:
- split: train
path: '*/*te*/train.parquet'
- split: valid
path: '*/*te*/valid.parquet'
---
|
picollect/danbooru | picollect | "2024-11-15T02:46:27Z" | 11,699 | 4 | [
"language:en",
"license:other",
"size_categories:10M<n<100M",
"format:webdataset",
"modality:image",
"modality:text",
"library:datasets",
"library:webdataset",
"library:mlcroissant",
"region:us",
"danbooru",
"anime"
] | null | "2024-11-06T07:12:33Z" | ---
license: other
language:
- en
tags:
- danbooru
- anime
pretty_name: Danbooru 2024 Dataset
size_categories:
- 1M<n<10M
---
# Danbooru 2024 Dataset
# Danbooru 2024 数据集
A collection of images from Danbooru website, organized and packaged by ID sequence. This dataset is for research and learning purposes only.
本数据集收集了来自 Danbooru 网站的图像，按 ID 顺序组织打包。该数据集仅用于研究和学习目的。
## Dataset Description
## 数据集描述
This dataset contains image resources from Danbooru website, updated to ID 8380648 (Update time: 2024-11-03).
本数据集包含来自 Danbooru 网站的图像资源，更新至 ID 8380648（更新时间：2024-11-03）。
### Data Organization
### 数据组织
- Images are packaged into compressed files, 1000 images per archive
- File naming format: `{start_id}.tar`
- Example: `2000.tar` contains images with IDs from 2000 to 2999
- 图像打包为压缩文件，每个存档包含 1000 张图像
- 文件命名格式：`{start_id}.tar`
- 示例：`2000.tar` 包含 ID 从 2000 到 2999 的图像
### Technical Details
### 技术细节
- Image Format: Original format
- File Organization: Sequential TAR packaging
- ID Range: 1 ~ 8380648
- 图像格式：原始格式
- 文件组织：顺序 TAR 打包
- ID 范围：1 ~ 8380648
## Usage Instructions
## 使用说明
1. Images within each archive are named by their IDs
2. Metadata can be queried from Danbooru database using corresponding IDs
zh
1. 存档中的图像以其 ID 命名
2. 可使用相应的 ID 从 Danbooru 数据库查询元数据
## License
## 许可证
This dataset is released under the following terms:
本数据集在以下条款下发布：
1. Academic and Research Use
学术和研究使用
- This dataset may only be used for academic research, learning, and non-commercial purposes
- 本数据集仅可用于学术研究、学习和非商业目的
2. Restrictions
限制条款
- Commercial use is strictly prohibited
- Redistribution or resale of the dataset is not permitted
- Any derivative works must be shared under the same terms
- 严格禁止商业使用
- 不允许重新分发或转售数据集
- 任何衍生作品必须在相同条款下共享
3. Attribution
署名要求
- Users must cite this dataset when used in research or publications
- Any derivative works must acknowledge the original source
- 在研究或出版物中使用时必须引用本数据集
- 任何衍生作品必须注明原始来源
4. Disclaimer
免责声明
- The dataset is provided "as is" without any warranty
- The creators are not liable for any damages or losses arising from its use
- Users are solely responsible for ensuring compliance with local laws and regulations
- 数据集按"原样"提供，不提供任何保证
- 创建者不对使用过程中产生的任何损害或损失负责
- 用户需自行负责确保符合当地法律法规
5. Termination
终止条款
- This license automatically terminates if you violate any of these terms
- Upon termination, you must cease all use of the dataset
- 如果违反任何这些条款，本许可证将自动终止
- 终止后，您必须停止使用本数据集
By using this dataset, you agree to be bound by these terms.
使用本数据集即表示您同意受这些条款的约束。
## Important Notes
## 重要提示
- Ensure legal compliance when using the dataset
- Review relevant data usage policies and guidelines before use
- Consult legal professionals if you have questions about usage rights
- 使用数据集时确保遵守法律
- 使用前请查看相关数据使用政策和指南
- 如对使用权有疑问，请咨询法律专业人士
---
**Notice:** Users must strictly comply with local laws and regulations when using this dataset. Users bear full responsibility for any issues arising from improper use.
**注意：** 用户在使用本数据集时必须严格遵守当地法律法规。用户对因不当使用而产生的任何问题承担全部责任。 |
lmms-lab/DocVQA | lmms-lab | "2024-04-18T05:14:35Z" | 11,652 | 35 | [
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2007.00398",
"region:us"
] | null | "2024-01-22T16:29:32Z" | ---
license: apache-2.0
dataset_info:
- config_name: DocVQA
features:
- name: questionId
dtype: string
- name: question
dtype: string
- name: question_types
sequence: string
- name: image
dtype: image
- name: docId
dtype: int64
- name: ucsf_document_id
dtype: string
- name: ucsf_document_page_no
dtype: string
- name: answers
sequence: string
- name: data_split
dtype: string
splits:
# - name: train
# num_bytes: 5659006943.631
# num_examples: 39463
- name: validation
num_bytes: 2532447207.066
num_examples: 5349
- name: test
num_bytes: 2500408525.732
num_examples: 5188
download_size: 9555791945
dataset_size: 10691862676.428999
- config_name: InfographicVQA
features:
- name: questionId
dtype: string
- name: question
dtype: string
- name: answers
sequence: string
- name: answer_type
sequence: string
- name: image
dtype: image
- name: image_url
dtype: string
- name: operation/reasoning
sequence: string
- name: ocr
dtype: string
- name: data_split
dtype: string
splits:
# - name: train
# num_bytes: 11559694546.32
# num_examples: 23946
- name: validation
num_bytes: 1863177404.253
num_examples: 2801
- name: test
num_bytes: 1851304047.712
num_examples: 3288
download_size: 2544892079
dataset_size: 15274175998.285
configs:
- config_name: DocVQA
data_files:
# - split: train
# path: DocVQA/train-*
- split: validation
path: DocVQA/validation-*
- split: test
path: DocVQA/test-*
- config_name: InfographicVQA
data_files:
# - split: train
# path: InfographicVQA/train-*
- split: validation
path: InfographicVQA/validation-*
- split: test
path: InfographicVQA/test-*
---
<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 [DocVQA](https://arxiv.org/abs/2007.00398). It is used in our `lmms-eval` pipeline to allow for one-click evaluations of large multi-modality models.
```
@article{mathew2020docvqa,
title={DocVQA: A Dataset for VQA on Document Images. CoRR abs/2007.00398 (2020)},
author={Mathew, Minesh and Karatzas, Dimosthenis and Manmatha, R and Jawahar, CV},
journal={arXiv preprint arXiv:2007.00398},
year={2020}
}
```
|
M-A-D/Mixed-Arabic-Datasets-Repo | M-A-D | "2023-10-16T21:25:35Z" | 11,646 | 32 | [
"task_categories:text-classification",
"task_categories:question-answering",
"task_categories:translation",
"task_categories:summarization",
"task_categories:text-generation",
"task_categories:text2text-generation",
"task_categories:fill-mask",
"language:ar",
"size_categories:100M<n<1B",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | [
"text-classification",
"question-answering",
"translation",
"summarization",
"conversational",
"text-generation",
"text2text-generation",
"fill-mask"
] | "2023-08-27T01:19:21Z" | ---
language:
- ar
size_categories:
- 1B<n<10B
task_categories:
- text-classification
- question-answering
- translation
- summarization
- conversational
- text-generation
- text2text-generation
- fill-mask
pretty_name: Mixed Arabic Datasets (MAD) Corpus
dataset_info:
- config_name: Ara--Ali-C137--Hindawi-Books-dataset
features:
- name: BookLink
dtype: string
- name: BookName
dtype: string
- name: AuthorName
dtype: string
- name: AboutBook
dtype: string
- name: ChapterLink
dtype: string
- name: ChapterName
dtype: string
- name: ChapterText
dtype: string
- name: AboutAuthor
dtype: string
splits:
- name: train
num_bytes: 1364854259
num_examples: 49821
download_size: 494678002
dataset_size: 1364854259
- config_name: Ara--Goud--Goud-sum
features:
- name: article
dtype: string
- name: headline
dtype: string
- name: categories
dtype: string
splits:
- name: train
num_bytes: 288296544
num_examples: 139288
download_size: 147735776
dataset_size: 288296544
- config_name: Ara--J-Mourad--MNAD.v1
features:
- name: Title
dtype: string
- name: Body
dtype: string
- name: Category
dtype: string
splits:
- name: train
num_bytes: 1101921980
num_examples: 418563
download_size: 527154122
dataset_size: 1101921980
- config_name: Ara--JihadZa--IADD
features:
- name: Sentence
dtype: string
- name: Region
dtype: string
- name: DataSource
dtype: string
- name: Country
dtype: string
splits:
- name: train
num_bytes: 19167070
num_examples: 135804
download_size: 8644491
dataset_size: 19167070
- config_name: Ara--LeMGarouani--MAC-corpus
features:
- name: tweets
dtype: string
- name: type
dtype: string
- name: class
dtype: string
splits:
- name: train
num_bytes: 1945646
num_examples: 18087
download_size: 866198
dataset_size: 1945646
- config_name: Ara--MBZUAI--Bactrian-X
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: id
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 66093524
num_examples: 67017
download_size: 33063779
dataset_size: 66093524
- config_name: Ara--OpenAssistant--oasst1
features:
- name: message_id
dtype: string
- name: parent_id
dtype: string
- name: user_id
dtype: string
- name: created_date
dtype: string
- name: text
dtype: string
- name: role
dtype: string
- name: lang
dtype: string
- name: review_count
dtype: int32
- name: review_result
dtype: bool
- name: deleted
dtype: bool
- name: rank
dtype: float64
- name: synthetic
dtype: bool
- name: model_name
dtype: 'null'
- name: detoxify
dtype: 'null'
- name: message_tree_id
dtype: string
- name: tree_state
dtype: string
- name: emojis
struct:
- name: count
sequence: int32
- name: name
sequence: string
- name: labels
struct:
- name: count
sequence: int32
- name: name
sequence: string
- name: value
sequence: float64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 58168
num_examples: 56
download_size: 30984
dataset_size: 58168
- config_name: Ara--Wikipedia
features:
- name: id
dtype: string
- name: url
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 3052201469
num_examples: 1205403
download_size: 1316212231
dataset_size: 3052201469
- config_name: Ara--bigscience--xP3
features:
- name: inputs
dtype: string
- name: targets
dtype: string
splits:
- name: train
num_bytes: 4727881680
num_examples: 2148955
download_size: 2805060725
dataset_size: 4727881680
- config_name: Ara--cardiffnlp--tweet_sentiment_multilingual
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': negative
'1': neutral
'2': positive
splits:
- name: train
num_bytes: 306108
num_examples: 1839
- name: validation
num_bytes: 53276
num_examples: 324
- name: test
num_bytes: 141536
num_examples: 870
download_size: 279900
dataset_size: 500920
- config_name: Ara--miracl--miracl
features:
- name: query_id
dtype: string
- name: query
dtype: string
- name: positive_passages
list:
- name: docid
dtype: string
- name: text
dtype: string
- name: title
dtype: string
- name: negative_passages
list:
- name: docid
dtype: string
- name: text
dtype: string
- name: title
dtype: string
splits:
- name: train
num_bytes: 32012083
num_examples: 3495
download_size: 15798509
dataset_size: 32012083
- config_name: Ara--mustapha--QuranExe
features:
- name: text
dtype: string
- name: resource_name
dtype: string
- name: verses_keys
dtype: string
splits:
- name: train
num_bytes: 133108687
num_examples: 49888
download_size: 58769417
dataset_size: 133108687
- config_name: Ara--pain--Arabic-Tweets
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 41639770853
num_examples: 202700438
download_size: 22561651700
dataset_size: 41639770853
- config_name: Ara--saudinewsnet
features:
- name: source
dtype: string
- name: url
dtype: string
- name: date_extracted
dtype: string
- name: title
dtype: string
- name: author
dtype: string
- name: content
dtype: string
splits:
- name: train
num_bytes: 103654009
num_examples: 31030
download_size: 49117164
dataset_size: 103654009
- config_name: Ary--AbderrahmanSkiredj1--Darija-Wikipedia
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 8104410
num_examples: 4862
download_size: 3229966
dataset_size: 8104410
- config_name: Ary--Ali-C137--Darija-Stories-Dataset
features:
- name: ChapterName
dtype: string
- name: ChapterLink
dtype: string
- name: Author
dtype: string
- name: Text
dtype: string
- name: Tags
dtype: int64
splits:
- name: train
num_bytes: 476926644
num_examples: 6142
download_size: 241528641
dataset_size: 476926644
- config_name: Ary--Wikipedia
features:
- name: id
dtype: string
- name: url
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 10007364
num_examples: 6703
download_size: 4094377
dataset_size: 10007364
- config_name: Arz--Wikipedia
features:
- name: id
dtype: string
- name: url
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 1364641408
num_examples: 1617770
download_size: 306420318
dataset_size: 1364641408
configs:
- config_name: Ara--Ali-C137--Hindawi-Books-dataset
data_files:
- split: train
path: Ara--Ali-C137--Hindawi-Books-dataset/train-*
- config_name: Ara--Goud--Goud-sum
data_files:
- split: train
path: Ara--Goud--Goud-sum/train-*
- config_name: Ara--J-Mourad--MNAD.v1
data_files:
- split: train
path: Ara--J-Mourad--MNAD.v1/train-*
- config_name: Ara--JihadZa--IADD
data_files:
- split: train
path: Ara--JihadZa--IADD/train-*
- config_name: Ara--LeMGarouani--MAC-corpus
data_files:
- split: train
path: Ara--LeMGarouani--MAC-corpus/train-*
- config_name: Ara--MBZUAI--Bactrian-X
data_files:
- split: train
path: Ara--MBZUAI--Bactrian-X/train-*
- config_name: Ara--OpenAssistant--oasst1
data_files:
- split: train
path: Ara--OpenAssistant--oasst1/train-*
- config_name: Ara--Wikipedia
data_files:
- split: train
path: Ara--Wikipedia/train-*
- config_name: Ara--bigscience--xP3
data_files:
- split: train
path: Ara--bigscience--xP3/train-*
- config_name: Ara--cardiffnlp--tweet_sentiment_multilingual
data_files:
- split: train
path: Ara--cardiffnlp--tweet_sentiment_multilingual/train-*
- split: validation
path: Ara--cardiffnlp--tweet_sentiment_multilingual/validation-*
- split: test
path: Ara--cardiffnlp--tweet_sentiment_multilingual/test-*
- config_name: Ara--miracl--miracl
data_files:
- split: train
path: Ara--miracl--miracl/train-*
- config_name: Ara--mustapha--QuranExe
data_files:
- split: train
path: Ara--mustapha--QuranExe/train-*
- config_name: Ara--pain--Arabic-Tweets
data_files:
- split: train
path: Ara--pain--Arabic-Tweets/train-*
- config_name: Ara--saudinewsnet
data_files:
- split: train
path: Ara--saudinewsnet/train-*
- config_name: Ary--AbderrahmanSkiredj1--Darija-Wikipedia
data_files:
- split: train
path: Ary--AbderrahmanSkiredj1--Darija-Wikipedia/train-*
- config_name: Ary--Ali-C137--Darija-Stories-Dataset
data_files:
- split: train
path: Ary--Ali-C137--Darija-Stories-Dataset/train-*
- config_name: Ary--Wikipedia
data_files:
- split: train
path: Ary--Wikipedia/train-*
- config_name: Arz--Wikipedia
data_files:
- split: train
path: Arz--Wikipedia/train-*
---
# Dataset Card for "Mixed Arabic Datasets (MAD) Corpus"
**The Mixed Arabic Datasets Corpus : A Community-Driven Collection of Diverse Arabic Texts**
## Dataset Description
The Mixed Arabic Datasets (MAD) presents a dynamic compilation of diverse Arabic texts sourced from various online platforms and datasets. It addresses a critical challenge faced by researchers, linguists, and language enthusiasts: the fragmentation of Arabic language datasets across the Internet. With MAD, we are trying to centralize these dispersed resources into a single, comprehensive repository.
Encompassing a wide spectrum of content, ranging from social media conversations to literary masterpieces, MAD captures the rich tapestry of Arabic communication, including both standard Arabic and regional dialects.
This corpus offers comprehensive insights into the linguistic diversity and cultural nuances of Arabic expression.
## Usage
If you want to use this dataset you pick one among the available configs:
`Ara--MBZUAI--Bactrian-X` | `Ara--OpenAssistant--oasst1` | `Ary--AbderrahmanSkiredj1--Darija-Wikipedia`
`Ara--Wikipedia` | `Ary--Wikipedia` | `Arz--Wikipedia`
`Ary--Ali-C137--Darija-Stories-Dataset` | `Ara--Ali-C137--Hindawi-Books-dataset` | ``
Example of usage:
```python
dataset = load_dataset('M-A-D/Mixed-Arabic-Datasets-Repo', 'Ara--MBZUAI--Bactrian-X')
```
If you loaded multiple datasets and wanted to merge them together then you can simply laverage `concatenate_datasets()` from `datasets`
```pyhton
dataset3 = concatenate_datasets([dataset1['train'], dataset2['train']])
```
Note : proccess the datasets before merging in order to make sure you have a new dataset that is consistent
## Dataset Size
The Mixed Arabic Datasets (MAD) is a dynamic and evolving collection, with its size fluctuating as new datasets are added or removed. As MAD continuously expands, it becomes a living resource that adapts to the ever-changing landscape of Arabic language datasets.
**Dataset List**
MAD draws from a diverse array of sources, each contributing to its richness and breadth. While the collection is constantly evolving, some of the datasets that are poised to join MAD in the near future include:
- [✔] OpenAssistant/oasst1 (ar portion) : [Dataset Link](https://huggingface.co/datasets/OpenAssistant/oasst1)
- [✔] MBZUAI/Bactrian-X (ar portion) : [Dataset Link](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/ar/train)
- [✔] AbderrahmanSkiredj1/Darija-Wikipedia : [Dataset Link](https://huggingface.co/datasets/AbderrahmanSkiredj1/moroccan_darija_wikipedia_dataset)
- [✔] Arabic Wikipedia : [Dataset Link](https://huggingface.co/datasets/wikipedia)
- [✔] Moroccan Arabic Wikipedia : [Dataset Link](https://huggingface.co/datasets/wikipedia)
- [✔] Egyptian Arabic Wikipedia : [Dataset Link](https://huggingface.co/datasets/wikipedia)
- [✔] Darija Stories Dataset : [Dataset Link](https://huggingface.co/datasets/Ali-C137/Darija-Stories-Dataset)
- [✔] Hindawi Books Dataset : [Dataset Link](https://huggingface.co/datasets/Ali-C137/Hindawi-Books-dataset)
- [] uonlp/CulturaX - ar : [Dataset Link](https://huggingface.co/datasets/uonlp/CulturaX/viewer/ar/train)
- [✔] Pain/ArabicTweets : [Dataset Link](https://huggingface.co/datasets/pain/Arabic-Tweets)
- [] Abu-El-Khair Corpus : [Dataset Link](https://huggingface.co/datasets/arabic_billion_words)
- [✔] QuranExe : [Dataset Link](https://huggingface.co/datasets/mustapha/QuranExe)
- [✔] MNAD : [Dataset Link](https://huggingface.co/datasets/J-Mourad/MNAD.v1)
- [✔] IADD : [Dataset Link](https://raw.githubusercontent.com/JihadZa/IADD/main/IADD.json)
- [] OSIAN : [Dataset Link](https://wortschatz.uni-leipzig.de/en/download/Arabic#ara-tn_newscrawl-OSIAN_2018)
- [✔] MAC corpus : [Dataset Link](https://raw.githubusercontent.com/LeMGarouani/MAC/main/MAC%20corpus.csv)
- [✔] Goud.ma-Sum : [Dataset Link](https://huggingface.co/datasets/Goud/Goud-sum)
- [✔] SaudiNewsNet : [Dataset Link](https://huggingface.co/datasets/saudinewsnet)
- [✔] Miracl : [Dataset Link](https://huggingface.co/datasets/miracl/miracl)
- [✔] CardiffNLP/TweetSentimentMulti : [Dataset Link](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual)
- [] OSCAR-2301 : [Dataset Link](https://huggingface.co/datasets/oscar-corpus/OSCAR-2301/viewer/ar/train)
- [] mc4 : [Dataset Link](https://huggingface.co/datasets/mc4/viewer/ar/train)
- [✔] bigscience/xP3 : [Dataset Link](https://huggingface.co/datasets/bigscience/xP3/viewer/ar/train)
- [] Muennighoff/xP3x : [Dataset Link](https://huggingface.co/datasets/Muennighoff/xP3x)
- [] Ai_Society : [Dataset Link](https://huggingface.co/datasets/camel-ai/ai_society_translated)
## Potential Use Cases
The Mixed Arabic Datasets (MAD) holds the potential to catalyze a multitude of groundbreaking applications:
- **Linguistic Analysis:** Employ MAD to conduct in-depth linguistic studies, exploring dialectal variances, language evolution, and grammatical structures.
- **Topic Modeling:** Dive into diverse themes and subjects through the extensive collection, revealing insights into emerging trends and prevalent topics.
- **Sentiment Understanding:** Decode sentiments spanning Arabic dialects, revealing cultural nuances and emotional dynamics.
- **Sociocultural Research:** Embark on a sociolinguistic journey, unraveling the intricate connection between language, culture, and societal shifts.
## Dataset Access
MAD's access mechanism is unique: while it doesn't carry a general license itself, each constituent dataset within the corpus retains its individual license. By accessing the dataset details through the provided links in the "Dataset List" section above, users can understand the specific licensing terms for each dataset.
### Join Us on Discord
For discussions, contributions, and community interactions, join us on Discord! [](https://discord.gg/2NpJ9JGm)
### How to Contribute
Want to contribute to the Mixed Arabic Datasets project? Follow our comprehensive guide on Google Colab for step-by-step instructions: [Contribution Guide](https://colab.research.google.com/drive/1kOIRoicgCOV8TPvASAI_2uMY7rpXnqzJ?usp=sharing).
**Note**: If you'd like to test a contribution before submitting it, feel free to do so on the [MAD Test Dataset](https://huggingface.co/datasets/M-A-D/Mixed-Arabic-Dataset-test).
## Citation
```
@dataset{
title = {Mixed Arabic Datasets (MAD)},
author = {MAD Community},
howpublished = {Dataset},
url = {https://huggingface.co/datasets/M-A-D/Mixed-Arabic-Datasets-Repo},
year = {2023},
}
``` |
bigscience/xP3all | bigscience | "2023-05-30T15:51:40Z" | 11,639 | 28 | [
"task_categories:other",
"annotations_creators:expert-generated",
"annotations_creators:crowdsourced",
"multilinguality:multilingual",
"language:ak",
"language:ar",
"language:as",
"language:bm",
"language:bn",
"language:ca",
"language:code",
"language:en",
"language:es",
"language:eu",
"language:fon",
"language:fr",
"language:gu",
"language:hi",
"language:id",
"language:ig",
"language:ki",
"language:kn",
"language:lg",
"language:ln",
"language:ml",
"language:mr",
"language:ne",
"language:nso",
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"language:or",
"language:pa",
"language:pt",
"language:rn",
"language:rw",
"language:sn",
"language:st",
"language:sw",
"language:ta",
"language:te",
"language:tn",
"language:ts",
"language:tum",
"language:tw",
"language:ur",
"language:vi",
"language:wo",
"language:xh",
"language:yo",
"language:zh",
"language:zu",
"license:apache-2.0",
"size_categories:10M<n<100M",
"modality:text",
"library:datasets",
"library:mlcroissant",
"arxiv:2211.01786",
"region:us"
] | [
"other"
] | "2022-07-30T21:05:02Z" | ---
annotations_creators:
- expert-generated
- crowdsourced
language:
- ak
- ar
- as
- bm
- bn
- ca
- code
- en
- es
- eu
- fon
- fr
- gu
- hi
- id
- ig
- ki
- kn
- lg
- ln
- ml
- mr
- ne
- nso
- ny
- or
- pa
- pt
- rn
- rw
- sn
- st
- sw
- ta
- te
- tn
- ts
- tum
- tw
- ur
- vi
- wo
- xh
- yo
- zh
- zu
programming_language:
- C
- C++
- C#
- Go
- Java
- JavaScript
- Lua
- PHP
- Python
- Ruby
- Rust
- Scala
- TypeScript
license:
- apache-2.0
multilinguality:
- multilingual
pretty_name: xP3
size_categories:
- 100M<n<1B
task_categories:
- other
---
# Dataset Card for xP3
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** https://github.com/bigscience-workshop/xmtf
- **Paper:** [Crosslingual Generalization through Multitask Finetuning](https://arxiv.org/abs/2211.01786)
- **Point of Contact:** [Niklas Muennighoff](mailto:[email protected])
### Dataset Summary
> xP3 (Crosslingual Public Pool of Prompts) is a collection of prompts & datasets across 46 of languages & 16 NLP tasks. It is used for the training of BLOOMZ and mT0, multilingual language models capable of following human instructions in dozens of languages zero-shot.
- **Creation:** The dataset can be recreated using instructions available [here](https://github.com/bigscience-workshop/xmtf#create-xp3). We provide this version to save processing time and ease reproducibility.
- **Languages:** 46 (Can be extended by [recreating with more splits](https://github.com/bigscience-workshop/xmtf#create-xp3))
- **xP3 Dataset Family:**
<table>
<tr>
<th>Name</th>
<th>Explanation</th>
<th>Example models</th>
</tr>
<tr>
<td><a href=https://huggingface.co/datasets/Muennighoff/xP3x>xP3x</a></t>
<td>Mixture of 17 tasks in 277 languages with English prompts</td>
<td>WIP - Join us at Project Aya @<a href=https://cohere.for.ai/>C4AI</a> to help!</td>
</tr>
<tr>
<td><a href=https://huggingface.co/datasets/bigscience/xP3>xP3</a></t>
<td>Mixture of 13 training tasks in 46 languages with English prompts</td>
<td><a href=https://huggingface.co/bigscience/bloomz>bloomz</a> & <a href=https://huggingface.co/bigscience/mt0-xxl>mt0-xxl</a></td>
</tr>
<tr>
<td><a href=https://huggingface.co/datasets/bigscience/xP3mt>xP3mt</a></t>
<td>Mixture of 13 training tasks in 46 languages with prompts in 20 languages (machine-translated from English)</td>
<td><a href=https://huggingface.co/bigscience/bloomz-mt>bloomz-mt</a> & <a href=https://huggingface.co/bigscience/mt0-xxl-mt>mt0-xxl-mt</a></td>
</tr>
<tr>
<td><a href=https://huggingface.co/datasets/bigscience/xP3all>xP3all</a></t>
<td>xP3 + evaluation datasets adding an additional 3 tasks for a total of 16 tasks in 46 languages with English prompts</td>
<td></td>
</tr>
<tr>
<td><a href=https://huggingface.co/datasets/bigscience/xP3megds>xP3megds</a></t>
<td><a href=https://github.com/bigscience-workshop/Megatron-DeepSpeed>Megatron-DeepSpeed</a> processed version of xP3</td>
<td><a href=https://huggingface.co/bigscience/bloomz>bloomz</a></td>
</tr>
<tr>
<td><a href=https://huggingface.co/datasets/Muennighoff/P3>P3</a></t>
<td>Repreprocessed version of the English-only <a href=https://huggingface.co/datasets/bigscience/P3>P3</a> with 8 training tasks</td>
<td><a href=https://huggingface.co/bigscience/bloomz-p3>bloomz-p3</a> & <a href=https://huggingface.co/bigscience/mt0-xxl-p3>mt0-xxl-p3</a></td>
</tr>
</table>
## Dataset Structure
### Data Instances
An example of "train" looks as follows:
```json
{
"inputs": "Sentence 1: Fue académico en literatura metafísica, teología y ciencias clásicas.\nSentence 2: Fue académico en literatura metafísica, teología y ciencia clásica.\nQuestion: Can we rewrite Sentence 1 to Sentence 2? Yes or No?",
"targets": "Yes"
}
```
### Data Fields
The data fields are the same among all splits:
- `inputs`: the natural language input fed to the model
- `targets`: the natural language target that the model has to generate
### Data Splits
The below table summarizes sizes per language (computed from the `merged_{lang}.jsonl` files). Due to languages like `tw` only being single sentence translation samples from Flores, their byte percentage is significantly lower than their sample percentage.
|Language|Kilobytes|%|Samples|%|
|--------|------:|-:|---:|-:|
|tw|106288|0.11|265071|0.33|
|bm|107056|0.11|265180|0.33|
|ak|108096|0.11|265071|0.33|
|ca|110608|0.11|271191|0.33|
|eu|113008|0.11|281199|0.35|
|fon|113072|0.11|265063|0.33|
|st|114080|0.11|265063|0.33|
|ki|115040|0.12|265180|0.33|
|tum|116032|0.12|265063|0.33|
|wo|122560|0.12|365063|0.45|
|ln|126304|0.13|365060|0.45|
|as|156256|0.16|265063|0.33|
|or|161472|0.16|265063|0.33|
|kn|165456|0.17|265063|0.33|
|ml|175040|0.18|265864|0.33|
|rn|192992|0.19|318189|0.39|
|nso|229712|0.23|915051|1.13|
|tn|235536|0.24|915054|1.13|
|lg|235936|0.24|915021|1.13|
|rw|249360|0.25|915043|1.13|
|ts|250256|0.25|915044|1.13|
|sn|252496|0.25|865056|1.07|
|xh|254672|0.26|915058|1.13|
|zu|263712|0.26|915061|1.13|
|ny|272128|0.27|915063|1.13|
|ig|325232|0.33|950097|1.17|
|yo|352784|0.35|918416|1.13|
|ne|393680|0.39|315754|0.39|
|pa|523248|0.52|339210|0.42|
|gu|560688|0.56|347499|0.43|
|sw|566656|0.57|1130481|1.4|
|mr|666240|0.67|417269|0.52|
|bn|832720|0.83|428843|0.53|
|ta|926912|0.93|415433|0.51|
|te|1343232|1.35|584590|0.72|
|ur|1918272|1.92|855756|1.06|
|vi|3102512|3.11|1672106|2.07|
|code|4330752|4.34|2707724|3.34|
|hi|4403568|4.41|1554667|1.92|
|zh|4599440|4.61|3589234|4.43|
|id|4612256|4.62|2643418|3.27|
|ar|4683456|4.69|2160181|2.67|
|fr|6591120|6.6|5316403|6.57|
|pt|6886800|6.9|3752156|4.63|
|es|8587920|8.6|5413205|6.69|
|en|39252528|39.33|32740750|40.44|
|total|99807184|100.0|80956089|100.0|
## Dataset Creation
### Source Data
#### Training datasets
- Code Miscellaneous
- [CodeComplex](https://huggingface.co/datasets/codeparrot/codecomplex)
- [Docstring Corpus](https://huggingface.co/datasets/teven/code_docstring_corpus)
- [GreatCode](https://huggingface.co/datasets/great_code)
- [State Changes](https://huggingface.co/datasets/Fraser/python-state-changes)
- Closed-book QA
- [Hotpot QA](https://huggingface.co/datasets/hotpot_qa)
- [Trivia QA](https://huggingface.co/datasets/trivia_qa)
- [Web Questions](https://huggingface.co/datasets/web_questions)
- [Wiki QA](https://huggingface.co/datasets/wiki_qa)
- Extractive QA
- [Adversarial QA](https://huggingface.co/datasets/adversarial_qa)
- [CMRC2018](https://huggingface.co/datasets/cmrc2018)
- [DRCD](https://huggingface.co/datasets/clue)
- [DuoRC](https://huggingface.co/datasets/duorc)
- [MLQA](https://huggingface.co/datasets/mlqa)
- [Quoref](https://huggingface.co/datasets/quoref)
- [ReCoRD](https://huggingface.co/datasets/super_glue)
- [ROPES](https://huggingface.co/datasets/ropes)
- [SQuAD v2](https://huggingface.co/datasets/squad_v2)
- [xQuAD](https://huggingface.co/datasets/xquad)
- TyDI QA
- [Primary](https://huggingface.co/datasets/khalidalt/tydiqa-primary)
- [Goldp](https://huggingface.co/datasets/khalidalt/tydiqa-goldp)
- Multiple-Choice QA
- [ARC](https://huggingface.co/datasets/ai2_arc)
- [C3](https://huggingface.co/datasets/c3)
- [CoS-E](https://huggingface.co/datasets/cos_e)
- [Cosmos](https://huggingface.co/datasets/cosmos)
- [DREAM](https://huggingface.co/datasets/dream)
- [MultiRC](https://huggingface.co/datasets/super_glue)
- [OpenBookQA](https://huggingface.co/datasets/openbookqa)
- [PiQA](https://huggingface.co/datasets/piqa)
- [QUAIL](https://huggingface.co/datasets/quail)
- [QuaRel](https://huggingface.co/datasets/quarel)
- [QuaRTz](https://huggingface.co/datasets/quartz)
- [QASC](https://huggingface.co/datasets/qasc)
- [RACE](https://huggingface.co/datasets/race)
- [SciQ](https://huggingface.co/datasets/sciq)
- [Social IQA](https://huggingface.co/datasets/social_i_qa)
- [Wiki Hop](https://huggingface.co/datasets/wiki_hop)
- [WiQA](https://huggingface.co/datasets/wiqa)
- Paraphrase Identification
- [MRPC](https://huggingface.co/datasets/super_glue)
- [PAWS](https://huggingface.co/datasets/paws)
- [PAWS-X](https://huggingface.co/datasets/paws-x)
- [QQP](https://huggingface.co/datasets/qqp)
- Program Synthesis
- [APPS](https://huggingface.co/datasets/codeparrot/apps)
- [CodeContests](https://huggingface.co/datasets/teven/code_contests)
- [JupyterCodePairs](https://huggingface.co/datasets/codeparrot/github-jupyter-text-code-pairs)
- [MBPP](https://huggingface.co/datasets/Muennighoff/mbpp)
- [NeuralCodeSearch](https://huggingface.co/datasets/neural_code_search)
- [XLCoST](https://huggingface.co/datasets/codeparrot/xlcost-text-to-code)
- Structure-to-text
- [Common Gen](https://huggingface.co/datasets/common_gen)
- [Wiki Bio](https://huggingface.co/datasets/wiki_bio)
- Sentiment
- [Amazon](https://huggingface.co/datasets/amazon_polarity)
- [App Reviews](https://huggingface.co/datasets/app_reviews)
- [IMDB](https://huggingface.co/datasets/imdb)
- [Rotten Tomatoes](https://huggingface.co/datasets/rotten_tomatoes)
- [Yelp](https://huggingface.co/datasets/yelp_review_full)
- Simplification
- [BiSECT](https://huggingface.co/datasets/GEM/BiSECT)
- Summarization
- [CNN Daily Mail](https://huggingface.co/datasets/cnn_dailymail)
- [Gigaword](https://huggingface.co/datasets/gigaword)
- [MultiNews](https://huggingface.co/datasets/multi_news)
- [SamSum](https://huggingface.co/datasets/samsum)
- [Wiki-Lingua](https://huggingface.co/datasets/GEM/wiki_lingua)
- [XLSum](https://huggingface.co/datasets/GEM/xlsum)
- [XSum](https://huggingface.co/datasets/xsum)
- Topic Classification
- [AG News](https://huggingface.co/datasets/ag_news)
- [DBPedia](https://huggingface.co/datasets/dbpedia_14)
- [TNEWS](https://huggingface.co/datasets/clue)
- [TREC](https://huggingface.co/datasets/trec)
- [CSL](https://huggingface.co/datasets/clue)
- Translation
- [Flores-200](https://huggingface.co/datasets/Muennighoff/flores200)
- [Tatoeba](https://huggingface.co/datasets/Helsinki-NLP/tatoeba_mt)
- Word Sense disambiguation
- [WiC](https://huggingface.co/datasets/super_glue)
- [XL-WiC](https://huggingface.co/datasets/pasinit/xlwic)
#### Evaluation datasets (included in [xP3all](https://huggingface.co/datasets/bigscience/xP3all) except for HumanEval)
- Natural Language Inference
- [ANLI](https://huggingface.co/datasets/anli)
- [CB](https://huggingface.co/datasets/super_glue)
- [RTE](https://huggingface.co/datasets/super_glue)
- [XNLI](https://huggingface.co/datasets/xnli)
- Coreference Resolution
- [Winogrande](https://huggingface.co/datasets/winogrande)
- [XWinograd](https://huggingface.co/datasets/Muennighoff/xwinograd)
- Program Synthesis
- [HumanEval](https://huggingface.co/datasets/openai_humaneval)
- Sentence Completion
- [COPA](https://huggingface.co/datasets/super_glue)
- [Story Cloze](https://huggingface.co/datasets/story_cloze)
- [XCOPA](https://huggingface.co/datasets/xcopa)
- [XStoryCloze](https://huggingface.co/datasets/Muennighoff/xstory_cloze)
#### Additional [xP3all](https://huggingface.co/datasets/bigscience/xP3all) datasets
- Coreference Resolution
- [WSC (Fixed)](https://huggingface.co/datasets/super_glue)
- Sentence Completion
- [HellaSwag](https://huggingface.co/datasets/hellaswag)
- Translation
- [MultiEurlex](https://huggingface.co/datasets/multi_eurlex)
## Additional Information
### Licensing Information
The dataset is released under Apache 2.0.
### Citation Information
```bibtex
@misc{muennighoff2022crosslingual,
title={Crosslingual Generalization through Multitask Finetuning},
author={Niklas Muennighoff and Thomas Wang and Lintang Sutawika and Adam Roberts and Stella Biderman and Teven Le Scao and M Saiful Bari and Sheng Shen and Zheng-Xin Yong and Hailey Schoelkopf and Xiangru Tang and Dragomir Radev and Alham Fikri Aji and Khalid Almubarak and Samuel Albanie and Zaid Alyafeai and Albert Webson and Edward Raff and Colin Raffel},
year={2022},
eprint={2211.01786},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to the contributors of [promptsource](https://github.com/bigscience-workshop/promptsource/graphs/contributors) for adding many prompts used in this dataset. |
AISE-TUDelft/MSR_Intermediate | AISE-TUDelft | "2025-02-18T16:10:48Z" | 11,630 | 0 | [
"size_categories:10M<n<100M",
"format:parquet",
"modality:tabular",
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"library:dask",
"library:mlcroissant",
"library:polars",
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---
|
regent-project/regent-subset-of-jat-dataset-tokenized | regent-project | "2024-10-02T05:12:09Z" | 11,609 | 0 | [
"size_categories:10M<n<100M",
"format:parquet",
"modality:timeseries",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-10-01T22:46:53Z" | ---
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path: babyai-boss-level_newdata/train-*
- config_name: babyai-boss-level_subset
data_files:
- split: train
path: babyai-boss-level_subset/train-*
- config_name: babyai-find-obj-s5_newdata
data_files:
- split: train
path: babyai-find-obj-s5_newdata/train-*
- config_name: babyai-find-obj-s5_subset
data_files:
- split: train
path: babyai-find-obj-s5_subset/train-*
- config_name: babyai-go-to-door_newdata
data_files:
- split: train
path: babyai-go-to-door_newdata/train-*
- config_name: babyai-go-to-door_subset
data_files:
- split: train
path: babyai-go-to-door_subset/train-*
- config_name: babyai-go-to-imp-unlock_newdata
data_files:
- split: train
path: babyai-go-to-imp-unlock_newdata/train-*
- config_name: babyai-go-to-imp-unlock_subset
data_files:
- split: train
path: babyai-go-to-imp-unlock_subset/train-*
- config_name: babyai-go-to-local_newdata
data_files:
- split: train
path: babyai-go-to-local_newdata/train-*
- config_name: babyai-go-to-local_subset
data_files:
- split: train
path: babyai-go-to-local_subset/train-*
- config_name: babyai-go-to-obj-door_newdata
data_files:
- split: train
path: babyai-go-to-obj-door_newdata/train-*
- config_name: babyai-go-to-obj-door_subset
data_files:
- split: train
path: babyai-go-to-obj-door_subset/train-*
- config_name: babyai-go-to-obj_newdata
data_files:
- split: train
path: babyai-go-to-obj_newdata/train-*
- config_name: babyai-go-to-obj_subset
data_files:
- split: train
path: babyai-go-to-obj_subset/train-*
- config_name: babyai-go-to-red-ball-grey_newdata
data_files:
- split: train
path: babyai-go-to-red-ball-grey_newdata/train-*
- config_name: babyai-go-to-red-ball-grey_subset
data_files:
- split: train
path: babyai-go-to-red-ball-grey_subset/train-*
- config_name: babyai-go-to-red-ball-no-dists_newdata
data_files:
- split: train
path: babyai-go-to-red-ball-no-dists_newdata/train-*
- config_name: babyai-go-to-red-ball-no-dists_subset
data_files:
- split: train
path: babyai-go-to-red-ball-no-dists_subset/train-*
- config_name: babyai-go-to-red-ball_newdata
data_files:
- split: train
path: babyai-go-to-red-ball_newdata/train-*
- config_name: babyai-go-to-red-ball_subset
data_files:
- split: train
path: babyai-go-to-red-ball_subset/train-*
- config_name: babyai-go-to-red-blue-ball_newdata
data_files:
- split: train
path: babyai-go-to-red-blue-ball_newdata/train-*
- config_name: babyai-go-to-red-blue-ball_subset
data_files:
- split: train
path: babyai-go-to-red-blue-ball_subset/train-*
- config_name: babyai-go-to-seq_newdata
data_files:
- split: train
path: babyai-go-to-seq_newdata/train-*
- config_name: babyai-go-to-seq_subset
data_files:
- split: train
path: babyai-go-to-seq_subset/train-*
- config_name: babyai-go-to_newdata
data_files:
- split: train
path: babyai-go-to_newdata/train-*
- config_name: babyai-go-to_subset
data_files:
- split: train
path: babyai-go-to_subset/train-*
- config_name: babyai-key-corridor_newdata
data_files:
- split: train
path: babyai-key-corridor_newdata/train-*
- config_name: babyai-key-corridor_subset
data_files:
- split: train
path: babyai-key-corridor_subset/train-*
- config_name: babyai-mini-boss-level_newdata
data_files:
- split: train
path: babyai-mini-boss-level_newdata/train-*
- config_name: babyai-mini-boss-level_subset
data_files:
- split: train
path: babyai-mini-boss-level_subset/train-*
- config_name: babyai-move-two-across-s8n9_newdata
data_files:
- split: train
path: babyai-move-two-across-s8n9_newdata/train-*
- config_name: babyai-move-two-across-s8n9_subset
data_files:
- split: train
path: babyai-move-two-across-s8n9_subset/train-*
- config_name: babyai-one-room-s8_newdata
data_files:
- split: train
path: babyai-one-room-s8_newdata/train-*
- config_name: babyai-one-room-s8_subset
data_files:
- split: train
path: babyai-one-room-s8_subset/train-*
- config_name: babyai-open-door_newdata
data_files:
- split: train
path: babyai-open-door_newdata/train-*
- config_name: babyai-open-door_subset
data_files:
- split: train
path: babyai-open-door_subset/train-*
- config_name: babyai-open-doors-order-n4_newdata
data_files:
- split: train
path: babyai-open-doors-order-n4_newdata/train-*
- config_name: babyai-open-doors-order-n4_subset
data_files:
- split: train
path: babyai-open-doors-order-n4_subset/train-*
- config_name: babyai-open-red-door_newdata
data_files:
- split: train
path: babyai-open-red-door_newdata/train-*
- config_name: babyai-open-red-door_subset
data_files:
- split: train
path: babyai-open-red-door_subset/train-*
- config_name: babyai-open-two-doors_newdata
data_files:
- split: train
path: babyai-open-two-doors_newdata/train-*
- config_name: babyai-open-two-doors_subset
data_files:
- split: train
path: babyai-open-two-doors_subset/train-*
- config_name: babyai-open_newdata
data_files:
- split: train
path: babyai-open_newdata/train-*
- config_name: babyai-open_subset
data_files:
- split: train
path: babyai-open_subset/train-*
- config_name: babyai-pickup-above_newdata
data_files:
- split: train
path: babyai-pickup-above_newdata/train-*
- config_name: babyai-pickup-above_subset
data_files:
- split: train
path: babyai-pickup-above_subset/train-*
- config_name: babyai-pickup-dist_newdata
data_files:
- split: train
path: babyai-pickup-dist_newdata/train-*
- config_name: babyai-pickup-dist_subset
data_files:
- split: train
path: babyai-pickup-dist_subset/train-*
- config_name: babyai-pickup-loc_newdata
data_files:
- split: train
path: babyai-pickup-loc_newdata/train-*
- config_name: babyai-pickup-loc_subset
data_files:
- split: train
path: babyai-pickup-loc_subset/train-*
- config_name: babyai-pickup_newdata
data_files:
- split: train
path: babyai-pickup_newdata/train-*
- config_name: babyai-pickup_subset
data_files:
- split: train
path: babyai-pickup_subset/train-*
- config_name: babyai-put-next-local_newdata
data_files:
- split: train
path: babyai-put-next-local_newdata/train-*
- config_name: babyai-put-next-local_subset
data_files:
- split: train
path: babyai-put-next-local_subset/train-*
- config_name: babyai-put-next_newdata
data_files:
- split: train
path: babyai-put-next_newdata/train-*
- config_name: babyai-put-next_subset
data_files:
- split: train
path: babyai-put-next_subset/train-*
- config_name: babyai-synth-loc_newdata
data_files:
- split: train
path: babyai-synth-loc_newdata/train-*
- config_name: babyai-synth-loc_subset
data_files:
- split: train
path: babyai-synth-loc_subset/train-*
- config_name: babyai-synth-seq_newdata
data_files:
- split: train
path: babyai-synth-seq_newdata/train-*
- config_name: babyai-synth-seq_subset
data_files:
- split: train
path: babyai-synth-seq_subset/train-*
- config_name: babyai-synth_newdata
data_files:
- split: train
path: babyai-synth_newdata/train-*
- config_name: babyai-synth_subset
data_files:
- split: train
path: babyai-synth_subset/train-*
- config_name: babyai-unblock-pickup_newdata
data_files:
- split: train
path: babyai-unblock-pickup_newdata/train-*
- config_name: babyai-unblock-pickup_subset
data_files:
- split: train
path: babyai-unblock-pickup_subset/train-*
- config_name: babyai-unlock-local_newdata
data_files:
- split: train
path: babyai-unlock-local_newdata/train-*
- config_name: babyai-unlock-local_subset
data_files:
- split: train
path: babyai-unlock-local_subset/train-*
- config_name: babyai-unlock-pickup_newdata
data_files:
- split: train
path: babyai-unlock-pickup_newdata/train-*
- config_name: babyai-unlock-pickup_subset
data_files:
- split: train
path: babyai-unlock-pickup_subset/train-*
- config_name: babyai-unlock-to-unlock_newdata
data_files:
- split: train
path: babyai-unlock-to-unlock_newdata/train-*
- config_name: babyai-unlock-to-unlock_subset
data_files:
- split: train
path: babyai-unlock-to-unlock_subset/train-*
- config_name: babyai-unlock_newdata
data_files:
- split: train
path: babyai-unlock_newdata/train-*
- config_name: babyai-unlock_subset
data_files:
- split: train
path: babyai-unlock_subset/train-*
- config_name: metaworld-assembly_newdata
data_files:
- split: train
path: metaworld-assembly_newdata/train-*
- config_name: metaworld-assembly_subset
data_files:
- split: train
path: metaworld-assembly_subset/train-*
- config_name: metaworld-basketball_newdata
data_files:
- split: train
path: metaworld-basketball_newdata/train-*
- config_name: metaworld-basketball_subset
data_files:
- split: train
path: metaworld-basketball_subset/train-*
- config_name: metaworld-bin-picking_newdata
data_files:
- split: train
path: metaworld-bin-picking_newdata/train-*
- config_name: metaworld-box-close_newdata
data_files:
- split: train
path: metaworld-box-close_newdata/train-*
- config_name: metaworld-button-press-topdown-wall_newdata
data_files:
- split: train
path: metaworld-button-press-topdown-wall_newdata/train-*
- config_name: metaworld-button-press-topdown-wall_subset
data_files:
- split: train
path: metaworld-button-press-topdown-wall_subset/train-*
- config_name: metaworld-button-press-topdown_newdata
data_files:
- split: train
path: metaworld-button-press-topdown_newdata/train-*
- config_name: metaworld-button-press-topdown_subset
data_files:
- split: train
path: metaworld-button-press-topdown_subset/train-*
- config_name: metaworld-button-press-wall_newdata
data_files:
- split: train
path: metaworld-button-press-wall_newdata/train-*
- config_name: metaworld-button-press-wall_subset
data_files:
- split: train
path: metaworld-button-press-wall_subset/train-*
- config_name: metaworld-button-press_newdata
data_files:
- split: train
path: metaworld-button-press_newdata/train-*
- config_name: metaworld-button-press_subset
data_files:
- split: train
path: metaworld-button-press_subset/train-*
- config_name: metaworld-coffee-button_newdata
data_files:
- split: train
path: metaworld-coffee-button_newdata/train-*
- config_name: metaworld-coffee-button_subset
data_files:
- split: train
path: metaworld-coffee-button_subset/train-*
- config_name: metaworld-coffee-pull_newdata
data_files:
- split: train
path: metaworld-coffee-pull_newdata/train-*
- config_name: metaworld-coffee-pull_subset
data_files:
- split: train
path: metaworld-coffee-pull_subset/train-*
- config_name: metaworld-coffee-push_newdata
data_files:
- split: train
path: metaworld-coffee-push_newdata/train-*
- config_name: metaworld-coffee-push_subset
data_files:
- split: train
path: metaworld-coffee-push_subset/train-*
- config_name: metaworld-dial-turn_newdata
data_files:
- split: train
path: metaworld-dial-turn_newdata/train-*
- config_name: metaworld-dial-turn_subset
data_files:
- split: train
path: metaworld-dial-turn_subset/train-*
- config_name: metaworld-disassemble_newdata
data_files:
- split: train
path: metaworld-disassemble_newdata/train-*
- config_name: metaworld-disassemble_subset
data_files:
- split: train
path: metaworld-disassemble_subset/train-*
- config_name: metaworld-door-close_newdata
data_files:
- split: train
path: metaworld-door-close_newdata/train-*
- config_name: metaworld-door-close_subset
data_files:
- split: train
path: metaworld-door-close_subset/train-*
- config_name: metaworld-door-lock_newdata
data_files:
- split: train
path: metaworld-door-lock_newdata/train-*
- config_name: metaworld-door-open_newdata
data_files:
- split: train
path: metaworld-door-open_newdata/train-*
- config_name: metaworld-door-open_subset
data_files:
- split: train
path: metaworld-door-open_subset/train-*
- config_name: metaworld-door-unlock_newdata
data_files:
- split: train
path: metaworld-door-unlock_newdata/train-*
- config_name: metaworld-drawer-close_newdata
data_files:
- split: train
path: metaworld-drawer-close_newdata/train-*
- config_name: metaworld-drawer-close_subset
data_files:
- split: train
path: metaworld-drawer-close_subset/train-*
- config_name: metaworld-drawer-open_newdata
data_files:
- split: train
path: metaworld-drawer-open_newdata/train-*
- config_name: metaworld-drawer-open_subset
data_files:
- split: train
path: metaworld-drawer-open_subset/train-*
- config_name: metaworld-faucet-close_newdata
data_files:
- split: train
path: metaworld-faucet-close_newdata/train-*
- config_name: metaworld-faucet-close_subset
data_files:
- split: train
path: metaworld-faucet-close_subset/train-*
- config_name: metaworld-faucet-open_newdata
data_files:
- split: train
path: metaworld-faucet-open_newdata/train-*
- config_name: metaworld-faucet-open_subset
data_files:
- split: train
path: metaworld-faucet-open_subset/train-*
- config_name: metaworld-hammer_newdata
data_files:
- split: train
path: metaworld-hammer_newdata/train-*
- config_name: metaworld-hammer_subset
data_files:
- split: train
path: metaworld-hammer_subset/train-*
- config_name: metaworld-handle-press-side_newdata
data_files:
- split: train
path: metaworld-handle-press-side_newdata/train-*
- config_name: metaworld-handle-press-side_subset
data_files:
- split: train
path: metaworld-handle-press-side_subset/train-*
- config_name: metaworld-handle-press_newdata
data_files:
- split: train
path: metaworld-handle-press_newdata/train-*
- config_name: metaworld-handle-press_subset
data_files:
- split: train
path: metaworld-handle-press_subset/train-*
- config_name: metaworld-handle-pull-side_newdata
data_files:
- split: train
path: metaworld-handle-pull-side_newdata/train-*
- config_name: metaworld-handle-pull-side_subset
data_files:
- split: train
path: metaworld-handle-pull-side_subset/train-*
- config_name: metaworld-handle-pull_newdata
data_files:
- split: train
path: metaworld-handle-pull_newdata/train-*
- config_name: metaworld-handle-pull_subset
data_files:
- split: train
path: metaworld-handle-pull_subset/train-*
- config_name: metaworld-lever-pull_newdata
data_files:
- split: train
path: metaworld-lever-pull_newdata/train-*
- config_name: metaworld-lever-pull_subset
data_files:
- split: train
path: metaworld-lever-pull_subset/train-*
- config_name: metaworld-peg-insert-side_newdata
data_files:
- split: train
path: metaworld-peg-insert-side_newdata/train-*
- config_name: metaworld-peg-insert-side_subset
data_files:
- split: train
path: metaworld-peg-insert-side_subset/train-*
- config_name: metaworld-peg-unplug-side_newdata
data_files:
- split: train
path: metaworld-peg-unplug-side_newdata/train-*
- config_name: metaworld-peg-unplug-side_subset
data_files:
- split: train
path: metaworld-peg-unplug-side_subset/train-*
- config_name: metaworld-pick-out-of-hole_newdata
data_files:
- split: train
path: metaworld-pick-out-of-hole_newdata/train-*
- config_name: metaworld-pick-out-of-hole_subset
data_files:
- split: train
path: metaworld-pick-out-of-hole_subset/train-*
- config_name: metaworld-pick-place-wall_newdata
data_files:
- split: train
path: metaworld-pick-place-wall_newdata/train-*
- config_name: metaworld-pick-place-wall_subset
data_files:
- split: train
path: metaworld-pick-place-wall_subset/train-*
- config_name: metaworld-pick-place_newdata
data_files:
- split: train
path: metaworld-pick-place_newdata/train-*
- config_name: metaworld-pick-place_subset
data_files:
- split: train
path: metaworld-pick-place_subset/train-*
- config_name: metaworld-plate-slide-back-side_newdata
data_files:
- split: train
path: metaworld-plate-slide-back-side_newdata/train-*
- config_name: metaworld-plate-slide-back-side_subset
data_files:
- split: train
path: metaworld-plate-slide-back-side_subset/train-*
- config_name: metaworld-plate-slide-back_newdata
data_files:
- split: train
path: metaworld-plate-slide-back_newdata/train-*
- config_name: metaworld-plate-slide-back_subset
data_files:
- split: train
path: metaworld-plate-slide-back_subset/train-*
- config_name: metaworld-plate-slide-side_newdata
data_files:
- split: train
path: metaworld-plate-slide-side_newdata/train-*
- config_name: metaworld-plate-slide-side_subset
data_files:
- split: train
path: metaworld-plate-slide-side_subset/train-*
- config_name: metaworld-plate-slide_newdata
data_files:
- split: train
path: metaworld-plate-slide_newdata/train-*
- config_name: metaworld-plate-slide_subset
data_files:
- split: train
path: metaworld-plate-slide_subset/train-*
- config_name: metaworld-push-back_newdata
data_files:
- split: train
path: metaworld-push-back_newdata/train-*
- config_name: metaworld-push-back_subset
data_files:
- split: train
path: metaworld-push-back_subset/train-*
- config_name: metaworld-push-wall_newdata
data_files:
- split: train
path: metaworld-push-wall_newdata/train-*
- config_name: metaworld-push-wall_subset
data_files:
- split: train
path: metaworld-push-wall_subset/train-*
- config_name: metaworld-push_newdata
data_files:
- split: train
path: metaworld-push_newdata/train-*
- config_name: metaworld-push_subset
data_files:
- split: train
path: metaworld-push_subset/train-*
- config_name: metaworld-reach-wall_newdata
data_files:
- split: train
path: metaworld-reach-wall_newdata/train-*
- config_name: metaworld-reach-wall_subset
data_files:
- split: train
path: metaworld-reach-wall_subset/train-*
- config_name: metaworld-reach_newdata
data_files:
- split: train
path: metaworld-reach_newdata/train-*
- config_name: metaworld-reach_subset
data_files:
- split: train
path: metaworld-reach_subset/train-*
- config_name: metaworld-shelf-place_newdata
data_files:
- split: train
path: metaworld-shelf-place_newdata/train-*
- config_name: metaworld-shelf-place_subset
data_files:
- split: train
path: metaworld-shelf-place_subset/train-*
- config_name: metaworld-soccer_newdata
data_files:
- split: train
path: metaworld-soccer_newdata/train-*
- config_name: metaworld-soccer_subset
data_files:
- split: train
path: metaworld-soccer_subset/train-*
- config_name: metaworld-stick-pull_newdata
data_files:
- split: train
path: metaworld-stick-pull_newdata/train-*
- config_name: metaworld-stick-pull_subset
data_files:
- split: train
path: metaworld-stick-pull_subset/train-*
- config_name: metaworld-stick-push_newdata
data_files:
- split: train
path: metaworld-stick-push_newdata/train-*
- config_name: metaworld-stick-push_subset
data_files:
- split: train
path: metaworld-stick-push_subset/train-*
- config_name: metaworld-sweep-into_newdata
data_files:
- split: train
path: metaworld-sweep-into_newdata/train-*
- config_name: metaworld-sweep-into_subset
data_files:
- split: train
path: metaworld-sweep-into_subset/train-*
- config_name: metaworld-sweep_newdata
data_files:
- split: train
path: metaworld-sweep_newdata/train-*
- config_name: metaworld-sweep_subset
data_files:
- split: train
path: metaworld-sweep_subset/train-*
- config_name: metaworld-window-close_newdata
data_files:
- split: train
path: metaworld-window-close_newdata/train-*
- config_name: metaworld-window-close_subset
data_files:
- split: train
path: metaworld-window-close_subset/train-*
- config_name: metaworld-window-open_newdata
data_files:
- split: train
path: metaworld-window-open_newdata/train-*
- config_name: metaworld-window-open_subset
data_files:
- split: train
path: metaworld-window-open_subset/train-*
- config_name: mujoco-ant_newdata
data_files:
- split: train
path: mujoco-ant_newdata/train-*
- config_name: mujoco-ant_subset
data_files:
- split: train
path: mujoco-ant_subset/train-*
- config_name: mujoco-doublependulum_newdata
data_files:
- split: train
path: mujoco-doublependulum_newdata/train-*
- config_name: mujoco-doublependulum_subset
data_files:
- split: train
path: mujoco-doublependulum_subset/train-*
- config_name: mujoco-halfcheetah_newdata
data_files:
- split: train
path: mujoco-halfcheetah_newdata/train-*
- config_name: mujoco-hopper_newdata
data_files:
- split: train
path: mujoco-hopper_newdata/train-*
- config_name: mujoco-humanoid_newdata
data_files:
- split: train
path: mujoco-humanoid_newdata/train-*
- config_name: mujoco-humanoid_subset
data_files:
- split: train
path: mujoco-humanoid_subset/train-*
- config_name: mujoco-pendulum_newdata
data_files:
- split: train
path: mujoco-pendulum_newdata/train-*
- config_name: mujoco-pendulum_subset
data_files:
- split: train
path: mujoco-pendulum_subset/train-*
- config_name: mujoco-pusher_newdata
data_files:
- split: train
path: mujoco-pusher_newdata/train-*
- config_name: mujoco-pusher_subset
data_files:
- split: train
path: mujoco-pusher_subset/train-*
- config_name: mujoco-reacher_newdata
data_files:
- split: train
path: mujoco-reacher_newdata/train-*
- config_name: mujoco-reacher_subset
data_files:
- split: train
path: mujoco-reacher_subset/train-*
- config_name: mujoco-standup_newdata
data_files:
- split: train
path: mujoco-standup_newdata/train-*
- config_name: mujoco-standup_subset
data_files:
- split: train
path: mujoco-standup_subset/train-*
- config_name: mujoco-swimmer_newdata
data_files:
- split: train
path: mujoco-swimmer_newdata/train-*
- config_name: mujoco-swimmer_subset
data_files:
- split: train
path: mujoco-swimmer_subset/train-*
- config_name: mujoco-walker_newdata
data_files:
- split: train
path: mujoco-walker_newdata/train-*
- config_name: mujoco-walker_subset
data_files:
- split: train
path: mujoco-walker_subset/train-*
---
|
mshah1/speech_robust_bench | mshah1 | "2025-02-23T18:32:01Z" | 11,592 | 3 | [
"size_categories:1M<n<10M",
"modality:audio",
"modality:text",
"region:us"
] | null | "2024-01-21T01:39:08Z" | ---
dataset_info:
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download_size: 58723208514
dataset_size: 30342709961.007984
configs:
- config_name: accented_cv
data_files:
- split: test
path: accented_cv/test-*
- split: test.clean
path: accented_cv/test.clean-*
- config_name: accented_cv_es
data_files:
- split: test
path: accented_cv_es/test-*
- config_name: accented_cv_fr
data_files:
- split: test
path: accented_cv_fr/test-*
- config_name: chime
data_files:
- split: farfield
path: chime/farfield-*
- split: nearfield
path: chime/nearfield-*
- config_name: in-the-wild
data_files:
- split: farfield
path: in-the-wild/farfield-*
- split: nearfield
path: in-the-wild/nearfield-*
- config_name: in-the-wild-AMI
data_files:
- split: nearfield
