davanstrien/dataset_cards_with_metadata · Datasets at Hugging Face

datasetId large_stringlengths 6 116	author large_stringlengths 2 42	last_modified large_stringdate 2021-05-20 00:57:22 2025-06-03 10:14:14	downloads int64 0 3.97M	likes int64 0 7.74k	tags large listlengths 1 2.03k	task_categories large listlengths 0 48	createdAt large_stringdate 2022-03-02 23:29:22 2025-06-03 10:13:51	trending_score float64 1 36 ⌀	card large_stringlengths 31 1.01M
self-label-zanette-lab/dapo_mixed_dataset_with_fraction_labeled_0.01	self-label-zanette-lab	2025-05-02T15:44:14Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-02T15:44:13Z	null	--- dataset_info: features: - name: prompt dtype: string - name: answer dtype: string - name: source dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 5694890 num_examples: 17398 download_size: 2676607 dataset_size: 5694890 configs: - config_name: default data_files: - split: train path: data/train-* ---
Mxode/Chinese-QA-Agriculture_Forestry_Animal_Husbandry_Fishery	Mxode	2025-05-02T10:50:18Z	106	0	[ "task_categories:text-generation", "task_categories:question-answering", "language:zh", "license:cc-by-sa-4.0", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[ "text-generation", "question-answering" ]	2025-04-22T16:15:08Z	null	--- license: cc-by-sa-4.0 task_categories: - text-generation - question-answering language: - zh size_categories: - 100K<n<1M --- <h1 align="center"> 中文农林牧渔问答数据集 </h1> <p align="center"> <a href="https://github.com/Mxoder/Maxs-Awesome-Datasets" target="_blank">💻 Github Repo</a> <br> </p> ## 简介中文农林牧渔问答数据集，涵盖农业、林业、畜牧业、渔业，数据量 900K+，均为简单的问答形式。 ## 数据格式每条数据的格式如下： ```json { "id": << 12位nanoid >>, "prompt": << 问题 >>, "response": << 答案 >> } ```
Mxode/NanoExperiment-Data-Mix-10M	Mxode	2025-05-02T10:46:03Z	18	0	[ "task_categories:translation", "language:en", "language:zh", "license:cc-by-sa-4.0", "size_categories:10M<n<100M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "chemistry", "biology", "finance", "legal", "music", "art", "climate", "medical", "synthetic" ]	[ "translation" ]	2024-09-13T16:58:49Z	null	--- license: cc-by-sa-4.0 task_categories: - translation language: - en - zh tags: - chemistry - biology - finance - legal - music - art - climate - medical - synthetic pretty_name: BiST size_categories: - 1M<n<10M configs: - config_name: raw data_files: - split: train path: raw/* - config_name: tokenized data_files: - split: train path: tokenized/* --- # Mxode/NanoExperiment-Data-Mix-10M Dataset of [NanoExperiment](https://github.com/Mxoder/NanoExperiment). Tokenized by [Bilingual-Tokenizer-2K](https://huggingface.co/Mxode/Bilingual-Tokenizer/tree/main/BilingualTokenizer-2K).
Mxode/Magpie-Pro-10K-GPT4o-mini	Mxode	2025-05-02T10:43:14Z	36	0	[ "task_categories:text-generation", "task_categories:question-answering", "language:en", "license:cc-by-sa-4.0", "size_categories:10K<n<100K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "magpie", "gpt-4o" ]	[ "text-generation", "question-answering" ]	2024-09-06T07:20:09Z	null	--- license: cc-by-sa-4.0 task_categories: - text-generation - question-answering language: - en tags: - magpie - gpt-4o size_categories: - 10K<n<100K ---
NONHUMAN-RESEARCH/tic-tac-toe-pos-eight	NONHUMAN-RESEARCH	2025-05-02T04:27:57Z	0	0	[ "task_categories:robotics", "license:apache-2.0", "size_categories:10K<n<100K", "format:parquet", "modality:tabular", "modality:timeseries", "modality:video", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "LeRobot", "so100", "test" ]	[ "robotics" ]	2025-05-02T04:26:56Z	null	--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - so100 - test configs: - config_name: default data_files: data//.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - Homepage: [More Information Needed] - Paper: [More Information Needed] - License: apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.1", "robot_type": "so100", "total_episodes": 25, "total_frames": 11175, "total_tasks": 1, "total_videos": 50, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:25" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "action": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.state": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.images.laptop": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "h264", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "observation.images.phone": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "h264", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "timestamp": { "dtype": "float32", "shape": [ 1 ], "names": null }, "frame_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "episode_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "task_index": { "dtype": "int64", "shape": [ 1 ], "names": null } } } ``` ## Citation BibTeX: ```bibtex [More Information Needed] ```
hai2131/OCR-dataset	hai2131	2025-05-02T01:09:46Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-02T01:09:42Z	null	--- dataset_info: features: - name: image dtype: image - name: label dtype: string splits: - name: train num_bytes: 13465855.2 num_examples: 8000 - name: val num_bytes: 3366481.8 num_examples: 2000 download_size: 13265496 dataset_size: 16832337.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: val path: data/val-* ---
mlfoundations-dev/d1_code_gpt_0.3k	mlfoundations-dev	2025-05-01T17:05:44Z	0	0	[ "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-01T17:05:31Z	null	--- dataset_info: features: - name: id dtype: string - name: instruction_seed dtype: string - name: output dtype: string - name: source dtype: string - name: license dtype: string - name: dataset dtype: string - name: split dtype: string - name: difficulty dtype: int64 - name: solution dtype: string - name: index dtype: string - name: _source dtype: string - name: difficulty_reasoning dtype: string - name: __original_row_idx dtype: int64 - name: ms_id dtype: int64 - name: reasoning sequence: string - name: deepseek_solution sequence: string - name: final_reasoning_trace sequence: string - name: correct sequence: bool - name: classifier_reasoning dtype: string - name: _majority_responses sequence: string - name: verified_final_reasoning_trace dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 664551210.79 num_examples: 316 download_size: 276573145 dataset_size: 664551210.79 configs: - config_name: default data_files: - split: train path: data/train-* ---
kwangchaeko/koch_test_3	kwangchaeko	2025-05-01T03:55:32Z	0	0	[ "task_categories:robotics", "license:apache-2.0", "size_categories:10K<n<100K", "format:parquet", "modality:tabular", "modality:timeseries", "modality:video", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "LeRobot", "koch", "tutorial" ]	[ "robotics" ]	2025-05-01T03:54:42Z	null	--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - koch - tutorial configs: - config_name: default data_files: data//.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - Homepage: [More Information Needed] - Paper: [More Information Needed] - License: apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.1", "robot_type": "koch", "total_episodes": 10, "total_frames": 17543, "total_tasks": 1, "total_videos": 10, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:10" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "action": { "dtype": "float32", "shape": [ 4 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex" ] }, "observation.state": { "dtype": "float32", "shape": [ 4 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex" ] }, "observation.images.laptop": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "timestamp": { "dtype": "float32", "shape": [ 1 ], "names": null }, "frame_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "episode_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "task_index": { "dtype": "int64", "shape": [ 1 ], "names": null } } } ``` ## Citation BibTeX: ```bibtex [More Information Needed] ```
DamianBoborzi/Objaverse_processed	DamianBoborzi	2025-04-16T14:14:51Z	19,856	0	[ "license:odc-by", "arxiv:2503.14002", "region:us" ]	[]	2025-02-28T15:01:58Z	null	--- license: odc-by pretty_name: Objaverse Processsing Data --- Contains information of Objaverse XL objects from the alignment and TRELLIS500K (over 1 Millionen processed objects) dataset. We downloaded and rendered 4 views of each object. We generat siglip embeddings and Yolov10 objects detections for fast filtering. We added TRELLIS and CAP3D Captions where available. If there were no captions we generated new captions with the large version of Florence 2. This is the base dataset we used to generate [MeshFleet](https://huggingface.co/datasets/DamianBoborzi/meshfleetXL) which is described in [MeshFleet: Filtered and Annotated 3D Vehicle Dataset for Domain Specific Generative Modeling](arxiv.org/abs/2503.14002). - The rendered views are in the data directory split into webdataset chunks. The file 'objaverse_xl_render_files.csv' shows which objects is in which chunk using the sha256 of the objects. You can simply extract all files from each chunk using something like `tar -xf chunk_0.tar -C extract_test` or you can use the WebDataset Library to access the content of each chunk. - objaverse_oxl_processing_df.csv includes the following information: sha256: The sha256 of the object from Objaverse XL yolo_detections: Detected objects cap3D Data: cap3D_caption: The Captions generation by Cap3D (https://cap3d-um.github.io/), cap3D_avg_clip_similarity: is the average similariy of the text siglip embeddings to the siglip embedding of filtered car objects TRELLIS500K Data: Captions (trellis_caption) and aesthetic scores (trellis_aesthetic_score) from TRELLIS500K (https://huggingface.co/datasets/JeffreyXiang/TRELLIS-500K). We also used the referenced aesthetic score prediciton model (aesthetic_score). The results are however very different. Florence_caption: Captions of objects which did not have a caption either from CAP3D or TRELLIS500 using Florence 2 (https://huggingface.co/microsoft/Florence-2-large) Car Quality Assessment: We processed all objects by generating SigLIP and DINOv2 Embeddings and estiomating if the object is a high quality vehicle. Results contain the estimated label(predicted_car_quality_label), the score of the model before taking the max of the output (output_score), and uncertainty estimates using monte carlo dropout: car_quality_uncertainty_entropy,car_quality_uncertainty_mutual_info,car_quality_uncertainty_variation_ratio - objaverse_combined_captions.csv combines the captions from three different sources (CAP3D, TRELLIS500K and newly generated ones with Florence 2) and stores them together with the corresponding sha256 of the object. - objaverse_vehicle_detections_textcategories.csv Contains the detection if a car is described based on the captions from CAP3D, TRELLIS500K and the Florence-2 generated ones. The result is given with text_category_car as 'car' and 'not a car'. If the text describes a car the text_category_vehicle gives an estimate what type of car. - objaverse_car_classification_results_df.csv Contains the Car Quality Assessment alone without the additional information from objaverse_oxl_processing_df.csv.
