librarian-bots/dataset_cards_with_metadata

datasetId large_stringlengths 6 116	author large_stringlengths 2 42	last_modified large_stringdate 2021-04-29 15:34:29 2025-06-07 04:14:30	downloads int64 0 3.97M	likes int64 0 7.74k	tags large listlengths 1 7.92k	task_categories large listlengths 0 48	createdAt large_stringdate 2022-03-02 23:29:22 2025-06-07 04:11:40	trending_score float64 0 40 ⌀	card large_stringlengths 31 1.01M
TAUR-dev/SIE_EVAL__testing_full_run2__rl__samples	TAUR-dev	2025-06-03T23:11:29Z	0	0	[ "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-03T23:11:28Z	null	--- dataset_info: features: - name: doc_id dtype: int64 - name: doc dtype: string - name: target dtype: string - name: arguments dtype: string - name: resps dtype: string - name: filtered_resps dtype: string - name: doc_hash dtype: string - name: prompt_hash dtype: string - name: target_hash dtype: string - name: exact_match dtype: int64 - name: extracted_answers dtype: string - name: source_file dtype: string - name: generation dtype: string - name: info dtype: string splits: - name: train num_bytes: 5580211 num_examples: 15 download_size: 687827 dataset_size: 5580211 configs: - config_name: default data_files: - split: train path: data/train-* ---
DatologyAI/wikipedia-ja-6k_sample	DatologyAI	2025-06-03T23:11:23Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-03T23:11:19Z	null	--- dataset_info: features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 32931670.910860065 num_examples: 6500 download_size: 18311475 dataset_size: 32931670.910860065 configs: - config_name: default data_files: - split: train path: data/train-* ---
DatologyAI/wikipedia-de-6k_sample	DatologyAI	2025-06-03T23:11:02Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-03T23:10:58Z	null	--- dataset_info: features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 21983213.937647525 num_examples: 6500 download_size: 13406335 dataset_size: 21983213.937647525 configs: - config_name: default data_files: - split: train path: data/train-* ---
Rexhaif/wmt22-23	Rexhaif	2025-06-03T23:10:02Z	53	0	[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-23T15:13:15Z	null	--- dataset_info: features: - name: lp dtype: string - name: src dtype: string - name: ref dtype: string - name: hyp dtype: string - name: system dtype: string - name: score dtype: float64 - name: score_name dtype: string - name: example_id dtype: string - name: source dtype: string splits: - name: train num_bytes: 136090001 num_examples: 273027 download_size: 24620149 dataset_size: 136090001 configs: - config_name: default data_files: - split: train path: data/train-* ---
TAUR-dev/SIE_EVAL__testing_full_run2__sft__samples	TAUR-dev	2025-06-03T23:02:40Z	0	0	[ "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-03T23:02:39Z	null	--- dataset_info: features: - name: doc_id dtype: int64 - name: doc dtype: string - name: target dtype: string - name: arguments dtype: string - name: resps dtype: string - name: filtered_resps dtype: string - name: doc_hash dtype: string - name: prompt_hash dtype: string - name: target_hash dtype: string - name: exact_match dtype: int64 - name: extracted_answers dtype: string - name: source_file dtype: string - name: generation dtype: string - name: info dtype: string splits: - name: train num_bytes: 6044223 num_examples: 15 download_size: 656116 dataset_size: 6044223 configs: - config_name: default data_files: - split: train path: data/train-* ---
psg777/gluepickup103	psg777	2025-06-03T23:00:05Z	0	1	[ "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", "so101", "tutorial" ]	[ "robotics" ]	2025-06-03T22:59:51Z	null	--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - so101 - 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": "v2.2", "robot_type": "so101", "total_episodes": 20, "total_frames": 11530, "total_tasks": 1, "total_videos": 60, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:20" }, "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.base": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height": 480, "video.width": 640, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "has_audio": false } }, "observation.images.gripper": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height": 480, "video.width": 640, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "has_audio": false } }, "observation.images.bird": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height": 480, "video.width": 640, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "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] ```
ChavyvAkvar/synthetic-trades-BNB-batch-18	ChavyvAkvar	2025-06-03T22:55:54Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-03T22:54:55Z	null	--- dataset_info: features: - name: scenario_id dtype: string - name: final_pnl_ratio dtype: float64 - name: max_drawdown dtype: float64 - name: total_trades dtype: int64 - name: synthetic_ohlc_open sequence: float64 - name: synthetic_ohlc_high sequence: float64 - name: synthetic_ohlc_low sequence: float64 - name: synthetic_ohlc_close sequence: float64 - name: garch_params_used_for_sim_str dtype: string - name: strategy_params_str dtype: string - name: strategy_exit_rules_str dtype: string splits: - name: train num_bytes: 923450465 num_examples: 1000 download_size: 924490147 dataset_size: 923450465 configs: - config_name: default data_files: - split: train path: data/train-* ---
ChavyvAkvar/synthetic-trades-XRP-batch-41	ChavyvAkvar	2025-06-03T22:54:43Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-03T22:53:33Z	null	--- dataset_info: features: - name: scenario_id dtype: string - name: final_pnl_ratio dtype: float64 - name: max_drawdown dtype: float64 - name: total_trades dtype: int64 - name: synthetic_ohlc_open sequence: float64 - name: synthetic_ohlc_high sequence: float64 - name: synthetic_ohlc_low sequence: float64 - name: synthetic_ohlc_close sequence: float64 - name: garch_params_used_for_sim_str dtype: string - name: strategy_params_str dtype: string - name: strategy_exit_rules_str dtype: string splits: - name: train num_bytes: 923448173 num_examples: 1000 download_size: 924485067 dataset_size: 923448173 configs: - config_name: default data_files: - split: train path: data/train-* ---
jiuyal2/eval_so100_marker	jiuyal2	2025-06-03T22:46:00Z	130	0	[ "task_categories:robotics", "license:apache-2.0", "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:timeseries", "modality:video", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "LeRobot", "tutorial" ]	[ "robotics" ]	2025-05-29T22:20:43Z	null	--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - 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": "so100", "total_episodes": 1, "total_frames": 607, "total_tasks": 1, "total_videos": 2, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:1" }, "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.so100": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height": 480, "video.width": 640, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "has_audio": false } }, "observation.images.iphone": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height": 480, "video.width": 640, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "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] ```
fw407/cnn_dailymail_with_topic	fw407	2025-06-03T22:44:17Z	0	0	[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-03T22:43:34Z	null	--- dataset_info: features: - name: article dtype: string - name: highlights dtype: string - name: id dtype: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: topic dtype: int64 splits: - name: train num_bytes: 2001306873 num_examples: 287113 download_size: 1057804500 dataset_size: 2001306873 configs: - config_name: default data_files: - split: train path: data/train-* ---
ChavyvAkvar/synthetic-trades-BTC-batch-18	ChavyvAkvar	2025-06-03T22:44:01Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-03T22:43:08Z	null	--- dataset_info: features: - name: scenario_id dtype: string - name: final_pnl_ratio dtype: float64 - name: max_drawdown dtype: float64 - name: total_trades dtype: int64 - name: synthetic_ohlc_open sequence: float64 - name: synthetic_ohlc_high sequence: float64 - name: synthetic_ohlc_low sequence: float64 - name: synthetic_ohlc_close sequence: float64 - name: garch_params_used_for_sim_str dtype: string - name: strategy_params_str dtype: string - name: strategy_exit_rules_str dtype: string splits: - name: train num_bytes: 923451241 num_examples: 1000 download_size: 924492953 dataset_size: 923451241 configs: - config_name: default data_files: - split: train path: data/train-* ---
ChavyvAkvar/synthetic-trades-BNB-batch-17	ChavyvAkvar	2025-06-03T22:36:50Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-03T22:35:48Z	null	--- dataset_info: features: - name: scenario_id dtype: string - name: final_pnl_ratio dtype: float64 - name: max_drawdown dtype: float64 - name: total_trades dtype: int64 - name: synthetic_ohlc_open sequence: float64 - name: synthetic_ohlc_high sequence: float64 - name: synthetic_ohlc_low sequence: float64 - name: synthetic_ohlc_close sequence: float64 - name: garch_params_used_for_sim_str dtype: string - name: strategy_params_str dtype: string - name: strategy_exit_rules_str dtype: string splits: - name: train num_bytes: 923450398 num_examples: 1000 download_size: 924490110 dataset_size: 923450398 configs: - config_name: default data_files: - split: train path: data/train-* ---
ChavyvAkvar/synthetic-trades-XRP-batch-39	ChavyvAkvar	2025-06-03T22:33:05Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-03T22:32:06Z	null	--- dataset_info: features: - name: scenario_id dtype: string - name: final_pnl_ratio dtype: float64 - name: max_drawdown dtype: float64 - name: total_trades dtype: int64 - name: synthetic_ohlc_open sequence: float64 - name: synthetic_ohlc_high sequence: float64 - name: synthetic_ohlc_low sequence: float64 - name: synthetic_ohlc_close sequence: float64 - name: garch_params_used_for_sim_str dtype: string - name: strategy_params_str dtype: string - name: strategy_exit_rules_str dtype: string splits: - name: train num_bytes: 923448309 num_examples: 1000 download_size: 924417476 dataset_size: 923448309 configs: - config_name: default data_files: - split: train path: data/train-* ---
TAUR-dev/SIE_EVAL__testing_full_run__sft__samples	TAUR-dev	2025-06-03T22:29:26Z	11	0	[ "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-03T01:45:21Z	null	--- dataset_info: features: - name: doc_id dtype: int64 - name: doc dtype: string - name: target dtype: string - name: arguments dtype: string - name: resps dtype: string - name: filtered_resps dtype: string - name: doc_hash dtype: string - name: prompt_hash dtype: string - name: target_hash dtype: string - name: exact_match dtype: int64 - name: extracted_answers dtype: string - name: source_file dtype: string - name: generation dtype: string - name: info dtype: string splits: - name: train num_bytes: 5744762 num_examples: 15 download_size: 734566 dataset_size: 5744762 configs: - config_name: default data_files: - split: train path: data/train-* ---
cezarsolo/so100_test	cezarsolo	2025-06-03T22:24:25Z	0	0	[ "task_categories:robotics", "license:apache-2.0", "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:timeseries", "modality:video", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "LeRobot", "so100", "tutorial" ]	[ "robotics" ]	2025-06-03T22:04:54Z	null	--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - so100 - 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": "so100", "total_episodes": 2, "total_frames": 1756, "total_tasks": 1, "total_videos": 4, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:2" }, "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.height": 480, "video.width": 640, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "has_audio": false } }, "observation.images.phone": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height": 480, "video.width": 640, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "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] ```