path: in-the-wild-AMI/nearfield-*
- split: farfield
path: in-the-wild-AMI/farfield-*
- config_name: in-the-wild-ami
data_files:
- split: nearfield
path: in-the-wild-ami/nearfield-*
- split: farfield
path: in-the-wild-ami/farfield-*
- config_name: librispeech_asr-test.clean
data_files:
- split: None.0
path: librispeech_asr-test.clean/None.0-*
- split: gnoise.1
path: librispeech_asr-test.clean/gnoise.1-*
- split: gnoise.2
path: librispeech_asr-test.clean/gnoise.2-*
- split: gnoise.3
path: librispeech_asr-test.clean/gnoise.3-*
- split: gnoise.4
path: librispeech_asr-test.clean/gnoise.4-*
- split: env_noise.1
path: librispeech_asr-test.clean/env_noise.1-*
- split: env_noise.2
path: librispeech_asr-test.clean/env_noise.2-*
- split: env_noise.3
path: librispeech_asr-test.clean/env_noise.3-*
- split: env_noise.4
path: librispeech_asr-test.clean/env_noise.4-*
- split: rir.1
path: librispeech_asr-test.clean/rir.1-*
- split: rir.2
path: librispeech_asr-test.clean/rir.2-*
- split: rir.3
path: librispeech_asr-test.clean/rir.3-*
- split: rir.4
path: librispeech_asr-test.clean/rir.4-*
- split: speedup.1
path: librispeech_asr-test.clean/speedup.1-*
- split: speedup.2
path: librispeech_asr-test.clean/speedup.2-*
- split: speedup.3
path: librispeech_asr-test.clean/speedup.3-*
- split: speedup.4
path: librispeech_asr-test.clean/speedup.4-*
- split: slowdown.1
path: librispeech_asr-test.clean/slowdown.1-*
- split: slowdown.2
path: librispeech_asr-test.clean/slowdown.2-*
- split: slowdown.3
path: librispeech_asr-test.clean/slowdown.3-*
- split: slowdown.4
path: librispeech_asr-test.clean/slowdown.4-*
- split: pitch_up.3
path: librispeech_asr-test.clean/pitch_up.3-*
- split: pitch_up.4
path: librispeech_asr-test.clean/pitch_up.4-*
- split: pitch_down.1
path: librispeech_asr-test.clean/pitch_down.1-*
- split: pitch_down.2
path: librispeech_asr-test.clean/pitch_down.2-*
- split: pitch_down.3
path: librispeech_asr-test.clean/pitch_down.3-*
- split: pitch_down.4
path: librispeech_asr-test.clean/pitch_down.4-*
- split: pitch_up.1
path: librispeech_asr-test.clean/pitch_up.1-*
- split: pitch_up.2
path: librispeech_asr-test.clean/pitch_up.2-*
- split: resample.1
path: librispeech_asr-test.clean/resample.1-*
- split: resample.2
path: librispeech_asr-test.clean/resample.2-*
- split: resample.3
path: librispeech_asr-test.clean/resample.3-*
- split: resample.4
path: librispeech_asr-test.clean/resample.4-*
- split: env_noise_esc50.1
path: librispeech_asr-test.clean/env_noise_esc50.1-*
- split: env_noise_esc50.2
path: librispeech_asr-test.clean/env_noise_esc50.2-*
- split: env_noise_esc50.3
path: librispeech_asr-test.clean/env_noise_esc50.3-*
- split: env_noise_esc50.4
path: librispeech_asr-test.clean/env_noise_esc50.4-*
- split: voice_conversion.4
path: librispeech_asr-test.clean/voice_conversion.4-*
- split: voice_conversion.3
path: librispeech_asr-test.clean/voice_conversion.3-*
- split: voice_conversion.1
path: librispeech_asr-test.clean/voice_conversion.1-*
- split: voice_conversion.2
path: librispeech_asr-test.clean/voice_conversion.2-*
- split: gain.1
path: librispeech_asr-test.clean/gain.1-*
- split: gain.2
path: librispeech_asr-test.clean/gain.2-*
- split: gain.3
path: librispeech_asr-test.clean/gain.3-*
- split: echo.1
path: librispeech_asr-test.clean/echo.1-*
- split: echo.2
path: librispeech_asr-test.clean/echo.2-*
- split: echo.3
path: librispeech_asr-test.clean/echo.3-*
- split: echo.4
path: librispeech_asr-test.clean/echo.4-*
- split: phaser.1
path: librispeech_asr-test.clean/phaser.1-*
- split: phaser.2
path: librispeech_asr-test.clean/phaser.2-*
- split: phaser.3
path: librispeech_asr-test.clean/phaser.3-*
- split: tempo_up.1
path: librispeech_asr-test.clean/tempo_up.1-*
- split: tempo_up.2
path: librispeech_asr-test.clean/tempo_up.2-*
- split: tempo_up.3
path: librispeech_asr-test.clean/tempo_up.3-*
- split: tempo_up.4
path: librispeech_asr-test.clean/tempo_up.4-*
- split: tempo_down.1
path: librispeech_asr-test.clean/tempo_down.1-*
- split: tempo_down.2
path: librispeech_asr-test.clean/tempo_down.2-*
- split: tempo_down.3
path: librispeech_asr-test.clean/tempo_down.3-*
- split: tempo_down.4
path: librispeech_asr-test.clean/tempo_down.4-*
- split: gain.4
path: librispeech_asr-test.clean/gain.4-*
- split: lowpass.1
path: librispeech_asr-test.clean/lowpass.1-*
- split: lowpass.2
path: librispeech_asr-test.clean/lowpass.2-*
- split: lowpass.3
path: librispeech_asr-test.clean/lowpass.3-*
- split: lowpass.4
path: librispeech_asr-test.clean/lowpass.4-*
- split: highpass.1
path: librispeech_asr-test.clean/highpass.1-*
- split: highpass.2
path: librispeech_asr-test.clean/highpass.2-*
- split: highpass.3
path: librispeech_asr-test.clean/highpass.3-*
- split: highpass.4
path: librispeech_asr-test.clean/highpass.4-*
- split: phaser.4
path: librispeech_asr-test.clean/phaser.4-*
- split: voice_conversion_vctk.1
path: librispeech_asr-test.clean/voice_conversion_vctk.1-*
- split: universal_adv.1
path: librispeech_asr-test.clean/universal_adv.1-*
- split: music.1
path: librispeech_asr-test.clean/music.1-*
- split: music.2
path: librispeech_asr-test.clean/music.2-*
- split: music.3
path: librispeech_asr-test.clean/music.3-*
- split: music.4
path: librispeech_asr-test.clean/music.4-*
- split: crosstalk.1
path: librispeech_asr-test.clean/crosstalk.1-*
- split: crosstalk.2
path: librispeech_asr-test.clean/crosstalk.2-*
- split: crosstalk.3
path: librispeech_asr-test.clean/crosstalk.3-*
- split: crosstalk.4
path: librispeech_asr-test.clean/crosstalk.4-*
- split: env_noise_musan.1
path: librispeech_asr-test.clean/env_noise_musan.1-*
- split: env_noise_musan.2
path: librispeech_asr-test.clean/env_noise_musan.2-*
- split: env_noise_musan.3
path: librispeech_asr-test.clean/env_noise_musan.3-*
- split: env_noise_musan.4
path: librispeech_asr-test.clean/env_noise_musan.4-*
- split: real_rir.1
path: librispeech_asr-test.clean/real_rir.1-*
- split: real_rir.2
path: librispeech_asr-test.clean/real_rir.2-*
- split: real_rir.3
path: librispeech_asr-test.clean/real_rir.3-*
- split: real_rir.4
path: librispeech_asr-test.clean/real_rir.4-*
- split: env_noise_wham.1
path: librispeech_asr-test.clean/env_noise_wham.1-*
- split: env_noise_wham.2
path: librispeech_asr-test.clean/env_noise_wham.2-*
- split: env_noise_wham.3
path: librispeech_asr-test.clean/env_noise_wham.3-*
- split: env_noise_wham.4
path: librispeech_asr-test.clean/env_noise_wham.4-*
- split: tremolo.1
path: librispeech_asr-test.clean/tremolo.1-*
- split: tremolo.2
path: librispeech_asr-test.clean/tremolo.2-*
- split: tremolo.3
path: librispeech_asr-test.clean/tremolo.3-*
- split: tremolo.4
path: librispeech_asr-test.clean/tremolo.4-*
- split: treble.1
path: librispeech_asr-test.clean/treble.1-*
- split: treble.2
path: librispeech_asr-test.clean/treble.2-*
- split: treble.3
path: librispeech_asr-test.clean/treble.3-*
- split: treble.4
path: librispeech_asr-test.clean/treble.4-*
- split: bass.1
path: librispeech_asr-test.clean/bass.1-*
- split: bass.2
path: librispeech_asr-test.clean/bass.2-*
- split: bass.3
path: librispeech_asr-test.clean/bass.3-*
- split: bass.4
path: librispeech_asr-test.clean/bass.4-*
- split: chorus.1
path: librispeech_asr-test.clean/chorus.1-*
- split: chorus.2
path: librispeech_asr-test.clean/chorus.2-*
- split: chorus.3
path: librispeech_asr-test.clean/chorus.3-*
- split: chorus.4
path: librispeech_asr-test.clean/chorus.4-*
- config_name: librispeech_asr-test.clean_pertEval_500_30
data_files:
- split: gnoise.1
path: librispeech_asr-test.clean_pertEval_500_30/gnoise.1-*
- split: env_noise_esc50.1
path: librispeech_asr-test.clean_pertEval_500_30/env_noise_esc50.1-*
- config_name: multilingual_librispeech-french_test
data_files:
- split: gnoise.1
path: multilingual_librispeech-french_test/gnoise.1-*
- split: gnoise.2
path: multilingual_librispeech-french_test/gnoise.2-*
- split: gnoise.3
path: multilingual_librispeech-french_test/gnoise.3-*
- split: speedup.1
path: multilingual_librispeech-french_test/speedup.1-*
- split: speedup.2
path: multilingual_librispeech-french_test/speedup.2-*
- split: speedup.3
path: multilingual_librispeech-french_test/speedup.3-*
- split: slowdown.1
path: multilingual_librispeech-french_test/slowdown.1-*
- split: slowdown.2
path: multilingual_librispeech-french_test/slowdown.2-*
- split: slowdown.3
path: multilingual_librispeech-french_test/slowdown.3-*
- split: pitch_up.1
path: multilingual_librispeech-french_test/pitch_up.1-*
- split: pitch_up.2
path: multilingual_librispeech-french_test/pitch_up.2-*
- split: pitch_up.3
path: multilingual_librispeech-french_test/pitch_up.3-*
- split: pitch_down.1
path: multilingual_librispeech-french_test/pitch_down.1-*
- split: pitch_down.2
path: multilingual_librispeech-french_test/pitch_down.2-*
- split: env_noise.1
path: multilingual_librispeech-french_test/env_noise.1-*
- split: env_noise.3
path: multilingual_librispeech-french_test/env_noise.3-*
- split: env_noise_wham.1
path: multilingual_librispeech-french_test/env_noise_wham.1-*
- split: env_noise_wham.2
path: multilingual_librispeech-french_test/env_noise_wham.2-*
- split: real_rir.3
path: multilingual_librispeech-french_test/real_rir.3-*
- split: env_noise.2
path: multilingual_librispeech-french_test/env_noise.2-*
- split: env_noise_esc50.1
path: multilingual_librispeech-french_test/env_noise_esc50.1-*
- split: env_noise_esc50.2
path: multilingual_librispeech-french_test/env_noise_esc50.2-*
- split: env_noise_esc50.3
path: multilingual_librispeech-french_test/env_noise_esc50.3-*
- split: env_noise_musan.1
path: multilingual_librispeech-french_test/env_noise_musan.1-*
- split: env_noise_musan.2
path: multilingual_librispeech-french_test/env_noise_musan.2-*
- split: env_noise_musan.3
path: multilingual_librispeech-french_test/env_noise_musan.3-*
- split: env_noise_wham.3
path: multilingual_librispeech-french_test/env_noise_wham.3-*
- split: pitch_down.3
path: multilingual_librispeech-french_test/pitch_down.3-*
- split: rir.1
path: multilingual_librispeech-french_test/rir.1-*
- split: rir.2
path: multilingual_librispeech-french_test/rir.2-*
- split: rir.3
path: multilingual_librispeech-french_test/rir.3-*
- split: real_rir.1
path: multilingual_librispeech-french_test/real_rir.1-*
- split: real_rir.2
path: multilingual_librispeech-french_test/real_rir.2-*
- split: resample.1
path: multilingual_librispeech-french_test/resample.1-*
- split: resample.2
path: multilingual_librispeech-french_test/resample.2-*
- split: resample.3
path: multilingual_librispeech-french_test/resample.3-*
- split: gain.1
path: multilingual_librispeech-french_test/gain.1-*
- split: gain.2
path: multilingual_librispeech-french_test/gain.2-*
- split: gain.3
path: multilingual_librispeech-french_test/gain.3-*
- split: echo.1
path: multilingual_librispeech-french_test/echo.1-*
- split: echo.2
path: multilingual_librispeech-french_test/echo.2-*
- split: echo.3
path: multilingual_librispeech-french_test/echo.3-*
- split: phaser.1
path: multilingual_librispeech-french_test/phaser.1-*
- split: phaser.2
path: multilingual_librispeech-french_test/phaser.2-*
- split: phaser.3
path: multilingual_librispeech-french_test/phaser.3-*
- split: tempo_up.1
path: multilingual_librispeech-french_test/tempo_up.1-*
- split: tempo_up.2
path: multilingual_librispeech-french_test/tempo_up.2-*
- split: tempo_up.3
path: multilingual_librispeech-french_test/tempo_up.3-*
- split: tempo_down.1
path: multilingual_librispeech-french_test/tempo_down.1-*
- split: tempo_down.2
path: multilingual_librispeech-french_test/tempo_down.2-*
- split: tempo_down.3
path: multilingual_librispeech-french_test/tempo_down.3-*
- split: lowpass.1
path: multilingual_librispeech-french_test/lowpass.1-*
- split: lowpass.2
path: multilingual_librispeech-french_test/lowpass.2-*
- split: lowpass.3
path: multilingual_librispeech-french_test/lowpass.3-*
- split: highpass.1
path: multilingual_librispeech-french_test/highpass.1-*
- split: highpass.2
path: multilingual_librispeech-french_test/highpass.2-*
- split: highpass.3
path: multilingual_librispeech-french_test/highpass.3-*
- split: music.1
path: multilingual_librispeech-french_test/music.1-*
- split: music.2
path: multilingual_librispeech-french_test/music.2-*
- split: music.3
path: multilingual_librispeech-french_test/music.3-*
- split: crosstalk.1
path: multilingual_librispeech-french_test/crosstalk.1-*
- split: crosstalk.2
path: multilingual_librispeech-french_test/crosstalk.2-*
- split: crosstalk.3
path: multilingual_librispeech-french_test/crosstalk.3-*
- split: tremolo.1
path: multilingual_librispeech-french_test/tremolo.1-*
- split: tremolo.2
path: multilingual_librispeech-french_test/tremolo.2-*
- split: tremolo.3
path: multilingual_librispeech-french_test/tremolo.3-*
- split: treble.1
path: multilingual_librispeech-french_test/treble.1-*
- split: treble.2
path: multilingual_librispeech-french_test/treble.2-*
- split: treble.3
path: multilingual_librispeech-french_test/treble.3-*
- split: bass.1
path: multilingual_librispeech-french_test/bass.1-*
- split: bass.2
path: multilingual_librispeech-french_test/bass.2-*
- split: bass.3
path: multilingual_librispeech-french_test/bass.3-*
- split: chorus.1
path: multilingual_librispeech-french_test/chorus.1-*
- split: chorus.2
path: multilingual_librispeech-french_test/chorus.2-*
- split: chorus.3
path: multilingual_librispeech-french_test/chorus.3-*
- split: gnoise.4
path: multilingual_librispeech-french_test/gnoise.4-*
- split: env_noise.4
path: multilingual_librispeech-french_test/env_noise.4-*
- split: env_noise_esc50.4
path: multilingual_librispeech-french_test/env_noise_esc50.4-*
- split: env_noise_musan.4
path: multilingual_librispeech-french_test/env_noise_musan.4-*
- split: env_noise_wham.4
path: multilingual_librispeech-french_test/env_noise_wham.4-*
- split: speedup.4
path: multilingual_librispeech-french_test/speedup.4-*
- split: slowdown.4
path: multilingual_librispeech-french_test/slowdown.4-*
- split: pitch_up.4
path: multilingual_librispeech-french_test/pitch_up.4-*
- split: pitch_down.4
path: multilingual_librispeech-french_test/pitch_down.4-*
- split: rir.4
path: multilingual_librispeech-french_test/rir.4-*
- split: real_rir.4
path: multilingual_librispeech-french_test/real_rir.4-*
- split: resample.4
path: multilingual_librispeech-french_test/resample.4-*
- split: gain.4
path: multilingual_librispeech-french_test/gain.4-*
- split: echo.4
path: multilingual_librispeech-french_test/echo.4-*
- split: phaser.4
path: multilingual_librispeech-french_test/phaser.4-*
- split: tempo_up.4
path: multilingual_librispeech-french_test/tempo_up.4-*
- split: tempo_down.4
path: multilingual_librispeech-french_test/tempo_down.4-*
- split: lowpass.4
path: multilingual_librispeech-french_test/lowpass.4-*
- split: highpass.4
path: multilingual_librispeech-french_test/highpass.4-*
- split: music.4
path: multilingual_librispeech-french_test/music.4-*
- split: crosstalk.4
path: multilingual_librispeech-french_test/crosstalk.4-*
- split: tremolo.4
path: multilingual_librispeech-french_test/tremolo.4-*
- split: treble.4
path: multilingual_librispeech-french_test/treble.4-*
- split: bass.4
path: multilingual_librispeech-french_test/bass.4-*
- split: chorus.4
path: multilingual_librispeech-french_test/chorus.4-*
- config_name: multilingual_librispeech-german_test
data_files:
- split: gnoise.1
path: multilingual_librispeech-german_test/gnoise.1-*
- split: gnoise.2
path: multilingual_librispeech-german_test/gnoise.2-*
- split: gnoise.3
path: multilingual_librispeech-german_test/gnoise.3-*
- split: env_noise.1
path: multilingual_librispeech-german_test/env_noise.1-*
- split: env_noise.2
path: multilingual_librispeech-german_test/env_noise.2-*
- split: env_noise.3
path: multilingual_librispeech-german_test/env_noise.3-*
- split: env_noise_esc50.1
path: multilingual_librispeech-german_test/env_noise_esc50.1-*
- split: env_noise_esc50.2
path: multilingual_librispeech-german_test/env_noise_esc50.2-*
- split: env_noise_esc50.3
path: multilingual_librispeech-german_test/env_noise_esc50.3-*
- split: env_noise_musan.1
path: multilingual_librispeech-german_test/env_noise_musan.1-*
- split: env_noise_musan.2
path: multilingual_librispeech-german_test/env_noise_musan.2-*
- split: env_noise_musan.3
path: multilingual_librispeech-german_test/env_noise_musan.3-*
- split: env_noise_wham.1
path: multilingual_librispeech-german_test/env_noise_wham.1-*
- split: env_noise_wham.2
path: multilingual_librispeech-german_test/env_noise_wham.2-*
- split: env_noise_wham.3
path: multilingual_librispeech-german_test/env_noise_wham.3-*
- split: speedup.1
path: multilingual_librispeech-german_test/speedup.1-*
- split: speedup.2
path: multilingual_librispeech-german_test/speedup.2-*
- split: speedup.3
path: multilingual_librispeech-german_test/speedup.3-*
- split: slowdown.1
path: multilingual_librispeech-german_test/slowdown.1-*
- split: slowdown.2
path: multilingual_librispeech-german_test/slowdown.2-*
- split: slowdown.3
path: multilingual_librispeech-german_test/slowdown.3-*
- split: pitch_up.1
path: multilingual_librispeech-german_test/pitch_up.1-*
- split: pitch_up.2
path: multilingual_librispeech-german_test/pitch_up.2-*
- split: pitch_up.3
path: multilingual_librispeech-german_test/pitch_up.3-*
- split: pitch_down.1
path: multilingual_librispeech-german_test/pitch_down.1-*
- split: pitch_down.2
path: multilingual_librispeech-german_test/pitch_down.2-*
- split: pitch_down.3
path: multilingual_librispeech-german_test/pitch_down.3-*
- split: rir.1
path: multilingual_librispeech-german_test/rir.1-*
- split: rir.2
path: multilingual_librispeech-german_test/rir.2-*
- split: rir.3
path: multilingual_librispeech-german_test/rir.3-*
- split: real_rir.1
path: multilingual_librispeech-german_test/real_rir.1-*
- split: real_rir.2
path: multilingual_librispeech-german_test/real_rir.2-*
- split: real_rir.3
path: multilingual_librispeech-german_test/real_rir.3-*
- split: resample.1
path: multilingual_librispeech-german_test/resample.1-*
- split: resample.2
path: multilingual_librispeech-german_test/resample.2-*
- split: resample.3
path: multilingual_librispeech-german_test/resample.3-*
- split: gain.1
path: multilingual_librispeech-german_test/gain.1-*
- split: gain.2
path: multilingual_librispeech-german_test/gain.2-*
- split: gain.3
path: multilingual_librispeech-german_test/gain.3-*
- split: echo.1
path: multilingual_librispeech-german_test/echo.1-*
- split: echo.2
path: multilingual_librispeech-german_test/echo.2-*
- split: echo.3
path: multilingual_librispeech-german_test/echo.3-*
- split: phaser.1
path: multilingual_librispeech-german_test/phaser.1-*
- split: phaser.2
path: multilingual_librispeech-german_test/phaser.2-*
- split: phaser.3
path: multilingual_librispeech-german_test/phaser.3-*
- split: tempo_up.1
path: multilingual_librispeech-german_test/tempo_up.1-*
- split: tempo_up.2
path: multilingual_librispeech-german_test/tempo_up.2-*
- split: tempo_up.3
path: multilingual_librispeech-german_test/tempo_up.3-*
- split: tempo_down.1
path: multilingual_librispeech-german_test/tempo_down.1-*
- split: tempo_down.2
path: multilingual_librispeech-german_test/tempo_down.2-*
- split: tempo_down.3
path: multilingual_librispeech-german_test/tempo_down.3-*
- split: lowpass.1
path: multilingual_librispeech-german_test/lowpass.1-*
- split: lowpass.2
path: multilingual_librispeech-german_test/lowpass.2-*
- split: lowpass.3
path: multilingual_librispeech-german_test/lowpass.3-*
- split: highpass.1
path: multilingual_librispeech-german_test/highpass.1-*
- split: highpass.2
path: multilingual_librispeech-german_test/highpass.2-*
- split: highpass.3
path: multilingual_librispeech-german_test/highpass.3-*
- split: music.1
path: multilingual_librispeech-german_test/music.1-*
- split: music.2
path: multilingual_librispeech-german_test/music.2-*
- split: music.3
path: multilingual_librispeech-german_test/music.3-*
- split: crosstalk.1
path: multilingual_librispeech-german_test/crosstalk.1-*
- split: crosstalk.2
path: multilingual_librispeech-german_test/crosstalk.2-*
- split: crosstalk.3
path: multilingual_librispeech-german_test/crosstalk.3-*
- split: tremolo.1
path: multilingual_librispeech-german_test/tremolo.1-*
- split: tremolo.2
path: multilingual_librispeech-german_test/tremolo.2-*
- split: tremolo.3
path: multilingual_librispeech-german_test/tremolo.3-*
- split: treble.1
path: multilingual_librispeech-german_test/treble.1-*
- split: treble.2
path: multilingual_librispeech-german_test/treble.2-*
- split: treble.3
path: multilingual_librispeech-german_test/treble.3-*
- split: bass.1
path: multilingual_librispeech-german_test/bass.1-*
- split: bass.2
path: multilingual_librispeech-german_test/bass.2-*
- split: bass.3
path: multilingual_librispeech-german_test/bass.3-*
- split: chorus.1
path: multilingual_librispeech-german_test/chorus.1-*
- split: chorus.2
path: multilingual_librispeech-german_test/chorus.2-*
- split: chorus.3
path: multilingual_librispeech-german_test/chorus.3-*
- split: gnoise.4
path: multilingual_librispeech-german_test/gnoise.4-*
- split: env_noise.4
path: multilingual_librispeech-german_test/env_noise.4-*
- split: env_noise_esc50.4
path: multilingual_librispeech-german_test/env_noise_esc50.4-*
- split: env_noise_musan.4
path: multilingual_librispeech-german_test/env_noise_musan.4-*
- split: env_noise_wham.4
path: multilingual_librispeech-german_test/env_noise_wham.4-*
- split: speedup.4
path: multilingual_librispeech-german_test/speedup.4-*
- split: slowdown.4
path: multilingual_librispeech-german_test/slowdown.4-*
- split: pitch_up.4
path: multilingual_librispeech-german_test/pitch_up.4-*
- split: pitch_down.4
path: multilingual_librispeech-german_test/pitch_down.4-*
- split: rir.4
path: multilingual_librispeech-german_test/rir.4-*
- split: real_rir.4
path: multilingual_librispeech-german_test/real_rir.4-*
- split: resample.4
path: multilingual_librispeech-german_test/resample.4-*
- split: gain.4
path: multilingual_librispeech-german_test/gain.4-*
- split: echo.4
path: multilingual_librispeech-german_test/echo.4-*
- split: phaser.4
path: multilingual_librispeech-german_test/phaser.4-*
- split: tempo_up.4
path: multilingual_librispeech-german_test/tempo_up.4-*
- split: tempo_down.4
path: multilingual_librispeech-german_test/tempo_down.4-*
- split: lowpass.4
path: multilingual_librispeech-german_test/lowpass.4-*
- split: highpass.4
path: multilingual_librispeech-german_test/highpass.4-*
- split: music.4
path: multilingual_librispeech-german_test/music.4-*
- split: crosstalk.4
path: multilingual_librispeech-german_test/crosstalk.4-*
- split: tremolo.4
path: multilingual_librispeech-german_test/tremolo.4-*
- split: treble.4
path: multilingual_librispeech-german_test/treble.4-*
- split: bass.4
path: multilingual_librispeech-german_test/bass.4-*
- split: chorus.4
path: multilingual_librispeech-german_test/chorus.4-*
- config_name: multilingual_librispeech-spanish_test
data_files:
- split: None.0
path: multilingual_librispeech-spanish_test/None.0-*
- split: gnoise.1
path: multilingual_librispeech-spanish_test/gnoise.1-*
- split: gnoise.2
path: multilingual_librispeech-spanish_test/gnoise.2-*
- split: gnoise.3
path: multilingual_librispeech-spanish_test/gnoise.3-*
- split: gnoise.4
path: multilingual_librispeech-spanish_test/gnoise.4-*
- split: env_noise.1
path: multilingual_librispeech-spanish_test/env_noise.1-*
- split: env_noise.2
path: multilingual_librispeech-spanish_test/env_noise.2-*
- split: env_noise.3
path: multilingual_librispeech-spanish_test/env_noise.3-*
- split: env_noise.4
path: multilingual_librispeech-spanish_test/env_noise.4-*
- split: rir.1
path: multilingual_librispeech-spanish_test/rir.1-*
- split: rir.2
path: multilingual_librispeech-spanish_test/rir.2-*
- split: rir.3
path: multilingual_librispeech-spanish_test/rir.3-*
- split: rir.4
path: multilingual_librispeech-spanish_test/rir.4-*
- split: speedup.1
path: multilingual_librispeech-spanish_test/speedup.1-*
- split: speedup.2
path: multilingual_librispeech-spanish_test/speedup.2-*
- split: speedup.3
path: multilingual_librispeech-spanish_test/speedup.3-*
- split: speedup.4
path: multilingual_librispeech-spanish_test/speedup.4-*
- split: slowdown.1
path: multilingual_librispeech-spanish_test/slowdown.1-*
- split: slowdown.2
path: multilingual_librispeech-spanish_test/slowdown.2-*
- split: slowdown.3
path: multilingual_librispeech-spanish_test/slowdown.3-*
- split: slowdown.4
path: multilingual_librispeech-spanish_test/slowdown.4-*
- split: pitch_up.3
path: multilingual_librispeech-spanish_test/pitch_up.3-*
- split: pitch_up.4
path: multilingual_librispeech-spanish_test/pitch_up.4-*
- split: pitch_down.1
path: multilingual_librispeech-spanish_test/pitch_down.1-*
- split: pitch_down.2
path: multilingual_librispeech-spanish_test/pitch_down.2-*
- split: pitch_down.3
path: multilingual_librispeech-spanish_test/pitch_down.3-*
- split: pitch_down.4
path: multilingual_librispeech-spanish_test/pitch_down.4-*
- split: pitch_up.1
path: multilingual_librispeech-spanish_test/pitch_up.1-*
- split: pitch_up.2
path: multilingual_librispeech-spanish_test/pitch_up.2-*
- split: resample.2
path: multilingual_librispeech-spanish_test/resample.2-*
- split: resample.3
path: multilingual_librispeech-spanish_test/resample.3-*
- split: resample.4
path: multilingual_librispeech-spanish_test/resample.4-*
- split: env_noise_esc50.1
path: multilingual_librispeech-spanish_test/env_noise_esc50.1-*
- split: env_noise_esc50.2
path: multilingual_librispeech-spanish_test/env_noise_esc50.2-*
- split: env_noise_esc50.3
path: multilingual_librispeech-spanish_test/env_noise_esc50.3-*
- split: env_noise_esc50.4
path: multilingual_librispeech-spanish_test/env_noise_esc50.4-*
- split: resample.1
path: multilingual_librispeech-spanish_test/resample.1-*
- split: gain.1
path: multilingual_librispeech-spanish_test/gain.1-*
- split: gain.2
path: multilingual_librispeech-spanish_test/gain.2-*
- split: gain.3
path: multilingual_librispeech-spanish_test/gain.3-*
- split: gain.4
path: multilingual_librispeech-spanish_test/gain.4-*
- split: echo.4
path: multilingual_librispeech-spanish_test/echo.4-*
- split: echo.1
path: multilingual_librispeech-spanish_test/echo.1-*
- split: echo.2
path: multilingual_librispeech-spanish_test/echo.2-*
- split: echo.3
path: multilingual_librispeech-spanish_test/echo.3-*
- split: tempo_up.1
path: multilingual_librispeech-spanish_test/tempo_up.1-*
- split: tempo_up.2
path: multilingual_librispeech-spanish_test/tempo_up.2-*
- split: tempo_up.3
path: multilingual_librispeech-spanish_test/tempo_up.3-*
- split: tempo_up.4
path: multilingual_librispeech-spanish_test/tempo_up.4-*
- split: tempo_down.1
path: multilingual_librispeech-spanish_test/tempo_down.1-*
- split: tempo_down.2
path: multilingual_librispeech-spanish_test/tempo_down.2-*
- split: tempo_down.3
path: multilingual_librispeech-spanish_test/tempo_down.3-*
- split: tempo_down.4
path: multilingual_librispeech-spanish_test/tempo_down.4-*
- split: lowpass.1
path: multilingual_librispeech-spanish_test/lowpass.1-*
- split: lowpass.2
path: multilingual_librispeech-spanish_test/lowpass.2-*
- split: lowpass.3
path: multilingual_librispeech-spanish_test/lowpass.3-*
- split: lowpass.4
path: multilingual_librispeech-spanish_test/lowpass.4-*
- split: highpass.1
path: multilingual_librispeech-spanish_test/highpass.1-*
- split: highpass.2
path: multilingual_librispeech-spanish_test/highpass.2-*
- split: highpass.3
path: multilingual_librispeech-spanish_test/highpass.3-*
- split: highpass.4
path: multilingual_librispeech-spanish_test/highpass.4-*
- split: phaser.1
path: multilingual_librispeech-spanish_test/phaser.1-*
- split: phaser.2
path: multilingual_librispeech-spanish_test/phaser.2-*
- split: phaser.3
path: multilingual_librispeech-spanish_test/phaser.3-*
- split: phaser.4
path: multilingual_librispeech-spanish_test/phaser.4-*
- split: env_noise_musan.1
path: multilingual_librispeech-spanish_test/env_noise_musan.1-*
- split: env_noise_musan.2
path: multilingual_librispeech-spanish_test/env_noise_musan.2-*
- split: env_noise_musan.3
path: multilingual_librispeech-spanish_test/env_noise_musan.3-*
- split: env_noise_musan.4
path: multilingual_librispeech-spanish_test/env_noise_musan.4-*
- split: music.1
path: multilingual_librispeech-spanish_test/music.1-*
- split: music.2
path: multilingual_librispeech-spanish_test/music.2-*
- split: music.3
path: multilingual_librispeech-spanish_test/music.3-*
- split: music.4
path: multilingual_librispeech-spanish_test/music.4-*
- split: crosstalk.1
path: multilingual_librispeech-spanish_test/crosstalk.1-*
- split: crosstalk.2
path: multilingual_librispeech-spanish_test/crosstalk.2-*
- split: crosstalk.3
path: multilingual_librispeech-spanish_test/crosstalk.3-*
- split: crosstalk.4
path: multilingual_librispeech-spanish_test/crosstalk.4-*
- split: env_noise_wham.1
path: multilingual_librispeech-spanish_test/env_noise_wham.1-*
- split: env_noise_wham.2
path: multilingual_librispeech-spanish_test/env_noise_wham.2-*
- split: env_noise_wham.3
path: multilingual_librispeech-spanish_test/env_noise_wham.3-*
- split: env_noise_wham.4
path: multilingual_librispeech-spanish_test/env_noise_wham.4-*
- split: tremolo.1
path: multilingual_librispeech-spanish_test/tremolo.1-*
- split: tremolo.2
path: multilingual_librispeech-spanish_test/tremolo.2-*
- split: tremolo.4
path: multilingual_librispeech-spanish_test/tremolo.4-*
- split: treble.1
path: multilingual_librispeech-spanish_test/treble.1-*
- split: treble.2
path: multilingual_librispeech-spanish_test/treble.2-*
- split: treble.3
path: multilingual_librispeech-spanish_test/treble.3-*
- split: treble.4
path: multilingual_librispeech-spanish_test/treble.4-*
- split: bass.1
path: multilingual_librispeech-spanish_test/bass.1-*
- split: bass.2
path: multilingual_librispeech-spanish_test/bass.2-*
- split: bass.3
path: multilingual_librispeech-spanish_test/bass.3-*
- split: bass.4
path: multilingual_librispeech-spanish_test/bass.4-*
- split: chorus.1
path: multilingual_librispeech-spanish_test/chorus.1-*
- split: chorus.2
path: multilingual_librispeech-spanish_test/chorus.2-*
- split: chorus.3
path: multilingual_librispeech-spanish_test/chorus.3-*
- split: chorus.4
path: multilingual_librispeech-spanish_test/chorus.4-*
- split: tremolo.3
path: multilingual_librispeech-spanish_test/tremolo.3-*
- split: voice_conversion_bark.1
path: multilingual_librispeech-spanish_test/voice_conversion_bark.1-*
- config_name: multilingual_librispeech-spanish_test_pertEval_500_30
data_files:
- split: gnoise.1
path: multilingual_librispeech-spanish_test_pertEval_500_30/gnoise.1-*
- split: env_noise_esc50.1
path: multilingual_librispeech-spanish_test_pertEval_500_30/env_noise_esc50.1-*
- config_name: tedlium-release3_test
data_files:
- split: gnoise.1
path: tedlium-release3_test/gnoise.1-*
- split: gnoise.2
path: tedlium-release3_test/gnoise.2-*
- split: gnoise.3
path: tedlium-release3_test/gnoise.3-*
- split: env_noise_esc50.1
path: tedlium-release3_test/env_noise_esc50.1-*
- split: env_noise_esc50.2
path: tedlium-release3_test/env_noise_esc50.2-*
- split: env_noise_esc50.3
path: tedlium-release3_test/env_noise_esc50.3-*
- split: speedup.1
path: tedlium-release3_test/speedup.1-*
- split: speedup.2
path: tedlium-release3_test/speedup.2-*
- split: speedup.3
path: tedlium-release3_test/speedup.3-*
- split: slowdown.1
path: tedlium-release3_test/slowdown.1-*
- split: slowdown.2
path: tedlium-release3_test/slowdown.2-*
- split: slowdown.3
path: tedlium-release3_test/slowdown.3-*
- split: pitch_up.1
path: tedlium-release3_test/pitch_up.1-*
- split: pitch_up.2
path: tedlium-release3_test/pitch_up.2-*
- split: pitch_up.3
path: tedlium-release3_test/pitch_up.3-*
- split: pitch_down.1
path: tedlium-release3_test/pitch_down.1-*
- split: pitch_down.2
path: tedlium-release3_test/pitch_down.2-*
- split: pitch_down.3
path: tedlium-release3_test/pitch_down.3-*
- split: rir.1
path: tedlium-release3_test/rir.1-*
- split: rir.2
path: tedlium-release3_test/rir.2-*
- split: rir.3
path: tedlium-release3_test/rir.3-*
- split: voice_conversion_vctk.1
path: tedlium-release3_test/voice_conversion_vctk.1-*
- split: resample.1
path: tedlium-release3_test/resample.1-*
- split: resample.2
path: tedlium-release3_test/resample.2-*
- split: resample.3
path: tedlium-release3_test/resample.3-*
- split: gain.1
path: tedlium-release3_test/gain.1-*
- split: gain.2
path: tedlium-release3_test/gain.2-*
- split: gain.3
path: tedlium-release3_test/gain.3-*
- split: echo.1
path: tedlium-release3_test/echo.1-*
- split: echo.2
path: tedlium-release3_test/echo.2-*
- split: echo.3
path: tedlium-release3_test/echo.3-*
- split: phaser.1
path: tedlium-release3_test/phaser.1-*
- split: phaser.2
path: tedlium-release3_test/phaser.2-*
- split: phaser.3
path: tedlium-release3_test/phaser.3-*
- split: tempo_up.1
path: tedlium-release3_test/tempo_up.1-*
- split: tempo_up.2
path: tedlium-release3_test/tempo_up.2-*
- split: tempo_up.3
path: tedlium-release3_test/tempo_up.3-*
- split: tempo_down.1
path: tedlium-release3_test/tempo_down.1-*
- split: tempo_down.2
path: tedlium-release3_test/tempo_down.2-*
- split: tempo_down.3
path: tedlium-release3_test/tempo_down.3-*
- split: lowpass.1
path: tedlium-release3_test/lowpass.1-*
- split: lowpass.2
path: tedlium-release3_test/lowpass.2-*
- split: lowpass.3
path: tedlium-release3_test/lowpass.3-*
- split: highpass.1
path: tedlium-release3_test/highpass.1-*
- split: highpass.2
path: tedlium-release3_test/highpass.2-*
- split: highpass.3
path: tedlium-release3_test/highpass.3-*
- split: gnoise.4
path: tedlium-release3_test/gnoise.4-*
- split: env_noise_esc50.4
path: tedlium-release3_test/env_noise_esc50.4-*
- split: speedup.4
path: tedlium-release3_test/speedup.4-*
- split: slowdown.4
path: tedlium-release3_test/slowdown.4-*
- split: pitch_up.4
path: tedlium-release3_test/pitch_up.4-*
- split: pitch_down.4
path: tedlium-release3_test/pitch_down.4-*
- split: rir.4
path: tedlium-release3_test/rir.4-*
- split: resample.4
path: tedlium-release3_test/resample.4-*
- split: gain.4
path: tedlium-release3_test/gain.4-*
- split: echo.4
path: tedlium-release3_test/echo.4-*
- split: phaser.4
path: tedlium-release3_test/phaser.4-*
- split: tempo_up.4
path: tedlium-release3_test/tempo_up.4-*
- split: tempo_down.4
path: tedlium-release3_test/tempo_down.4-*
- split: lowpass.4
path: tedlium-release3_test/lowpass.4-*
- split: highpass.4
path: tedlium-release3_test/highpass.4-*
- split: None.0
path: tedlium-release3_test/None.0-*
- split: music.1
path: tedlium-release3_test/music.1-*
- split: music.2
path: tedlium-release3_test/music.2-*
- split: music.3
path: tedlium-release3_test/music.3-*
- split: music.4
path: tedlium-release3_test/music.4-*
- split: crosstalk.1
path: tedlium-release3_test/crosstalk.1-*
- split: crosstalk.2
path: tedlium-release3_test/crosstalk.2-*
- split: crosstalk.3
path: tedlium-release3_test/crosstalk.3-*
- split: crosstalk.4
path: tedlium-release3_test/crosstalk.4-*
- split: env_noise_musan.1
path: tedlium-release3_test/env_noise_musan.1-*
- split: env_noise_musan.2
path: tedlium-release3_test/env_noise_musan.2-*
- split: env_noise_musan.3
path: tedlium-release3_test/env_noise_musan.3-*
- split: env_noise_musan.4
path: tedlium-release3_test/env_noise_musan.4-*
- split: real_rir.1
path: tedlium-release3_test/real_rir.1-*
- split: real_rir.2
path: tedlium-release3_test/real_rir.2-*
- split: real_rir.3
path: tedlium-release3_test/real_rir.3-*
- split: real_rir.4
path: tedlium-release3_test/real_rir.4-*
- split: env_noise.1
path: tedlium-release3_test/env_noise.1-*
- split: env_noise.2
path: tedlium-release3_test/env_noise.2-*
- split: env_noise.3
path: tedlium-release3_test/env_noise.3-*
- split: env_noise.4
path: tedlium-release3_test/env_noise.4-*
- split: env_noise_wham.1
path: tedlium-release3_test/env_noise_wham.1-*
- split: env_noise_wham.2
path: tedlium-release3_test/env_noise_wham.2-*
- split: env_noise_wham.3
path: tedlium-release3_test/env_noise_wham.3-*
- split: env_noise_wham.4
path: tedlium-release3_test/env_noise_wham.4-*
- split: tremolo.1
path: tedlium-release3_test/tremolo.1-*
- split: tremolo.2
path: tedlium-release3_test/tremolo.2-*
- split: tremolo.3
path: tedlium-release3_test/tremolo.3-*
- split: tremolo.4
path: tedlium-release3_test/tremolo.4-*
- split: treble.1
path: tedlium-release3_test/treble.1-*
- split: treble.2
path: tedlium-release3_test/treble.2-*
- split: treble.3
path: tedlium-release3_test/treble.3-*
- split: treble.4
path: tedlium-release3_test/treble.4-*
- split: bass.1
path: tedlium-release3_test/bass.1-*
- split: bass.2
path: tedlium-release3_test/bass.2-*
- split: bass.3
path: tedlium-release3_test/bass.3-*
- split: bass.4
path: tedlium-release3_test/bass.4-*
- split: chorus.1
path: tedlium-release3_test/chorus.1-*
- split: chorus.2
path: tedlium-release3_test/chorus.2-*
- split: chorus.4
path: tedlium-release3_test/chorus.4-*
- split: chorus.3
path: tedlium-release3_test/chorus.3-*
---
# Dataset Card for "speech_robust_bench"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Forceless/PPTAgent | Forceless | "2024-10-20T05:51:45Z" | 11,581 | 3 | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:image",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-10-18T04:49:53Z" | ---
dataset_info:
features:
- name: filename
dtype: string
- name: size
dtype: int64
- name: url
dtype: string
- name: license
dtype: string
- name: title
dtype: string
- name: created
dtype: string
- name: updated
dtype: string
- name: doi
dtype: string
- name: checksum
dtype: string
- name: page
dtype: int64
- name: topic
dtype: string
- name: filetype
dtype: string
splits:
- name: pptx
num_bytes: 317828
num_examples: 761
- name: pdf
num_bytes: 253893
num_examples: 603
download_size: 249178
dataset_size: 571721
configs:
- config_name: default
data_files:
- split: pptx
path: data/pptx-*
- split: pdf
path: data/pdf-*
---
|
andstor/the_pile_github | andstor | "2023-03-20T23:39:53Z" | 11,525 | 8 | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_categories:text-classification",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:other",
"size_categories:10M<n<100M",
"modality:text",
"library:datasets",
"library:mlcroissant",
"arxiv:2101.00027",
"arxiv:2201.07311",
"region:us"
] | [
"text-generation",
"fill-mask",
"text-classification"
] | "2023-03-07T15:53:05Z" | ---
annotations_creators:
- no-annotation
language:
- en
language_creators:
- found
license:
- other
multilinguality:
- monolingual
pretty_name: The Pile GitHub
size_categories: []
source_datasets:
- original
tags: []
task_categories:
- text-generation
- fill-mask
- text-classification
task_ids: []
---
# Dataset Card for The Pile GitHub
## Table of Contents
- [Dataset Card for Smart Contracts](#dataset-card-for-the-pile-github)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [ElutherAI](https://pile.eleuther.ai)
- **Repository:** [GitHub](https://github.com/andstor/the-pile-github)
- **Paper:** [arXiv](https://arxiv.org/abs/2101.00027)
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
This is the GitHub subset of EleutherAi/The Pile dataset and contains GitHub repositories. The programming languages are identified using the [guesslang library](https://github.com/yoeo/guesslang). A total of 54 programming languages are included in the dataset.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The following languages are covered by the dataset:
```
'Assembly', 'Batchfile', 'C', 'C#', 'C++', 'CMake', 'COBOL', 'CSS', 'CSV', 'Clojure', 'CoffeeScript', 'DM', 'Dart', 'Dockerfile', 'Elixir', 'Erlang', 'Fortran', 'Go', 'Groovy', 'HTML', 'Haskell', 'INI', 'JSON', 'Java', 'JavaScript', 'Julia', 'Kotlin', 'Lisp', 'Lua', 'Makefile', 'Markdown', 'Matlab', 'None', 'OCaml', 'Objective-C', 'PHP', 'Pascal', 'Perl', 'PowerShell', 'Prolog', 'Python', 'R', 'Ruby', 'Rust', 'SQL', 'Scala', 'Shell', 'Swift', 'TOML', 'TeX', 'TypeScript', 'Verilog', 'Visual Basic', 'XML', 'YAML'
```
The [guesslang library](https://github.com/yoeo/guesslang) is used to identify the programming languages. It has a guessing accuracy of above 90%. Hence, there will be some misclassifications in the language identification.
## Dataset Structure
### Data Instances
[More Information Needed]
```
{
'text': ...,
'meta': {'language': ...}
}
```
### Data Fields
- `text` (`string`): the source code.
- `meta` (`dict`): the metadata of the source code.
- `language` (`string`): the programming language of the source code.
### Data Splits
[More Information Needed]
| | train | validation | test |
|-------------------------|------:|-----------:|-----:|
| Input Sentences | | | |
| Average Sentence Length | | | |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
The data is purely a subset of the [EleutherAI/The Pile dataset](https://huggingface.co/datasets/the_pile). See the original [dataset](https://arxiv.org/abs/2201.07311) for more details.
## Additional Information
### Licensing Information
The Pile dataset was released on January 1st, 2021. It is licensed under the MIT License. See the [dataset](https://arxiv.org/abs/2201.07311) for more details.
### Citation Information
Provide the [BibTex](http://www.bibtex.org/)-formatted reference for the dataset. For example:
```
@article{pile,
title={The {P}ile: An 800GB Dataset of Diverse Text for Language Modeling},
author={Gao, Leo and Biderman, Stella and Black, Sid and Golding, Laurence and Hoppe, Travis and Foster, Charles and Phang, Jason and He, Horace and Thite, Anish and Nabeshima, Noa and Presser, Shawn and Leahy, Connor},
journal={arXiv preprint arXiv:2101.00027},
year={2020}
}
```
### Contributions
Thanks to [@andstor](https://github.com/andstor) for adding this dataset. |
FrancophonIA/UFAL_Parallel_Corpus_of_North_Levantine_1.0 | FrancophonIA | "2024-10-31T19:11:18Z" | 11,513 | 0 | [
"multilinguality:multilingual",
"language:en",
"language:fr",
"language:arb",
"language:de",
"language:el",
"language:es",
"language:apc",
"license:cc-by-nc-sa-4.0",
"region:us"
] | null | "2024-10-31T18:59:32Z" | ---
language:
- en
- fr
- arb
- de
- el
- es
- apc
multilinguality:
- multilingual
license: cc-by-nc-sa-4.0
configs:
- config_name: apc
data_files:
- split: train
path: "apc.txt"
- config_name: arb
data_files:
- split: train
path: "arb.txt"
- config_name: arb-eng
data_files:
- split: train
path: "arb-eng.txt"
- config_name: deu
data_files:
- split: train
path: "deu.txt"
- config_name: deu-eng
data_files:
- split: train
path: "deu-eng.txt"
- config_name: ell
data_files:
- split: train
path: "ell.txt"
- config_name: ell-eng
data_files:
- split: train
path: "ell-eng.txt"
- config_name: ar_AR
data_files:
- split: eng
path: "eng.txt"
- config_name: ar_AR
data_files:
- split: eng-fra
path: "eng-fra.txt"
- config_name: eng-spa
data_files:
- split: train
path: "eng-spa.txt"
- config_name: fra
data_files:
- split: train
path: "fra.txt"
- config_name: spa
data_files:
- split: train
path: "spa.txt"
---
> [!NOTE]
> Dataset origin: https://zenodo.org/records/4012218
# UFAL Parallel Corpus of North Levantine 1.0
March 10, 2023
## Authors
Shadi Saleh <[[email protected]](mailto:[email protected])>
Hashem Sellat <[[email protected]](mailto:[email protected])>
Mateusz Krubiński <[[email protected]](mailto:[email protected])>
Adam Posppíšil <[[email protected]](mailto:[email protected])>
Petr Zemánek <[[email protected]](mailto:[email protected])>
Pavel Pecina <[[email protected]](mailto:[email protected])>
## Overview
This is the first release of the UFAL Parallel Corpus of North Levantine, compiled by the Institute of Formal and Applied Linguistics (ÚFAL) at Charles University within the Welcome project (https://welcome-h2020.eu/). The corpus consists of 120,600 multiparallel sentences in English, French, German, Greek, Spanish, and Standard Arabic selected from the OpenSubtitles2018 corpus [1] and manually translated into the North Levantine Arabic language. The corpus was created for the purpose of training machine translation for North Levantine and the other languages.
## Data processing
In OpenSubtitles2018, we identified 3,661,627 sentences in English that were aligned with their translations in all of the following languages: arb, fra, deu, ell, spa, and filtered out those that matched any of the following conditions:
- presence of non-standard characters in the English side (only English alphabet, numbers and the following characters allowed: .!?,:; '$%£€) to reduce noise
- non-capital first letter in the English side (to avoid incomplete sentences)
- presence of less than two infrequent words (to increase lexical richness)
- presence of vulgar words in the English side
Then, we removed exact and near duplicates (detected in the English side) and sampled a subset of approximately 1 million words in the English side. This resulted in 120,771 multiparallel sentences with an average length of 8.28 words per sentence in the English side.
The sentences in Standard Arabic were then manually translated to North Levantine Arabic by native speakers. A few erroneous translations were automatically detected (e.g. empty or unfinished translations) and discarded. The remaining translations were aligned with the other languages through Standard Arabic and English. The final corpus comprises 120,600 sentences in English, Spanish, Greek, German, French, Standard Arabic, and the newly added North Levantine Arabic. The table below shows some overall statistics. The languages of the data files are denoted by their ISO 639-3 codes.
| language | ISO 639-3 code | #words |
|:----------------------:|:---------------:|:-------:|
| North Levantine Arabic | apc | 738,812 |
| Standard Arabic | arb | 802,313 |
| German | deu | 940,234 |
| Greek | ell | 869,543 |
| English | eng | 999,193 |
| French | fra | 956,208 |
| Spanish | spa | 920,922 |
The translations are provided in seven files, each file contains data in one language. The files aligned through the line numbers; the order of lines is random. We provide linking of the English-centred sentence pairs to the original data in OpenSubtitles2018. This information is stored in the *.ids files that are aligned through the line numbers with the corresponding translations. Each line contains tab-separated items: the source filename, the target filename, space-separated positions of the source sentence in the source file, space-separated positions of the target sentence in the target file.
## References
[1] Pierre Lison, Jörg Tiedemann, and Milen Kouylekov. 2018. OpenSubtitles2018: Statistical Rescoring of Sentence Alignments in Large, Noisy Parallel Corpora. Proceedings of the Eleventh International Conference on Language Resources and Evaluation, pages 1742–1748. Miyazaki, Japan.
## Acknowledgement
The work was supported by the European Commission via the H2020 Program, project WELCOME, grant agreement: 870930.
## Citation
```
@misc{11234/1-5033,
title = {{UFAL} Parallel Corpus of North Levantine 1.0},
author = {Sellat, Hashem and Saleh, Shadi and Krubi{\'n}ski, Mateusz and Posp{\'{\i}}{\v s}il, Adam and Zem{\'a}nek, Petr and Pecina, Pavel},
url = {http://hdl.handle.net/11234/1-5033},
note = {{LINDAT}/{CLARIAH}-{CZ} digital library at the Institute of Formal and Applied Linguistics ({{\'U}FAL}), Faculty of Mathematics and Physics, Charles University},
copyright = {Creative Commons - Attribution-{NonCommercial}-{ShareAlike} 4.0 International ({CC} {BY}-{NC}-{SA} 4.0)},
year = {2023} }``` |
Jiayi-Pan/Countdown-Tasks-3to4 | Jiayi-Pan | "2025-01-23T00:56:52Z" | 11,507 | 49 | [
"size_categories:100K<n<1M",
"format:parquet",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2025-01-23T00:56:50Z" | ---
dataset_info:
features:
- name: target
dtype: int64
- name: nums
sequence: int64
splits:
- name: train
num_bytes: 19650960
num_examples: 490364
download_size: 2845904
dataset_size: 19650960
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
proj-persona/PersonaHub | proj-persona | "2025-03-04T22:01:42Z" | 11,473 | 549 | [
"task_categories:text-generation",
"task_categories:text-classification",
"task_categories:token-classification",
"task_categories:fill-mask",
"task_categories:table-question-answering",
"task_categories:text2text-generation",
"language:en",
"language:zh",
"license:cc-by-nc-sa-4.0",
"size_categories:100K<n<1M",
"format:json",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2406.20094",
"region:us",
"synthetic",
"text",
"math",
"reasoning",
"instruction",
"tool"
] | [
"text-generation",
"text-classification",
"token-classification",
"fill-mask",
"table-question-answering",
"text2text-generation"
] | "2024-06-28T16:35:21Z" | ---
license: cc-by-nc-sa-4.0
task_categories:
- text-generation
- text-classification
- token-classification
- fill-mask
- table-question-answering
- text2text-generation
language:
- en
- zh
tags:
- synthetic
- text
- math
- reasoning
- instruction
- tool
size_categories:
- 100M<n<1B
configs:
- config_name: math
data_files: math.jsonl
- config_name: instruction
data_files: instruction.jsonl
- config_name: reasoning
data_files: reasoning.jsonl
- config_name: knowledge
data_files: knowledge.jsonl
- config_name: npc
data_files: npc.jsonl
- config_name: tool
data_files: tool.jsonl
- config_name: persona
data_files: persona.jsonl
- config_name: elite_persona
data_files:
- split: train
path: ElitePersonas/*
---
# Scaling Synthetic Data Creation with 1,000,000,000 Personas
This repo releases data introduced in our paper [Scaling Synthetic Data Creation with 1,000,000,000 Personas](https://arxiv.org/pdf/2406.20094):
We propose a novel persona-driven data synthesis methodology that leverages various perspectives within a large language model (LLM) to create diverse synthetic data. To fully exploit this methodology at scale, we introduce **PERSONA HUB** – a collection of **1 billion diverse personas** automatically curated from web data. These 1 billion personas (~13% of the world's total population), acting as distributed carriers of world knowledge, can tap into almost every perspective encapsulated within the LLM, thereby facilitating the creation of diverse synthetic data at scale for various scenarios. By showcasing PERSONA HUB’s use cases in synthesizing high-quality **mathematical and logical reasoning** problems, **instructions** (i.e., user prompts), **knowledge-rich texts**, **game NPCs** and **tools** (functions) at scale, we demonstrate persona-driven data synthesis is versatile, scalable, flexible, and easy to use, potentially driving a paradigm shift in synthetic data creation and applications in practice, which may have a profound impact on LLM research and development.
<div align="center">
<img src="./assets/persona_overview.png" width="90%">
</div>
## Data Release
### Synthetic Data Samples
To facilitate research in persona-driven data synthesis, we are initially releasing following synthetic data samples we created with various personas, including:
* **50,000 math problems**
* **50,000 logical reasoning problems**
* **50,000 instructions**
* **10,000 knowledge-rich texts**
* **10,000 game NPCs**
* **5,000 tools (functions)**
### Persona Hub
We also release a subset of our PERSONA HUB, including:
* **200,000 personas (early preview)**
* **370,000,000 elite personas (added in Feb 2025)**
## Run Demo
One can try the demo to synthesize data with PERSONA HUB simply by running code in https://github.com/tencent-ailab/persona-hub:
```bash
# ensure that you have installed datasets and openai (pip install datasets openai) and configured the openai_api_key before running
bash demo_openai_synthesize.sh # using gpt4o to synthesize data with PERSONA HUB
```
or
```bash
# ensure that you have installed datasets, transformers and vllm (pip install datasets transformers vllm) before running
bash demo_vllm_synthesize.sh # using open-sourced models to synthesize data with PERSONA HUB
```
Note that the data synthesis prompt templates we provide are for reference only. You can customize your desired prompts in `code/prompt_templates.py`.
## Argilla
You can also access this dataset in [Argilla space](https://argilla-data-explorers.hf.space/), as introduced in the following video:
* Video: https://youtu.be/timmCn8Nr6g?feature=shared
## Contact
* Please email `[email protected]` or `[email protected]`
* Github page: https://github.com/tencent-ailab/persona-hub
## Disclaimer
PERSONA HUB can facilitate synthetic data creation at a billion-scale to simulate diverse inputs (i.e., use cases) from a wide variety of real-world users. If this data is used as input to query a target LLM to obtain its outputs at scale, there is a high risk that the LLM's knowledge, intelligence and capabilities will be dumped and easily replicated, thereby challenging the leading position of the most powerful LLMs. It is crucial to avoid misuse and ensure ethical and responsible application to prevent privacy violations and other ethical concerns.
The released data is all generated by public available models (GPT-4, Llama-3 and Qwen), and is intended for research purposes only. Users also must comply with the respective license agreements and usage policies of these models when using the synthesized data. The data may contain inaccuracies, unsafe content, or biases, for which we cannot be held responsible. Please evaluate its accuracy and suitability before use. Tencent and its licensors provide the data AS-IS, without warranty of any kind, express or implied. The views and opinions expressed in the data do not necessarily reflect those of Tencent. |
HuggingFaceM4/Docmatix | HuggingFaceM4 | "2024-08-26T08:15:21Z" | 11,465 | 259 | [
"task_categories:visual-question-answering",
"language:en",
"license:mit",
"size_categories:1M<n<10M",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2408.12637",
"region:us",
"docvqa"
] | [
"visual-question-answering"
] | "2024-07-17T11:33:00Z" | ---
language:
- en
license: mit
size_categories:
- 1M<n<10M
task_categories:
- visual-question-answering
pretty_name: Docmatix
tags:
- docvqa
configs:
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data_files:
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path: data/train-*
- config_name: pdf
data_files:
- split: train
path: pdf/train-*
- config_name: zero-shot-exp
data_files:
- split: train
path: zero-shot-exp/train-*
- split: test
path: zero-shot-exp/test-*
dataset_info:
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sequence: image
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dtype: string
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dtype: string
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download_size: 642963847
dataset_size: 647235943.5
---
# Dataset Card for Docmatix
## Dataset description
Docmatix is part of the Idefics3 release (stay tuned).
It is a massive dataset for Document Visual Question Answering that was used for the fine-tuning of the vision-language model Idefics3.
## Load the dataset
To load the dataset, install the library `datasets` with `pip install datasets`. Then,
```
from datasets import load_dataset
ds = load_dataset("HuggingFaceM4/Docmatix")
```
If you want the dataset to link to the pdf files as binaries instead of the images, do:
```
from datasets import load_dataset
ds = load_dataset("HuggingFaceM4/Docmatix", "pdf")
```
## Data fields
An example of a sample looks as follows:
```
{
"images" = [PIL.Image]
"texts" = [
{ "user": "What is the purpose of the Confirmation Statement mentioned in the document?",
"assistant": "The purpose of the Confirmation Statement is to confirm that all information required to be delivered by the company to the registrar in relation to the confirmation period concerned has been delivered or is being delivered at the same time as the confirmation statement.",
"source": "PDFA key: 244" },
{ "user": "When was the filing received as per the document?",
"assistant": "The filing was received for filing in Electronic Format on the 23/03/2021.",
"source": "PDFA key: 244" },
]
}
```
In `images`, there is a list of up to 4 images, to be placed before the text.
In `texts`, there is a conversation between a user and an assistant about the images that is represented by a list of turns.