AnonRes/OpenMind	AnonRes	2025-04-03T11:51:07Z	2,050	16	[ "task_categories:image-feature-extraction", "license:cc-by-4.0", "modality:3d", "modality:image", "region:us", "3d", "image" ]	[ "image-feature-extraction" ]	2025-03-11T14:19:10Z	2	--- license: cc-by-4.0 task_categories: - "image-feature-extraction" pretty_name: "The OpenMind Dataset" tags: - 3d - image --- # The OpenMind Dataset: A large-scale Head-And-Neck 3D MRI Dataset for self-supervised learning ![OpenMind Dataset](./assets/OpenMindDataset.png) ## Description The OpenMind Dataset is a large-scale 3D MRI dataset of the head and neck region featuring 114k MRI Images. Its purpose is to provide access of large amounts of 3D medical imaging data to accelerate the development of self-supervised learning methods for 3D medical imaging. This data was pooled from exactly 800 datasets from the OpenNeuro platform and provides 23 different MRI modalities/techniques from over 30 different scanners, representing a highly variable pre-training dataset. ## Additional Features Aside from the 3D MRI Images, we provide stratified metadata for each of the 114k images when made available by the original dataset in a unified format. Moreover, we provide a) `deface_masks`, which delineate anonymized/defaced regions, allowing to take them into account when developing reconstruction based pre-training methods and b) `anatomy_masks`, which delineate areas which holds anatomy, e.g. for cases where images were brain extracted. Similarly to the `deface_masks` this allows to either ignore regions outside of this during reconstruction and allows sampling regions with anatomy, avoiding empty regions for contrastive learning approaches. ## Dataset structure The dataset is structured akin to the original OpenNeuro datasets, following a modified BIDS format, exemplified below. We recommend using the `openneuro_metadata.csv` which holds the relative paths from the root directory to the images and their associated masks as well as metadata. ``` -- Readme.md // this readme -- openneuro_metadata.csv // metadata file containing the relative paths to the images and their metadata -- openmind_dataset \|-- openneuro_metadata.csv # Contains the relative paths to the images and their metadata \|-- OpenMind \|-- ds_000001 \|-- sub-01 \|-- anat \|-- sub-01_ses-01_T1w.nii.gz # 3D Image in Nifti format \|-- sub-01_T1w__Data # Associated Folder holding Masks to Image \|-- deface_mask.nii.gz # deface mask \-- fb_mask.nii.gz # anatomy mask \|-- sub-01_inplaneT2.nii.gz \-- sub-01_inplaneT2__Data # Associated Folder holding Masks to Image \|-- deface_mask.nii.gz \-- fb_mask.nii.gz ... ... ... \|-- ds_xxxxxx ... ``` Available meta-data tags (Name [Percent of images with this metadata]) within the metadata.csv file are: - MR Modalitiy/Technique [100%] - Scanner Manufacturer [100%] - Scanner Model [100%] - Scanner Field Strength [100%] - Age [70%] - Sex [77.4%] - Weight [1.5%] - BMI [15.7%] - Race [11.7%] - Handedness (right/left/ambidexterous) [35.4%] - Health Status (healthy/ill) [26.4%]
open-r1/OpenR1-Math-220k	open-r1	2025-02-18T11:45:27Z	32,044	564	[ "language:en", "license:apache-2.0", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-02-10T13:41:48Z	null	--- license: apache-2.0 language: - en configs: - config_name: all data_files: - split: train path: all/train-* - config_name: default data_files: - split: train path: data/train-* - config_name: extended data_files: - split: train path: extended/train-* dataset_info: - config_name: all features: - name: problem dtype: string - name: solution dtype: string - name: answer dtype: string - name: problem_type dtype: string - name: question_type dtype: string - name: source dtype: string - name: uuid dtype: string - name: is_reasoning_complete sequence: bool - name: generations sequence: string - name: correctness_math_verify sequence: bool - name: correctness_llama sequence: bool - name: finish_reasons sequence: string - name: correctness_count dtype: int64 - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 9734110026.0 num_examples: 225129 download_size: 4221672067 dataset_size: 9734110026.0 - config_name: default features: - name: problem dtype: string - name: solution dtype: string - name: answer dtype: string - name: problem_type dtype: string - name: question_type dtype: string - name: source dtype: string - name: uuid dtype: string - name: is_reasoning_complete sequence: bool - name: generations sequence: string - name: correctness_math_verify sequence: bool - name: correctness_llama sequence: bool - name: finish_reasons sequence: string - name: correctness_count dtype: int64 - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 4964543659 num_examples: 93733 download_size: 2149897914 dataset_size: 4964543659 - config_name: extended features: - name: problem dtype: string - name: solution dtype: string - name: answer dtype: string - name: problem_type dtype: string - name: question_type dtype: string - name: source dtype: string - name: uuid dtype: string - name: is_reasoning_complete sequence: bool - name: generations sequence: string - name: correctness_math_verify sequence: bool - name: correctness_llama sequence: bool - name: finish_reasons sequence: string - name: correctness_count dtype: int64 - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 4769566550 num_examples: 131396 download_size: 2063936457 dataset_size: 4769566550 --- # OpenR1-Math-220k ## Dataset description OpenR1-Math-220k is a large-scale dataset for mathematical reasoning. It consists of 220k math problems with two to four reasoning traces generated by [DeepSeek R1](https://huggingface.co/deepseek-ai/DeepSeek-R1) for problems from NuminaMath 1.5. The traces were verified using [Math Verify](https://github.com/huggingface/Math-Verify) for most samples and [Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct) as a judge for 12% of the samples, and each problem contains at least one reasoning trace with a correct answer. The dataset consists of two splits: - `default` with 94k problems and that achieves the best performance after SFT. - `extended` with 131k samples where we add data sources like `cn_k12`. This provides more reasoning traces, but we found that the performance after SFT to be lower than the `default` subset, likely because the questions from `cn_k12` are less difficult than other sources. You can load the dataset as follows: ```python from datasets import load_dataset ds = load_dataset("open-r1/OpenR1-Math-220k", "default") ``` ## Dataset curation To build OpenR1-Math-220k, we prompt [DeepSeek R1](https://huggingface.co/deepseek-ai/DeepSeek-R1) model to generate solutions for 400k problems from [NuminaMath 1.5](https://huggingface.co/datasets/AI-MO/NuminaMath-1.5) using [SGLang](https://github.com/sgl-project/sglang), the generation code is available [here](https://github.com/huggingface/open-r1/tree/main/slurm). We follow the model card’s recommended generation parameters and prepend the following instruction to the user prompt: `"Please reason step by step, and put your final answer within \boxed{}."` We set a 16k token limit per generation, as our analysis showed that only 75% of problems could be solved in under 8k tokens, and most of the remaining problems required the full 16k tokens. We were able to generate 25 solutions per hour per H100, enabling us to generate 300k problem solutions per day on 512 H100s. We generate two solutions per problem—and in some cases, four—to provide flexibility in filtering and training. This approach allows for rejection sampling, similar to DeepSeek R1’s methodology, and also makes the dataset suitable for preference optimisation methods like DPO. ## License The dataset is licensed under Apache 2.0
NebulaByte/E-Commerce_Customer_Support_Conversations	NebulaByte	2024-04-01T05:57:55Z	272	36	[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2023-07-24T05:56:30Z	2	--- dataset_info: features: - name: issue_area dtype: string - name: issue_category dtype: string - name: issue_sub_category dtype: string - name: issue_category_sub_category dtype: string - name: customer_sentiment dtype: string - name: product_category dtype: string - name: product_sub_category dtype: string - name: issue_complexity dtype: string - name: agent_experience_level dtype: string - name: agent_experience_level_desc dtype: string - name: conversation dtype: string splits: - name: train num_bytes: 2537279 num_examples: 1000 download_size: 827367 dataset_size: 2537279 --- # Dataset Card for "E-Commerce_Customer_Support_Conversations" The dataset is synthetically generated with OpenAI ChatGPT model (gpt-3.5-turbo). [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
coastalcph/lex_glue	coastalcph	2024-01-04T14:25:27Z	27,251	57	[ "task_categories:question-answering", "task_categories:text-classification", "task_ids:multi-class-classification", "task_ids:multi-label-classification", "task_ids:multiple-choice-qa", "task_ids:topic-classification", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "source_datasets:extended", "language:en", "license:cc-by-4.0", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2110.00976", "arxiv:2109.00904", "arxiv:1805.01217", "arxiv:2104.08671", "region:us" ]	[ "question-answering", "text-classification" ]	2022-03-02T23:29:22Z	null	--- annotations_creators: - found language_creators: - found language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - extended task_categories: - question-answering - text-classification task_ids: - multi-class-classification - multi-label-classification - multiple-choice-qa - topic-classification pretty_name: LexGLUE config_names: - case_hold - ecthr_a - ecthr_b - eurlex - ledgar - scotus - unfair_tos dataset_info: - config_name: case_hold features: - name: context dtype: string - name: endings sequence: string - name: label dtype: class_label: names: '0': '0' '1': '1' '2': '2' '3': '3' '4': '4' splits: - name: train num_bytes: 74781706 num_examples: 45000 - name: test num_bytes: 5989952 num_examples: 3600 - name: validation num_bytes: 6474603 num_examples: 3900 download_size: 47303537 dataset_size: 87246261 - config_name: ecthr_a features: - name: text sequence: string - name: labels sequence: class_label: names: '0': '2' '1': '3' '2': '5' '3': '6' '4': '8' '5': '9' '6': '10' '7': '11' '8': '14' '9': P1-1 splits: - name: train num_bytes: 89637449 num_examples: 9000 - name: test num_bytes: 11884168 num_examples: 1000 - name: validation num_bytes: 10985168 num_examples: 1000 download_size: 53352586 dataset_size: 112506785 - config_name: ecthr_b features: - name: text sequence: string - name: labels sequence: class_label: names: '0': '2' '1': '3' '2': '5' '3': '6' '4': '8' '5': '9' '6': '10' '7': '11' '8': '14' '9': P1-1 splits: - name: train num_bytes: 89657649 num_examples: 9000 - name: test num_bytes: 11886928 num_examples: 1000 - name: validation num_bytes: 10987816 num_examples: 1000 download_size: 53352494 dataset_size: 112532393 - config_name: eurlex features: - name: text dtype: string - name: labels sequence: class_label: names: '0': '100163' '1': '100168' '2': '100169' '3': '100170' '4': '100171' '5': '100172' '6': '100173' '7': '100174' '8': '100175' '9': '100176' '10': '100177' '11': '100179' '12': '100180' '13': '100183' '14': '100184' '15': '100185' '16': '100186' '17': '100187' '18': '100189' '19': '100190' '20': '100191' '21': '100192' '22': '100193' '23': '100194' '24': '100195' '25': '100196' '26': '100197' '27': '100198' '28': '100199' '29': '100200' '30': '100201' '31': '100202' '32': '100204' '33': '100205' '34': '100206' '35': '100207' '36': '100212' '37': '100214' '38': '100215' '39': '100220' '40': '100221' '41': '100222' '42': '100223' '43': '100224' '44': '100226' '45': '100227' '46': '100229' '47': '100230' '48': '100231' '49': '100232' '50': '100233' '51': '100234' '52': '100235' '53': '100237' '54': '100238' '55': '100239' '56': '100240' '57': '100241' '58': '100242' '59': '100243' '60': '100244' '61': '100245' '62': '100246' '63': '100247' '64': '100248' '65': '100249' '66': '100250' '67': '100252' '68': '100253' '69': '100254' '70': '100255' '71': '100256' '72': '100257' '73': '100258' '74': '100259' '75': '100260' '76': '100261' '77': '100262' '78': '100263' '79': '100264' '80': '100265' '81': '100266' '82': '100268' '83': '100269' '84': '100270' '85': '100271' '86': '100272' '87': '100273' '88': '100274' '89': '100275' '90': '100276' '91': '100277' '92': '100278' '93': '100279' '94': '100280' '95': '100281' '96': '100282' '97': '100283' '98': '100284' '99': '100285' splits: - name: train num_bytes: 390770241 num_examples: 55000 - name: test num_bytes: 59739094 num_examples: 5000 - name: validation num_bytes: 41544476 num_examples: 5000 download_size: 208028049 dataset_size: 492053811 - config_name: ledgar features: - name: text dtype: string - name: label dtype: class_label: names: '0': Adjustments '1': Agreements '2': Amendments '3': Anti-Corruption Laws '4': Applicable Laws '5': Approvals '6': Arbitration '7': Assignments '8': Assigns '9': Authority '10': Authorizations '11': Base Salary '12': Benefits '13': Binding Effects '14': Books '15': Brokers '16': Capitalization '17': Change In Control '18': Closings '19': Compliance With Laws '20': Confidentiality '21': Consent To Jurisdiction '22': Consents '23': Construction '24': Cooperation '25': Costs '26': Counterparts '27': Death '28': Defined Terms '29': Definitions '30': Disability '31': Disclosures '32': Duties '33': Effective Dates '34': Effectiveness '35': Employment '36': Enforceability '37': Enforcements '38': Entire Agreements '39': Erisa '40': Existence '41': Expenses '42': Fees '43': Financial Statements '44': Forfeitures '45': Further Assurances '46': General '47': Governing Laws '48': Headings '49': Indemnifications '50': Indemnity '51': Insurances '52': Integration '53': Intellectual Property '54': Interests '55': Interpretations '56': Jurisdictions '57': Liens '58': Litigations '59': Miscellaneous '60': Modifications '61': No Conflicts '62': No Defaults '63': No Waivers '64': Non-Disparagement '65': Notices '66': Organizations '67': Participations '68': Payments '69': Positions '70': Powers '71': Publicity '72': Qualifications '73': Records '74': Releases '75': Remedies '76': Representations '77': Sales '78': Sanctions '79': Severability '80': Solvency '81': Specific Performance '82': Submission To Jurisdiction '83': Subsidiaries '84': Successors '85': Survival '86': Tax Withholdings '87': Taxes '88': Terminations '89': Terms '90': Titles '91': Transactions With Affiliates '92': Use Of Proceeds '93': Vacations '94': Venues '95': Vesting '96': Waiver Of Jury Trials '97': Waivers '98': Warranties '99': Withholdings splits: - name: train num_bytes: 43358291 num_examples: 60000 - name: test num_bytes: 6845581 num_examples: 10000 - name: validation num_bytes: 7143588 num_examples: 10000 download_size: 27650585 dataset_size: 57347460 - config_name: scotus features: - name: text dtype: string - name: label dtype: class_label: names: '0': '1' '1': '2' '2': '3' '3': '4' '4': '5' '5': '6' '6': '7' '7': '8' '8': '9' '9': '10' '10': '11' '11': '12' '12': '13' splits: - name: train num_bytes: 178959316 num_examples: 5000 - name: test num_bytes: 76213279 num_examples: 1400 - name: validation num_bytes: 75600243 num_examples: 1400 download_size: 173411399 dataset_size: 330772838 - config_name: unfair_tos features: - name: text dtype: string - name: labels sequence: class_label: names: '0': Limitation of liability '1': Unilateral termination '2': Unilateral change '3': Content removal '4': Contract by using '5': Choice of law '6': Jurisdiction '7': Arbitration splits: - name: train num_bytes: 1041782 num_examples: 5532 - name: test num_bytes: 303099 num_examples: 1607 - name: validation num_bytes: 452111 num_examples: 2275 download_size: 865604 dataset_size: 1796992 configs: - config_name: case_hold data_files: - split: train path: case_hold/train-* - split: test path: case_hold/test-* - split: validation path: case_hold/validation-* - config_name: ecthr_a data_files: - split: train path: ecthr_a/train-* - split: test path: ecthr_a/test-* - split: validation path: ecthr_a/validation-* - config_name: ecthr_b data_files: - split: train path: ecthr_b/train-* - split: test path: ecthr_b/test-* - split: validation path: ecthr_b/validation-* - config_name: eurlex data_files: - split: train path: eurlex/train-* - split: test path: eurlex/test-* - split: validation path: eurlex/validation-* - config_name: ledgar data_files: - split: train path: ledgar/train-* - split: test path: ledgar/test-* - split: validation path: ledgar/validation-* - config_name: scotus data_files: - split: train path: scotus/train-* - split: test path: scotus/test-* - split: validation path: scotus/validation-* - config_name: unfair_tos data_files: - split: train path: unfair_tos/train-* - split: test path: unfair_tos/test-* - split: validation path: unfair_tos/validation-* --- # Dataset Card for "LexGLUE" ## 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/coastalcph/lex-glue - Repository: https://github.com/coastalcph/lex-glue - Paper: https://arxiv.org/abs/2110.00976 - Leaderboard: https://github.com/coastalcph/lex-glue - Point of Contact: [Ilias Chalkidis](mailto:ilias.chalkidis@di.ku.dk) ### Dataset Summary Inspired by the recent widespread use of the GLUE multi-task benchmark NLP dataset (Wang et al., 2018), the subsequent more difficult SuperGLUE (Wang et al., 2019), other previous multi-task NLP benchmarks (Conneau and Kiela, 2018; McCann et al., 2018), and similar initiatives in other domains (Peng et al., 2019), we introduce the Legal General Language Understanding Evaluation (LexGLUE) benchmark, a benchmark dataset to evaluate the performance of NLP methods in legal tasks. LexGLUE is based on seven existing legal NLP datasets, selected using criteria largely from SuperGLUE. As in GLUE and SuperGLUE (Wang et al., 2019b,a), one of our goals is to push towards generic (or ‘foundation’) models that can cope with multiple NLP tasks, in our case legal NLP tasks possibly with limited task-specific fine-tuning. Another goal is to provide a convenient and informative entry point for NLP researchers and practitioners wishing to explore or develop methods for legalNLP. Having these goals in mind, the datasets we include in LexGLUE and the tasks they address have been simplified in several ways to make it easier for newcomers and generic models to address all tasks. LexGLUE benchmark is accompanied by experimental infrastructure that relies on Hugging Face Transformers library and resides at: https://github.com/coastalcph/lex-glue. ### Supported Tasks and Leaderboards The supported tasks are the following: <table> <tr><td>Dataset</td><td>Source</td><td>Sub-domain</td><td>Task Type</td><td>Classes</td><tr> <tr><td>ECtHR (Task A)</td><td> <a href="https://aclanthology.org/P19-1424/">Chalkidis et al. (2019)</a> </td><td>ECHR</td><td>Multi-label classification</td><td>10+1</td></tr> <tr><td>ECtHR (Task B)</td><td> <a href="https://aclanthology.org/2021.naacl-main.22/">Chalkidis et al. (2021a)</a> </td><td>ECHR</td><td>Multi-label classification </td><td>10+1</td></tr> <tr><td>SCOTUS</td><td> <a href="http://scdb.wustl.edu">Spaeth et al. (2020)</a></td><td>US Law</td><td>Multi-class classification</td><td>14</td></tr> <tr><td>EUR-LEX</td><td> <a href="https://arxiv.org/abs/2109.00904">Chalkidis et al. (2021b)</a></td><td>EU Law</td><td>Multi-label classification</td><td>100</td></tr> <tr><td>LEDGAR</td><td> <a href="https://aclanthology.org/2020.lrec-1.155/">Tuggener et al. (2020)</a></td><td>Contracts</td><td>Multi-class classification</td><td>100</td></tr> <tr><td>UNFAIR-ToS</td><td><a href="https://arxiv.org/abs/1805.01217"> Lippi et al. (2019)</a></td><td>Contracts</td><td>Multi-label classification</td><td>8+1</td></tr> <tr><td>CaseHOLD</td><td><a href="https://arxiv.org/abs/2104.08671">Zheng et al. (2021)</a></td><td>US Law</td><td>Multiple choice QA</td><td>n/a</td></tr> </table> #### ecthr_a The European Court of Human Rights (ECtHR) hears allegations that a state has breached human rights provisions of the European Convention of Human Rights (ECHR). For each case, the dataset provides a list of factual paragraphs (facts) from the case description. Each case is mapped to articles of the ECHR that were violated (if any). #### ecthr_b The European Court of Human Rights (ECtHR) hears allegations that a state has breached human rights provisions of the European Convention of Human Rights (ECHR). For each case, the dataset provides a list of factual paragraphs (facts) from the case description. Each case is mapped to articles of ECHR that were allegedly violated (considered by the court). #### scotus The US Supreme Court (SCOTUS) is the highest federal court in the United States of America and generally hears only the most controversial or otherwise complex cases which have not been sufficiently well solved by lower courts. This is a single-label multi-class classification task, where given a document (court opinion), the task is to predict the relevant issue areas. The 14 issue areas cluster 278 issues whose focus is on the subject matter of the controversy (dispute). #### eurlex European Union (EU) legislation is published in EUR-Lex portal. All EU laws are annotated by EU's Publications Office with multiple concepts from the EuroVoc thesaurus, a multilingual thesaurus maintained by the Publications Office. The current version of EuroVoc contains more than 7k concepts referring to various activities of the EU and its Member States (e.g., economics, health-care, trade). Given a document, the task is to predict its EuroVoc labels (concepts). #### ledgar LEDGAR dataset aims contract provision (paragraph) classification. The contract provisions come from contracts obtained from the US Securities and Exchange Commission (SEC) filings, which are publicly available from EDGAR. Each label represents the single main topic (theme) of the corresponding contract provision. #### unfair_tos The UNFAIR-ToS dataset contains 50 Terms of Service (ToS) from on-line platforms (e.g., YouTube, Ebay, Facebook, etc.). The dataset has been annotated on the sentence-level with 8 types of unfair contractual terms (sentences), meaning terms that potentially violate user rights according to the European consumer law. #### case_hold The CaseHOLD (Case Holdings on Legal Decisions) dataset includes multiple choice questions about holdings of US court cases from the Harvard Law Library case law corpus. Holdings are short summaries of legal rulings accompany referenced decisions relevant for the present case. The input consists of an excerpt (or prompt) from a court decision, containing a reference to a particular case, while the holding statement is masked out. The model must identify the correct (masked) holding statement from a selection of five choices. The current leaderboard includes several Transformer-based (Vaswaniet al., 2017) pre-trained language models, which achieve state-of-the-art performance in most NLP tasks (Bommasani et al., 2021) and NLU benchmarks (Wang et al., 2019a). Results reported by [Chalkidis et al. (2021)](https://arxiv.org/abs/2110.00976): Task-wise Test Results <table> <tr><td><b>Dataset</b></td><td><b>ECtHR A</b></td><td><b>ECtHR B</b></td><td><b>SCOTUS</b></td><td><b>EUR-LEX</b></td><td><b>LEDGAR</b></td><td><b>UNFAIR-ToS</b></td><td><b>CaseHOLD</b></td></tr> <tr><td><b>Model</b></td><td>μ-F1 / m-F1 </td><td>μ-F1 / m-F1 </td><td>μ-F1 / m-F1 </td><td>μ-F1 / m-F1 </td><td>μ-F1 / m-F1 </td><td>μ-F1 / m-F1</td><td>μ-F1 / m-F1 </td></tr> <tr><td>TFIDF+SVM</td><td> 64.7 / 51.7 </td><td>74.6 / 65.1 </td><td> <b>78.2</b> / <b>69.5</b> </td><td>71.3 / 51.4 </td><td>87.2 / 82.4 </td><td>95.4 / 78.8</td><td>n/a </td></tr> <tr><td colspan="8" style='text-align:center'><b>Medium-sized Models (L=12, H=768, A=12)</b></td></tr> <td>BERT</td> <td> 71.2 / 63.6 </td> <td> 79.7 / 73.4 </td> <td> 68.3 / 58.3 </td> <td> 71.4 / 57.2 </td> <td> 87.6 / 81.8 </td> <td> 95.6 / 81.3 </td> <td> 70.8 </td> </tr> <td>RoBERTa</td> <td> 69.2 / 59.0 </td> <td> 77.3 / 68.9 </td> <td> 71.6 / 62.0 </td> <td> 71.9 / <b>57.9</b> </td> <td> 87.9 / 82.3 </td> <td> 95.2 / 79.2 </td> <td> 71.4 </td> </tr> <td>DeBERTa</td> <td> 70.0 / 60.8 </td> <td> 78.8 / 71.0 </td> <td> 71.1 / 62.7 </td> <td> <b>72.1</b> / 57.4 </td> <td> 88.2 / 83.1 </td> <td> 95.5 / 80.3 </td> <td> 72.6 </td> </tr> <td>Longformer</td> <td> 69.9 / 64.7 </td> <td> 79.4 / 71.7 </td> <td> 72.9 / 64.0 </td> <td> 71.6 / 57.7 </td> <td> 88.2 / 83.0 </td> <td> 95.5 / 80.9 </td> <td> 71.9 </td> </tr> <td>BigBird</td> <td> 70.0 / 62.9 </td> <td> 78.8 / 70.9 </td> <td> 72.8 / 62.0 </td> <td> 71.5 / 56.8 </td> <td> 87.8 / 82.6 </td> <td> 95.7 / 81.3 </td> <td> 70.8 </td> </tr> <td>Legal-BERT</td> <td> 70.0 / 64.0 </td> <td> <b>80.4</b> / <b>74.7</b> </td> <td> 76.4 / 66.5 </td> <td> <b>72.1</b> / 57.4 </td> <td> 88.2 / 83.0 </td> <td> <b>96.0</b> / <b>83.0</b> </td> <td> 75.3 </td> </tr> <td>CaseLaw-BERT</td> <td> 69.8 / 62.9 </td> <td> 78.8 / 70.3 </td> <td> 76.6 / 65.9 </td> <td> 70.7 / 56.6 </td> <td> 88.3 / 83.0 </td> <td> <b>96.0</b> / 82.3 </td> <td> <b>75.4</b> </td> </tr> <tr><td colspan="8" style='text-align:center'><b>Large-sized Models (L=24, H=1024, A=18)</b></td></tr> <tr><td>RoBERTa</td> <td> <b>73.8</b> / <b>67.6</b> </td> <td> 79.8 / 71.6 </td> <td> 75.5 / 66.3 </td> <td> 67.9 / 50.3 </td> <td> <b>88.6</b> / <b>83.6</b> </td> <td> 95.8 / 81.6 </td> <td> 74.4 </td> </tr> </table> Averaged (Mean over Tasks) Test Results <table> <tr><td><b>Averaging</b></td><td><b>Arithmetic</b></td><td><b>Harmonic</b></td><td><b>Geometric</b></td></tr> <tr><td><b>Model</b></td><td>μ-F1 / m-F1 </td><td>μ-F1 / m-F1 </td><td>μ-F1 / m-F1 </td></tr> <tr><td colspan="4" style='text-align:center'><b>Medium-sized Models (L=12, H=768, A=12)</b></td></tr> <tr><td>BERT</td><td> 77.8 / 69.5 </td><td> 76.7 / 68.2 </td><td> 77.2 / 68.8 </td></tr> <tr><td>RoBERTa</td><td> 77.8 / 68.7 </td><td> 76.8 / 67.5 </td><td> 77.3 / 68.1 </td></tr> <tr><td>DeBERTa</td><td> 78.3 / 69.7 </td><td> 77.4 / 68.5 </td><td> 77.8 / 69.1 </td></tr> <tr><td>Longformer</td><td> 78.5 / 70.5 </td><td> 77.5 / 69.5 </td><td> 78.0 / 70.0 </td></tr> <tr><td>BigBird</td><td> 78.2 / 69.6 </td><td> 77.2 / 68.5 </td><td> 77.7 / 69.0 </td></tr> <tr><td>Legal-BERT</td><td> <b>79.8</b> / <b>72.0</b> </td><td> <b>78.9</b> / <b>70.8</b> </td><td> <b>79.3</b> / <b>71.4</b> </td></tr> <tr><td>CaseLaw-BERT</td><td> 79.4 / 70.9 </td><td> 78.5 / 69.7 </td><td> 78.9 / 70.3 </td></tr> <tr><td colspan="4" style='text-align:center'><b>Large-sized Models (L=24, H=1024, A=18)</b></td></tr> <tr><td>RoBERTa</td><td> 79.4 / 70.8 </td><td> 78.4 / 69.1 </td><td> 78.9 / 70.0 </td></tr> </table> ### Languages We only consider English datasets, to make experimentation easier for researchers across the globe. ## Dataset Structure ### Data Instances #### ecthr_a An example of 'train' looks as follows. ```json { "text": ["8. The applicant was arrested in the early morning of 21 October 1990 ...", ...], "labels": [6] } ``` #### ecthr_b An example of 'train' looks as follows. ```json { "text": ["8. The applicant was arrested in the early morning of 21 October 1990 ...", ...], "label": [5, 6] } ``` #### scotus An example of 'train' looks as follows. ```json { "text": "Per Curiam\nSUPREME COURT OF THE UNITED STATES\nRANDY WHITE, WARDEN v. ROGER L. WHEELER\n Decided December 14, 2015\nPER CURIAM.