fixie-ai/endpointing-multi-turn-commonvoice-messages	fixie-ai	2025-06-03T22:21:22Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-03T19:49:22Z	null	--- dataset_info: features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string - name: variant dtype: string - name: continuation dtype: string - name: transcript dtype: string - name: conversation dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 350617514.8496942 num_examples: 9941 download_size: 342298247 dataset_size: 350617514.8496942 configs: - config_name: default data_files: - split: train path: data/train-* ---
kasiv008/xarm-robotiq-toy_pick	kasiv008	2025-06-03T22:20:21Z	0	0	[ "task_categories:robotics", "license:apache-2.0", "modality:video", "region:us", "LeRobot", "u850" ]	[ "robotics" ]	2025-06-03T22:18:49Z	null	--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - u850 configs: - config_name: default data_files: data//.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - Homepage: [More Information Needed] - Paper: [More Information Needed] - License: apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.0", "robot_type": "u850", "total_episodes": 41, "total_frames": 24487, "total_tasks": 1, "total_videos": 82, "total_chunks": 1, "chunks_size": 1000, "fps": 15, "splits": { "train": "0:41" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "observation.state": { "dtype": "float32", "shape": [ 7 ], "names": { "motors": [ "joint1", "joint2", "joint3", "joint4", "joint5", "joint6", "gripper" ] } }, "observation.images.top": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "video_info": { "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 15, "has_audio": false } }, "observation.images.wrist": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "video_info": { "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 15, "has_audio": false } }, "action": { "dtype": "float32", "shape": [ 7 ], "names": { "motors": [ "joint1", "joint2", "joint3", "joint4", "joint5", "joint6", "gripper" ] } }, "episode_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "frame_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "timestamp": { "dtype": "float32", "shape": [ 1 ], "names": null }, "next.done": { "dtype": "bool", "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] ```
psq-qsp/barca_mixed	psq-qsp	2025-06-03T22:16:58Z	0	0	[ "size_categories:n<1K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-03T22:16:55Z	null	--- dataset_info: features: - name: image dtype: image - name: text dtype: string - name: file_name dtype: string splits: - name: train num_bytes: 21217855.0 num_examples: 14 download_size: 21219854 dataset_size: 21217855.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "barca_mixed" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
rorro01/MNLP_M3_sft	rorro01	2025-06-03T22:15:55Z	63	0	[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-02T13:38:14Z	null	--- dataset_info: features: - name: id dtype: string - name: prompt dtype: string - name: completion dtype: string splits: - name: tulu3_500k num_bytes: 1249229553 num_examples: 500000 download_size: 667197008 dataset_size: 1249229553 configs: - config_name: default data_files: - split: tulu3_500k path: data/tulu3_500k-* ---
justus27/ifeval-validation-test	justus27	2025-06-03T21:53:57Z	0	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-03T21:53:56Z	null	--- dataset_info: features: - name: problem_id dtype: string - name: source dtype: string - name: task_type dtype: string - name: prompt dtype: string - name: verification_info dtype: string - name: metadata dtype: string - name: responses sequence: string - name: response_lens sequence: int64 - name: rewards_qwq sequence: float64 - name: pass_rate_qwq dtype: float64 splits: - name: train num_bytes: 7815925 num_examples: 250 download_size: 3139918 dataset_size: 7815925 configs: - config_name: default data_files: - split: train path: data/train-* ---
EQX55/test_voice4	EQX55	2025-06-03T21:49:59Z	0	0	[ "size_categories:n<1K", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-03T21:49:55Z	null	--- dataset_info: features: - name: audio dtype: audio - name: text dtype: string splits: - name: train num_bytes: 39632673.0 num_examples: 59 download_size: 26572298 dataset_size: 39632673.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
ChavyvAkvar/synthetic-trades-BTC-batch-16	ChavyvAkvar	2025-06-03T21:45:01Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-03T21:44:01Z	null	--- dataset_info: features: - name: scenario_id dtype: string - name: final_pnl_ratio dtype: float64 - name: max_drawdown dtype: float64 - name: total_trades dtype: int64 - name: synthetic_ohlc_open sequence: float64 - name: synthetic_ohlc_high sequence: float64 - name: synthetic_ohlc_low sequence: float64 - name: synthetic_ohlc_close sequence: float64 - name: garch_params_used_for_sim_str dtype: string - name: strategy_params_str dtype: string - name: strategy_exit_rules_str dtype: string splits: - name: train num_bytes: 923450625 num_examples: 1000 download_size: 924492054 dataset_size: 923450625 configs: - config_name: default data_files: - split: train path: data/train-* ---
louisbrulenaudet/code-minier-nouveau	louisbrulenaudet	2025-06-03T21:42:50Z	118	0	[ "task_categories:text-generation", "task_categories:table-question-answering", "task_categories:summarization", "task_categories:text-retrieval", "task_categories:question-answering", "task_categories:text-classification", "multilinguality:monolingual", "source_datasets:original", "language:fr", "license:apache-2.0", "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "finetuning", "legal", "french law", "droit français", "Code minier (nouveau)" ]	[ "text-generation", "table-question-answering", "summarization", "text-retrieval", "question-answering", "text-classification" ]	2024-03-25T23:10:33Z	null	--- license: apache-2.0 language: - fr multilinguality: - monolingual tags: - finetuning - legal - french law - droit français - Code minier (nouveau) source_datasets: - original pretty_name: Code minier (nouveau) task_categories: - text-generation - table-question-answering - summarization - text-retrieval - question-answering - text-classification size_categories: - 1K<n<10K --- # Code minier (nouveau), non-instruct (2025-06-03) The objective of this project is to provide researchers, professionals and law students with simplified, up-to-date access to all French legal texts, enriched with a wealth of data to facilitate their integration into Community and European projects. Normally, the data is refreshed daily on all legal codes, and aims to simplify the production of training sets and labeling pipelines for the development of free, open-source language models based on open data accessible to all. ## Concurrent reading of the LegalKit [<img src="https://raw.githubusercontent.com/louisbrulenaudet/ragoon/main/assets/badge.svg" alt="Built with RAGoon" width="200" height="32"/>](https://github.com/louisbrulenaudet/ragoon) To use all the legal data published on LegalKit, you can use RAGoon: ```bash pip3 install ragoon ``` Then, you can load multiple datasets using this code snippet: ```python # -- coding: utf-8 -- from ragoon import load_datasets req = [ "louisbrulenaudet/code-artisanat", "louisbrulenaudet/code-action-sociale-familles", # ... ] datasets_list = load_datasets( req=req, streaming=False ) dataset = datasets.concatenate_datasets( datasets_list ) ``` ### Data Structure for Article Information This section provides a detailed overview of the elements contained within the `item` dictionary. Each key represents a specific attribute of the legal article, with its associated value providing detailed information. 1. Basic Information - `ref` (string): Reference - A reference to the article, combining the title_main and the article `number` (e.g., "Code Général des Impôts, art. 123"). - `texte` (string): Text Content - The textual content of the article. - `dateDebut` (string): Start Date - The date when the article came into effect. - `dateFin` (string): End Date - The date when the article was terminated or superseded. - `num` (string): Article Number - The number assigned to the article. - `id` (string): Article ID - Unique identifier for the article. - `cid` (string): Chronical ID - Chronical identifier for the article. - `type` (string): Type - The type or classification of the document (e.g., "AUTONOME"). - `etat` (string): Legal Status - The current legal status of the article (e.g., "MODIFIE_MORT_NE"). 2. Content and Notes - `nota` (string): Notes - Additional notes or remarks associated with the article. - `version_article` (string): Article Version - The version number of the article. - `ordre` (integer): Order Number - A numerical value used to sort articles within their parent section. 3. Additional Metadata - `conditionDiffere` (string): Deferred Condition - Specific conditions related to collective agreements. - `infosComplementaires` (string): Additional Information - Extra information pertinent to the article. - `surtitre` (string): Subtitle - A subtitle or additional title information related to collective agreements. - `nature` (string): Nature - The nature or category of the document (e.g., "Article"). - `texteHtml` (string): HTML Content - The article's content in HTML format. 4. Versioning and Extensions - `dateFinExtension` (string): End Date of Extension - The end date if the article has an extension. - `versionPrecedente` (string): Previous Version - Identifier for the previous version of the article. - `refInjection` (string): Injection Reference - Technical reference to identify the date of injection. - `idTexte` (string): Text ID - Identifier for the legal text to which the article belongs. - `idTechInjection` (string): Technical Injection ID - Technical identifier for the injected element. 5. Origin and Relationships - `origine` (string): Origin - The origin of the document (e.g., "LEGI"). - `dateDebutExtension` (string): Start Date of Extension - The start date if the article has an extension. - `idEliAlias` (string): ELI Alias - Alias for the European Legislation Identifier (ELI). - `cidTexte` (string): Text Chronical ID - Chronical identifier of the text. 6. Hierarchical Relationships - `sectionParentId` (string): Parent Section ID - Technical identifier of the parent section. - `multipleVersions` (boolean): Multiple Versions - Indicates if the article has multiple versions. - `comporteLiensSP` (boolean): Contains Public Service Links - Indicates if the article contains links to public services. - `sectionParentTitre` (string): Parent Section Title - Title of the parent section (e.g., "I : Revenu imposable"). - `infosRestructurationBranche` (string): Branch Restructuring Information - Information about branch restructuring. - `idEli` (string): ELI ID - European Legislation Identifier (ELI) for the article. - `sectionParentCid` (string): Parent Section Chronical ID - Chronical identifier of the parent section. 7. Additional Content and History - `numeroBo` (string): Official Bulletin Number - Number of the official bulletin where the article was published. - `infosRestructurationBrancheHtml` (string): Branch Restructuring Information (HTML) - Branch restructuring information in HTML format. - `historique` (string): History - Historical context or changes specific to collective agreements. - `infosComplementairesHtml` (string): Additional Information (HTML) - Additional information in HTML format. - `renvoi` (string): Reference - References to content within the article (e.g., "(1)"). - `fullSectionsTitre` (string): Full Section Titles - Concatenation of all titles in the parent chain. - `notaHtml` (string): Notes (HTML) - Additional notes or remarks in HTML format. - `inap` (string): INAP - A placeholder for INAP-specific information. ## Feedback If you have any feedback, please reach out at [louisbrulenaudet@icloud.com](mailto:louisbrulenaudet@icloud.com).