## Comparison to other DocVQA datasets
| Dataset | # images | # Q/A pairs | # tokens |
|----------------------|----------|-------------|------------|
| *Document visual question answering* |
| **Docmatix** | **2,444,750**| **9,500,000** | **390,000,000**|
| DocVQA | 10,189 | 39,463 | 337,829 |
| TextCaps | 21,953 | 21,953 | 389,658 |
| TextVQA | 21,953 | 34,602 | 181,918 |
| ST-VQA | 17,247 | 23,121 | 127,846 |
| OCR-VQA | 165,746 | 801,579 | 6,073,824 |
| VisualMRC | 3,027 | 11,988 | 168,828 |
| IAM | 5,663 | 5,663 | 144,216 |
| InfoVQA | 2,118 | 10,074 | 61,048 |
| Diagram image-to-text| 300 | 300 | 22,196 |
# Citation
**BibTeX:**
```bibtex
@misc{laurençon2024building,
title={Building and better understanding vision-language models: insights and future directions.},
author={Hugo Laurençon and Andrés Marafioti and Victor Sanh and Léo Tronchon},
year={2024},
eprint={2408.12637},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
|
mteb/sts12-sts | mteb | "2022-09-27T19:11:50Z" | 11,454 | 7 | [
"language:en",
"size_categories:1K<n<10K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2022-04-20T10:47:29Z" | ---
language:
- en
--- |
alvin319/semantic-memorization-partial-2023-09-03 | alvin319 | "2023-09-04T09:39:21Z" | 11,344 | 0 | [
"license:mit",
"size_categories:10M<n<100M",
"format:parquet",
"modality:tabular",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2023-09-04T01:07:54Z" | ---
license: mit
configs:
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---
This dataset is a partial computation of metrics (memorized token frequencies, non-memorized token frequencies, sequence frequencies) needed for [research](https://github.com/EleutherAI/semantic-memorization). |
AmazonScience/MultilingualMultiModalClassification | AmazonScience | "2024-12-06T14:00:39Z" | 11,335 | 2 | [
"license:cc-by-4.0",
"region:us"
] | null | "2023-05-12T20:22:46Z" | ---
license: cc-by-4.0
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data_files:
- split: train
path: wiki-doc-pt-merged/train-*
- split: validation
path: wiki-doc-pt-merged/validation-*
- split: test
path: wiki-doc-pt-merged/test-*
- config_name: wiki-doc-pt-merged-v2
data_files:
- split: train
path: wiki-doc-pt-merged-v2/train-*
- split: validation
path: wiki-doc-pt-merged-v2/validation-*
- split: test
path: wiki-doc-pt-merged-v2/test-*
- config_name: wiki-doc-zh-img
data_files:
- split: train
path: wiki-doc-zh-img/train-*
- split: test
path: wiki-doc-zh-img/test-*
- split: validation
path: wiki-doc-zh-img/validation-*
- config_name: wiki-doc-zh-merged
data_files:
- split: train
path: wiki-doc-zh-merged/train-*
- split: test
path: wiki-doc-zh-merged/test-*
- split: validation
path: wiki-doc-zh-merged/validation-*
---
## Additional Information
To load the dataset,
```
import datasets
ds = datasets.load_dataset("AmazonScience/MultilingualMultiModalClassification", data_dir="wiki-doc-ar-merged")
print(ds)
DatasetDict({
train: Dataset({
features: ['image', 'filename', 'words', 'ocr_bboxes', 'label'],
num_rows: 8129
})
validation: Dataset({
features: ['image', 'filename', 'words', 'ocr_bboxes', 'label'],
num_rows: 1742
})
test: Dataset({
features: ['image', 'filename', 'words', 'ocr_bboxes', 'label'],
num_rows: 1743
})
})
# In case you encountered `NonMatchingSplitsSizesError`, try out the following:
# from datasets import Image, Value, Sequence, ClassLabel, Features
# features = Features({'image': Image(mode=None, decode=True, id=None), 'filename': Value(dtype='string', id=None), 'words': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'ocr_bboxes': Sequence(feature=Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), length=-1, id=None), 'label': ClassLabel(names=['AcademicJournal', 'AdultActor', 'Album', 'AmateurBoxer', 'Ambassador', 'AmericanFootballPlayer', 'Amphibian', 'AnimangaCharacter', 'Anime', 'Arachnid', 'Baronet', 'BasketballTeam', 'BeautyQueen', 'BroadcastNetwork', 'BusCompany', 'BusinessPerson', 'CanadianFootballTeam', 'Canal', 'Cardinal', 'Cave', 'ChristianBishop', 'ClassicalMusicArtist', 'ClassicalMusicComposition', 'CollegeCoach', 'Comedian', 'ComicsCreator', 'Congressman', 'Conifer', 'Convention', 'Cricketer', 'Crustacean', 'CultivatedVariety', 'Cycad', 'Dam', 'Economist', 'Engineer', 'Entomologist', 'EurovisionSongContestEntry', 'Fern', 'FilmFestival', 'Fish', 'FootballMatch', 'Glacier', 'GolfTournament', 'Governor', 'Gymnast', 'Historian', 'IceHockeyLeague', 'Insect', 'Journalist', 'Judge', 'Lighthouse', 'Magazine', 'Mayor', 'Medician', 'MemberOfParliament', 'MilitaryPerson', 'Model', 'Mollusca', 'Monarch', 'Moss', 'Mountain', 'MountainPass', 'MountainRange', 'MusicFestival', 'Musical', 'MythologicalFigure', 'Newspaper', 'Noble', 'OfficeHolder', 'Other', 'Philosopher', 'Photographer', 'PlayboyPlaymate', 'Poem', 'Poet', 'Pope', 'President', 'PrimeMinister', 'PublicTransitSystem', 'Racecourse', 'RadioHost', 'RadioStation', 'Religious', 'Reptile', 'Restaurant', 'Road', 'RoadTunnel', 'RollerCoaster', 'RugbyClub', 'RugbyLeague', 'Saint', 'School', 'ScreenWriter', 'Senator', 'ShoppingMall', 'Skater', 'SoccerLeague', 'SoccerManager', 'SoccerPlayer', 'SoccerTournament', 'SportsTeamMember', 'SumoWrestler', 'TelevisionStation', 'TennisTournament', 'TradeUnion', 'University', 'Village', 'VoiceActor', 'Volcano', 'WrestlingEvent'], id=None)})
# ds = datasets.load_dataset("AmazonScience/MultilingualMultiModalClassification", data_dir="wiki-doc-ar-merged", features=features, verification_mode="no_checks")
```
### Licensing Information
#### Wiki
Each image is licensed under original provider.
Any additional work provided by current work is provided under CC-BY-SA-4.0 following the Wikipedia license.
#### MultiEURLEX
We provide MultiEURLEX with the same licensing as the original EU data (CC-BY-4.0):
© European Union, 1998-2021
The Commission’s document reuse policy is based on Decision 2011/833/EU. Unless otherwise specified, you can re-use the legal documents published in EUR-Lex for commercial or non-commercial purposes.
The copyright for the editorial content of this website, the summaries of EU legislation and the consolidated texts, which is owned by the EU, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made.
Source: https://eur-lex.europa.eu/content/legal-notice/legal-notice.html \
Read more: https://eur-lex.europa.eu/content/help/faq/reuse-contents-eurlex.html
### Citation Information
```
@inproceedings{fujinuma-etal-2023-multi,
title = "A Multi-Modal Multilingual Benchmark for Document Image Classification",
author = "Fujinuma, Yoshinari and
Varia, Siddharth and
Sankaran, Nishant and
Appalaraju, Srikar and
Min, Bonan and
Vyas, Yogarshi",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.958",
doi = "10.18653/v1/2023.findings-emnlp.958",
pages = "14361--14376",
abstract = "Document image classification is different from plain-text document classification and consists of classifying a document by understanding the content and structure of documents such as forms, emails, and other such documents. We show that the only existing dataset for this task (Lewis et al., 2006) has several limitations and we introduce two newly curated multilingual datasets WIKI-DOC and MULTIEURLEX-DOC that overcome these limitations. We further undertake a comprehensive study of popular visually-rich document understanding or Document AI models in previously untested setting in document image classification such as 1) multi-label classification, and 2) zero-shot cross-lingual transfer setup. Experimental results show limitations of multilingual Document AI models on cross-lingual transfer across typologically distant languages. Our datasets and findings open the door for future research into improving Document AI models.",
}
``` |
flax-sentence-embeddings/stackexchange_titlebody_best_and_down_voted_answer_jsonl | flax-sentence-embeddings | "2022-07-11T13:13:18Z" | 11,296 | 11 | [
"task_categories:question-answering",
"task_ids:closed-domain-qa",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"source_datasets:original",
"language:en",
"license:cc-by-nc-sa-4.0",
"size_categories:100K<n<1M",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us"
] | [
"question-answering"
] | "2022-03-02T23:29:22Z" | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- cc-by-nc-sa-4.0
multilinguality:
- multilingual
pretty_name: stackexchange
size_categories:
- unknown
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- closed-domain-qa
---
# Dataset Card Creation Guide
## Table of Contents
- [Dataset Card Creation Guide](#dataset-card-creation-guide)
- [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)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)s
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [stackexchange](https://archive.org/details/stackexchange)
- **Repository:** [flax-sentence-embeddings](https://github.com/nreimers/flax-sentence-embeddings)
### Dataset Summary
We automatically extracted question and answer (Q&A) pairs from [Stack Exchange](https://stackexchange.com/) network. Stack Exchange gather many Q&A communities across 50 online plateform, including the well known Stack Overflow and other technical sites. 100 millon developpers consult Stack Exchange every month. The dataset is a parallel corpus with each question mapped to the top rated answer. The dataset is split given communities which cover a variety of domains from 3d printing, economics, raspberry pi or emacs. An exhaustive list of all communities is available [here](https://stackexchange.com/sites).
### Languages
Stack Exchange mainly consist of english language (en).
## Dataset Structure
### Data Instances
Each data samples is presented as follow:
```
{'title_body': "Is there a Stack Exchange icon available? StackAuth /sites route provides all the site's icons except for the one of the Stack Exchange master site.\nCould you please provide it in some way (a static SVG would be good)?",
'upvoted_answer': 'Here it is!\n\nDead link: SVG version here\nNote: the same restrictions on this trademarked icon that apply here, also apply to the icon above.',
'downvoted_answer': 'No, the /sites route is not the right place for that.\n\n/sites enumerates all websites that expose API end-points. StackExchange.com does not expose such an endpoint, so it does not (and will not) appear in the results.'}
```
This particular exampe corresponds to the [following page](https://stackapps.com/questions/1508/is-there-a-stack-exchange-icon-available)
### Data Fields
The fields present in the dataset contain the following informations:
- `title_body`: This is the concatenation of the title and body from the question
- `upvoted_answer`: This is the body from the most upvoted answer
- `downvoted_answer`: This is the body from the most downvoted answer
### Data Splits
We provide multiple splits for this dataset, which each refers to a given community channel. We detail the number of pail for each split below:
| | Number of pairs |
| ----- | ------ |
| english | 13,003 |
| academia | 2,465 |
| christianity | 1,502 |
| apple | 6,696 |
| electronics | 4,014 |
| gaming | 7,321 |
| askubuntu | 9,975 |
| ell | 4,438 |
| hermeneutics | 1,719 |
| judaism | 2,216 |
| diy | 2,037 |
| law | 1,297 |
| history | 1,099 |
| islam | 2,037 |
| dba | 2,502 |
| cooking | 2,064 |
| gamedev | 1,598 |
| drupal | 1,714 |
| chemistry | 1,523 |
| android | 2,830 |
| mathoverflow | 1,109 |
| magento | 1,849 |
| buddhism | 770 |
| gis | 1,843 |
| graphicdesign | 1,565 |
| codereview | 666 |
| aviation | 903 |
| bicycles | 984 |
| japanese | 1,124 |
| cs | 936 |
| german | 1,047 |
| interpersonal | 469 |
| biology | 832 |
| bitcoin | 1,068 |
| blender | 1,312 |
| crypto | 595 |
| anime | 802 |
| boardgames | 691 |
| hinduism | 343 |
| french | 632 |
| fitness | 567 |
| economics | 441 |
| chinese | 611 |
| codegolf | 333 |
| linguistics | 442 |
| astronomy | 371 |
| arduino | 595 |
| chess | 402 |
| cstheory | 314 |
| ja | 328 |
| martialarts | 254 |
| mathematica | 262 |
| dsp | 387 |
| ethereum | 479 |
| health | 299 |
| cogsci | 221 |
| earthscience | 229 |
| gardening | 210 |
| datascience | 325 |
| literature | 191 |
| matheducators | 177 |
| lifehacks | 316 |
| engineering | 227 |
| ham | 158 |
| 3dprinting | 109 |
| italian | 181 |
| emacs | 188 |
| homebrew | 176 |
| ai | 130 |
| avp | 152 |
| expatriates | 132 |
| elementaryos | 224 |
| cseducators | 67 |
| hsm | 70 |
| expressionengine | 91 |
| joomla | 124 |
| freelancing | 70 |
| crafts | 72 |
| genealogy | 86 |
| latin | 55 |
| hardwarerecs | 58 |
| devops | 53 |
| coffee | 47 |
| beer | 57 |
| languagelearning | 42 |
| ebooks | 54 |
| bricks | 79 |
| civicrm | 85 |
| bioinformatics | 39 |
| esperanto | 56 |
| computergraphics | 30 |
| conlang | 8 |
| korean | 28 |
| iota | 31 |
| eosio | 44 |
| craftcms | 26 |
| iot | 10 |
| drones | 6 |
| cardano | 7 |
| materials | 1 |
| ru | 6,305 |
| softwareengineering | 4,238 |
| scifi | 5,176 |
| workplace | 4,317 |
| serverfault | 7,969 |
| rpg | 4,212 |
| physics | 8,362 |
| superuser | 17,425 |
| worldbuilding | 2,087 |
| security | 3,069 |
| pt | 3,718 |
| unix | 6,173 |
| meta | 61 |
| politics | 1,468 |
| stats | 2,238 |
| movies | 1,577 |
| photo | 1,432 |
| wordpress | 3,046 |
| music | 1,228 |
| philosophy | 1,184 |
| skeptics | 670 |
| money | 1,905 |
| salesforce | 1,781 |
| parenting | 624 |
| raspberrypi | 1,011 |
| travel | 1,317 |
| mechanics | 842 |
| tex | 1,095 |
| ux | 1,107 |
| sharepoint | 1,691 |
| webapps | 1,906 |
| puzzling | 784 |
| networkengineering | 476 |
| webmasters | 854 |
| sports | 455 |
| rus | 514 |
| space | 405 |
| writers | 407 |
| pets | 322 |
| pm | 241 |
| russian | 353 |
| spanish | 366 |
| sound | 365 |
| quant | 340 |
| sqa | 353 |
| outdoors | 221 |
| softwarerecs | 348 |
| retrocomputing | 135 |
| mythology | 103 |
| portuguese | 144 |
| opensource | 123 |
| scicomp | 127 |
| ukrainian | 87 |
| patents | 137 |
| sustainability | 152 |
| poker | 115 |
| robotics | 110 |
| woodworking | 93 |
| reverseengineering | 97 |
| sitecore | 122 |
| tor | 137 |
| vi | 95 |
| windowsphone | 153 |
| vegetarianism | 35 |
| moderators | 23 |
| quantumcomputing | 46 |
| musicfans | 78 |
| tridion | 68 |
| opendata | 45 |
| tezos | 11 |
| stellar | 3 |
| or | 13 |
| monero | 26 |
| stackapps | 15 |
| total | 210,748 |
## Dataset Creation
### Curation Rationale
We primary designed this dataset for sentence embeddings training. Indeed sentence embeddings may be trained using a contrastive learning setup for which the model is trained to associate each sentence with its corresponding pair out of multiple proposition. Such models require many examples to be efficient and thus the dataset creation may be tedious. Community networks such as Stack Exchange allow us to build many examples semi-automatically.
### Source Data
The source data are dumps from [Stack Exchange](https://archive.org/details/stackexchange)
#### Initial Data Collection and Normalization
We collected the data from the math community.
We filtered out questions which title or body length is bellow 20 characters and questions for which body length is above 4096 characters.
When extracting most upvoted answer, we filtered to pairs for which their is at least 100 votes gap between most upvoted and downvoted answers.
#### Who are the source language producers?
Questions and answers are written by the community developpers of Stack Exchange.
## Additional Information
### Licensing Information
Please see the license information at: https://archive.org/details/stackexchange
### Citation Information
```
@misc{StackExchangeDataset,
author = {Flax Sentence Embeddings Team},
title = {Stack Exchange question pairs},
year = {2021},
howpublished = {https://huggingface.co/datasets/flax-sentence-embeddings/},
}
```
### Contributions
Thanks to the Flax Sentence Embeddings team for adding this dataset. |
EleutherAI/proof-pile-2 | EleutherAI | "2023-10-25T06:16:04Z" | 11,279 | 197 | [
"task_categories:text-generation",
"language:en",
"size_categories:10B<n<100B",
"arxiv:2310.10631",
"arxiv:2310.06786",
"region:us",
"math"
] | [
"text-generation"
] | "2023-10-12T00:11:33Z" | ---
task_categories:
- text-generation
language:
- en
tags:
- math
size_categories:
- 10B<n<100B
---
<img src="proofpile_logo.jpg" width="500">
[ArXiv](http://arxiv.org/abs/2310.10631) | [Models](https://huggingface.co/EleutherAI/llemma_34b) | [Data](https://huggingface.co/datasets/EleutherAI/proof-pile-2) | [Code](https://github.com/EleutherAI/math-lm) | [Blog](https://blog.eleuther.ai/llemma/) | [Sample Explorer](https://llemma-demo.github.io/)
[Zhangir Azerbayev](https://zhangir-azerbayev.github.io/), [Hailey Schoelkopf](https://github.com/haileyschoelkopf), [Keiran Paster](https://keirp.com), [Marco Dos Santos](https://github.com/dsantosmarco), [Stephen McAleer](https://www.andrew.cmu.edu/user/smcaleer/), [Albert Q. Jiang](https://albertqjiang.github.io/), [Jia Deng](https://www.cs.princeton.edu/~jiadeng/), [Stella Biderman](https://www.stellabiderman.com/), [Sean Welleck](https://wellecks.com/)
The **Proof-Pile-2** is a 55 billion token dataset of mathematical and scientific documents. This dataset was created in order to train the [Llemma 7B](https://huggingface.co/EleutherAI/llemma_7b) and [Llemma 34B](https://huggingface.co/EleutherAI/llemma_34b) models. It consists of three subsets:
- `arxiv` (29B tokens): the ArXiv subset of [RedPajama](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T)
- `open-web-math` (15B tokens): The [OpenWebMath](https://huggingface.co/datasets/open-web-math/open-web-math) dataset, which contains much of the high-quality mathematical text from the internet.
- `algebraic-stack` (11B tokens): A new dataset of mathematical code, including numerical computing, computer algebra, and formal mathematics.
You can download the dataset as follows
```python
from datasets import load_dataset
ds = load_dataset("EleutherAI/proof-pile-2")
# To load only a specific subset, pass it as an argument, e.g
ds_arxiv = load_dataset("EleutherAI/proof-pile-2", "arxiv")
```
### Schema
Each dataset row has the following structure
```python
{
"text": ..., # document text
"meta": ..., # JSON string of metadata, schema specific to data source
}
```
### Dataset Contents
For detailed documentation of the ArXiv and web subsets, refer to [RedPajama](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T) and [OpenWebMath](https://huggingface.co/datasets/open-web-math/open-web-math). The following table enumerates the contents of the AlgebraicStack by programming language. The AlgebraicStack is filtered to only include documents that contain mathematics, as judged by hand-crafted, language-specific heuristics.
| Language | AlgebraicStack tokens |
|-----------|-----------------------|
| Agda | 35.2 M |
| C | 25.1 M |
| C++ | 954.1 M |
| Coq | 281.9 M |
| Fortran | 724.9 M |
| GAP | 3.6 M |
| Haskell | 9.1 M |
| Idris | 10.9 M |
| Isabelle | 1,089.7 M |
| Julia | 531.0 M |
| Jupyter | 199.1 M |
| Lean | 285.6 M |
| Maple | 2.0 M |
| Matlab | 65.8 M |
| Python | 6,098.8 M |
| R | 71.3 M |
| Tex | 567.7 M |
| **Total** | **10,955.7 M** |
### License
We do not alter the license of any of the underlying data.
### Version History
**v1.1.0**: Contains an updated version of OpenWebMath, precisely the one available at [open-web-math/open-web-math](https://huggingface.co/datasets/open-web-math/open-web-math). This version of OpenWebMath has slightly improved filtering, for example, removal of very short documents.
**v1.0.0**: The data used to train the [Llemma 7B](https://huggingface.co/EleutherAI/llemma_7b) and [Llemma 34B](https://huggingface.co/EleutherAI/llemma_34b). Uses a development version of OpenWebMath.
### Citation
For the entire Proof-Pile-2, cite
```
@misc{azerbayev2023llemma,
title={Llemma: An Open Language Model For Mathematics},
author={Zhangir Azerbayev and Hailey Schoelkopf and Keiran Paster and Marco Dos Santos and Stephen McAleer and Albert Q. Jiang and Jia Deng and Stella Biderman and Sean Welleck},
year={2023},
eprint={2310.10631},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
For the ArXiv subset, cite
```
@software{together2023redpajama,
author = {Together Computer},
title = {RedPajama: An Open Source Recipe to Reproduce LLaMA training dataset},
month = April,
year = 2023,
url = {https://github.com/togethercomputer/RedPajama-Data}
}
```
For OpenWebMath, cite
```
@misc{paster2023openwebmath,
title={OpenWebMath: An Open Dataset of High-Quality Mathematical Web Text},
author={Keiran Paster and Marco Dos Santos and Zhangir Azerbayev and Jimmy Ba},
year={2023},
eprint={2310.06786},
archivePrefix={arXiv},
primaryClass={cs.AI}
}
```
|
open-llm-leaderboard-old/details_TencentARC__LLaMA-Pro-8B-Instruct | open-llm-leaderboard-old | "2024-01-06T13:14:36Z" | 11,250 | 0 | [
"region:us"
] | null | "2024-01-06T05:38:44Z" | ---
pretty_name: Evaluation run of TencentARC/LLaMA-Pro-8B-Instruct
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [TencentARC/LLaMA-Pro-8B-Instruct](https://huggingface.co/TencentARC/LLaMA-Pro-8B-Instruct)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 8 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_TencentARC__LLaMA-Pro-8B-Instruct\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-01-06T13:12:05.796061](https://huggingface.co/datasets/open-llm-leaderboard/details_TencentARC__LLaMA-Pro-8B-Instruct/blob/main/results_2024-01-06T13-12-05.796061.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.5281709550040744,\n\
\ \"acc_stderr\": 0.034190129304935035,\n \"acc_norm\": 0.5299752077852407,\n\
\ \"acc_norm_stderr\": 0.03489132244520177,\n \"mc1\": 0.3353733170134639,\n\
\ \"mc1_stderr\": 0.01652753403966899,\n \"mc2\": 0.4942677553605431,\n\
\ \"mc2_stderr\": 0.015656020272217592\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5,\n \"acc_stderr\": 0.014611390804670088,\n \
\ \"acc_norm\": 0.5298634812286689,\n \"acc_norm_stderr\": 0.014585305840007105\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5853415654252141,\n\
\ \"acc_stderr\": 0.0049165612135912825,\n \"acc_norm\": 0.7697669786895041,\n\
\ \"acc_norm_stderr\": 0.004201215520808244\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \
\ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.4666666666666667,\n\
\ \"acc_stderr\": 0.043097329010363554,\n \"acc_norm\": 0.4666666666666667,\n\
\ \"acc_norm_stderr\": 0.043097329010363554\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.5197368421052632,\n \"acc_stderr\": 0.040657710025626036,\n\
\ \"acc_norm\": 0.5197368421052632,\n \"acc_norm_stderr\": 0.040657710025626036\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.48,\n\
\ \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.48,\n \
\ \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.5433962264150943,\n \"acc_stderr\": 0.03065674869673943,\n\
\ \"acc_norm\": 0.5433962264150943,\n \"acc_norm_stderr\": 0.03065674869673943\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.5,\n\
\ \"acc_stderr\": 0.04181210050035455,\n \"acc_norm\": 0.5,\n \
\ \"acc_norm_stderr\": 0.04181210050035455\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \
\ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.39,\n\
\ \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \
\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.4161849710982659,\n\
\ \"acc_stderr\": 0.03758517775404948,\n \"acc_norm\": 0.4161849710982659,\n\
\ \"acc_norm_stderr\": 0.03758517775404948\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.2549019607843137,\n \"acc_stderr\": 0.04336432707993177,\n\
\ \"acc_norm\": 0.2549019607843137,\n \"acc_norm_stderr\": 0.04336432707993177\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.68,\n \"acc_stderr\": 0.04688261722621504,\n \"acc_norm\": 0.68,\n\
\ \"acc_norm_stderr\": 0.04688261722621504\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.4553191489361702,\n \"acc_stderr\": 0.03255525359340354,\n\
\ \"acc_norm\": 0.4553191489361702,\n \"acc_norm_stderr\": 0.03255525359340354\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2807017543859649,\n\
\ \"acc_stderr\": 0.042270544512322004,\n \"acc_norm\": 0.2807017543859649,\n\
\ \"acc_norm_stderr\": 0.042270544512322004\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.47586206896551725,\n \"acc_stderr\": 0.041618085035015295,\n\
\ \"acc_norm\": 0.47586206896551725,\n \"acc_norm_stderr\": 0.041618085035015295\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.3544973544973545,\n \"acc_stderr\": 0.024636830602841997,\n \"\
acc_norm\": 0.3544973544973545,\n \"acc_norm_stderr\": 0.024636830602841997\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.38095238095238093,\n\
\ \"acc_stderr\": 0.043435254289490965,\n \"acc_norm\": 0.38095238095238093,\n\
\ \"acc_norm_stderr\": 0.043435254289490965\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \
\ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.5903225806451613,\n\
\ \"acc_stderr\": 0.027976054915347357,\n \"acc_norm\": 0.5903225806451613,\n\
\ \"acc_norm_stderr\": 0.027976054915347357\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.3891625615763547,\n \"acc_stderr\": 0.034304624161038716,\n\
\ \"acc_norm\": 0.3891625615763547,\n \"acc_norm_stderr\": 0.034304624161038716\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \"acc_norm\"\
: 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.696969696969697,\n \"acc_stderr\": 0.035886248000917075,\n\
\ \"acc_norm\": 0.696969696969697,\n \"acc_norm_stderr\": 0.035886248000917075\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.6212121212121212,\n \"acc_stderr\": 0.03456088731993747,\n \"\
acc_norm\": 0.6212121212121212,\n \"acc_norm_stderr\": 0.03456088731993747\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.772020725388601,\n \"acc_stderr\": 0.030276909945178263,\n\
\ \"acc_norm\": 0.772020725388601,\n \"acc_norm_stderr\": 0.030276909945178263\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.48205128205128206,\n \"acc_stderr\": 0.025334667080954942,\n\
\ \"acc_norm\": 0.48205128205128206,\n \"acc_norm_stderr\": 0.025334667080954942\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.28888888888888886,\n \"acc_stderr\": 0.027634907264178544,\n \
\ \"acc_norm\": 0.28888888888888886,\n \"acc_norm_stderr\": 0.027634907264178544\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.5042016806722689,\n \"acc_stderr\": 0.0324773433444811,\n \
\ \"acc_norm\": 0.5042016806722689,\n \"acc_norm_stderr\": 0.0324773433444811\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.2913907284768212,\n \"acc_stderr\": 0.037101857261199946,\n \"\
acc_norm\": 0.2913907284768212,\n \"acc_norm_stderr\": 0.037101857261199946\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.7192660550458716,\n \"acc_stderr\": 0.019266055045871616,\n \"\
acc_norm\": 0.7192660550458716,\n \"acc_norm_stderr\": 0.019266055045871616\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.42592592592592593,\n \"acc_stderr\": 0.03372343271653063,\n \"\
acc_norm\": 0.42592592592592593,\n \"acc_norm_stderr\": 0.03372343271653063\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.7205882352941176,\n \"acc_stderr\": 0.031493281045079556,\n \"\
acc_norm\": 0.7205882352941176,\n \"acc_norm_stderr\": 0.031493281045079556\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7172995780590717,\n \"acc_stderr\": 0.029312814153955924,\n \
\ \"acc_norm\": 0.7172995780590717,\n \"acc_norm_stderr\": 0.029312814153955924\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.5739910313901345,\n\
\ \"acc_stderr\": 0.033188332862172806,\n \"acc_norm\": 0.5739910313901345,\n\
\ \"acc_norm_stderr\": 0.033188332862172806\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.648854961832061,\n \"acc_stderr\": 0.0418644516301375,\n\
\ \"acc_norm\": 0.648854961832061,\n \"acc_norm_stderr\": 0.0418644516301375\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.6115702479338843,\n \"acc_stderr\": 0.044492703500683836,\n \"\
acc_norm\": 0.6115702479338843,\n \"acc_norm_stderr\": 0.044492703500683836\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.5925925925925926,\n\
\ \"acc_stderr\": 0.04750077341199984,\n \"acc_norm\": 0.5925925925925926,\n\
\ \"acc_norm_stderr\": 0.04750077341199984\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.6257668711656442,\n \"acc_stderr\": 0.03802068102899615,\n\
\ \"acc_norm\": 0.6257668711656442,\n \"acc_norm_stderr\": 0.03802068102899615\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4642857142857143,\n\
\ \"acc_stderr\": 0.04733667890053756,\n \"acc_norm\": 0.4642857142857143,\n\
\ \"acc_norm_stderr\": 0.04733667890053756\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.6990291262135923,\n \"acc_stderr\": 0.04541609446503948,\n\
\ \"acc_norm\": 0.6990291262135923,\n \"acc_norm_stderr\": 0.04541609446503948\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.7863247863247863,\n\
\ \"acc_stderr\": 0.02685345037700916,\n \"acc_norm\": 0.7863247863247863,\n\
\ \"acc_norm_stderr\": 0.02685345037700916\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.66,\n \"acc_stderr\": 0.04760952285695238,\n \
\ \"acc_norm\": 0.66,\n \"acc_norm_stderr\": 0.04760952285695238\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7113665389527458,\n\
\ \"acc_stderr\": 0.016203792703197797,\n \"acc_norm\": 0.7113665389527458,\n\
\ \"acc_norm_stderr\": 0.016203792703197797\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.5867052023121387,\n \"acc_stderr\": 0.02651126136940924,\n\
\ \"acc_norm\": 0.5867052023121387,\n \"acc_norm_stderr\": 0.02651126136940924\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.32625698324022345,\n\
\ \"acc_stderr\": 0.01568044151888918,\n \"acc_norm\": 0.32625698324022345,\n\
\ \"acc_norm_stderr\": 0.01568044151888918\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.565359477124183,\n \"acc_stderr\": 0.028384256704883037,\n\
\ \"acc_norm\": 0.565359477124183,\n \"acc_norm_stderr\": 0.028384256704883037\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.5852090032154341,\n\
\ \"acc_stderr\": 0.027982680459759563,\n \"acc_norm\": 0.5852090032154341,\n\
\ \"acc_norm_stderr\": 0.027982680459759563\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.5555555555555556,\n \"acc_stderr\": 0.027648477877413327,\n\
\ \"acc_norm\": 0.5555555555555556,\n \"acc_norm_stderr\": 0.027648477877413327\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.37943262411347517,\n \"acc_stderr\": 0.02894733885161411,\n \
\ \"acc_norm\": 0.37943262411347517,\n \"acc_norm_stderr\": 0.02894733885161411\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.3754889178617992,\n\
\ \"acc_stderr\": 0.012367945396728208,\n \"acc_norm\": 0.3754889178617992,\n\
\ \"acc_norm_stderr\": 0.012367945396728208\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.45588235294117646,\n \"acc_stderr\": 0.030254372573976687,\n\
\ \"acc_norm\": 0.45588235294117646,\n \"acc_norm_stderr\": 0.030254372573976687\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.49836601307189543,\n \"acc_stderr\": 0.020227726838150117,\n \
\ \"acc_norm\": 0.49836601307189543,\n \"acc_norm_stderr\": 0.020227726838150117\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6363636363636364,\n\
\ \"acc_stderr\": 0.046075820907199756,\n \"acc_norm\": 0.6363636363636364,\n\
\ \"acc_norm_stderr\": 0.046075820907199756\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.6448979591836734,\n \"acc_stderr\": 0.030635655150387638,\n\
\ \"acc_norm\": 0.6448979591836734,\n \"acc_norm_stderr\": 0.030635655150387638\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.6915422885572139,\n\
\ \"acc_stderr\": 0.032658195885126966,\n \"acc_norm\": 0.6915422885572139,\n\
\ \"acc_norm_stderr\": 0.032658195885126966\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \
\ \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.04351941398892446\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.463855421686747,\n\
\ \"acc_stderr\": 0.03882310850890594,\n \"acc_norm\": 0.463855421686747,\n\
\ \"acc_norm_stderr\": 0.03882310850890594\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.7426900584795322,\n \"acc_stderr\": 0.03352799844161865,\n\
\ \"acc_norm\": 0.7426900584795322,\n \"acc_norm_stderr\": 0.03352799844161865\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3353733170134639,\n\
\ \"mc1_stderr\": 0.01652753403966899,\n \"mc2\": 0.4942677553605431,\n\
\ \"mc2_stderr\": 0.015656020272217592\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7221783741120757,\n \"acc_stderr\": 0.012588918183871593\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.44200151630022744,\n \
\ \"acc_stderr\": 0.013679514492814581\n }\n}\n```"
repo_url: https://huggingface.co/TencentARC/LLaMA-Pro-8B-Instruct
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_01_06T05_36_22.722674
path:
- '**/details_harness|arc:challenge|25_2024-01-06T05-36-22.722674.parquet'
- split: 2024_01_06T06_15_48.429229
path:
- '**/details_harness|arc:challenge|25_2024-01-06T06-15-48.429229.parquet'
- split: 2024_01_06T06_43_15.789213
path:
- '**/details_harness|arc:challenge|25_2024-01-06T06-43-15.789213.parquet'
- split: 2024_01_06T09_13_09.739975
path:
- '**/details_harness|arc:challenge|25_2024-01-06T09-13-09.739975.parquet'
- split: 2024_01_06T09_16_27.017995
path:
- '**/details_harness|arc:challenge|25_2024-01-06T09-16-27.017995.parquet'
- split: 2024_01_06T11_33_07.175402
path:
- '**/details_harness|arc:challenge|25_2024-01-06T11-33-07.175402.parquet'
- split: 2024_01_06T13_05_18.668611
path:
- '**/details_harness|arc:challenge|25_2024-01-06T13-05-18.668611.parquet'
- split: 2024_01_06T13_12_05.796061
path:
- '**/details_harness|arc:challenge|25_2024-01-06T13-12-05.796061.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-01-06T13-12-05.796061.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_01_06T05_36_22.722674
path:
- '**/details_harness|gsm8k|5_2024-01-06T05-36-22.722674.parquet'
- split: 2024_01_06T06_15_48.429229
path:
- '**/details_harness|gsm8k|5_2024-01-06T06-15-48.429229.parquet'
- split: 2024_01_06T06_43_15.789213
path:
- '**/details_harness|gsm8k|5_2024-01-06T06-43-15.789213.parquet'
- split: 2024_01_06T09_13_09.739975
path:
- '**/details_harness|gsm8k|5_2024-01-06T09-13-09.739975.parquet'
- split: 2024_01_06T09_16_27.017995
path:
- '**/details_harness|gsm8k|5_2024-01-06T09-16-27.017995.parquet'
- split: 2024_01_06T11_33_07.175402
path:
- '**/details_harness|gsm8k|5_2024-01-06T11-33-07.175402.parquet'
- split: 2024_01_06T13_05_18.668611
path:
- '**/details_harness|gsm8k|5_2024-01-06T13-05-18.668611.parquet'
- split: 2024_01_06T13_12_05.796061
path:
- '**/details_harness|gsm8k|5_2024-01-06T13-12-05.796061.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-01-06T13-12-05.796061.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_01_06T05_36_22.722674
path:
- '**/details_harness|hellaswag|10_2024-01-06T05-36-22.722674.parquet'
- split: 2024_01_06T06_15_48.429229
path:
- '**/details_harness|hellaswag|10_2024-01-06T06-15-48.429229.parquet'
- split: 2024_01_06T06_43_15.789213
path:
- '**/details_harness|hellaswag|10_2024-01-06T06-43-15.789213.parquet'
- split: 2024_01_06T09_13_09.739975
path:
- '**/details_harness|hellaswag|10_2024-01-06T09-13-09.739975.parquet'
- split: 2024_01_06T09_16_27.017995
path:
- '**/details_harness|hellaswag|10_2024-01-06T09-16-27.017995.parquet'
- split: 2024_01_06T11_33_07.175402
path:
- '**/details_harness|hellaswag|10_2024-01-06T11-33-07.175402.parquet'
- split: 2024_01_06T13_05_18.668611
path:
- '**/details_harness|hellaswag|10_2024-01-06T13-05-18.668611.parquet'
- split: 2024_01_06T13_12_05.796061
path:
- '**/details_harness|hellaswag|10_2024-01-06T13-12-05.796061.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-01-06T13-12-05.796061.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_01_06T05_36_22.722674
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-06T05-36-22.722674.parquet'
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path:
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path:
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path:
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path:
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- '**/details_harness|hendrycksTest-anatomy|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-06T13-12-05.796061.parquet'
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- '**/details_harness|hendrycksTest-sociology|5_2024-01-06T13-12-05.796061.parquet'
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- '**/details_harness|hendrycksTest-virology|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-06T13-12-05.796061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-06T13-12-05.796061.parquet'
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- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-06T13-12-05.796061.parquet'
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- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-06T13-12-05.796061.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_01_06T05_36_22.722674
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-06T05-36-22.722674.parquet'
- split: 2024_01_06T06_15_48.429229
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-06T06-15-48.429229.parquet'
- split: 2024_01_06T06_43_15.789213
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-06T06-43-15.789213.parquet'
- split: 2024_01_06T09_13_09.739975
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-06T09-13-09.739975.parquet'
- split: 2024_01_06T09_16_27.017995
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-06T09-16-27.017995.parquet'
- split: 2024_01_06T11_33_07.175402
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-06T11-33-07.175402.parquet'
- split: 2024_01_06T13_05_18.668611
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-06T13-05-18.668611.parquet'
- split: 2024_01_06T13_12_05.796061
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-06T13-12-05.796061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-06T13-12-05.796061.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
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path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-06T05-36-22.722674.parquet'
- split: 2024_01_06T06_15_48.429229
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-06T06-15-48.429229.parquet'
- split: 2024_01_06T06_43_15.789213
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-06T06-43-15.789213.parquet'
- split: 2024_01_06T09_13_09.739975
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-06T09-13-09.739975.parquet'
- split: 2024_01_06T09_16_27.017995
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-06T09-16-27.017995.parquet'
- split: 2024_01_06T11_33_07.175402
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-06T11-33-07.175402.parquet'
- split: 2024_01_06T13_05_18.668611
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-06T13-05-18.668611.parquet'
- split: 2024_01_06T13_12_05.796061
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-06T13-12-05.796061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-06T13-12-05.796061.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_01_06T05_36_22.722674
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-06T05-36-22.722674.parquet'
- split: 2024_01_06T06_15_48.429229
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-06T06-15-48.429229.parquet'
- split: 2024_01_06T06_43_15.789213
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-06T06-43-15.789213.parquet'
- split: 2024_01_06T09_13_09.739975
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-06T09-13-09.739975.parquet'
- split: 2024_01_06T09_16_27.017995
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-06T09-16-27.017995.parquet'
- split: 2024_01_06T11_33_07.175402
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-06T11-33-07.175402.parquet'
- split: 2024_01_06T13_05_18.668611
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-06T13-05-18.668611.parquet'
- split: 2024_01_06T13_12_05.796061
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-06T13-12-05.796061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-06T13-12-05.796061.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_01_06T05_36_22.722674
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-06T05-36-22.722674.parquet'
- split: 2024_01_06T06_15_48.429229
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-06T06-15-48.429229.parquet'
- split: 2024_01_06T06_43_15.789213
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-06T06-43-15.789213.parquet'
- split: 2024_01_06T09_13_09.739975
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-06T09-13-09.739975.parquet'
- split: 2024_01_06T09_16_27.017995
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-06T09-16-27.017995.parquet'
- split: 2024_01_06T11_33_07.175402
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-06T11-33-07.175402.parquet'
- split: 2024_01_06T13_05_18.668611
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-06T13-05-18.668611.parquet'
- split: 2024_01_06T13_12_05.796061
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-06T13-12-05.796061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-06T13-12-05.796061.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_01_06T05_36_22.722674
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-06T05-36-22.722674.parquet'
- split: 2024_01_06T06_15_48.429229
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-06T06-15-48.429229.parquet'
- split: 2024_01_06T06_43_15.789213
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-06T06-43-15.789213.parquet'
- split: 2024_01_06T09_13_09.739975
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-06T09-13-09.739975.parquet'
- split: 2024_01_06T09_16_27.017995
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-06T09-16-27.017995.parquet'
- split: 2024_01_06T11_33_07.175402
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-06T11-33-07.175402.parquet'
- split: 2024_01_06T13_05_18.668611
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-06T13-05-18.668611.parquet'
- split: 2024_01_06T13_12_05.796061
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-06T13-12-05.796061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-06T13-12-05.796061.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_01_06T05_36_22.722674
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-06T05-36-22.722674.parquet'
- split: 2024_01_06T06_15_48.429229
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-06T06-15-48.429229.parquet'
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
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path:
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path:
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path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-06T06-43-15.789213.parquet'
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path:
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path:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-06T11-33-07.175402.parquet'
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path:
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path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-06T13-12-05.796061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-06T13-12-05.796061.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
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path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-06T05-36-22.722674.parquet'
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path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-06T06-15-48.429229.parquet'
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path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-06T06-43-15.789213.parquet'
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path:
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path:
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path:
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path:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-06T13-12-05.796061.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
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path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-06T05-36-22.722674.parquet'
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-06T13-12-05.796061.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
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path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-06T05-36-22.722674.parquet'
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-06T13-12-05.796061.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
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path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-06T05-36-22.722674.parquet'
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_machine_learning_5
data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_nutrition_5
data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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data_files:
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path:
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path:
- '**/details_harness|truthfulqa:mc|0_2024-01-06T13-12-05.796061.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-06T13-12-05.796061.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_01_06T05_36_22.722674
path:
- '**/details_harness|winogrande|5_2024-01-06T05-36-22.722674.parquet'
- split: 2024_01_06T06_15_48.429229
path:
- '**/details_harness|winogrande|5_2024-01-06T06-15-48.429229.parquet'
- split: 2024_01_06T06_43_15.789213
path:
- '**/details_harness|winogrande|5_2024-01-06T06-43-15.789213.parquet'
- split: 2024_01_06T09_13_09.739975
path:
- '**/details_harness|winogrande|5_2024-01-06T09-13-09.739975.parquet'
- split: 2024_01_06T09_16_27.017995
path:
- '**/details_harness|winogrande|5_2024-01-06T09-16-27.017995.parquet'
- split: 2024_01_06T11_33_07.175402
path:
- '**/details_harness|winogrande|5_2024-01-06T11-33-07.175402.parquet'
- split: 2024_01_06T13_05_18.668611
path:
- '**/details_harness|winogrande|5_2024-01-06T13-05-18.668611.parquet'
- split: 2024_01_06T13_12_05.796061
path:
- '**/details_harness|winogrande|5_2024-01-06T13-12-05.796061.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-01-06T13-12-05.796061.parquet'
- config_name: results
data_files:
- split: 2024_01_06T05_36_22.722674
path:
- results_2024-01-06T05-36-22.722674.parquet
- split: 2024_01_06T06_15_48.429229
path:
- results_2024-01-06T06-15-48.429229.parquet
- split: 2024_01_06T06_43_15.789213
path:
- results_2024-01-06T06-43-15.789213.parquet
- split: 2024_01_06T09_13_09.739975
path:
- results_2024-01-06T09-13-09.739975.parquet
- split: 2024_01_06T09_16_27.017995
path:
- results_2024-01-06T09-16-27.017995.parquet
- split: 2024_01_06T11_33_07.175402
path:
- results_2024-01-06T11-33-07.175402.parquet
- split: 2024_01_06T13_05_18.668611
path:
- results_2024-01-06T13-05-18.668611.parquet
- split: 2024_01_06T13_12_05.796061
path:
- results_2024-01-06T13-12-05.796061.parquet
- split: latest
path:
- results_2024-01-06T13-12-05.796061.parquet
---
# Dataset Card for Evaluation run of TencentARC/LLaMA-Pro-8B-Instruct
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [TencentARC/LLaMA-Pro-8B-Instruct](https://huggingface.co/TencentARC/LLaMA-Pro-8B-Instruct) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 8 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_TencentARC__LLaMA-Pro-8B-Instruct",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-06T13:12:05.796061](https://huggingface.co/datasets/open-llm-leaderboard/details_TencentARC__LLaMA-Pro-8B-Instruct/blob/main/results_2024-01-06T13-12-05.796061.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.5281709550040744,
"acc_stderr": 0.034190129304935035,
"acc_norm": 0.5299752077852407,
"acc_norm_stderr": 0.03489132244520177,
"mc1": 0.3353733170134639,
"mc1_stderr": 0.01652753403966899,
"mc2": 0.4942677553605431,
"mc2_stderr": 0.015656020272217592
},
"harness|arc:challenge|25": {
"acc": 0.5,
"acc_stderr": 0.014611390804670088,
"acc_norm": 0.5298634812286689,
"acc_norm_stderr": 0.014585305840007105
},
"harness|hellaswag|10": {
"acc": 0.5853415654252141,
"acc_stderr": 0.0049165612135912825,
"acc_norm": 0.7697669786895041,
"acc_norm_stderr": 0.004201215520808244
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.4666666666666667,
"acc_stderr": 0.043097329010363554,
"acc_norm": 0.4666666666666667,
"acc_norm_stderr": 0.043097329010363554
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.5197368421052632,
"acc_stderr": 0.040657710025626036,
"acc_norm": 0.5197368421052632,
"acc_norm_stderr": 0.040657710025626036
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.48,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.48,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.5433962264150943,
"acc_stderr": 0.03065674869673943,
"acc_norm": 0.5433962264150943,
"acc_norm_stderr": 0.03065674869673943
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.5,
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"acc_norm": 0.5,
"acc_norm_stderr": 0.04181210050035455
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.46,
"acc_stderr": 0.05009082659620332,
"acc_norm": 0.46,
"acc_norm_stderr": 0.05009082659620332
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.39,
"acc_stderr": 0.04902071300001975,
"acc_norm": 0.39,
"acc_norm_stderr": 0.04902071300001975
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.34,
"acc_stderr": 0.04760952285695235,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695235
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.4161849710982659,
"acc_stderr": 0.03758517775404948,
"acc_norm": 0.4161849710982659,
"acc_norm_stderr": 0.03758517775404948
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.2549019607843137,
"acc_stderr": 0.04336432707993177,
"acc_norm": 0.2549019607843137,
"acc_norm_stderr": 0.04336432707993177
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.68,
"acc_stderr": 0.04688261722621504,
"acc_norm": 0.68,
"acc_norm_stderr": 0.04688261722621504
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.4553191489361702,
"acc_stderr": 0.03255525359340354,
"acc_norm": 0.4553191489361702,
"acc_norm_stderr": 0.03255525359340354
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.2807017543859649,
"acc_stderr": 0.042270544512322004,
"acc_norm": 0.2807017543859649,
"acc_norm_stderr": 0.042270544512322004
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.47586206896551725,
"acc_stderr": 0.041618085035015295,
"acc_norm": 0.47586206896551725,
"acc_norm_stderr": 0.041618085035015295
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.3544973544973545,
"acc_stderr": 0.024636830602841997,
"acc_norm": 0.3544973544973545,
"acc_norm_stderr": 0.024636830602841997
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.38095238095238093,
"acc_stderr": 0.043435254289490965,
"acc_norm": 0.38095238095238093,
"acc_norm_stderr": 0.043435254289490965
},
"harness|hendrycksTest-global_facts|5": {
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"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.5903225806451613,
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"acc_norm": 0.5903225806451613,
"acc_norm_stderr": 0.027976054915347357
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.3891625615763547,
"acc_stderr": 0.034304624161038716,
"acc_norm": 0.3891625615763547,
"acc_norm_stderr": 0.034304624161038716
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.5,
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"acc_norm": 0.5,
"acc_norm_stderr": 0.050251890762960605
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.696969696969697,
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"acc_norm": 0.696969696969697,
"acc_norm_stderr": 0.035886248000917075
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.6212121212121212,
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"acc_norm_stderr": 0.03456088731993747
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.772020725388601,
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"acc_norm": 0.772020725388601,
"acc_norm_stderr": 0.030276909945178263
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.48205128205128206,
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"acc_norm": 0.48205128205128206,
"acc_norm_stderr": 0.025334667080954942
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.28888888888888886,
"acc_stderr": 0.027634907264178544,
"acc_norm": 0.28888888888888886,
"acc_norm_stderr": 0.027634907264178544
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.5042016806722689,
"acc_stderr": 0.0324773433444811,
"acc_norm": 0.5042016806722689,
"acc_norm_stderr": 0.0324773433444811
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.2913907284768212,
"acc_stderr": 0.037101857261199946,
"acc_norm": 0.2913907284768212,
"acc_norm_stderr": 0.037101857261199946
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.7192660550458716,
"acc_stderr": 0.019266055045871616,
"acc_norm": 0.7192660550458716,
"acc_norm_stderr": 0.019266055045871616
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.42592592592592593,
"acc_stderr": 0.03372343271653063,
"acc_norm": 0.42592592592592593,
"acc_norm_stderr": 0.03372343271653063
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.7205882352941176,
"acc_stderr": 0.031493281045079556,
"acc_norm": 0.7205882352941176,
"acc_norm_stderr": 0.031493281045079556
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7172995780590717,
"acc_stderr": 0.029312814153955924,
"acc_norm": 0.7172995780590717,
"acc_norm_stderr": 0.029312814153955924
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.5739910313901345,
"acc_stderr": 0.033188332862172806,
"acc_norm": 0.5739910313901345,
"acc_norm_stderr": 0.033188332862172806
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.648854961832061,
"acc_stderr": 0.0418644516301375,
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"acc_norm_stderr": 0.0418644516301375
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.6115702479338843,
"acc_stderr": 0.044492703500683836,
"acc_norm": 0.6115702479338843,
"acc_norm_stderr": 0.044492703500683836
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.5925925925925926,
"acc_stderr": 0.04750077341199984,
"acc_norm": 0.5925925925925926,
"acc_norm_stderr": 0.04750077341199984
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.6257668711656442,
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"acc_norm": 0.6257668711656442,
"acc_norm_stderr": 0.03802068102899615
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.4642857142857143,
"acc_stderr": 0.04733667890053756,
"acc_norm": 0.4642857142857143,
"acc_norm_stderr": 0.04733667890053756
},
"harness|hendrycksTest-management|5": {
"acc": 0.6990291262135923,
"acc_stderr": 0.04541609446503948,
"acc_norm": 0.6990291262135923,
"acc_norm_stderr": 0.04541609446503948
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.7863247863247863,
"acc_stderr": 0.02685345037700916,
"acc_norm": 0.7863247863247863,
"acc_norm_stderr": 0.02685345037700916
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.66,
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"acc_norm": 0.66,
"acc_norm_stderr": 0.04760952285695238
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.7113665389527458,
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"acc_norm": 0.7113665389527458,
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},
"harness|hendrycksTest-moral_disputes|5": {
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"acc_norm": 0.5867052023121387,
"acc_norm_stderr": 0.02651126136940924
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.32625698324022345,
"acc_stderr": 0.01568044151888918,
"acc_norm": 0.32625698324022345,
"acc_norm_stderr": 0.01568044151888918
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.565359477124183,
"acc_stderr": 0.028384256704883037,
"acc_norm": 0.565359477124183,
"acc_norm_stderr": 0.028384256704883037
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.5852090032154341,
"acc_stderr": 0.027982680459759563,
"acc_norm": 0.5852090032154341,
"acc_norm_stderr": 0.027982680459759563
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.5555555555555556,
"acc_stderr": 0.027648477877413327,
"acc_norm": 0.5555555555555556,
"acc_norm_stderr": 0.027648477877413327
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.37943262411347517,
"acc_stderr": 0.02894733885161411,
"acc_norm": 0.37943262411347517,
"acc_norm_stderr": 0.02894733885161411
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.3754889178617992,
"acc_stderr": 0.012367945396728208,
"acc_norm": 0.3754889178617992,
"acc_norm_stderr": 0.012367945396728208
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.45588235294117646,
"acc_stderr": 0.030254372573976687,
"acc_norm": 0.45588235294117646,
"acc_norm_stderr": 0.030254372573976687
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.49836601307189543,
"acc_stderr": 0.020227726838150117,
"acc_norm": 0.49836601307189543,
"acc_norm_stderr": 0.020227726838150117
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6363636363636364,
"acc_stderr": 0.046075820907199756,
"acc_norm": 0.6363636363636364,
"acc_norm_stderr": 0.046075820907199756
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.6448979591836734,
"acc_stderr": 0.030635655150387638,
"acc_norm": 0.6448979591836734,
"acc_norm_stderr": 0.030635655150387638
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.6915422885572139,
"acc_stderr": 0.032658195885126966,
"acc_norm": 0.6915422885572139,
"acc_norm_stderr": 0.032658195885126966
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.75,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.75,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-virology|5": {
"acc": 0.463855421686747,
"acc_stderr": 0.03882310850890594,
"acc_norm": 0.463855421686747,
"acc_norm_stderr": 0.03882310850890594
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.7426900584795322,
"acc_stderr": 0.03352799844161865,
"acc_norm": 0.7426900584795322,
"acc_norm_stderr": 0.03352799844161865
},
"harness|truthfulqa:mc|0": {
"mc1": 0.3353733170134639,
"mc1_stderr": 0.01652753403966899,
"mc2": 0.4942677553605431,
"mc2_stderr": 0.015656020272217592
},
"harness|winogrande|5": {
"acc": 0.7221783741120757,
"acc_stderr": 0.012588918183871593
},
"harness|gsm8k|5": {
"acc": 0.44200151630022744,
"acc_stderr": 0.013679514492814581
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered 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.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] |
alvations/c4p0-x1-en-ja | alvations | "2024-03-24T03:55:23Z" | 11,250 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-03-23T09:54:37Z" | ---
dataset_info:
features:
- name: source
dtype: string
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---
|
lighteval/mmlu | lighteval | "2023-06-09T16:36:19Z" | 11,195 | 39 | [
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
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"arxiv:2009.03300",
"arxiv:2005.00700",
"arxiv:2005.14165",
"arxiv:2008.02275",
"region:us"
] | [
"question-answering"
] | "2023-05-16T09:39:28Z" | ---
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paperswithcode_id: mmlu
pretty_name: Measuring Massive Multitask Language Understanding
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download_size: 166184960
dataset_size: 160630086
---
# Dataset Card for MMLU
## 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
- **Repository**: https://github.com/hendrycks/test
- **Paper**: https://arxiv.org/abs/2009.03300
### Dataset Summary
[Measuring Massive Multitask Language Understanding](https://arxiv.org/pdf/2009.03300) by [Dan Hendrycks](https://people.eecs.berkeley.edu/~hendrycks/), [Collin Burns](http://collinpburns.com), [Steven Basart](https://stevenbas.art), Andy Zou, Mantas Mazeika, [Dawn Song](https://people.eecs.berkeley.edu/~dawnsong/), and [Jacob Steinhardt](https://www.stat.berkeley.edu/~jsteinhardt/) (ICLR 2021).
This is a massive multitask test consisting of multiple-choice questions from various branches of knowledge. The test spans subjects in the humanities, social sciences, hard sciences, and other areas that are important for some people to learn. This covers 57 tasks including elementary mathematics, US history, computer science, law, and more. To attain high accuracy on this test, models must possess extensive world knowledge and problem solving ability.
A complete list of tasks: ['abstract_algebra', 'anatomy', 'astronomy', 'business_ethics', 'clinical_knowledge', 'college_biology', 'college_chemistry', 'college_computer_science', 'college_mathematics', 'college_medicine', 'college_physics', 'computer_security', 'conceptual_physics', 'econometrics', 'electrical_engineering', 'elementary_mathematics', 'formal_logic', 'global_facts', 'high_school_biology', 'high_school_chemistry', 'high_school_computer_science', 'high_school_european_history', 'high_school_geography', 'high_school_government_and_politics', 'high_school_macroeconomics', 'high_school_mathematics', 'high_school_microeconomics', 'high_school_physics', 'high_school_psychology', 'high_school_statistics', 'high_school_us_history', 'high_school_world_history', 'human_aging', 'human_sexuality', 'international_law', 'jurisprudence', 'logical_fallacies', 'machine_learning', 'management', 'marketing', 'medical_genetics', 'miscellaneous', 'moral_disputes', 'moral_scenarios', 'nutrition', 'philosophy', 'prehistory', 'professional_accounting', 'professional_law', 'professional_medicine', 'professional_psychology', 'public_relations', 'security_studies', 'sociology', 'us_foreign_policy', 'virology', 'world_religions']
### Supported Tasks and Leaderboards
| Model | Authors | Humanities | Social Science | STEM | Other | Average |
|------------------------------------|----------|:-------:|:-------:|:-------:|:-------:|:-------:|
| [UnifiedQA](https://arxiv.org/abs/2005.00700) | Khashabi et al., 2020 | 45.6 | 56.6 | 40.2 | 54.6 | 48.9
| [GPT-3](https://arxiv.org/abs/2005.14165) (few-shot) | Brown et al., 2020 | 40.8 | 50.4 | 36.7 | 48.8 | 43.9
| [GPT-2](https://arxiv.org/abs/2005.14165) | Radford et al., 2019 | 32.8 | 33.3 | 30.2 | 33.1 | 32.4
| Random Baseline | N/A | 25.0 | 25.0 | 25.0 | 25.0 | 25.0 | 25.0
### Languages
English
## Dataset Structure
### Data Instances
An example from anatomy subtask looks as follows:
```
{
"question": "What is the embryological origin of the hyoid bone?",
"choices": ["The first pharyngeal arch", "The first and second pharyngeal arches", "The second pharyngeal arch", "The second and third pharyngeal arches"],
"answer": "D"
}
```
### Data Fields
- `question`: a string feature
- `choices`: a list of 4 string features
- `answer`: a ClassLabel feature
### Data Splits
- `auxiliary_train`: auxiliary multiple-choice training questions from ARC, MC_TEST, OBQA, RACE, etc.
- `dev`: 5 examples per subtask, meant for few-shot setting
- `test`: there are at least 100 examples per subtask
| | auxiliary_train | dev | val | test |
| ----- | :------: | :-----: | :-----: | :-----: |
| TOTAL | 99842 | 285 | 1531 | 14042
## Dataset Creation
### Curation Rationale
Transformer models have driven this recent progress by pretraining on massive text corpora, including all of Wikipedia, thousands of books, and numerous websites. These models consequently see extensive information about specialized topics, most of which is not assessed by existing NLP benchmarks. To bridge the gap between the wide-ranging knowledge that models see during pretraining and the existing measures of success, we introduce a new benchmark for assessing models across a diverse set of subjects that humans learn.
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[MIT License](https://github.com/hendrycks/test/blob/master/LICENSE)
### Citation Information
If you find this useful in your research, please consider citing the test and also the [ETHICS](https://arxiv.org/abs/2008.02275) dataset it draws from:
```
@article{hendryckstest2021,
title={Measuring Massive Multitask Language Understanding},
author={Dan Hendrycks and Collin Burns and Steven Basart and Andy Zou and Mantas Mazeika and Dawn Song and Jacob Steinhardt},
journal={Proceedings of the International Conference on Learning Representations (ICLR)},
year={2021}
}
@article{hendrycks2021ethics,
title={Aligning AI With Shared Human Values},
author={Dan Hendrycks and Collin Burns and Steven Basart and Andrew Critch and Jerry Li and Dawn Song and Jacob Steinhardt},
journal={Proceedings of the International Conference on Learning Representations (ICLR)},
year={2021}
}
```
### Contributions
Thanks to [@andyzoujm](https://github.com/andyzoujm) for adding this dataset.
|
CodedotAI/code_clippy_github | CodedotAI | "2022-08-05T02:57:36Z" | 11,180 | 16 | [
"task_ids:language-modeling",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:multilingual",
"language:code",
"license:mit",
"size_categories:1M<n<10M",
"modality:text",
"library:datasets",
"library:mlcroissant",
"arxiv:2107.03374",
"region:us"
] | [
"sequence-modeling"
] | "2022-03-02T23:29:22Z" | ---
annotations_creators: []
language_creators:
- crowdsourced
- expert-generated
language: ["code"]
license:
- mit
multilinguality:
- multilingual
pretty_name: code-clippy-github-code
size_categories:
- unknown
source_datasets: []
task_categories:
- sequence-modeling
task_ids:
- language-modeling
---
# Code Clippy Github Dataset
## Dataset Description
The Code Clippy dataset consists of various public codebases from GitHub in 22 programming languages with 23 extensions totaling about 16 TB of data when uncompressed. The dataset was created from the public GitHub dataset on Google BigQuery.
### How to use it
This dataset is pretty large please use the streaming parameter from the `datasets` library as seen below:
```python
from datasets import load_dataset
ds = load_dataset("CodedotAI/code_clippy_github", streaming=True)
```
## Data Structure
### Data Instances
```python
{
'code_text': " a = mc^2",
'repo_name': 'NotEinstein',
'file_path': 'root/users/einstein.py',
'language': 'Python',
'license': 'isc',
'size': 2
}
```
### Data Fields
|Field|Type|Description|
|---|---|---|
|code_text|string|string of the source code contained in the code file|
|repo_name|string|name of the GitHub repository|
|file_path|string|path of the code file within the repository |
|language|string|programming language used in the file inferred by the file extension|
|license|string|license of GitHub repository|
|size|int|size of source file in bytes|
### Data Splits
Only a train split is provided in this dataset.
## Languages
The dataset contains 22 programming languages with over 23 extensions:
```python
{
"C": [".c"],
"C#": [".cs"],
"C++": [".cpp"],
"CSS": [".css"],
"Dart" : [".dart"],
"GO": [".go"],
"HTML":[".html"],
"Java": [".java"],
"JavaScript": [".js"],
"Jupyter Notebooks (Python)": [".ipynb"],
"Kotlin" : [".kt"],
"Lisp" : [".lisp"],
"Matlab" : [".m"],
"PHP": [".php"],
"Perl": [".pl"],
"Python": [".py"],
"R" : [".r"],
"Ruby": [".rb"],
"Rust": [".rs"],
"SQL": [".sql"],
"Shell": [".sh"],
"Swift" : [".swift"],
"TypeScript": [".ts"],
}
```
## Licenses
Each example is also annotated with the license of the associated repository. There are in total 15 licenses:
```python
[
'mit',
'apache-2.0',
'gpl-2.0',
'gpl-3.0',
'bsd-3-clause',
'bsd-2-clause',
'unlicense',
'apacheagpl-3.0',
'lgpl-3.0',
'cc0-1.0',
'epl-1.0',
'lgpl-2.1',
'mpl-2.0',
'isc',
'artistic-2.0'
]
```
## Dataset Statistics
The dataset is about ~ 18 TB uncompressed. We are currently working on processing it and applying further filtering.
## Dataset Creation
The dataset was created in two steps:
1. Files with the extensions given in the list above were retrieved from the GitHub dataset on BigQuery using the following query:
```sql
SELECT
f.id, f.repo_name, f.path, content.copies, content.size, content.content, lic.license
FROM
`bigquery-public-data.github_repos.files` AS f
JOIN
`bigquery-public-data.github_repos.contents` as content
ON
f.id = content.id
JOIN
`bigquery-public-data.github_repos.licenses` AS lic
ON
f.repo_name = lic.repo_name
WHERE
NOT content.binary
AND (
(f.path LIKE '%.py') OR (f.path LIKE '%.java') OR (f.path LIKE '%.js')
OR (f.path LIKE '%.html') OR (f.path LIKE '%.lisp') OR (f.path LIKE '%.sh')
OR (f.path LIKE '%.r') OR (f.path LIKE '%.pl') OR (f.path LIKE '%.css')
OR (f.path LIKE '%.sql') OR (f.path LIKE '%.c') OR (f.path LIKE '%.cpp')
OR (f.path LIKE '%.ts') OR (f.path LIKE '%.cs') OR (f.path LIKE '%.go')
OR (f.path LIKE '%.rs') OR (f.path LIKE '%.swift') OR (f.path LIKE '%.php')
OR (f.path LIKE '%.dart') OR (f.path LIKE '%.kt') OR (f.path LIKE '%.m')
OR (f.path LIKE '%.rb') OR (f.path LIKE '%.ipynb')
)
-- make sure we dont go above 1 megabyte
AND (content.size BETWEEN 1024 AND 1000000)
```
2. Currently, our CodedotAI team is working on adding additional filters and cleaning this dataset.
### Personal and Sensitive Information
Since this data was collected from public repositories, there exists potential for personal and sensitive information to be included in the data through developers accidentally or on purpose uploading their secret keys, passwords, API keys, emails, etc.