\nA death sentence imposed by a Kentucky trial court and\naffirmed by the ...", "label": 8 } ``` #### eurlex An example of 'train' looks as follows. ```json { "text": "COMMISSION REGULATION (EC) No 1629/96 of 13 August 1996 on an invitation to tender for the refund on export of wholly milled round grain rice to certain third countries ...", "labels": [4, 20, 21, 35, 68] } ``` #### ledgar An example of 'train' looks as follows. ```json { "text": "All Taxes shall be the financial responsibility of the party obligated to pay such Taxes as determined by applicable law and neither party is or shall be liable at any time for any of the other party ...", "label": 32 } ``` #### unfair_tos An example of 'train' looks as follows. ```json { "text": "tinder may terminate your account at any time without notice if it believes that you have violated this agreement.", "label": 2 } ``` #### casehold An example of 'test' looks as follows. ```json { "context": "In Granato v. City and County of Denver, No. CIV 11-0304 MSK/BNB, 2011 WL 3820730 (D.Colo. Aug. 20, 2011), the Honorable Marcia S. Krieger, now-Chief United States District Judge for the District of Colorado, ruled similarly: At a minimum, a party asserting a Mo-nell claim must plead sufficient facts to identify ... to act pursuant to City or State policy, custom, decision, ordinance, re d 503, 506-07 (3d Cir.l985)(<HOLDING>).", "endings": ["holding that courts are to accept allegations in the complaint as being true including monell policies and writing that a federal court reviewing the sufficiency of a complaint has a limited task", "holding that for purposes of a class certification motion the court must accept as true all factual allegations in the complaint and may draw reasonable inferences therefrom", "recognizing that the allegations of the complaint must be accepted as true on a threshold motion to dismiss", "holding that a court need not accept as true conclusory allegations which are contradicted by documents referred to in the complaint", "holding that where the defendant was in default the district court correctly accepted the fact allegations of the complaint as true" ], "label": 0 } ``` ### Data Fields #### ecthr_a - `text`: a list of `string` features (list of factual paragraphs (facts) from the case description). - `labels`: a list of classification labels (a list of violated ECHR articles, if any) . <details> <summary>List of ECHR articles</summary> "Article 2", "Article 3", "Article 5", "Article 6", "Article 8", "Article 9", "Article 10", "Article 11", "Article 14", "Article 1 of Protocol 1" </details> #### ecthr_b - `text`: a list of `string` features (list of factual paragraphs (facts) from the case description) - `labels`: a list of classification labels (a list of articles considered). <details> <summary>List of ECHR articles</summary> "Article 2", "Article 3", "Article 5", "Article 6", "Article 8", "Article 9", "Article 10", "Article 11", "Article 14", "Article 1 of Protocol 1" </details> #### scotus - `text`: a `string` feature (the court opinion). - `label`: a classification label (the relevant issue area). <details> <summary>List of issue areas</summary> (1, Criminal Procedure), (2, Civil Rights), (3, First Amendment), (4, Due Process), (5, Privacy), (6, Attorneys), (7, Unions), (8, Economic Activity), (9, Judicial Power), (10, Federalism), (11, Interstate Relations), (12, Federal Taxation), (13, Miscellaneous), (14, Private Action) </details> #### eurlex - `text`: a `string` feature (an EU law). - `labels`: a list of classification labels (a list of relevant EUROVOC concepts). <details> <summary>List of EUROVOC concepts</summary> The list is very long including 100 EUROVOC concepts. You can find the EUROVOC concepts descriptors <a href="https://raw.githubusercontent.com/nlpaueb/multi-eurlex/master/data/eurovoc_descriptors.json">here</a>. </details> #### ledgar - `text`: a `string` feature (a contract provision/paragraph). - `label`: a classification label (the type of contract provision). <details> <summary>List of contract provision types</summary> "Adjustments", "Agreements", "Amendments", "Anti-Corruption Laws", "Applicable Laws", "Approvals", "Arbitration", "Assignments", "Assigns", "Authority", "Authorizations", "Base Salary", "Benefits", "Binding Effects", "Books", "Brokers", "Capitalization", "Change In Control", "Closings", "Compliance With Laws", "Confidentiality", "Consent To Jurisdiction", "Consents", "Construction", "Cooperation", "Costs", "Counterparts", "Death", "Defined Terms", "Definitions", "Disability", "Disclosures", "Duties", "Effective Dates", "Effectiveness", "Employment", "Enforceability", "Enforcements", "Entire Agreements", "Erisa", "Existence", "Expenses", "Fees", "Financial Statements", "Forfeitures", "Further Assurances", "General", "Governing Laws", "Headings", "Indemnifications", "Indemnity", "Insurances", "Integration", "Intellectual Property", "Interests", "Interpretations", "Jurisdictions", "Liens", "Litigations", "Miscellaneous", "Modifications", "No Conflicts", "No Defaults", "No Waivers", "Non-Disparagement", "Notices", "Organizations", "Participations", "Payments", "Positions", "Powers", "Publicity", "Qualifications", "Records", "Releases", "Remedies", "Representations", "Sales", "Sanctions", "Severability", "Solvency", "Specific Performance", "Submission To Jurisdiction", "Subsidiaries", "Successors", "Survival", "Tax Withholdings", "Taxes", "Terminations", "Terms", "Titles", "Transactions With Affiliates", "Use Of Proceeds", "Vacations", "Venues", "Vesting", "Waiver Of Jury Trials", "Waivers", "Warranties", "Withholdings", </details> #### unfair_tos - `text`: a `string` feature (a ToS sentence) - `labels`: a list of classification labels (a list of unfair types, if any). <details> <summary>List of unfair types</summary> "Limitation of liability", "Unilateral termination", "Unilateral change", "Content removal", "Contract by using", "Choice of law", "Jurisdiction", "Arbitration" </details> #### casehold - `context`: a `string` feature (a context sentence incl. a masked holding statement). - `holdings`: a list of `string` features (a list of candidate holding statements). - `label`: a classification label (the id of the original/correct holding). ### Data Splits <table> <tr><td>Dataset </td><td>Training</td><td>Development</td><td>Test</td><td>Total</td></tr> <tr><td>ECtHR (Task A)</td><td>9,000</td><td>1,000</td><td>1,000</td><td>11,000</td></tr> <tr><td>ECtHR (Task B)</td><td>9,000</td><td>1,000</td><td>1,000</td><td>11,000</td></tr> <tr><td>SCOTUS</td><td>5,000</td><td>1,400</td><td>1,400</td><td>7,800</td></tr> <tr><td>EUR-LEX</td><td>55,000</td><td>5,000</td><td>5,000</td><td>65,000</td></tr> <tr><td>LEDGAR</td><td>60,000</td><td>10,000</td><td>10,000</td><td>80,000</td></tr> <tr><td>UNFAIR-ToS</td><td>5,532</td><td>2,275</td><td>1,607</td><td>9,414</td></tr> <tr><td>CaseHOLD</td><td>45,000</td><td>3,900</td><td>3,900</td><td>52,800</td></tr> </table> ## 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 <table> <tr><td>Dataset</td><td>Source</td><td>Sub-domain</td><td>Task Type</td><tr> <tr><td>ECtHR (Task A)</td><td> <a href="https://aclanthology.org/P19-1424/">Chalkidis et al. (2019)</a> </td><td>ECHR</td><td>Multi-label classification</td></tr> <tr><td>ECtHR (Task B)</td><td> <a href="https://aclanthology.org/2021.naacl-main.22/">Chalkidis et al. (2021a)</a> </td><td>ECHR</td><td>Multi-label classification </td></tr> <tr><td>SCOTUS</td><td> <a href="http://scdb.wustl.edu">Spaeth et al. (2020)</a></td><td>US Law</td><td>Multi-class classification</td></tr> <tr><td>EUR-LEX</td><td> <a href="https://arxiv.org/abs/2109.00904">Chalkidis et al. (2021b)</a></td><td>EU Law</td><td>Multi-label classification</td></tr> <tr><td>LEDGAR</td><td> <a href="https://aclanthology.org/2020.lrec-1.155/">Tuggener et al. (2020)</a></td><td>Contracts</td><td>Multi-class classification</td></tr> <tr><td>UNFAIR-ToS</td><td><a href="https://arxiv.org/abs/1805.01217"> Lippi et al. (2019)</a></td><td>Contracts</td><td>Multi-label classification</td></tr> <tr><td>CaseHOLD</td><td><a href="https://arxiv.org/abs/2104.08671">Zheng et al. (2021)</a></td><td>US Law</td><td>Multiple choice QA</td></tr> </table> #### 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 [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Dataset Curators Ilias Chalkidis, Abhik Jana, Dirk Hartung, Michael Bommarito, Ion Androutsopoulos, Daniel Martin Katz, and Nikolaos Aletras. LexGLUE: A Benchmark Dataset for Legal Language Understanding in English. 2022. In the Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics. Dublin, Ireland. ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information [Ilias Chalkidis, Abhik Jana, Dirk Hartung, Michael Bommarito, Ion Androutsopoulos, Daniel Martin Katz, and Nikolaos Aletras. LexGLUE: A Benchmark Dataset for Legal Language Understanding in English. 2022. In the Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics. Dublin, Ireland.](https://arxiv.org/abs/2110.00976) ``` @inproceedings{chalkidis-etal-2021-lexglue, title={LexGLUE: A Benchmark Dataset for Legal Language Understanding in English}, author={Chalkidis, Ilias and Jana, Abhik and Hartung, Dirk and Bommarito, Michael and Androutsopoulos, Ion and Katz, Daniel Martin and Aletras, Nikolaos}, year={2022}, booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics}, address={Dubln, Ireland}, } ``` ### Contributions Thanks to [@iliaschalkidis](https://github.com/iliaschalkidis) for adding this dataset.
lighteval/siqa	lighteval	2023-10-07T08:03:32Z	1,598	7	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2023-10-07T08:03:29Z	1	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: context dtype: string - name: question dtype: string - name: answerA dtype: string - name: answerB dtype: string - name: answerC dtype: string - name: label dtype: string splits: - name: train num_bytes: 6327209 num_examples: 33410 - name: validation num_bytes: 372815 num_examples: 1954 download_size: 3678635 dataset_size: 6700024 --- # Dataset Card for "siqa" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
GBaker/MedQA-USMLE-4-options	GBaker	2023-01-24T19:18:09Z	2,121	57	[ "language:en", "license:cc-by-4.0", "size_categories:10K<n<100K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2023-01-24T19:08:56Z	2	--- license: cc-by-4.0 language: - en --- Original dataset introduced by Jin et al. in [What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams](https://paperswithcode.com/paper/what-disease-does-this-patient-have-a-large) <h4>Citation information:</h4> @article{jin2020disease, title={What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams}, author={Jin, Di and Pan, Eileen and Oufattole, Nassim and Weng, Wei-Hung and Fang, Hanyi and Szolovits, Peter}, journal={arXiv preprint arXiv:2009.13081}, year={2020} }