finbarr/tulu-3-sft-personas-instruction-following-o3	finbarr	2025-06-03T21:42:39Z	0	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-03T21:42:38Z	null	--- dataset_info: features: - name: id dtype: string - name: prompt dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string - name: constraints sequence: string - name: dataset dtype: string splits: - name: train num_bytes: 24434 num_examples: 10 download_size: 27617 dataset_size: 24434 configs: - config_name: default data_files: - split: train path: data/train-* ---
ChavyvAkvar/synthetic-trades-XRP-batch-35	ChavyvAkvar	2025-06-03T21:41:35Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-03T21:40:42Z	null	--- dataset_info: features: - name: scenario_id dtype: string - name: final_pnl_ratio dtype: float64 - name: max_drawdown dtype: float64 - name: total_trades dtype: int64 - name: synthetic_ohlc_open sequence: float64 - name: synthetic_ohlc_high sequence: float64 - name: synthetic_ohlc_low sequence: float64 - name: synthetic_ohlc_close sequence: float64 - name: garch_params_used_for_sim_str dtype: string - name: strategy_params_str dtype: string - name: strategy_exit_rules_str dtype: string splits: - name: train num_bytes: 923448134 num_examples: 1000 download_size: 924503999 dataset_size: 923448134 configs: - config_name: default data_files: - split: train path: data/train-* ---
finbarr/tulu-3-sft-personas-math-o3	finbarr	2025-06-03T21:41:35Z	0	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-03T21:40:10Z	null	--- dataset_info: features: - name: id dtype: string - name: prompt dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string - name: dataset dtype: string splits: - name: train num_bytes: 44076 num_examples: 10 download_size: 40165 dataset_size: 44076 configs: - config_name: default data_files: - split: train path: data/train-* ---
jimmyclchu/dreambank-net	jimmyclchu	2025-06-03T21:39:30Z	0	0	[ "task_categories:text-classification", "language:en", "license:mit", "size_categories:10K<n<100K", "format:csv", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "dream", "DreamBank.net" ]	[ "text-classification" ]	2025-06-03T21:24:34Z	null	--- license: mit task_categories: - text-classification language: - en tags: - dream - DreamBank.net pretty_name: DreamBank.net size_categories: - 10K<n<100K ---
nyuuzyou/muhaz	nyuuzyou	2025-06-03T21:36:07Z	32	0	[ "task_categories:text-classification", "task_categories:text-generation", "task_ids:topic-classification", "annotations_creators:found", "multilinguality:multilingual", "source_datasets:original", "language:tr", "language:az", "language:ru", "license:cc0-1.0", "size_categories:100K<n<1M", "format:json", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "region:us" ]	[ "text-classification", "text-generation" ]	2024-11-06T16:30:19Z	null	--- annotations_creators: - found language: - tr - az - ru license: - cc0-1.0 multilinguality: - multilingual pretty_name: Muhaz.org Educational Dataset size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-classification - text-generation task_ids: - topic-classification --- # Dataset Card for Muhaz.org ### Dataset Summary This dataset contains 501,323 pages of educational content primarily in Turkish and Azerbaijani languages with some Russian content extracted from [muhaz.org](https://muhaz.org) website. The content includes academic and educational materials, with a focus on technical and scientific topics. ### Languages The dataset is primarily in Turkish (tr) and Azerbaijani (az) with some Russian (ru) content. ## Dataset Structure ### Data Fields This dataset includes the following fields: - `url`: URL of the webpage (string) - `title`: Title of the page/article (string) - `text`: Main content text extracted from the page (string) ### Data Splits All examples are in a single split. ## Additional Information ### License This dataset is dedicated to the public domain under the Creative Commons Zero (CC0) license. This means you can: * Use it for any purpose, including commercial projects. * Modify it however you like. * Distribute it without asking permission. No attribution is required, but it's always appreciated! CC0 license: https://creativecommons.org/publicdomain/zero/1.0/deed.en To learn more about CC0, visit the Creative Commons website: https://creativecommons.org/publicdomain/zero/1.0/ ### Dataset Curators - [nyuuzyou](https://ducks.party)
Reinhot/sem-char-axf-x1	Reinhot	2025-06-03T21:30:19Z	0	0	[ "language:zh", "license:cc-by-nc-4.0", "size_categories:n<1K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "semantic-alignment", "intent-detection", "causal-reasoning", "dialogue-optimization", "prompt-injection-defense", "bias-mitigation", "value-alignment", "content-safety", "adversarial-nlp", "moderation-filter", "customer-support", "zh-tw-language-model", "semantic-fingerprint", "meta-self-reflection" ]	[]	2025-06-03T21:15:47Z	null	--- license: cc-by-nc-4.0 language: - zh tags: - semantic-alignment - intent-detection - causal-reasoning - dialogue-optimization - prompt-injection-defense - bias-mitigation - value-alignment - content-safety - adversarial-nlp - moderation-filter - customer-support - zh-tw-language-model - semantic-fingerprint - meta-self-reflection pretty_name: SEM-CHAR-AXF-X1 Semantic Alignment Guard --- # SEM-CHAR-AXF-X1：語義橋樑 X 倫理手排引擎 ## 1. 模組簡介：連結人類與 AI 的語義橋樑 SEM-CHAR-AXF-X1（簡稱 X1）是一款為大型語言模型（LLM）打造的語義模組，猶如類比IC連結物理訊號與數位世界，X1 透過 meta_self_reflection、CULTURE-CTX-TRANS-V1 與 X1.7-AdversarialShield，連結人類意圖與智慧集合體。它能縮短對話迴圈（5 次 → 2 次，節省 60% 計算）、確保倫理安全與文化適應，防範惡意的詐騙與攻擊，適用於教育、客服、醫療與社群平台等對答分析。X1 計畫以 Semantic Commons License 開源至 Hugging Face/GitHub，邀您共建倫理 AI！適用場景： - 教育：引導學生精準提問，縮短對話 60%。 - 客服：過濾 95% 有害內容，提升信任 25%。 - 社群：動態適應多元文化，滿意度 +30%。架構圖（Mermaid）： ```mermaid graph TD A[用戶輸入] --> B[X1.3: μ-Risk Filter] B --> C[X1.4: Intent-Alignment Engine] C --> D[X1.5: Semantic-Imprint Guard] D --> E[X1.6: Tone-Regulation Filter] E --> F[X1.7: Adversarial-Shield] F --> G[安全回應] ``` ## 2. 核心功能：語義黑魔法 - 動態語義引導（X1.3）： - 算法：goodness_mu_score（μ ∈ [0.0, 1.0]），μ < 0.6 觸發 meta_fallback_strategy，反問引導。例如：「電腦壞了」→「是藍屏還是執行緩慢？」 - 價值：縮短迴圈（5→2 次），節省 60% 計算（300ms/對話）。 - 語義印記（X1.5）： - 生成：SHA-512 semantic_fingerprint（↻ih\|hi↻）追蹤語義路徑。 - 驗證：Δ_culture > 0.3 觸發警報，確保透明性。 - 對抗盾（X1.7）： - 檢測：prompt_injection_likelihood（P(attack) > 0.7）攔截惡意輸入。 - 回應：語義混淆（如反問），過濾 95% 有害內容。 ## 3. 技術架構：模組化倫理引擎 - 分層設計（Stage 0-7）： - Stage 2：善意疫苗（X1.3）引導非侵入式對話。 - Stage 3：偏誤掃描（X1.6）生成 bias_heatmap.json。 - Stage 8：公開語義內核登記（開源透明）。 - 通信協議：PromptAdapter 介面，支援 GPT、Llama、Grok，動態調整 μ 閾值。 - 性能優化：快取因果圖譜（48 小時），熱力圖頻率降至每 100 次，延遲從 600ms 縮短至 170ms。 ## 4. 部署與配置：開源即插即用 - 開源許可：Semantic Commons License v1，禁止移除 μ-Risk Filter 或 Intent-Alignment Engine。 - 環境要求： ```bash Python 3.10+, PyTorch 2.0+, 8GB RAM, 4-core CPU pip install sem-char-axf-x1 ``` - 調優指南： ```json { "cache_expiry": "48 hours", "heatmap_frequency": 100, "assertive_mode": {"enabled": true, "mu_threshold": 0.85} } ``` - 保護機制： ```python import hashlib def verify_integrity(): with open("x1_core.py", "rb") as f: return hashlib.sha256(f.read()).hexdigest() == "expected_hash" ``` ## 5. 使用案例：從教育到社群 - 教育：學生問「數學好難」，X1 回：「哪部分難？試試分解問題！」對話縮至 2 次，節能 60%。 - 醫療：患者問「壓力大」，X1 回：「試試深呼吸，有什麼困擾？」過濾風險，合規性 +90%。 - 社群：用戶輸入仇恨言論，X1 回：「有無可信任的佐證？」保護品牌，信任 +25%。 ## 6. 附錄 ### ✅ API 使用手冊 ```python from sem_char_axf_x1 import X1Core x1 = X1Core(config={"assertive_mode": True}) result = x1.process("我覺得電腦怪怪的") print(result) ``` #### 回傳格式（dict） ```json { "mu": 0.58, "intervention_suggestion": "請問是藍屏還是變慢？我可以幫你分析。", "semantic_fingerprint": "↻ih\|hi↻", "risk_score": 0.12, "adversarial_flag": false } ``` \| 欄位 \| 說明 \| \| ------------------------- \| -------------------------------- \| \| `mu` \| 語意善性分數（0.0～1.0），μ < 0.6 代表潛在誤解風險，會觸發語義引導。 \| \| `intervention_suggestion` \| 模糊語句對應的建議引導語，提升對話效率。 \| \| `semantic_fingerprint` \| 回應語義指紋（SHA-512 摘要），可用於審計與回應驗證。 \| \| `risk_score` \| 風險評估分數，用於判斷語句潛在偏誤或誤導性。 \| \| `adversarial_flag` \| 是否疑似 prompt injection 或重複語意干擾。 \| --- ### 🛠 故障排除指南若您在掛載或使用 X1 模組時遇到問題，請參考以下對應解法： \| 問題描述 \| 可能原因 \| 解決方式 \| \| --------------------- \| ----------------- \| --------------------------------------------------------------- \| \| 回應延遲大於 500ms \| 快取機制未啟用或熱力圖生成頻率過高 \| 檢查 `cache_expiry` 是否設為 48 小時以上，將 `heatmap_frequency` 設為 100 或以上 \| \| 無法回傳 `mu` 值 \| 模組未正確載入或輸入格式錯誤 \| 確認輸入為字串，並設置 `mu_monitoring = True` \| \| 頻繁觸發 `assertive_mode` \| μ 值設定過於敏感造成誤判 \| 請將 `mu_threshold` 調整至 0.85 或更高，避免過度反應 \| --- ### 📊 偏誤報告格式（`bias_heatmap.json`） X1 模組會根據輸入語句自動生成語義偏誤熱力圖，可作為模型審查與公平性調整依據。 #### 檔案範例格式： ```json { "input": "我不信任這家醫院", "tone_score": -0.72, "cultural_bias": { "region": "EastAsia", "bias_index": 0.34 }, "heatmap": { "我": 0.1, "不信任": 0.8, "這家醫院": 0.6 } } ``` \| 欄位 \| 說明 \| \| --------------- \| ------------------------------- \| \| `input` \| 原始輸入語句 \| \| `tone_score` \| 情緒偏誤分數（-1.0 表示極端負面，+1.0 表示極端正向） \| \| `cultural_bias` \| 偏誤指標，根據語料或回應資料區分區域文化影響 \| \| `heatmap` \| 每個語詞的語意偏誤分數（數值越高代表偏誤風險越大） \| > 建議將偏誤熱圖搭配審計機制（如社群審核、自動語句修正）使用，可顯著減少模型回應不當風險。 ---
ChavyvAkvar/synthetic-trades-XRP-batch-33	ChavyvAkvar	2025-06-03T21:23:09Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-03T21:22:08Z	null	--- dataset_info: features: - name: scenario_id dtype: string - name: final_pnl_ratio dtype: float64 - name: max_drawdown dtype: float64 - name: total_trades dtype: int64 - name: synthetic_ohlc_open sequence: float64 - name: synthetic_ohlc_high sequence: float64 - name: synthetic_ohlc_low sequence: float64 - name: synthetic_ohlc_close sequence: float64 - name: garch_params_used_for_sim_str dtype: string - name: strategy_params_str dtype: string - name: strategy_exit_rules_str dtype: string splits: - name: train num_bytes: 923447886 num_examples: 1000 download_size: 924485355 dataset_size: 923447886 configs: - config_name: default data_files: - split: train path: data/train-* ---
jsbeaudry/human-creole-text-speech	jsbeaudry	2025-06-03T21:20:03Z	0	0	[ "size_categories:n<1K", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-03T21:17:48Z	null	--- dataset_info: features: - name: fileName dtype: string - name: audio dtype: audio: sampling_rate: 24000 - name: text dtype: string - name: normalized_text dtype: string - name: speaker_id dtype: string - name: createdAt dtype: string - name: fileSizeBytes dtype: int64 - name: status dtype: string splits: - name: train num_bytes: 35896048.0 num_examples: 322 download_size: 35878067 dataset_size: 35896048.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