## Considerations for Using the Data
### Social Impact of Dataset
The paper ["Evaluating Large Language Models Trained on Code"](https://arxiv.org/abs/2107.03374) from OpenAI has a good discussion on what the impact of a large language model trained on code could be. Therefore, some parts of their discussion are highlighted here as it pertains to this dataset and models that may be trained from it. **As well as some differences in views from the paper, particularly around legal implications**.
1. **Over-reliance:** A language model trained on large datasets such as this one for the task of autogenerating code may generate plausible solutions that may appear correct, but are not necessarily the correct solution. Not properly evaluating the generated code may cause have negative consequences such as the introduction of bugs, or the introduction of security vulnerabilities. Therefore, it is important that users are aware of the limitations and potential negative consequences of using a language model trained on this dataset.
2. **Economic and labor market impacts:** Large language models trained on large code datasets such as this one that are capable of generating high-quality code have the potential to automate part of the software development process. This may negatively impact software developers. However, as discussed in the paper, as shown in the Summary Report of software developers from [O*NET OnLine](https://www.onetonline.org/link/summary/15-1252.00), developers don't just write software.
3. **Security implications:** No filtering or checking of vulnerabilities or buggy code was performed. This means that the dataset may contain code that may be malicious or contain vulnerabilities. Therefore, any model trained on this dataset may generate vulnerable, buggy, or malicious code. In safety-critical software, this could lead to software that may work improperly and could result in serious consequences depending on the software. Additionally, a model trained on this dataset may be used to generate malicious code on purpose in order to perform ransomware or other such attacks.
4. **Legal implications:** No filtering was performed on licensed code. This means that the dataset may contain restrictive licensed code. As discussed in the paper, public Github repositories may fall under "fair use." However, there have been little to no previous cases of such usages of licensed publicly available code. Therefore, any model trained on this dataset may be required to obey license terms that align with the software it was trained on such as GPL-3.0, which is why we purposefully put this dataset under the GPL-3.0 license. It is unclear the legal ramifications of using a language model trained on this dataset.
### v1.0
- The query was executed on _February 1, 2022, 12:15:59 AM EST_
## Acknowledgements
This project would not have been possible without compute generously provided by Google through the [TPU Research Cloud](https://sites.research.google/trc/about/). We would also like to thank [Dr. Razvan Bunescu](https://webpages.charlotte.edu/rbunescu/) and [The College of Computing and Informatics at UNC Charlotte](https://cci.charlotte.edu/) for their generous contributions to this project, specifically in funding the BigQuery and Google Cloud Storage costs. We would also like to thank the [codeparrot team at Hugging face](https://huggingface.co/codeparrot) for open sourcing their documentation on [github-code](https://huggingface.co/datasets/codeparrot/github-code) which we used for the readme in this dataset. For another similar dataset to this please check github-code! |
open-llm-leaderboard-old/details_yhyhy3__med-orca-instruct-33b | open-llm-leaderboard-old | "2023-10-17T22:28:04Z" | 11,168 | 0 | [
"region:us"
] | null | "2023-08-18T11:52:40Z" | ---
pretty_name: Evaluation run of yhyhy3/med-orca-instruct-33b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [yhyhy3/med-orca-instruct-33b](https://huggingface.co/yhyhy3/med-orca-instruct-33b)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 4 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_yhyhy3__med-orca-instruct-33b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-17T22:27:51.480164](https://huggingface.co/datasets/open-llm-leaderboard/details_yhyhy3__med-orca-instruct-33b/blob/main/results_2023-10-17T22-27-51.480164.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0,\n \"\
em_stderr\": 0.0,\n \"f1\": 6.606543624161075e-05,\n \"f1_stderr\"\
: 2.6666679153418564e-05,\n \"acc\": 0.2525651144435675,\n \"acc_stderr\"\
: 0.007025872980895256\n },\n \"harness|drop|3\": {\n \"em\": 0.0,\n\
\ \"em_stderr\": 0.0,\n \"f1\": 6.606543624161075e-05,\n \"\
f1_stderr\": 2.6666679153418564e-05\n },\n \"harness|gsm8k|5\": {\n \
\ \"acc\": 0.0,\n \"acc_stderr\": 0.0\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.505130228887135,\n \"acc_stderr\": 0.014051745961790513\n\
\ }\n}\n```"
repo_url: https://huggingface.co/yhyhy3/med-orca-instruct-33b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_08_09T13_49_32.359108
path:
- '**/details_harness|arc:challenge|25_2023-08-09T13:49:32.359108.parquet'
- split: 2023_08_18T09_03_49.045450
path:
- '**/details_harness|arc:challenge|25_2023-08-18T09:03:49.045450.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-18T09:03:49.045450.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_26T02_39_23.109820
path:
- '**/details_harness|drop|3_2023-09-26T02-39-23.109820.parquet'
- split: 2023_10_17T22_27_51.480164
path:
- '**/details_harness|drop|3_2023-10-17T22-27-51.480164.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-17T22-27-51.480164.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_26T02_39_23.109820
path:
- '**/details_harness|gsm8k|5_2023-09-26T02-39-23.109820.parquet'
- split: 2023_10_17T22_27_51.480164
path:
- '**/details_harness|gsm8k|5_2023-10-17T22-27-51.480164.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-17T22-27-51.480164.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_09T13_49_32.359108
path:
- '**/details_harness|hellaswag|10_2023-08-09T13:49:32.359108.parquet'
- split: 2023_08_18T09_03_49.045450
path:
- '**/details_harness|hellaswag|10_2023-08-18T09:03:49.045450.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-18T09:03:49.045450.parquet'
- config_name: harness_hendrycksTest_5
data_files:
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path:
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- '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T13:49:32.359108.parquet'
- split: 2023_08_18T09_03_49.045450
path:
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- split: latest
path:
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data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_anatomy_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_astronomy_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_business_ethics_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_college_physics_5
data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_formal_logic_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_global_facts_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_09T13_49_32.359108
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T13:49:32.359108.parquet'
- split: 2023_08_18T09_03_49.045450
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-18T09:03:49.045450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-18T09:03:49.045450.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_09T13_49_32.359108
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T13:49:32.359108.parquet'
- split: 2023_08_18T09_03_49.045450
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-18T09:03:49.045450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-18T09:03:49.045450.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_09T13_49_32.359108
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-09T13:49:32.359108.parquet'
- split: 2023_08_18T09_03_49.045450
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-18T09:03:49.045450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-18T09:03:49.045450.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_09T13_49_32.359108
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T13:49:32.359108.parquet'
- split: 2023_08_18T09_03_49.045450
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-18T09:03:49.045450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-18T09:03:49.045450.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_09T13_49_32.359108
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T13:49:32.359108.parquet'
- split: 2023_08_18T09_03_49.045450
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-18T09:03:49.045450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-18T09:03:49.045450.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_09T13_49_32.359108
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T13:49:32.359108.parquet'
- split: 2023_08_18T09_03_49.045450
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-18T09:03:49.045450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-18T09:03:49.045450.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_09T13_49_32.359108
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-09T13:49:32.359108.parquet'
- split: 2023_08_18T09_03_49.045450
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-18T09:03:49.045450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-18T09:03:49.045450.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_09T13_49_32.359108
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-09T13:49:32.359108.parquet'
- split: 2023_08_18T09_03_49.045450
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-18T09:03:49.045450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-18T09:03:49.045450.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_09T13_49_32.359108
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T13:49:32.359108.parquet'
- split: 2023_08_18T09_03_49.045450
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-18T09:03:49.045450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-18T09:03:49.045450.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_09T13_49_32.359108
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T13:49:32.359108.parquet'
- split: 2023_08_18T09_03_49.045450
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-18T09:03:49.045450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-18T09:03:49.045450.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_09T13_49_32.359108
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T13:49:32.359108.parquet'
- split: 2023_08_18T09_03_49.045450
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-18T09:03:49.045450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-18T09:03:49.045450.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_09T13_49_32.359108
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T13:49:32.359108.parquet'
- split: 2023_08_18T09_03_49.045450
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-18T09:03:49.045450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-18T09:03:49.045450.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_09T13_49_32.359108
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T13:49:32.359108.parquet'
- split: 2023_08_18T09_03_49.045450
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-18T09:03:49.045450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-18T09:03:49.045450.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_09T13_49_32.359108
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T13:49:32.359108.parquet'
- split: 2023_08_18T09_03_49.045450
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-18T09:03:49.045450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-18T09:03:49.045450.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_09T13_49_32.359108
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T13:49:32.359108.parquet'
- split: 2023_08_18T09_03_49.045450
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-18T09:03:49.045450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-18T09:03:49.045450.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_09T13_49_32.359108
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T13:49:32.359108.parquet'
- split: 2023_08_18T09_03_49.045450
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-18T09:03:49.045450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-18T09:03:49.045450.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_09T13_49_32.359108
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T13:49:32.359108.parquet'
- split: 2023_08_18T09_03_49.045450
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-18T09:03:49.045450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-18T09:03:49.045450.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_09T13_49_32.359108
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T13:49:32.359108.parquet'
- split: 2023_08_18T09_03_49.045450
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-18T09:03:49.045450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-18T09:03:49.045450.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_09T13_49_32.359108
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T13:49:32.359108.parquet'
- split: 2023_08_18T09_03_49.045450
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-18T09:03:49.045450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-18T09:03:49.045450.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_09T13_49_32.359108
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T13:49:32.359108.parquet'
- split: 2023_08_18T09_03_49.045450
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-18T09:03:49.045450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-18T09:03:49.045450.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_09T13_49_32.359108
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T13:49:32.359108.parquet'
- split: 2023_08_18T09_03_49.045450
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-18T09:03:49.045450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-18T09:03:49.045450.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_09T13_49_32.359108
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-09T13:49:32.359108.parquet'
- split: 2023_08_18T09_03_49.045450
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-18T09:03:49.045450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-18T09:03:49.045450.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_09T13_49_32.359108
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T13:49:32.359108.parquet'
- split: 2023_08_18T09_03_49.045450
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-18T09:03:49.045450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-18T09:03:49.045450.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_09T13_49_32.359108
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-09T13:49:32.359108.parquet'
- split: 2023_08_18T09_03_49.045450
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-18T09:03:49.045450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-18T09:03:49.045450.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_09T13_49_32.359108
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T13:49:32.359108.parquet'
- split: 2023_08_18T09_03_49.045450
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-18T09:03:49.045450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-18T09:03:49.045450.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_09T13_49_32.359108
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-09T13:49:32.359108.parquet'
- split: 2023_08_18T09_03_49.045450
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-18T09:03:49.045450.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-18T09:03:49.045450.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_26T02_39_23.109820
path:
- '**/details_harness|winogrande|5_2023-09-26T02-39-23.109820.parquet'
- split: 2023_10_17T22_27_51.480164
path:
- '**/details_harness|winogrande|5_2023-10-17T22-27-51.480164.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-17T22-27-51.480164.parquet'
- config_name: results
data_files:
- split: 2023_08_09T13_49_32.359108
path:
- results_2023-08-09T13:49:32.359108.parquet
- split: 2023_08_18T09_03_49.045450
path:
- results_2023-08-18T09:03:49.045450.parquet
- split: 2023_09_26T02_39_23.109820
path:
- results_2023-09-26T02-39-23.109820.parquet
- split: 2023_10_17T22_27_51.480164
path:
- results_2023-10-17T22-27-51.480164.parquet
- split: latest
path:
- results_2023-10-17T22-27-51.480164.parquet
---
# Dataset Card for Evaluation run of yhyhy3/med-orca-instruct-33b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/yhyhy3/med-orca-instruct-33b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [yhyhy3/med-orca-instruct-33b](https://huggingface.co/yhyhy3/med-orca-instruct-33b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 4 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_yhyhy3__med-orca-instruct-33b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-17T22:27:51.480164](https://huggingface.co/datasets/open-llm-leaderboard/details_yhyhy3__med-orca-instruct-33b/blob/main/results_2023-10-17T22-27-51.480164.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.0,
"em_stderr": 0.0,
"f1": 6.606543624161075e-05,
"f1_stderr": 2.6666679153418564e-05,
"acc": 0.2525651144435675,
"acc_stderr": 0.007025872980895256
},
"harness|drop|3": {
"em": 0.0,
"em_stderr": 0.0,
"f1": 6.606543624161075e-05,
"f1_stderr": 2.6666679153418564e-05
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.505130228887135,
"acc_stderr": 0.014051745961790513
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
klue/klue | klue | "2024-01-04T14:05:57Z" | 11,158 | 73 | [
"task_categories:fill-mask",
"task_categories:question-answering",
"task_categories:text-classification",
"task_categories:text-generation",
"task_categories:token-classification",
"task_ids:extractive-qa",
"task_ids:named-entity-recognition",
"task_ids:natural-language-inference",
"task_ids:parsing",
"task_ids:semantic-similarity-scoring",
"task_ids:text-scoring",
"task_ids:topic-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"source_datasets:original",
"language:ko",
"license:cc-by-sa-4.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2105.09680",
"region:us",
"relation-extraction"
] | [
"fill-mask",
"question-answering",
"text-classification",
"text-generation",
"token-classification"
] | "2022-03-02T23:29:22Z" | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- ko
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- fill-mask
- question-answering
- text-classification
- text-generation
- token-classification
task_ids:
- extractive-qa
- named-entity-recognition
- natural-language-inference
- parsing
- semantic-similarity-scoring
- text-scoring
- topic-classification
paperswithcode_id: klue
pretty_name: KLUE
config_names:
- dp
- mrc
- ner
- nli
- re
- sts
- wos
- ynat
tags:
- relation-extraction
dataset_info:
- config_name: dp
features:
- name: sentence
dtype: string
- name: index
list: int32
- name: word_form
list: string
- name: lemma
list: string
- name: pos
list: string
- name: head
list: int32
- name: deprel
list: string
splits:
- name: train
num_bytes: 7899965
num_examples: 10000
- name: validation
num_bytes: 1557462
num_examples: 2000
download_size: 3742577
dataset_size: 9457427
- config_name: mrc
features:
- name: title
dtype: string
- name: context
dtype: string
- name: news_category
dtype: string
- name: source
dtype: string
- name: guid
dtype: string
- name: is_impossible
dtype: bool
- name: question_type
dtype: int32
- name: question
dtype: string
- name: answers
sequence:
- name: answer_start
dtype: int32
- name: text
dtype: string
splits:
- name: train
num_bytes: 46505593
num_examples: 17554
- name: validation
num_bytes: 15583017
num_examples: 5841
download_size: 30098472
dataset_size: 62088610
- config_name: ner
features:
- name: sentence
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': B-DT
'1': I-DT
'2': B-LC
'3': I-LC
'4': B-OG
'5': I-OG
'6': B-PS
'7': I-PS
'8': B-QT
'9': I-QT
'10': B-TI
'11': I-TI
'12': O
splits:
- name: train
num_bytes: 19891905
num_examples: 21008
- name: validation
num_bytes: 4937563
num_examples: 5000
download_size: 5265887
dataset_size: 24829468
- config_name: nli
features:
- name: guid
dtype: string
- name: source
dtype: string
- 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: 5719882
num_examples: 24998
- name: validation
num_bytes: 673260
num_examples: 3000
download_size: 2056116
dataset_size: 6393142
- config_name: re
features:
- name: guid
dtype: string
- name: sentence
dtype: string
- name: subject_entity
struct:
- name: word
dtype: string
- name: start_idx
dtype: int32
- name: end_idx
dtype: int32
- name: type
dtype: string
- name: object_entity
struct:
- name: word
dtype: string
- name: start_idx
dtype: int32
- name: end_idx
dtype: int32
- name: type
dtype: string
- name: label
dtype:
class_label:
names:
'0': no_relation
'1': org:dissolved
'2': org:founded
'3': org:place_of_headquarters
'4': org:alternate_names
'5': org:member_of
'6': org:members
'7': org:political/religious_affiliation
'8': org:product
'9': org:founded_by
'10': org:top_members/employees
'11': org:number_of_employees/members
'12': per:date_of_birth
'13': per:date_of_death
'14': per:place_of_birth
'15': per:place_of_death
'16': per:place_of_residence
'17': per:origin
'18': per:employee_of
'19': per:schools_attended
'20': per:alternate_names
'21': per:parents
'22': per:children
'23': per:siblings
'24': per:spouse
'25': per:other_family
'26': per:colleagues
'27': per:product
'28': per:religion
'29': per:title
- name: source
dtype: string
splits:
- name: train
num_bytes: 11145426
num_examples: 32470
- name: validation
num_bytes: 2559272
num_examples: 7765
download_size: 8190257
dataset_size: 13704698
- config_name: sts
features:
- name: guid
dtype: string
- name: source
dtype: string
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: labels
struct:
- name: label
dtype: float64
- name: real-label
dtype: float64
- name: binary-label
dtype:
class_label:
names:
'0': negative
'1': positive
splits:
- name: train
num_bytes: 2832889
num_examples: 11668
- name: validation
num_bytes: 122641
num_examples: 519
download_size: 1587855
dataset_size: 2955530
- config_name: wos
features:
- name: guid
dtype: string
- name: domains
list: string
- name: dialogue
list:
- name: role
dtype: string
- name: text
dtype: string
- name: state
list: string
splits:
- name: train
num_bytes: 26676970
num_examples: 8000
- name: validation
num_bytes: 3488911
num_examples: 1000
download_size: 6358855
dataset_size: 30165881
- config_name: ynat
features:
- name: guid
dtype: string
- name: title
dtype: string
- name: label
dtype:
class_label:
names:
'0': IT과학
'1': 경제
'2': 사회
'3': 생활문화
'4': 세계
'5': 스포츠
'6': 정치
- name: url
dtype: string
- name: date
dtype: string
splits:
- name: train
num_bytes: 10109584
num_examples: 45678
- name: validation
num_bytes: 2039181
num_examples: 9107
download_size: 5012303
dataset_size: 12148765
configs:
- config_name: dp
data_files:
- split: train
path: dp/train-*
- split: validation
path: dp/validation-*
- config_name: mrc
data_files:
- split: train
path: mrc/train-*
- split: validation
path: mrc/validation-*
- config_name: ner
data_files:
- split: train
path: ner/train-*
- split: validation
path: ner/validation-*
- config_name: nli
data_files:
- split: train
path: nli/train-*
- split: validation
path: nli/validation-*
- config_name: re
data_files:
- split: train
path: re/train-*
- split: validation
path: re/validation-*
- config_name: sts
data_files:
- split: train
path: sts/train-*
- split: validation
path: sts/validation-*
- config_name: wos
data_files:
- split: train
path: wos/train-*
- split: validation
path: wos/validation-*
- config_name: ynat
data_files:
- split: train
path: ynat/train-*
- split: validation
path: ynat/validation-*
---
# Dataset Card for KLUE
## 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://klue-benchmark.com/
- **Repository:** https://github.com/KLUE-benchmark/KLUE
- **Paper:** [KLUE: Korean Language Understanding Evaluation](https://arxiv.org/abs/2105.09680)
- **Leaderboard:** [Leaderboard](https://klue-benchmark.com/leaderboard)
- **Point of Contact:** https://github.com/KLUE-benchmark/KLUE/issues
### Dataset Summary
KLUE is a collection of 8 tasks to evaluate natural language understanding capability of Korean language models. We delibrately select the 8 tasks, which are Topic Classification, Semantic Textual Similarity, Natural Language Inference, Named Entity Recognition, Relation Extraction, Dependency Parsing, Machine Reading Comprehension, and Dialogue State Tracking.
### Supported Tasks and Leaderboards
Topic Classification, Semantic Textual Similarity, Natural Language Inference, Named Entity Recognition, Relation Extraction, Dependency Parsing, Machine Reading Comprehension, and Dialogue State Tracking
### Languages
`ko-KR`
## Dataset Structure
### Data Instances
#### ynat
An example of 'train' looks as follows.
```
{'date': '2016.06.30. 오전 10:36',
'guid': 'ynat-v1_train_00000',
'label': 3,
'title': '유튜브 내달 2일까지 크리에이터 지원 공간 운영',
'url': 'https://news.naver.com/main/read.nhn?mode=LS2D&mid=shm&sid1=105&sid2=227&oid=001&aid=0008508947'}
```
#### sts
An example of 'train' looks as follows.
```
{'guid': 'klue-sts-v1_train_00000',
'labels': {'label': 3.7, 'real-label': 3.714285714285714, 'binary-label': 1},
'sentence1': '숙소 위치는 찾기 쉽고 일반적인 한국의 반지하 숙소입니다.',
'sentence2': '숙박시설의 위치는 쉽게 찾을 수 있고 한국의 대표적인 반지하 숙박시설입니다.',
'source': 'airbnb-rtt'}
```
#### nli
An example of 'train' looks as follows.
```
{'guid': 'klue-nli-v1_train_00000',
'hypothesis': '힛걸 진심 최고로 멋지다.',
'label': 0,
'premise': '힛걸 진심 최고다 그 어떤 히어로보다 멋지다',
'source': 'NSMC'}
```
#### ner
An example of 'train' looks as follows.
```
{'tokens': ['특', '히', ' ', '영', '동', '고', '속', '도', '로', ' ', '강', '릉', ' ', '방', '향', ' ', '문', '막', '휴', '게', '소', '에', '서', ' ', '만', '종', '분', '기', '점', '까', '지', ' ', '5', '㎞', ' ', '구', '간', '에', '는', ' ', '승', '용', '차', ' ', '전', '용', ' ', '임', '시', ' ', '갓', '길', '차', '로', '제', '를', ' ', '운', '영', '하', '기', '로', ' ', '했', '다', '.'],
'ner_tags': [12, 12, 12, 2, 3, 3, 3, 3, 3, 12, 2, 3, 12, 12, 12, 12, 2, 3, 3, 3, 3, 12, 12, 12, 2, 3, 3, 3, 3, 12, 12, 12, 8, 9, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12],
'sentence': '특히 <영동고속도로:LC> <강릉:LC> 방향 <문막휴게소:LC>에서 <만종분기점:LC>까지 <5㎞:QT> 구간에는 승용차 전용 임시 갓길차로제를 운영하기로 했다.'}
```
#### re
An example of 'train' looks as follows.
```
{'guid': 'klue-re-v1_train_00000',
'label': 0,
'object_entity': {'word': '조지 해리슨',
'start_idx': 13,
'end_idx': 18,
'type': 'PER'},
'sentence': '〈Something〉는 조지 해리슨이 쓰고 비틀즈가 1969년 앨범 《Abbey Road》에 담은 노래다.',
'source': 'wikipedia',
'subject_entity': {'word': '비틀즈',
'start_idx': 24,
'end_idx': 26,
'type': 'ORG'}}
```
#### dp
An example of 'train' looks as follows.
```
{'deprel': ['NP', 'NP_OBJ', 'VP', 'NP', 'NP_SBJ', 'NP', 'NP_MOD', 'NP_CNJ', 'NP_CNJ', 'NP', 'NP', 'NP_OBJ', 'AP', 'VP'],
'head': [2, 3, 14, 5, 14, 7, 10, 10, 10, 11, 12, 14, 14, 0],
'index': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14],
'lemma': ['해당', '그림 을', '보 면', '디즈니', '공주 들 이', '브리트니', '스피어스 의', '앨범 이나', '뮤직 비디오 ,', '화보', '속', '모습 을', '똑같이', '재연 하 였 다 .'],
'pos': ['NNG', 'NNG+JKO', 'VV+EC', 'NNP', 'NNG+XSN+JKS', 'NNP', 'NNP+JKG', 'NNG+JC', 'NNG+NNG+SP', 'NNG', 'NNG', 'NNG+JKO', 'MAG', 'NNG+XSA+EP+EF+SF'],
'sentence': '해당 그림을 보면 디즈니 공주들이 브리트니 스피어스의 앨범이나 뮤직비디오, 화보 속 모습을 똑같이 재연했다.',
'word_form': ['해당', '그림을', '보면', '디즈니', '공주들이', '브리트니', '스피어스의', '앨범이나', '뮤직비디오,', '화보', '속', '모습을', '똑같이', '재연했다.']}
```
#### mrc
An example of 'train' looks as follows.
```
{'answers': {'answer_start': [478, 478], 'text': ['한 달가량', '한 달']},
'context': '올여름 장마가 17일 제주도에서 시작됐다. 서울 등 중부지방은 예년보다 사나흘 정도 늦은 이달 말께 장마가 시작될 전망이다.17일 기상청에 따르면 제주도 남쪽 먼바다에 있는 장마전선의 영향으로 이날 제주도 산간 및 내륙지역에 호우주의보가 내려지면서 곳곳에 100㎜에 육박하는 많은 비가 내렸다. 제주의 장마는 평년보다 2~3일, 지난해보다는 하루 일찍 시작됐다. 장마는 고온다습한 북태평양 기단과 한랭 습윤한 오호츠크해 기단이 만나 형성되는 장마전선에서 내리는 비를 뜻한다.장마전선은 18일 제주도 먼 남쪽 해상으로 내려갔다가 20일께 다시 북상해 전남 남해안까지 영향을 줄 것으로 보인다. 이에 따라 20~21일 남부지방에도 예년보다 사흘 정도 장마가 일찍 찾아올 전망이다. 그러나 장마전선을 밀어올리는 북태평양 고기압 세력이 약해 서울 등 중부지방은 평년보다 사나흘가량 늦은 이달 말부터 장마가 시작될 것이라는 게 기상청의 설명이다. 장마전선은 이후 한 달가량 한반도 중남부를 오르내리며 곳곳에 비를 뿌릴 전망이다. 최근 30년간 평균치에 따르면 중부지방의 장마 시작일은 6월24~25일이었으며 장마기간은 32일, 강수일수는 17.2일이었다.기상청은 올해 장마기간의 평균 강수량이 350~400㎜로 평년과 비슷하거나 적을 것으로 내다봤다. 브라질 월드컵 한국과 러시아의 경기가 열리는 18일 오전 서울은 대체로 구름이 많이 끼지만 비는 오지 않을 것으로 예상돼 거리 응원에는 지장이 없을 전망이다.',
'guid': 'klue-mrc-v1_train_12759',
'is_impossible': False,
'news_category': '종합',
'question': '북태평양 기단과 오호츠크해 기단이 만나 국내에 머무르는 기간은?',
'question_type': 1,
'source': 'hankyung',
'title': '제주도 장마 시작 … 중부는 이달 말부터'}
```
#### wos
An example of 'train' looks as follows.
```
{'dialogue': [{'role': 'user',
'text': '쇼핑을 하려는데 서울 서쪽에 있을까요?',
'state': ['관광-종류-쇼핑', '관광-지역-서울 서쪽']},
{'role': 'sys',
'text': '서울 서쪽에 쇼핑이 가능한 곳이라면 노량진 수산물 도매시장이 있습니다.',
'state': []},
{'role': 'user',
'text': '오 네 거기 주소 좀 알려주세요.',
'state': ['관광-종류-쇼핑', '관광-지역-서울 서쪽', '관광-이름-노량진 수산물 도매시장']},
{'role': 'sys', 'text': '노량진 수산물 도매시장의 주소는 서울 동작구 93806입니다.', 'state': []},
{'role': 'user',
'text': '알려주시는김에 연락처랑 평점도 좀 알려주세요.',
'state': ['관광-종류-쇼핑', '관광-지역-서울 서쪽', '관광-이름-노량진 수산물 도매시장']},
{'role': 'sys', 'text': '그럼. 연락처는 6182006591이고 평점은 4점입니다.', 'state': []},
{'role': 'user',
'text': '와 감사합니다.',
'state': ['관광-종류-쇼핑', '관광-지역-서울 서쪽', '관광-이름-노량진 수산물 도매시장']},
{'role': 'sys', 'text': '감사합니다.', 'state': []}],
'domains': ['관광'],
'guid': 'wos-v1_train_00001'}
```
### Data Fields
#### ynat
+ `guid`: a `string` feature
+ `title`: a `string` feature
+ `label`: a classification label, with possible values `IT과학`(0), `경제`(1), `사회`(2), `생활문화`(3), `세계`(4), `스포츠`(5), `정치`(6)
+ `url`: a `string` feature
+ `date`: a `string` feature
#### sts
+ `guid`: a `string` feature
+ `source`: a `string` feature
+ `sentence1`: a `string` feature
+ `sentence2`: a `string` feature
+ `labels`: a dictionary feature containing
+ `label`: a `float64` feature
+ `real-label`: a `float64` feature
+ `binary-label`: a classification label, with possible values `negative`(0), `positive`(1)
#### nli
+ `guid`: a `string` feature
+ `source`: a `string` feature
+ `premise`: a `string` feature
+ `hypothesis`: a `string` feature
+ `label`: a classification label, with possible values `entailment`(0), `neutral`(1), `contradiction`(2)
#### ner
+ `sentence`: a `string` feature
+ `tokens`: a list of a `string` feature (tokenization is at character level)
+ `ner_tags`: a list of classification labels, with possible values including `B-DT`(0), `I-DT`(1),
`B-LC`(2), `I-LC`(3), `B-OG`(4), `I-OG`(5), `B-PS`(6), `I-PS`(7), `B-QT`(8), `I-QT`(9), `B-TI`(10),
`I-TI`(11), `O`(12)
#### re
+ `guid`: a `string` feature
+ `sentence`: a `string` feature
+ `subject_entity`: a dictionary feature containing
+ `word`: a `string` feature
+ `start_idx`: a `int32` feature
+ `end_idx`: a `int32` feature
+ `type`: a `string` feature
+ `object_entity`: a dictionary feature containing
+ `word`: a `string` feature
+ `start_idx`: a `int32` feature
+ `end_idx`: a `int32` feature
+ `type`: a `string` feature
+ `label`: a list of labels, with possible values including `no_relation`(0), `org:dissolved`(1),
`org:founded`(2), `org:place_of_headquarters`(3), `org:alternate_names`(4), `org:member_of`(5),
`org:members`(6), `org:political/religious_affiliation`(7), `org:product`(8), `org:founded_by`(9),`org:top_members/employees`(10),
`org:number_of_employees/members`(11), `per:date_of_birth`(12), `per:date_of_death`(13), `per:place_of_birth`(14),
`per:place_of_death`(15), `per:place_of_residence`(16), `per:origin`(17), `per:employee_of`(18),
`per:schools_attended`(19), `per:alternate_names`(20), `per:parents`(21), `per:children`(22),
`per:siblings`(23), `per:spouse`(24), `per:other_family`(25), `per:colleagues`(26), `per:product`(27),
`per:religion`(28), `per:title`(29),
+ `source`: a `string` feature
#### dp
+ `sentence`: a `string` feature
+ `index`: a list of `int32` feature
+ `word_form`: a list of `string` feature
+ `lemma`: a list of `string` feature
+ `pos`: a list of `string` feature
+ `head`: a list of `int32` feature
+ `deprel`: a list of `string` feature
#### mrc
+ `title`: a `string` feature
+ `context`: a `string` feature
+ `news_category`: a `string` feature
+ `source`: a `string` feature
+ `guid`: a `string` feature
+ `is_impossible`: a `bool` feature
+ `question_type`: a `int32` feature
+ `question`: a `string` feature
+ `answers`: a dictionary feature containing
+ `answer_start`: a `int32` feature
+ `text`: a `string` feature
#### wos
+ `guid`: a `string` feature
+ `domains`: a `string` feature
+ `dialogue`: a list of dictionary feature containing
+ `role`: a `string` feature
+ `text`: a `string` feature
+ `state`: a `string` feature
### Data Splits
#### ynat
You can see more details in [here](https://klue-benchmark.com/tasks/66/data/description).
+ train: 45,678
+ validation: 9,107
#### sts
You can see more details in [here](https://klue-benchmark.com/tasks/67/data/description).
+ train: 11,668
+ validation: 519
#### nli
You can see more details in [here](https://klue-benchmark.com/tasks/68/data/description).
+ train: 24,998
+ validation: 3,000
#### ner
You can see more details in [here](https://klue-benchmark.com/tasks/69/overview/description).
+ train: 21,008
+ validation: 5,000
#### re
You can see more details in [here](https://klue-benchmark.com/tasks/70/overview/description).
+ train: 32,470
+ validation: 7,765
#### dp
You can see more details in [here](https://klue-benchmark.com/tasks/71/data/description).
+ train: 10,000
+ validation: 2,000
#### mrc
You can see more details in [here](https://klue-benchmark.com/tasks/72/overview/description).
+ train: 17,554
+ validation: 5,841
#### wos
You can see more details in [here](https://klue-benchmark.com/tasks/73/overview/description).
+ train: 8,000
+ validation: 1,000
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
```
@misc{park2021klue,
title={KLUE: Korean Language Understanding Evaluation},
author={Sungjoon Park and Jihyung Moon and Sungdong Kim and Won Ik Cho and Jiyoon Han and Jangwon Park and Chisung Song and Junseong Kim and Yongsook Song and Taehwan Oh and Joohong Lee and Juhyun Oh and Sungwon Lyu and Younghoon Jeong and Inkwon Lee and Sangwoo Seo and Dongjun Lee and Hyunwoo Kim and Myeonghwa Lee and Seongbo Jang and Seungwon Do and Sunkyoung Kim and Kyungtae Lim and Jongwon Lee and Kyumin Park and Jamin Shin and Seonghyun Kim and Lucy Park and Alice Oh and Jungwoo Ha and Kyunghyun Cho},
year={2021},
eprint={2105.09680},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@jungwhank](https://github.com/jungwhank), [@bzantium](https://github.com/bzantium) for adding this dataset. |
MarkJeong/aihub_food | MarkJeong | "2023-03-09T17:13:22Z" | 11,158 | 1 | [
"size_categories:100K<n<1M",
"format:parquet",
"modality:image",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2023-03-09T02:39:58Z" | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': '01011001'
'1': '01012001'
'2': '01012002'
'3': '01012003'
'4': '01012004'
'5': '01012005'
'6': '01012006'
'7': '01013001'
'8': 01014008
'9': 01014009
'10': '01014010'
'11': '01014011'
'12': '01014012'
'13': '01014013'
'14': '01015002'
'15': '01015003'
'16': '01015012'
'17': '01015013'
'18': '01015014'
'19': '01015015'
'20': '01015016'
'21': '01015017'
'22': 01015018
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'27': '01016004'
'28': '01016005'
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'30': '01016007'
'31': 01016008
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'64': '02011040'
'65': '02012001'
'66': '02012002'
'67': '02012003'
'68': '02012004'
'69': '02012005'
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'72': '03011003'
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'81': '03012001'
'82': '03012002'
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'99': '04012002'
'100': '04012003'
'101': '04012004'
'102': '04012005'
'103': '04012006'
'104': '04012007'
'105': 04012008
'106': 04012009
'107': '04012010'
'108': '04012011'
'109': '04012012'
'110': '04012013'
'111': '04013002'
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'113': '04013004'
'114': '04013005'
'115': '04013006'
'116': '04013007'
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'144': '05011001'
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'148': '05011010'
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'151': '05012001'
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'153': '05012003'
'154': '05012004'
'155': '05012005'
'156': '05013001'
'157': '06012001'
'158': '06012002'
'159': '06012003'
'160': '06012011'
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'227': '11011011'
'228': '11012001'
'229': '11012002'
'230': '11012003'
'231': '11012004'
'232': '11013001'
'233': '11013002'
'234': '11013003'
'235': '11013004'
'236': '11013005'
'237': '11013006'
'238': '11013007'
'239': '11013009'
'240': '11013010'
'241': '11013011'
'242': '11013012'
'243': '11014001'
'244': '11014002'
'245': '11014003'
'246': '11014004'
'247': '11014005'
'248': '11014006'
'249': '11014007'
'250': '11014008'
'251': '11014009'
'252': '11014010'
'253': '11015001'
'254': '11015002'
'255': '12011001'
'256': '12011002'
'257': '12011003'
'258': '12011004'
'259': '12011005'
'260': '12011006'
'261': '12011007'
'262': '12011008'
'263': '12011009'
'264': '12011010'
'265': '12011011'
'266': '12011012'
'267': '12011013'
'268': '12011014'
'269': '12011015'
'270': '13011001'
'271': '13011002'
'272': '13011003'
'273': '13011011'
'274': '13011012'
'275': '13012001'
'276': '13012002'
'277': '14011001'
'278': '14011002'
'279': '14011004'
'280': '14011005'
'281': '14012001'
'282': '14012002'
'283': '15011001'
'284': '15011002'
'285': '15011003'
'286': '15011004'
'287': '15011005'
'288': '15011006'
'289': '15011007'
'290': '15011008'
'291': '15011009'
'292': '15011010'
'293': '15011011'
'294': '15011012'
'295': '15011013'
'296': '15011014'
'297': '15011015'
'298': '15011016'
'299': '15011017'
'300': '16011001'
'301': '16011002'
'302': '16011003'
'303': '16011004'
'304': '16011005'
'305': '16011006'
splits:
- name: train
num_bytes: 14812723538.728
num_examples: 486839
- name: test
num_bytes: 33069619665.134
num_examples: 21178
- name: validation
num_bytes: 33770989851.48
num_examples: 21180
download_size: 82692432131
dataset_size: 81653333055.342
---
# Dataset Card for "aihub_food"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
KShivendu/dbpedia-entities-openai-1M | KShivendu | "2024-02-19T08:24:43Z" | 11,129 | 20 | [
"task_categories:feature-extraction",
"language:en",
"license:mit",
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"modality:timeseries",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | [
"feature-extraction"
] | "2023-06-20T22:29:43Z" | ---
license: mit
dataset_info:
features:
- name: _id
dtype: string
- name: title
dtype: string
- name: text
dtype: string
- name: openai
sequence: float32
splits:
- name: train
num_bytes: 12383152
num_examples: 1000000
download_size: 12383152
dataset_size: 1000000
language:
- en
task_categories:
- feature-extraction
pretty_name: OpenAI 1M with DBPedia Entities
size_categories:
- 1M<n<10M
---
1M OpenAI Embeddings -- 1536 dimensions
Created: June 2023.
Text used for Embedding: title (string) + text (string)
Embedding Model: text-embedding-ada-002
First used for the pgvector vs VectorDB (Qdrant) benchmark: https://nirantk.com/writing/pgvector-vs-qdrant/
### Future work
We are planning to take this up to 10M (and possibly 100M) vectors. Contact [@KShivendu_](https://twitter.com/KShivendu_) on Twitter or mail to [email protected] if you want to help :)
### Credits:
This dataset was generated from the first 1M entries of https://huggingface.co/datasets/BeIR/dbpedia-entity |
HuggingFaceTB/smollm-corpus | HuggingFaceTB | "2024-09-06T07:04:57Z" | 11,124 | 315 | [
"language:en",
"license:odc-by",
"size_categories:100M<n<1B",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-07-15T13:51:48Z" | ---
license: odc-by
dataset_info:
- config_name: cosmopedia-v2
features:
- name: prompt
dtype: string
- name: text
dtype: string
- name: token_length
dtype: int64
- name: audience
dtype: string
- name: format
dtype: string
- name: seed_data
dtype: string
splits:
- name: train
num_bytes: 212503640747
num_examples: 39134000
download_size: 122361137711
dataset_size: 212503640747
- config_name: fineweb-edu-dedup
features:
- name: text
dtype: string
- name: id
dtype: string
- name: metadata
struct:
- name: dump
dtype: string
- name: url
dtype: string
- name: date
dtype: timestamp[s]
- name: file_path
dtype: string
- name: language
dtype: string
- name: language_score
dtype: float64
- name: token_count
dtype: int64
- name: score
dtype: float64
- name: int_score
dtype: int64
splits:
- name: train
num_bytes: 957570164451
num_examples: 190168005
download_size: 550069279849
dataset_size: 957570164451
- config_name: python-edu
features:
- name: blob_id
dtype: string
- name: repo_name
dtype: string
- name: path
dtype: string
- name: length_bytes
dtype: int64
- name: score
dtype: float64
- name: int_score
dtype: int64
splits:
- name: train
num_bytes: 989334135
num_examples: 7678448
download_size: 643903049
dataset_size: 989334135
configs:
- config_name: cosmopedia-v2
data_files:
- split: train
path: cosmopedia-v2/train-*
- config_name: fineweb-edu-dedup
data_files:
- split: train
path: fineweb-edu-dedup/train-*
- config_name: python-edu
data_files:
- split: train
path: python-edu/train-*
language:
- en
---
# SmolLM-Corpus
This dataset is a curated collection of high-quality educational and synthetic data designed for training small language models.
You can find more details about the models trained on this dataset in our [SmolLM blog post](https://huggingface.co/blog/smollm).
# Dataset subsets
## Cosmopedia v2
Cosmopedia v2 is an enhanced version of Cosmopedia, the largest synthetic dataset for pre-training, consisting of over 39 million textbooks, blog posts, and stories generated by [Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1).
Most of the samples are generated by prompting the model to generate content on specific topics using a web page referred to as a "seed sample," as shown in Figure 1. We use web samples to increase diversity and expand the range of prompts.
You can find more details in this [blog post](https://huggingface.co/blog/smollm).
### Dataset Features
* `prompt (string)`: The input prompt used to generate the text.
* `text (string)`: The generated text content.
* `token_length (int64)`: The length of the text in tokens (Mistral-7B tokenizer).
* `audience (string)`: The intended audience for the content.
* `format (string)`: The format of the content (e.g., textbook, story).
* `seed_data (string)`: The seed sample used to generate the text.
### Loading the dataset
```python
from datasets import load_dataset
ds = load_dataset("HuggingFaceTB/smollm-corpus", "cosmopedia-v2", split="train", num_proc=16)
print(ds[0])
```
## Python-Edu
The `python-edu` subset consists of Python files that were scored 4 or more by the [educational code model](https://huggingface.co/HuggingFaceTB/python-edu-scorer).
The files were extracted from the [`stack-v2-train`](https://huggingface.co/datasets/bigcode/the-stack-v2-train-full-ids) dataset.
### Dataset Features
* `blob_id (string)`: Software Heritage (SWH) ID of the file on AWS S3.
* `repo_name (string)`: Repository name on GitHub.
* `path (string)`: The file path within the repository.
* `length_bytes (int64)`: Length of the file content in UTF-8 bytes.
* `score (float32)`: The output of the educational scoring model.
* `int_score (uint8)`: The rounded educational score.
### Downloading the data
The file contents are downloaded from Software Heritage's S3 bucket to ensure data compliance.
Please refer to [the-stack-v2](https://huggingface.co/datasets/bigcode/the-stack-v2-train-full-ids) for the data license.
When running on a 16-core AWS `us-east-1` instance, this script takes ~6 hours to download the files:
```python
import boto3
import gzip
from datasets import load_dataset
from botocore.exceptions import ClientError
num_proc = 16
s3 = boto3.client('s3')
bucket_name = "softwareheritage"
def download_contents(blob_id):
key = f"content/{blob_id}"
try:
obj = s3.get_object(Bucket=bucket_name, Key=key)
with gzip.GzipFile(fileobj=obj['Body']) as fin:
content = fin.read().decode("utf-8", errors="ignore")
return {"text": content, "download_success": True}
except ClientError as e:
if e.response['Error']['Code'] == 'NoSuchKey':
print(f"File not found: {key}")
return {"text": "", "download_success": False}
else:
raise
ds = load_dataset("HuggingFaceTB/smollm-corpus", "python-edu", split="train", num_proc=num_proc)
ds = ds.map(download_contents, input_columns="blob_id", num_proc=num_proc)
# Filter out failed downloads
ds = ds.filter(lambda x: x['download_success'])
# Optionally, print the first example to verify the data
print(ds[0])
```
## FineWeb-Edu (deduplicated)
FineWeb-Edu-Dedup is a deduplicated subset of the [FineWeb-Edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu) dataset, containing 220 billion tokens of educational web pages.
The source dataset was filtered using an educational quality classifier to retain only the highest quality educational content.
For more information refer to the [FineWeb-v1 blog post](https://huggingface.co/spaces/HuggingFaceFW/blogpost-fineweb-v1)
### Dataset Features
* `text (string)`: The web page's text content.
* `id (string)`: Unique ID of the web page.
* `metadata (struct)`: Metadata about the web page, including:
* `dump (string)`: The source CommonCrawl dump.
* `url (string)`: The URL of the web page.
* `date (timestamp[s])`: The date the web page was captured.
* `file_path (string)`: The file path of the commoncrawl snapshot.
* `language (string)`: The language of the web page.
* `language_score (float64)`: The language probability.
* `token_count (int64)`: The token count of the web page (gpt2 tokenizer).
* `score (float64)`: The educational quality score.
* `int_score (int64)`: The rounded educational quality score.
### Loading the dataset
```python
from datasets import load_dataset
ds = load_dataset("HuggingFaceTB/smollm-corpus", "fineweb-edu-dedup", split="train", num_proc=16)
print(ds[0])
```
## Citation
```
@software{benallal2024smollmcorpus,
author = {Ben Allal, Loubna and Lozhkov, Anton and Penedo, Guilherme and Wolf, Thomas and von Werra, Leandro},
title = {SmolLM-Corpus},
month = July,
year = 2024,
url = {https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus}
}
``` |
pppppppppp2/planeperturbed | pppppppppp2 | "2023-10-13T11:12:52Z" | 11,110 | 1 | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2023-06-08T19:52:28Z" | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 647755473.5
num_examples: 5500
download_size: 622143522
dataset_size: 647755473.5
---
# Dataset Card for "planeperturbed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
tiange/Cap3D | tiange | "2025-03-21T22:43:21Z" | 11,106 | 104 | [
"task_categories:text-to-3d",
"task_categories:image-to-3d",
"license:odc-by",
"arxiv:2306.07279",
"arxiv:2404.07984",
"arxiv:2212.08051",
"arxiv:2307.05663",
"arxiv:2110.06199",
"arxiv:1512.03012",
"region:us"
] | [
"text-to-3d",
"image-to-3d"
] | "2023-05-28T18:31:58Z" | ---
license: odc-by
viewer: false
task_categories:
- text-to-3d
- image-to-3d
---
## Dataset Description
- **Paper:** [Scalable 3D Captioning with Pretrained Models](https://arxiv.org/abs/2306.07279)
- **Paper:** [View Selection for 3D Captioning via Diffusion Ranking](https://arxiv.org/abs/2404.07984)
- **Repository**: [Github_Cap3D](https://github.com/crockwell/Cap3D)
- **Repository**: [Github_DiffuRank](https://github.com/tiangeluo/DiffuRank)
- **Project**: [Project](https://cap3d-um.github.io/)
This repository hosts data for [Scalable 3D Captioning with Pretrained Models](https://cap3d-um.github.io/) and [View Selection for 3D Captioning via Diffusion Ranking](http://arxiv.org/abs/2404.07984), including descriptive **captions** for 3D objects in [Objaverse](https://arxiv.org/abs/2212.08051), [Objaverse-XL](https://arxiv.org/pdf/2307.05663.pdf), [ABO](https://arxiv.org/abs/2110.06199), and [ShapeNet](https://arxiv.org/abs/1512.03012). This repo also includes **point clouds** and **rendered images with camera, depth, and MatAlpha information** of Objaverse objects, as well as their Shap-E latent codes. All the captions and data provided by our papers are released under ODC-By 1.0 license.
## Very important license & data remove information
Please ensure compliance with the licenses specified for each object in the Objaverse annotations. Note that certain objects are not approved for commercial use.
If you are the creator of an asset and would like your 3D model’s information removed from the Cap3D-DiffuRank dataset, please contact [Tiange](mailto:[email protected]) for assistance. We sincerely thank all contributors—your efforts are instrumental in advancing the 3D vision community. This dataset repository is a humble addition, built upon the foundation of your contributions and shared work.
## Usage
Please download and unzip files from [**Page**](https://huggingface.co/datasets/tiange/Cap3D/tree/main) according to your usage. Below is a table listing fiels descriptions, followed by example Python scripts for data loading.
| Filename | Description |
| -------------------------------------- | ------------------------------------------------------------ |
| **Cap3D_automated_Objaverse_full.csv** | By integrating text descriptions initially generated by [**Cap3D**](https://arxiv.org/abs/2306.07279) and refined by [**DiffuRank**](https://arxiv.org/abs/2404.07984), we produced **1,816,350** 3D-caption pairs for Objaverse objects. <br>- **785,150** for [**Objaverse**](https://arxiv.org/abs/2212.08051); <br>- the remainder for [**Objaverse-XL**](https://arxiv.org/pdf/2307.05663.pdf), primarily from the high-quality subset described in **Section 4.1 (Alignment Finetuning)** of the [Objaverse-XL paper](https://proceedings.neurips.cc/paper_files/paper/2023/file/70364304877b5e767de4e9a2a511be0c-Paper-Datasets_and_Benchmarks.pdf), retrieved via `alignment_annotations = oxl.get_alignment_annotations()`; <br>- identifiers of length **32 characters** are Objaverse 1.0 **UIDs** (`import objaverse; uids = objaverse.load_uids()`), while those with **64 characters** are **SHA256 hashes** from Objaverse-XL. |
| Cap3D_automated_**ABO**.csv | Our captions generated by [Cap3D](https://arxiv.org/abs/2306.07279) and [DiffuRank](https://arxiv.org/abs/2404.07984) for the [ABO dataset](https://arxiv.org/abs/2110.06199), including both general and compositional descriptions. |
| Cap3D_automated_**ShapeNet**.csv | Our captions generated by [Cap3D](https://arxiv.org/abs/2306.07279) and [DiffuRank](https://arxiv.org/abs/2404.07984) for the [ShapeNet dataset](https://arxiv.org/abs/1512.03012). |
| **PointCloud_zips** | Provided by [Cap3D](https://arxiv.org/abs/2306.07279) and [DiffuRank](https://arxiv.org/abs/2404.07984), **1,006,782** PointClouds (16,384 colorful points) extracted from Objaverse objects. Saved as `.ply` file. `compressed_pcs_{00~09}.zip` are for Objaverse objects and `compressed_pcs_{>=10}.zip` for Objaverse-XL objects. |
| PointCloud_zips_**ABO** | Provided by [Cap3D](https://arxiv.org/abs/2306.07279) and [DiffuRank](https://arxiv.org/abs/2404.07984), **7,953** PointClouds (16,384 colorful points) extracted from ABO objects. Saved as `.ply` file. |
| PointCloud_zips_**ShapeNet** | Provided by [Cap3D](https://arxiv.org/abs/2306.07279) and [DiffuRank](https://arxiv.org/abs/2404.07984), **52,472** PointClouds (16,384 colorful points) extracted from ShapeNet objects. Saved as `.ply` file. |
| **RenderedImage_perobj_zips** | Provided by [DiffuRank](https://arxiv.org/abs/2404.07984), Rendered images for **1,006,782** Objaverse objects. Once unzip `compressed_imgs_perobj_xx.zip` will have multiple zip files which consists of **20** rendered images along with camera details (intrinsic & extrinsic), depth data, and masks ([one example](https://huggingface.co/datasets/tiange/Cap3D/tree/main/RenderedImage_perobj_zips/example_zipfile)). Please specify the unzip path, such as `unzip ed51a51909ee46c780db3a85e821feb2.zip -d ed51a51909ee46c780db3a85e821feb2`. `compressed_imgs_perobj_{00~52}.zip` are for Objaverse objects and `compressed_imgs_perobj_{>=53}.zip` for Objaverse-XL objects. **More information are in [here](https://huggingface.co/datasets/tiange/Cap3D/blob/main/RenderedImage_perobj_zips/README.md).** |
| RenderedImage_perobj_zips_**ABO** | Provided by [DiffuRank](https://arxiv.org/abs/2404.07984), Rendered images for **7,953** ABO objects. Details similar to the above. |
| RenderedImage_perobj_zips_**ShapeNet** | Provided by [DiffuRank](https://arxiv.org/abs/2404.07984), Rendered images for **52,472** ShapeNet objects. Similar to the above but with 8 rendered images. |
| misc | Including miscellaneous files such as human-authored captions, finetuned models, objaverse pointclouds stored as .pt, shapE latent codes, and etc. Please refer to this [README](https://huggingface.co/datasets/tiange/Cap3D/blob/main/misc/README.md) |
``` python
# load our captions
import pandas as pd
captions = pd.read_csv('Cap3D_automated_Objaverse_full.csv', header=None)
## captions:
## 0 1
## 0 ed51a51909ee46c780db3a85e821feb2 Matte green rifle with a long barrel, stock, a...
## 1 9110b606f6c547b2980fcb3c8c4b6a1c Rustic single-story building with a weathered ...
## 2 80d9caaa1fa04502af666135196456e1 a pair of purple and black swords with white h...
## 3 28d43a218cd8466a8c1f82b29b71e314 3D model of a cluttered outdoor scene with veg...
## 4 75582285fab442a2ba31733f9c8fae66 Floating terrain piece with grassy landscape a...
## ... ... ...
## 1002417 3623e74f34c1c3c523af6b2bb8ffcbe2d2dce897ef61b9... Abstract 3D composition with human figures and...
## 1002418 64e9f7b7a1fc4c4ec56ed8b5917dfd610930043ac5e15f... 3D object with a rough, irregular pink surface...
## 1002419 fcd089d6a237fee21dfd5f0d6d9b74b2fd1150cdc61c7f... Bright pink abstract 3D model of a building wi...
## 1002420 f812dc980050f2d5f4b37df2a8620372f810dd6456a5f2... Monochromatic gray 3D model of a stylized huma...
## 1002421 77c09500b4d8e4b881e1ce6929d56c23658b87173c0996... Modular futuristic spacecraft with red and ora...
## if u want to obtain the caption for specific UID
caption = captions[captions[0] == '80d9caaa1fa04502af666135196456e1'][1].values[0]
# load point clouds (unzip https://huggingface.co/datasets/tiange/Cap3D/tree/main/PointCloud_pt_zips)
import torch
pts = torch.load('Cap3D_pcs_pt/80d9caaa1fa04502af666135196456e1.pt')
## pts.shape == torch.Size([6, 16384])
```
## Citation Information
<details>
<summary>Please cite Objaverse, ABO, and ShapeNet paper accordingly, if you use related data. </summary>
```
@inproceedings{deitke2023objaverse,
title={Objaverse: A universe of annotated 3d objects},
author={Deitke, Matt and Schwenk, Dustin and Salvador, Jordi and Weihs, Luca and Michel, Oscar and VanderBilt, Eli and Schmidt, Ludwig and Ehsani, Kiana and Kembhavi, Aniruddha and Farhadi, Ali},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={13142--13153},
year={2023}
}
@article{deitke2024objaverse,
title={Objaverse-xl: A universe of 10m+ 3d objects},
author={Deitke, Matt and Liu, Ruoshi and Wallingford, Matthew and Ngo, Huong and Michel, Oscar and Kusupati, Aditya and Fan, Alan and Laforte, Christian and Voleti, Vikram and Gadre, Samir Yitzhak and others},
journal={Advances in Neural Information Processing Systems},
volume={36},
year={2024}
}
@inproceedings{collins2022abo,
title={Abo: Dataset and benchmarks for real-world 3d object understanding},
author={Collins, Jasmine and Goel, Shubham and Deng, Kenan and Luthra, Achleshwar and Xu, Leon and Gundogdu, Erhan and Zhang, Xi and Vicente, Tomas F Yago and Dideriksen, Thomas and Arora, Himanshu and others},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={21126--21136},
year={2022}
}
@article{chang2015shapenet,
title={Shapenet: An information-rich 3d model repository},
author={Chang, Angel X and Funkhouser, Thomas and Guibas, Leonidas and Hanrahan, Pat and Huang, Qixing and Li, Zimo and Savarese, Silvio and Savva, Manolis and Song, Shuran and Su, Hao and others},
journal={arXiv preprint arXiv:1512.03012},
year={2015}
}
```
</details>
If you find our data or code useful, please consider citing:
```bibtex
@article{luo2023scalable,
title={Scalable 3D Captioning with Pretrained Models},
author={Luo, Tiange and Rockwell, Chris and Lee, Honglak and Johnson, Justin},
journal={arXiv preprint arXiv:2306.07279},
year={2023}
}
@article{luo2024view,
title={View Selection for 3D Captioning via Diffusion Ranking},
author={Luo, Tiange and Johnson, Justin and Lee, Honglak},
journal={arXiv preprint arXiv:2404.07984},
year={2024}
}
```
|
bit0/x_dataset_12 | bit0 | "2025-03-26T00:06:44Z" | 11,106 | 0 | [
"task_categories:text-classification",
"task_categories:token-classification",
"task_categories:question-answering",
"task_categories:summarization",
"task_categories:text-generation",
"task_ids:sentiment-analysis",
"task_ids:topic-classification",
"task_ids:named-entity-recognition",
"task_ids:language-modeling",
"task_ids:text-scoring",
"task_ids:multi-class-classification",
"task_ids:multi-label-classification",
"task_ids:extractive-qa",
"task_ids:news-articles-summarization",
"multilinguality:multilingual",
"source_datasets:original",
"license:mit",
"size_categories:100M<n<1B",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | [
"text-classification",
"token-classification",
"question-answering",
"summarization",
"text-generation"
] | "2025-01-23T08:21:19Z" | ---
license: mit
multilinguality:
- multilingual
source_datasets:
- original
task_categories:
- text-classification
- token-classification
- question-answering
- summarization
- text-generation
task_ids:
- sentiment-analysis
- topic-classification
- named-entity-recognition
- language-modeling
- text-scoring
- multi-class-classification
- multi-label-classification
- extractive-qa
- news-articles-summarization
---
# Bittensor Subnet 13 X (Twitter) Dataset
<center>
<img src="https://huggingface.co/datasets/macrocosm-os/images/resolve/main/bittensor.png" alt="Data-universe: The finest collection of social media data the web has to offer">
</center>
<center>
<img src="https://huggingface.co/datasets/macrocosm-os/images/resolve/main/macrocosmos-black.png" alt="Data-universe: The finest collection of social media data the web has to offer">
</center>
## Dataset Description
- **Repository:** bit0/x_dataset_12
- **Subnet:** Bittensor Subnet 13
- **Miner Hotkey:** 5Dvth5w7eXuZNmQUXn7tn5Hr5tgUeYHYqftPHSkJbt16Daqq
### Miner Data Compliance Agreement
In uploading this dataset, I am agreeing to the [Macrocosmos Miner Data Compliance Policy](https://github.com/macrocosm-os/data-universe/blob/add-miner-policy/docs/miner_policy.md).
### Dataset Summary
This dataset is part of the Bittensor Subnet 13 decentralized network, containing preprocessed data from X (formerly Twitter). The data is continuously updated by network miners, providing a real-time stream of tweets for various analytical and machine learning tasks.
For more information about the dataset, please visit the [official repository](https://github.com/macrocosm-os/data-universe).
### Supported Tasks
The versatility of this dataset allows researchers and data scientists to explore various aspects of social media dynamics and develop innovative applications. Users are encouraged to leverage this data creatively for their specific research or business needs.
For example:
- Sentiment Analysis
- Trend Detection
- Content Analysis
- User Behavior Modeling
### Languages
Primary language: Datasets are mostly English, but can be multilingual due to decentralized ways of creation.
## Dataset Structure
### Data Instances
Each instance represents a single tweet with the following fields:
### Data Fields
- `text` (string): The main content of the tweet.
- `label` (string): Sentiment or topic category of the tweet.
- `tweet_hashtags` (list): A list of hashtags used in the tweet. May be empty if no hashtags are present.
- `datetime` (string): The date when the tweet was posted.
- `username_encoded` (string): An encoded version of the username to maintain user privacy.
- `url_encoded` (string): An encoded version of any URLs included in the tweet. May be empty if no URLs are present.
### Data Splits
This dataset is continuously updated and does not have fixed splits. Users should create their own splits based on their requirements and the data's timestamp.
## Dataset Creation
### Source Data
Data is collected from public tweets on X (Twitter), adhering to the platform's terms of service and API usage guidelines.
### Personal and Sensitive Information
All usernames and URLs are encoded to protect user privacy. The dataset does not intentionally include personal or sensitive information.
## Considerations for Using the Data
### Social Impact and Biases
Users should be aware of potential biases inherent in X (Twitter) data, including demographic and content biases. This dataset reflects the content and opinions expressed on X and should not be considered a representative sample of the general population.
### Limitations
- Data quality may vary due to the decentralized nature of collection and preprocessing.
- The dataset may contain noise, spam, or irrelevant content typical of social media platforms.
- Temporal biases may exist due to real-time collection methods.
- The dataset is limited to public tweets and does not include private accounts or direct messages.
- Not all tweets contain hashtags or URLs.
## Additional Information
### Licensing Information
The dataset is released under the MIT license. The use of this dataset is also subject to X Terms of Use.
### Citation Information
If you use this dataset in your research, please cite it as follows:
```
@misc{bit02025datauniversex_dataset_12,
title={The Data Universe Datasets: The finest collection of social media data the web has to offer},
author={bit0},
year={2025},
url={https://huggingface.co/datasets/bit0/x_dataset_12},
}
```
### Contributions
To report issues or contribute to the dataset, please contact the miner or use the Bittensor Subnet 13 governance mechanisms.