self-label-zanette-lab/dapo_mixed_dataset_with_fraction_labeled_0.01

self-label-zanette-lab

2025-05-02T15:44:14Z

0

[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-05-02T15:44:13Z

null

--- dataset_info: features: - name: prompt dtype: string - name: answer dtype: string - name: source dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 5694890 num_examples: 17398 download_size: 2676607 dataset_size: 5694890 configs: - config_name: default data_files: - split: train path: data/train-* ---

Mxode/Chinese-QA-Agriculture_Forestry_Animal_Husbandry_Fishery

Mxode

2025-05-02T10:50:18Z

106

0

[ "task_categories:text-generation", "task_categories:question-answering", "language:zh", "license:cc-by-sa-4.0", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[ "text-generation", "question-answering" ]

2025-04-22T16:15:08Z

null

--- license: cc-by-sa-4.0 task_categories: - text-generation - question-answering language: - zh size_categories: - 100K<n<1M --- <h1 align="center"> 中文农林牧渔问答数据集 </h1> <a href="https://github.com/Mxoder/Maxs-Awesome-Datasets" target="_blank">💻 Github Repo</a> ## 简介中文农林牧渔问答数据集，涵盖农业、林业、畜牧业、渔业，数据量 900K+，均为简单的问答形式。 ## 数据格式每条数据的格式如下： ```json { "id": << 12位nanoid >>, "prompt": << 问题 >>, "response": << 答案 >> } ```

Mxode/NanoExperiment-Data-Mix-10M

Mxode

2025-05-02T10:46:03Z

18

0

[ "task_categories:translation", "language:en", "language:zh", "license:cc-by-sa-4.0", "size_categories:10M<n<100M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "chemistry", "biology", "finance", "legal", "music", "art", "climate", "medical", "synthetic" ]

[ "translation" ]

2024-09-13T16:58:49Z

null

--- license: cc-by-sa-4.0 task_categories: - translation language: - en - zh tags: - chemistry - biology - finance - legal - music - art - climate - medical - synthetic pretty_name: BiST size_categories: - 1M<n<10M configs: - config_name: raw data_files: - split: train path: raw/* - config_name: tokenized data_files: - split: train path: tokenized/* --- # **Mxode/NanoExperiment-Data-Mix-10M** Dataset of [NanoExperiment](https://github.com/Mxoder/NanoExperiment). Tokenized by [Bilingual-Tokenizer-2K](https://huggingface.co/Mxode/Bilingual-Tokenizer/tree/main/BilingualTokenizer-2K).

Mxode/Magpie-Pro-10K-GPT4o-mini

Mxode

2025-05-02T10:43:14Z

36

0

[ "task_categories:text-generation", "task_categories:question-answering", "language:en", "license:cc-by-sa-4.0", "size_categories:10K<n<100K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "magpie", "gpt-4o" ]

[ "text-generation", "question-answering" ]

2024-09-06T07:20:09Z

null

--- license: cc-by-sa-4.0 task_categories: - text-generation - question-answering language: - en tags: - magpie - gpt-4o size_categories: - 10K<n<100K ---

NONHUMAN-RESEARCH/tic-tac-toe-pos-eight

NONHUMAN-RESEARCH

2025-05-02T04:27:57Z

0

[ "task_categories:robotics", "license:apache-2.0", "size_categories:10K<n<100K", "format:parquet", "modality:tabular", "modality:timeseries", "modality:video", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "LeRobot", "so100", "test" ]

[ "robotics" ]

2025-05-02T04:26:56Z

null

--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - so100 - test configs: - config_name: default data_files: data/*/*.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - **Homepage:** [More Information Needed] - **Paper:** [More Information Needed] - **License:** apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.1", "robot_type": "so100", "total_episodes": 25, "total_frames": 11175, "total_tasks": 1, "total_videos": 50, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:25" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "action": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.state": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.images.laptop": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "h264", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "observation.images.phone": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "h264", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "timestamp": { "dtype": "float32", "shape": [ 1 ], "names": null }, "frame_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "episode_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "task_index": { "dtype": "int64", "shape": [ 1 ], "names": null } } } ``` ## Citation **BibTeX:** ```bibtex [More Information Needed] ```

hai2131/OCR-dataset

hai2131

2025-05-02T01:09:46Z

0

[ "size_categories:10K<n<100K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-05-02T01:09:42Z

null

--- dataset_info: features: - name: image dtype: image - name: label dtype: string splits: - name: train num_bytes: 13465855.2 num_examples: 8000 - name: val num_bytes: 3366481.8 num_examples: 2000 download_size: 13265496 dataset_size: 16832337.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: val path: data/val-* ---

mlfoundations-dev/d1_code_gpt_0.3k

mlfoundations-dev

2025-05-01T17:05:44Z

0

[ "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-05-01T17:05:31Z

null

--- dataset_info: features: - name: id dtype: string - name: instruction_seed dtype: string - name: output dtype: string - name: source dtype: string - name: license dtype: string - name: dataset dtype: string - name: split dtype: string - name: difficulty dtype: int64 - name: solution dtype: string - name: index dtype: string - name: _source dtype: string - name: difficulty_reasoning dtype: string - name: __original_row_idx dtype: int64 - name: ms_id dtype: int64 - name: reasoning sequence: string - name: deepseek_solution sequence: string - name: final_reasoning_trace sequence: string - name: correct sequence: bool - name: classifier_reasoning dtype: string - name: _majority_responses sequence: string - name: verified_final_reasoning_trace dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 664551210.79 num_examples: 316 download_size: 276573145 dataset_size: 664551210.79 configs: - config_name: default data_files: - split: train path: data/train-* ---

kwangchaeko/koch_test_3

kwangchaeko

2025-05-01T03:55:32Z

0

[ "task_categories:robotics", "license:apache-2.0", "size_categories:10K<n<100K", "format:parquet", "modality:tabular", "modality:timeseries", "modality:video", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "LeRobot", "koch", "tutorial" ]

[ "robotics" ]

2025-05-01T03:54:42Z

null

--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - koch - tutorial configs: - config_name: default data_files: data/*/*.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - **Homepage:** [More Information Needed] - **Paper:** [More Information Needed] - **License:** apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.1", "robot_type": "koch", "total_episodes": 10, "total_frames": 17543, "total_tasks": 1, "total_videos": 10, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:10" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "action": { "dtype": "float32", "shape": [ 4 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex" ] }, "observation.state": { "dtype": "float32", "shape": [ 4 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex" ] }, "observation.images.laptop": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "timestamp": { "dtype": "float32", "shape": [ 1 ], "names": null }, "frame_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "episode_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "task_index": { "dtype": "int64", "shape": [ 1 ], "names": null } } } ``` ## Citation **BibTeX:** ```bibtex [More Information Needed] ```

DamianBoborzi/Objaverse_processed

DamianBoborzi

2025-04-16T14:14:51Z

19,856

0

[ "license:odc-by", "arxiv:2503.14002", "region:us" ]

[]

2025-02-28T15:01:58Z

null

--- license: odc-by pretty_name: Objaverse Processsing Data --- Contains information of Objaverse XL objects from the alignment and TRELLIS500K (over 1 Millionen processed objects) dataset. We downloaded and rendered 4 views of each object. We generat siglip embeddings and Yolov10 objects detections for fast filtering. We added TRELLIS and CAP3D Captions where available. If there were no captions we generated new captions with the large version of Florence 2. This is the base dataset we used to generate [MeshFleet](https://huggingface.co/datasets/DamianBoborzi/meshfleetXL) which is described in [MeshFleet: Filtered and Annotated 3D Vehicle Dataset for Domain Specific Generative Modeling](arxiv.org/abs/2503.14002). - The rendered views are in the data directory split into webdataset chunks. The file 'objaverse_xl_render_files.csv' shows which objects is in which chunk using the sha256 of the objects. You can simply extract all files from each chunk using something like `tar -xf chunk_0.tar -C extract_test` or you can use the WebDataset Library to access the content of each chunk. - objaverse_oxl_processing_df.csv includes the following information: sha256: The sha256 of the object from Objaverse XL yolo_detections: Detected objects cap3D Data: cap3D_caption: The Captions generation by Cap3D (https://cap3d-um.github.io/), cap3D_avg_clip_similarity: is the average similariy of the text siglip embeddings to the siglip embedding of filtered car objects TRELLIS500K Data: Captions (trellis_caption) and aesthetic scores (trellis_aesthetic_score) from TRELLIS500K (https://huggingface.co/datasets/JeffreyXiang/TRELLIS-500K). We also used the referenced aesthetic score prediciton model (aesthetic_score). The results are however very different. Florence_caption: Captions of objects which did not have a caption either from CAP3D or TRELLIS500 using Florence 2 (https://huggingface.co/microsoft/Florence-2-large) Car Quality Assessment: We processed all objects by generating SigLIP and DINOv2 Embeddings and estiomating if the object is a high quality vehicle. Results contain the estimated label(predicted_car_quality_label), the score of the model before taking the max of the output (output_score), and uncertainty estimates using monte carlo dropout: car_quality_uncertainty_entropy,car_quality_uncertainty_mutual_info,car_quality_uncertainty_variation_ratio - objaverse_combined_captions.csv combines the captions from three different sources (CAP3D, TRELLIS500K and newly generated ones with Florence 2) and stores them together with the corresponding sha256 of the object. - objaverse_vehicle_detections_textcategories.csv Contains the detection if a car is described based on the captions from CAP3D, TRELLIS500K and the Florence-2 generated ones. The result is given with text_category_car as 'car' and 'not a car'. If the text describes a car the text_category_vehicle gives an estimate what type of car. - objaverse_car_classification_results_df.csv Contains the Car Quality Assessment alone without the additional information from objaverse_oxl_processing_df.csv.

AnonRes/OpenMind

AnonRes

2025-04-03T11:51:07Z

2,050

16

[ "task_categories:image-feature-extraction", "license:cc-by-4.0", "modality:3d", "modality:image", "region:us", "3d", "image" ]

[ "image-feature-extraction" ]

2025-03-11T14:19:10Z

2

--- license: cc-by-4.0 task_categories: - "image-feature-extraction" pretty_name: "The OpenMind Dataset" tags: - 3d - image --- # The OpenMind Dataset: A large-scale Head-And-Neck 3D MRI Dataset for self-supervised learning ![OpenMind Dataset](./assets/OpenMindDataset.png) ## Description The OpenMind Dataset is a large-scale 3D MRI dataset of the head and neck region featuring 114k MRI Images. Its purpose is to provide access of large amounts of 3D medical imaging data to accelerate the development of self-supervised learning methods for 3D medical imaging. This data was pooled from exactly 800 datasets from the OpenNeuro platform and provides 23 different MRI modalities/techniques from over 30 different scanners, representing a highly variable pre-training dataset. ## Additional Features Aside from the 3D MRI Images, we provide stratified metadata for each of the 114k images when made available by the original dataset in a unified format. Moreover, we provide a) `deface_masks`, which delineate anonymized/defaced regions, allowing to take them into account when developing reconstruction based pre-training methods and b) `anatomy_masks`, which delineate areas which holds anatomy, e.g. for cases where images were brain extracted. Similarly to the `deface_masks` this allows to either ignore regions outside of this during reconstruction and allows sampling regions with anatomy, avoiding empty regions for contrastive learning approaches. ## Dataset structure The dataset is structured akin to the original OpenNeuro datasets, following a modified BIDS format, exemplified below. We recommend using the `openneuro_metadata.csv` which holds the relative paths from the root directory to the images and their associated masks as well as metadata. ``` -- Readme.md // this readme -- openneuro_metadata.csv // metadata file containing the relative paths to the images and their metadata -- openmind_dataset |-- openneuro_metadata.csv # Contains the relative paths to the images and their metadata |-- OpenMind |-- ds_000001 |-- sub-01 |-- anat |-- sub-01_ses-01_T1w.nii.gz # 3D Image in Nifti format |-- sub-01_T1w__Data # Associated Folder holding Masks to Image |-- deface_mask.nii.gz # deface mask \-- fb_mask.nii.gz # anatomy mask |-- sub-01_inplaneT2.nii.gz \-- sub-01_inplaneT2__Data # Associated Folder holding Masks to Image |-- deface_mask.nii.gz \-- fb_mask.nii.gz ... ... ... |-- ds_xxxxxx ... ``` Available meta-data tags (Name [Percent of images with this metadata]) within the metadata.csv file are: - MR Modalitiy/Technique [100%] - Scanner Manufacturer [100%] - Scanner Model [100%] - Scanner Field Strength [100%] - Age [70%] - Sex [77.4%] - Weight [1.5%] - BMI [15.7%] - Race [11.7%] - Handedness (right/left/ambidexterous) [35.4%] - Health Status (healthy/ill) [26.4%]