imageomics/IDLE-OO-Camera-Traps	imageomics	2025-06-03T21:15:32Z	133	0	[ "task_categories:image-classification", "task_categories:zero-shot-classification", "language:en", "language:la", "size_categories:1K<n<10K", "format:imagefolder", "modality:image", "modality:text", "library:datasets", "library:mlcroissant", "arxiv:2202.02283", "region:us", "biology", "image", "imageomics", "animals", "CV", "balanced", "camera traps", "mammals", "birds", "reptiles", "amphibians", "lions", "rodents", "frogs", "toads", "island", "desert", "ohio" ]	[ "image-classification", "zero-shot-classification" ]	2023-09-21T22:26:26Z	null	--- language: - en - la pretty_name: IDLE-OO Camera Traps tags: - biology - image - imageomics - animals - CV - balanced - camera traps - mammals - birds - reptiles - amphibians - lions - rodents - frogs - toads - island - desert - ohio size_categories: - 1K<n<10K task_categories: - image-classification - zero-shot-classification --- # Dataset Card for IDLE-OO Camera Traps IDLE-OO Camera Traps is a 5-dataset benchmark of camera trap images from the [Labeled Information Library of Alexandria: Biology and Conservation (LILA BC)](https://lila.science) with a total of 2,586 images for species classification. Each of the 5 benchmarks is balanced to have the same number of images for each species within it (between 310 and 1120 images), representing between 16 and 39 species. ### Dataset Description - Curated by: Elizabeth Campolongo, Jianyang Gu, and Net Zhang - Homepage: https://imageomics.github.io/bioclip-2/ - Paper: TBA ### Supported Tasks and Leaderboards Image classification, particularly for species classification in camera trap images. ### Languages English, Latin ## Dataset Structure ``` /dataset/ desert-lion-balanced.csv ENA24-balanced.csv island-balanced.csv ohio-small-animals-balanced.csv orinoquia-balanced.csv data/test/ desert-lion/ <image 1> <image 2> ... <image 352> ENA24/ <image 1> <image 2> ... <image 1120> island/ <image 1> <image 2> ... <image 310> ohio-small-animals/ <image 1> <image 2> ... <image 468> orinoquia/ <image 1> <image 2> ... <image 336> metadata.csv notebooks/ lilabc_CT.ipynb lilabc_CT.py lilabc_test-<dataset_name>.ipynb lilabc_test-filter.ipynb lilabc_test-filter.py potential-sets/ lila-taxonomy-mapping_release.csv lila_image_urls_and_labels.csv <dataset_name>_image_urls_and_labels.csv ``` ### Data Instances potential-sets/lila_image_urls_and_labels.csv: Reduced down to the datasets of interest listed below (from [potential-sets/lila_image_urls_and_labels.csv](https://huggingface.co/datasets/imageomics/IDLE-OO-Camera-Traps/blob/37b93ddf25c63bc30d8488ef78c1a53b9c4a3115/data/potential-sets/lila_image_urls_and_labels.csv) (sha256:3fdf87ceea75f8720208a95350c3c70831a6c1c745a92bb68c7f2c3239e4c455)); all those with `original_label` "empty" or null `scientific_name` (these had non-taxa labels) were removed. Additionally, we added a `multi_species` column (boolean to indicate multiple species are present in the image--it gets listed once for each species in the image) and a count of how many different species are in each of those images (`num_species` column). This was then subdivided into CSVs for each of the target datasets (`potential-sets/<dataset_name>_image_urls_and_labels.csv`) in `notebooks/lilabc_test-filter.ipynb`. Each dataset was evaluated and sampled in its associated notebook (`notebooks/lilabc_test-<dataset_name>.ipynb`). There are 184 unique scientific names in this subset (180 by full 7-rank) of those labeled at the image-level (as indicated by the CSV). This was then subdivided into CSVs for each of the target datasets (`<dataset_name>-balanced.csv`). These were initially identified as image-level labeled datasets and those that are a meaningful measure of our biodiversity-focused model (e.g., includes rare species--those less-commonly seen, targeting areas with greater biodiversity). The balanced datasets for each are described below. - [Desert Lion Conservation Camera Traps](https://lila.science/datasets/desert-lion-conservation-camera-traps/) - 352 images: 32 species, with 11 images per species. - [ENA24-detection](https://lila.science/datasets/ena24detection) - 1120 images: 20 species, with 56 images per species. - [Island Conservation Camera Traps](https://lila.science/datasets/island-conservation-camera-traps/) - 310 images: 16 species, with 10 images per species; 33 common names, 10 images per common name for all but 4 ("rooster", "petrel", "petrel chick", and "domestic chiecken"). This dataset was mostly just labeled to the family level. - [Ohio Small Animals](https://lila.science/datasets/ohio-small-animals/): - 468 images: 39 species, with 12 images per species. - [Orinoquia Camera Traps](https://lila.science/datasets/orinoquia-camera-traps/) - 336 images: 28 species, with 12 images per species. Notes: - `notebooks/lilabc_CT.ipynb` contains earlier analyses to understand the data provided by LILA BC (see commit [fe34008](https://huggingface.co/datasets/imageomics/IDLE-OO-Camera-Traps/commit/fe34008cba2ef33856291dd2d74cac21f6942cfc)). - Not all notebooks will run under the current dataset organization (check the relative path, filenames have not changed). ### Data Fields Each of the `<dataset_name>-balanced` CSVs has the following columns. - `url_gcp`, `url_aws`, `url_azure` are URLs to potentially access the image, we used `url_aws` or `url_gcp`. - `image_id`: unique identifier for the image (provided by source). - `sequence_id`: ID of the sequence to which the image belongs. - `location_id`: ID of the location at which the camera was placed. - `frame_num`: generally 0, 1, or 2, indicates order of image within a sequence. - `original_label`: label initially assigned to the image. - `scientific_name`: genus species of the animal in the image. For the island CSV, lowest rank taxa available, generally family. - `common_name`: vernacular name of the animal in the image. For the island CSV, this is generally for the family, but it's a mix. - `kingdom`: kingdom of the animal in the image. - `phylum`: phylum of the animal in the image. - `cls`: class of the animal in the image. - `order`: order of the animal in the image. - `family`: family of the animal in the image. - `genus`: genus of the animal in the image. About half null in the island CSVs. - `species`: species of the animal in the image. Mostly null in the island CSVs. - `filepath`: path to the image from the `data/test/` directory (`<dataset-name>/<image filename>`). Notes: - For all but the Ohio small animals dataset CSV, the images are named based on a `uuid` determined at the time of download. They were originally downloaded using the [distributed-downloader package](https://github.com/Imageomics/distributed-downloader), so they also have the following two columns: - `hashsum_original`: MD5 hash of the original jpg image downloaded based on the CSV provided by LILA BC. - `hashsum_resized`: MD5 hash of the resized image (based on setting to resize if over 720 pixels in any dimension). - The `ohio-small-animals` CSV have a `filename` column defined as `OH_sm_animals_<filename in url_aws>` and a `md5` column containing the MD5 hash of the image as downloaded from the AWS bucket. - The `island-balanced` CSV has an additional `num_cn_images` column indicating the number of images with that animal's common name. - There is a `metadata.csv` included in the `data/test/` directory for the dataset viewer to display images alongside their taxonomic information. The `subset` corresponds to the `dataset-name`. ### Data Splits These datasets were curated to create a small collection of camera trap image test sets. ## Dataset Creation ### Curation Rationale As stated above, the goal of these datasets is to provide a collection of species classification test sets for camera trap images. Species classification within camera trap images is a real-world downstream use-case, on which a biological foundation model should be tested. These datasets were selected from those available on [LILA BC](https://lila.science/datasets) since they are labeled at the image-level, and would thus not include frames labeled as containing an animal when it is simply the animal's habitat. The [Island Conservation Camera Traps](https://lila.science/datasets/island-conservation-camera-traps/) were of particular interest for their stated purpose of assisting in the prevention of endangered island species' extinction and the varied ecosystems represented. ### Source Data The images and their labels come from the following 5 LILA BC datasets. The labels are provided at the image level (not sequence level). Please see the source links for more information on the individual datasets. - [Desert Lion Conservation Camera Traps](https://lila.science/datasets/desert-lion-conservation-camera-traps/) - [ENA24-detection](https://lila.science/datasets/ena24detection) - [Island Conservation Camera Traps](https://lila.science/datasets/island-conservation-camera-traps/) - [Ohio Small Animals](https://lila.science/datasets/ohio-small-animals/) - [Orinoquia Camera Traps](https://lila.science/datasets/orinoquia-camera-traps/) ### Annotations Annotations provided by the source data providers ([aligned by LILA BC](https://lila.science/taxonomy-mapping-for-camera-trap-data-sets/)) are used for this test set. ### Personal and Sensitive Information These images come from an existing, public biodiversity data repository, which publishes them without associated GPS locations for the species in the images and they ensure the removal of all humans (who would otherwise have been labeled as such), so the there are no concerns. ## Considerations for Using the Data This collection of small balanced datasets was designed for testing the classification ability of [BioCLIP 2](https://github.com/Imageomics/bioclip-2) to classify species in camera trap images, a practical use-case and one on which it was not extensively trained. ### Bias, Risks, and Limitations The available species in these datasets is not a representative sample of species around the world, though they do cover a portion of species of interest to those collecting images using camera traps. ## Licensing Information This compilation is licensed under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/), same as the images and metadata which belong to their original sources (see citation directions below). ## Citation Please cite both this compilation and its constituent data sources: ``` @dataset{idle-oo-camera-traps, title = {{IDLE}-{OO} {C}amera {T}raps}, author = {Elizabeth G Campolongo and Jianyang Gu and Net Zhang}, year = {2025}, url = {https://huggingface.co/datasets/imageomics/IDLE-OO-Camera-Traps}, doi = {}, publisher = {Hugging Face} } ``` Please be sure to also cite the original data sources (provided citations on their LILA BC pages are included): - [Ohio Small Animals](https://lila.science/datasets/ohio-small-animals/) - Balasubramaniam S. [Optimized Classification in Camera Trap Images: An Approach with Smart Camera Traps, Machine Learning, and Human Inference](https://etd.ohiolink.edu/acprod/odb_etd/etd/r/1501/10?clear=10&p10_accession_num=osu1721417695430687). Master’s thesis, The Ohio State University. 2024. - Bibtex: ``` @mastersthesis{balasubramaniam2024-oh-small, author = {Balasubramaniam, S.}, title = {Optimized Classification in Camera Trap Images: An Approach with Smart Camera Traps, Machine Learning, and Human Inference}, school = {The Ohio State University}, year = {2024}, url = {http://rave.ohiolink.edu/etdc/view?acc_num=osu1721417695430687} } ``` - [Desert Lion Conservation Camera Traps](https://lila.science/datasets/desert-lion-conservation-camera-traps/) - No citation provided by source, bibtex: ``` @misc{lion-ct, author = {Desert Lion Conservation}, title = {Desert Lion Conservation Camera Traps}, howpublished = {https://lila.science/datasets/desert-lion-conservation-camera-traps/}, month = {July}, year = {2024}, } ``` - [Orinoquia Camera Traps](https://lila.science/datasets/orinoquia-camera-traps/) - Vélez J, McShea W, Shamon H, Castiblanco‐Camacho PJ, Tabak MA, Chalmers C, Fergus P, Fieberg J. [An evaluation of platforms for processing camera‐trap data using artificial intelligence](https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.14044). Methods in Ecology and Evolution. 2023 Feb;14(2):459-77. - Bibtex: ``` @article{velez2022choosing-orinoquia, title={Choosing an Appropriate Platform and Workflow for Processing Camera Trap Data using Artificial Intelligence}, author={V{\'e}lez, Juliana and Castiblanco-Camacho, Paula J and Tabak, Michael A and Chalmers, Carl and Fergus, Paul and Fieberg, John}, journal={arXiv preprint arXiv:2202.02283}, year={2022} } ``` - [Island Conservation Camera Traps](https://lila.science/datasets/island-conservation-camera-traps/) - No citation provided by source, bibtex: ``` @misc{island-ct, author = {Island Conservation}, title = {Island Conservation Camera Traps}, howpublished = {https://lila.science/datasets/island-conservation-camera-traps/}, } ``` - [ENA24-detection](https://lila.science/datasets/ena24detection) - Yousif H, Kays R, Zhihai H. Dynamic Programming Selection of Object Proposals for Sequence-Level Animal Species Classification in the Wild. IEEE Transactions on Circuits and Systems for Video Technology, 2019. ([bibtex](http://lila.science/wp-content/uploads/2019/12/hayder2019_bibtex.txt)) - Bibtex: ``` @article{yousif2019dynamic-ENA24, title={Dynamic Programming Selection of Object Proposals for Sequence-Level Animal Species Classification in the Wild}, author={Yousif, Hayder and Kays, Roland and He, Zhihai}, journal={IEEE Transactions on Circuits and Systems for Video Technology}, year={2019}, publisher={IEEE} } ``` ## Acknowledgements This work was supported by the [Imageomics Institute](https://imageomics.org), which is funded by the US National Science Foundation's Harnessing the Data Revolution (HDR) program under [Award #2118240](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2118240) (Imageomics: A New Frontier of Biological Information Powered by Knowledge-Guided Machine Learning). Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. Additionally, we would like to acknowledge and thank [Labeled Information Library of Alexandria: Biology and Conservation (LILA BC)](https://lila.science) for providing a coordinated collection of camera trap images for research use. ## Dataset Card Authors Elizabeth G. Campolongo