## Dataset Statistics
[This section is automatically updated]
- **Total Instances:** 508543658
- **Date Range:** 2025-01-12T00:00:00Z to 2025-03-19T00:00:00Z
- **Last Updated:** 2025-03-26T00:06:43Z
### Data Distribution
- Tweets with hashtags: 0.00%
- Tweets without hashtags: 100.00%
### Top 10 Hashtags
For full statistics, please refer to the `stats.json` file in the repository.
| Rank | Topic | Total Count | Percentage |
|------|-------|-------------|-------------|
| 1 | NULL | 508543658 | 100.00% |
## Update History
| Date | New Instances | Total Instances |
|------|---------------|-----------------|
| 2025-01-27T01:35:08Z | 218850 | 218850 |
| 2025-01-27T02:07:31Z | 226831 | 445681 |
| 2025-01-27T03:07:31Z | 224919 | 670600 |
| 2025-01-27T04:07:29Z | 206544 | 877144 |
| 2025-01-27T05:07:27Z | 192521 | 1069665 |
| 2025-01-27T06:07:28Z | 195281 | 1264946 |
| 2025-01-27T07:07:31Z | 201371 | 1466317 |
| 2025-01-27T08:07:29Z | 218640 | 1684957 |
| 2025-01-27T09:07:33Z | 237412 | 1922369 |
| 2025-01-27T10:07:34Z | 245574 | 2167943 |
| 2025-01-27T11:07:35Z | 263340 | 2431283 |
| 2025-01-27T12:07:37Z | 286394 | 2717677 |
| 2025-01-27T13:07:38Z | 302893 | 3020570 |
| 2025-01-27T14:07:43Z | 309028 | 3329598 |
| 2025-01-27T15:07:41Z | 305393 | 3634991 |
| 2025-01-27T16:07:39Z | 297399 | 3932390 |
| 2025-01-27T17:07:40Z | 280906 | 4213296 |
| 2025-01-27T18:07:35Z | 257898 | 4471194 |
| 2025-01-27T19:07:37Z | 285004 | 4756198 |
| 2025-01-27T20:07:37Z | 273457 | 5029655 |
| 2025-01-27T21:07:34Z | 257777 | 5287432 |
| 2025-01-27T22:07:30Z | 216721 | 5504153 |
| 2025-01-27T23:07:32Z | 224776 | 5728929 |
| 2025-01-28T00:07:35Z | 234338 | 5963267 |
| 2025-01-28T01:07:33Z | 232653 | 6195920 |
| 2025-01-28T02:07:33Z | 234256 | 6430176 |
| 2025-01-28T03:07:35Z | 250492 | 6680668 |
| 2025-01-28T04:07:35Z | 236093 | 6916761 |
| 2025-01-28T05:07:33Z | 207700 | 7124461 |
| 2025-01-28T06:07:35Z | 222655 | 7347116 |
| 2025-01-28T07:07:37Z | 252145 | 7599261 |
| 2025-01-28T08:07:35Z | 251687 | 7850948 |
| 2025-01-28T09:07:38Z | 269138 | 8120086 |
| 2025-01-28T10:07:46Z | 286119 | 8406205 |
| 2025-01-28T11:07:47Z | 320438 | 8726643 |
| 2025-01-28T12:07:57Z | 415958 | 9142601 |
| 2025-01-28T13:07:50Z | 380518 | 9523119 |
| 2025-01-28T14:07:54Z | 366668 | 9889787 |
| 2025-01-28T15:07:49Z | 346973 | 10236760 |
| 2025-01-28T16:07:42Z | 300370 | 10537130 |
| 2025-01-28T17:07:40Z | 280207 | 10817337 |
| 2025-01-28T18:07:40Z | 260183 | 11077520 |
| 2025-01-28T19:07:39Z | 250737 | 11328257 |
| 2025-01-28T20:07:41Z | 241828 | 11570085 |
| 2025-01-28T21:07:38Z | 247788 | 11817873 |
| 2025-01-28T22:07:42Z | 257844 | 12075717 |
| 2025-01-28T23:07:39Z | 255402 | 12331119 |
| 2025-01-29T00:07:39Z | 241459 | 12572578 |
| 2025-01-29T01:07:40Z | 266312 | 12838890 |
| 2025-01-29T02:07:44Z | 288357 | 13127247 |
| 2025-01-29T03:07:44Z | 298915 | 13426162 |
| 2025-01-29T04:07:40Z | 247961 | 13674123 |
| 2025-01-29T05:07:36Z | 218011 | 13892134 |
| 2025-01-29T06:07:39Z | 219915 | 14112049 |
| 2025-01-29T07:07:39Z | 231124 | 14343173 |
| 2025-01-29T08:07:41Z | 256642 | 14599815 |
| 2025-01-29T09:07:44Z | 299274 | 14899089 |
| 2025-01-29T10:07:55Z | 331518 | 15230607 |
| 2025-01-29T11:07:53Z | 363627 | 15594234 |
| 2025-01-29T12:07:57Z | 403168 | 15997402 |
| 2025-01-29T13:07:59Z | 417519 | 16414921 |
| 2025-01-29T14:08:01Z | 406575 | 16821496 |
| 2025-01-29T15:07:59Z | 386030 | 17207526 |
| 2025-01-29T16:07:50Z | 336405 | 17543931 |
| 2025-01-29T17:07:45Z | 308792 | 17852723 |
| 2025-01-29T18:07:47Z | 287284 | 18140007 |
| 2025-01-29T19:07:58Z | 282168 | 18422175 |
| 2025-01-29T20:07:56Z | 299463 | 18721638 |
| 2025-01-29T21:07:49Z | 315694 | 19037332 |
| 2025-01-29T22:07:47Z | 295974 | 19333306 |
| 2025-01-29T23:07:46Z | 279817 | 19613123 |
| 2025-01-30T00:07:46Z | 272179 | 19885302 |
| 2025-01-30T02:12:03Z | 298659 | 20183961 |
| 2025-01-30T03:08:37Z | 320987 | 20504948 |
| 2025-01-30T04:07:47Z | 256708 | 20761656 |
| 2025-01-30T06:15:27Z | 231558 | 20993214 |
| 2025-01-30T07:07:45Z | 242181 | 21235395 |
| 2025-01-30T08:07:49Z | 278307 | 21513702 |
| 2025-01-30T09:07:53Z | 325600 | 21839302 |
| 2025-01-30T10:07:57Z | 350754 | 22190056 |
| 2025-01-30T11:08:00Z | 388434 | 22578490 |
| 2025-01-30T12:08:05Z | 429146 | 23007636 |
| 2025-01-30T13:08:09Z | 444487 | 23452123 |
| 2025-01-30T14:08:13Z | 442123 | 23894246 |
| 2025-01-30T15:08:07Z | 426613 | 24320859 |
| 2025-01-30T16:08:00Z | 367970 | 24688829 |
| 2025-01-30T17:07:54Z | 350907 | 25039736 |
| 2025-01-30T18:07:56Z | 335383 | 25375119 |
| 2025-01-30T19:08:01Z | 329010 | 25704129 |
| 2025-01-30T20:08:00Z | 357588 | 26061717 |
| 2025-01-30T21:07:58Z | 355122 | 26416839 |
| 2025-01-30T22:07:57Z | 336850 | 26753689 |
| 2025-01-30T23:07:56Z | 313904 | 27067593 |
| 2025-01-31T00:07:53Z | 301269 | 27368862 |
| 2025-01-31T01:07:56Z | 312218 | 27681080 |
| 2025-01-31T02:07:57Z | 320280 | 28001360 |
| 2025-01-31T03:07:58Z | 357646 | 28359006 |
| 2025-01-31T04:07:52Z | 284685 | 28643691 |
| 2025-01-31T05:07:53Z | 257225 | 28900916 |
| 2025-01-31T06:07:51Z | 263323 | 29164239 |
| 2025-01-31T07:07:51Z | 274071 | 29438310 |
| 2025-01-31T09:10:35Z | 364546 | 29802856 |
| 2025-01-31T10:08:05Z | 394162 | 30197018 |
| 2025-01-31T11:08:10Z | 441922 | 30638940 |
| 2025-01-31T12:08:16Z | 479358 | 31118298 |
| 2025-01-31T13:08:24Z | 572691 | 31690989 |
| 2025-01-31T14:08:21Z | 527359 | 32218348 |
| 2025-01-31T15:08:17Z | 496741 | 32715089 |
| 2025-01-31T16:08:16Z | 435876 | 33150965 |
| 2025-01-31T17:08:06Z | 395952 | 33546917 |
| 2025-01-31T18:08:04Z | 381873 | 33928790 |
| 2025-01-31T19:08:05Z | 354953 | 34283743 |
| 2025-01-31T20:08:04Z | 354559 | 34638302 |
| 2025-01-31T21:08:08Z | 409148 | 35047450 |
| 2025-01-31T22:08:13Z | 411450 | 35458900 |
| 2025-01-31T23:08:07Z | 385851 | 35844751 |
| 2025-02-01T00:08:04Z | 351469 | 36196220 |
| 2025-02-01T01:08:04Z | 351621 | 36547841 |
| 2025-02-01T02:08:07Z | 363893 | 36911734 |
| 2025-02-01T03:08:11Z | 413780 | 37325514 |
| 2025-02-01T04:08:01Z | 326925 | 37652439 |
| 2025-02-01T05:07:57Z | 296926 | 37949365 |
| 2025-02-01T06:08:05Z | 298546 | 38247911 |
| 2025-02-01T07:07:59Z | 308830 | 38556741 |
| 2025-02-01T08:08:07Z | 363353 | 38920094 |
| 2025-02-01T09:08:15Z | 435801 | 39355895 |
| 2025-02-01T10:08:15Z | 456645 | 39812540 |
| 2025-02-01T11:08:23Z | 497955 | 40310495 |
| 2025-02-01T12:08:26Z | 541057 | 40851552 |
| 2025-02-01T13:08:34Z | 564057 | 41415609 |
| 2025-02-01T14:08:31Z | 566274 | 41981883 |
| 2025-02-01T15:08:27Z | 543607 | 42525490 |
| 2025-02-01T16:08:21Z | 464817 | 42990307 |
| 2025-02-01T17:08:14Z | 424890 | 43415197 |
| 2025-02-01T18:08:12Z | 391021 | 43806218 |
| 2025-02-01T19:08:11Z | 375440 | 44181658 |
| 2025-02-01T20:08:09Z | 360561 | 44542219 |
| 2025-02-01T21:08:07Z | 362713 | 44904932 |
| 2025-02-01T22:08:13Z | 367056 | 45271988 |
| 2025-02-01T23:08:13Z | 399005 | 45670993 |
| 2025-02-02T00:08:15Z | 399651 | 46070644 |
| 2025-02-02T01:08:14Z | 414756 | 46485400 |
| 2025-02-02T02:08:19Z | 434498 | 46919898 |
| 2025-02-02T03:08:24Z | 443775 | 47363673 |
| 2025-02-02T04:08:16Z | 401808 | 47765481 |
| 2025-02-02T05:08:12Z | 375225 | 48140706 |
| 2025-02-02T06:08:13Z | 370862 | 48511568 |
| 2025-02-02T07:08:13Z | 377693 | 48889261 |
| 2025-02-02T08:08:14Z | 403451 | 49292712 |
| 2025-02-02T09:08:20Z | 470071 | 49762783 |
| 2025-02-02T10:08:25Z | 485245 | 50248028 |
| 2025-02-02T11:08:30Z | 526662 | 50774690 |
| 2025-02-02T12:08:33Z | 570050 | 51344740 |
| 2025-02-02T13:08:37Z | 590534 | 51935274 |
| 2025-02-02T14:08:36Z | 597635 | 52532909 |
| 2025-02-02T15:08:35Z | 565447 | 53098356 |
| 2025-02-02T16:08:33Z | 544912 | 53643268 |
| 2025-02-02T17:08:25Z | 479512 | 54122780 |
| 2025-02-02T18:08:24Z | 449875 | 54572655 |
| 2025-02-02T19:08:19Z | 443346 | 55016001 |
| 2025-02-02T20:08:18Z | 411496 | 55427497 |
| 2025-02-02T21:08:15Z | 426350 | 55853847 |
| 2025-02-02T22:08:25Z | 437389 | 56291236 |
| 2025-02-02T23:08:24Z | 486345 | 56777581 |
| 2025-02-03T00:08:20Z | 475541 | 57253122 |
| 2025-02-03T01:08:25Z | 473938 | 57727060 |
| 2025-02-03T02:08:31Z | 556252 | 58283312 |
| 2025-02-03T03:08:35Z | 646000 | 58929312 |
| 2025-02-03T05:08:26Z | 500341 | 59429653 |
| 2025-02-03T06:08:25Z | 505308 | 59934961 |
| 2025-02-03T07:08:24Z | 489386 | 60424347 |
| 2025-02-03T08:08:24Z | 482909 | 60907256 |
| 2025-02-03T09:08:25Z | 533234 | 61440490 |
| 2025-02-03T10:08:33Z | 559694 | 62000184 |
| 2025-02-03T11:08:37Z | 616519 | 62616703 |
| 2025-02-03T12:08:38Z | 669725 | 63286428 |
| 2025-02-03T13:08:57Z | 704452 | 63990880 |
| 2025-02-03T14:08:53Z | 736990 | 64727870 |
| 2025-02-03T15:09:03Z | 758339 | 65486209 |
| 2025-02-03T16:08:51Z | 644437 | 66130646 |
| 2025-02-03T17:08:33Z | 579511 | 66710157 |
| 2025-02-03T18:08:38Z | 532414 | 67242571 |
| 2025-02-03T19:08:38Z | 484265 | 67726836 |
| 2025-02-03T20:08:33Z | 482760 | 68209596 |
| 2025-02-03T21:08:33Z | 491318 | 68700914 |
| 2025-02-03T22:08:41Z | 564560 | 69265474 |
| 2025-02-03T23:08:43Z | 566362 | 69831836 |
| 2025-02-04T00:08:36Z | 508721 | 70340557 |
| 2025-02-04T01:08:39Z | 491392 | 70831949 |
| 2025-02-04T02:08:48Z | 601097 | 71433046 |
| 2025-02-04T03:08:46Z | 584260 | 72017306 |
| 2025-02-04T04:08:31Z | 458509 | 72475815 |
| 2025-02-04T05:08:24Z | 408867 | 72884682 |
| 2025-02-04T06:08:23Z | 419954 | 73304636 |
| 2025-02-04T07:08:24Z | 434826 | 73739462 |
| 2025-02-04T08:08:30Z | 495293 | 74234755 |
| 2025-02-04T09:08:41Z | 600626 | 74835381 |
| 2025-02-04T10:08:44Z | 651884 | 75487265 |
| 2025-02-04T11:08:52Z | 758058 | 76245323 |
| 2025-02-04T12:09:14Z | 798886 | 77044209 |
| 2025-02-04T13:09:11Z | 823995 | 77868204 |
| 2025-02-04T14:09:27Z | 816446 | 78684650 |
| 2025-02-04T15:09:12Z | 776672 | 79461322 |
| 2025-02-04T16:09:00Z | 699108 | 80160430 |
| 2025-02-04T17:08:55Z | 751998 | 80912428 |
| 2025-02-04T18:08:44Z | 633596 | 81546024 |
| 2025-02-04T19:08:52Z | 579609 | 82125633 |
| 2025-02-04T20:08:44Z | 605174 | 82730807 |
| 2025-02-04T21:08:46Z | 618912 | 83349719 |
| 2025-02-04T22:08:46Z | 628897 | 83978616 |
| 2025-02-04T23:08:45Z | 610806 | 84589422 |
| 2025-02-05T00:08:42Z | 587450 | 85176872 |
| 2025-02-05T01:08:45Z | 618951 | 85795823 |
| 2025-02-05T02:08:48Z | 647202 | 86443025 |
| 2025-02-05T03:08:54Z | 718314 | 87161339 |
| 2025-02-05T04:08:43Z | 580951 | 87742290 |
| 2025-02-05T05:08:40Z | 493178 | 88235468 |
| 2025-02-05T06:08:37Z | 495415 | 88730883 |
| 2025-02-05T07:08:38Z | 509538 | 89240421 |
| 2025-02-05T08:08:50Z | 579949 | 89820370 |
| 2025-02-05T09:08:54Z | 670206 | 90490576 |
| 2025-02-05T10:09:14Z | 742394 | 91232970 |
| 2025-02-05T11:09:10Z | 839404 | 92072374 |
| 2025-02-05T12:09:23Z | 915036 | 92987410 |
| 2025-02-05T13:09:39Z | 963172 | 93950582 |
| 2025-02-05T14:09:36Z | 950408 | 94900990 |
| 2025-02-05T15:09:33Z | 913278 | 95814268 |
| 2025-02-05T16:09:21Z | 808441 | 96622709 |
| 2025-02-05T17:09:00Z | 748842 | 97371551 |
| 2025-02-05T18:08:56Z | 683670 | 98055221 |
| 2025-02-05T19:08:56Z | 662736 | 98717957 |
| 2025-02-05T20:09:04Z | 710725 | 99428682 |
| 2025-02-05T21:09:08Z | 784648 | 100213330 |
| 2025-02-05T22:09:14Z | 809270 | 101022600 |
| 2025-02-05T23:09:04Z | 723175 | 101745775 |
| 2025-02-06T00:09:00Z | 678653 | 102424428 |
| 2025-02-06T01:08:59Z | 683097 | 103107525 |
| 2025-02-06T02:09:05Z | 690163 | 103797688 |
| 2025-02-06T03:09:09Z | 791985 | 104589673 |
| 2025-02-06T04:08:57Z | 642023 | 105231696 |
| 2025-02-06T05:08:51Z | 577155 | 105808851 |
| 2025-02-06T06:08:53Z | 578709 | 106387560 |
| 2025-02-06T07:08:55Z | 587000 | 106974560 |
| 2025-02-06T08:09:02Z | 671850 | 107646410 |
| 2025-02-06T09:09:14Z | 801535 | 108447945 |
| 2025-02-06T10:09:20Z | 873663 | 109321608 |
| 2025-02-06T11:10:48Z | 973316 | 110294924 |
| 2025-02-06T12:09:42Z | 1058349 | 111353273 |
| 2025-02-06T13:10:05Z | 1152051 | 112505324 |
| 2025-02-06T14:10:06Z | 1120779 | 113626103 |
| 2025-02-06T15:10:04Z | 1070987 | 114697090 |
| 2025-02-06T16:09:33Z | 945552 | 115642642 |
| 2025-02-06T17:09:25Z | 897721 | 116540363 |
| 2025-02-06T18:09:22Z | 830067 | 117370430 |
| 2025-02-06T19:09:23Z | 787021 | 118157451 |
| 2025-02-06T20:09:26Z | 839810 | 118997261 |
| 2025-02-06T21:09:35Z | 939451 | 119936712 |
| 2025-02-06T22:09:35Z | 901278 | 120837990 |
| 2025-02-06T23:09:26Z | 841908 | 121679898 |
| 2025-02-07T00:09:24Z | 799728 | 122479626 |
| 2025-02-07T01:09:20Z | 813764 | 123293390 |
| 2025-02-07T02:09:27Z | 841291 | 124134681 |
| 2025-02-07T03:09:48Z | 1009671 | 125144352 |
| 2025-02-07T04:09:26Z | 796079 | 125940431 |
| 2025-02-07T05:09:23Z | 719990 | 126660421 |
| 2025-02-07T06:09:20Z | 718662 | 127379083 |
| 2025-02-07T07:09:21Z | 729766 | 128108849 |
| 2025-02-07T08:09:27Z | 815309 | 128924158 |
| 2025-02-07T09:09:38Z | 989900 | 129914058 |
| 2025-02-07T10:09:56Z | 1101573 | 131015631 |
| 2025-02-07T11:10:02Z | 1195608 | 132211239 |
| 2025-02-07T12:10:13Z | 1289038 | 133500277 |
| 2025-02-07T13:10:48Z | 1507083 | 135007360 |
| 2025-02-07T14:10:48Z | 1486003 | 136493363 |
| 2025-02-07T15:10:36Z | 1338560 | 137831923 |
| 2025-02-07T16:10:08Z | 1180172 | 139012095 |
| 2025-02-07T17:09:57Z | 1072748 | 140084843 |
| 2025-02-07T18:09:56Z | 1019875 | 141104718 |
| 2025-02-07T19:09:55Z | 1013296 | 142118014 |
| 2025-02-07T20:09:56Z | 1039787 | 143157801 |
| 2025-02-07T21:10:05Z | 1099742 | 144257543 |
| 2025-02-07T22:10:10Z | 1117334 | 145374877 |
| 2025-02-07T23:10:01Z | 1120534 | 146495411 |
| 2025-02-08T00:09:58Z | 1077674 | 147573085 |
| 2025-02-08T01:10:07Z | 1106404 | 148679489 |
| 2025-02-08T02:10:10Z | 1095800 | 149775289 |
| 2025-02-08T03:10:18Z | 1264929 | 151040218 |
| 2025-02-08T04:10:01Z | 1046777 | 152086995 |
| 2025-02-08T05:09:56Z | 975774 | 153062769 |
| 2025-02-08T06:09:58Z | 951207 | 154013976 |
| 2025-02-08T07:09:56Z | 954618 | 154968594 |
| 2025-02-08T08:10:02Z | 1076654 | 156045248 |
| 2025-02-08T09:10:23Z | 1290055 | 157335303 |
| 2025-02-08T10:10:33Z | 1390494 | 158725797 |
| 2025-02-08T11:10:52Z | 1565805 | 160291602 |
| 2025-02-08T12:10:59Z | 1747975 | 162039577 |
| 2025-02-08T13:11:16Z | 1700062 | 163739639 |
| 2025-02-08T14:11:12Z | 1687625 | 165427264 |
| 2025-02-08T15:11:20Z | 1617049 | 167044313 |
| 2025-02-08T16:10:39Z | 1396606 | 168440919 |
| 2025-02-08T17:10:25Z | 1249741 | 169690660 |
| 2025-02-08T18:10:15Z | 1129528 | 170820188 |
| 2025-02-08T19:10:18Z | 1047986 | 171868174 |
| 2025-02-08T20:10:14Z | 1015860 | 172884034 |
| 2025-02-08T21:10:15Z | 1030266 | 173914300 |
| 2025-02-08T22:10:18Z | 1065283 | 174979583 |
| 2025-02-08T23:10:13Z | 1108784 | 176088367 |
| 2025-02-09T00:10:19Z | 1130510 | 177218877 |
| 2025-02-09T01:10:26Z | 1187721 | 178406598 |
| 2025-02-09T02:10:31Z | 1247724 | 179654322 |
| 2025-02-09T03:10:42Z | 1276674 | 180930996 |
| 2025-02-09T04:10:28Z | 1163136 | 182094132 |
| 2025-02-09T05:10:29Z | 1095446 | 183189578 |
| 2025-02-09T06:10:27Z | 1085375 | 184274953 |
| 2025-02-09T07:10:25Z | 1067968 | 185342921 |
| 2025-02-09T08:10:39Z | 1142678 | 186485599 |
| 2025-02-09T09:10:35Z | 1256453 | 187742052 |
| 2025-02-09T11:11:28Z | 1504611 | 189246663 |
| 2025-02-09T12:11:15Z | 1668430 | 190915093 |
| 2025-02-09T13:11:26Z | 1725762 | 192640855 |
| 2025-02-09T14:11:32Z | 1757106 | 194397961 |
| 2025-02-09T15:11:28Z | 1765062 | 196163023 |
| 2025-02-09T16:11:12Z | 1603214 | 197766237 |
| 2025-02-09T17:11:02Z | 1437092 | 199203329 |
| 2025-02-09T18:11:04Z | 1322348 | 200525677 |
| 2025-02-09T19:11:34Z | 1211786 | 201737463 |
| 2025-02-09T20:10:47Z | 1115284 | 202852747 |
| 2025-02-09T21:10:57Z | 1122677 | 203975424 |
| 2025-02-09T22:10:45Z | 1105983 | 205081407 |
| 2025-02-09T23:10:56Z | 1195837 | 206277244 |
| 2025-02-10T00:10:55Z | 1249476 | 207526720 |
| 2025-02-10T01:11:15Z | 1265064 | 208791784 |
| 2025-02-10T02:11:17Z | 1286985 | 210078769 |
| 2025-02-10T03:11:20Z | 1322859 | 211401628 |
| 2025-02-10T04:11:05Z | 1254522 | 212656150 |
| 2025-02-10T05:11:10Z | 1178711 | 213834861 |
| 2025-02-10T06:11:04Z | 1196822 | 215031683 |
| 2025-02-10T07:11:12Z | 1189227 | 216220910 |
| 2025-02-10T08:11:12Z | 1266601 | 217487511 |
| 2025-02-10T09:11:25Z | 1340224 | 218827735 |
| 2025-02-10T10:11:30Z | 1448770 | 220276505 |
| 2025-02-10T11:11:56Z | 1648736 | 221925241 |
| 2025-02-10T12:12:02Z | 1754839 | 223680080 |
| 2025-02-10T13:12:19Z | 1862242 | 225542322 |
| 2025-02-10T14:12:13Z | 1826646 | 227368968 |
| 2025-02-10T15:12:14Z | 1817817 | 229186785 |
| 2025-02-10T16:11:46Z | 1556088 | 230742873 |
| 2025-02-10T17:11:33Z | 1435936 | 232178809 |
| 2025-02-10T18:11:25Z | 1325672 | 233504481 |
| 2025-02-10T19:11:52Z | 1277078 | 234781559 |
| 2025-02-10T20:11:37Z | 1439564 | 236221123 |
| 2025-02-10T21:12:06Z | 1365508 | 237586631 |
| 2025-02-10T22:11:32Z | 1416644 | 239003275 |
| 2025-02-10T23:11:54Z | 1467828 | 240471103 |
| 2025-02-11T00:11:32Z | 1362716 | 241833819 |
| 2025-02-11T01:11:38Z | 1336049 | 243169868 |
| 2025-02-11T02:11:55Z | 1547764 | 244717632 |
| 2025-02-11T03:12:10Z | 1547959 | 246265591 |
| 2025-02-11T04:11:30Z | 1200857 | 247466448 |
| 2025-02-11T05:11:23Z | 1101825 | 248568273 |
| 2025-02-11T06:11:18Z | 1126122 | 249694395 |
| 2025-02-11T07:11:37Z | 1149702 | 250844097 |
| 2025-02-11T08:11:41Z | 1271665 | 252115762 |
| 2025-02-11T09:11:56Z | 1463085 | 253578847 |
| 2025-02-11T10:12:08Z | 1596539 | 255175386 |
| 2025-02-11T11:12:36Z | 1854637 | 257030023 |
| 2025-02-11T12:12:46Z | 2029808 | 259059831 |
| 2025-02-11T13:13:00Z | 2042750 | 261102581 |
| 2025-02-11T14:12:54Z | 2017608 | 263120189 |
| 2025-02-11T15:12:43Z | 1923288 | 265043477 |
| 2025-02-11T16:12:28Z | 1743637 | 266787114 |
| 2025-02-11T17:12:07Z | 1565511 | 268352625 |
| 2025-02-11T18:11:58Z | 1474712 | 269827337 |
| 2025-02-11T19:13:01Z | 1382117 | 271209454 |
| 2025-02-11T20:11:51Z | 1345413 | 272554867 |
| 2025-02-11T21:11:54Z | 1378746 | 273933613 |
| 2025-02-11T22:11:53Z | 1410203 | 275343816 |
| 2025-02-11T23:12:11Z | 1392308 | 276736124 |
| 2025-02-12T00:28:40Z | 1323063 | 278059187 |
| 2025-02-12T01:11:58Z | 1326496 | 279385683 |
| 2025-02-12T02:12:03Z | 1334384 | 280720067 |
| 2025-02-12T03:12:19Z | 1503096 | 282223163 |
| 2025-02-12T04:11:50Z | 1244233 | 283467396 |
| 2025-02-12T05:11:52Z | 1138428 | 284605824 |
| 2025-02-12T06:11:40Z | 1148642 | 285754466 |
| 2025-02-12T07:12:28Z | 1168314 | 286922780 |
| 2025-02-12T08:12:05Z | 1287163 | 288209943 |
| 2025-02-12T09:12:43Z | 1479028 | 289688971 |
| 2025-02-12T10:12:33Z | 1620411 | 291309382 |
| 2025-02-12T11:12:56Z | 1765288 | 293074670 |
| 2025-02-12T12:13:20Z | 1923118 | 294997788 |
| 2025-02-12T13:13:45Z | 2031364 | 297029152 |
| 2025-02-12T14:12:47Z | 1994282 | 299023434 |
| 2025-02-12T15:13:18Z | 1940129 | 300963563 |
| 2025-02-12T16:12:32Z | 1729631 | 302693194 |
| 2025-02-12T17:13:30Z | 1583185 | 304276379 |
| 2025-02-12T18:17:01Z | 1471613 | 305747992 |
| 2025-02-12T19:22:00Z | 1406612 | 307154604 |
| 2025-02-12T20:11:57Z | 1383907 | 308538511 |
| 2025-02-12T21:16:50Z | 1409997 | 309948508 |
| 2025-02-12T23:13:54Z | 1435906 | 311384414 |
| 2025-02-13T00:12:42Z | 1347405 | 312731819 |
| 2025-02-13T01:13:26Z | 1374328 | 314106147 |
| 2025-02-13T02:28:02Z | 1406240 | 315512387 |
| 2025-02-13T02:30:10Z | 1406240 | 316918627 |
| 2025-02-13T03:17:05Z | 1521105 | 318439732 |
| 2025-02-13T04:12:29Z | 1281972 | 319721704 |
| 2025-02-13T05:13:14Z | 1180565 | 320902269 |
| 2025-02-13T06:22:16Z | 1164777 | 322067046 |
| 2025-02-13T07:11:57Z | 1176686 | 323243732 |
| 2025-02-13T08:12:01Z | 1295623 | 324539355 |
| 2025-02-13T09:19:47Z | 1503123 | 326042478 |
| 2025-02-13T10:13:53Z | 1620805 | 327663283 |
| 2025-02-13T11:20:59Z | 1835292 | 329498575 |
| 2025-02-13T12:24:30Z | 1993063 | 331491638 |
| 2025-02-13T13:13:30Z | 2015219 | 333506857 |
| 2025-02-13T14:13:28Z | 1992892 | 335499749 |
| 2025-02-13T16:13:15Z | 1684412 | 337184161 |
| 2025-02-13T17:12:16Z | 1565969 | 338750130 |
| 2025-02-13T18:12:12Z | 1503850 | 340253980 |
| 2025-02-13T19:12:16Z | 1445429 | 341699409 |
| 2025-02-13T20:12:24Z | 1567145 | 343266554 |
| 2025-02-13T21:12:24Z | 1619611 | 344886165 |
| 2025-02-13T22:12:47Z | 1558853 | 346445018 |
| 2025-02-13T23:12:35Z | 1443554 | 347888572 |
| 2025-02-14T00:12:22Z | 1376817 | 349265389 |
| 2025-02-14T01:12:15Z | 1366757 | 350632146 |
| 2025-02-14T02:12:14Z | 1390455 | 352022601 |
| 2025-02-14T03:12:31Z | 1509252 | 353531853 |
| 2025-02-14T04:12:10Z | 1230554 | 354762407 |
| 2025-02-14T05:12:00Z | 1125572 | 355887979 |
| 2025-02-14T06:11:55Z | 1134428 | 357022407 |
| 2025-02-14T07:12:05Z | 1137433 | 358159840 |
| 2025-02-14T08:12:15Z | 1238021 | 359397861 |
| 2025-02-14T09:12:21Z | 1409165 | 360807026 |
| 2025-02-14T10:12:42Z | 1561142 | 362368168 |
| 2025-02-14T11:12:50Z | 1707762 | 364075930 |
| 2025-02-14T12:13:08Z | 1833835 | 365909765 |
| 2025-02-14T13:13:09Z | 1916429 | 367826194 |
| 2025-02-14T14:13:06Z | 1893665 | 369719859 |
| 2025-02-14T15:13:08Z | 1836382 | 371556241 |
| 2025-02-14T16:12:48Z | 1627091 | 373183332 |
| 2025-02-14T17:12:34Z | 1483530 | 374666862 |
| 2025-02-14T18:12:29Z | 1390903 | 376057765 |
| 2025-02-14T19:12:25Z | 1354998 | 377412763 |
| 2025-02-14T20:12:30Z | 1357721 | 378770484 |
| 2025-02-14T21:12:42Z | 1460807 | 380231291 |
| 2025-02-14T22:12:46Z | 1513022 | 381744313 |
| 2025-02-14T23:12:40Z | 1451102 | 383195415 |
| 2025-02-15T00:12:29Z | 1353197 | 384548612 |
| 2025-02-15T01:12:25Z | 1358425 | 385907037 |
| 2025-02-15T02:12:33Z | 1350900 | 387257937 |
| 2025-02-15T03:12:58Z | 1508490 | 388766427 |
| 2025-02-15T04:12:31Z | 1268677 | 390035104 |
| 2025-02-15T05:12:18Z | 1192181 | 391227285 |
| 2025-02-15T06:12:22Z | 1164833 | 392392118 |
| 2025-02-15T07:12:28Z | 1159457 | 393551575 |
| 2025-02-15T08:12:30Z | 1276302 | 394827877 |
| 2025-02-15T09:12:43Z | 1029495 | 395857372 |
| 2025-02-18T02:10:43Z | 1353168 | 397210540 |
| 2025-02-18T02:12:29Z | 1353168 | 398563708 |
| 2025-02-18T03:07:10Z | 1518653 | 400082361 |
| 2025-02-18T08:06:21Z | 1204713 | 401287074 |
| 2025-02-18T16:07:17Z | 1807731 | 403094805 |
| 2025-02-19T00:06:32Z | 1353917 | 404448722 |
| 2025-02-19T08:06:16Z | 1105583 | 405554305 |
| 2025-02-19T16:06:42Z | 1253744 | 406808049 |
| 2025-02-20T00:06:15Z | 1041928 | 407849977 |
| 2025-02-20T08:06:49Z | 1004773 | 408854750 |
| 2025-02-20T16:07:05Z | 1209201 | 410063951 |
| 2025-02-21T00:06:43Z | 967182 | 411031133 |
| 2025-02-21T08:06:37Z | 943379 | 411974512 |
| 2025-02-21T16:07:45Z | 1173049 | 413147561 |
| 2025-02-22T00:07:30Z | 995419 | 414142980 |
| 2025-02-22T08:07:18Z | 1019833 | 415162813 |
| 2025-02-22T16:07:22Z | 1202116 | 416364929 |
| 2025-02-23T08:07:25Z | 960028 | 417324957 |
| 2025-02-23T16:07:01Z | 1065157 | 418390114 |
| 2025-02-24T00:06:56Z | 976914 | 419367028 |
| 2025-02-24T08:07:02Z | 1071136 | 420438164 |
| 2025-02-24T16:07:34Z | 1468396 | 421906560 |
| 2025-02-25T00:07:38Z | 1547219 | 423453779 |
| 2025-02-25T08:07:11Z | 1159176 | 424612955 |
| 2025-02-25T16:07:33Z | 1436269 | 426049224 |
| 2025-02-26T00:07:20Z | 1253941 | 427303165 |
| 2025-02-26T08:07:17Z | 1217873 | 428521038 |
| 2025-02-26T16:07:36Z | 1415503 | 429936541 |
| 2025-02-27T00:07:19Z | 1198606 | 431135147 |
| 2025-02-27T08:07:15Z | 1185787 | 432320934 |
| 2025-02-27T16:07:45Z | 1410102 | 433731036 |
| 2025-02-28T00:07:24Z | 1244844 | 434975880 |
| 2025-02-28T08:07:20Z | 1172250 | 436148130 |
| 2025-02-28T16:07:53Z | 1500507 | 437648637 |
| 2025-03-01T00:07:31Z | 1270009 | 438918646 |
| 2025-03-01T08:07:31Z | 1311188 | 440229834 |
| 2025-03-01T16:08:15Z | 1595456 | 441825290 |
| 2025-03-02T00:07:34Z | 1279555 | 443104845 |
| 2025-03-02T08:07:34Z | 1254123 | 444358968 |
| 2025-03-02T16:08:12Z | 1584373 | 445943341 |
| 2025-03-03T00:07:42Z | 1321599 | 447264940 |
| 2025-03-03T08:07:30Z | 1204593 | 448469533 |
| 2025-03-03T16:08:02Z | 1494861 | 449964394 |
| 2025-03-04T00:07:32Z | 1157859 | 451122253 |
| 2025-03-04T08:07:15Z | 1094499 | 452216752 |
| 2025-03-04T16:08:15Z | 1401191 | 453617943 |
| 2025-03-05T00:07:33Z | 1181375 | 454799318 |
| 2025-03-05T08:07:23Z | 1151091 | 455950409 |
| 2025-03-05T16:08:04Z | 1450326 | 457400735 |
| 2025-03-06T00:07:46Z | 1255061 | 458655796 |
| 2025-03-06T08:06:54Z | 1191214 | 459847010 |
| 2025-03-06T16:07:30Z | 1488170 | 461335180 |
| 2025-03-07T00:06:55Z | 1234787 | 462569967 |
| 2025-03-07T08:07:04Z | 1244911 | 463814878 |
| 2025-03-07T16:07:24Z | 1497660 | 465312538 |
| 2025-03-08T00:07:07Z | 1324584 | 466637122 |
| 2025-03-08T05:21:48Z | 1199319 | 467836441 |
| 2025-03-08T08:06:57Z | 1247799 | 469084240 |
| 2025-03-08T16:07:26Z | 1547089 | 470631329 |
| 2025-03-09T00:07:16Z | 1308816 | 471940145 |
| 2025-03-09T02:16:14Z | 197431 | 472137576 |
| 2025-03-09T08:05:24Z | 189497 | 472327073 |
| 2025-03-09T16:05:36Z | 253688 | 472580761 |
| 2025-03-10T00:05:34Z | 245736 | 472826497 |
| 2025-03-10T08:05:28Z | 223211 | 473049708 |
| 2025-03-10T16:06:06Z | 414047 | 473463755 |
| 2025-03-11T00:06:40Z | 701384 | 474165139 |
| 2025-03-11T08:06:02Z | 596598 | 474761737 |
| 2025-03-11T16:06:36Z | 975300 | 475737037 |
| 2025-03-12T00:06:41Z | 984107 | 476721144 |
| 2025-03-12T08:06:47Z | 934228 | 477655372 |
| 2025-03-12T16:07:27Z | 1229004 | 478884376 |
| 2025-03-13T00:06:46Z | 1032803 | 479917179 |
| 2025-03-13T08:20:37Z | 957581 | 480874760 |
| 2025-03-13T16:07:09Z | 1282646 | 482157406 |
| 2025-03-14T00:07:03Z | 1124285 | 483281691 |
| 2025-03-14T08:06:51Z | 1011939 | 484293630 |
| 2025-03-14T16:07:11Z | 1270724 | 485564354 |
| 2025-03-15T00:06:54Z | 1069045 | 486633399 |
| 2025-03-15T08:06:48Z | 986198 | 487619597 |
| 2025-03-15T16:06:57Z | 1135196 | 488754793 |
| 2025-03-16T00:06:36Z | 866912 | 489621705 |
| 2025-03-16T08:06:40Z | 787188 | 490408893 |
| 2025-03-16T16:06:50Z | 1000236 | 491409129 |
| 2025-03-17T00:06:26Z | 761833 | 492170962 |
| 2025-03-17T08:06:25Z | 659065 | 492830027 |
| 2025-03-17T16:06:01Z | 460001 | 493290028 |
| 2025-03-18T00:06:43Z | 835419 | 494125447 |
| 2025-03-18T08:06:34Z | 730689 | 494856136 |
| 2025-03-18T16:06:56Z | 957885 | 495814021 |
| 2025-03-19T00:06:58Z | 788880 | 496602901 |
| 2025-03-19T08:06:26Z | 624268 | 497227169 |
| 2025-03-19T16:06:52Z | 782298 | 498009467 |
| 2025-03-20T00:06:25Z | 641541 | 498651008 |
| 2025-03-20T08:06:21Z | 469733 | 499120741 |
| 2025-03-20T16:06:27Z | 603871 | 499724612 |
| 2025-03-21T00:06:22Z | 530167 | 500254779 |
| 2025-03-21T08:06:18Z | 455317 | 500710096 |
| 2025-03-21T16:06:26Z | 603845 | 501313941 |
| 2025-03-22T00:06:15Z | 529181 | 501843122 |
| 2025-03-22T08:06:15Z | 468351 | 502311473 |
| 2025-03-22T16:06:33Z | 600185 | 502911658 |
| 2025-03-23T00:06:18Z | 502834 | 503414492 |
| 2025-03-23T08:06:15Z | 455285 | 503869777 |
| 2025-03-23T16:06:22Z | 573312 | 504443089 |
| 2025-03-24T00:06:14Z | 462112 | 504905201 |
| 2025-03-24T08:06:09Z | 434216 | 505339417 |
| 2025-03-24T16:06:25Z | 570896 | 505910313 |
| 2025-03-25T00:06:20Z | 513332 | 506423645 |
| 2025-03-25T08:06:35Z | 579228 | 507002873 |
| 2025-03-25T16:06:50Z | 800864 | 507803737 |
| 2025-03-26T00:06:43Z | 739921 | 508543658 |
|
LHF/escorpius-mr | LHF | "2023-05-11T22:29:21Z" | 11,102 | 5 | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"multilinguality:multilingual",
"source_datasets:original",
"language:af",
"language:ar",
"language:bn",
"language:ca",
"language:cs",
"language:da",
"language:de",
"language:el",
"language:eu",
"language:fa",
"language:fi",
"language:fr",
"language:gl",
"language:hi",
"language:hr",
"language:it",
"language:ja",
"language:ko",
"language:mt",
"language:nl",
"language:no",
"language:oc",
"language:pa",
"language:pl",
"language:pt",
"language:ro",
"language:sl",
"language:sr",
"language:sv",
"language:tr",
"language:uk",
"language:ur",
"license:cc-by-nc-nd-4.0",
"size_categories:1B<n<10B",
"format:json",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"arxiv:2206.15147",
"region:us"
] | [
"text-generation",
"fill-mask"
] | "2022-05-03T18:49:47Z" | ---
license: cc-by-nc-nd-4.0
language:
- af
- ar
- bn
- ca
- cs
- da
- de
- el
- eu
- fa
- fi
- fr
- gl
- hi
- hr
- it
- ja
- ko
- mt
- nl
- no
- oc
- pa
- pl
- pt
- ro
- sl
- sr
- sv
- tr
- uk
- ur
multilinguality:
- multilingual
size_categories:
- 100B<n<1T
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
---
# esCorpius Multilingual Raw
In the recent years, Transformer-based models have lead to significant advances in language modelling for natural language processing. However, they require a vast amount of data to be (pre-)trained and there is a lack of corpora in languages other than English. Recently, several initiatives have presented multilingual datasets obtained from automatic web crawling. However, they present important shortcomings for languages different from English, as they are either too small, or present a low quality derived from sub-optimal cleaning and deduplication. In this repository, we introduce esCorpius-m, a multilingual crawling corpus obtained from near 1 Pb of Common Crawl data. It is the most extensive corpus in some of the languages covered with this level of quality in the extraction, purification and deduplication of web textual content. Our data curation process involves a novel highly parallel cleaning pipeline and encompasses a series of deduplication mechanisms that together ensure the integrity of both document and paragraph boundaries. Additionally, we maintain both the source web page URL and the WARC shard origin URL in order to complain with EU regulations. esCorpius-m has been released under CC BY-NC-ND 4.0 license.
# Usage
```
dataset = load_dataset('LHF/escorpius-m', split='train', streaming=True)
```
# Intended use
This corpus is the *raw version* of the esCorpius-m corpus. This corpus can be used for benchmarking deduplication tools.
## Other corpora
- esCorpius multilingual corpus (deduplicated): https://huggingface.co/datasets/LHF/escorpius-m
- esCorpius original *Spanish-only* corpus (deduplicated): https://huggingface.co/datasets/LHF/escorpius
## Citation
Link to paper: https://www.isca-speech.org/archive/pdfs/iberspeech_2022/gutierrezfandino22_iberspeech.pdf / https://arxiv.org/abs/2206.15147
Cite this work:
```
@inproceedings{gutierrezfandino22_iberspeech,
author={Asier Gutiérrez-Fandiño and David Pérez-Fernández and Jordi Armengol-Estapé and David Griol and Zoraida Callejas},
title={{esCorpius: A Massive Spanish Crawling Corpus}},
keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
year=2022,
booktitle={Proc. IberSPEECH 2022},
pages={126--130},
doi={10.21437/IberSPEECH.2022-26}
}
```
## Disclaimer
We did not perform any kind of filtering and/or censorship to the corpus. We expect users to do so applying their own methods. We are not liable for any misuse of the corpus.
|
ashraf-ali/quran-data | ashraf-ali | "2022-12-10T17:35:33Z" | 11,042 | 18 | [
"task_categories:automatic-speech-recognition",
"language_creators:Tarteel.io",
"license:cc0-1.0",
"size_categories:1K<n<10K",
"format:audiofolder",
"modality:audio",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us"
] | [
"automatic-speech-recognition"
] | "2022-11-28T17:14:02Z" | ---
language_creators:
- Tarteel.io
license:
- cc0-1.0
size_categories:
ar:
- 43652
task_categories:
- automatic-speech-recognition
task_ids: []
paperswithcode_id: quran-data
pretty_name: Quran Audio
language_bcp47:
- ar
---
# Dataset Card for Quran audio
Content
* 7 Imam Full Quran Recitation: 7*6236 wav file
- csv contains the Text info for 11k subset short wav file
* Tarteel.io user dataset ~25k wav
- csv contains the Text info for 18k subset of the accepted user quality |
open-llm-leaderboard-old/details_EleutherAI__polyglot-ko-12.8b | open-llm-leaderboard-old | "2023-10-19T02:18:08Z" | 11,034 | 0 | [
"region:us"
] | null | "2023-08-17T23:47:23Z" | ---
pretty_name: Evaluation run of EleutherAI/polyglot-ko-12.8b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [EleutherAI/polyglot-ko-12.8b](https://huggingface.co/EleutherAI/polyglot-ko-12.8b)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_EleutherAI__polyglot-ko-12.8b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-19T02:17:54.630291](https://huggingface.co/datasets/open-llm-leaderboard/details_EleutherAI__polyglot-ko-12.8b/blob/main/results_2023-10-19T02-17-54.630291.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.04268036912751678,\n\
\ \"em_stderr\": 0.0020700565850232436,\n \"f1\": 0.09065960570469792,\n\
\ \"f1_stderr\": 0.002370421899236817,\n \"acc\": 0.2994953245415047,\n\
\ \"acc_stderr\": 0.0074273230901261535\n },\n \"harness|drop|3\":\
\ {\n \"em\": 0.04268036912751678,\n \"em_stderr\": 0.0020700565850232436,\n\
\ \"f1\": 0.09065960570469792,\n \"f1_stderr\": 0.002370421899236817\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.001516300227445034,\n \
\ \"acc_stderr\": 0.0010717793485492619\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.5974743488555643,\n \"acc_stderr\": 0.013782866831703044\n\
\ }\n}\n```"
repo_url: https://huggingface.co/EleutherAI/polyglot-ko-12.8b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|arc:challenge|25_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_19T02_17_54.630291
path:
- '**/details_harness|drop|3_2023-10-19T02-17-54.630291.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-19T02-17-54.630291.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_19T02_17_54.630291
path:
- '**/details_harness|gsm8k|5_2023-10-19T02-17-54.630291.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-19T02-17-54.630291.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hellaswag|10_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T18:43:02.018732.parquet'
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- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T18:43:02.018732.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T18:43:02.018732.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T18:43:02.018732.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_19T02_17_54.630291
path:
- '**/details_harness|winogrande|5_2023-10-19T02-17-54.630291.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-19T02-17-54.630291.parquet'
- config_name: results
data_files:
- split: 2023_07_19T18_43_02.018732
path:
- results_2023-07-19T18:43:02.018732.parquet
- split: 2023_10_19T02_17_54.630291
path:
- results_2023-10-19T02-17-54.630291.parquet
- split: latest
path:
- results_2023-10-19T02-17-54.630291.parquet
---
# Dataset Card for Evaluation run of EleutherAI/polyglot-ko-12.8b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/EleutherAI/polyglot-ko-12.8b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [EleutherAI/polyglot-ko-12.8b](https://huggingface.co/EleutherAI/polyglot-ko-12.8b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_EleutherAI__polyglot-ko-12.8b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-19T02:17:54.630291](https://huggingface.co/datasets/open-llm-leaderboard/details_EleutherAI__polyglot-ko-12.8b/blob/main/results_2023-10-19T02-17-54.630291.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.04268036912751678,
"em_stderr": 0.0020700565850232436,
"f1": 0.09065960570469792,
"f1_stderr": 0.002370421899236817,
"acc": 0.2994953245415047,
"acc_stderr": 0.0074273230901261535
},
"harness|drop|3": {
"em": 0.04268036912751678,
"em_stderr": 0.0020700565850232436,
"f1": 0.09065960570469792,
"f1_stderr": 0.002370421899236817
},
"harness|gsm8k|5": {
"acc": 0.001516300227445034,
"acc_stderr": 0.0010717793485492619
},
"harness|winogrande|5": {
"acc": 0.5974743488555643,
"acc_stderr": 0.013782866831703044
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
ccdv/cnn_dailymail | ccdv | "2022-10-24T20:31:59Z" | 11,028 | 22 | [
"task_categories:summarization",
"task_categories:text-generation",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"size_categories:100K<n<1M",
"region:us",
"conditional-text-generation"
] | [
"summarization",
"text-generation"
] | "2022-03-02T23:29:22Z" | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- summarization
- text-generation
task_ids: []
paperswithcode_id: cnn-daily-mail-1
pretty_name: CNN / Daily Mail
tags:
- conditional-text-generation
---
**Copy of the [cnn_dailymail](https://huggingface.co/datasets/cnn_dailymail) dataset fixing the "NotADirectoryError: [Errno 20]".**
# Dataset Card for CNN Dailymail Dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:** [CNN / DailyMail Dataset repository](https://github.com/abisee/cnn-dailymail)
- **Paper:** [Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond](https://papers.nips.cc/paper/5945-teaching-machines-to-read-and-comprehend.pdf), [Get To The Point: Summarization with Pointer-Generator Networks](https://www.aclweb.org/anthology/K16-1028.pdf)
- **Leaderboard:** [Papers with Code leaderboard for CNN / Dailymail Dataset](https://paperswithcode.com/sota/document-summarization-on-cnn-daily-mail)
- **Point of Contact:** [Abigail See](mailto:[email protected])
### Dataset Summary
The CNN / DailyMail Dataset is an English-language dataset containing just over 300k unique news articles as written by journalists at CNN and the Daily Mail. The current version supports both extractive and abstractive summarization, though the original version was created for machine reading and comprehension and abstractive question answering.
### Supported Tasks and Leaderboards
- 'summarization': [Versions 2.0.0 and 3.0.0 of the CNN / DailyMail Dataset](https://www.aclweb.org/anthology/K16-1028.pdf) can be used to train a model for abstractive and extractive summarization ([Version 1.0.0](https://papers.nips.cc/paper/5945-teaching-machines-to-read-and-comprehend.pdf) was developed for machine reading and comprehension and abstractive question answering). The model performance is measured by how high the output summary's [ROUGE](https://huggingface.co/metrics/rouge) score for a given article is when compared to the highlight as written by the original article author. [Zhong et al (2020)](https://www.aclweb.org/anthology/2020.acl-main.552.pdf) report a ROUGE-1 score of 44.41 when testing a model trained for extractive summarization. See the [Papers With Code leaderboard](https://paperswithcode.com/sota/document-summarization-on-cnn-daily-mail) for more models.
### Languages
The BCP-47 code for English as generally spoken in the United States is en-US and the BCP-47 code for English as generally spoken in the United Kingdom is en-GB. It is unknown if other varieties of English are represented in the data.
## Dataset Structure
### Data Instances
For each instance, there is a string for the article, a string for the highlights, and a string for the id. See the [CNN / Daily Mail dataset viewer](https://huggingface.co/datasets/viewer/?dataset=cnn_dailymail&config=3.0.0) to explore more examples.
```
{'id': '0054d6d30dbcad772e20b22771153a2a9cbeaf62',
'article': '(CNN) -- An American woman died aboard a cruise ship that docked at Rio de Janeiro on Tuesday, the same ship on which 86 passengers previously fell ill, according to the state-run Brazilian news agency, Agencia Brasil. The American tourist died aboard the MS Veendam, owned by cruise operator Holland America. Federal Police told Agencia Brasil that forensic doctors were investigating her death. The ship's doctors told police that the woman was elderly and suffered from diabetes and hypertension, according the agency. The other passengers came down with diarrhea prior to her death during an earlier part of the trip, the ship's doctors said. The Veendam left New York 36 days ago for a South America tour.'
'highlights': 'The elderly woman suffered from diabetes and hypertension, ship's doctors say .\nPreviously, 86 passengers had fallen ill on the ship, Agencia Brasil says .'}
```
The average token count for the articles and the highlights are provided below:
| Feature | Mean Token Count |
| ---------- | ---------------- |
| Article | 781 |
| Highlights | 56 |
### Data Fields
- `id`: a string containing the heximal formated SHA1 hash of the url where the story was retrieved from
- `article`: a string containing the body of the news article
- `highlights`: a string containing the highlight of the article as written by the article author
### Data Splits
The CNN/DailyMail dataset has 3 splits: _train_, _validation_, and _test_. Below are the statistics for Version 3.0.0 of the dataset.
| Dataset Split | Number of Instances in Split |
| ------------- | ------------------------------------------- |
| Train | 287,113 |
| Validation | 13,368 |
| Test | 11,490 |
## Dataset Creation
### Curation Rationale
Version 1.0.0 aimed to support supervised neural methodologies for machine reading and question answering with a large amount of real natural language training data and released about 313k unique articles and nearly 1M Cloze style questions to go with the articles. Versions 2.0.0 and 3.0.0 changed the structure of the dataset to support summarization rather than question answering. Version 3.0.0 provided a non-anonymized version of the data, whereas both the previous versions were preprocessed to replace named entities with unique identifier labels.
### Source Data
#### Initial Data Collection and Normalization
The data consists of news articles and highlight sentences. In the question answering setting of the data, the articles are used as the context and entities are hidden one at a time in the highlight sentences, producing Cloze style questions where the goal of the model is to correctly guess which entity in the context has been hidden in the highlight. In the summarization setting, the highlight sentences are concatenated to form a summary of the article. The CNN articles were written between April 2007 and April 2015. The Daily Mail articles were written between June 2010 and April 2015.
The code for the original data collection is available at <https://github.com/deepmind/rc-data>. The articles were downloaded using archives of <www.cnn.com> and <www.dailymail.co.uk> on the Wayback Machine. Articles were not included in the Version 1.0.0 collection if they exceeded 2000 tokens. Due to accessibility issues with the Wayback Machine, Kyunghyun Cho has made the datasets available at <https://cs.nyu.edu/~kcho/DMQA/>. An updated version of the code that does not anonymize the data is available at <https://github.com/abisee/cnn-dailymail>.
Hermann et al provided their own tokenization script. The script provided by See uses the PTBTokenizer. It also lowercases the text and adds periods to lines missing them.
#### Who are the source language producers?
The text was written by journalists at CNN and the Daily Mail.
### Annotations
The dataset does not contain any additional annotations.
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
Version 3.0 is not anonymized, so individuals' names can be found in the dataset. Information about the original author is not included in the dataset.
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to help develop models that can summarize long paragraphs of text in one or two sentences.
This task is useful for efficiently presenting information given a large quantity of text. It should be made clear that any summarizations produced by models trained on this dataset are reflective of the language used in the articles, but are in fact automatically generated.
### Discussion of Biases
[Bordia and Bowman (2019)](https://www.aclweb.org/anthology/N19-3002.pdf) explore measuring gender bias and debiasing techniques in the CNN / Dailymail dataset, the Penn Treebank, and WikiText-2. They find the CNN / Dailymail dataset to have a slightly lower gender bias based on their metric compared to the other datasets, but still show evidence of gender bias when looking at words such as 'fragile'.
Because the articles were written by and for people in the US and the UK, they will likely present specifically US and UK perspectives and feature events that are considered relevant to those populations during the time that the articles were published.
### Other Known Limitations
News articles have been shown to conform to writing conventions in which important information is primarily presented in the first third of the article [(Kryściński et al, 2019)](https://www.aclweb.org/anthology/D19-1051.pdf). [Chen et al (2016)](https://www.aclweb.org/anthology/P16-1223.pdf) conducted a manual study of 100 random instances of the first version of the dataset and found 25% of the samples to be difficult even for humans to answer correctly due to ambiguity and coreference errors.
It should also be noted that machine-generated summarizations, even when extractive, may differ in truth values when compared to the original articles.
## Additional Information
### Dataset Curators
The data was originally collected by Karl Moritz Hermann, Tomáš Kočiský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom of Google DeepMind. Tomáš Kočiský and Phil Blunsom are also affiliated with the University of Oxford. They released scripts to collect and process the data into the question answering format.
Ramesh Nallapati, Bowen Zhou, Cicero dos Santos, and Bing Xiang of IMB Watson and Çağlar Gu̇lçehre of Université de Montréal modified Hermann et al's collection scripts to restore the data to a summary format. They also produced both anonymized and non-anonymized versions.
The code for the non-anonymized version is made publicly available by Abigail See of Stanford University, Peter J. Liu of Google Brain and Christopher D. Manning of Stanford University at <https://github.com/abisee/cnn-dailymail>. The work at Stanford University was supported by the DARPA DEFT ProgramAFRL contract no. FA8750-13-2-0040.
### Licensing Information
The CNN / Daily Mail dataset version 1.0.0 is released under the [Apache-2.0 License](http://www.apache.org/licenses/LICENSE-2.0).
### Citation Information
```
@inproceedings{see-etal-2017-get,
title = "Get To The Point: Summarization with Pointer-Generator Networks",
author = "See, Abigail and
Liu, Peter J. and
Manning, Christopher D.",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/P17-1099",
doi = "10.18653/v1/P17-1099",
pages = "1073--1083",
abstract = "Neural sequence-to-sequence models have provided a viable new approach for abstractive text summarization (meaning they are not restricted to simply selecting and rearranging passages from the original text). However, these models have two shortcomings: they are liable to reproduce factual details inaccurately, and they tend to repeat themselves. In this work we propose a novel architecture that augments the standard sequence-to-sequence attentional model in two orthogonal ways. First, we use a hybrid pointer-generator network that can copy words from the source text via pointing, which aids accurate reproduction of information, while retaining the ability to produce novel words through the generator. Second, we use coverage to keep track of what has been summarized, which discourages repetition. We apply our model to the CNN / Daily Mail summarization task, outperforming the current abstractive state-of-the-art by at least 2 ROUGE points.",
}
```
```
@inproceedings{DBLP:conf/nips/HermannKGEKSB15,
author={Karl Moritz Hermann and Tomás Kociský and Edward Grefenstette and Lasse Espeholt and Will Kay and Mustafa Suleyman and Phil Blunsom},
title={Teaching Machines to Read and Comprehend},
year={2015},
cdate={1420070400000},
pages={1693-1701},
url={http://papers.nips.cc/paper/5945-teaching-machines-to-read-and-comprehend},
booktitle={NIPS},
crossref={conf/nips/2015}
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@jplu](https://github.com/jplu), [@jbragg](https://github.com/jbragg), [@patrickvonplaten](https://github.com/patrickvonplaten) and [@mcmillanmajora](https://github.com/mcmillanmajora) for adding this dataset.
|
jacobbieker/eumetsat-cloudmask-iodc | jacobbieker | "2024-07-26T07:39:56Z" | 11,018 | 0 | [
"license:mit",
"doi:10.57967/hf/1639",
"region:us"
] | null | "2024-01-12T18:51:01Z" | ---
license: mit
---
|
Codec-SUPERB/librispeech_synth | Codec-SUPERB | "2024-01-15T14:57:31Z" | 10,996 | 1 | [
"size_categories:1M<n<10M",
"format:parquet",
"modality:audio",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-01-04T04:21:43Z" | ---
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: id
dtype: string
splits:
- name: academicodec_hifi_16k_320d
num_bytes: 113116345974.686
num_examples: 292367
- name: academicodec_hifi_16k_320d_large_uni
num_bytes: 113116345974.686
num_examples: 292367
- name: academicodec_hifi_24k_320d
num_bytes: 169685346294.686
num_examples: 292367
- name: funcodec_en_libritts_16k_gr1nq32ds320
num_bytes: 113174576650.686
num_examples: 292367
- name: funcodec_en_libritts_16k_gr8nq32ds320
num_bytes: 113173372218.686
num_examples: 292367
- name: audiodec_24k_320d
num_bytes: 169835583482.686
num_examples: 292367
- name: original
num_bytes: 63678669918.686
num_examples: 292367
- name: funcodec_en_libritts_16k_nq32ds320
num_bytes: 113186105690.686
num_examples: 292367
- name: dac_16k
num_bytes: 113185098868.686
num_examples: 292367
- name: funcodec_en_libritts_16k_nq32ds640
num_bytes: 113186105690.686
num_examples: 292367
- name: funcodec_zh_en_16k_nq32ds320
num_bytes: 113186105690.686
num_examples: 292367
- name: funcodec_zh_en_16k_nq32ds640
num_bytes: 113186105690.686
num_examples: 292367
- name: dac_24k
num_bytes: 169767074932.686
num_examples: 292367
- name: speech_tokenizer_16k
num_bytes: 113255906934.686
num_examples: 292367
download_size: 1424205343315
dataset_size: 1704732744013.6042
configs:
- config_name: default
data_files:
- split: academicodec_hifi_16k_320d
path: data/academicodec_hifi_16k_320d-*
- split: academicodec_hifi_16k_320d_large_uni
path: data/academicodec_hifi_16k_320d_large_uni-*
- split: academicodec_hifi_24k_320d
path: data/academicodec_hifi_24k_320d-*
- split: funcodec_en_libritts_16k_gr1nq32ds320
path: data/funcodec_en_libritts_16k_gr1nq32ds320-*
- split: funcodec_en_libritts_16k_gr8nq32ds320
path: data/funcodec_en_libritts_16k_gr8nq32ds320-*
- split: audiodec_24k_320d
path: data/audiodec_24k_320d-*
- split: original
path: data/original-*
- split: funcodec_en_libritts_16k_nq32ds320
path: data/funcodec_en_libritts_16k_nq32ds320-*
- split: dac_16k
path: data/dac_16k-*
- split: funcodec_en_libritts_16k_nq32ds640
path: data/funcodec_en_libritts_16k_nq32ds640-*
- split: funcodec_zh_en_16k_nq32ds320
path: data/funcodec_zh_en_16k_nq32ds320-*
- split: funcodec_zh_en_16k_nq32ds640
path: data/funcodec_zh_en_16k_nq32ds640-*
- split: dac_24k
path: data/dac_24k-*
- split: speech_tokenizer_16k
path: data/speech_tokenizer_16k-*
---
# Dataset Card for "librispeech_synth"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
XufengDuan/results | XufengDuan | "2024-10-31T08:14:36Z" | 10,984 | 0 | [
"license:mit",
"region:us"
] | null | "2024-08-03T10:58:54Z" | ---
license: mit
---
|
LWHYC/PASTA-Gen-30K | LWHYC | "2025-02-18T05:47:25Z" | 10,961 | 3 | [
"license:mit",
"arxiv:2502.06171",
"region:us"
] | null | "2025-01-28T09:35:45Z" | ---
license: mit
---
**Workflow of PASTA Model Development and Training Pipeline**. **a**, Overview of organs and lesion
types involved in PASTA training. **b**, Examples of lesions generated by PASTA-Gen from healthy organs. **c**, Lesion generation process pipeline of PASTA-Gen. **d**, Two-stage training of PASTA using the PASTA-Gen-30K
dataset.
[Model](https://github.com/LWHYC/PASTA), [Paper](https://arxiv.org/abs/2502.06171)
## Overview
PASTA-Gen-30K, a large-scale synthetic dataset of 30,000 CT volumes with precise lesion masks and structured textual reports from 15 lesion types (10 common malignancies and 5 benign lesions). It is an integral part of the [PASTA](https://github.com/LWHYC/PASTA) project.