open-r1/OpenR1-Math-220k

open-r1

2025-02-18T11:45:27Z

32,044

564

[ "language:en", "license:apache-2.0", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-02-10T13:41:48Z

null

--- license: apache-2.0 language: - en configs: - config_name: all data_files: - split: train path: all/train-* - config_name: default data_files: - split: train path: data/train-* - config_name: extended data_files: - split: train path: extended/train-* dataset_info: - config_name: all features: - name: problem dtype: string - name: solution dtype: string - name: answer dtype: string - name: problem_type dtype: string - name: question_type dtype: string - name: source dtype: string - name: uuid dtype: string - name: is_reasoning_complete sequence: bool - name: generations sequence: string - name: correctness_math_verify sequence: bool - name: correctness_llama sequence: bool - name: finish_reasons sequence: string - name: correctness_count dtype: int64 - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 9734110026.0 num_examples: 225129 download_size: 4221672067 dataset_size: 9734110026.0 - config_name: default features: - name: problem dtype: string - name: solution dtype: string - name: answer dtype: string - name: problem_type dtype: string - name: question_type dtype: string - name: source dtype: string - name: uuid dtype: string - name: is_reasoning_complete sequence: bool - name: generations sequence: string - name: correctness_math_verify sequence: bool - name: correctness_llama sequence: bool - name: finish_reasons sequence: string - name: correctness_count dtype: int64 - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 4964543659 num_examples: 93733 download_size: 2149897914 dataset_size: 4964543659 - config_name: extended features: - name: problem dtype: string - name: solution dtype: string - name: answer dtype: string - name: problem_type dtype: string - name: question_type dtype: string - name: source dtype: string - name: uuid dtype: string - name: is_reasoning_complete sequence: bool - name: generations sequence: string - name: correctness_math_verify sequence: bool - name: correctness_llama sequence: bool - name: finish_reasons sequence: string - name: correctness_count dtype: int64 - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 4769566550 num_examples: 131396 download_size: 2063936457 dataset_size: 4769566550 --- # OpenR1-Math-220k ## Dataset description OpenR1-Math-220k is a large-scale dataset for mathematical reasoning. It consists of 220k math problems with two to four reasoning traces generated by [DeepSeek R1](https://huggingface.co/deepseek-ai/DeepSeek-R1) for problems from NuminaMath 1.5. The traces were verified using [Math Verify](https://github.com/huggingface/Math-Verify) for most samples and [Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct) as a judge for 12% of the samples, and each problem contains at least one reasoning trace with a correct answer. The dataset consists of two splits: - `default` with 94k problems and that achieves the best performance after SFT. - `extended` with 131k samples where we add data sources like `cn_k12`. This provides more reasoning traces, but we found that the performance after SFT to be lower than the `default` subset, likely because the questions from `cn_k12` are less difficult than other sources. You can load the dataset as follows: ```python from datasets import load_dataset ds = load_dataset("open-r1/OpenR1-Math-220k", "default") ``` ## Dataset curation To build OpenR1-Math-220k, we prompt [DeepSeek R1](https://huggingface.co/deepseek-ai/DeepSeek-R1) model to generate solutions for 400k problems from [NuminaMath 1.5](https://huggingface.co/datasets/AI-MO/NuminaMath-1.5) using [SGLang](https://github.com/sgl-project/sglang), the generation code is available [here](https://github.com/huggingface/open-r1/tree/main/slurm). We follow the model card’s recommended generation parameters and prepend the following instruction to the user prompt: `"Please reason step by step, and put your final answer within \boxed{}."` We set a 16k token limit per generation, as our analysis showed that only 75% of problems could be solved in under 8k tokens, and most of the remaining problems required the full 16k tokens. We were able to generate 25 solutions per hour per H100, enabling us to generate 300k problem solutions per day on 512 H100s. We generate two solutions per problem—and in some cases, four—to provide flexibility in filtering and training. This approach allows for rejection sampling, similar to DeepSeek R1’s methodology, and also makes the dataset suitable for preference optimisation methods like DPO. ## License The dataset is licensed under Apache 2.0

NebulaByte/E-Commerce_Customer_Support_Conversations

NebulaByte

2024-04-01T05:57:55Z

272

36

[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2023-07-24T05:56:30Z

2

--- dataset_info: features: - name: issue_area dtype: string - name: issue_category dtype: string - name: issue_sub_category dtype: string - name: issue_category_sub_category dtype: string - name: customer_sentiment dtype: string - name: product_category dtype: string - name: product_sub_category dtype: string - name: issue_complexity dtype: string - name: agent_experience_level dtype: string - name: agent_experience_level_desc dtype: string - name: conversation dtype: string splits: - name: train num_bytes: 2537279 num_examples: 1000 download_size: 827367 dataset_size: 2537279 --- # Dataset Card for "E-Commerce_Customer_Support_Conversations" The dataset is synthetically generated with OpenAI ChatGPT model (gpt-3.5-turbo). [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

coastalcph/lex_glue

coastalcph

2024-01-04T14:25:27Z

27,251

57

[ "task_categories:question-answering", "task_categories:text-classification", "task_ids:multi-class-classification", "task_ids:multi-label-classification", "task_ids:multiple-choice-qa", "task_ids:topic-classification", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "source_datasets:extended", "language:en", "license:cc-by-4.0", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2110.00976", "arxiv:2109.00904", "arxiv:1805.01217", "arxiv:2104.08671", "region:us" ]

[ "question-answering", "text-classification" ]