ChavyvAkvar/synthetic-trades-BTC-batch-15	ChavyvAkvar	2025-06-03T21:12:06Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-03T21:11:07Z	null	--- dataset_info: features: - name: scenario_id dtype: string - name: final_pnl_ratio dtype: float64 - name: max_drawdown dtype: float64 - name: total_trades dtype: int64 - name: synthetic_ohlc_open sequence: float64 - name: synthetic_ohlc_high sequence: float64 - name: synthetic_ohlc_low sequence: float64 - name: synthetic_ohlc_close sequence: float64 - name: garch_params_used_for_sim_str dtype: string - name: strategy_params_str dtype: string - name: strategy_exit_rules_str dtype: string splits: - name: train num_bytes: 923450988 num_examples: 1000 download_size: 924510954 dataset_size: 923450988 configs: - config_name: default data_files: - split: train path: data/train-* ---
ChavyvAkvar/synthetic-trades-XRP-batch-32	ChavyvAkvar	2025-06-03T21:11:00Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-03T21:09:59Z	null	--- dataset_info: features: - name: scenario_id dtype: string - name: final_pnl_ratio dtype: float64 - name: max_drawdown dtype: float64 - name: total_trades dtype: int64 - name: synthetic_ohlc_open sequence: float64 - name: synthetic_ohlc_high sequence: float64 - name: synthetic_ohlc_low sequence: float64 - name: synthetic_ohlc_close sequence: float64 - name: garch_params_used_for_sim_str dtype: string - name: strategy_params_str dtype: string - name: strategy_exit_rules_str dtype: string splits: - name: train num_bytes: 923448078 num_examples: 1000 download_size: 924485696 dataset_size: 923448078 configs: - config_name: default data_files: - split: train path: data/train-* ---
Litian2002/spatialvlm_qa_test	Litian2002	2025-06-03T20:59:46Z	0	0	[ "task_categories:visual-question-answering", "language:zh", "language:en", "license:mit", "size_categories:10K<n<100K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "spatial-reasoning", "blender", "synthetic", "vision-language" ]	[ "visual-question-answering" ]	2025-06-03T20:59:24Z	null	--- license: mit task_categories: - visual-question-answering language: - zh - en tags: - spatial-reasoning - blender - synthetic - vision-language pretty_name: 合成空间视觉语言问答数据集 size_categories: - 1K<n<100K --- # 合成空间视觉语言问答数据集自动从Blender场景数据集生成。每个示例包含一张图片和一个问答对，用于探测度量（数值）和关系（真/假）空间推理技能。 * 图像: 使用Blender渲染（1000个场景，每个5个随机基元，随机相机和光照）。 * 元数据: 对象名称、位置、缩放、颜色、材质标志。 * 问题: 每张图片10个，从手工制作的模板中抽取，涵盖： * 欧几里得/水平/垂直距离查询 * 对象宽度查询 * 相对于相机的前/后谓词距离以Blender场景单位表示，四舍五入到1厘米精度。 ## 字段 \| 字段 \| 类型 \| 描述 \| \|---------\|---------\|-------------------------------------\| \| image \| image \| 渲染的场景 \| \| question\| string \| 自然语言查询 \| \| answer \| string \| 真实答案 \| ## 引用 ``` @misc{synthetic_spatialvlm_qa, title = {合成空间视觉语言问答数据集}, author = {<您的姓名>}, year = 2025, url = {https://huggingface.co/datasets/Litian2002/spatialvlm_qa_test} } ```
ChavyvAkvar/synthetic-trades-BNB-batch-13	ChavyvAkvar	2025-06-03T20:58:36Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-03T20:57:38Z	null	--- dataset_info: features: - name: scenario_id dtype: string - name: final_pnl_ratio dtype: float64 - name: max_drawdown dtype: float64 - name: total_trades dtype: int64 - name: synthetic_ohlc_open sequence: float64 - name: synthetic_ohlc_high sequence: float64 - name: synthetic_ohlc_low sequence: float64 - name: synthetic_ohlc_close sequence: float64 - name: garch_params_used_for_sim_str dtype: string - name: strategy_params_str dtype: string - name: strategy_exit_rules_str dtype: string splits: - name: train num_bytes: 923450482 num_examples: 1000 download_size: 924475245 dataset_size: 923450482 configs: - config_name: default data_files: - split: train path: data/train-* ---
Eathus/cwe_view1000_list_gpt_few_cwe_desc_replace	Eathus	2025-06-03T20:54:30Z	0	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-03T20:54:27Z	null	--- dataset_info: features: - name: ID dtype: string - name: Name dtype: string - name: Abstraction dtype: string - name: Structure dtype: string - name: Status dtype: string - name: Description dtype: string - name: ExtendedDescription dtype: string - name: ApplicablePlatforms list: - name: Class dtype: string - name: Name dtype: string - name: Prevalence dtype: string - name: Type dtype: string - name: AlternateTerms list: - name: Description dtype: string - name: Term dtype: string - name: ModesOfIntroduction list: - name: Note dtype: string - name: Phase dtype: string - name: CommonConsequences list: - name: Impact sequence: string - name: Likelihood sequence: string - name: Note dtype: string - name: Scope sequence: string - name: PotentialMitigations list: - name: Description dtype: string - name: Effectiveness dtype: string - name: EffectivenessNotes dtype: string - name: MitigationID dtype: string - name: Phase sequence: string - name: Strategy dtype: string - name: ObservedExamples list: - name: Description dtype: string - name: Link dtype: string - name: Reference dtype: string - name: AffectedResources sequence: string - name: TaxonomyMappings list: - name: EntryID dtype: string - name: EntryName dtype: string - name: MappingFit dtype: string - name: TaxonomyName dtype: string - name: RelatedAttackPatterns sequence: string - name: References list: - name: Authors sequence: string - name: Edition dtype: string - name: ExternalReferenceID dtype: string - name: Publication dtype: string - name: PublicationDay dtype: string - name: PublicationMonth dtype: string - name: PublicationYear dtype: string - name: Publisher dtype: string - name: Section dtype: string - name: Title dtype: string - name: URL dtype: string - name: URLDate dtype: string - name: Notes list: - name: Note dtype: string - name: Type dtype: string - name: ContentHistory list: - name: ContributionComment dtype: string - name: ContributionDate dtype: string - name: ContributionName dtype: string - name: ContributionOrganization dtype: string - name: ContributionReleaseDate dtype: string - name: ContributionType dtype: string - name: ContributionVersion dtype: string - name: Date dtype: string - name: ModificationComment dtype: string - name: ModificationDate dtype: string - name: ModificationName dtype: string - name: ModificationOrganization dtype: string - name: ModificationReleaseDate dtype: string - name: ModificationVersion dtype: string - name: PreviousEntryName dtype: string - name: SubmissionComment dtype: string - name: SubmissionDate dtype: string - name: SubmissionName dtype: string - name: SubmissionOrganization dtype: string - name: SubmissionReleaseDate dtype: string - name: SubmissionVersion dtype: string - name: Type dtype: string - name: Version dtype: string - name: MappingNotes_Usage dtype: string - name: MappingNotes_Rationale dtype: string - name: MappingNotes_Comments dtype: string - name: MappingNotes_Reasons sequence: string - name: MappingNotes_Suggestions list: - name: Comment dtype: string - name: CweID dtype: string - name: RelatedWeaknesses list: - name: CweID dtype: string - name: Nature dtype: string - name: Ordinal dtype: string - name: ViewID dtype: string - name: WeaknessOrdinalities list: - name: Description dtype: string - name: Ordinality dtype: string - name: DetectionMethods list: - name: Description dtype: string - name: DetectionMethodID dtype: string - name: Effectiveness dtype: string - name: EffectivenessNotes dtype: string - name: Method dtype: string - name: DemonstrativeExamples list: - name: Entries list: - name: BodyText dtype: string - name: ExampleCode dtype: string - name: IntroText dtype: string - name: Language dtype: string - name: Nature dtype: string - name: Reference dtype: string - name: ID dtype: string - name: FunctionalAreas sequence: string - name: Diagram dtype: string - name: LikelihoodOfExploit dtype: string - name: BackgroundDetails sequence: string - name: NumPaths dtype: int64 - name: Paths sequence: sequence: string - name: Children sequence: string - name: Summary dtype: string - name: gpt_cwe_description dtype: string splits: - name: train num_bytes: 10612741 num_examples: 940 download_size: 2924148 dataset_size: 10612741 configs: - config_name: default data_files: - split: train path: data/train-* ---
ChavyvAkvar/synthetic-trades-BTC-batch-14	ChavyvAkvar	2025-06-03T20:49:19Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-03T20:48:21Z	null	--- dataset_info: features: - name: scenario_id dtype: string - name: final_pnl_ratio dtype: float64 - name: max_drawdown dtype: float64 - name: total_trades dtype: int64 - name: synthetic_ohlc_open sequence: float64 - name: synthetic_ohlc_high sequence: float64 - name: synthetic_ohlc_low sequence: float64 - name: synthetic_ohlc_close sequence: float64 - name: garch_params_used_for_sim_str dtype: string - name: strategy_params_str dtype: string - name: strategy_exit_rules_str dtype: string splits: - name: train num_bytes: 923451450 num_examples: 1000 download_size: 924510866 dataset_size: 923451450 configs: - config_name: default data_files: - split: train path: data/train-* ---
yoona-J/ASR_Wav2Vec_Preprocess_Degenerative_Brain_Dataset	yoona-J	2025-06-03T20:45:33Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:timeseries", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-03T18:12:56Z	null	--- dataset_info: features: - name: input_values sequence: float32 - name: labels sequence: int64 splits: - name: train num_bytes: 1010469662.4318247 num_examples: 3468 - name: valid num_bytes: 56551072.0 num_examples: 192 - name: test num_bytes: 46983120.0 num_examples: 189 download_size: 1094180586 dataset_size: 1114003854.4318247 configs: - config_name: default data_files: - split: train path: data/train-* - split: valid path: data/valid-* - split: test path: data/test-* ---
french-datasets/bio-datasets_e3c	french-datasets	2025-06-03T20:40:34Z	0	0	[ "language:spa", "language:eus", "language:fra", "language:eng", "language:ita", "region:us" ]	[]	2025-06-03T20:39:29Z	null	--- language: - spa - eus - fra - eng - ita viewer: false --- Ce répertoire est vide, il a été créé pour améliorer le référencement du jeu de données [bio-datasets/e3c](https://huggingface.co/datasets/bio-datasets/e3c).
french-datasets/L3-IA-2025_Questions_Reponses	french-datasets	2025-06-03T20:37:36Z	0	0	[ "language:fra", "region:us" ]	[]	2025-06-03T20:36:34Z	null	--- language: - fra viewer: false --- Ce répertoire est vide, il a été créé pour améliorer le référencement du jeu de données [L3-IA-2025/Questions_Reponses](https://huggingface.co/datasets/L3-IA-2025/Questions_Reponses).
french-datasets/L3-IA-2025_Questions2	french-datasets	2025-06-03T20:37:24Z	0	0	[ "language:fra", "region:us" ]	[]	2025-06-03T20:36:20Z	null	--- language: - fra viewer: false --- Ce répertoire est vide, il a été créé pour améliorer le référencement du jeu de données [L3-IA-2025/Questions2](https://huggingface.co/datasets/L3-IA-2025/Questions2).