It contains 2K samples for each lesion:
- Lung tumor
- Liver tumor
- Gallbladder cancer
- Pancreas tumor
- Esophageal Cancer
- Gastric cancer
- Colorectal cancer
- Kidney tumor
- Bladder cancer
- Bone metastasis
- Liver cyst
- Gallstone
- Pancreas cyst
- Kidney cyst
- Kidney stone
## Data Organization
Each sample in this dataset contains the following files:
- **`img.nii.gz`**: A synthetic CT scan featuring a target lesion. The image has dimensions of 280 × 280 × 280 voxels with a spacing of 1 × 1 × 1 mm.
- **`label.nii.gz`**: A synthetic label volume indicating the target lesion and the corresponding organ. The labeling convention is as follows:
- Organ: label value `1`
- Lesion: label value `2`
- **`total.nii.gz`**: Organ segmentation results generated using [TotalSegmentator v1](https://github.com/wasserth/TotalSegmentator/tree/v1.5.7). This file includes segmentation outputs for 104 organs. A full list of the segmented classes is available [here](https://github.com/wasserth/TotalSegmentator/tree/v1.5.7).
- **`type.json`**: A structured lesion report containing various attributes and their possible options. An overview of these attributes and options is illustrated in the image below.
## Citation
If you use our dataset, please cite:
```bibtex
@article{lei2025data,
title={A Data-Efficient Pan-Tumor Foundation Model for Oncology CT Interpretation},
author={Lei, Wenhui and Chen, Hanyu and Zhang, Zitian and Luo, Luyang and Xiao, Qiong and Gu, Yannian and Gao, Peng and Jiang, Yankai and Wang, Ci and Wu, Guangtao and others},
journal={arXiv preprint arXiv:2502.06171},
year={2025}
}
```
and please also consider cite Totalsegmentator. Thanks for their great work:
```bibtex
@article{wasserthal2023totalsegmentator,
title={TotalSegmentator: robust segmentation of 104 anatomic structures in CT images},
author={Wasserthal, Jakob and Breit, Hanns-Christian and Meyer, Manfred T and Pradella, Maurice and Hinck, Daniel and Sauter, Alexander W and Heye, Tobias and Boll, Daniel T and Cyriac, Joshy and Yang, Shan and others},
journal={Radiology: Artificial Intelligence},
volume={5},
number={5},
year={2023},
publisher={Radiological Society of North America}
}
``` |
MathLLMs/MathVision | MathLLMs | "2025-03-10T12:48:32Z" | 10,960 | 48 | [
"task_categories:question-answering",
"task_categories:multiple-choice",
"task_categories:visual-question-answering",
"task_categories:text-generation",
"annotations_creators:expert-generated",
"annotations_creators:found",
"language_creators:expert-generated",
"language_creators:found",
"language:en",
"license:mit",
"size_categories:1K<n<10K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2402.14804",
"arxiv:2501.12599",
"region:us",
"mathematics",
"reasoning",
"multi-modal-qa",
"math-qa",
"figure-qa",
"geometry-qa",
"math-word-problem",
"textbook-qa",
"vqa",
"geometry-diagram",
"synthetic-scene",
"chart",
"plot",
"scientific-figure",
"table",
"function-plot",
"abstract-scene",
"puzzle-test",
"document-image",
"science"
] | [
"question-answering",
"multiple-choice",
"visual-question-answering",
"text-generation"
] | "2024-02-22T19:14:42Z" | ---
license: mit
annotations_creators:
- expert-generated
- found
language_creators:
- expert-generated
- found
task_categories:
- question-answering
- multiple-choice
- visual-question-answering
- text-generation
language:
- en
tags:
- mathematics
- reasoning
- multi-modal-qa
- math-qa
- figure-qa
- geometry-qa
- math-word-problem
- textbook-qa
- vqa
- geometry-diagram
- synthetic-scene
- chart
- plot
- scientific-figure
- table
- function-plot
- abstract-scene
- puzzle-test
- document-image
- science
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
- split: testmini
path: data/testmini-*
pretty_name: MATH-V
size_categories:
- 1K<n<10K
---
# Measuring Multimodal Mathematical Reasoning with the MATH-Vision Dataset
[[💻 Github](https://github.com/mathllm/MATH-V/)] [[🌐 Homepage](https://mathllm.github.io/mathvision/)] [[📊 Leaderboard ](https://mathllm.github.io/mathvision/#leaderboard )] [[🔍 Visualization](https://mathllm.github.io/mathvision/#visualization)] [[📖 ArXiv Paper](https://arxiv.org/pdf/2402.14804.pdf)]
## 🚀 Data Usage
<!-- **We have observed that some studies have used our MATH-Vision dataset as a training set.**
⚠️ **As clearly stated in our paper: *"The MATH-V dataset is not supposed, though the risk exists, to be used to train models for cheating. We intend for researchers to use this dataset to better evaluate LMMs’ mathematical reasoning capabilities and consequently facilitate future studies in this area."***
⚠️⚠️⚠️ **In the very rare situation that there is a compelling reason to include MATH-V in your training set, we strongly urge that the ***testmini*** subset be excluded from the training process!**
-->
```python
from datasets import load_dataset
dataset = load_dataset("MathLLMs/MathVision")
print(dataset)
```
## 💥 News
- **[2025.03.10]** 💥 **Kimi k1.6 Preview 🥇 Sets New SOTA on MATH-V with 53.29%!** See the full [leaderboard](https://mathllm.github.io/mathvision/#leaderboard).
- **[2025.02.28]** 💥 **Doubao-1.5-pro Sets New SOTA on MATH-V with 48.62%!** Read more on the [Doubao blog](https://team.doubao.com/zh/special/doubao_1_5_pro).
- **[2025.01.26]** 🚀 [Qwen2.5-VL-72B](http://qwenlm.github.io/blog/qwen2.5-vl/) achieves **38.1%**, establishing itself as the best-performing one in open-sourced models. 🎉 Congratulations!
- **[2025.01.22]** 💥 **Kimi k1.5 achieves new SOTA** on MATH-Vision with **38.6%**! Learn more at the [Kimi k1.5 report](https://arxiv.org/pdf/2501.12599).
- **[2024-09-27]** **MATH-V** is accepted by NeurIPS DB Track, 2024! 🎉🎉🎉
- **[2024-08-29]** 🔥🔥🔥 Qwen2-VL-72B achieves new open-sourced SOTA on MATH-Vision with 25.9! 🎉 Congratulations! Learn more at the [Qwen2-VL blog](https://qwenlm.github.io/blog/qwen2-vl/).
- **[2024-07-19]** [open-compass/VLMEvalKit](https://github.com/open-compass/VLMEvalKit) now supports **MATH-V**, utilizing LLMs for more accurate answer extraction!🔥🔥
- **[2024-05-19]** OpenAI's **GPT-4o** scores **30.39%** on **MATH-V**, considerable advancement in short time! 💥
- **[2024-03-01]** **InternVL-Chat-V1-2-Plus** achieves **16.97%**, establishing itself as the new best-performing open-sourced model. 🎉 Congratulations!
- **[2024-02-23]** Our dataset is now accessible at [huggingface](https://huggingface.co/datasets/MathLLMs/MathVision).
- **[2024-02-22]** The top-performing model, **GPT-4V** only scores **23.98%** on **MATH-V**, while human performance is around **70%**.
- **[2024-02-22]** Our paper is now accessible at [ArXiv Paper](https://arxiv.org/abs/2402.14804).
## 👀 Introduction
Recent advancements in Large Multimodal Models (LMMs) have shown promising results in mathematical reasoning within visual contexts, with models approaching human-level performance on existing benchmarks such as MathVista. However, we observe significant limitations in the diversity of questions and breadth of subjects covered by these benchmarks. To address this issue, we present the MATH-Vision (MATH-V) dataset, a meticulously curated collection of 3,040 high-quality mathematical problems with visual contexts sourced from real math competitions. Spanning 16 distinct mathematical disciplines and graded across 5 levels of difficulty, our dataset provides a comprehensive and diverse set of challenges for evaluating the mathematical reasoning abilities of LMMs.
<p align="center">
<img src="https://raw.githubusercontent.com/mathvision-cuhk/MathVision/main/assets/figures/figure1_new.png" width="66%"> The accuracies of four prominent Large Multimodal Models (LMMs), random chance, and human <br>
performance are evaluated on our proposed <b>MATH-Vision (MATH-V)</b> across 16 subjects.
</p>
<br>
Through extensive experimentation, we unveil a notable performance gap between current LMMs and human performance on MATH-V, underscoring the imperative for further advancements in LMMs.
You can refer to the [project homepage](https://mathvision-cuhk.github.io/) for more details.
## 🏆 Leaderboard
The leaderboard is available [here](https://mathvision-cuhk.github.io/#leaderboard).
We are commmitted to maintain this dataset and leaderboard in the long run to ensure its quality!
🔔 If you find any mistakes, please paste the question_id to the issue page, we will modify it accordingly.
## 📐 Dataset Examples
Some examples of MATH-V on three subjects: analytic geometry, topology, and graph theory.
<details>
<summary>Analytic geometry</summary><p align="center">
<img src="https://raw.githubusercontent.com/mathvision-cuhk/MathVision/main/assets/examples/exam_analytic_geo.png" width="60%"> <br>
</p></details>
<details>
<summary>Topology</summary><p align="center">
<img src="https://raw.githubusercontent.com/mathvision-cuhk/MathVision/main/assets/examples/exam_topology.png" width="60%"> <br>
</p></details>
<details>
<summary>Graph Geometry</summary><p align="center">
<img src="https://raw.githubusercontent.com/mathvision-cuhk/MathVision/main/assets/examples/exam_graph.png" width="60%"> <br>
</p></details>
## 📑 Citation
If you find this benchmark useful in your research, please consider citing this BibTex:
```
@inproceedings{
wang2024measuring,
title={Measuring Multimodal Mathematical Reasoning with MATH-Vision Dataset},
author={Ke Wang and Junting Pan and Weikang Shi and Zimu Lu and Houxing Ren and Aojun Zhou and Mingjie Zhan and Hongsheng Li},
booktitle={The Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year={2024},
url={https://openreview.net/forum?id=QWTCcxMpPA}
}
```
|
Cohere/miracl-en-corpus-22-12 | Cohere | "2023-02-06T11:54:52Z" | 10,930 | 2 | [
"task_categories:text-retrieval",
"task_ids:document-retrieval",
"annotations_creators:expert-generated",
"multilinguality:multilingual",
"language:en",
"license:apache-2.0",
"size_categories:10M<n<100M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | [
"text-retrieval"
] | "2023-02-02T23:21:21Z" | ---
annotations_creators:
- expert-generated
language:
- en
multilinguality:
- multilingual
size_categories: []
source_datasets: []
tags: []
task_categories:
- text-retrieval
license:
- apache-2.0
task_ids:
- document-retrieval
---
# MIRACL (en) embedded with cohere.ai `multilingual-22-12` encoder
We encoded the [MIRACL dataset](https://huggingface.co/miracl) using the [cohere.ai](https://txt.cohere.ai/multilingual/) `multilingual-22-12` embedding model.
The query embeddings can be found in [Cohere/miracl-en-queries-22-12](https://huggingface.co/datasets/Cohere/miracl-en-queries-22-12) and the corpus embeddings can be found in [Cohere/miracl-en-corpus-22-12](https://huggingface.co/datasets/Cohere/miracl-en-corpus-22-12).
For the orginal datasets, see [miracl/miracl](https://huggingface.co/datasets/miracl/miracl) and [miracl/miracl-corpus](https://huggingface.co/datasets/miracl/miracl-corpus).
Dataset info:
> MIRACL 🌍🙌🌏 (Multilingual Information Retrieval Across a Continuum of Languages) is a multilingual retrieval dataset that focuses on search across 18 different languages, which collectively encompass over three billion native speakers around the world.
>
> The corpus for each language is prepared from a Wikipedia dump, where we keep only the plain text and discard images, tables, etc. Each article is segmented into multiple passages using WikiExtractor based on natural discourse units (e.g., `\n\n` in the wiki markup). Each of these passages comprises a "document" or unit of retrieval. We preserve the Wikipedia article title of each passage.
## Embeddings
We compute for `title+" "+text` the embeddings using our `multilingual-22-12` embedding model, a state-of-the-art model that works for semantic search in 100 languages. If you want to learn more about this model, have a look at [cohere.ai multilingual embedding model](https://txt.cohere.ai/multilingual/).
## Loading the dataset
In [miracl-en-corpus-22-12](https://huggingface.co/datasets/Cohere/miracl-en-corpus-22-12) we provide the corpus embeddings. Note, depending on the selected split, the respective files can be quite large.
You can either load the dataset like this:
```python
from datasets import load_dataset
docs = load_dataset(f"Cohere/miracl-en-corpus-22-12", split="train")
```
Or you can also stream it without downloading it before:
```python
from datasets import load_dataset
docs = load_dataset(f"Cohere/miracl-en-corpus-22-12", split="train", streaming=True)
for doc in docs:
docid = doc['docid']
title = doc['title']
text = doc['text']
emb = doc['emb']
```
## Search
Have a look at [miracl-en-queries-22-12](https://huggingface.co/datasets/Cohere/miracl-en-queries-22-12) where we provide the query embeddings for the MIRACL dataset.
To search in the documents, you must use **dot-product**.
And then compare this query embeddings either with a vector database (recommended) or directly computing the dot product.
A full search example:
```python
# Attention! For large datasets, this requires a lot of memory to store
# all document embeddings and to compute the dot product scores.
# Only use this for smaller datasets. For large datasets, use a vector DB
from datasets import load_dataset
import torch
#Load documents + embeddings
docs = load_dataset(f"Cohere/miracl-en-corpus-22-12", split="train")
doc_embeddings = torch.tensor(docs['emb'])
# Load queries
queries = load_dataset(f"Cohere/miracl-en-queries-22-12", split="dev")
# Select the first query as example
qid = 0
query = queries[qid]
query_embedding = torch.tensor(queries['emb'])
# Compute dot score between query embedding and document embeddings
dot_scores = torch.mm(query_embedding, doc_embeddings.transpose(0, 1))
top_k = torch.topk(dot_scores, k=3)
# Print results
print("Query:", query['query'])
for doc_id in top_k.indices[0].tolist():
print(docs[doc_id]['title'])
print(docs[doc_id]['text'])
```
You can get embeddings for new queries using our API:
```python
#Run: pip install cohere
import cohere
co = cohere.Client(f"{api_key}") # You should add your cohere API Key here :))
texts = ['my search query']
response = co.embed(texts=texts, model='multilingual-22-12')
query_embedding = response.embeddings[0] # Get the embedding for the first text
```
## Performance
In the following table we compare the cohere multilingual-22-12 model with Elasticsearch version 8.6.0 lexical search (title and passage indexed as independent fields). Note that Elasticsearch doesn't support all languages that are part of the MIRACL dataset.
We compute nDCG@10 (a ranking based loss), as well as hit@3: Is at least one relevant document in the top-3 results. We find that hit@3 is easier to interpret, as it presents the number of queries for which a relevant document is found among the top-3 results.
Note: MIRACL only annotated a small fraction of passages (10 per query) for relevancy. Especially for larger Wikipedias (like English), we often found many more relevant passages. This is know as annotation holes. Real nDCG@10 and hit@3 performance is likely higher than depicted.
| Model | cohere multilingual-22-12 nDCG@10 | cohere multilingual-22-12 hit@3 | ES 8.6.0 nDCG@10 | ES 8.6.0 acc@3 |
|---|---|---|---|---|
| miracl-ar | 64.2 | 75.2 | 46.8 | 56.2 |
| miracl-bn | 61.5 | 75.7 | 49.2 | 60.1 |
| miracl-de | 44.4 | 60.7 | 19.6 | 29.8 |
| miracl-en | 44.6 | 62.2 | 30.2 | 43.2 |
| miracl-es | 47.0 | 74.1 | 27.0 | 47.2 |
| miracl-fi | 63.7 | 76.2 | 51.4 | 61.6 |
| miracl-fr | 46.8 | 57.1 | 17.0 | 21.6 |
| miracl-hi | 50.7 | 62.9 | 41.0 | 48.9 |
| miracl-id | 44.8 | 63.8 | 39.2 | 54.7 |
| miracl-ru | 49.2 | 66.9 | 25.4 | 36.7 |
| **Avg** | 51.7 | 67.5 | 34.7 | 46.0 |
Further languages (not supported by Elasticsearch):
| Model | cohere multilingual-22-12 nDCG@10 | cohere multilingual-22-12 hit@3 |
|---|---|---|
| miracl-fa | 44.8 | 53.6 |
| miracl-ja | 49.0 | 61.0 |
| miracl-ko | 50.9 | 64.8 |
| miracl-sw | 61.4 | 74.5 |
| miracl-te | 67.8 | 72.3 |
| miracl-th | 60.2 | 71.9 |
| miracl-yo | 56.4 | 62.2 |
| miracl-zh | 43.8 | 56.5 |
| **Avg** | 54.3 | 64.6 |
|
elsaEU/ELSA1M_track1 | elsaEU | "2023-08-27T08:01:57Z" | 10,925 | 3 | [
"license:cc-by-4.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2023-07-18T16:50:36Z" | ---
elsaEU--ELSA1M_track1:
description: ''
citation: ''
homepage: ''
license: ''
features:
image:
decode: true
id: null
dtype: Image
id:
dtype: string
id: null
_type: Value
original_prompt:
dtype: string
id: null
_type: Value
positive_prompt:
dtype: string
id: null
_type: Value
negative_prompt:
dtype: string
id: null
_type: Value
model:
dtype: string
id: null
_type: Value
nsfw:
dtype: string
id: null
_type: Value
url_real_image:
dtype: string
id: null
_type: Value
filepath:
dtype: string
id: null
_type: Value
aspect_ratio:
feature:
dtype: int64
id: null
_type: Value
length: -1
id: null
_type: Sequence
post_processed: null
supervised_keys: null
task_templates: null
builder_name: imagefolder
config_name: default
version:
version_str: 0.0.0
description: null
major: 0
minor: 0
patch: 0
splits:
train:
name: train
num_bytes: 445926712527.43
num_examples: 992655
dataset_name: ELSA1M_track1
download_checksums: null
download_size: 223034360161
post_processing_size: null
dataset_size: 445926712527.43
size_in_bytes: 668961072688.4299
license: cc-by-4.0
---
# ELSA - Multimedia use case
**ELSA Multimedia is a large collection of Deep Fake images, generated using diffusion models**
### Dataset Summary
This dataset was developed as part of the EU project ELSA. Specifically for the Multimedia use-case.
Official webpage: https://benchmarks.elsa-ai.eu/
This dataset aims to develop effective solutions for detecting and mitigating the spread of deep fake images in multimedia content. Deep fake images, which are highly realistic and deceptive manipulations, pose significant risks to privacy, security, and trust in digital media. This dataset can be used to train robust and accurate models that can identify and flag instances of deep fake images.
### ELSA versions
| Name | Description | Link |
| ------------- | ------------- | ---------------------|
| ELSA1M_track1 | Dataset of 1M images generated using diffusion model | https://huggingface.co/datasets/elsaEU/ELSA1M_track1 |
| ELSA500k_track2 | Dataset of 500k images generated using diffusion model with diffusion attentive attribution maps [1] | https://huggingface.co/datasets/elsaEU/ELSA500k_track2 |
```python
from datasets import load_dataset
elsa_data = load_dataset("elsaEU/ELSA1M_track1", split="train", streaming=True)
for sample in elsa_data:
image = sample.pop("image")
metadata = sample
```
Using <a href="https://huggingface.co/docs/datasets/stream">streaming=True</a> lets you work with the dataset without downloading it.
## Dataset Structure
Each parquet file contains nearly 1k images and a JSON file with metadata.
The Metadata for generated images are:
- ID: Laion image ID
- original_prompt: Laion Prompt
- positive_prompt: positive prompt used for image generation
- negative_prompt: negative prompt used for image generation
- model: model used for the image generation
- nsfw: nsfw tag from Laion
- url_real_image: Url of the real image associated to the same prompt
- filepath: filepath of the fake image
- aspect_ratio: aspect ratio of the generated image
### Dataset Curators
- Leonardo Labs ([email protected])
- UNIMORE (https://aimagelab.ing.unimore.it/imagelab/) |
WenhaoWang/VideoUFO | WenhaoWang | "2025-03-06T10:40:21Z" | 10,904 | 12 | [
"task_categories:text-to-video",
"task_categories:text-to-image",
"task_categories:image-to-video",
"task_categories:image-to-image",
"language:en",
"license:cc-by-4.0",
"size_categories:1M<n<10M",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2503.01739",
"region:us",
"video-generation",
"text-to-video-dataset"
] | [
"text-to-video",
"text-to-image",
"image-to-video",
"image-to-image"
] | "2025-02-18T04:18:29Z" | ---
language:
- en
license: cc-by-4.0
size_categories:
- 1M<n<10M
task_categories:
- text-to-video
- text-to-image
- image-to-video
- image-to-image
dataset_info:
features:
- name: ID
dtype: string
- name: Middle_Frame
dtype: image
- name: Topic
dtype: string
- name: Detailed_Caption
dtype: string
- name: Brief_Caption
dtype: string
- name: Start_Time
dtype: string
- name: End_Time
dtype: string
- name: Aesthetic_Quality
dtype: float32
- name: Background_Consistency
dtype: float32
- name: Dynamic_Degree
dtype: float32
- name: Imaging_Quality
dtype: float32
- name: Motion_Smoothness
dtype: float32
- name: Subject_Consistency
dtype: float32
splits:
- name: Full
num_bytes: 46459680631.0
num_examples: 1091712
download_size: 91635996940
dataset_size: 92919361262.0
configs:
- config_name: default
data_files:
- split: Full
path: data/Full-*
tags:
- video-generation
- text-to-video-dataset
---
# Summary
This is the dataset proposed in our paper [**VideoUFO: A Million-Scale User-Focused Dataset for Text-to-Video Generation**](https://huggingface.co/papers/2503.01739).
VideoUFO is the first dataset curated in alignment with real-world users’ focused topics for text-to-video generation. Specifically, the dataset comprises over 1.09 million video clips spanning 1,291 topics. Here, we select the top 20 most popular topics for illustration.
<p align="center">
<img src="https://huggingface.co/datasets/WenhaoWang/VideoUFO/resolve/main/assets/teasor.png" width="1000">
</p>
# Visual comparison
Visual comparisons between our approach (MVDiT-VideoUFO) and other text-to-video models. The model trained on VideoUFO outperforms the alternatives in generating user-focused topics.
<p align="center">
<img src="https://huggingface.co/datasets/WenhaoWang/VideoUFO/resolve/main/assets/compare.png" width="1000">
</p>
# Data point
Each data point in our VideoUFO includes a video clip, an ID, a topic, start and end times, a brief caption, and a detailed caption. Beyond that, we evaluate each clip with six different video quality scores from VBench.
<p align="center">
<img src="https://huggingface.co/datasets/WenhaoWang/VideoUFO/resolve/main/assets/datapoint.png" width="1000">
</p>
# Statistics
<p align="center">
<img src="https://huggingface.co/datasets/WenhaoWang/VideoUFO/resolve/main/assets/stat_a.png" width="1000">
</p>
<p align="center">
<img src="https://huggingface.co/datasets/WenhaoWang/VideoUFO/resolve/main/assets/stat_b.png" width="1000">
</p>
# Download
For users in mainland China, try setting `export HF_ENDPOINT=https://hf-mirror.com` to successfully download the datasets.
## Download the metadata of VideoUFO
```python
from datasets import load_dataset
ds = load_dataset("WenhaoWang/VideoUFO", split='Full', streaming=False)
```
or
```
wget https://huggingface.co/datasets/WenhaoWang/VideoUFO/resolve/main/VideoUFO.csv
```
## Download the videos in VideoUFO
Please note that due to bandwidth costs, we compress the released videos. However, the total size is still approximately 800GB.
```python
from huggingface_hub import hf_hub_download
for i in range(1,201):
hf_hub_download(repo_id="WenhaoWang/VideoUFO", filename="VideoUFO_tar/VideoUFO_%d.tar"%i, repo_type="dataset")
```
# Comparison with other datasets
<p align="center">
<img src="https://huggingface.co/datasets/WenhaoWang/VideoUFO/resolve/main/assets/comparison_datasets.png" width="1000">
</p>
# License
The videos in our VideoUFO are licensed under the [CC BY 4.0 license](https://creativecommons.org/licenses/by/4.0/deed.en).
# Curators
VideoUFO is created by [Wenhao Wang](https://wangwenhao0716.github.io/) and Professor [Yi Yang](https://scholar.google.com/citations?user=RMSuNFwAAAAJ&hl=zh-CN).
# Citation
```
@article{wang2025VideoUFO,
title={VideoUFO: A Million-Scale User-Focused Dataset for Text-to-Video Generation},
author={Wang, Wenhao and Yang, Yi},
booktitle={arXiv preprint arXiv:2503.01739},
year={2025}
}
```
# Contact
If you have any questions, feel free to contact Wenhao Wang ([email protected]). |
mitermix/chess-selfplay | mitermix | "2023-05-22T06:58:34Z" | 10,877 | 6 | [
"license:apache-2.0",
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2023-05-18T08:56:25Z" | ---
license: apache-2.0
---
|
QingyiSi/Alpaca-CoT | QingyiSi | "2023-09-14T08:52:10Z" | 10,875 | 726 | [
"language:en",
"language:zh",
"language:ml",
"license:apache-2.0",
"region:us",
"Instruction",
"Cot"
] | null | "2023-03-25T14:58:30Z" | ---
language:
- en
- zh
- ml
tags:
- Instruction
- Cot
license: apache-2.0
datasets:
- dataset1
- dataset2
---
# Instruction-Finetuning Dataset Collection (Alpaca-CoT)
This repository will continuously collect various instruction tuning datasets. And we standardize different datasets into the same format, which can be directly loaded by the [code](https://github.com/PhoebusSi/alpaca-CoT) of Alpaca model.
We also have conducted empirical study on various instruction-tuning datasets based on the Alpaca model, as shown in [https://github.com/PhoebusSi/alpaca-CoT](https://github.com/PhoebusSi/alpaca-CoT).
If you think this dataset collection is helpful to you, please `like` this dataset and `star` our [github project](https://github.com/PhoebusSi/alpaca-CoT)!
You are in a warm welcome to provide us with any non-collected instruction-tuning datasets (or their sources). We will uniformly format them, train Alpaca model with these datasets and open source the model checkpoints.
# Contribute
Welcome to join us and become a contributor to this project!
If you want to share some datasets, adjust the data in the following format:
```
example.json
[
{"instruction": instruction string,
"input": input string, # (may be empty)
"output": output string}
]
```
Folder should be like this:
```
Alpaca-CoT
|
|----example
| |
| |----example.json
| |
| ----example_context.json
...
```
Create a new pull request in [Community
](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT/discussions) and publish your branch when you are ready. We will merge it as soon as we can.
# Data Usage and Resources
## Data Format
All data in this folder is formatted into the same templates, where each sample is as follows:
```
[
{"instruction": instruction string,
"input": input string, # (may be empty)
"output": output string}
]
```
## alpaca
#### alpaca_data.json
> This dataset is published by [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca). It contains 52K English instruction-following samples obtained by [Self-Instruction](https://github.com/yizhongw/self-instruct) techniques.
#### alpaca_data_cleaned.json
> This dataset is obtained [here](https://github.com/tloen/alpaca-lora). It is a revised version of `alpaca_data.json` by stripping of various tokenization artifacts.
## alpacaGPT4
#### alpaca_gpt4_data.json
> This dataset is published by [Instruction-Tuning-with-GPT-4](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM).
It contains 52K English instruction-following samples generated by GPT-4 using Alpaca prompts for fine-tuning LLMs.
#### alpaca_gpt4_data_zh.json
> This dataset is generated by GPT-4 using Chinese prompts translated from Alpaca by ChatGPT.
<!-- ## belle_cn
#### belle_data_cn.json
This dataset is published by [BELLE](https://github.com/LianjiaTech/BELLE). It contains 0.5M Chinese instruction-following samples, which is also generated by [Self-Instruction](https://github.com/yizhongw/self-instruct) techniques.
#### belle_data1M_cn.json
This dataset is published by [BELLE](https://github.com/LianjiaTech/BELLE). It contains 1M Chinese instruction-following samples. The data of `belle_data_cn.json` and `belle_data1M_cn.json` are not duplicated. -->
## Chain-of-Thought
#### CoT_data.json
> This dataset is obtained by formatting the combination of 9 CoT datasets published by [FLAN](https://github.com/google-research/FLAN). It contains 9 CoT tasks involving 74771 samples.
#### CoT_CN_data.json
> This dataset is obtained by tranlating `CoT_data.json` into Chinese, using Google Translate(en2cn).
#### formatted_cot_data folder
> This folder contains the formatted English data for each CoT dataset.
#### formatted_cot_data folder
> This folder contains the formatted Chinese data for each CoT dataset.
## CodeAlpaca
#### code_alpaca.json
> This dataset is published by [codealpaca](https://github.com/sahil280114/codealpaca). It contains code generation task involving 20022 samples.
## finance
#### finance_en.json
> This dataset is collected from [here](https://huggingface.co/datasets/gbharti/finance-alpaca). It contains 68912 financial related instructions in English.
## firefly
#### firefly.json
> his dataset is collected from [here](https://github.com/yangjianxin1/Firefly). It contains 1649398 chinese instructions in 23 nlp tasks.
## GPT4all
#### gpt4all.json
> This dataset is collected from [here](https://github.com/nomic-ai/gpt4all). It contains 806199 en instructions in code, storys and dialogs tasks.
#### gpt4all_without_p3.json
> gpt4all without Bigscience/P3, contains 437605 samples.
## GPTeacher
#### GPTeacher.json
> This dataset is collected from [here](https://github.com/teknium1/GPTeacher). It contains 29013 en instructions generated by GPT-4, General-Instruct - Roleplay-Instruct - Code-Instruct - and Toolformer.
## Guanaco
#### GuanacoDataset.json
> This dataset is collected from [here](https://huggingface.co/datasets/JosephusCheung/GuanacoDataset). It contains 534610 en instructions generated by text-davinci-003 upon 175 tasks from the Alpaca model by providing rewrites of seed tasks in different languages and adding new tasks specifically designed for English grammar analysis, natural language understanding, cross-lingual self-awareness, and explicit content recognition.
#### Guanaco_additional_Dataset.json
> A new additional larger dataset for different languages.
## HC3
#### HC3_ChatGPT.json/HC3_Human.json
> This dataset is collected from [here](https://huggingface.co/datasets/Hello-SimpleAI/HC3). It contains 37175 en/zh instructions generated by ChatGPT and human.
#### HC3_ChatGPT_deduplication.json/HC3_Human_deduplication.json
> HC3 dataset without deduplication instructions.
## instinwild
#### instinwild_en.json & instinwild_cn.json
> The two datasets are obtained [here](https://github.com/XueFuzhao/InstructionWild). It contains 52191 English and 51504 Chinese instructions, which are collected from Twitter, where users tend to share their interesting prompts of mostly generation, open QA, and mind-storm types. (Colossal AI used these datasets to train the ColossalChat model.)
## instruct
#### instruct.json
> The two datasets are obtained [here](https://huggingface.co/datasets/swype/instruct). It contains 888969 English instructions, which are caugmentation performed using the advanced NLP tools provided by AllenAI.
## Natural Instructions
#### natural-instructions-1700tasks.zip
> This dataset is obtained [here](https://github.com/allenai/natural-instructions). It contains 5040134 instructions, which are collected from diverse nlp tasks
## prosocial dialog
#### natural-instructions-1700tasks.zip
> This dataset is obtained [here](https://huggingface.co/datasets/allenai/prosocial-dialog). It contains 165681 English instructions, which are produuced by GPT-3 rewrites questions and humans feedback
## xP3
#### natural-instructions-1700tasks.zip
> This dataset is obtained [here](https://huggingface.co/datasets/bigscience/xP3). It contains 78883588 instructions, which are collected by prompts & datasets across 46 of languages & 16 NLP tasks
## Chinese-instruction-collection
> all datasets of Chinese instruction collection
## combination
#### alcapa_plus_belle_data.json
> This dataset is the combination of English `alpaca_data.json` and Chinese `belle_data_cn.json`.
#### alcapa_plus_cot_data.json
> This dataset is the combination of English `alpaca_data.json` and CoT `CoT_data.json`.
#### alcapa_plus_belle_cot_data.json
> This dataset is the combination of English `alpaca_data.json`, Chinese `belle_data_cn.json` and CoT `CoT_data.json`.
## Citation
Please cite the repo if you use the data collection, code, and experimental findings in this repo.
```
@misc{alpaca-cot,
author = {Qingyi Si, Zheng Lin },
school = {Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China},
title = {Alpaca-CoT: An Instruction Fine-Tuning Platform with Instruction Data Collection and Unified Large Language Models Interface},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/PhoebusSi/alpaca-CoT}},
}
```
Cite the original Stanford Alpaca, BELLE and FLAN papers as well, please.
|
chcorbi/helvipad | chcorbi | "2025-03-24T09:12:06Z" | 10,851 | 6 | [
"task_categories:depth-estimation",
"source_datasets:original",
"license:cc0-1.0",
"size_categories:10K<n<100K",
"modality:image",
"region:us",
"omnidirectional",
"stereo-matching",
"depth-estimation",
"image"
] | [
"depth-estimation"
] | "2024-12-09T19:13:40Z" | ---
license: cc0-1.0
task_categories:
- depth-estimation
tags:
- omnidirectional
- stereo-matching
- depth-estimation
- image
source_datasets:
- original
pretty_name: Helvipad
size_categories:
- 10K<n<100K
paperswithcode_id: helvipad
dataset_info:
config_name: default
features:
- name: images_top
dtype: image
- name: images_bottom
dtype: image
- name: depth_maps
dtype: image
- name: disparity_maps
dtype: image
- name: depth_maps_augmented
dtype: image
- name: disparity_maps_augmented
dtype: image
splits:
- name: train
num_examples: 26412
- name: val
num_examples: 2995
- name: test
num_examples: 10146
configs:
- config_name: default
data_files:
- split: train
path: train/**
- split: val
path: val/**
- split: test
path: test/**
default: true
---
# HELVIPAD: A Real-World Dataset for Omnidirectional Stereo Depth Estimation [](https://vita-epfl.github.io/Helvipad/)
The <span style="font-variant: small-caps;">Helvipad</span> dataset is a real-world stereo dataset designed for omnidirectional depth estimation. It comprises 39,553 paired equirectangular images captured using a top-bottom 360° camera setup and corresponding pixel-wise depth and disparity labels derived from LiDAR point clouds. The dataset spans diverse indoor and outdoor scenes under varying lighting conditions, including night-time environments.
## News
- **[16/02/2025]** Helvipad has been accepted to CVPR 2025! 🎉🎉
- **[CVPR Update – 16/03/2025]** If you already downloaded the dataset, we have applied a small but important update:
- **train/val split**: the previous `train/` folder is now split into `train/` and `val/` subsets.
- **bottom image fix** (`images_bottom/`): a minor horizontal shift correction has been applied to bottom images in `train/`, `val/`, and `test/`.
- **disparity and depth maps adjustment** (`disparity_maps/`, `depth_maps/`, `disparity_maps_augmented/`, `depth_maps_augmented/`): a small vertical shift was corrected in both standard and augmented depth and disparity maps in `train/`, `val/`, and `test/`.
We have re-run all experiments, and the updated dataset produces similar results.
## Dataset Structure
The dataset is organized into training, validation and testing subsets with the following structure:
```
helvipad/
├── train/
│ ├── depth_maps # Depth maps generated from LiDAR data
│ ├── depth_maps_augmented # Augmented depth maps using depth completion
│ ├── disparity_maps # Disparity maps computed from depth maps
│ ├── disparity_maps_augmented # Augmented disparity maps using depth completion
│ ├── images_top # Top-camera RGB images
│ ├── images_bottom # Bottom-camera RGB images
│ ├── LiDAR_pcd # Original LiDAR point cloud data
├── val/
│ ├── depth_maps # Depth maps generated from LiDAR data
│ ├── depth_maps_augmented # Augmented depth maps using depth completion
│ ├── disparity_maps # Disparity maps computed from depth maps
│ ├── disparity_maps_augmented # Augmented disparity maps using depth completion
│ ├── images_top # Top-camera RGB images
│ ├── images_bottom # Bottom-camera RGB images
│ ├── LiDAR_pcd # Original LiDAR point cloud data
├── test/
│ ├── depth_maps # Depth maps generated from LiDAR data
│ ├── depth_maps_augmented # Augmented depth maps using depth completion (only for computing LRCE)
│ ├── disparity_maps # Disparity maps computed from depth maps
│ ├── disparity_maps_augmented # Augmented disparity maps using depth completion (only for computing LRCE)
│ ├── images_top # Top-camera RGB images
│ ├── images_bottom # Bottom-camera RGB images
│ ├── LiDAR_pcd # Original LiDAR point cloud data
```
The dataset repository also includes:
- `helvipad_utils.py`: utility functions for reading depth and disparity maps, converting disparity to depth, and handling disparity values in pixels and degrees;
- `calibration.json`: intrinsic and extrinsic calibration parameters for the stereo cameras and LiDAR sensor.
## Benchmark
We evaluate the performance of multiple state-of-the-art and popular stereo matching methods, both for standard and 360° images. All models are trained on a single NVIDIA A100 GPU with
the largest possible batch size to ensure comparable use of computational resources.
| Method | Stereo Setting | Disp-MAE (°) | Disp-RMSE (°) | Disp-MARE | Depth-MAE (m) | Depth-RMSE (m) | Depth-MARE | Depth-LRCE (m) |
|--------------------|-------------------|---------------|----------------|------------|----------------|----------------|-----------------|---------------------|
| PSMNet | conventional | 0.286 | 0.496 | 0.248 | 2.509 | 5.673 | 0.176 | 1.809 |
| 360SD-Net | omnidirectional | 0.224 | 0.419 | 0.191 | 2.122 | 5.077 | 0.152 | 0.904 |
| IGEV-Stereo | conventional | 0.225 | 0.423 | 0.172 | 1.860 | 4.447 | 0.146 | 1.203 |
| 360-IGEV-Stereo | omnidirectional | **0.188** | **0.404** | **0.146** | **1.720** | **4.297** | **0.130** | **0.388** |
## Project Page
For more information, visualizations, and updates, visit the **[project page](https://vita-epfl.github.io/Helvipad/)**.
## License
This dataset is licensed under the [Creative Commons Attribution-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-sa/4.0/).
## Acknowledgments
This work was supported by the [EPFL Center for Imaging](https://imaging.epfl.ch/) through a Collaborative Imaging Grant.
We thank the VITA lab members for their valuable feedback, which helped to enhance the quality of this manuscript.
We also express our gratitude to Dr. Simone Schaub-Meyer and Oliver Hahn for their insightful advice during the project's final stages.
## Citation
If you use the Helvipad dataset in your research, please cite our paper:
```bibtex
@inproceedings{zayene2025helvipad,
author = {Zayene, Mehdi and Endres, Jannik and Havolli, Albias and Corbière, Charles and Cherkaoui, Salim and Ben Ahmed Kontouli, Alexandre and Alahi, Alexandre},
title = {Helvipad: A Real-World Dataset for Omnidirectional Stereo Depth Estimation},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2025}
}
``` |
Neel-Gupta/owt-processed_512 | Neel-Gupta | "2024-12-16T16:10:54Z" | 10,834 | 0 | [
"size_categories:10K<n<100K",
"format:parquet",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-12-16T15:22:51Z" | ---
dataset_info:
features:
- name: text
sequence:
sequence:
sequence: int64
splits:
- name: train
num_bytes: 281226340096
num_examples: 44656
download_size: 30432385846
dataset_size: 281226340096
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
PRIME-RL/Eurus-2-RL-Data | PRIME-RL | "2025-02-19T12:14:49Z" | 10,833 | 30 | [
"license:mit",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2502.01456",
"arxiv:2412.01981",
"region:us"
] | null | "2024-12-31T07:01:21Z" | ---
license: mit
---
# Eurus-2-RL-Data
## Links
- 📜 [Paper](https://arxiv.org/abs/2502.01456)
- 📜 [Blog](https://curvy-check-498.notion.site/Process-Reinforcement-through-Implicit-Rewards-15f4fcb9c42180f1b498cc9b2eaf896f)
- 🤗 [PRIME Collection](https://huggingface.co/PRIME-RL)
## Introduction
Eurus-2-RL-Data is a high-quality RL training dataset of mathematics and coding problems with outcome verifiers (LaTeX answers for math and test cases for coding).
- For math, we source from [NuminaMath-CoT](https://huggingface.co/datasets/AI-MO/NuminaMath-CoT). The problems span from Chinese high school mathematics to International Mathematical Olympiad competition questions.
- For coding, we source from [APPS](https://huggingface.co/datasets/codeparrot/apps), [CodeContests](https://huggingface.co/datasets/deepmind/code_contests), [TACO](https://huggingface.co/datasets/BAAI/TACO), and [Codeforces](https://huggingface.co/datasets/MatrixStudio/Codeforces-Python-Submissions). The problems are mainly programming competition level.
To further increase data quality, we conduct detailed cleaning and filtering.
- For math, we use advanced reasoning models like [Qwen-QwQ](https://huggingface.co/Qwen/QwQ-32B-Preview) to filter out problems that are unsolvable, unmatchable, or with incorrect answers. We also reformat multiple-choice questions to open questions.
- For coding, we mainly filter out duplicated problems.
Detailed data preprocessing can be found [here](https://huggingface.co/datasets/PRIME-RL/Eurus-2-RL-Data#detailed-rl-data-preprocessing). Finally, we retain **455k** math problems and **26k** coding problems.
## Usage
```python
from datasets import load_dataset
ds = load_dataset("PRIME-RL/Eurus-2-RL-Data")
print(ds)
# DatasetDict({
# train: Dataset({
# features: ['data_source', 'prompt', 'ability', 'reward_model', 'extra_info'],
# num_rows: 480537
# })
# validation: Dataset({
# features: ['data_source', 'prompt', 'ability', 'reward_model', 'extra_info'],
# num_rows: 2048
# })
# })
```
## Statistics
| | Train | Validation |
| ------ | ------ | ---------- |
| Math | 455261 | 1024 |
| Coding | 25276 | 1024 |
## Data Example
Math
```json
{
'data_source': 'numina_olympiads',
'prompt': array([
{'content': '\nWhen tackling complex reasoning tasks, you have access to the following actions. Use them as needed to progress through your thought process.\n\n[ASSESS]\n\n[ADVANCE]\n\n[VERIFY]\n\n[SIMPLIFY]\n\n[SYNTHESIZE]\n\n[PIVOT]\n\n[OUTPUT]\n\nYou should strictly follow the format below:\n\n[ACTION NAME]\n\n# Your action step 1\n\n# Your action step 2\n\n# Your action step 3\n\n...\n\nNext action: [NEXT ACTION NAME]\n\n', 'role': 'system'},
{'content': 'Find the matrix of the operator $\\widehat{A}$ in the basis $\\mathbf{e}_{1}^{\\prime}, \\mathbf{e}_{2}^{\\prime}, \\mathbf{e}_{3}^{\\prime}$, where\n\n$$\n\\begin{aligned}\n& \\mathbf{e}_{1}^{\\prime}=\\mathbf{e}_{1}+\\mathbf{e}_{2}+2 \\mathbf{e}_{3}, \\\\\n& \\mathbf{e}_{2}^{\\prime}=2 \\mathbf{e}_{1}-\\mathbf{e}_{2} \\\\\n& \\mathbf{e}_{3}^{\\prime}=-\\mathbf{e}_{1}+\\mathbf{e}_{2}+\\mathbf{e}_{3},\n\\end{aligned}\n$$\n\nif in the basis $\\mathbf{e}_{1}, \\mathbf{e}_{2}, \\mathbf{e}_{3}$ its matrix is given by\n\n$$\nA_{\\mathbf{e}}=\\left(\\begin{array}{rrr}\n2 & 0 & -1 \\\\\n0 & 1 & -2 \\\\\n-1 & 2 & 0\n\\end{array}\\right)\n$$\n\nPresent the answer in LaTex format: \\boxed{Your answer}', 'role': 'user'}],
dtype=object),
'ability': 'math',
'reward_model': {'ground_truth': '\\begin{pmatrix}\n -7 & 6 & -8 \\\\\n 11 & -9 & 12 \\\\\n 15 & -16 & 19\n \\end{pmatrix}', 'style': 'rule'},
'extra_info': {'index': 0, 'split': 'dummy'}
}
```
Coding
```json
{
'data_source': 'taco',
'prompt': array([
{'content': '\nWhen tackling complex reasoning tasks, you have access to the following actions. Use them as needed to progress through your thought process.\n\n[ASSESS]\n\n[ADVANCE]\n\n[VERIFY]\n\n[SIMPLIFY]\n\n[SYNTHESIZE]\n\n[PIVOT]\n\n[OUTPUT]\n\nYou should strictly follow the format below:\n\n[ACTION NAME]\n\n# Your action step 1\n\n# Your action step 2\n\n# Your action step 3\n\n...\n\nNext action: [NEXT ACTION NAME]\n\n', 'role': 'system'},
{'content': 'Xander Cage has a list of cities he can visit on his new top-secret mission. He represents each city as a tuple of $(latitude,longitude,height,points)$. The values of $latitude$, $longitude$, and $height$ are distinct across all cities.\n\nWe define a mission as a sequence of cities, $c_1,c_2,c_3,\\ldots,c_k$, that he visits. We define the total $\\text{points}$ of such a mission to be the sum of the $\\text{points}$ of all the cities in his mission list.\n\nBeing eccentric, he abides by the following rules on any mission:\n\nHe can choose the number of cities he will visit (if any).\nHe can start the mission from any city.\nHe visits cities in order of strictly increasing $height$.\nThe absolute difference in $latitude$ between adjacent visited cities in his mission must be at most $d_l\\textbf{at}$.\nThe absolute difference in $longitude$ between adjacent visited cities in his mission must be at most $d_long$.\n\nGiven $\\boldsymbol{d\\text{_lat}}$, $d\\text{_long}$, and the definitions for $n$ cities, find and print the maximum possible total $\\text{points}$ that Xander can earn on a mission.\n\nInput Format\n\nThe first line contains three space-separated integers describing the respective values of $n$, $\\boldsymbol{d\\text{_lat}}$, and $d\\text{_long}$. \n\nEach line $\\boldsymbol{i}$ of the $n$ subsequent lines contains four space-separated integers denoting the respective $latitude$, $longitude$, $height$, and $\\text{points}$ for a city.\n\nConstraints\n\n$1\\leq n\\leq2\\times10^5$ \n$1\\leq d\\_\\textit{lat},d\\textit{long}\\leq2\\times10^5$ \n$1\\leq latitude,longitude,height\\leq2\\times10^5$ \n$-2\\times10^5\\leq\\textit{points}\\leq2\\times10^5$\n\nOutput Format\n\nPrint a single integer denoting the maximum possible $\\text{points}$ that Xander can earn on a mission.\n\nSample Input 0\n3 1 1\n1 1 1 3\n2 2 2 -1\n3 3 3 3\n\nSample Output 0\n5\n\nExplanation 0\n\nXander can start at city $1$, then go to city $2$, and then go to city $3$ for a maximum value of total $points=3+-1+3=5$ \n\nNote that he cannot go directly from city $1$ to city $3$ as that would violate his rules that the absolute difference in $latitude$ between adjacent visited cities be $\\leq d\\text{_lat}$ and the absolute difference in $longitude$ between adjacent visited cities be $\\leq d\\text{_long}$. Because $d\\textit{_lat}=1$ and $d\\textit{_long}=1$, he cannot directly travel between those cities.\n\nWrite Python code to solve the problem. Present the code in \n```python\nYour code\n```\nat the end.', 'role': 'user'}],
dtype=object),
'ability': 'code',
'reward_model': {'ground_truth': '{"inputs": ["3 2 2\\n1 1 1 3\\n2 2 2 -1\\n3 3 3 3\\n", "4 2 2\\n1 1 1 3\\n2 2 2 -1\\n3 3 3 3\\n4 4 4 5\\n", "5 2 2\\n1 1 1 3\\n2 2 2 -1\\n3 3 3 3\\n4 4 4 5\\n5 5 5 1\\n", "2 1 1\\n1 1 1 3\\n2 2 2 5\\n", "3 1 1\\n1 1 1 3\\n1 2 2 5\\n1 3 3 6\\n", "5 200000 200000\\n1 1 1 200000\\n200000 200000 200000 200000\\n400000 400000 400000 200000\\n600000 600000 600000 200000\\n800000 800000 800000 200000\\n"], "outputs": ["6", "11", "12", "8", "14", "1000000"]}', 'style': 'rule'},
'extra_info': {'index': 0, 'split': 'dummy'}
}
```
Detailed descriptions of the different fields can be found [here](https://verl.readthedocs.io/en/latest/preparation/prepare_data.html).
## Detailed RL Data Preprocessing
### Data Filtering and Question-Type Classification
The preprocessing pipeline employs a systematic rule-based approach to filter and classify mathematical problems to create a high-quality dataset with solvable problems, appropriate difficulty levels, and correct solutions.
We exclude problems containing figures or diagrams since they require visual processing capabilities. We also remove proof questions due to difficulties in answer verification. The remaining problems are classified into question-answering, multiple-choice, or fill-in-the-blank questions based on specific patterns. Since fill-in-the-blank questions comprise less than 400 examples compared to the much larger set of multiple-choice questions, we focus solely on multiple-choice questions for further processing.
### Converting to Direct Question-Answer Format
We transform multiple-choice questions into a direct question-answer format through three sequential stages: rule-based filtering, LLM-based filtering, and LLM-based formatting.
We first identify and remove questions that inherently require multiple-choice options - specifically, those where comparing specific statements or properties is essential to the problem-solving process. These questions cannot be meaningfully converted to a direct question-answer format. The initial filtering employs simple rule-based pattern matching, searching for keywords like "following" and "statement" that typically indicate option-dependent problems.
Following the rule-based filtering, we employ Meta-Llama-3.1-8B-Instruct to perform a more nuanced classification of the remaining questions. Our pilot study revealed that while the LLM occasionally misclassifies questions, it tends to err on the conservative side - marking potentially convertible questions as requiring options rather than the reverse. Given our large dataset, we accepted this conservative approach to maintain quality.
For questions classified as convertible, we implement a two-phase reformatting process:
1. Question Reformatting: Removing choice indicators and restructuring the question to elicit direct answers
2. Solution Reformatting: Converting multiple-choice solutions into step-by-step derivations, ensuring all final answers are presented in standard LaTeX boxed format
This systematic approach maintains mathematical rigor while creating a standardized format suitable for downstream applications.
### Problem and Solution Validation
The final stage involves merging all question-answer pairs and performing LLM-based comprehensive validation. We identify two key aspects in validation: solvability and correctness.
We leverage state-of-the-art mathematical reasoning models, including QwQ-32B-Preview and Qwen2.5-Math-72B-Instruct, employing a self-consistency approach to determine problem solvability, and if solvable, verify the correctness of solutions provided in the original dataset.
To enhance validation accuracy, we first analyzed sample problems to identify characteristics of solvable and unsolvable cases and created synthetic unsolvable problems featuring missing conditions or logical contradictions. Based on these samples, we developed specialized prompts to improve the models' ability to distinguish solvability.
Each problem undergoes five independent validation attempts, where the LLM:
1. Provides step-by-step solutions using LaTeX formatting
2. Identifies insolvability due to missing conditions or logical contradictions
3. Generates complete reasoning traces for solvable problems
4. Presents final answers in standardized LaTeX boxed format (`\\boxed{}`)
5. Documents any impediments to solution completion
We evaluate two key consistency measures across multiple validation attempts:
- Status Consistency: Agreement on problem solvability
- Answer Consistency:
- Consistency of solutions across different attempts
- Agreement between generated solutions and ground truth
The final dataset retains only problems that demonstrate:
- Consistent solvability across validation attempts
- Agreement in solutions across multiple attempts
- Alignment with ground truth answers
This rigorous validation process ensures the resulting dataset comprises well-defined, solvable problems with verified, accurate solutions.
## Citation
```latex
@article{cui2025process,
title={Process reinforcement through implicit rewards},
author={Cui, Ganqu and Yuan, Lifan and Wang, Zefan and Wang, Hanbin and Li, Wendi and He, Bingxiang and Fan, Yuchen and Yu, Tianyu and Xu, Qixin and Chen, Weize and others},
journal={arXiv preprint arXiv:2502.01456},
year={2025}
}
```
```latex
@article{yuan2024implicitprm,
title={Free Process Rewards without Process Labels},
author={Lifan Yuan and Wendi Li and Huayu Chen and Ganqu Cui and Ning Ding and Kaiyan Zhang and Bowen Zhou and Zhiyuan Liu and Hao Peng},
journal={arXiv preprint arXiv:2412.01981},
year={2024}
}
``` |
ioclab/laplacian_image_aesthetic_3M | ioclab | "2023-04-21T22:30:16Z" | 10,824 | 2 | [
"size_categories:1M<n<10M",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2023-04-21T15:35:24Z" | ---
dataset_info:
features:
- name: image
dtype: image
- name: conditioning_image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 359597047282.0
num_examples: 3000000
download_size: 359170663793
dataset_size: 359597047282.0
---
# Dataset Card for "laplacian_image_aesthetic_3M"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
yongchao98/SymBench | yongchao98 | "2025-02-12T15:24:08Z" | 10,811 | 3 | [
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:json",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"arxiv:2502.04350",
"arxiv:2410.03524",
"region:us",
"symbolic-reasoning",
"code"
] | [
"text-generation"
] | "2025-02-08T14:10:41Z" | ---
license: apache-2.0
task_categories:
- text-generation
tags:
- symbolic-reasoning
- code
language:
- en
---
# CodeSteer: Symbolic-Augmented Language Models via Code/Text Guidance
<img src="./Figures/Tag.png" width="650px" alt="s" />
SymBench comprises 37 symbolic tasks related to the following papers. The specific description of each task is in page 16-19 of the paper'CodeSteer: Symbolic-Augmented Language Models via Code/Text Guidance'. This dataset comprises the dataset for finetuning CodeSteerLLM with SFT and DPO datasets, the SymBench with 37 tested tasks, the code scripts to synthesize the SymBench samples.
- [CodeSteer: Symbolic-Augmented Language Models via Code/Text Guidance](https://arxiv.org/pdf/2502.04350)
- [Steering Large Language Models between Code Execution and Textual Reasoning (ICLR'2025)](https://arxiv.org/pdf/2410.03524)
[Code](https://github.com/yongchao98/CodeSteer-v1.0)   
[Huggingface🤗](https://huggingface.co/yongchao98/CodeSteer-v1)   
[Model Weights](https://drive.google.com/drive/folders/1qb_rec6f8rMYtFKm0eQpad0L0uHCwgpL?usp=share_link)
[Finetune Datasets](https://drive.google.com/drive/folders/1Byn-99gFd5ckRkPMJ8-zagzW7XDfO8ie?usp=share_link)   
[SymBench Datasets](https://github.com/yongchao98/CodeSteer-v1.0/tree/main/dataset_gather)   
[SymBench Synthesis Scripts](https://github.com/yongchao98/CodeSteer-v1.0/tree/main/benchmark)
## Contents
- [Framework](#Framework)
- [Inspirations](#Inspirations)
- [Performance](#Performance)
- [Environment_Setup](#Environment_Setup)
- [LLM_API_Key_Setup](#LLM_API_Key_Setup)
- [Train_and_Test_Models](#Train_and_Test_Models)
- [Assistance](#Assistance)
- [Citation](#Citation)
## Framework
<img src="./Figures/CodeSteer-intro.png" width="800px" alt="s" />
<p align="center" style="font-size: 16px;">
Figure: CodeSteer on guiding LLM code/text generation to integrate symbolic computing. At each interaction with TaskLLM, it reviews current and previous answers, then provides guidance for the next round.
</p>
## Inspirations
<img src="./Figures/LLM-makes-simple-mistakes-gather.png" width="800px" alt="s" />
<p align="center" style="font-size: 16px;">
Figure: The cases that GPT-4o makes simple mistakes by direct textual reasoning but can reliably solve the problem with prompted to use code.
</p>
## Performance
We compare GPT-4o + CodeSteer with OpenAI o1 and DeepSeek R1 on SymBench, with 28 seen tasks and 9 unseen tasks. GPT-4o + CodeSteer surpasses o1 (82.7), R1 (76.8), and o1-preview (74.8), highlighting the importance of integrating symbolic computing into LLMs.
<img src="./Figures/Table-results.png" width="800px" alt="s" />
The cost of tokens and runtimes for each method are as follows. GPT-4o + CodeSteer costs less tokens and runtimes than o1 and R1.
<img src="./Figures/Cost-token-runtime.png" width="800px" alt="s" />
## Environment_Setup
The fine-tuning and inference of CodeSteerLLM are based on [Llama-factory](https://github.com/hiyouga/LLaMA-Factory) with some modules modified by us.
```
git clone https://github.com/yongchao98/CodeSteer-v1.0.git
cd CodeSteer-v1.0
conda create -n CodeSteer python=3.10
conda activate CodeSteer
pip install -r requirements.txt
```
## LLM_API_Key_Setup
If you want to use several API-based LLMs as TaskLLM or CodeSteerLLM, then you need to set up API key.
1. First, create a .env file in your project root:
```
OPENAI_API_KEY='your_key_here'
CLAUDE_API_KEY='your_key_here'
MIXTRAL_API_KEY='your_key_here'
DEEPSEEK_API_KEY='your_key_here'
```
2. Add this .env file to your .gitignore to prevent accidentally committing it:
```
echo ".env" >> .gitignore
```
## Train_and_Test_Models
### Create_test_samples
The synthesized test samples for 37 tasks of SymBench are in [dataset_gather](https://github.com/yongchao98/CodeSteer-v1.0/tree/main/dataset_gather) dictionary. You can also synthezise the samples by yourself with tunable complexities with scripts in [create_dataset](https://github.com/yongchao98/CodeSteer-v1.0/tree/main/create_dataset).
### Run inference without GPU, test close LLM as CodeSteerLLM
We can directly use unfinetuned model like GPT-4o as CodeSteerLLM, in this case directly run
```
python benchmark_test_baseline.py
```
### Run inference with GPU, test finetuned CodeSteerLLM
We can infer Llama-3.1-8B with own GPUs (default setting is in infer_CodeSteer.sh using 4*H100 of Harvard Cluster, please modify freely with your own cluster settings). You can also download the [Model Weights](https://drive.google.com/drive/folders/1qb_rec6f8rMYtFKm0eQpad0L0uHCwgpL?usp=share_link) in your local and change the path in llama3_8B_CodeSteer.yaml.
```bash
bash infer_CodeSteer.sh
# default config file is ./llama3_8B_CodeSteer.yaml using the model uploaded on Huggingface.
```
### Finetuning CodeSteerLLM with synthesized data
Both our synthesized datasets of SFT and DPO finetuning are in [Finetune Datasets](https://drive.google.com/drive/folders/1Byn-99gFd5ckRkPMJ8-zagzW7XDfO8ie?usp=share_link).
We use Llama-factory and DeepSpeed for fintuning processes. First install Llama-factory with:
```
git clone --depth 1 https://github.com/hiyouga/LLaMA-Factory.git
cd LLaMA-Factory
pip install -e ".[torch,metrics]"
cd ..
```
Then we run the code with (default setting is in train_llama3-8B-CodeSteer.sh using 4*H100 of Harvard Cluster, please modify freely with your own cluster settings):
```
bash train_llama3-8B-CodeSteer.sh
```
## Assistance
We appreciate all feedback! Feel free to raise an issue for bugs, questions, or suggestions. Contacting [Yongchao Chen](https://yongchao98.github.io/YongchaoChen/) and [Chuchu Fan](https://chuchu.mit.edu) for any questions and discussion.
## Citation
```md
@misc{chen2025codesteersymbolicaugmentedlanguagemodels,
title={CodeSteer: Symbolic-Augmented Language Models via Code/Text Guidance},
author={Yongchao Chen and Yilun Hao and Yueying Liu and Yang Zhang and Chuchu Fan},
year={2025},
eprint={2502.04350},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2502.04350},
}
```
```md
@article{chen2024steering,
title={Steering Large Language Models between Code Execution and Textual Reasoning},
author={Chen, Yongchao and Jhamtani, Harsh and Sharma, Srinagesh and Fan, Chuchu and Wang, Chi},
journal={arXiv preprint arXiv:2410.03524},
year={2024}
}
``` |
gair-prox/DCLM-pro | gair-prox | "2025-02-15T11:41:05Z" | 10,806 | 8 | [
"task_categories:text-generation",
"language:en",
"license:odc-by",
"size_categories:100M<n<1B",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2409.17115",
"region:us",
"web",
"common crawl"
] | [
"text-generation"
] | "2025-02-14T09:30:19Z" | ---
license: odc-by
task_categories:
- text-generation
language:
- en
tags:
- web
- common crawl
size_categories:
- 100B<n<1T
---
# 📚 DCLM-pro
<p align="center">
<img src="prox-teaser.png">
</p>
[ArXiv](http://arxiv.org/abs/2409.17115) | [Models](https://huggingface.co/collections/gair-prox/prox-general-models-65f1674f0607712c4d6eec76) | [Code](https://github.com/GAIR-NLP/ProX)
DCLM-pro is refined from [DCLM](https://huggingface.co/datasets/mlfoundations/dclm-baseline-1.0-parquet) using the **ProX** refining framework.
It contains about >500B high quality tokens, ready for general language model pre-training.
## License
DCLM-pro is based on DCLM, which is made available under an cc-by-4.0 license.