2022-03-02T23:29:22Z

null

--- annotations_creators: - found language_creators: - found language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - extended task_categories: - question-answering - text-classification task_ids: - multi-class-classification - multi-label-classification - multiple-choice-qa - topic-classification pretty_name: LexGLUE config_names: - case_hold - ecthr_a - ecthr_b - eurlex - ledgar - scotus - unfair_tos dataset_info: - config_name: case_hold features: - name: context dtype: string - name: endings sequence: string - name: label dtype: class_label: names: '0': '0' '1': '1' '2': '2' '3': '3' '4': '4' splits: - name: train num_bytes: 74781706 num_examples: 45000 - name: test num_bytes: 5989952 num_examples: 3600 - name: validation num_bytes: 6474603 num_examples: 3900 download_size: 47303537 dataset_size: 87246261 - config_name: ecthr_a features: - name: text sequence: string - name: labels sequence: class_label: names: '0': '2' '1': '3' '2': '5' '3': '6' '4': '8' '5': '9' '6': '10' '7': '11' '8': '14' '9': P1-1 splits: - name: train num_bytes: 89637449 num_examples: 9000 - name: test num_bytes: 11884168 num_examples: 1000 - name: validation num_bytes: 10985168 num_examples: 1000 download_size: 53352586 dataset_size: 112506785 - config_name: ecthr_b features: - name: text sequence: string - name: labels sequence: class_label: names: '0': '2' '1': '3' '2': '5' '3': '6' '4': '8' '5': '9' '6': '10' '7': '11' '8': '14' '9': P1-1 splits: - name: train num_bytes: 89657649 num_examples: 9000 - name: test num_bytes: 11886928 num_examples: 1000 - name: validation num_bytes: 10987816 num_examples: 1000 download_size: 53352494 dataset_size: 112532393 - config_name: eurlex features: - name: text dtype: string - name: labels sequence: class_label: names: '0': '100163' '1': '100168' '2': '100169' '3': '100170' '4': '100171' '5': '100172' '6': '100173' '7': '100174' '8': '100175' '9': '100176' '10': '100177' '11': '100179' '12': '100180' '13': '100183' '14': '100184' '15': '100185' '16': '100186' '17': '100187' '18': '100189' '19': '100190' '20': '100191' '21': '100192' '22': '100193' '23': '100194' '24': '100195' '25': '100196' '26': '100197' '27': '100198' '28': '100199' '29': '100200' '30': '100201' '31': '100202' '32': '100204' '33': '100205' '34': '100206' '35': '100207' '36': '100212' '37': '100214' '38': '100215' '39': '100220' '40': '100221' '41': '100222' '42': '100223' '43': '100224' '44': '100226' '45': '100227' '46': '100229' '47': '100230' '48': '100231' '49': '100232' '50': '100233' '51': '100234' '52': '100235' '53': '100237' '54': '100238' '55': '100239' '56': '100240' '57': '100241' '58': '100242' '59': '100243' '60': '100244' '61': '100245' '62': '100246' '63': '100247' '64': '100248' '65': '100249' '66': '100250' '67': '100252' '68': '100253' '69': '100254' '70': '100255' '71': '100256' '72': '100257' '73': '100258' '74': '100259' '75': '100260' '76': '100261' '77': '100262' '78': '100263' '79': '100264' '80': '100265' '81': '100266' '82': '100268' '83': '100269' '84': '100270' '85': '100271' '86': '100272' '87': '100273' '88': '100274' '89': '100275' '90': '100276' '91': '100277' '92': '100278' '93': '100279' '94': '100280' '95': '100281' '96': '100282' '97': '100283' '98': '100284' '99': '100285' splits: - name: train num_bytes: 390770241 num_examples: 55000 - name: test num_bytes: 59739094 num_examples: 5000 - name: validation num_bytes: 41544476 num_examples: 5000 download_size: 208028049 dataset_size: 492053811 - config_name: ledgar features: - name: text dtype: string - name: label dtype: class_label: names: '0': Adjustments '1': Agreements '2': Amendments '3': Anti-Corruption Laws '4': Applicable Laws '5': Approvals '6': Arbitration '7': Assignments '8': Assigns '9': Authority '10': Authorizations '11': Base Salary '12': Benefits '13': Binding Effects '14': Books '15': Brokers '16': Capitalization '17': Change In Control '18': Closings '19': Compliance With Laws '20': Confidentiality '21': Consent To Jurisdiction '22': Consents '23': Construction '24': Cooperation '25': Costs '26': Counterparts '27': Death '28': Defined Terms '29': Definitions '30': Disability '31': Disclosures '32': Duties '33': Effective Dates '34': Effectiveness '35': Employment '36': Enforceability '37': Enforcements '38': Entire Agreements '39': Erisa '40': Existence '41': Expenses '42': Fees '43': Financial Statements '44': Forfeitures '45': Further Assurances '46': General '47': Governing Laws '48': Headings '49': Indemnifications '50': Indemnity '51': Insurances '52': Integration '53': Intellectual Property '54': Interests '55': Interpretations '56': Jurisdictions '57': Liens '58': Litigations '59': Miscellaneous '60': Modifications '61': No Conflicts '62': No Defaults '63': No Waivers '64': Non-Disparagement '65': Notices '66': Organizations '67': Participations '68': Payments '69': Positions '70': Powers '71': Publicity '72': Qualifications '73': Records '74': Releases '75': Remedies '76': Representations '77': Sales '78': Sanctions '79': Severability '80': Solvency '81': Specific Performance '82': Submission To Jurisdiction '83': Subsidiaries '84': Successors '85': Survival '86': Tax Withholdings '87': Taxes '88': Terminations '89': Terms '90': Titles '91': Transactions With Affiliates '92': Use Of Proceeds '93': Vacations '94': Venues '95': Vesting '96': Waiver Of Jury Trials '97': Waivers '98': Warranties '99': Withholdings splits: - name: train num_bytes: 43358291 num_examples: 60000 - name: test num_bytes: 6845581 num_examples: 10000 - name: validation num_bytes: 7143588 num_examples: 10000 download_size: 27650585 dataset_size: 57347460 - config_name: scotus features: - name: text dtype: string - name: label dtype: class_label: names: '0': '1' '1': '2' '2': '3' '3': '4' '4': '5' '5': '6' '6': '7' '7': '8' '8': '9' '9': '10' '10': '11' '11': '12' '12': '13' splits: - name: train num_bytes: 178959316 num_examples: 5000 - name: test num_bytes: 76213279 num_examples: 1400 - name: validation num_bytes: 75600243 num_examples: 1400 download_size: 173411399 dataset_size: 330772838 - config_name: unfair_tos features: - name: text dtype: string - name: labels sequence: class_label: names: '0': Limitation of liability '1': Unilateral termination '2': Unilateral change '3': Content removal '4': Contract by using '5': Choice of law '6': Jurisdiction '7': Arbitration splits: - name: train num_bytes: 1041782 num_examples: 5532 - name: test num_bytes: 303099 num_examples: 1607 - name: validation num_bytes: 452111 num_examples: 2275 download_size: 865604 dataset_size: 1796992 configs: - config_name: case_hold data_files: - split: train path: case_hold/train-* - split: test path: case_hold/test-* - split: validation path: case_hold/validation-* - config_name: ecthr_a data_files: - split: train path: ecthr_a/train-* - split: test path: ecthr_a/test-* - split: validation path: ecthr_a/validation-* - config_name: ecthr_b data_files: - split: train path: ecthr_b/train-* - split: test path: ecthr_b/test-* - split: validation path: ecthr_b/validation-* - config_name: eurlex data_files: - split: train path: eurlex/train-* - split: test path: eurlex/test-* - split: validation path: eurlex/validation-* - config_name: ledgar data_files: - split: train path: ledgar/train-* - split: test path: ledgar/test-* - split: validation path: ledgar/validation-* - config_name: scotus data_files: - split: train path: scotus/train-* - split: test path: scotus/test-* - split: validation path: scotus/validation-* - config_name: unfair_tos data_files: - split: train path: unfair_tos/train-* - split: test path: unfair_tos/test-* - split: validation path: unfair_tos/validation-* --- # Dataset Card for "LexGLUE" ## 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/coastalcph/lex-glue - **Repository:** https://github.com/coastalcph/lex-glue - **Paper:** https://arxiv.org/abs/2110.00976 - **Leaderboard:** https://github.com/coastalcph/lex-glue - **Point of Contact:** [Ilias Chalkidis](mailto:ilias.chalkidis@di.ku.dk) ### Dataset Summary Inspired by the recent widespread use of the GLUE multi-task benchmark NLP dataset (Wang et al., 2018), the subsequent more difficult SuperGLUE (Wang et al., 2019), other previous multi-task NLP benchmarks (Conneau and Kiela, 2018; McCann et al., 2018), and similar initiatives in other domains (Peng et al., 2019), we introduce the *Legal General Language Understanding Evaluation (LexGLUE) benchmark*, a benchmark dataset to evaluate the performance of NLP methods in legal tasks. LexGLUE is based on seven existing legal NLP datasets, selected using criteria largely from SuperGLUE. As in GLUE and SuperGLUE (Wang et al., 2019b,a), one of our goals is to push towards generic (or ‘foundation’) models that can cope with multiple NLP tasks, in our case legal NLP tasks possibly with limited task-specific fine-tuning. Another goal is to provide a convenient and informative entry point for NLP researchers and practitioners wishing to explore or develop methods for legalNLP. Having these goals in mind, the datasets we include in LexGLUE and the tasks they address have been simplified in several ways to make it easier for newcomers and generic models to address all tasks. LexGLUE benchmark is accompanied by experimental infrastructure that relies on Hugging Face Transformers library and resides at: https://github.com/coastalcph/lex-glue. ### Supported Tasks and Leaderboards The supported tasks are the following: <table> <tr><td>Dataset</td><td>Source</td><td>Sub-domain</td><td>Task Type</td><td>Classes</td><tr> <tr><td>ECtHR (Task A)</td><td> <a href="https://aclanthology.org/P19-1424/">Chalkidis et al. (2019)</a> </td><td>ECHR</td><td>Multi-label classification</td><td>10+1</td></tr> <tr><td>ECtHR (Task B)</td><td> <a href="https://aclanthology.org/2021.naacl-main.22/">Chalkidis et al. (2021a)</a> </td><td>ECHR</td><td>Multi-label classification </td><td>10+1</td></tr> <tr><td>SCOTUS</td><td> <a href="http://scdb.wustl.edu">Spaeth et al. (2020)</a></td><td>US Law</td><td>Multi-class classification</td><td>14</td></tr> <tr><td>EUR-LEX</td><td> <a href="https://arxiv.org/abs/2109.00904">Chalkidis et al. (2021b)</a></td><td>EU Law</td><td>Multi-label classification</td><td>100</td></tr> <tr><td>LEDGAR</td><td> <a href="https://aclanthology.org/2020.lrec-1.155/">Tuggener et al. (2020)</a></td><td>Contracts</td><td>Multi-class classification</td><td>100</td></tr> <tr><td>UNFAIR-ToS</td><td><a href="https://arxiv.org/abs/1805.01217"> Lippi et al. (2019)</a></td><td>Contracts</td><td>Multi-label classification</td><td>8+1</td></tr> <tr><td>CaseHOLD</td><td><a href="https://arxiv.org/abs/2104.08671">Zheng et al. (2021)</a></td><td>US Law</td><td>Multiple choice QA</td><td>n/a</td></tr> </table> #### ecthr_a The European Court of Human Rights (ECtHR) hears allegations that a state has breached human rights provisions of the European Convention of Human Rights (ECHR). For each case, the dataset provides a list of factual paragraphs (facts) from the case description. Each case is mapped to articles of the ECHR that were violated (if any). #### ecthr_b The European Court of Human Rights (ECtHR) hears allegations that a state has breached human rights provisions of the European Convention of Human Rights (ECHR). For each case, the dataset provides a list of factual paragraphs (facts) from the case description. Each case is mapped to articles of ECHR that were allegedly violated (considered by the court). #### scotus The US Supreme Court (SCOTUS) is the highest federal court in the United States of America and generally hears only the most controversial or otherwise complex cases which have not been sufficiently well solved by lower courts. This is a single-label multi-class classification task, where given a document (court opinion), the task is to predict the relevant issue areas. The 14 issue areas cluster 278 issues whose focus is on the subject matter of the controversy (dispute). #### eurlex European Union (EU) legislation is published in EUR-Lex portal. All EU laws are annotated by EU's Publications Office with multiple concepts from the EuroVoc thesaurus, a multilingual thesaurus maintained by the Publications Office. The current version of EuroVoc contains more than 7k concepts referring to various activities of the EU and its Member States (e.g., economics, health-care, trade). Given a document, the task is to predict its EuroVoc labels (concepts). #### ledgar LEDGAR dataset aims contract provision (paragraph) classification. The contract provisions come from contracts obtained from the US Securities and Exchange Commission (SEC) filings, which are publicly available from EDGAR. Each label represents the single main topic (theme) of the corresponding contract provision. #### unfair_tos The UNFAIR-ToS dataset contains 50 Terms of Service (ToS) from on-line platforms (e.g., YouTube, Ebay, Facebook, etc.). The dataset has been annotated on the sentence-level with 8 types of unfair contractual terms (sentences), meaning terms that potentially violate user rights according to the European consumer law. #### case_hold The CaseHOLD (Case Holdings on Legal Decisions) dataset includes multiple choice questions about holdings of US court cases from the Harvard Law Library case law corpus. Holdings are short summaries of legal rulings accompany referenced decisions relevant for the present case. The input consists of an excerpt (or prompt) from a court decision, containing a reference to a particular case, while the holding statement is masked out. The model must identify the correct (masked) holding statement from a selection of five choices. The current leaderboard includes several Transformer-based (Vaswaniet al., 2017) pre-trained language models, which achieve state-of-the-art performance in most NLP tasks (Bommasani et al., 2021) and NLU benchmarks (Wang et al., 2019a). Results reported by [Chalkidis et al. (2021)](https://arxiv.org/abs/2110.00976): *Task-wise Test Results* <table> <tr><td>Dataset</td><td>ECtHR A</td><td>ECtHR B</td><td>SCOTUS</td><td>EUR-LEX</td><td>LEDGAR</td><td>UNFAIR-ToS</td><td>CaseHOLD</td></tr> <tr><td>Model</td><td>μ-F1 / m-F1 </td><td>μ-F1 / m-F1 </td><td>μ-F1 / m-F1 </td><td>μ-F1 / m-F1 </td><td>μ-F1 / m-F1 </td><td>μ-F1 / m-F1</td><td>μ-F1 / m-F1 </td></tr> <tr><td>TFIDF+SVM</td><td> 64.7 / 51.7 </td><td>74.6 / 65.1 </td><td> 78.2 / 69.5 </td><td>71.3 / 51.4 </td><td>87.2 / 82.4 </td><td>95.4 / 78.8</td><td>n/a </td></tr> <tr><td colspan="8" style='text-align:center'>Medium-sized Models (L=12, H=768, A=12)</td></tr> <td>BERT</td> <td> 71.2 / 63.6 </td> <td> 79.7 / 73.4 </td> <td> 68.3 / 58.3 </td> <td> 71.4 / 57.2 </td> <td> 87.6 / 81.8 </td> <td> 95.6 / 81.3 </td> <td> 70.8 </td> </tr> <td>RoBERTa</td> <td> 69.2 / 59.0 </td> <td> 77.3 / 68.9 </td> <td> 71.6 / 62.0 </td> <td> 71.9 / 57.9 </td> <td> 87.9 / 82.3 </td> <td> 95.2 / 79.2 </td> <td> 71.4 </td> </tr> <td>DeBERTa</td> <td> 70.0 / 60.8 </td> <td> 78.8 / 71.0 </td> <td> 71.1 / 62.7 </td> <td> 72.1 / 57.4 </td> <td> 88.2 / 83.1 </td> <td> 95.5 / 80.3 </td> <td> 72.6 </td> </tr> <td>Longformer</td> <td> 69.9 / 64.7 </td> <td> 79.4 / 71.7 </td> <td> 72.9 / 64.0 </td> <td> 71.6 / 57.7 </td> <td> 88.2 / 83.0 </td> <td> 95.5 / 80.9 </td> <td> 71.9 </td> </tr> <td>BigBird</td> <td> 70.0 / 62.9 </td> <td> 78.8 / 70.9 </td> <td> 72.8 / 62.0 </td> <td> 71.5 / 56.8 </td> <td> 87.8 / 82.6 </td> <td> 95.7 / 81.3 </td> <td> 70.8 </td> </tr> <td>Legal-BERT</td> <td> 70.0 / 64.0 </td> <td> 80.4 / 74.7 </td> <td> 76.4 / 66.5 </td> <td> 72.1 / 57.4 </td> <td> 88.2 / 83.0 </td> <td> 96.0 / 83.0 </td> <td> 75.3 </td> </tr> <td>CaseLaw-BERT</td> <td> 69.8 / 62.9 </td> <td> 78.8 / 70.3 </td> <td> 76.6 / 65.9 </td> <td> 70.7 / 56.6 </td> <td> 88.3 / 83.0 </td> <td> 96.0 / 82.3 </td> <td> 75.4 </td> </tr> <tr><td colspan="8" style='text-align:center'>Large-sized Models (L=24, H=1024, A=18)</td></tr> <tr><td>RoBERTa</td> <td> 73.8 / 67.6 </td> <td> 79.8 / 71.6 </td> <td> 75.5 / 66.3 </td> <td> 67.9 / 50.3 </td> <td> 88.6 / 83.6 </td> <td> 95.8 / 81.6 </td> <td> 74.4 </td> </tr> </table> *Averaged (Mean over Tasks) Test Results* <table> <tr><td>Averaging</td><td>Arithmetic</td><td>Harmonic</td><td>Geometric</td></tr> <tr><td>Model</td><td>μ-F1 / m-F1 </td><td>μ-F1 / m-F1 </td><td>μ-F1 / m-F1 </td></tr> <tr><td colspan="4" style='text-align:center'>Medium-sized Models (L=12, H=768, A=12)</td></tr> <tr><td>BERT</td><td> 77.8 / 69.5 </td><td> 76.7 / 68.2 </td><td> 77.2 / 68.8 </td></tr> <tr><td>RoBERTa</td><td> 77.8 / 68.7 </td><td> 76.8 / 67.5 </td><td> 77.3 / 68.1 </td></tr> <tr><td>DeBERTa</td><td> 78.3 / 69.7 </td><td> 77.4 / 68.5 </td><td> 77.8 / 69.1 </td></tr> <tr><td>Longformer</td><td> 78.5 / 70.5 </td><td> 77.5 / 69.5 </td><td> 78.0 / 70.0 </td></tr> <tr><td>BigBird</td><td> 78.2 / 69.6 </td><td> 77.2 / 68.5 </td><td> 77.7 / 69.0 </td></tr> <tr><td>Legal-BERT</td><td> 79.8 / 72.0 </td><td> 78.9 / 70.8 </td><td> 79.3 / 71.4 </td></tr> <tr><td>CaseLaw-BERT</td><td> 79.4 / 70.9 </td><td> 78.5 / 69.7 </td><td> 78.9 / 70.3 </td></tr> <tr><td colspan="4" style='text-align:center'>Large-sized Models (L=24, H=1024, A=18)</td></tr> <tr><td>RoBERTa</td><td> 79.4 / 70.8 </td><td> 78.4 / 69.1 </td><td> 78.9 / 70.0 </td></tr> </table> ### Languages We only consider English datasets, to make experimentation easier for researchers across the globe. ## Dataset Structure ### Data Instances #### ecthr_a An example of 'train' looks as follows. ```json { "text": ["8. The applicant was arrested in the early morning of 21 October 1990 ...", ...], "labels": [6] } ``` #### ecthr_b An example of 'train' looks as follows. ```json { "text": ["8. The applicant was arrested in the early morning of 21 October 1990 ...", ...], "label": [5, 6] } ``` #### scotus An example of 'train' looks as follows. ```json { "text": "Per Curiam\nSUPREME COURT OF THE UNITED STATES\nRANDY WHITE, WARDEN v. ROGER L. WHEELER\n Decided December 14, 2015\nPER CURIAM.\nA death sentence imposed by a Kentucky trial court and\naffirmed by the ...", "label": 8 } ``` #### eurlex An example of 'train' looks as follows. ```json { "text": "COMMISSION REGULATION (EC) No 1629/96 of 13 August 1996 on an invitation to tender for the refund on export of wholly milled round grain rice to certain third countries ...", "labels": [4, 20, 21, 35, 68] } ``` #### ledgar An example of 'train' looks as follows. ```json { "text": "All Taxes shall be the financial responsibility of the party obligated to pay such Taxes as determined by applicable law and neither party is or shall be liable at any time for any of the other party ...", "label": 32 } ``` #### unfair_tos An example of 'train' looks as follows. ```json { "text": "tinder may terminate your account at any time without notice if it believes that you have violated this agreement.", "label": 2 } ``` #### casehold An example of 'test' looks as follows. ```json { "context": "In Granato v. City and County of Denver, No. CIV 11-0304 MSK/BNB, 2011 WL 3820730 (D.Colo. Aug. 20, 2011), the Honorable Marcia S. Krieger, now-Chief United States District Judge for the District of Colorado, ruled similarly: At a minimum, a party asserting a Mo-nell claim must plead sufficient facts to identify ... to act pursuant to City or State policy, custom, decision, ordinance, re d 503, 506-07 (3d Cir.l985)(<HOLDING>).", "endings": ["holding that courts are to accept allegations in the complaint as being true including monell policies and writing that a federal court reviewing the sufficiency of a complaint has a limited task", "holding that for purposes of a class certification motion the court must accept as true all factual allegations in the complaint and may draw reasonable inferences therefrom", "recognizing that the allegations of the complaint must be accepted as true on a threshold motion to dismiss", "holding that a court need not accept as true conclusory allegations which are contradicted by documents referred to in the complaint", "holding that where the defendant was in default the district court correctly accepted the fact allegations of the complaint as true" ], "label": 0 } ``` ### Data Fields #### ecthr_a - `text`: a list of `string` features (list of factual paragraphs (facts) from the case description). - `labels`: a list of classification labels (a list of violated ECHR articles, if any) . <details> <summary>List of ECHR articles</summary> "Article 2", "Article 3", "Article 5", "Article 6", "Article 8", "Article 9", "Article 10", "Article 11", "Article 14", "Article 1 of Protocol 1" </details> #### ecthr_b - `text`: a list of `string` features (list of factual paragraphs (facts) from the case description) - `labels`: a list of classification labels (a list of articles considered). <details> <summary>List of ECHR articles</summary> "Article 2", "Article 3", "Article 5", "Article 6", "Article 8", "Article 9", "Article 10", "Article 11", "Article 14", "Article 1 of Protocol 1" </details> #### scotus - `text`: a `string` feature (the court opinion). - `label`: a classification label (the relevant issue area). <details> <summary>List of issue areas</summary> (1, Criminal Procedure), (2, Civil Rights), (3, First Amendment), (4, Due Process), (5, Privacy), (6, Attorneys), (7, Unions), (8, Economic Activity), (9, Judicial Power), (10, Federalism), (11, Interstate Relations), (12, Federal Taxation), (13, Miscellaneous), (14, Private Action) </details> #### eurlex - `text`: a `string` feature (an EU law). - `labels`: a list of classification labels (a list of relevant EUROVOC concepts). <details> <summary>List of EUROVOC concepts</summary> The list is very long including 100 EUROVOC concepts. You can find the EUROVOC concepts descriptors <a href="https://raw.githubusercontent.com/nlpaueb/multi-eurlex/master/data/eurovoc_descriptors.json">here</a>. </details> #### ledgar - `text`: a `string` feature (a contract provision/paragraph). - `label`: a classification label (the type of contract provision). <details> <summary>List of contract provision types</summary> "Adjustments", "Agreements", "Amendments", "Anti-Corruption Laws", "Applicable Laws", "Approvals", "Arbitration", "Assignments", "Assigns", "Authority", "Authorizations", "Base Salary", "Benefits", "Binding Effects", "Books", "Brokers", "Capitalization", "Change In Control", "Closings", "Compliance With Laws", "Confidentiality", "Consent To Jurisdiction", "Consents", "Construction", "Cooperation", "Costs", "Counterparts", "Death", "Defined Terms", "Definitions", "Disability", "Disclosures", "Duties", "Effective Dates", "Effectiveness", "Employment", "Enforceability", "Enforcements", "Entire Agreements", "Erisa", "Existence", "Expenses", "Fees", "Financial Statements", "Forfeitures", "Further Assurances", "General", "Governing Laws", "Headings", "Indemnifications", "Indemnity", "Insurances", "Integration", "Intellectual Property", "Interests", "Interpretations", "Jurisdictions", "Liens", "Litigations", "Miscellaneous", "Modifications", "No Conflicts", "No Defaults", "No Waivers", "Non-Disparagement", "Notices", "Organizations", "Participations", "Payments", "Positions", "Powers", "Publicity", "Qualifications", "Records", "Releases", "Remedies", "Representations", "Sales", "Sanctions", "Severability", "Solvency", "Specific Performance", "Submission To Jurisdiction", "Subsidiaries", "Successors", "Survival", "Tax Withholdings", "Taxes", "Terminations", "Terms", "Titles", "Transactions With Affiliates", "Use Of Proceeds", "Vacations", "Venues", "Vesting", "Waiver Of Jury Trials", "Waivers", "Warranties", "Withholdings", </details> #### unfair_tos - `text`: a `string` feature (a ToS sentence) - `labels`: a list of classification labels (a list of unfair types, if any). <details> <summary>List of unfair types</summary> "Limitation of liability", "Unilateral termination", "Unilateral change", "Content removal", "Contract by using", "Choice of law", "Jurisdiction", "Arbitration" </details> #### casehold - `context`: a `string` feature (a context sentence incl. a masked holding statement). - `holdings`: a list of `string` features (a list of candidate holding statements). - `label`: a classification label (the id of the original/correct holding). ### Data Splits <table> <tr><td>Dataset </td><td>Training</td><td>Development</td><td>Test</td><td>Total</td></tr> <tr><td>ECtHR (Task A)</td><td>9,000</td><td>1,000</td><td>1,000</td><td>11,000</td></tr> <tr><td>ECtHR (Task B)</td><td>9,000</td><td>1,000</td><td>1,000</td><td>11,000</td></tr> <tr><td>SCOTUS</td><td>5,000</td><td>1,400</td><td>1,400</td><td>7,800</td></tr> <tr><td>EUR-LEX</td><td>55,000</td><td>5,000</td><td>5,000</td><td>65,000</td></tr> <tr><td>LEDGAR</td><td>60,000</td><td>10,000</td><td>10,000</td><td>80,000</td></tr> <tr><td>UNFAIR-ToS</td><td>5,532</td><td>2,275</td><td>1,607</td><td>9,414</td></tr> <tr><td>CaseHOLD</td><td>45,000</td><td>3,900</td><td>3,900</td><td>52,800</td></tr> </table> ## 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 <table> <tr><td>Dataset</td><td>Source</td><td>Sub-domain</td><td>Task Type</td><tr> <tr><td>ECtHR (Task A)</td><td> <a href="https://aclanthology.org/P19-1424/">Chalkidis et al. (2019)</a> </td><td>ECHR</td><td>Multi-label classification</td></tr> <tr><td>ECtHR (Task B)</td><td> <a href="https://aclanthology.org/2021.naacl-main.22/">Chalkidis et al. (2021a)</a> </td><td>ECHR</td><td>Multi-label classification </td></tr> <tr><td>SCOTUS</td><td> <a href="http://scdb.wustl.edu">Spaeth et al. (2020)</a></td><td>US Law</td><td>Multi-class classification</td></tr> <tr><td>EUR-LEX</td><td> <a href="https://arxiv.org/abs/2109.00904">Chalkidis et al. (2021b)</a></td><td>EU Law</td><td>Multi-label classification</td></tr> <tr><td>LEDGAR</td><td> <a href="https://aclanthology.org/2020.lrec-1.155/">Tuggener et al. (2020)</a></td><td>Contracts</td><td>Multi-class classification</td></tr> <tr><td>UNFAIR-ToS</td><td><a href="https://arxiv.org/abs/1805.01217"> Lippi et al. (2019)</a></td><td>Contracts</td><td>Multi-label classification</td></tr> <tr><td>CaseHOLD</td><td><a href="https://arxiv.org/abs/2104.08671">Zheng et al. (2021)</a></td><td>US Law</td><td>Multiple choice QA</td></tr> </table> #### 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 [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Dataset Curators *Ilias Chalkidis, Abhik Jana, Dirk Hartung, Michael Bommarito, Ion Androutsopoulos, Daniel Martin Katz, and Nikolaos Aletras.* *LexGLUE: A Benchmark Dataset for Legal Language Understanding in English.* *2022. In the Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics. Dublin, Ireland.* ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information [*Ilias Chalkidis, Abhik Jana, Dirk Hartung, Michael Bommarito, Ion Androutsopoulos, Daniel Martin Katz, and Nikolaos Aletras.* *LexGLUE: A Benchmark Dataset for Legal Language Understanding in English.* *2022. In the Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics. Dublin, Ireland.*](https://arxiv.org/abs/2110.00976) ``` @inproceedings{chalkidis-etal-2021-lexglue, title={LexGLUE: A Benchmark Dataset for Legal Language Understanding in English}, author={Chalkidis, Ilias and Jana, Abhik and Hartung, Dirk and Bommarito, Michael and Androutsopoulos, Ion and Katz, Daniel Martin and Aletras, Nikolaos}, year={2022}, booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics}, address={Dubln, Ireland}, } ``` ### Contributions Thanks to [@iliaschalkidis](https://github.com/iliaschalkidis) for adding this dataset.

lighteval/siqa

lighteval

2023-10-07T08:03:32Z

1,598

7

[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2023-10-07T08:03:29Z

1

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: context dtype: string - name: question dtype: string - name: answerA dtype: string - name: answerB dtype: string - name: answerC dtype: string - name: label dtype: string splits: - name: train num_bytes: 6327209 num_examples: 33410 - name: validation num_bytes: 372815 num_examples: 1954 download_size: 3678635 dataset_size: 6700024 --- # Dataset Card for "siqa" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

GBaker/MedQA-USMLE-4-options

GBaker

2023-01-24T19:18:09Z

2,121

57

[ "language:en", "license:cc-by-4.0", "size_categories:10K<n<100K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2023-01-24T19:08:56Z

2

--- license: cc-by-4.0 language: - en --- Original dataset introduced by Jin et al. in [What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams](https://paperswithcode.com/paper/what-disease-does-this-patient-have-a-large) <h4>Citation information:</h4> @article{jin2020disease, title={What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams}, author={Jin, Di and Pan, Eileen and Oufattole, Nassim and Weng, Wei-Hung and Fang, Hanyi and Szolovits, Peter}, journal={arXiv preprint arXiv:2009.13081}, year={2020} }