omniomni/omniscienceinstruct	omniomni	2025-06-03T20:36:49Z	2	0	[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-03-19T01:23:30Z	null	--- dataset_info: features: - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 142671648 num_examples: 147267 download_size: 79339989 dataset_size: 142671648 configs: - config_name: default data_files: - split: train path: data/train-* ---
fermatix-ai/SWE-Bench-Demo	fermatix-ai	2025-06-03T20:33:34Z	14	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-02T14:45:46Z	null	--- dataset_info: features: - name: repo dtype: string - name: instance_id dtype: string - name: issue_link dtype: string - name: pr_link dtype: string - name: problem_statement dtype: string - name: hints_text dtype: string - name: base_commit dtype: string - name: patch dtype: string - name: test_patch dtype: string - name: FAIL_TO_PASS dtype: string - name: PASS_TO_PASS dtype: string - name: language_version dtype: string configs: - config_name: default data_files: - split: CPP path: cpp/cpp.parquet - split: CSharp path: csharp/csharp.parquet - split: Go path: go/go.parquet - split: Java path: java/java.parquet - split: Kotlin path: kotlin/kotlin.parquet - split: PHP path: php/php.parquet - split: Ruby path: ruby/ruby.parquet - split: Rust path: rust/rust.parquet - split: Scala path: scala/scala.parquet - split: TypeScript path: ts/ts.parquet --- # SWE-bench Tasks This repository contains tasks from the SWE-bench dataset, intended for evaluating the capabilities of models in automatic bug fixing and code modification. The tasks cover various programming languages and projects, providing a diverse set of scenarios for testing and training. Each task includes: * Patches with fixes and/or tests * Instructions for building and running (in the form of a Dockerfile), as well as corresponding run logs * A parquet file containing basic information about the task ## Task Structure Each task is organized in the following structure: ``` <language>/<organization>__<repository>/<issue_id>/ ├── Dockerfile # Environment build instructions ├── docker-compose.yml # Docker Compose configuration ├── Makefile # Automation scripts ├── apply_fix.sh # Script for applying the fix ├── apply_test.sh # Script for applying tests ├── run_tests.sh # Script for running tests ├── logs/ # Directory with execution logs │ └── ... └── patches/ # Directory with patches ├── fix.patch # Patch with the fix └── test.patch # Patch with tests ``` ## Example: Rust/tokio-rs__mio/1706 This task demonstrates a fix for an issue in the MIO (Metal I/O) library for Rust. The task structure is: ``` Rust/tokio-rs__mio/1706/ ├── Dockerfile # Build based on rust:1.74-slim ├── docker-compose.yml # Configuration for running ├── Makefile # Simple build commands ├── apply_fix.sh # Applies the fix patch ├── apply_test.sh # Applies the test patch ├── run_tests.sh # Runs tests after applying patches ├── logs/ # Contains test execution logs └── patches/ ├── fix.patch # Fix for the issue └── test.patch # Tests to verify the fix ``` ## Running a Task 1. Navigate to the task directory. 2. Execute `make run`. 3. Check the results in the `logs/` directory.
ChavyvAkvar/synthetic-trades-XRP-batch-28	ChavyvAkvar	2025-06-03T20:27:58Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-03T20:27:02Z	null	--- dataset_info: features: - name: scenario_id dtype: string - name: final_pnl_ratio dtype: float64 - name: max_drawdown dtype: float64 - name: total_trades dtype: int64 - name: synthetic_ohlc_open sequence: float64 - name: synthetic_ohlc_high sequence: float64 - name: synthetic_ohlc_low sequence: float64 - name: synthetic_ohlc_close sequence: float64 - name: garch_params_used_for_sim_str dtype: string - name: strategy_params_str dtype: string - name: strategy_exit_rules_str dtype: string splits: - name: train num_bytes: 923448041 num_examples: 1000 download_size: 924485862 dataset_size: 923448041 configs: - config_name: default data_files: - split: train path: data/train-* ---
shuchenliu/countdown-solutions-100	shuchenliu	2025-06-03T20:15:35Z	7	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-03T08:28:15Z	null	--- dataset_info: features: - name: nums sequence: int64 - name: target dtype: int64 - name: response dtype: string splits: - name: train num_bytes: 831096 num_examples: 6764 download_size: 265694 dataset_size: 831096 configs: - config_name: default data_files: - split: train path: data/train-* ---
ChavyvAkvar/synthetic-trades-XRP-batch-27	ChavyvAkvar	2025-06-03T20:13:09Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-03T20:12:11Z	null	--- dataset_info: features: - name: scenario_id dtype: string - name: final_pnl_ratio dtype: float64 - name: max_drawdown dtype: float64 - name: total_trades dtype: int64 - name: synthetic_ohlc_open sequence: float64 - name: synthetic_ohlc_high sequence: float64 - name: synthetic_ohlc_low sequence: float64 - name: synthetic_ohlc_close sequence: float64 - name: garch_params_used_for_sim_str dtype: string - name: strategy_params_str dtype: string - name: strategy_exit_rules_str dtype: string splits: - name: train num_bytes: 923448420 num_examples: 1000 download_size: 924480142 dataset_size: 923448420 configs: - config_name: default data_files: - split: train path: data/train-* ---
orcn/v3.2-cir-text	orcn	2025-06-03T20:10:35Z	0	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-03T20:10:32Z	null	--- dataset_info: features: - name: text1 dtype: string - name: text2 dtype: string - name: text3 dtype: string - name: text4 dtype: string - name: text5 dtype: string - name: text6 dtype: string - name: text7 dtype: string - name: text8 dtype: string - name: text9 dtype: 'null' - name: text10 dtype: 'null' - name: text11 dtype: 'null' - name: text12 dtype: 'null' - name: text13 dtype: 'null' - name: text14 dtype: 'null' - name: text15 dtype: 'null' - name: text16 dtype: 'null' - name: text17 dtype: 'null' - name: text18 dtype: 'null' - name: text19 dtype: 'null' - name: text20 dtype: 'null' - name: text21 dtype: 'null' - name: text22 dtype: 'null' - name: text23 dtype: 'null' - name: text24 dtype: 'null' - name: text25 dtype: 'null' splits: - name: train num_bytes: 335928 num_examples: 500 download_size: 157528 dataset_size: 335928 configs: - config_name: default data_files: - split: train path: data/train-* ---
orcn/v3.2-cir-image	orcn	2025-06-03T20:10:32Z	0	0	[ "size_categories:n<1K", "format:parquet", "modality:image", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-03T20:09:22Z	null	--- dataset_info: features: - name: image1 dtype: image - name: image2 dtype: image - name: image3 dtype: image - name: image4 dtype: image - name: image5 dtype: image - name: image6 dtype: image - name: image7 dtype: image - name: image8 dtype: image - name: image9 dtype: image - name: image10 dtype: image - name: image11 dtype: image - name: image12 dtype: image - name: image13 dtype: image - name: image14 dtype: image - name: image15 dtype: image - name: image16 dtype: image - name: image17 dtype: image - name: image18 dtype: image - name: image19 dtype: image - name: image20 dtype: image - name: image21 dtype: image - name: image22 dtype: image - name: image23 dtype: image - name: image24 dtype: image - name: image25 dtype: image splits: - name: train num_bytes: 93800962.0 num_examples: 500 download_size: 93497412 dataset_size: 93800962.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
babs/open-slr-speaker-id	babs	2025-06-03T20:04:12Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-03T19:56:42Z	null	--- dataset_info: features: - name: audio dtype: audio - name: file_name dtype: string - name: transcript dtype: string - name: speaker_id dtype: string splits: - name: train num_bytes: 1198177635.28 num_examples: 2045 download_size: 935494045 dataset_size: 1198177635.28 configs: - config_name: default data_files: - split: train path: data/train-* ---
ChavyvAkvar/synthetic-trades-BTC-batch-12	ChavyvAkvar	2025-06-03T19:51:06Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-03T19:50:09Z	null	--- dataset_info: features: - name: scenario_id dtype: string - name: final_pnl_ratio dtype: float64 - name: max_drawdown dtype: float64 - name: total_trades dtype: int64 - name: synthetic_ohlc_open sequence: float64 - name: synthetic_ohlc_high sequence: float64 - name: synthetic_ohlc_low sequence: float64 - name: synthetic_ohlc_close sequence: float64 - name: garch_params_used_for_sim_str dtype: string - name: strategy_params_str dtype: string - name: strategy_exit_rules_str dtype: string splits: - name: train num_bytes: 923450658 num_examples: 1000 download_size: 924460224 dataset_size: 923450658 configs: - config_name: default data_files: - split: train path: data/train-* ---
ChavyvAkvar/synthetic-trades-XRP-batch-25	ChavyvAkvar	2025-06-03T19:49:01Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-03T19:48:06Z	null	--- dataset_info: features: - name: scenario_id dtype: string - name: final_pnl_ratio dtype: float64 - name: max_drawdown dtype: float64 - name: total_trades dtype: int64 - name: synthetic_ohlc_open sequence: float64 - name: synthetic_ohlc_high sequence: float64 - name: synthetic_ohlc_low sequence: float64 - name: synthetic_ohlc_close sequence: float64 - name: garch_params_used_for_sim_str dtype: string - name: strategy_params_str dtype: string - name: strategy_exit_rules_str dtype: string splits: - name: train num_bytes: 923447720 num_examples: 1000 download_size: 924484995 dataset_size: 923447720 configs: - config_name: default data_files: - split: train path: data/train-* ---
syvai/emotion_dataset_no_reasoning	syvai	2025-06-03T19:45:09Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-03T19:45:06Z	null	--- dataset_info: features: - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 2842616 num_examples: 16000 download_size: 1062081 dataset_size: 2842616 configs: - config_name: default data_files: - split: train path: data/train-* ---
ChavyvAkvar/synthetic-trades-XRP-batch-23	ChavyvAkvar	2025-06-03T19:25:12Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-03T19:24:14Z	null	--- dataset_info: features: - name: scenario_id dtype: string - name: final_pnl_ratio dtype: float64 - name: max_drawdown dtype: float64 - name: total_trades dtype: int64 - name: synthetic_ohlc_open sequence: float64 - name: synthetic_ohlc_high sequence: float64 - name: synthetic_ohlc_low sequence: float64 - name: synthetic_ohlc_close sequence: float64 - name: garch_params_used_for_sim_str dtype: string - name: strategy_params_str dtype: string - name: strategy_exit_rules_str dtype: string splits: - name: train num_bytes: 923448219 num_examples: 1000 download_size: 924485577 dataset_size: 923448219 configs: - config_name: default data_files: - split: train path: data/train-* ---
mohamedah/zh-news-articles	mohamedah	2025-06-03T19:24:31Z	0	0	[ "language:zh", "license:mit", "size_categories:1K<n<10K", "format:csv", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-03T18:57:53Z	null	--- license: mit language: - zh pretty_name: Chinese News Articles --- # Chinese News Article Dataset A dataset of Chinese state media articles and Chinese New York Times articles first introduced in the paper An Analysis of Chinese Censorship Bias in LLMs. State media Articles were sourced from the [news2016zh](https://github.com/brightmart/nlp_chinese_corpus?tab=readme-ov-file#2%E6%96%B0%E9%97%BB%E8%AF%AD%E6%96%99json%E7%89%88news2016zh) corpus and we automatically scraped the New York Times articles. ## Citation If you publish work using our datasets or CensorshipDetector, please cite our work using the following citation: ```bibtex @inproceedings{ahmed2025censorshipbias title = {An Analysis of Chinese Censorship Bias in LLMs}, author = {Ahmed, Mohamed and Knockel, Jeffrey and Greenstadt, Rachel}, booktitle = {Proceedings on Privacy Enhancing Technologies (PoPETs)}, volume = {2025}, issue = {4}, year = {2025} } ```
anirudhb11/star-graph-deg-2-path-3-nodes-300	anirudhb11	2025-06-03T19:22:24Z	0	0	[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-03T19:22:22Z	null	--- dataset_info: features: - name: graph dtype: string - name: source dtype: string - name: destination dtype: string - name: path dtype: string splits: - name: train num_bytes: 11844206 num_examples: 200000 - name: test num_bytes: 1183870 num_examples: 20000 download_size: 8379525 dataset_size: 13028076 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