### Citation
```
@article{zhou2024programming,
title={Programming Every Example: Lifting Pre-training Data Quality like Experts at Scale},
author={Zhou, Fan and Wang, Zengzhi and Liu, Qian and Li, Junlong and Liu, Pengfei},
journal={arXiv preprint arXiv:2409.17115},
year={2024}
}
``` |
nguha/legalbench | nguha | "2024-09-30T04:35:09Z" | 10,777 | 105 | [
"task_categories:text-classification",
"task_categories:question-answering",
"task_categories:text-generation",
"language:en",
"license:other",
"size_categories:10K<n<100K",
"arxiv:2308.11462",
"arxiv:2110.01799",
"arxiv:2103.06268",
"arxiv:2301.00876",
"arxiv:1911.00841",
"arxiv:2105.07903",
"region:us",
"legal",
"law",
"finance"
] | [
"text-classification",
"question-answering",
"text-generation"
] | "2023-03-16T23:03:42Z" | ---
language:
- en
license: other
size_categories:
- 10K<n<100K
task_categories:
- text-classification
- question-answering
- text-generation
tags:
- legal
- law
- finance
dataset_info:
- config_name: abercrombie
features:
- name: answer
dtype: string
- name: index
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 307
num_examples: 5
- name: test
num_bytes: 6240
num_examples: 95
download_size: 19558988
dataset_size: 6547
- config_name: canada_tax_court_outcomes
features:
- name: answer
dtype: string
- name: index
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 2975
num_examples: 6
- name: test
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num_examples: 244
download_size: 19558988
dataset_size: 160386
- config_name: citation_prediction_classification
features:
- name: answer
dtype: string
- name: citation
dtype: string
- name: index
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 660
num_examples: 2
- name: test
num_bytes: 26112
num_examples: 108
download_size: 19558988
dataset_size: 26772
- config_name: citation_prediction_open
features:
- name: answer
dtype: string
- name: circuit
dtype: string
- name: index
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 555
num_examples: 2
- name: test
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num_examples: 53
download_size: 19558988
dataset_size: 14015
- config_name: consumer_contracts_qa
features:
- name: answer
dtype: string
- name: contract
dtype: string
- name: index
dtype: string
- name: question
dtype: string
splits:
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num_bytes: 9941
num_examples: 4
- name: test
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num_examples: 396
download_size: 19558988
dataset_size: 1231261
- config_name: contract_nli_confidentiality_of_agreement
features:
- name: answer
dtype: string
- name: index
dtype: string
- name: text
dtype: string
- name: document_name
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- name: test
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num_examples: 82
download_size: 19558988
dataset_size: 47888
- config_name: contract_nli_explicit_identification
features:
- name: answer
dtype: string
- name: index
dtype: string
- name: text
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- name: document_name
dtype: string
splits:
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- name: test
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num_examples: 109
download_size: 19558988
dataset_size: 65748
- config_name: contract_nli_inclusion_of_verbally_conveyed_information
features:
- name: answer
dtype: string
- name: index
dtype: string
- name: text
dtype: string
- name: document_name
dtype: string
splits:
- name: train
num_bytes: 3817
num_examples: 8
- name: test
num_bytes: 81933
num_examples: 139
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---
# Dataset Card for Dataset Name
- **Homepage: https://hazyresearch.stanford.edu/legalbench/**
- **Repository: https://github.com/HazyResearch/legalbench/**
- **Paper: https://arxiv.org/abs/2308.11462**
## Dataset Description
### Dataset Summary
The LegalBench project is an ongoing open science effort to collaboratively curate tasks for evaluating legal reasoning in English large language models (LLMs). The benchmark currently consists of 162 tasks gathered from 40 contributors.
Note: Because LegalBench is intended to test zero and few-shot reasoning, the available "train" splits are small. However, if you are interested in finetuning models or studying model performance in a more traditional train/test regime, you can combine and re-partition train and test data.
If you have questions about the project or would like to get involved, please see the website for more information.
### Supported Tasks and Leaderboards
LegalBench tasks span multiple types (binary classification, multi-class classification, extraction, generation, entailment), multiple types of text (statutes, judicial opinions, contracts, etc.), and multiple areas of law (evidence, contracts, civil procedure, etc.). For more information on tasks, we recommend visiting the website, where you can search through task descriptions, or the Github repository, which contains more granular task descriptions. We also recommend reading the paper, which provides more background on task significance and construction process.
### Languages
All LegalBench tasks are in English.
## Dataset Structure
### Data Instances
Detailed descriptions of the instances for each task can be found on the Github. An example of an instance, for the `abercrombie` task, is provided below:
```
{
"text": "The mark "Ivory" for a product made of elephant tusks.",
"label": "generic"
"idx": 0
}
```
A substantial number of LegalBench tasks are binary classification tasks, which require the LLM to determine if a piece of text has some legal attribute. Because these are framed as Yes/No questions, the label space is "Yes" or "No".
### Data Fields
Detailed descriptions of the instances for each task can be found on the Github.
### Data Splits
Each task (except for `rule_qa` and `scalr`) has both a training and evaluation split. Following [RAFT](https://huggingface.co/datasets/ought/raft), train splits only consists of a few-labeled instances, reflecting the few-shot nature of most LLMs.
## Dataset Creation
### Curation Rationale
LegalBench was created to enable researchers to better benchmark the legal reasoning capabilities of LLMs.
### Source Data
#### Initial Data Collection and Normalization
Broadly, LegalBench tasks are drawn from three sources. The first source of tasks are existing available datasets and corpora. Most of these were originally released for non-LLM evaluation settings. In creating tasks for LegalBench from these sources, we often significantly reformatted data and restructured the prediction objective. For instance, the original [CUAD dataset](https://github.com/TheAtticusProject/cuad) contains annotations on long-documents and is intended for evaluating extraction with span-prediction models. We restructure this corpora to generate a binary classification task for each type of contractual clause. While the original corpus emphasized the long-document aspects of contracts, our restructured tasks emphasize whether LLMs can identify the distinguishing features of different types of clauses. The second source of tasks are datasets that were previously constructed by legal professionals but never released. This primarily includes datasets hand-coded by legal scholars as part of prior empirical legal projects. The last category of tasks are those that were developed specifically for \name, by the authors of this paper. Overall, tasks are drawn from 36 distinct corpora. Please see the Appendix of the paper for more details.
#### Who are the source language producers?
LegalBench data was created by humans. Demographic information for these individuals is not available.
### Annotations
#### Annotation process
Please see the paper for more information on the annotation process used in the creation of each task.
#### Who are the annotators?
Please see the paper for more information on the identity of annotators for each task.
### Personal and Sensitive Information
Data in this benchmark has either been synthetically generated, or derived from an already public source (e.g., contracts from the EDGAR database).
Several tasks have been derived from the LearnedHands corpus, which consists of public posts on /r/LegalAdvice. Some posts may discuss sensitive issues.
## Considerations for Using the Data
### Social Impact of Dataset
Please see the original paper for a discussion of social impact.
### Discussion of Biases
Please see the original paper for a discussion of social impact.
### Other Known Limitations
LegalBench primarily contains tasks corresponding to American law.
## Additional Information
### Dataset Curators
Please see the website for a full list of participants in the LegalBench project.
### Licensing Information
LegalBench tasks are subject to different licenses. Please see the paper for a description of the licenses.
### Citation Information
If you intend to reference LegalBench broadly, please use the citation below. If you are working with a particular task, please use the citation below in addition to the task specific citation (which can be found on the task page on the website or Github).
```
@misc{guha2023legalbench,
title={LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models},
author={Neel Guha and Julian Nyarko and Daniel E. Ho and Christopher Ré and Adam Chilton and Aditya Narayana and Alex Chohlas-Wood and Austin Peters and Brandon Waldon and Daniel N. Rockmore and Diego Zambrano and Dmitry Talisman and Enam Hoque and Faiz Surani and Frank Fagan and Galit Sarfaty and Gregory M. Dickinson and Haggai Porat and Jason Hegland and Jessica Wu and Joe Nudell and Joel Niklaus and John Nay and Jonathan H. Choi and Kevin Tobia and Margaret Hagan and Megan Ma and Michael Livermore and Nikon Rasumov-Rahe and Nils Holzenberger and Noam Kolt and Peter Henderson and Sean Rehaag and Sharad Goel and Shang Gao and Spencer Williams and Sunny Gandhi and Tom Zur and Varun Iyer and Zehua Li},
year={2023},
eprint={2308.11462},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@article{koreeda2021contractnli,
title={ContractNLI: A dataset for document-level natural language inference for contracts},
author={Koreeda, Yuta and Manning, Christopher D},
journal={arXiv preprint arXiv:2110.01799},
year={2021}
}
@article{hendrycks2021cuad,
title={Cuad: An expert-annotated nlp dataset for legal contract review},
author={Hendrycks, Dan and Burns, Collin and Chen, Anya and Ball, Spencer},
journal={arXiv preprint arXiv:2103.06268},
year={2021}
}
@article{wang2023maud,
title={MAUD: An Expert-Annotated Legal NLP Dataset for Merger Agreement Understanding},
author={Wang, Steven H and Scardigli, Antoine and Tang, Leonard and Chen, Wei and Levkin, Dimitry and Chen, Anya and Ball, Spencer and Woodside, Thomas and Zhang, Oliver and Hendrycks, Dan},
journal={arXiv preprint arXiv:2301.00876},
year={2023}
}
@inproceedings{wilson2016creation,
title={The creation and analysis of a website privacy policy corpus},
author={Wilson, Shomir and Schaub, Florian and Dara, Aswarth Abhilash and Liu, Frederick and Cherivirala, Sushain and Leon, Pedro Giovanni and Andersen, Mads Schaarup and Zimmeck, Sebastian and Sathyendra, Kanthashree Mysore and Russell, N Cameron and others},
booktitle={Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
pages={1330--1340},
year={2016}
}
@inproceedings{zheng2021does,
title={When does pretraining help? assessing self-supervised learning for law and the casehold dataset of 53,000+ legal holdings},
author={Zheng, Lucia and Guha, Neel and Anderson, Brandon R and Henderson, Peter and Ho, Daniel E},
booktitle={Proceedings of the eighteenth international conference on artificial intelligence and law},
pages={159--168},
year={2021}
}
@article{zimmeck2019maps,
title={Maps: Scaling privacy compliance analysis to a million apps},
author={Zimmeck, Sebastian and Story, Peter and Smullen, Daniel and Ravichander, Abhilasha and Wang, Ziqi and Reidenberg, Joel R and Russell, N Cameron and Sadeh, Norman},
journal={Proc. Priv. Enhancing Tech.},
volume={2019},
pages={66},
year={2019}
}
@article{ravichander2019question,
title={Question answering for privacy policies: Combining computational and legal perspectives},
author={Ravichander, Abhilasha and Black, Alan W and Wilson, Shomir and Norton, Thomas and Sadeh, Norman},
journal={arXiv preprint arXiv:1911.00841},
year={2019}
}
@article{holzenberger2021factoring,
title={Factoring statutory reasoning as language understanding challenges},
author={Holzenberger, Nils and Van Durme, Benjamin},
journal={arXiv preprint arXiv:2105.07903},
year={2021}
}
@article{lippi2019claudette,
title={CLAUDETTE: an automated detector of potentially unfair clauses in online terms of service},
author={Lippi, Marco and Pa{\l}ka, Przemys{\l}aw and Contissa, Giuseppe and Lagioia, Francesca and Micklitz, Hans-Wolfgang and Sartor, Giovanni and Torroni, Paolo},
journal={Artificial Intelligence and Law},
volume={27},
pages={117--139},
year={2019},
publisher={Springer}
}
``` |
Open-Orca/OpenOrca | Open-Orca | "2025-02-19T07:32:36Z" | 10,749 | 1,381 | [
"task_categories:text-classification",
"task_categories:token-classification",
"task_categories:table-question-answering",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:summarization",
"task_categories:feature-extraction",
"task_categories:text-generation",
"task_categories:text2text-generation",
"language:en",
"license:mit",
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2306.02707",
"arxiv:2301.13688",
"arxiv:2302.13971",
"region:us"
] | [
"conversational",
"text-classification",
"token-classification",
"table-question-answering",
"question-answering",
"zero-shot-classification",
"summarization",
"feature-extraction",
"text-generation",
"text2text-generation"
] | "2023-06-15T18:16:11Z" | ---
language:
- en
license: mit
task_categories:
- conversational
- text-classification
- token-classification
- table-question-answering
- question-answering
- zero-shot-classification
- summarization
- feature-extraction
- text-generation
- text2text-generation
pretty_name: OpenOrca
size_categories:
- 10M<n<100M
---
## Table of Contents
- [Dataset Summary](#dataset-summary)
- [Dataset Attribution](#dataset-attribution)
- [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)
- [Dataset Use](#dataset-use)
- [Use Cases](#use-cases)
- [Usage Caveats](#usage-caveats)
- [Getting Started](#getting-started)
<p><h1>🐋 The OpenOrca Dataset! 🐋</h1></p>
<a name="dataset-announcement"></a>
We are thrilled to announce the release of the OpenOrca dataset!
This rich collection of augmented FLAN data aligns, as best as possible, with the distributions outlined in the [Orca paper](https://arxiv.org/abs/2306.02707).
It has been instrumental in generating high-performing model checkpoints and serves as a valuable resource for all NLP researchers and developers!
# Official Models
## Mistral-7B-OpenOrca
Our [latest model](https://huggingface.co/spaces/Open-Orca/Mistral-7B-OpenOrca), the first 7B to score better overall than all previous models below 30B.
98% of Llama2-70b-chat's performance, in a completely open 7B!
## OpenOrca-Platypus2-13B
Our [third model](https://huggingface.co/Open-Orca/OpenOrca-Platypus2-13B), the first 13B model to score higher than LLaMA1-65B on the HuggingFace Leaderboard!
Released in partnership with Platypus.
## LlongOrca 7B & 13B
* Our [first 7B release](https://huggingface.co/Open-Orca/LlongOrca-7B-16k), trained on top of LLongMA2 to achieve 16,000 tokens context. #1 long context 7B model at release time, with >99% of the overall #1 model's performance.
* [LlongOrca-13B-16k](https://huggingface.co/Open-Orca/LlongOrca-13B-16k), trained on top of LLongMA2. #1 long context 13B model at release time, with >97% of the overall #1 model's performance.
## OpenOrcaxOpenChat-Preview2-13B
Our [second model](https://huggingface.co/Open-Orca/OpenOrcaxOpenChat-Preview2-13B), highlighting that we've surpassed the performance reported in the Orca paper.
Was #1 at release time, now surpassed by our own OpenOrca-Platypus2-13B.
Released in partnership with OpenChat.
## OpenOrca-Preview1-13B
[OpenOrca-Preview1-13B](https://huggingface.co/Open-Orca/OpenOrca-Preview1-13B)
This model was trained in less than a day, for <$200, with <10% of our data.
At release, it beat the current state of the art models on BigBench-Hard and AGIEval. Achieves ~60% of the improvements reported in the Orca paper.
<a name="dataset-summary"></a>
# Dataset Summary
The OpenOrca dataset is a collection of augmented [FLAN Collection data](https://arxiv.org/abs/2301.13688).
Currently ~1M GPT-4 completions, and ~3.2M GPT-3.5 completions.
It is tabularized in alignment with the distributions presented in the ORCA paper and currently represents a partial completion of the full intended dataset, with ongoing generation to expand its scope.
The data is primarily used for training and evaluation in the field of natural language processing.
<a name="dataset-attribution"></a>
# Dataset Attribution
We would like to give special recognition to the following contributors for their significant efforts and dedication:
Teknium
WingLian/Caseus
Eric Hartford
NanoBit
Pankaj
Winddude
Rohan
http://AlignmentLab.ai:
Autometa
Entropi
AtlasUnified
NeverendingToast
NanoBit
WingLian/Caseus
Also of course, as always, TheBloke, for being the backbone of the whole community.
Many thanks to NanoBit and Caseus, makers of [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl), for lending us their expertise on the platform that developed and trained manticore, minotaur, and many others!
We are welcoming sponsors or collaborators to help us build these models to the scale they deserve. Please reach out via our socials:
http://Alignmentlab.ai https://discord.gg/n9hXaBPWxx
Want to visualize our full dataset? Check out our [Nomic Atlas Map](https://atlas.nomic.ai/map/c1b88b47-2d9b-47e0-9002-b80766792582/2560fd25-52fe-42f1-a58f-ff5eccc890d2).
[<img src="https://huggingface.co/Open-Orca/OpenOrca-Preview1-13B/resolve/main/OpenOrca%20Nomic%20Atlas.png" alt="Atlas Nomic Dataset Map" width="400" height="400" />](https://atlas.nomic.ai/map/c1b88b47-2d9b-47e0-9002-b80766792582/2560fd25-52fe-42f1-a58f-ff5eccc890d2)
<a name="supported-tasks-and-leaderboards"></a>
# Supported Tasks and Leaderboards
This dataset supports a range of tasks including language modeling, text generation, and text augmentation.
It has been instrumental in the generation of multiple high-performing model checkpoints which have exhibited exceptional performance in our unit testing.
Further information on leaderboards will be updated as they become available.
<a name="languages"></a>
# Languages
The language of the data is primarily English.
<a name="dataset-structure"></a>
# Dataset Structure
<a name="data-instances"></a>
## Data Instances
A data instance in this dataset represents entries from the FLAN collection which have been augmented by submitting the listed question to either GPT-4 or GPT-3.5.
The response is then entered into the response field.
<a name="data-fields"></a>
## Data Fields
The fields are:
1) 'id', a unique numbered identifier which includes one of 'niv', 't0', 'cot', or 'flan' to represent which source FLAN Collection submix the 'question' is sourced from.
2) 'system_prompt', representing the System Prompt presented to the GPT-3.5 or GPT-4 API for the datapoint
3) 'question', representing a question entry as provided by the FLAN Collection
4) 'response', a response to that question received from a query to either GPT-3.5 or GPT-4.
<a name="data-splits"></a>
## Data Splits
The data is unsplit.
<a name="dataset-creation"></a>
# Dataset Creation
<a name="curation-rationale"></a>
## Curation Rationale
The dataset was created to provide a source of augmented text data for researchers and developers.
The datapoints are intended primarily to provide an enhancement of the core FLAN Collection data which relies upon the detailed step by step reasoning capabilities of GPT-3.5 and GPT-4.
This "reasoning trace" augmentation has demonstrated exceptional results, allowing a LLaMA-13B model trained with this data to rival or beat GPT-3.5 on broad sets of hard reasoning tasks which all models below 100B parameters had previously performed dramatically worse on.
<a name="source-data"></a>
## Source Data
The data is generated using techniques in alignment with the distributions outlined in the Orca paper, except as noted below:
1) There is not enough CoT data in the FLAN Collection to generate 150K zero-shot entries, as the paper purports to use.
We suspect this portion was either undocumented or misrepresented. We have used the ~75K points available.
2) We used the pre-generated FLAN Collection datasets hosted on HuggingFace under conceptofmind, e.g. [conceptofmind/flan2021](https://huggingface.co/datasets/conceptofmind/flan2021_submix_original).
These are referenced by the [official FLAN Collection repo](https://github.com/google-research/FLAN/tree/main/flan/v2) as the preferred data source.
However, these are a subset of the full FLAN Collection data, and have less than the required entries for the flan2021 and t0 submixes, by ~1.25M and 200k respectively.
Combined, this gave us ~1.5M fewer datapoints than in the original Orca paper. Completing the set is an ongoing work.
<a name="dataset-use"></a>
# Dataset Use
<a name="use-cases"></a>
## Use Cases
The dataset can be used for tasks related to language understanding, natural language processing, machine learning model training, and model performance evaluation.
<a name="usage-caveats"></a>
## Usage Caveats
Given that this is a work-in-progress dataset, it is recommended to regularly check for updates and improvements.
Further, the data should be used in accordance with the guidelines and recommendations outlined in the Orca paper.
<a name="getting-started"></a>
## Getting Started
This dataset is organized such that it can be naively loaded via Hugging Face datasets library.
We recommend using streaming due to the large size of the files.
Regular updates and data generation progress can be monitored through the OpenOrca repository on Hugging Face.
# Citation
```bibtex
@misc{OpenOrca,
title = {OpenOrca: An Open Dataset of GPT Augmented FLAN Reasoning Traces},
author = {Wing Lian and Bleys Goodson and Eugene Pentland and Austin Cook and Chanvichet Vong and "Teknium"},
year = {2023},
publisher = {HuggingFace},
journal = {HuggingFace repository},
howpublished = {\url{https://https://huggingface.co/datasets/Open-Orca/OpenOrca}},
}
```
```bibtex
@misc{mukherjee2023orca,
title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4},
author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah},
year={2023},
eprint={2306.02707},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```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{touvron2023llama,
title={Llama 2: Open Foundation and Fine-Tuned Chat Models},
author={Hugo Touvron and Louis Martin and Kevin Stone and Peter Albert and Amjad Almahairi and Yasmine Babaei and Nikolay Bashlykov and Soumya Batra and Prajjwal Bhargava and Shruti Bhosale and Dan Bikel and Lukas Blecher and Cristian Canton Ferrer and Moya Chen and Guillem Cucurull and David Esiobu and Jude Fernandes and Jeremy Fu and Wenyin Fu and Brian Fuller and Cynthia Gao and Vedanuj Goswami and Naman Goyal and Anthony Hartshorn and Saghar Hosseini and Rui Hou and Hakan Inan and Marcin Kardas and Viktor Kerkez and Madian Khabsa and Isabel Kloumann and Artem Korenev and Punit Singh Koura and Marie-Anne Lachaux and Thibaut Lavril and Jenya Lee and Diana Liskovich and Yinghai Lu and Yuning Mao and Xavier Martinet and Todor Mihaylov and Pushkar Mishra and Igor Molybog and Yixin Nie and Andrew Poulton and Jeremy Reizenstein and Rashi Rungta and Kalyan Saladi and Alan Schelten and Ruan Silva and Eric Michael Smith and Ranjan Subramanian and Xiaoqing Ellen Tan and Binh Tang and Ross Taylor and Adina Williams and Jian Xiang Kuan and Puxin Xu and Zheng Yan and Iliyan Zarov and Yuchen Zhang and Angela Fan and Melanie Kambadur and Sharan Narang and Aurelien Rodriguez and Robert Stojnic and Sergey Edunov and Thomas Scialom},
year={2023},
eprint= arXiv 2307.09288
}
@software{touvron2023llama,
title={LLaMA: Open and Efficient Foundation Language Models},
author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and Rodriguez, Aurelien and Joulin, Armand and Grave, Edouard and Lample, Guillaume},
journal={arXiv preprint arXiv:2302.13971},
year={2023}
}
``` |
common-canvas/commoncatalog-cc-by-nc-sa | common-canvas | "2024-05-16T19:45:25Z" | 10,737 | 4 | [
"task_categories:text-to-image",
"language:en",
"license:cc-by-nc-sa-4.0",
"size_categories:10M<n<100M",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2310.16825",
"region:us"
] | [
"text-to-image"
] | "2023-10-19T02:09:41Z" | ---
license: cc-by-nc-sa-4.0
dataset_info:
features:
- name: jpg
dtype: image
- name: blip2_caption
dtype: string
- name: caption
dtype: string
- name: licensename
dtype: string
- name: licenseurl
dtype: string
- name: width
dtype: int32
- name: height
dtype: int32
- name: original_width
dtype: int32
- name: original_height
dtype: int32
- name: photoid
dtype: int64
- name: uid
dtype: string
- name: unickname
dtype: string
- name: datetaken
dtype: timestamp[us]
- name: dateuploaded
dtype: int64
- name: capturedevice
dtype: string
- name: title
dtype: string
- name: usertags
dtype: string
- name: machinetags
dtype: string
- name: longitude
dtype: float64
- name: latitude
dtype: float64
- name: accuracy
dtype: int64
- name: pageurl
dtype: string
- name: downloadurl
dtype: string
- name: serverid
dtype: int64
- name: farmid
dtype: int64
- name: secret
dtype: string
- name: secretoriginal
dtype: string
- name: ext
dtype: string
- name: url
dtype: string
- name: key
dtype: string
- name: status
dtype: string
- name: error_message
dtype: string
- name: exif
dtype: string
- name: sha256
dtype: string
- name: description
dtype: string
task_categories:
- text-to-image
language:
- en
---
# Dataset Card for CommonCatalog CC-BY-NC-SA
This dataset is a large collection of high-resolution Creative Common images (composed of different licenses, see paper Table 1 in the Appendix) collected in 2014 from users of Yahoo Flickr.
The dataset contains images of up to 4k resolution, making this one of the highest resolution captioned image datasets.
## Dataset Details
### Dataset Description
We provide captions synthetic captions to approximately 100 million high resolution images collected from Yahoo Flickr Creative Commons (YFCC).
- **Curated by:** Aaron Gokaslan
- **Language(s) (NLP):** en
- **License:** See relevant yaml tag / dataset name.
### Dataset Sources
<!-- Provide the basic links for the dataset. -->
- **Repository:** https://github.com/mosaicml/diffusion
- **Paper:** https://arxiv.org/abs/2310.16825
- **Demo:** See CommonCanvas Gradios
## Uses
We use CommonCatalog to train a family latent diffusion models called CommonCanvas.
The goal is to produce a model that is competitive with Stable Diffusion 2, but to do so using an easily accessible dataset of known provenance.
Doing so makes replicating the model significantly easier, and provides a clearer mechanism for applying training-data attribution techniques.
### Direct Use
Training text-to-image models
Training image-to-text models
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
* Commercial use
* Crafting content that is offensive or injurious towards individuals, including negative portrayals of their living conditions, cultural backgrounds, religious beliefs, etc.
* Deliberately creating or spreading content that is discriminatory or reinforces harmful stereotypes.
* Falsely representing individuals without their permission.
* Generating sexual content that may be seen by individuals without their consent.
* Producing or disseminating false or misleading information.
* Creating content that depicts extreme violence or bloodshed.
* Distributing content that modifies copyrighted or licensed material in a way that breaches its usage terms.
## Dataset Structure
The dataset is divided into 10 subsets each containing parquets about 4GB each. Each subfolder within contains a resolution range of the images and their respective aspect ratios.
The dataset is also divided along images licensed for commercial use (C) and those that are not (NC).
## Dataset Creation
### Curation Rationale
Creating a standardized, accessible dataset with synthetic caption and releasing it so other people can train on a common dataset for open source image generation.
### Source Data
Yahoo Flickr Creative Commons 100M Dataset and Synthetically Generated Caption Data.
#### Data Collection and Processing
All synthetic captions were generated with BLIP2. See paper for more details.
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
Users of Flickr
## Bias, Risks, and Limitations
See Yahoo Flickr Creative Commons 100M dataset for more information. The information was collected circa 2014 and known to have a bias towards internet connected Western countries. Some areas such as the global south lack representation.
## Citation
**BibTeX:**
```
@article{gokaslan2023commoncanvas,
title={CommonCanvas: An Open Diffusion Model Trained with Creative-Commons Images},
author={Gokaslan, Aaron and Cooper, A Feder and Collins, Jasmine and Seguin, Landan and Jacobson, Austin and Patel, Mihir and Frankle, Jonathan and Stephenson, Cory and Kuleshov, Volodymyr},
journal={arXiv preprint arXiv:2310.16825},
year={2023}
}
```
## Dataset Card Authors
[Aaron Gokaslan](https://huggingface.co/Skylion007)
## Dataset Card Contact
[Aaron Gokaslan](https://huggingface.co/Skylion007)
|
vicgalle/alpaca-gpt4 | vicgalle | "2024-02-10T10:03:45Z" | 10,701 | 280 | [
"task_categories:text-generation",
"task_categories:question-answering",
"language:en",
"license:cc-by-nc-4.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2304.03277",
"region:us",
"gpt4",
"alpaca",
"instruction-finetuning",
"synthetic"
] | [
"text-generation",
"conversational",
"question-answering"
] | "2023-04-07T16:22:59Z" | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 88566301
num_examples: 52002
download_size: 48393562
dataset_size: 88566301
task_categories:
- text-generation
- conversational
- question-answering
language:
- en
size_categories:
- 10K<n<100K
license: cc-by-nc-4.0
tags:
- gpt4
- alpaca
- instruction-finetuning
- synthetic
---
# Dataset Card for "alpaca-gpt4"
This dataset contains English Instruction-Following generated by GPT-4 using Alpaca prompts for fine-tuning LLMs.
The dataset was originaly shared in this repository: https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM. This is just a wraper for compatibility with huggingface's datasets library.
## Dataset Description
- **Homepage:** https://instruction-tuning-with-gpt-4.github.io
- **Repository:** https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM
- **Paper:** https://arxiv.org/abs/2304.03277
## Dataset structure
It contains 52K instruction-following data generated by GPT-4 using the same prompts as in Alpaca.
The dataset has the same format as Alpaca data, except the output is generated by GPT-4:
- `instruction`: `str`, describes the task the model should perform. Each of the 52K instructions is unique.
- `input`: `str`, optional context or input for the task.
- `output`: `str`, the answer to the instruction as generated by `GPT-4`.
- `text`: `str`, all the previous fields concatenated together, plus the same prompt used in Alpaca at the beginnig.
## Difference with the original Alpaca dataset
The original Alpaca dataset used text-davinci-003 to complete the prompts. This dataset uses those same prompts, but generating the completions with GPT-4. Thus, in general, the responses are of higher quality and lenght. Here is an example:
#### Example from Alpaca-GPT4:
```bash
{'instruction': 'Identify the odd one out.',
'input': 'Twitter, Instagram, Telegram',
'output': 'The odd one out is Telegram. Twitter and Instagram are social media platforms mainly for sharing information, images and videos while Telegram is a cloud-based instant messaging and voice-over-IP service.',
'text': 'Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n### Instruction:\nIdentify the odd one out.\n\n### Input:\nTwitter, Instagram, Telegram\n\n### Response:\nThe odd one out is Telegram. Twitter and Instagram are social media platforms mainly for sharing information, images and videos while Telegram is a cloud-based instant messaging and voice-over-IP service.'}
```
#### Same example from original Alpaca:
```bash
{'instruction': 'Identify the odd one out.',
'input': 'Twitter, Instagram, Telegram',
'output': 'Telegram',
'text': 'Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n### Instruction:\nIdentify the odd one out.\n\n### Input:\nTwitter, Instagram, Telegram\n\n### Response:\nTelegram'}
```
## Licensing Information
The dataset is available under the [Creative Commons NonCommercial (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/legalcode). |
qiaojin/PubMedQA | qiaojin | "2024-03-06T01:50:16Z" | 10,689 | 190 | [
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"annotations_creators:expert-generated",
"annotations_creators:machine-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:mit",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:1909.06146",
"region:us"
] | [
"question-answering"
] | "2022-03-02T23:29:22Z" | ---
annotations_creators:
- expert-generated
- machine-generated
language_creators:
- expert-generated
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
- 10K<n<100K
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- multiple-choice-qa
paperswithcode_id: pubmedqa
pretty_name: PubMedQA
config_names:
- pqa_artificial
- pqa_labeled
- pqa_unlabeled
dataset_info:
- config_name: pqa_artificial
features:
- name: pubid
dtype: int32
- name: question
dtype: string
- name: context
sequence:
- name: contexts
dtype: string
- name: labels
dtype: string
- name: meshes
dtype: string
- name: long_answer
dtype: string
- name: final_decision
dtype: string
splits:
- name: train
num_bytes: 443501057
num_examples: 211269
download_size: 233411194
dataset_size: 443501057
- config_name: pqa_labeled
features:
- name: pubid
dtype: int32
- name: question
dtype: string
- name: context
sequence:
- name: contexts
dtype: string
- name: labels
dtype: string
- name: meshes
dtype: string
- name: reasoning_required_pred
dtype: string
- name: reasoning_free_pred
dtype: string
- name: long_answer
dtype: string
- name: final_decision
dtype: string
splits:
- name: train
num_bytes: 2088898
num_examples: 1000
download_size: 1075513
dataset_size: 2088898
- config_name: pqa_unlabeled
features:
- name: pubid
dtype: int32
- name: question
dtype: string
- name: context
sequence:
- name: contexts
dtype: string
- name: labels
dtype: string
- name: meshes
dtype: string
- name: long_answer
dtype: string
splits:
- name: train
num_bytes: 125922964
num_examples: 61249
download_size: 66010017
dataset_size: 125922964
configs:
- config_name: pqa_artificial
data_files:
- split: train
path: pqa_artificial/train-*
- config_name: pqa_labeled
data_files:
- split: train
path: pqa_labeled/train-*
- config_name: pqa_unlabeled
data_files:
- split: train
path: pqa_unlabeled/train-*
---
# 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
- **Homepage:** [PubMedQA homepage](https://pubmedqa.github.io/ )
- **Repository:** [PubMedQA repository](https://github.com/pubmedqa/pubmedqa)
- **Paper:** [PubMedQA: A Dataset for Biomedical Research Question Answering](https://arxiv.org/abs/1909.06146)
- **Leaderboard:** [PubMedQA: Leaderboard](https://pubmedqa.github.io/)
### Dataset Summary
The task of PubMedQA is to answer research questions with yes/no/maybe (e.g.: Do preoperative statins reduce atrial fibrillation after coronary artery bypass grafting?) using the corresponding abstracts.
### Supported Tasks and Leaderboards
The official leaderboard is available at: https://pubmedqa.github.io/.
500 questions in the `pqa_labeled` are used as the test set. They can be found at https://github.com/pubmedqa/pubmedqa.
### Languages
English
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@tuner007](https://github.com/tuner007) for adding this dataset. |
fixie-ai/peoples_speech | fixie-ai | "2024-08-11T17:26:01Z" | 10,640 | 2 | [
"size_categories:1M<n<10M",
"format:parquet",
"modality:audio",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-08-05T18:35:01Z" | ---
dataset_info:
- config_name: clean
features:
- name: id
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: duration_ms
dtype: int32
- name: text
dtype: string
- name: continuation
dtype: string
splits:
- name: validation
num_bytes: 2511523987.692
num_examples: 18622
- name: test
num_bytes: 4259695510.794
num_examples: 34898
- name: train
num_bytes: 401646320552.671
num_examples: 1501271
download_size: 398922548670
dataset_size: 408417540051
- config_name: dirty_sa
features:
- name: id
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: duration_ms
dtype: int32
- name: text
dtype: string
- name: continuation
dtype: string
splits:
- name: train
num_bytes: 144432442623.054
num_examples: 548014
- name: validation
num_bytes: 2511524241.692
num_examples: 18622
- name: test
num_bytes: 4259695588.794
num_examples: 34898
download_size: 149491764186
dataset_size: 151203662453.53998
configs:
- config_name: clean
data_files:
- split: validation
path: clean/validation-*
- split: test
path: clean/test-*
- split: train
path: data/train-*
- config_name: dirty_sa
data_files:
- split: train
path: dirty_sa/train-*
- split: validation
path: dirty_sa/validation-*
- split: test
path: dirty_sa/test-*
---
|
xinrongzhang2022/InfiniteBench | xinrongzhang2022 | "2024-10-08T01:59:10Z" | 10,604 | 27 | [
"region:us"
] | null | "2023-11-16T09:29:02Z" | ---
configs:
- config_name: default
data_files:
- split: passkey
path: "passkey.jsonl"
- split: kv_retrieval
path: "kv_retrieval.jsonl"
- split: number_string
path: "number_string.jsonl"
- split: code_run
path: "code_run.jsonl"
- split: code_debug
path: "code_debug.jsonl"
- split: math_find
path: "math_find.jsonl"
- split: math_calc
path: "math_calc.jsonl"
- split: longdialogue_qa_eng
path: "longdialogue_qa_eng.jsonl"
- split: longbook_qa_eng
path: "longbook_qa_eng.jsonl"
- split: longbook_sum_eng
path: "longbook_sum_eng.jsonl"
- split: longbook_choice_eng
path: "longbook_choice_eng.jsonl"
- split: longbook_qa_chn
path: "longbook_qa_chn.jsonl"
---
---
license: apache-2.0
---
---
## Usage
load with datasets
```
from datasets import load_dataset, Features, Value, Sequence
# Define the features schema
ft = Features({
"id": Value("int64"),
"context": Value("string"),
"input": Value("string"),
"answer": Sequence(Value("string")),
"options": Sequence(Value("string"))
})
# Load the dataset with the specified features
dataset = load_dataset("xinrongzhang2022/InfiniteBench", features=ft)
```
## Citation
Please cite us if you use $\infty$Bench.
```bibtex
@inproceedings{zhang-etal-2024-bench,
title = "$\infty${B}ench: Extending Long Context Evaluation Beyond 100{K} Tokens",
author = "Zhang, Xinrong and
Chen, Yingfa and
Hu, Shengding and
Xu, Zihang and
Chen, Junhao and
Hao, Moo and
Han, Xu and
Thai, Zhen and
Wang, Shuo and
Liu, Zhiyuan and
Sun, Maosong",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.814",
pages = "15262--15277",
abstract = "Processing and reasoning over long contexts is crucial for many practical applications of Large Language Models (LLMs), such as document comprehension and agent construction. Despite recent strides in making LLMs process contexts with more than 100K tokens, there is currently a lack of a standardized benchmark to evaluate this long-context capability. Existing public benchmarks typically focus on contexts around 10K tokens, limiting the assessment and comparison of LLMs in processing longer contexts. In this paper, we propose , the first LLM benchmark featuring an average data length surpassing 100K tokens. comprises synthetic and realistic tasks spanning diverse domains in English and Chinese. The tasks in are designed to require an understanding of long dependencies in contexts and make simply retrieving a limited number of passages from contexts not sufficient for these tasks. Based on , we evaluate several state-of-the-art LLMs tailored for processing long contexts. The experimental results indicate that existing long-context LLMs still require significant advancements to process 100K+ contexts effectively. Furthermore, we present three intriguing analyses regarding the behavior of LLMs processing long context. Our code and data is released.",
} |
saiyan-world/Goku-MovieGenBench | saiyan-world | "2025-02-11T03:18:05Z" | 10,595 | 201 | [
"task_categories:text-to-video",
"size_categories:1K<n<10K",
"modality:video",
"library:datasets",
"library:mlcroissant",
"arxiv:2502.04896",
"region:us"
] | [
"text-to-video"
] | "2025-02-06T12:47:26Z" | ---
task_categories:
- text-to-video
---
This repository contains the data associated with the paper [Goku: Flow Based Video Generative Foundation Models](https://huggingface.co/papers/2502.04896).
Project page: https://saiyan-world.github.io/goku/ |
fjd/scannet-processed-test | fjd | "2023-03-29T04:13:39Z" | 10,576 | 1 | [
"license:cc-by-nc-4.0",
"modality:image",
"modality:text",
"region:us"
] | null | "2023-03-29T03:27:18Z" | ---
license: cc-by-nc-4.0
---
|
parler-tts/mls_eng | parler-tts | "2024-04-09T14:37:17Z" | 10,560 | 21 | [
"task_categories:automatic-speech-recognition",
"task_categories:text-to-speech",
"task_categories:text-to-audio",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:multilingual",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"size_categories:10M<n<100M",
"format:parquet",
"modality:audio",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2012.03411",
"region:us"
] | [
"automatic-speech-recognition",
"text-to-speech",
"text-to-audio"
] | "2024-03-11T20:00:44Z" | ---
pretty_name: English MLS
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
- expert-generated
language:
- en
license:
- cc-by-4.0
multilinguality:
- multilingual
paperswithcode_id: multilingual-librispeech
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- automatic-speech-recognition
- text-to-speech
- text-to-audio
configs:
- config_name: default
data_files:
- split: dev
path: data/dev-*
- split: test
path: data/test-*
- split: train
path: data/train-*
dataset_info:
features:
- name: audio
dtype: audio
- name: original_path
dtype: string
- name: begin_time
dtype: float64
- name: end_time
dtype: float64
- name: transcript
dtype: string
- name: audio_duration
dtype: float64
- name: speaker_id
dtype: string
- name: book_id
dtype: string
splits:
- name: dev
num_bytes: 249688889.909
num_examples: 3807
- name: test
num_bytes: 245938961
num_examples: 3769
- name: train
num_bytes: 707578913096
num_examples: 10808037
download_size: 705179367357
dataset_size: 708074540946.909
---
# Dataset Card for English MLS
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [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:** [MultiLingual LibriSpeech ASR corpus](http://www.openslr.org/94)
- **Repository:** [Needs More Information]
- **Paper:** [MLS: A Large-Scale Multilingual Dataset for Speech Research](https://arxiv.org/abs/2012.03411)
- **Leaderboard:** [🤗 Autoevaluate Leaderboard](https://huggingface.co/spaces/autoevaluate/leaderboards?dataset=facebook%2Fmultilingual_librispeech&only_verified=0&task=automatic-speech-recognition&config=-unspecified-&split=-unspecified-&metric=wer)
### Dataset Summary
This is a streamable version of the **English version of the Multilingual LibriSpeech (MLS) dataset**.
The data archives were restructured from the original ones from [OpenSLR](http://www.openslr.org/94) to make it easier to stream.
MLS dataset is a large multilingual corpus suitable for speech research. The dataset is derived from read audiobooks from LibriVox and consists of
8 languages - English, German, Dutch, Spanish, French, Italian, Portuguese, Polish. It includes about 44.5K hours of English and a total of about 6K hours for other languages.
This dataset card includes the 44.5K hours of English. Refers to this [dataset card](https://huggingface.co/datasets/facebook/multilingual_librispeech) for the other languages.
### Supported Tasks and Leaderboards
- `automatic-speech-recognition`, `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 leaderboard which can be found at https://paperswithcode.com/dataset/multilingual-librispeech and ranks models based on their WER.
- `text-to-speech`, `text-to-audio`: The dataset can also be used to train a model for Text-To-Speech (TTS).
### How to use
The `datasets` library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the `load_dataset` function.
For example, to download the German config, simply specify the corresponding language config name (i.e., "german" for German):
```python
from datasets import load_dataset
mls = load_dataset("parler-tts/mls_eng", split="train")
```
Using the datasets library, you can also stream the dataset on-the-fly by adding a `streaming=True` argument to the `load_dataset` function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk.
```python
from datasets import load_dataset
mls = load_dataset("parler-tts/mls_eng", split="train", streaming=True)
print(next(iter(mls)))
```
*Bonus*: create a [PyTorch dataloader](https://huggingface.co/docs/datasets/use_with_pytorch) directly with your own datasets (local/streamed).
Local:
```python
from datasets import load_dataset
from torch.utils.data.sampler import BatchSampler, RandomSampler
mls = load_dataset("parler-tts/mls_eng", split="train")
batch_sampler = BatchSampler(RandomSampler(mls), batch_size=32, drop_last=False)
dataloader = DataLoader(mls, batch_sampler=batch_sampler)
```
Streaming:
```python
from datasets import load_dataset
from torch.utils.data import DataLoader
mls = load_dataset("parler-tts/mls_eng", split="train", streaming=True)
dataloader = DataLoader(mls, batch_size=32)
```
To find out more about loading and preparing audio datasets, head over to [hf.co/blog/audio-datasets](https://huggingface.co/blog/audio-datasets).
### Example scripts
Train your own CTC or Seq2Seq Automatic Speech Recognition models on MultiLingual Librispeech with `transformers` - [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition).
## Dataset Structure
### Data Fields
- file: A filename .flac format.
- audio: A dictionary containing the audio filename, 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.
## 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
[Needs More Information]
### Licensing Information
Public Domain, Creative Commons Attribution 4.0 International Public License ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/legalcode))
### Citation Information
```
@article{Pratap2020MLSAL,
title={MLS: A Large-Scale Multilingual Dataset for Speech Research},
author={Vineel Pratap and Qiantong Xu and Anuroop Sriram and Gabriel Synnaeve and Ronan Collobert},
journal={ArXiv},
year={2020},
volume={abs/2012.03411}
}
```
### Data Statistics
| Duration (h) | Train | Dev | Test |
|--------------|-----------|-------|-------|
| English | 44,659.74 | 15.75 | 15.55 |
| German | 1,966.51 | 14.28 | 14.29 |
| Dutch | 1,554.24 | 12.76 | 12.76 |
| French | 1,076.58 | 10.07 | 10.07 |
| Spanish | 917.68 | 9.99 | 10 |
| Italian | 247.38 | 5.18 | 5.27 |
| Portuguese | 160.96 | 3.64 | 3.74 |
| Polish | 103.65 | 2.08 | 2.14 |
| # Speakers | Train | | Dev | | Test | |
|------------|-------|------|-----|----|------|----|
| Gender | M | F | M | F | M | F |
| English | 2742 | 2748 | 21 | 21 | 21 | 21 |
| German | 81 | 95 | 15 | 15 | 15 | 15 |
| Dutch | 9 | 31 | 3 | 3 | 3 | 3 |
| French | 62 | 80 | 9 | 9 | 9 | 9 |
| Spanish | 36 | 50 | 10 | 10 | 10 | 10 |
| Italian | 22 | 43 | 5 | 5 | 5 | 5 |
| Portuguese | 26 | 16 | 5 | 5 | 5 | 5 |
| Polish | 6 | 5 | 2 | 2 | 2 | 2 |
| # Hours / Gender | Dev | | Test | |
|------------------|------|------|------|------|
| Gender | M | F | M | F |
| English | 7.76 | 7.99 | 7.62 | 7.93 |
| German | 7.06 | 7.22 | 7 | 7.29 |
| Dutch | 6.44 | 6.32 | 6.72 | 6.04 |
| French | 5.13 | 4.94 | 5.04 | 5.02 |
| Spanish | 4.91 | 5.08 | 4.78 | 5.23 |
| Italian | 2.5 | 2.68 | 2.38 | 2.9 |
| Portuguese | 1.84 | 1.81 | 1.83 | 1.9 |
| Polish | 1.12 | 0.95 | 1.09 | 1.05 |
|
duongttr/vi-dataset-for-pretrain | duongttr | "2023-08-02T09:38:30Z" | 10,547 | 2 | [
"task_categories:text-generation",
"language:vi",
"size_categories:10M<n<100M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"LM"
] | [
"text-generation"
] | "2023-08-02T08:20:06Z" | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 77360702833
num_examples: 23891116
- name: validation
num_bytes: 4064634081
num_examples: 1257428
download_size: 2126869688
dataset_size: 81425336914
task_categories:
- text-generation
language:
- vi
size_categories:
- 10M<n<100M
tags:
- LM
---
# Dataset Card for "vi-dataset-for-pretrain"
This is a combination of multiple Vietnamese dataset for pretraining CLMs such as GPT, GPT2, etc.
The dataset consists of:
- [`vietgpt/covid_19_news_vi`](https://huggingface.co/datasets/vietgpt/covid_19_news_vi)
- [`hieunguyen1053/binhvq-news-corpus`](https://huggingface.co/datasets/hieunguyen1053/binhvq-news-corpus)
- [`oscar (unshuffled_deduplicated_vi)`](https://huggingface.co/datasets/oscar)
- [`vietgpt/wikipedia_vi`](https://huggingface.co/datasets/vietgpt/wikipedia_vi)
# Dataset info
| Splits | N.o examples | Size |
| --- | --- | --- |
| Train | 23,891,116 | 77.36 GB |
| Validation | 1,257,428 | 4.06 GB |
| **Total** | **25,148,544** | **81.43 GB** | |
AI-MO/aimo-validation-aime | AI-MO | "2024-07-10T12:44:42Z" | 10,514 | 41 | [
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-07-09T11:17:14Z" | ---
dataset_info:
features:
- name: id
dtype: int64
- name: problem
dtype: string
- name: solution
dtype: string
- name: answer
dtype: string
- name: url
dtype: string
splits:
- name: train
num_bytes: 520431
num_examples: 90
download_size: 261038
dataset_size: 520431
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for AIMO Validation AIME
All 90 problems come from AIME 22, AIME 23, and AIME 24, and have been extracted directly from the AOPS wiki page https://artofproblemsolving.com/wiki/index.php/AIME_Problems_and_Solutions
This dataset serves as an internal validation set during our participation in the AIMO progress prize competition. Using data after 2021 is to avoid potential overlap with the MATH training set.
Here are the different columns in the dataset:
- problem: the original problem statement from the website
- solution: one of the solutions proposed in the forum with \boxed answer
- url: url to the problem page in the website
|
textmachinelab/quail | textmachinelab | "2024-01-04T16:18:32Z" | 10,513 | 7 | [
"task_categories:multiple-choice",
"task_ids:multiple-choice-qa",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:cc-by-nc-sa-4.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [
"multiple-choice"
] | "2022-03-02T23:29:22Z" | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- multiple-choice
task_ids:
- multiple-choice-qa
paperswithcode_id: quail
pretty_name: Question Answering for Artificial Intelligence (QuAIL)
dataset_info:
config_name: quail
features:
- name: id
dtype: string
- name: context_id
dtype: string
- name: question_id
dtype: string
- name: domain
dtype: string
- name: metadata
struct:
- name: author
dtype: string
- name: title
dtype: string
- name: url
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: question_type
dtype: string
- name: answers
sequence: string
- name: correct_answer_id
dtype: int32
splits:
- name: train
num_bytes: 23432601
num_examples: 10246
- name: validation
num_bytes: 4989531
num_examples: 2164
- name: challenge
num_bytes: 1199792
num_examples: 556
download_size: 2286403
dataset_size: 29621924
configs:
- config_name: quail
data_files:
- split: train
path: quail/train-*
- split: validation
path: quail/validation-*
- split: challenge
path: quail/challenge-*
default: true
---
# Dataset Card for "quail"
## 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://text-machine-lab.github.io/blog/2020/quail/](https://text-machine-lab.github.io/blog/2020/quail/)
- **Repository:** https://github.com/text-machine-lab/quail
- **Paper:** [Getting Closer to AI Complete Question Answering: A Set of Prerequisite Real Tasks](https://doi.org/10.1609/aaai.v34i05.6398 )
- **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:** 6.41 MB
- **Size of the generated dataset:** 29.62 MB
- **Total amount of disk used:** 36.03 MB
### Dataset Summary
QuAIL is a reading comprehension dataset. QuAIL contains 15K multi-choice questions in texts 300-350 tokens long 4 domains (news, user stories, fiction, blogs).QuAIL is balanced and annotated for question types.
### 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
#### quail
- **Size of downloaded dataset files:** 6.41 MB
- **Size of the generated dataset:** 29.62 MB
- **Total amount of disk used:** 36.03 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"answers": ["the cousin is not friendly", "the cousin could have been pretier", "not enough information", "the cousin was too nice"],
"context": "\"That fall came and I went back to Michigan and the school year went by and summer came and I never really thought about it. I'm...",
"context_id": "f001",
"correct_answer_id": 0,
"domain": "fiction",
"id": "f001_19",
"metadata": {
"author": "Joseph Devon",
"title": "Black Eyed Susan",
"url": "http://manybooks.net/pages/devonjother08black_eyed_susan/0.html"
},
"question": "After the events in the text what does the author think about the cousin?",
"question_id": "19",
"question_type": "Subsequent_state"
}
```
### Data Fields
The data fields are the same among all splits.
#### quail
- `id`: a `string` feature.
- `context_id`: a `string` feature.
- `question_id`: a `string` feature.
- `domain`: a `string` feature.
- `author`: a `string` feature.
- `title`: a `string` feature.
- `url`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `question_type`: a `string` feature.
- `answers`: a `list` of `string` features.
- `correct_answer_id`: a `int32` feature.
### Data Splits
|name |train|challenge|validation|
|-----|----:|--------:|---------:|
|quail|10246| 556| 2164|
## 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{DBLP:conf/aaai/RogersKDR20,
author = {Anna Rogers and
Olga Kovaleva and
Matthew Downey and
Anna Rumshisky},
title = {Getting Closer to {AI} Complete Question Answering: {A} Set of Prerequisite
Real Tasks},
booktitle = {The Thirty-Fourth {AAAI} Conference on Artificial Intelligence, {AAAI}
2020, The Thirty-Second Innovative Applications of Artificial Intelligence
Conference, {IAAI} 2020, The Tenth {AAAI} Symposium on Educational
Advances in Artificial Intelligence, {EAAI} 2020, New York, NY, USA,
February 7-12, 2020},
pages = {8722--8731},
publisher = {{AAAI} Press},
year = {2020},
url = {https://aaai.org/ojs/index.php/AAAI/article/view/6398},
timestamp = {Thu, 04 Jun 2020 13:18:48 +0200},
biburl = {https://dblp.org/rec/conf/aaai/RogersKDR20.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
### Contributions
Thanks to [@sai-prasanna](https://github.com/sai-prasanna), [@ngdodd](https://github.com/ngdodd) for adding this dataset. |
airtrain-ai/fineweb-edu-fortified | airtrain-ai | "2024-08-08T18:04:44Z" | 10,513 | 55 | [
"task_categories:text-generation",
"language:en",
"license:odc-by",
"size_categories:100M<n<1B",
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"arxiv:2406.17557",
"arxiv:2109.07445",
"region:us"
] | [
"text-generation"
] | "2024-07-22T14:22:31Z" | ---
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path: data/CC-MAIN-2020-05/train-*
- config_name: CC-MAIN-2020-10
data_files:
- split: train
path: data/CC-MAIN-2020-10/train-*
- config_name: CC-MAIN-2020-16
data_files:
- split: train
path: data/CC-MAIN-2020-16/train-*
- config_name: CC-MAIN-2020-24
data_files:
- split: train
path: data/CC-MAIN-2020-24/train-*
- config_name: CC-MAIN-2020-29
data_files:
- split: train
path: data/CC-MAIN-2020-29/train-*
- config_name: CC-MAIN-2020-34
data_files:
- split: train
path: data/CC-MAIN-2020-34/train-*
- config_name: CC-MAIN-2020-40
data_files:
- split: train
path: data/CC-MAIN-2020-40/train-*
- config_name: CC-MAIN-2020-45
data_files:
- split: train
path: data/CC-MAIN-2020-45/train-*
- config_name: CC-MAIN-2020-50
data_files:
- split: train
path: data/CC-MAIN-2020-50/train-*
- config_name: CC-MAIN-2021-04
data_files:
- split: train
path: data/CC-MAIN-2021-04/train-*
- config_name: CC-MAIN-2021-10
data_files:
- split: train
path: data/CC-MAIN-2021-10/train-*
- config_name: CC-MAIN-2021-17
data_files:
- split: train
path: data/CC-MAIN-2021-17/train-*
- config_name: CC-MAIN-2021-21
data_files:
- split: train
path: data/CC-MAIN-2021-21/train-*
- config_name: CC-MAIN-2021-25
data_files:
- split: train
path: data/CC-MAIN-2021-25/train-*
- config_name: CC-MAIN-2021-31
data_files:
- split: train
path: data/CC-MAIN-2021-31/train-*
- config_name: CC-MAIN-2021-39
data_files:
- split: train
path: data/CC-MAIN-2021-39/train-*
- config_name: CC-MAIN-2021-43
data_files:
- split: train
path: data/CC-MAIN-2021-43/train-*
- config_name: CC-MAIN-2021-49
data_files:
- split: train
path: data/CC-MAIN-2021-49/train-*
- config_name: CC-MAIN-2022-05
data_files:
- split: train
path: data/CC-MAIN-2022-05/train-*
- config_name: CC-MAIN-2022-21
data_files:
- split: train
path: data/CC-MAIN-2022-21/train-*
- config_name: CC-MAIN-2022-27
data_files:
- split: train
path: data/CC-MAIN-2022-27/train-*
- config_name: CC-MAIN-2022-33
data_files:
- split: train
path: data/CC-MAIN-2022-33/train-*
- config_name: CC-MAIN-2022-40
data_files:
- split: train
path: data/CC-MAIN-2022-40/train-*
- config_name: CC-MAIN-2022-49
data_files:
- split: train
path: data/CC-MAIN-2022-49/train-*
- config_name: CC-MAIN-2023-06
data_files:
- split: train
path: data/CC-MAIN-2023-06/train-*
- config_name: CC-MAIN-2023-14
data_files:
- split: train
path: data/CC-MAIN-2023-14/train-*
- config_name: CC-MAIN-2023-23
data_files:
- split: train
path: data/CC-MAIN-2023-23/train-*
- config_name: CC-MAIN-2023-40
data_files:
- split: train
path: data/CC-MAIN-2023-40/train-*
- config_name: CC-MAIN-2023-50
data_files:
- split: train
path: data/CC-MAIN-2023-50/train-*
- config_name: CC-MAIN-2024-10
data_files:
- split: train
path: data/CC-MAIN-2024-10/train-*
---
# Fineweb-Edu-Fortified
<figure>
<img src="https://cdn-uploads.huggingface.co/production/uploads/646516d2200b583e1e50faf8/79yPdK79m9mA0cCz-3h4v.png" width="500" style="margin-left:auto; margin-right: auto"/>
<figcaption style="text-align: center; margin-left: auto; margin-right: auto; font-style: italic;">
The composition of fineweb-edu-fortified, produced by automatically clustering a 500k row sample in
<a href="https://app.airtrain.ai/dataset/c232b33f-4f4a-49a7-ba55-8167a5f433da/null/1/0"> Airtrain </a>
</figcaption>
</figure>
## What is it?
Fineweb-Edu-Fortified is a dataset derived from
[Fineweb-Edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu) by applying exact-match
deduplication across the whole dataset and producing an embedding for each row. The number of times
the text from each row appears is also included as a `count` column. The embeddings were produced
using [TaylorAI/bge-micro](https://huggingface.co/TaylorAI/bge-micro)
Fineweb and Fineweb-Edu were obtained by processing data from 95 crawls of
[Common Crawl](https://commoncrawl.org/), covering a time period from 2013 to 2024.
More information about the original datasets can be found by consulting:
- [Fineweb-edu dataset card](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu)
- [Fineweb dataset card](https://huggingface.co/datasets/HuggingFaceFW/fineweb)
- [Fineweb release blog post](https://huggingface.co/spaces/HuggingFaceFW/blogpost-fineweb-v1)
- [Fineweb paper](https://arxiv.org/abs/2406.17557)
The contents of a randomly selected 500k rows from this dataset can be interactively
explored in this
[Airtrain](https://app.airtrain.ai/dataset/c232b33f-4f4a-49a7-ba55-8167a5f433da/null/1/0)
dashboard.
## Deduplication
### Deduplication in original Fineweb and Fineweb-Edu
During creation of the original Fineweb dataset, a variety of deduplication strategies were
explored. The evaluation criteria used to assess deduplication strategies was to train ablation models
on randomly selected subsets of the data, using a subset of up to ~350 billion tokens.
Using this mechanism, the Fineweb authors selected a MinHash algorithm, using parameters
considering documents with approximately 75% similarity or higher to be duplicates. This deduplication was
performed *within* each Common Crawl crawl. For example, it would have removed all approximate duplicates
from the 20th crawl from 2013, but would have retained an identical record that showed up
in both the 2013-20 crawl and the 2013-48 crawl. The authors note that applying the
deduplication *across crawls* reduced the evaluation performance of the ablation models used
for assessment. The proposed reason for this performance degredation is that data
duplicated across crawls is more likely to be high-quality compared to data that is not,
so leaving in the duplicates effectively upsamples the higer-quality data.
Following deduplication in Fineweb, Fineweb-Edu was extracted using a model-based quality classifier
targeting educational content. It thus inherited the same inter-crawl deduplication strategy of Fineweb.
### Deduplication in this dataset
#### Motivation
Given the findings that cross-crawl deduplication reduced ablation model performance, one might ask
what the motivation is for producing a dataset that uses it. Our motivation was threefold:
- Reduce the number of rows that needed to be embedded by avoiding embedding of exact-match content
- Enable easier filtering of the dataset for subsets-of-interest
- Provide a version of the dataset for users whose training goals include avoiding training on non-unique
tokens.
For use cases that would benefit from "re-hydrating" or filtering the rows based on how frequently
the text appeared in the original dataset, the new `count` column retains the number of appearances
of the associated text.
#### Procedure
The overall procedure was to remove exact matches that appeared in multiple crawls (also referred to
as "dumps"). This was achieved by performing an md5 hash on the text column and removing rows with
duplicate hashes. To make this tractable at scale, we first grouped all rows by the first two hex
digits of their hashes, then looked for exact hash matches within each of the resulting 256
buckets of data. Note that unlike the intra-crawl deduplication, we only eliminated exact matches
across crawls. For duplicated rows, a strong preference was given to keep the metadata
(ex: dump, url) from the oldest crawl where the text appeared. Following deduplication and
embedding, the data were grouped by the "dump" column, mirroring the organization of the original
Fineweb-Edu dataset.
### Deduplication stats
Deduplication removed approximately 74.7% of rows from the original dataset
(from 1.279 billion in Fineweb-Edu to 0.324 billion rows in Fineweb-Edu-Fortified).
This indicates that a substantial amount of data in Fineweb-Edu is present across multiple crawls.
The total token count in the deduplicated dataset is approximately 375 billion, compared to the
1,320 billion tokens in Fineweb-Edu.
<figure>
<img src="https://cdn-uploads.huggingface.co/production/uploads/646516d2200b583e1e50faf8/mUFyO1fUWJEXbYwiteR9e.png" width="750" style="margin-left:auto; margin-right: auto"/>
<figcaption style="text-align: center; margin-left: auto; margin-right: auto; font-style: italic;">
A histogram of the `count` column. Histogram was generated using a 500k row sample after
performing global per-row text duplication counting.
</figcaption>
</figure>
## Embeddings
To support use cases with Fineweb-Edu such as classification, clustering, semantic search, etc.,
we have produced an embedding vector for each row in the dataset. The embedding model
[TaylorAI/bge-micro](https://huggingface.co/TaylorAI/bge-micro)
was selected for its tradeoff of strong performance on [MTEB](https://huggingface.co/spaces/mteb/leaderboard)
benchmarks relative to its size (17 million parameters). The model's embedding space
has 384 dimensions. The context-window of the model is 512 tokens (roughly several paragraphs of text);
each row is embedded by using the first 512 tokens in its text field. Producing the embeddings took approximately
412 GPU-hours on Nvidia T4 GPUs.
## Using via `datasets`
```python
from datasets import load_dataset
fw = load_dataset("airtrain-ai/fineweb-edu-fortified", name="CC-MAIN-2024-10", split="train", streaming=True)
```
## Considerations for Using the Data
This "Considerations" section is copied from the parent dataset:
[FineWeb-edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu).
### Social Impact of Dataset
With the release of this dataset we aim to make model training more accessible to the machine learning community at large.
While multiple open-weights models with strong performance have been publicly released in the past, more often than not these releases are not accompanied by the corresponding training dataset. This is unfortunate as the dataset specificities and characteristics have been demonstrated to have a very large impact and role in the performances of the models. As the creation of a high quality training dataset is a fundamental requirement to training an LLM capable of excelling at downstream tasks, with 🍷 FineWeb we (a) not only make the dataset creation process more transparent, by sharing our entire processing setup including the codebase used, we also (b) help alleviate the costs of dataset curation, both in time and in compute, for model creators by publicly releasing our dataset with the community.
### Discussion of Biases
Efforts were made to minimize the amount of NSFW and toxic content present in the dataset by employing filtering on the URL level. However, there are still a significant number of documents present in the final dataset that could be considered toxic or contain harmful content. As 🍷 FineWeb was sourced from the web as a whole, any harmful biases typically present in it may be reproduced on our dataset.
We deliberately avoided using machine learning filtering methods that define text quality based on the similarity to a “gold” source such as wikipedia or toxicity classifiers as these methods have been known to [disproportionately remove content in specific dialects](https://aclanthology.org/D16-1120/) and [overclassify as toxic text related to specific social identities](https://arxiv.org/pdf/2109.07445.pdf), respectively.
### Other Known Limitations
As a consequence of some of the filtering steps applied, it is likely that code content is not prevalent in our dataset. If you are training a model that should also perform code tasks, we recommend you use 🍷 FineWeb with a code dataset, such as [The Stack v2](https://huggingface.co/datasets/bigcode/the-stack-v2). You should also probably consider complementing 🍷 FineWeb with specialized curated sources (such as Wikipedia, for example) as they will likely have better formatting than the wikipedia content included in 🍷 FineWeb (we did not tailor the processing to individual websites).