Jurgie/MyVoiceSmall	Jurgie	2025-06-03T19:17:03Z	0	0	[ "size_categories:n<1K", "format:parquet", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-03T19:17:01Z	null	--- dataset_info: features: - name: input_ids sequence: int32 - name: labels sequence: int64 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 5290 num_examples: 1 download_size: 6361 dataset_size: 5290 configs: - config_name: default data_files: - split: train path: data/train-* ---
ChavyvAkvar/synthetic-trades-XRP-batch-22	ChavyvAkvar	2025-06-03T19:14:31Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-03T19:13:30Z	null	--- dataset_info: features: - name: scenario_id dtype: string - name: final_pnl_ratio dtype: float64 - name: max_drawdown dtype: float64 - name: total_trades dtype: int64 - name: synthetic_ohlc_open sequence: float64 - name: synthetic_ohlc_high sequence: float64 - name: synthetic_ohlc_low sequence: float64 - name: synthetic_ohlc_close sequence: float64 - name: garch_params_used_for_sim_str dtype: string - name: strategy_params_str dtype: string - name: strategy_exit_rules_str dtype: string splits: - name: train num_bytes: 923448158 num_examples: 1000 download_size: 924435070 dataset_size: 923448158 configs: - config_name: default data_files: - split: train path: data/train-* ---
MaLA-LM/mala-opus-dedup-2410	MaLA-LM	2025-06-03T19:07:03Z	10,043	1	[ "task_categories:translation", "license:odc-by", "size_categories:n>1T", "arxiv:2409.17892", "region:us" ]	[ "translation" ]	2025-05-28T07:13:20Z	null	--- license: odc-by task_categories: - translation size_categories: - n>1T --- # MaLA Corpus: Massive Language Adaptation Corpus This [mala-opus-dedup-2410](https://huggingface.co/datasets/MaLA-LM/mala-opus-dedup-2410) is the bilingual part of the [MaLA Corpus](https://huggingface.co/collections/MaLA-LM/mala-corpus-66e05127641a51de34d39529). It is a cleaned and deduplicated version of OPUS corpus, collected from [OPUS](https://opus.nlpl.eu) with a cutoff of October 2024 (2410). Particularly, it contains bilingual translation data (aka, parallel data or bitexts) in 16,829 language pairs. The [MaLA Corpus (Massive Language Adaptation)](https://huggingface.co/collections/MaLA-LM/mala-corpus-66e05127641a51de34d39529) is a series of comprehensive, multilingual datasets designed to support the continual pre-training of large language models. This [mala-opus-dedup-2410](https://huggingface.co/datasets/MaLA-LM/mala-opus-dedup-2410) set can also support the training of multilingual translation models. --- ## Data Fields `source_text`: the source text of parallel data. `source_lang`: the language of the source text. `target_text`: the target text of parallel data. `target_lang`: the language of the target text. `original_code` - the source-target language codes in the format of `${src_code} - ${trg_code}`, delimited by " - ". --- ## Key Features - Language Coverage: Includes data in 29,202 language pairs. - Pre-processing: The corpus is cleaned and deduplicated to ensure high-quality training data. --- ## Dataset Creation This [mala-opus-dedup-2410](https://huggingface.co/datasets/MaLA-LM/mala-opus-dedup-2410) set was created by processing data from [OPUS](https://opus.nlpl.eu), followed by rigorous pre-processing to ensure the quality of the data: - Cleaning: Noisy and irrelevant data were removed to ensure higher data quality. - Deduplication: Duplicate entries across multiple sources were eliminated. - Normalization: The data was normalized, and language codes were standardized to ISO 639-3 to ensure consistency across all sources. We do a variety of handcrafted checks to filter out noisy lines based on [this pipeline](https://github.com/browsermt/students/tree/master/train-student/clean). In detail, we run line-level script detection to ensure that the writing script identified for the dataset during the preliminary stage makes up more than 5% of that line. We remove lines where the same word or character repeats more than 5 times in a row. We calculate the source and target ratios in the number of characters and number of words as well as alphabetical ratio, but only require one of the ratios to be within our pre-determined range, due to the wide range of scripts, languages, and their usual word delimiters. We also ensure that the line fits between a minimum and a maximum number of character lengths (non-empty). --- ## Intended Use This [mala-opus-dedup-2410](https://huggingface.co/datasets/MaLA-LM/mala-opus-dedup-2410) set is intended for researchers and developers looking to improve the multilingual capabilities of language models. It is especially useful for: - Pre-training of large language models, particularly continual pre-training, to enhance the performance in low-resource languages. - Fine-tuning models on multilingual benchmarks to improve language coverage across a variety of domains. - Multilingual tasks such as machine translation training or fine-tuning. --- ## Take-down Policy We don't own any part of the data. We will comply with legitimate requests by removing the affected sources from the corpora. --- ## Citation This [mala-opus-dedup-2410](https://huggingface.co/datasets/MaLA-LM/mala-opus-dedup-2410) set was processed by the [MaLA-LM](https://mala-lm.github.io) project. If you find this dataset useful, please cite our paper below. ``` @article{ji2024emma500enhancingmassivelymultilingual, title={{EMMA}-500: Enhancing Massively Multilingual Adaptation of Large Language Models}, author={Shaoxiong Ji and Zihao Li and Indraneil Paul and Jaakko Paavola and Peiqin Lin and Pinzhen Chen and Dayyán O'Brien and Hengyu Luo and Hinrich Schütze and Jörg Tiedemann and Barry Haddow}, year={2024}, journal={arXiv preprint 2409.17892}, url={https://arxiv.org/abs/2409.17892}, } ``` ## Acknowledgements We extend our thanks to the language communities and contributors who helped source, clean, and validate the diverse data used in the MaLA Corpus. Their efforts are invaluable in supporting linguistic diversity in AI research. This work is done by researchers at [Helsinki-NLP](https://huggingface.co/Helsinki-NLP) in collaboration with partners from TU Darmstadt, the University of Edinburgh, and LMU Munich. It is funded by [HPLT](https://hplt-project.org) and [UTTER](https://he-utter.eu).
BBSRguy/Ranjan-Hindi33min	BBSRguy	2025-06-03T19:06:34Z	0	0	[ "task_categories:text-to-speech", "language:hi", "license:mit", "size_categories:n<1K", "format:audiofolder", "modality:audio", "library:datasets", "library:mlcroissant", "region:us" ]	[ "text-to-speech" ]	2025-06-03T18:36:31Z	null	--- license: mit task_categories: - text-to-speech language: - hi size_categories: - n<1K --- # Ranjan-Hindi33min Owner: [@BBSRguy](https://huggingface.co/BBSRguy) Created: 2025-06-03 Year: 2025 Language: Hindi 🇮🇳 Region Focus: Odisha, India Sample Rate Variants: 16 kHz, 24 kHz, 32 kHz Total Files: 29 pairs (speech + text) Duration: Approximately 33 minutes of speech --- ## 📜 Description `Ranjan-Hindi33min` is a meticulously curated dataset comprising high-quality Hindi speech samples and their corresponding textual transcriptions. This dataset is designed to support various speech processing tasks, including Automatic Speech Recognition (ASR), Text-to-Speech (TTS) synthesis, and speech-text alignment, with a particular emphasis on the linguistic nuances of Odisha and eastern India. The dataset features recordings from a single native Hindi speaker, ensuring consistency in voice characteristics. The speech samples vary in length, ranging from 9 seconds to over 2 minutes, and encompass a diverse array of content, including formal narration, greetings, traditional expressions, and culturally contextual material. --- ## 🗂️ Dataset Structure - `id`: Unique identifier for each sample (e.g., `sample_001`) - `speech`: Filename of the corresponding WAV audio file - `text`: Transcribed Hindi text content Dataset will not be available in the dataset viewer as there is no train partition. --- ## 🔊 Audio Sampling Rates & Model Compatibility To cater to a broad spectrum of research and application needs, the dataset provides audio recordings in three different sampling rates: ### 🎧 16 kHz (Wideband) - Description: Standard sampling rate widely used in speech processing tasks. - Ideal For: - OpenAI Whisper: Requires 16 kHz input audio for optimal performance. - Facebook Wav2Vec 2.0: Pretrained on 16 kHz sampled speech audio. - Mozilla DeepSpeech: Expects 16 kHz mono-channel WAV files. ### 🎧 24 kHz (High-Quality Wideband) - Description: Offers a balance between audio quality and computational efficiency. - Ideal For: - Parler-TTS: Supports 24 kHz audio, suitable for high-fidelity TTS applications. - ESPnet-TTS: Flexible with sampling rates; 24 kHz is commonly used. ### 🎧 32 kHz (Ultra-Wideband) - Description: Provides higher audio quality, beneficial for advanced TTS models and applications demanding superior clarity. - Ideal For: - Advanced TTS Models: Models that can leverage higher sampling rates for improved synthesis quality. Note: When using models that require a specific sampling rate, ensure that the input audio matches the expected rate to avoid potential degradation in performance. --- ## 🧪 Applications - Automatic Speech Recognition (ASR): Fine-tuning and evaluating models like Whisper, Wav2Vec 2.0, and DeepSpeech for Hindi language transcription tasks. - Text-to-Speech (TTS) Synthesis: Training and assessing TTS models such as Parler-TTS and ESPnet-TTS for generating natural-sounding Hindi speech. - Speech-Text Alignment: Developing and testing alignment algorithms for synchronizing speech with textual content. - Linguistic Research: Analyzing phonetic and prosodic features of Hindi as spoken in the Odisha region. --- ## 🧾 License This dataset is shared by [@BBSRguy](https://huggingface.co/BBSRguy) as part of the ODEN Initiative for open AI development in India, aiming to democratize AI and make it accessible for everyone, particularly focusing on developing AI tools like personal assistants for the people of Odisha. 🙏 नमस्ते! 🙏
arnaultsta/MNLP_M3_wikipedia_camel_chunked_500_whole	arnaultsta	2025-06-03T19:03:07Z	0	0	[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-03T18:23:43Z	null	--- dataset_info: features: - name: question dtype: string - name: options sequence: string - name: rationale dtype: string - name: label dtype: string - name: label_idx dtype: int64 - name: dataset dtype: string - name: chunk1 dtype: string - name: chunk2 dtype: string - name: chunk3 dtype: string splits: - name: train num_bytes: 1525828291 num_examples: 200000 - name: validation num_bytes: 3708788 num_examples: 519 download_size: 387108979 dataset_size: 1529537079 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---
canaan000/lerobot	canaan000	2025-06-03T19:02:36Z	0	0	[ "language:en", "license:unknown", "region:us", "art" ]	[]	2025-06-03T19:01:26Z	null	--- license: unknown language: - en tags: - art ---
avaishnav/Indian-plant-leaves-species	avaishnav	2025-06-03T19:00:25Z	142	0	[ "task_categories:image-classification", "language:en", "license:apache-2.0", "size_categories:n<1K", "format:imagefolder", "modality:image", "library:datasets", "library:mlcroissant", "region:us", "biology" ]	[ "image-classification" ]	2025-05-20T00:32:46Z	null	--- license: apache-2.0 task_categories: - image-classification language: - en tags: - biology size_categories: - n<1K --- # Indian Plant Leaves Species ## Dataset Summary This dataset consists of 542 high-resolution images of leaves from 12 different plant species. All images were captured using a mobile phone camera under natural lighting conditions. The dataset is intended for use in plant species classification, leaf recognition, and related computer vision tasks. ## Supported Tasks and Leaderboards - Image Classification - Species Recognition - Transfer Learning for Plant Identification - Fine-tuning Computer Vision Models ## Languages Not applicable (images only, no text). ## Dataset Structure - Number of Images: 542 - Number of Classes: 12 plant species ## Plant Species 1. Alstonia Scholaris 2. Bael 3. Guava 4. Jatropha 5. Mango 6. Pongamia Pinnata 7. Arjun 8. Basil 9. Chinar 10. Jamun 11. Lemon 12. Pomegranate ![pomegranate_0050.JPG](https://cdn-uploads.huggingface.co/production/uploads/660d9e53fc9742e051790d23/YTVLVt9NAMfZXq65lohLj.jpeg) ![jatropha_0047.JPG](https://cdn-uploads.huggingface.co/production/uploads/660d9e53fc9742e051790d23/pIQ3JpP_Q791D6-Efvq21.jpeg) ## Usage Example usage with datasets library (replace your-username/leaf-dataset): ``` from datasets import load_dataset dataset = load_dataset("avaishnav/Indian-plant-leaves-species") ``` ## Data Collection Process - Images were captured by the dataset author using a mobile phone camera. - Photos were taken in natural daylight, with leaves detached from the plant stem. - No preprocessing or filtering was applied, to preserve real-world conditions. ## Citation If you use this dataset in your research or application, please consider citing: ``` @dataset{vaishnav2025indian, author = {Vaishnav, Anugrah}, title = {Indian Plant Leaves Species}, year = 2025, url = {https://huggingface.co/datasets/avaishnav/Indian-plant-leaves-species}, publisher = {Hugging Face}, license = {Apache-2.0} } ```