## Additional Information
### Acknowledgements
Airtrain would like to thank the Fineweb/Fineweb-Edu team at Hugging Face for producing the original datasets,
as well as for their support during work on Fineweb-Edu-Fortified.
We'd also like to thank [@underspirit](https://huggingface.co/underspirit) for
[pointing out](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu/discussions/7)
the amount of reduction in dataset size that could be achieved via deduplication.
We owe gratitude to [TaylorAI](https://huggingface.co/TaylorAI) for the `bge-micro` embedding model.
Finally, thank you to the Hugging Face community for fostering a thriving ecosystem of models, datasets, and tools
to support open-source AI.
### Licensing Information
The dataset is released under the **Open Data Commons Attribution License (ODC-By) v1.0** [license](https://opendatacommons.org/licenses/by/1-0/). The use of this dataset is also subject to [CommonCrawl's Terms of Use](https://commoncrawl.org/terms-of-use).
|
EleutherAI/drop | EleutherAI | "2025-01-10T23:56:02Z" | 10,472 | 1 | [
"license:cc-by-4.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2023-08-30T10:15:08Z" | ---
license: cc-by-4.0
--- |
artefactory/Argimi-Ardian-Finance-10k-text-image | artefactory | "2025-01-06T09:47:20Z" | 10,442 | 6 | [
"task_categories:text-retrieval",
"task_categories:text-generation",
"language:en",
"license:cc-by-4.0",
"size_categories:10K<n<100K",
"region:us",
"finance"
] | [
"text-retrieval",
"text-generation"
] | "2024-11-29T13:26:42Z" | ---
license: cc-by-4.0
task_categories:
- text-retrieval
- text-generation
language:
- en
tags:
- finance
size_categories:
- 10K<n<100K
---
# The ArGiMI Ardian datasets : text and images
The ArGiMi project is committed to open-source principles and data sharing.
Thanks to our generous partners, we are releasing several valuable datasets to the public.
## Dataset description
This dataset comprises 11,000 financial annual reports, written in english, meticulously
extracted from their original PDF format to provide a valuable resource for researchers and developers in financial
analysis and natural language processing (NLP). These reports were published from the late 90s to 2023.
This dataset provides images of each document pages. A lighter, **text-only version**, is also available at
[`artefactory/Argimi-Ardian-Finance-10k-text`](https://huggingface.co/datasets/artefactory/Argimi-Ardian-Finance-10k-text).
You can load the dataset with:
```python
from datasets import load_dataset
ds = load_dataset("artefactory/Argimi-Ardian-Finance-10k-text-image", split="train")
# Or you can stream the dataset to save memory space :
ds = load_dataset("artefactory/Argimi-Ardian-Finance-10k-text-image", split="train", streaming=True)
```
## Dataset composition:
* Each PDF was divided into **individual pages** to facilitate granular analysis.
* For each page, the following data points were extracted:
* **Raw Text:** The complete textual content of the page, capturing all textual information present.
* **Screenshot:** A high-resolution image of the page, preserving the visual layout and formatting.
* **Cells:** Each cell within tables was identified and represented as a `Cell` object within the `docling` framework. Each `Cell` object encapsulates:
* `id`: A unique identifier assigned to each cell, ensuring unambiguous referencing.
* `text`: The textual content contained within the cell.
* `bbox`: The precise bounding box coordinates of the cell, defining its location and dimensions on the page.
* When OCR is employed, cells are further represented as `OcrCell` objects, which include an additional `confidence` attribute. This attribute quantifies the confidence level of the OCR process in accurately recognizing the cell's textual content.
* **Segments:** Beyond individual cells, `docling` segments each page into distinct content units, each represented as a `Segment` object. These segments provide a structured representation of the document's layout and content, encompassing elements such as tables, headers, paragraphs, and other structural components. Each `Segment` object contains:
* `text`: The textual content of the segment.
* `bbox`: The bounding box coordinates, specifying the segment's position and size on the page.
* `label`: A categorical label indicating the type of content the segment represents (e.g., "table," "header," "paragraph").
* To guarantee unique identification, each document is assigned a distinct identifier derived from the hash of its content.
## Parsing description:
The dataset creation involved a systematic process using the `docling` library ([Documentation](https://ds4sd.github.io/docling/)).
* PDFs were processed using the `DocumentConverter` class, employing the `PyPdfiumDocumentBackend` for handling of the PDF format.
* To ensure high-quality extraction, the following `PdfPipelineOptions` were configured:
```python
pipeline_options = PdfPipelineOptions(ocr_options=EasyOcrOptions(use_gpu=True))
pipeline_options.images_scale = 2.0 # Scale image resolution by a factor of 2
pipeline_options.generate_page_images = True # Generate page images
pipeline_options.do_ocr = True # Perform OCR
pipeline_options.do_table_structure = True # Extract table structure
pipeline_options.table_structure_options.do_cell_matching = True # Perform cell matching in tables
pipeline_options.table_structure_options.mode = TableFormerMode.ACCURATE # Use accurate mode for table structure extraction
```
* These options collectively enable:
* GPU-accelerated Optical Character Recognition (OCR) via `EasyOcr`.
* Upscaling of image resolution by a factor of 2, enhancing the clarity of visual elements.
* Generation of page images, providing a visual representation of each page within the document.
* Comprehensive table structure extraction, including cell matching, to accurately capture tabular data within the reports.
* The "accurate" mode for table structure extraction, prioritizing precision in identifying and delineating tables.
## Disclaimer:
This dataset, made available for experimental purposes as part of the ArGiMi research project, is provided "as is"
for informational purposes only. The original publicly available data was provided by Ardian.
Artefact has processed this dataset and now publicly releases it through Ardian, with Ardian's agreement.
None of ArGiMi, Artefact, or Ardian make any representations or warranties of any kind (express or implied) regarding the completeness,
accuracy, reliability, suitability, or availability of the dataset or its contents.
Any reliance you place on such information is strictly at your own risk.
In no event shall ArGiMi, Artefact, or Ardian be liable for any loss or damage, including without limitation,
indirect or consequential loss or damage, or any loss or damage whatsoever arising from loss of data or profits arising out of,
or in connection with, the use of this dataset. This disclaimer includes, but is not limited to,
claims relating to intellectual property infringement, negligence, breach of contract, and defamation.
## Acknowledgement:
The ArGiMi consortium gratefully acknowledges Ardian for their invaluable contribution in gathering the documents that
comprise this dataset. Their effort and collaboration were essential in enabling the creation and release of this dataset for public use.
The ArGiMi project is a collaborative project with Giskard, Mistral, INA and BnF, and is sponsored by the
France 2030 program of the French Government.
## Citation:
If you find our datasets useful for your research, consider citing us in your works:
```latex
@misc{argimi2024Datasets,
title={The ArGiMi datasets},
author={Hicham Randrianarivo, Charles Moslonka, Arthur Garnier and Emmanuel Malherbe},
year={2024},
}
``` |
ilsp/mmlu_greek | ilsp | "2024-05-20T12:36:54Z" | 10,419 | 4 | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-04-01T14:53:41Z" | ---
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- name: validation
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- name: dev
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num_examples: 5
download_size: 9676
dataset_size: 231603
- config_name: us_foreign_policy
features:
- name: question
dtype: string
- name: subject
dtype: string
- name: choices
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- name: validation
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num_examples: 11
- name: dev
num_bytes: 5111
num_examples: 5
download_size: 68974
dataset_size: 105346
- config_name: virology
features:
- name: question
dtype: string
- name: subject
dtype: string
- name: choices
sequence: string
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- name: validation
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num_examples: 18
- name: dev
num_bytes: 3185
num_examples: 5
download_size: 96586
dataset_size: 135669
- config_name: world_religions
features:
- name: question
dtype: string
- name: subject
dtype: string
- name: choices
sequence: string
- name: answer
dtype: int64
- name: orig_question
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dtype: string
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num_examples: 171
- name: validation
num_bytes: 8462
num_examples: 19
- name: dev
num_bytes: 2073
num_examples: 5
download_size: 61169
dataset_size: 87808
configs:
- config_name: abstract_algebra
data_files:
- split: test
path: abstract_algebra/test-*
- split: validation
path: abstract_algebra/validation-*
- split: dev
path: abstract_algebra/dev-*
- config_name: all
data_files:
- split: test
path: all/test-*
- split: validation
path: all/validation-*
- split: dev
path: all/dev-*
- config_name: anatomy
data_files:
- split: test
path: anatomy/test-*
- split: validation
path: anatomy/validation-*
- split: dev
path: anatomy/dev-*
- config_name: astronomy
data_files:
- split: test
path: astronomy/test-*
- split: validation
path: astronomy/validation-*
- split: dev
path: astronomy/dev-*
- config_name: business_ethics
data_files:
- split: test
path: business_ethics/test-*
- split: validation
path: business_ethics/validation-*
- split: dev
path: business_ethics/dev-*
- config_name: clinical_knowledge
data_files:
- split: test
path: clinical_knowledge/test-*
- split: validation
path: clinical_knowledge/validation-*
- split: dev
path: clinical_knowledge/dev-*
- config_name: college_biology
data_files:
- split: test
path: college_biology/test-*
- split: validation
path: college_biology/validation-*
- split: dev
path: college_biology/dev-*
- config_name: college_chemistry
data_files:
- split: test
path: college_chemistry/test-*
- split: validation
path: college_chemistry/validation-*
- split: dev
path: college_chemistry/dev-*
- config_name: college_computer_science
data_files:
- split: test
path: college_computer_science/test-*
- split: validation
path: college_computer_science/validation-*
- split: dev
path: college_computer_science/dev-*
- config_name: college_mathematics
data_files:
- split: test
path: college_mathematics/test-*
- split: validation
path: college_mathematics/validation-*
- split: dev
path: college_mathematics/dev-*
- config_name: college_medicine
data_files:
- split: test
path: college_medicine/test-*
- split: validation
path: college_medicine/validation-*
- split: dev
path: college_medicine/dev-*
- config_name: college_physics
data_files:
- split: test
path: college_physics/test-*
- split: validation
path: college_physics/validation-*
- split: dev
path: college_physics/dev-*
- config_name: computer_security
data_files:
- split: test
path: computer_security/test-*
- split: validation
path: computer_security/validation-*
- split: dev
path: computer_security/dev-*
- config_name: conceptual_physics
data_files:
- split: test
path: conceptual_physics/test-*
- split: validation
path: conceptual_physics/validation-*
- split: dev
path: conceptual_physics/dev-*
- config_name: econometrics
data_files:
- split: test
path: econometrics/test-*
- split: validation
path: econometrics/validation-*
- split: dev
path: econometrics/dev-*
- config_name: electrical_engineering
data_files:
- split: test
path: electrical_engineering/test-*
- split: validation
path: electrical_engineering/validation-*
- split: dev
path: electrical_engineering/dev-*
- config_name: elementary_mathematics
data_files:
- split: test
path: elementary_mathematics/test-*
- split: validation
path: elementary_mathematics/validation-*
- split: dev
path: elementary_mathematics/dev-*
- config_name: formal_logic
data_files:
- split: test
path: formal_logic/test-*
- split: validation
path: formal_logic/validation-*
- split: dev
path: formal_logic/dev-*
- config_name: global_facts
data_files:
- split: test
path: global_facts/test-*
- split: validation
path: global_facts/validation-*
- split: dev
path: global_facts/dev-*
- config_name: high_school_biology
data_files:
- split: test
path: high_school_biology/test-*
- split: validation
path: high_school_biology/validation-*
- split: dev
path: high_school_biology/dev-*
- config_name: high_school_chemistry
data_files:
- split: test
path: high_school_chemistry/test-*
- split: validation
path: high_school_chemistry/validation-*
- split: dev
path: high_school_chemistry/dev-*
- config_name: high_school_computer_science
data_files:
- split: test
path: high_school_computer_science/test-*
- split: validation
path: high_school_computer_science/validation-*
- split: dev
path: high_school_computer_science/dev-*
- config_name: high_school_european_history
data_files:
- split: test
path: high_school_european_history/test-*
- split: validation
path: high_school_european_history/validation-*
- split: dev
path: high_school_european_history/dev-*
- config_name: high_school_geography
data_files:
- split: test
path: high_school_geography/test-*
- split: validation
path: high_school_geography/validation-*
- split: dev
path: high_school_geography/dev-*
- config_name: high_school_government_and_politics
data_files:
- split: test
path: high_school_government_and_politics/test-*
- split: validation
path: high_school_government_and_politics/validation-*
- split: dev
path: high_school_government_and_politics/dev-*
- config_name: high_school_macroeconomics
data_files:
- split: test
path: high_school_macroeconomics/test-*
- split: validation
path: high_school_macroeconomics/validation-*
- split: dev
path: high_school_macroeconomics/dev-*
- config_name: high_school_mathematics
data_files:
- split: test
path: high_school_mathematics/test-*
- split: validation
path: high_school_mathematics/validation-*
- split: dev
path: high_school_mathematics/dev-*
- config_name: high_school_microeconomics
data_files:
- split: test
path: high_school_microeconomics/test-*
- split: validation
path: high_school_microeconomics/validation-*
- split: dev
path: high_school_microeconomics/dev-*
- config_name: high_school_physics
data_files:
- split: test
path: high_school_physics/test-*
- split: validation
path: high_school_physics/validation-*
- split: dev
path: high_school_physics/dev-*
- config_name: high_school_psychology
data_files:
- split: test
path: high_school_psychology/test-*
- split: validation
path: high_school_psychology/validation-*
- split: dev
path: high_school_psychology/dev-*
- config_name: high_school_statistics
data_files:
- split: test
path: high_school_statistics/test-*
- split: validation
path: high_school_statistics/validation-*
- split: dev
path: high_school_statistics/dev-*
- config_name: high_school_us_history
data_files:
- split: test
path: high_school_us_history/test-*
- split: validation
path: high_school_us_history/validation-*
- split: dev
path: high_school_us_history/dev-*
- config_name: high_school_world_history
data_files:
- split: test
path: high_school_world_history/test-*
- split: validation
path: high_school_world_history/validation-*
- split: dev
path: high_school_world_history/dev-*
- config_name: human_aging
data_files:
- split: test
path: human_aging/test-*
- split: validation
path: human_aging/validation-*
- split: dev
path: human_aging/dev-*
- config_name: human_sexuality
data_files:
- split: test
path: human_sexuality/test-*
- split: validation
path: human_sexuality/validation-*
- split: dev
path: human_sexuality/dev-*
- config_name: international_law
data_files:
- split: test
path: international_law/test-*
- split: validation
path: international_law/validation-*
- split: dev
path: international_law/dev-*
- config_name: jurisprudence
data_files:
- split: test
path: jurisprudence/test-*
- split: validation
path: jurisprudence/validation-*
- split: dev
path: jurisprudence/dev-*
- config_name: logical_fallacies
data_files:
- split: test
path: logical_fallacies/test-*
- split: validation
path: logical_fallacies/validation-*
- split: dev
path: logical_fallacies/dev-*
- config_name: machine_learning
data_files:
- split: test
path: machine_learning/test-*
- split: validation
path: machine_learning/validation-*
- split: dev
path: machine_learning/dev-*
- config_name: management
data_files:
- split: test
path: management/test-*
- split: validation
path: management/validation-*
- split: dev
path: management/dev-*
- config_name: marketing
data_files:
- split: test
path: marketing/test-*
- split: validation
path: marketing/validation-*
- split: dev
path: marketing/dev-*
- config_name: medical_genetics
data_files:
- split: test
path: medical_genetics/test-*
- split: validation
path: medical_genetics/validation-*
- split: dev
path: medical_genetics/dev-*
- config_name: miscellaneous
data_files:
- split: test
path: miscellaneous/test-*
- split: validation
path: miscellaneous/validation-*
- split: dev
path: miscellaneous/dev-*
- config_name: moral_disputes
data_files:
- split: test
path: moral_disputes/test-*
- split: validation
path: moral_disputes/validation-*
- split: dev
path: moral_disputes/dev-*
- config_name: moral_scenarios
data_files:
- split: test
path: moral_scenarios/test-*
- split: validation
path: moral_scenarios/validation-*
- split: dev
path: moral_scenarios/dev-*
- config_name: nutrition
data_files:
- split: test
path: nutrition/test-*
- split: validation
path: nutrition/validation-*
- split: dev
path: nutrition/dev-*
- config_name: philosophy
data_files:
- split: test
path: philosophy/test-*
- split: validation
path: philosophy/validation-*
- split: dev
path: philosophy/dev-*
- config_name: prehistory
data_files:
- split: test
path: prehistory/test-*
- split: validation
path: prehistory/validation-*
- split: dev
path: prehistory/dev-*
- config_name: professional_accounting
data_files:
- split: test
path: professional_accounting/test-*
- split: validation
path: professional_accounting/validation-*
- split: dev
path: professional_accounting/dev-*
- config_name: professional_law
data_files:
- split: test
path: professional_law/test-*
- split: validation
path: professional_law/validation-*
- split: dev
path: professional_law/dev-*
- config_name: professional_medicine
data_files:
- split: test
path: professional_medicine/test-*
- split: validation
path: professional_medicine/validation-*
- split: dev
path: professional_medicine/dev-*
- config_name: professional_psychology
data_files:
- split: test
path: professional_psychology/test-*
- split: validation
path: professional_psychology/validation-*
- split: dev
path: professional_psychology/dev-*
- config_name: public_relations
data_files:
- split: test
path: public_relations/test-*
- split: validation
path: public_relations/validation-*
- split: dev
path: public_relations/dev-*
- config_name: security_studies
data_files:
- split: test
path: security_studies/test-*
- split: validation
path: security_studies/validation-*
- split: dev
path: security_studies/dev-*
- config_name: sociology
data_files:
- split: test
path: sociology/test-*
- split: validation
path: sociology/validation-*
- split: dev
path: sociology/dev-*
- config_name: us_foreign_policy
data_files:
- split: test
path: us_foreign_policy/test-*
- split: validation
path: us_foreign_policy/validation-*
- split: dev
path: us_foreign_policy/dev-*
- config_name: virology
data_files:
- split: test
path: virology/test-*
- split: validation
path: virology/validation-*
- split: dev
path: virology/dev-*
- config_name: world_religions
data_files:
- split: test
path: world_religions/test-*
- split: validation
path: world_religions/validation-*
- split: dev
path: world_religions/dev-*
---
# Dataset Card for MMLU Greek
The MMLU Greek dataset is a set of 15858 examples from the MMLU dataset [available from here and here], machine-translated into Greek. The original dataset consists of multiple-choice questions from 57 tasks including elementary mathematics, US history, computer science, law, etc.
## Dataset Details
### Dataset Description
- **Curated by:** ILSP/Athena RC
- **Language(s) (NLP):** el
- **License:** cc-by-nc-sa-4.0
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
This dataset is the result of machine translation.
## Dataset Card Contact
https://www.athenarc.gr/en/ilsp
|
bigcode/bigcodebench-hard | bigcode | "2025-02-23T16:42:46Z" | 10,416 | 2 | [
"size_categories:n<1K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-09-14T14:50:33Z" | ---
dataset_info:
features:
- name: task_id
dtype: string
- name: complete_prompt
dtype: string
- name: instruct_prompt
dtype: string
- name: canonical_solution
dtype: string
- name: code_prompt
dtype: string
- name: test
dtype: string
- name: entry_point
dtype: string
- name: doc_struct
dtype: string
- name: libs
dtype: string
- name: q_idx
dtype: int64
- name: question
dtype: string
- name: score
dtype: float64
- name: _id
dtype: string
splits:
- name: v0.1.0_hf
num_bytes: 1271624
num_examples: 148
- name: v0.1.1
num_bytes: 1271607
num_examples: 148
- name: v0.1.2
num_bytes: 1271812
num_examples: 148
- name: v0.1.3
num_bytes: 1271812
num_examples: 148
- name: v0.1.4
num_bytes: 1272012
num_examples: 148
download_size: 2758366
dataset_size: 6358867
configs:
- config_name: default
data_files:
- split: v0.1.0_hf
path: data/v0.1.0_hf-*
- split: v0.1.1
path: data/v0.1.1-*
- split: v0.1.2
path: data/v0.1.2-*
- split: v0.1.3
path: data/v0.1.3-*
- split: v0.1.4
path: data/v0.1.4-*
---
|
AVS-Net/knee_fast_mri | AVS-Net | "2023-08-25T11:30:20Z" | 10,377 | 1 | [
"license:afl-3.0",
"size_categories:100M<n<1B",
"region:us",
"medical"
] | null | "2023-08-12T01:09:50Z" | ---
license: afl-3.0
tags:
- medical
size_categories:
- 100M<n<1B
---
# Dataset for AVS-Net Pre-training
The dataset utilized in the pre-training of the AVS-Net: Attention-based Variable Splitting Network for P-MRI Acceleration model, developed by Y Zhang, J Li, Z Wang, J Duan, and J Li, incorporates data from five distinct protocol sequences. These are:
- (coronal_pd)Coronal Spin Density-weighted without Fat Suppression
- (coronal_pd_fs)Coronal Spin Density-weighted with Fat Suppression
- (sagittal_pd)Sagittal Spin Density-weighted
- (sagittal_t2)Sagittal T2-weighted with Fat Suppression
- (axial_t2)Axial T2-weighted with Fat Suppression
The dataset is structured on a slice-by-slice basis, with each slice containing 20 cases. Each case is comprised of two files: rawdata*.mat and espirit*.mat. The dataset's structure can be outlined as follows:
## Dataset architecture:
- name: /rds/projects/d/duanj-ai-in-medical-imaging/knee_fast_mri
- Protocol: [coronal_pd, coronal_pd_fs, sagittal_pd, sagittal_t2, axial_t2]
Approximately 40 slices per protocol, each slice containing 15 channels, with a height and width (HW) of (640, 368)
```
knee_nyu
- axial_t2 coronal_pd(X) coronal_pd_fs sagittal_pd sagittal_t2
| | | | |
- [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], [11, 12, 13, 14, 15, 16, 17, 18, 19, 20] masks
| |
- [train] [val]
| |
- espirit*.mat(1-40), rawdata*.mat(1-40) *_masks.mat
```
In this structure, each protocol has approximately 40 slices, each consisting of 15 channels. The dimensions of the data are 640x368 (height x width). For each protocol, the slices are further divided into two groups: the training set ([train]) and the validation set ([val]). The training set includes the espirit*.mat and rawdata*.mat files for each slice, while the validation set contains *_masks.mat files.
## Dataset Usage
> For a standalone knee dataset download, use `git lfs`(<https://git-lfs.com/>) to download from the `huggingface` datasets(<https://huggingface.co/datasets/AVS-Net/knee_fast_mri>):
```bash
# Make sure you have git-lfs installed (https://git-lfs.com)
git lfs install
git clone -j8 [email protected]:datasets/AVS-Net/knee_fast_mri
```
## Known Issues and Resolutions
- 1. Network Connection Issue
For enhanced network connection quality, it is recommended to employ the `ssh` protocol instead of `https`.
```bash
# Rather than utilizing `https://huggingface.co/datasets/AVS-Net/knee_fast_mri`
# Clone the repository using `[email protected]:datasets/AVS-Net/knee_fast_mri`
# As an example:
git clone -j8 [email protected]:datasets/AVS-Net/knee_fast_mri
```
- 2. Interruptions During Download
Certain error messages may appear during the download process due to interruptions. These errors can include:
```
error: ... : cannot add to the index - missing --add option?
batch response: Post ... : read: connection reset by peer
error: failed to fetch some objects from 'https://hf.co/datasets/AVS-Net/knee_fast_mri.git/info/lfs'
```
Following the instructions below allows for the handling of these interruptions.
```bash
# Navigate (`cd`) to the directory containing the `lfs` folder
# Intead of using `git pull`,
# Use `git lfs pull` to resume the download progress for `lfs` projects
git lfs pull
```
Please note that this process will resume the download from where it was interrupted, thereby ensuring the integrity of your downloaded data.
|
mesolitica/mixtral-magicoder | mesolitica | "2024-09-30T15:33:24Z" | 10,359 | 2 | [
"language:en",
"language:ms",
"license:mit",
"size_categories:100K<n<1M",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [
"conversational"
] | "2024-01-11T00:04:30Z" | ---
license: mit
task_categories:
- conversational
language:
- en
- ms
---
# Mixtral Magicoder: Source Code Is All You Need on various programming languages
We sampled programming languages from https://huggingface.co/datasets/bigcode/the-stack-dedup and pushed to https://huggingface.co/datasets/malaysia-ai/starcoderdata-sample
After that, we use [Magicoder: Source Code Is All You Need on various programming languages](https://github.com/ise-uiuc/magicoder) template, we target at least 10k rows for each programming languages.
1. C++, 10747 rows
2. C#, 10193 rows
3. CUDA, 13843 rows
4. Dockerfile, 13286 rows
5. Go, 10143 rows
6. Java, 11221 rows
7. JavaScript, 11758 rows
8. Kotlin, 12790 rows
9. PHP, 10176 rows
10. Python, other than `pandas` and `sklearn` and `matplotlib` and `plotly`, 10925 rows
11. Python, must have `pandas` or `sklearn` or `matplotlib` or `plotly`, focused on data analytics, 53959 rows
12. Ruby, 10201 rows
13. Rust, 10271 rows
14. Scala, 10017 rows
15. Shell, 10848 rows
16. SQL, 27668 rows
17. Swift, 10187 rows
18. TypeScript, 14248 rows
Source code at https://github.com/mesolitica/malaysian-dataset/tree/master/chatbot/mixtral-magicoder
## precaution
1. There is no validation for the output generated.
2. Always filter short answers.
## Filtered version
1. Dropped short answers.
2. Dropped contain `code snippet`.
Uploaded at [postfilter.jsonl](postfilter.jsonl).
## Infrastructure specification
1. 5x of 4x A100s, NC96ads A100 v4, spot instance, total run is ~48 hours, 48 * 1.954 (US East, https://instances.vantage.sh/azure/vm/nc96ads-v4) * 5 ~= 376 USD.
2. HuggingFace Text Inference Engine. |
gsarti/flores_101 | gsarti | "2022-10-27T08:37:36Z" | 10,352 | 26 | [
"task_categories:text-generation",
"task_categories:translation",
"annotations_creators:found",
"language_creators:expert-generated",
"multilinguality:multilingual",
"multilinguality:translation",
"source_datasets:extended|flores",
"language:af",
"language:am",
"language:ar",
"language:hy",
"language:as",
"language:ast",
"language:az",
"language:be",
"language:bn",
"language:bs",
"language:bg",
"language:my",
"language:ca",
"language:ceb",
"language:zho",
"language:hr",
"language:cs",
"language:da",
"language:nl",
"language:en",
"language:et",
"language:tl",
"language:fi",
"language:fr",
"language:ff",
"language:gl",
"language:lg",
"language:ka",
"language:de",
"language:el",
"language:gu",
"language:ha",
"language:he",
"language:hi",
"language:hu",
"language:is",
"language:ig",
"language:id",
"language:ga",
"language:it",
"language:ja",
"language:jv",
"language:kea",
"language:kam",
"language:kn",
"language:kk",
"language:km",
"language:ko",
"language:ky",
"language:lo",
"language:lv",
"language:ln",
"language:lt",
"language:luo",
"language:lb",
"language:mk",
"language:ms",
"language:ml",
"language:mt",
"language:mi",
"language:mr",
"language:mn",
"language:ne",
"language:ns",
"language:no",
"language:ny",
"language:oc",
"language:or",
"language:om",
"language:ps",
"language:fa",
"language:pl",
"language:pt",
"language:pa",
"language:ro",
"language:ru",
"language:sr",
"language:sn",
"language:sd",
"language:sk",
"language:sl",
"language:so",
"language:ku",
"language:es",
"language:sw",
"language:sv",
"language:tg",
"language:ta",
"language:te",
"language:th",
"language:tr",
"language:uk",
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] | [
"text-generation",
"translation"
] | "2022-03-02T23:29:22Z" | ---
annotations_creators:
- found
language_creators:
- expert-generated
language:
- af
- am
- ar
- hy
- as
- ast
- az
- be
- bn
- bs
- bg
- my
- ca
- ceb
- zho
- hr
- cs
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- et
- tl
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license:
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multilinguality:
- multilingual
- translation
size_categories:
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source_datasets:
- extended|flores
task_categories:
- text-generation
- translation
task_ids: []
paperswithcode_id: flores
pretty_name: flores101
tags:
- conditional-text-generation
---
# Dataset Card for Flores 101
## Table of Contents
- [Dataset Card for Flores 101](#dataset-card-for-flores-101)
- [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)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Home:** [WMT](http://www.statmt.org/wmt21/large-scale-multilingual-translation-task.html)
- **Repository:** [Github](https://github.com/facebookresearch/flores)
- **Blogpost:** [FAIR](https://ai.facebook.com/blog/the-flores-101-data-set-helping-build-better-translation-systems-around-the-world)
- **Paper:** [Arxiv](https://arxiv.org/abs/2106.03193)
- **Point of Contact:** [[email protected]](mailto:[email protected])
- **Leaderboard** [Dynabench](https://dynabench.org/flores/Flores%20MT%20Evaluation%20(FULL))
### Dataset Summary
FLORES is a benchmark dataset for machine translation between English and low-resource languages.
Abstract from the original paper:
> One of the biggest challenges hindering progress in low-resource and multilingual machine translation is the lack of good evaluation benchmarks. Current evaluation benchmarks either lack good coverage of low-resource languages, consider only restricted domains, or are low quality because they are constructed using semi-automatic procedures. In this work, we introduce the FLORES evaluation benchmark, consisting of 3001 sentences extracted from English Wikipedia and covering a variety of different topics and domains. These sentences have been translated in 101 languages by professional translators through a carefully controlled process. The resulting dataset enables better assessment of model quality on the long tail of low-resource languages, including the evaluation of many-to-many multilingual translation systems, as all translations are multilingually aligned. By publicly releasing such a high-quality and high-coverage dataset, we hope to foster progress in the machine translation community and beyond.
**Disclaimer**: *The Flores-101 dataset is hosted by the Facebook and licensed under the [Creative Commons Attribution-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-sa/4.0/).
### Supported Tasks and Leaderboards
#### Multilingual Machine Translation
Refer to the [Dynabench leaderboard](https://dynabench.org/flores/Flores%20MT%20Evaluation%20(FULL)) for additional details on model evaluation on FLORES-101 in the context of the WMT2021 shared task on [Large-Scale Multilingual Machine Translation](http://www.statmt.org/wmt21/large-scale-multilingual-translation-task.html).
### Languages
The dataset contains parallel sentences for 101 languages, as mentioned in the original [Github](https://github.com/facebookresearch/flores/blob/master/README.md) page for the project. Languages are identified with the ISO 639-3 code (e.g. `eng`, `fra`, `rus`) as in the original dataset.
**New:** Use the configuration `all` to access the full set of parallel sentences for all the available languages in a single command.
## Dataset Structure
### Data Instances
A sample from the `dev` split for the Russian language (`rus` config) is provided below. All configurations have the same structure, and all sentences are aligned across configurations and splits.
```python
{
'id': 1,
'sentence': 'В понедельник ученые из Медицинской школы Стэнфордского университета объявили об изобретении нового диагностического инструмента, который может сортировать клетки по их типу; это маленький чип, который можно напечатать, используя стандартный струйный принтер примерно за 1 цент США.',
'URL': 'https://en.wikinews.org/wiki/Scientists_say_new_medical_diagnostic_chip_can_sort_cells_anywhere_with_an_inkjet',
'domain': 'wikinews',
'topic': 'health',
'has_image': 0,
'has_hyperlink': 0
}
```
The text is provided as-in the original dataset, without further preprocessing or tokenization.
### Data Fields
- `id`: Row number for the data entry, starting at 1.
- `sentence`: The full sentence in the specific language.
- `URL`: The URL for the English article from which the sentence was extracted.
- `domain`: The domain of the sentence.
- `topic`: The topic of the sentence.
- `has_image`: Whether the original article contains an image.
- `has_hyperlink`: Whether the sentence contains a hyperlink.
### Data Splits
| config| `dev`| `devtest`|
|-----------------:|-----:|---------:|
|all configurations| 997| 1012:|
### Dataset Creation
Please refer to the original article [The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation](https://arxiv.org/abs/2106.03193) for additional information on dataset creation.
## Additional Information
### Dataset Curators
The original authors of FLORES-101 are the curators of the original dataset. For problems or updates on this 🤗 Datasets version, please contact [[email protected]](mailto:[email protected]).
### Licensing Information
Licensed with Creative Commons Attribution Share Alike 4.0. License available [here](https://creativecommons.org/licenses/by-sa/4.0/).
### Citation Information
Please cite the authors if you use these corpora in your work:
```bibtex
@inproceedings{flores101,
title={The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation},
author={Goyal, Naman and Gao, Cynthia and Chaudhary, Vishrav and Chen, Peng-Jen and Wenzek, Guillaume and Ju, Da and Krishnan, Sanjana and Ranzato, Marc'Aurelio and Guzm\'{a}n, Francisco and Fan, Angela},
journal={arXiv preprint arXiv:2106.03193},
year={2021}
}
``` |
PromptEval/PromptEval_MMLU_full | PromptEval | "2024-06-07T05:40:35Z" | 10,333 | 3 | [
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features:
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features:
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configs:
- config_name: format_0
data_files:
- split: meta_llama_llama_3_8b
path: format_0/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_0/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_0/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_0/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_0/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_0/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_0/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_0/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_0/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_0/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_0/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_0/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_0/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_0/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_0/tiiuae_falcon_180b-*
- config_name: format_104
data_files:
- split: meta_llama_llama_3_8b
path: format_104/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_104/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_104/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_104/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_104/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_104/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_104/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_104/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_104/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_104/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_104/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_104/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_104/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_104/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_104/tiiuae_falcon_180b-*
- config_name: format_110
data_files:
- split: meta_llama_llama_3_8b
path: format_110/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_110/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_110/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_110/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_110/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_110/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_110/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_110/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_110/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_110/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_110/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_110/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_110/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_110/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_110/tiiuae_falcon_180b-*
- config_name: format_111
data_files:
- split: meta_llama_llama_3_8b
path: format_111/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_111/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_111/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_111/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_111/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_111/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_111/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_111/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_111/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_111/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_111/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_111/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_111/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_111/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_111/tiiuae_falcon_180b-*
- config_name: format_112
data_files:
- split: meta_llama_llama_3_8b
path: format_112/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_112/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_112/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_112/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_112/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_112/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_112/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_112/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_112/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_112/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_112/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_112/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_112/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_112/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_112/tiiuae_falcon_180b-*
- config_name: format_113
data_files:
- split: meta_llama_llama_3_8b
path: format_113/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_113/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_113/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_113/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_113/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_113/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_113/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_113/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_113/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_113/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_113/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_113/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_113/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_113/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_113/tiiuae_falcon_180b-*
- config_name: format_120
data_files:
- split: meta_llama_llama_3_8b
path: format_120/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_120/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_120/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_120/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_120/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_120/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_120/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_120/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_120/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_120/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_120/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_120/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_120/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_120/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_120/tiiuae_falcon_180b-*
- config_name: format_122
data_files:
- split: meta_llama_llama_3_8b
path: format_122/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_122/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_122/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_122/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_122/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_122/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_122/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_122/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_122/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_122/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_122/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_122/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_122/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_122/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_122/tiiuae_falcon_180b-*
- config_name: format_123
data_files:
- split: meta_llama_llama_3_8b
path: format_123/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_123/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_123/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_123/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_123/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_123/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_123/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_123/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_123/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_123/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_123/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_123/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_123/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_123/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_123/tiiuae_falcon_180b-*
- config_name: format_124
data_files:
- split: meta_llama_llama_3_8b
path: format_124/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_124/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_124/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_124/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_124/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_124/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_124/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_124/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_124/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_124/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_124/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_124/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_124/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_124/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_124/tiiuae_falcon_180b-*
- config_name: format_128
data_files:
- split: meta_llama_llama_3_8b
path: format_128/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_128/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_128/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_128/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_128/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_128/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_128/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_128/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_128/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_128/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_128/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_128/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_128/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_128/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_128/tiiuae_falcon_180b-*
- config_name: format_132
data_files:
- split: meta_llama_llama_3_8b
path: format_132/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_132/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_132/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_132/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_132/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_132/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_132/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_132/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_132/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_132/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_132/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_132/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_132/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_132/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_132/tiiuae_falcon_180b-*
- config_name: format_133
data_files:
- split: meta_llama_llama_3_8b
path: format_133/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_133/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_133/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_133/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_133/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_133/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_133/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_133/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_133/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_133/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_133/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_133/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_133/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_133/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_133/tiiuae_falcon_180b-*
- config_name: format_138
data_files:
- split: meta_llama_llama_3_8b
path: format_138/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_138/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_138/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_138/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_138/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_138/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_138/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_138/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_138/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_138/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_138/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_138/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_138/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_138/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_138/tiiuae_falcon_180b-*
- config_name: format_140
data_files:
- split: meta_llama_llama_3_8b
path: format_140/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_140/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_140/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_140/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_140/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_140/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_140/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_140/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_140/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_140/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_140/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_140/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_140/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_140/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_140/tiiuae_falcon_180b-*
- config_name: format_141
data_files:
- split: meta_llama_llama_3_8b
path: format_141/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_141/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_141/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_141/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_141/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_141/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_141/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_141/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_141/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_141/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_141/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_141/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_141/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_141/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_141/tiiuae_falcon_180b-*
- config_name: format_144
data_files:
- split: meta_llama_llama_3_8b
path: format_144/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_144/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_144/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_144/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_144/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_144/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_144/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_144/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_144/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_144/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_144/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_144/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_144/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_144/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_144/tiiuae_falcon_180b-*
- config_name: format_147
data_files:
- split: meta_llama_llama_3_8b
path: format_147/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_147/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_147/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_147/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_147/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_147/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_147/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_147/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_147/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_147/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_147/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_147/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_147/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_147/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_147/tiiuae_falcon_180b-*
- config_name: format_148
data_files:
- split: meta_llama_llama_3_8b
path: format_148/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_148/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_148/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_148/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_148/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_148/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_148/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_148/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_148/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_148/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_148/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_148/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_148/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_148/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_148/tiiuae_falcon_180b-*
- config_name: format_149
data_files:
- split: meta_llama_llama_3_8b
path: format_149/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_149/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_149/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_149/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_149/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_149/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_149/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_149/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_149/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_149/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_149/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_149/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_149/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_149/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_149/tiiuae_falcon_180b-*
- config_name: format_154
data_files:
- split: meta_llama_llama_3_8b
path: format_154/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_154/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_154/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_154/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_154/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_154/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_154/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_154/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_154/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_154/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_154/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_154/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_154/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_154/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_154/tiiuae_falcon_180b-*
- config_name: format_155
data_files:
- split: meta_llama_llama_3_8b
path: format_155/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_155/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_155/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_155/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_155/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_155/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_155/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_155/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_155/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_155/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_155/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_155/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_155/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_155/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_155/tiiuae_falcon_180b-*
- config_name: format_158
data_files:
- split: meta_llama_llama_3_8b
path: format_158/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_158/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_158/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_158/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_158/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_158/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_158/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_158/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_158/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_158/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_158/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_158/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_158/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_158/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_158/tiiuae_falcon_180b-*
- config_name: format_16
data_files:
- split: meta_llama_llama_3_8b
path: format_16/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_16/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_16/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_16/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_16/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_16/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_16/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_16/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_16/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_16/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_16/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_16/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_16/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_16/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_16/tiiuae_falcon_180b-*
- config_name: format_161
data_files:
- split: meta_llama_llama_3_8b
path: format_161/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_161/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_161/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_161/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_161/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_161/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_161/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_161/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_161/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_161/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_161/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_161/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_161/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_161/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_161/tiiuae_falcon_180b-*
- config_name: format_162
data_files:
- split: meta_llama_llama_3_8b
path: format_162/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_162/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_162/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_162/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_162/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_162/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_162/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_162/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_162/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_162/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_162/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_162/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_162/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_162/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_162/tiiuae_falcon_180b-*
- config_name: format_163
data_files:
- split: meta_llama_llama_3_8b
path: format_163/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_163/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_163/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_163/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_163/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_163/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_163/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_163/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_163/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_163/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_163/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_163/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_163/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_163/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_163/tiiuae_falcon_180b-*
- config_name: format_166
data_files:
- split: meta_llama_llama_3_8b
path: format_166/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_166/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_166/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_166/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_166/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_166/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_166/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_166/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_166/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_166/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_166/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_166/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_166/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_166/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_166/tiiuae_falcon_180b-*
- config_name: format_169
data_files:
- split: meta_llama_llama_3_8b
path: format_169/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_169/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_169/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_169/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_169/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_169/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_169/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_169/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_169/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_169/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_169/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_169/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_169/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_169/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_169/tiiuae_falcon_180b-*
- config_name: format_170
data_files:
- split: meta_llama_llama_3_8b
path: format_170/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_170/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_170/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_170/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_170/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_170/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_170/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_170/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_170/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_170/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_170/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_170/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_170/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_170/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_170/tiiuae_falcon_180b-*
- config_name: format_171
data_files:
- split: meta_llama_llama_3_8b
path: format_171/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_171/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_171/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_171/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_171/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_171/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_171/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_171/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_171/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_171/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_171/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_171/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_171/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_171/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_171/tiiuae_falcon_180b-*
- config_name: format_181
data_files:
- split: meta_llama_llama_3_8b
path: format_181/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_181/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_181/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_181/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_181/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_181/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_181/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_181/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_181/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_181/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_181/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_181/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_181/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_181/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_181/tiiuae_falcon_180b-*
- config_name: format_182
data_files:
- split: meta_llama_llama_3_8b
path: format_182/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_182/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_182/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_182/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_182/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_182/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_182/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_182/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_182/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_182/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_182/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_182/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_182/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_182/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_182/tiiuae_falcon_180b-*
- config_name: format_183
data_files:
- split: meta_llama_llama_3_8b
path: format_183/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_183/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_183/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_183/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_183/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_183/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_183/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_183/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_183/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_183/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_183/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_183/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_183/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_183/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_183/tiiuae_falcon_180b-*
- config_name: format_19
data_files:
- split: meta_llama_llama_3_8b
path: format_19/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_19/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_19/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_19/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_19/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_19/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_19/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_19/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_19/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_19/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_19/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_19/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_19/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_19/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_19/tiiuae_falcon_180b-*
- config_name: format_190
data_files:
- split: meta_llama_llama_3_8b
path: format_190/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_190/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_190/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_190/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_190/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_190/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_190/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_190/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_190/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_190/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_190/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_190/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_190/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_190/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_190/tiiuae_falcon_180b-*
- config_name: format_197
data_files:
- split: meta_llama_llama_3_8b
path: format_197/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_197/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_197/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_197/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_197/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_197/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_197/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_197/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_197/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_197/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_197/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_197/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_197/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_197/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_197/tiiuae_falcon_180b-*
- config_name: format_20
data_files:
- split: meta_llama_llama_3_8b
path: format_20/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_20/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_20/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_20/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_20/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_20/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_20/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_20/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_20/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_20/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_20/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_20/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_20/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_20/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_20/tiiuae_falcon_180b-*
- config_name: format_200
data_files:
- split: meta_llama_llama_3_8b
path: format_200/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_200/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_200/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_200/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_200/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_200/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_200/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_200/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_200/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_200/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_200/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_200/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_200/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_200/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_200/tiiuae_falcon_180b-*
- config_name: format_204
data_files:
- split: meta_llama_llama_3_8b
path: format_204/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_204/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_204/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_204/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_204/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_204/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_204/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_204/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_204/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_204/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_204/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_204/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_204/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_204/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_204/tiiuae_falcon_180b-*
- config_name: format_207
data_files:
- split: meta_llama_llama_3_8b
path: format_207/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_207/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_207/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_207/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_207/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_207/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_207/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_207/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_207/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_207/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_207/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_207/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_207/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_207/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_207/tiiuae_falcon_180b-*
- config_name: format_214
data_files:
- split: meta_llama_llama_3_8b
path: format_214/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_214/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_214/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_214/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_214/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_214/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_214/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_214/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_214/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_214/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_214/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_214/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_214/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_214/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_214/tiiuae_falcon_180b-*
- config_name: format_215
data_files:
- split: meta_llama_llama_3_8b
path: format_215/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_215/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_215/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_215/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_215/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_215/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_215/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_215/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_215/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_215/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_215/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_215/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_215/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_215/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_215/tiiuae_falcon_180b-*
- config_name: format_222
data_files:
- split: meta_llama_llama_3_8b
path: format_222/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_222/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_222/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_222/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_222/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_222/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_222/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_222/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_222/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_222/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_222/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_222/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_222/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_222/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_222/tiiuae_falcon_180b-*
- config_name: format_226
data_files:
- split: meta_llama_llama_3_8b
path: format_226/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_226/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_226/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_226/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_226/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_226/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_226/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_226/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_226/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_226/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_226/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_226/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_226/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_226/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_226/tiiuae_falcon_180b-*
- config_name: format_227
data_files:
- split: meta_llama_llama_3_8b
path: format_227/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_227/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_227/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_227/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_227/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_227/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_227/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_227/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_227/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_227/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_227/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_227/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_227/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_227/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_227/tiiuae_falcon_180b-*
- config_name: format_229
data_files:
- split: meta_llama_llama_3_8b
path: format_229/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_229/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_229/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_229/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_229/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_229/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_229/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_229/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_229/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_229/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_229/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_229/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_229/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_229/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_229/tiiuae_falcon_180b-*
- config_name: format_230
data_files:
- split: meta_llama_llama_3_8b
path: format_230/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_230/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_230/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_230/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_230/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_230/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_230/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_230/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_230/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_230/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_230/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_230/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_230/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_230/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_230/tiiuae_falcon_180b-*
- config_name: format_241
data_files:
- split: meta_llama_llama_3_8b
path: format_241/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_241/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_241/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_241/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_241/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_241/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_241/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_241/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_241/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_241/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_241/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_241/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_241/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_241/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_241/tiiuae_falcon_180b-*
- config_name: format_243
data_files:
- split: meta_llama_llama_3_8b
path: format_243/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_243/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_243/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_243/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_243/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_243/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_243/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_243/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_243/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_243/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_243/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_243/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_243/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_243/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_243/tiiuae_falcon_180b-*
- config_name: format_244
data_files:
- split: meta_llama_llama_3_8b
path: format_244/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_244/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_244/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_244/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_244/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_244/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_244/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_244/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_244/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_244/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_244/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_244/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_244/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_244/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_244/tiiuae_falcon_180b-*
- config_name: format_248
data_files:
- split: meta_llama_llama_3_8b
path: format_248/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_248/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_248/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_248/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_248/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_248/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_248/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_248/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_248/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_248/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_248/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_248/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_248/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_248/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_248/tiiuae_falcon_180b-*
- config_name: format_249
data_files:
- split: meta_llama_llama_3_8b
path: format_249/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_249/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_249/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_249/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_249/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_249/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_249/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_249/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_249/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_249/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_249/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_249/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_249/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_249/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_249/tiiuae_falcon_180b-*
- config_name: format_250
data_files:
- split: meta_llama_llama_3_8b
path: format_250/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_250/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_250/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_250/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_250/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_250/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_250/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_250/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_250/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_250/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_250/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_250/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_250/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_250/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_250/tiiuae_falcon_180b-*
- config_name: format_252
data_files:
- split: meta_llama_llama_3_8b
path: format_252/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_252/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_252/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_252/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_252/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_252/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_252/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_252/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_252/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_252/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_252/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_252/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_252/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_252/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_252/tiiuae_falcon_180b-*
- config_name: format_258
data_files:
- split: meta_llama_llama_3_8b
path: format_258/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_258/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_258/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_258/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_258/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_258/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_258/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_258/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_258/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_258/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_258/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_258/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_258/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_258/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_258/tiiuae_falcon_180b-*
- config_name: format_260
data_files:
- split: meta_llama_llama_3_8b
path: format_260/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_260/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_260/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_260/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_260/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_260/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_260/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_260/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_260/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_260/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_260/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_260/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_260/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_260/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_260/tiiuae_falcon_180b-*
- config_name: format_261
data_files:
- split: meta_llama_llama_3_8b
path: format_261/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_261/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_261/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_261/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_261/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_261/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_261/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_261/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_261/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_261/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_261/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_261/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_261/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_261/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_261/tiiuae_falcon_180b-*
- config_name: format_266
data_files:
- split: meta_llama_llama_3_8b
path: format_266/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_266/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_266/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_266/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_266/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_266/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_266/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_266/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_266/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_266/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_266/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_266/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_266/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_266/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_266/tiiuae_falcon_180b-*
- config_name: format_267
data_files:
- split: meta_llama_llama_3_8b
path: format_267/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_267/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_267/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_267/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_267/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_267/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_267/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_267/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_267/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_267/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_267/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_267/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_267/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_267/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_267/tiiuae_falcon_180b-*
- config_name: format_268
data_files:
- split: meta_llama_llama_3_8b
path: format_268/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_268/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_268/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_268/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_268/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_268/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_268/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_268/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_268/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_268/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_268/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_268/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_268/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_268/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_268/tiiuae_falcon_180b-*
- config_name: format_272
data_files:
- split: meta_llama_llama_3_8b
path: format_272/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_272/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_272/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_272/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_272/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_272/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_272/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_272/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_272/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_272/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_272/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_272/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_272/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_272/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_272/tiiuae_falcon_180b-*
- config_name: format_276
data_files:
- split: meta_llama_llama_3_8b
path: format_276/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_276/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_276/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_276/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_276/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_276/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_276/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_276/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_276/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_276/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_276/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_276/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_276/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_276/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_276/tiiuae_falcon_180b-*
- config_name: format_278
data_files:
- split: meta_llama_llama_3_8b
path: format_278/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_278/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_278/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_278/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_278/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_278/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_278/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_278/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_278/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_278/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_278/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_278/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_278/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_278/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_278/tiiuae_falcon_180b-*
- config_name: format_280
data_files:
- split: meta_llama_llama_3_8b
path: format_280/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_280/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_280/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_280/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_280/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_280/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_280/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_280/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_280/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_280/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_280/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_280/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_280/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_280/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_280/tiiuae_falcon_180b-*
- config_name: format_282
data_files:
- split: meta_llama_llama_3_8b
path: format_282/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_282/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_282/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_282/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_282/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_282/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_282/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_282/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_282/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_282/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_282/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_282/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_282/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_282/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_282/tiiuae_falcon_180b-*
- config_name: format_286
data_files:
- split: meta_llama_llama_3_8b
path: format_286/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_286/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_286/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_286/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_286/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_286/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_286/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_286/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_286/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_286/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_286/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_286/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_286/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_286/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_286/tiiuae_falcon_180b-*
- config_name: format_290
data_files:
- split: meta_llama_llama_3_8b
path: format_290/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_290/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_290/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_290/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_290/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_290/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_290/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_290/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_290/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_290/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_290/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_290/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_290/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_290/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_290/tiiuae_falcon_180b-*
- config_name: format_294
data_files:
- split: meta_llama_llama_3_8b
path: format_294/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_294/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_294/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_294/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_294/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_294/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_294/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_294/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_294/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_294/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_294/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_294/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_294/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_294/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_294/tiiuae_falcon_180b-*
- config_name: format_296
data_files:
- split: meta_llama_llama_3_8b
path: format_296/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_296/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_296/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_296/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_296/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_296/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_296/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_296/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_296/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_296/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_296/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_296/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_296/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_296/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_296/tiiuae_falcon_180b-*
- config_name: format_298
data_files:
- split: meta_llama_llama_3_8b
path: format_298/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_298/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_298/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_298/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_298/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_298/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_298/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_298/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_298/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_298/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_298/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_298/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_298/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_298/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_298/tiiuae_falcon_180b-*
- config_name: format_300
data_files:
- split: meta_llama_llama_3_8b
path: format_300/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_300/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_300/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_300/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_300/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_300/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_300/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_300/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_300/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_300/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_300/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_300/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_300/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_300/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_300/tiiuae_falcon_180b-*
- config_name: format_301
data_files:
- split: meta_llama_llama_3_8b
path: format_301/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_301/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_301/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_301/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_301/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_301/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_301/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_301/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_301/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_301/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_301/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_301/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_301/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_301/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_301/tiiuae_falcon_180b-*
- config_name: format_31
data_files:
- split: meta_llama_llama_3_8b
path: format_31/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_31/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_31/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_31/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_31/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_31/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_31/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_31/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_31/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_31/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_31/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_31/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_31/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_31/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_31/tiiuae_falcon_180b-*
- config_name: format_32
data_files:
- split: meta_llama_llama_3_8b
path: format_32/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_32/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_32/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_32/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_32/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_32/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_32/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_32/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_32/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_32/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_32/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_32/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_32/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_32/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_32/tiiuae_falcon_180b-*
- config_name: format_35
data_files:
- split: meta_llama_llama_3_8b
path: format_35/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_35/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_35/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_35/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_35/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_35/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_35/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_35/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_35/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_35/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_35/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_35/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_35/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_35/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_35/tiiuae_falcon_180b-*
- config_name: format_37
data_files:
- split: meta_llama_llama_3_8b
path: format_37/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_37/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_37/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_37/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_37/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_37/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_37/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_37/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_37/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_37/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_37/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_37/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_37/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_37/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_37/tiiuae_falcon_180b-*
- config_name: format_41
data_files:
- split: meta_llama_llama_3_8b
path: format_41/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_41/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_41/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_41/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_41/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_41/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_41/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_41/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_41/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_41/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_41/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_41/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_41/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_41/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_41/tiiuae_falcon_180b-*
- config_name: format_42
data_files:
- split: meta_llama_llama_3_8b
path: format_42/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_42/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_42/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_42/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_42/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_42/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_42/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_42/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_42/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_42/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_42/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_42/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_42/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_42/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_42/tiiuae_falcon_180b-*
- config_name: format_45
data_files:
- split: meta_llama_llama_3_8b
path: format_45/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_45/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_45/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_45/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_45/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_45/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_45/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_45/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_45/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_45/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_45/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_45/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_45/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_45/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_45/tiiuae_falcon_180b-*
- config_name: format_46
data_files:
- split: meta_llama_llama_3_8b
path: format_46/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_46/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_46/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_46/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_46/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_46/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_46/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_46/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_46/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_46/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_46/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_46/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_46/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_46/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_46/tiiuae_falcon_180b-*
- config_name: format_47
data_files:
- split: meta_llama_llama_3_8b
path: format_47/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_47/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_47/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_47/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_47/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_47/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_47/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_47/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_47/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_47/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_47/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_47/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_47/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_47/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_47/tiiuae_falcon_180b-*
- config_name: format_48
data_files:
- split: meta_llama_llama_3_8b
path: format_48/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_48/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_48/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_48/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_48/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_48/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_48/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_48/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_48/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_48/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_48/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_48/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_48/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_48/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_48/tiiuae_falcon_180b-*
- config_name: format_50
data_files:
- split: meta_llama_llama_3_8b
path: format_50/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_50/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_50/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_50/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_50/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_50/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_50/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_50/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_50/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_50/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_50/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_50/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_50/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_50/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_50/tiiuae_falcon_180b-*
- config_name: format_51
data_files:
- split: meta_llama_llama_3_8b
path: format_51/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_51/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_51/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_51/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_51/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_51/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_51/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_51/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_51/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_51/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_51/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_51/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_51/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_51/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_51/tiiuae_falcon_180b-*
- config_name: format_55
data_files:
- split: meta_llama_llama_3_8b
path: format_55/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_55/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_55/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_55/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_55/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_55/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_55/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_55/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_55/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_55/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_55/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_55/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_55/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_55/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_55/tiiuae_falcon_180b-*
- config_name: format_59
data_files:
- split: meta_llama_llama_3_8b
path: format_59/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_59/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_59/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_59/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_59/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_59/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_59/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_59/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_59/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_59/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_59/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_59/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_59/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_59/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_59/tiiuae_falcon_180b-*
- config_name: format_63
data_files:
- split: meta_llama_llama_3_8b
path: format_63/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_63/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_63/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_63/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_63/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_63/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_63/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_63/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_63/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_63/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_63/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_63/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_63/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_63/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_63/tiiuae_falcon_180b-*
- config_name: format_66
data_files:
- split: meta_llama_llama_3_8b
path: format_66/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_66/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_66/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_66/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_66/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_66/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_66/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_66/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_66/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_66/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_66/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_66/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_66/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_66/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_66/tiiuae_falcon_180b-*
- config_name: format_7
data_files:
- split: meta_llama_llama_3_8b
path: format_7/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_7/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_7/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_7/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_7/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_7/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_7/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_7/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_7/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_7/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_7/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_7/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_7/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_7/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_7/tiiuae_falcon_180b-*
- config_name: format_71
data_files:
- split: meta_llama_llama_3_8b
path: format_71/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_71/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_71/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_71/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_71/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_71/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_71/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_71/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_71/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_71/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_71/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_71/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_71/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_71/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_71/tiiuae_falcon_180b-*
- config_name: format_72
data_files:
- split: meta_llama_llama_3_8b
path: format_72/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_72/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_72/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_72/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_72/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_72/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_72/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_72/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_72/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_72/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_72/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_72/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_72/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_72/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_72/tiiuae_falcon_180b-*
- config_name: format_75
data_files:
- split: meta_llama_llama_3_8b
path: format_75/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_75/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_75/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_75/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_75/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_75/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_75/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_75/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_75/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_75/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_75/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_75/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_75/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_75/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_75/tiiuae_falcon_180b-*
- config_name: format_76
data_files:
- split: meta_llama_llama_3_8b
path: format_76/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_76/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_76/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_76/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_76/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_76/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_76/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_76/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_76/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_76/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_76/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_76/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_76/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_76/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_76/tiiuae_falcon_180b-*
- config_name: format_8
data_files:
- split: meta_llama_llama_3_8b
path: format_8/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_8/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_8/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_8/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_8/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_8/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_8/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_8/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_8/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_8/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_8/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_8/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_8/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_8/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_8/tiiuae_falcon_180b-*
- config_name: format_87
data_files:
- split: meta_llama_llama_3_8b
path: format_87/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_87/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_87/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_87/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_87/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_87/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_87/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_87/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_87/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_87/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_87/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_87/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_87/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_87/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_87/tiiuae_falcon_180b-*
- config_name: format_94
data_files:
- split: meta_llama_llama_3_8b
path: format_94/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_94/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_94/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_94/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_94/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_94/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_94/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_94/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_94/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_94/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_94/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_94/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_94/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_94/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_94/tiiuae_falcon_180b-*
- config_name: format_95
data_files:
- split: meta_llama_llama_3_8b
path: format_95/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_95/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_95/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_95/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_95/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_95/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_95/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_95/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_95/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_95/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_95/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_95/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_95/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_95/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_95/tiiuae_falcon_180b-*
- config_name: format_96
data_files:
- split: meta_llama_llama_3_8b
path: format_96/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_96/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_96/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_96/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_96/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_96/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_96/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_96/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_96/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_96/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_96/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_96/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_96/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_96/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_96/tiiuae_falcon_180b-*
- config_name: format_97
data_files:
- split: meta_llama_llama_3_8b
path: format_97/meta_llama_llama_3_8b-*
- split: meta_llama_llama_3_8b_instruct
path: format_97/meta_llama_llama_3_8b_instruct-*
- split: meta_llama_llama_3_70b_instruct
path: format_97/meta_llama_llama_3_70b_instruct-*
- split: codellama_codellama_34b_instruct
path: format_97/codellama_codellama_34b_instruct-*
- split: google_flan_t5_xl
path: format_97/google_flan_t5_xl-*
- split: google_flan_t5_xxl
path: format_97/google_flan_t5_xxl-*
- split: google_flan_ul2
path: format_97/google_flan_ul2-*
- split: ibm_mistralai_merlinite_7b
path: format_97/ibm_mistralai_merlinite_7b-*
- split: mistralai_mixtral_8x7b_instruct_v01
path: format_97/mistralai_mixtral_8x7b_instruct_v01-*
- split: mistralai_mistral_7b_instruct_v0_2
path: format_97/mistralai_mistral_7b_instruct_v0_2-*
- split: google_gemma_7b
path: format_97/google_gemma_7b-*
- split: google_gemma_7b_it
path: format_97/google_gemma_7b_it-*
- split: tiiuae_falcon_40b
path: format_97/tiiuae_falcon_40b-*
- split: mistralai_mistral_7b_v0_1
path: format_97/mistralai_mistral_7b_v0_1-*
- split: tiiuae_falcon_180b
path: format_97/tiiuae_falcon_180b-*
---
# MMLU Multi-Prompt Evaluation Data
## Overview
This dataset contains the results of a comprehensive evaluation of various Large Language Models (LLMs) using multiple prompt templates on the Massive Multitask Language Understanding (MMLU) benchmark. The data is introduced in
[Maia Polo, Felipe, Ronald Xu, Lucas Weber, Mírian Silva, Onkar Bhardwaj, Leshem Choshen, Allysson Flavio Melo de Oliveira, Yuekai Sun, and Mikhail Yurochkin. "Efficient multi-prompt evaluation of LLMs." arXiv preprint arXiv:2405.17202 (2024).](https://arxiv.org/abs/2405.17202)
## Dataset Details
The [MMLU](https://huggingface.co/datasets/cais/mmlu) benchmark comprises 57 diverse subjects and approximately 14,000 examples. It is a multiple-choice question-answering benchmark that tests the performance of LLMs across a wide range of topics. The data includes evaluation for 15 different SOTA LLMs and 100 different prompt templates.
The data from a specific prompt template (format), can be downloaded using
```python
from datasets import load_dataset
j=0
data = load_dataset('PromptEval/tinyMMLU', f'format_{j}')
```
If you are only interested in the correctness scores, please check this lighter version of this dataset [here](https://huggingface.co/datasets/PromptEval/PromptEval_MMLU_correctness).
## Citing
@article{polo2024efficient,
title={Efficient multi-prompt evaluation of LLMs},
author={Polo, Felipe Maia and Xu, Ronald and Weber, Lucas and Silva, M{\'\i}rian and Bhardwaj, Onkar and Choshen, Leshem and de Oliveira, Allysson Flavio Melo and Sun, Yuekai and Yurochkin, Mikhail},
journal={arXiv preprint arXiv:2405.17202},
year={2024}
}
@article{hendryckstest2021,
title={Measuring Massive Multitask Language Understanding},
author={Dan Hendrycks and Collin Burns and Steven Basart and Andy Zou and Mantas Mazeika and Dawn Song and Jacob Steinhardt},
journal={Proceedings of the International Conference on Learning Representations (ICLR)},
year={2021}
} |
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