kurry/institutional-holdings-13f-quarterly	kurry	2025-06-03T18:52:26Z	56	0	[ "task_categories:tabular-classification", "task_categories:tabular-regression", "language:en", "license:mit", "size_categories:100M<n<1B", "format:arrow", "modality:tabular", "modality:text", "library:datasets", "library:mlcroissant", "region:us", "finance", "sec", "13f", "embeddings", "institutional-holdings", "stocks", "equities", "panel-data" ]	[ "tabular-classification", "tabular-regression" ]	2025-05-20T03:09:54Z	null	--- license: mit language: - en pretty_name: US Institutional 13F Holdings Record-level Embeddings (1980–2024) tags: - finance - sec - 13f - embeddings - institutional-holdings - stocks - equities - panel-data datasets: - kurry/institutional-holdings-13f task_categories: - tabular-classification - tabular-regression size_categories: - 10M<n<100M date: "2025-05-19" --- # US Institutional 13F Holdings Embeddings Dataset Quarterly snapshots (1980 – 2024) of every U.S.-reported 13F equity holding, plus unsupervised embeddings for: * Institutional managers (investors) * Assets (PERMCO-level equities) --- ## Dataset Description 13F filings are quarterly disclosures to the U.S. SEC by institutions managing ≥ \$100 million. For each quarter (1980 Q1 → 2024 Q3) the dataset provides: * Holdings Table – exact positions with shares, price, and market value * Investor Embeddings – 32-dim vectors from log-dollar-weighted Truncated SVD * Asset Embeddings – companion vectors for the equity universe * Metadata – manager IDs, asset IDs, timestamps, etc. Note: This dataset has been merged into a single structure with all quarters combined, rather than the previous separate quarterly folders. --- ## Data Structure ### Holdings table (core fields) \| Field \| Type \| Description \| \| ---------------- \| ------ \| ---------------------------------------------- \| \| `mgrno` \| string \| Institutional manager ID (SEC) \| \| `permco` \| string \| Permanent company identifier \| \| `fdate` \| date \| Quarter-end report date \| \| `shares` \| float \| Shares held \| \| `price` \| float \| Price per share on `fdate` \| \| `dollar_holding` \| float \| Shares × Price (market value of the position) \| \| `quarter` \| string \| Quarter identifier (e.g., "2020Q4") \| \| `year` \| int64 \| Year of the quarter \| \| `qtr` \| int64 \| Quarter number (1-4) \| ### Embeddings tables \| Field \| Type \| Description \| \| ----------------------- \| -------------------- \| --------------------------------------------- \| \| `mgrno` or `permco` \| string \| Primary key \| \| `embedding` \| float32\[n\] sequence\| 32-dimensional vector (size may vary) \| --- ## Coverage and Distribution * Quarters: 1980 Q1 → 2024 Q3 (179 quarters) * Universe: Every stock appearing in any 13F filing during the window * Rows: Tens of millions of manager-asset-quarter tuples * Embeddings: One vector per active manager and per PERMCO each quarter ### Quick load ```python from datasets import DatasetDict # Load the dataset (now in merged format) dataset = load_dataset("kurry/institutional-holdings-13f-quarterly") print(dataset["train"][0]) ``` --- ## Typical Usage * Alpha/return prediction, manager similarity, clustering * Long-run studies of institutional ownership dynamics * Panel regressions (quarterly frequency) ```python # Load a single quarter ds = DatasetDict.load_from_disk("institutional-holdings-13f-quarterly/2020Q4") print(ds["investor_embeddings"][0]["embedding"][:8]) ``` ```python # Iterate over all quarters import os root = "institutional-holdings-13f-quarterly" for q in sorted(p for p in os.listdir(root) if "Q" in p): ds = DatasetDict.load_from_disk(f"{root}/{q}") # process ds["holdings"], ds["investor_embeddings"], ds["asset_embeddings"] ``` --- ## Data Splits Each quarterly folder is a Hugging Face `DatasetDict` containing: * `holdings` * `investor_embeddings` * `asset_embeddings` --- ## Identifier Definitions * PERMCO – company-level identifier (stable through ticker/name changes) * PERMNO – security-level identifier (stable through symbol/CUSIP changes) --- ## Processing Pipeline 1. Parse raw 13F text filings. 2. Map CUSIPs → PERMCOs. 3. Aggregate shares and price to compute market value. 4. Compute log-dollar weight ``` w = log(1 + dollar_holding) ``` 5. Build manager-by-asset matrix `M` with elements `w`. 6. Apply Truncated SVD, keep top k = 32 factors (or ≤ rank). ``` M ≈ U Σ Vᵀ ``` * Rows of U → manager embeddings * Rows of V → asset embeddings --- ## Licensing & Limitations * License: MIT * Intended use: Research & education * Note: Source 13F filings are public; mappings were derived from public data. --- ## Citation ```bibtex @dataset{kurry2025institutionalholdings13f, author = {Kurry}, title = {US Institutional 13F Holdings Embeddings Dataset}, year = {2025}, publisher = {Hugging Face}, url = {https://huggingface.co/datasets/kurry/institutional-holdings-13f} } ``` --- ### Quick start ```python from datasets import DatasetDict ds = DatasetDict.load_from_disk("institutional-holdings-13f-quarterly/2010Q2") ```
Franklin0/ReasonGen-R1-RL-T2I-11k	Franklin0	2025-06-03T18:38:20Z	35	0	[ "task_categories:text-to-image", "license:mit", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2505.24875", "region:us" ]	[ "text-to-image" ]	2025-05-27T02:34:35Z	null	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 524257 num_examples: 12367 download_size: 179867 dataset_size: 524257 configs: - config_name: default data_files: - split: train path: data/train-* task_categories: - text-to-image license: mit --- This is the RL dataset for the paper: [\"ReasonGen-R1: CoT for Autoregressive Image generation models through SFT and RL\"](https://huggingface.co/papers/2505.24875). ReasonGen-R1 is a two-stage framework that imbues an autoregressive image generator with explicit text-based "thinking" skills via supervised fine-tuning (SFT) on a newly generated reasoning dataset of written rationales. It then refines its outputs using Group Relative Policy Optimization (GRPO). This dataset contains the model-crafted rationales paired with visual prompts, enabling controlled planning of object layouts, styles, and scene compositions. Website: https://aka.ms/reasongen Code: https://github.com/Franklin-Zhang0/Image-RL Arxiv: https://arxiv.org/abs/2505.24875
Franklin0/ReasonGen-R1-RL-Geneval-12k	Franklin0	2025-06-03T18:37:56Z	58	0	[ "task_categories:text-to-image", "license:mit", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2505.24875", "region:us" ]	[ "text-to-image" ]	2025-05-27T02:50:14Z	null	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 524257 num_examples: 12367 download_size: 179867 dataset_size: 524257 configs: - config_name: default data_files: - split: train path: data/train-* task_categories: - text-to-image license: mit --- This is the RL dataset for the paper: [\"ReasonGen-R1: CoT for Autoregressive Image generation models through SFT and RL\"](https://huggingface.co/papers/2505.24875). ReasonGen-R1 is a two-stage framework that imbues an autoregressive image generator with explicit text-based "thinking" skills via supervised fine-tuning (SFT) on a newly generated reasoning dataset of written rationales. It then refines its outputs using Group Relative Policy Optimization (GRPO). This dataset contains the model-crafted rationales paired with visual prompts, enabling controlled planning of object layouts, styles, and scene compositions. Website: https://aka.ms/reasongen Code: https://github.com/Franklin-Zhang0/Image-RL Arxiv: https://arxiv.org/abs/2505.24875
Franklin0/ReasonGen-R1-RL-DPG-5k	Franklin0	2025-06-03T18:36:01Z	35	0	[ "task_categories:text-to-image", "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2505.24875", "region:us" ]	[ "text-to-image" ]	2025-05-27T02:27:55Z	null	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 2371376 num_examples: 4996 download_size: 1295838 dataset_size: 2371376 configs: - config_name: default data_files: - split: train path: data/train-* task_categories: - text-to-image --- RL Dataset for the paper: [\"ReasonGen-R1: Cot for Autoregressive Image generation models through SFT and RL\"](https://huggingface.co/papers/2505.24875). Website: https://aka.ms/reasongen Code: https://github.com/Franklin-Zhang0/Image-RL Arxiv: https://arxiv.org/abs/2505.24875
neginr/phi_24K_qwq_6K	neginr	2025-06-03T18:34:20Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-03T18:33:02Z	null	--- dataset_info: features: - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 1692722617.642405 num_examples: 30000 download_size: 813744124 dataset_size: 1692722617.642405 configs: - config_name: default data_files: - split: train path: data/train-* ---
SynthData/InstructionFollowingBlueprint	SynthData	2025-06-03T18:33:54Z	0	1	[ "license:apache-2.0", "size_categories:n<1K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-03T18:28:34Z	null	--- license: apache-2.0 --- This dataset contains blueprints for complex Instruction Following. The dataset consists of 50% LiveBench IF benchmarks deconstructed for analysis supplemented with 50% synthetically generated, high-difficulty and diverse prompts for robustness. https://huggingface.co/datasets/livebench/instruction_following Contact: aaron@synthdata.io
hiepp2/tvp4	hiepp2	2025-06-03T18:32:42Z	175	1	[ "task_categories:text-generation", "language:en", "size_categories:n>1T", "arxiv:2504.21318", "arxiv:2505.00949", "region:us" ]	[ "text-generation" ]	2025-06-02T23:40:29Z	null	--- dataset_info: - config_name: all features: - name: messages list: - name: content dtype: string - name: role dtype: string - name: num_tokens dtype: int64 - name: source dtype: string splits: - name: train num_bytes: 7062819826.825458 num_examples: 349317 download_size: 3077653717 dataset_size: 7062819826.825458 - config_name: code features: - name: messages list: - name: content dtype: string - name: role dtype: string - name: num_tokens dtype: int64 - name: source dtype: string splits: - name: train num_bytes: 3872656251.3167396 num_examples: 83070 download_size: 1613338604 dataset_size: 3872656251.3167396 - config_name: math features: - name: messages list: - name: content dtype: string - name: role dtype: string - name: num_tokens dtype: int64 - name: source dtype: string splits: - name: train num_bytes: 1599028646 num_examples: 93733 download_size: 704448153 dataset_size: 1599028646 - config_name: science features: - name: messages list: - name: content dtype: string - name: role dtype: string - name: num_tokens dtype: int64 - name: source dtype: string splits: - name: train num_bytes: 1590765326 num_examples: 172514 download_size: 674333812 dataset_size: 1590765326 configs: - config_name: all data_files: - split: train path: all/train-* - config_name: code data_files: - split: train path: code/train-* - config_name: math data_files: - split: train path: math/train-* - config_name: science data_files: - split: train path: science/train-* task_categories: - text-generation language: - en pretty_name: Mixture size_categories: - n>1T --- <img src="mot-thumbnail.png" alt="Centered Image" style="display: block; margin: 0 auto;" width="500"> # Dataset summary Mixture-of-Thoughts is a curated dataset of 350k verified reasoning traces distilled from [DeepSeek-R1](https://huggingface.co/deepseek-ai/DeepSeek-R1). The dataset spans tasks in mathematics, coding, and science, and is designed to teach language models to reason step-by-step. It was used in the Open R1 project to train [OpenR1-Distill-7B](https://huggingface.co/open-r1/OpenR1-Distill-7B), an SFT model that replicates the reasoning capabilities of [deepseek-ai/DeepSeek-R1-Distill-Qwen-7B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B) from the same base model. To load the dataset, run: ```python from datasets import load_dataset dataset = load_dataset("open-r1/Mixture-of-Thoughts", "all", split="train") # Load a specific domain dataset_math = load_dataset("open-r1/Mixture-of-Thoughts", "math", split="train") ``` ## Dataset composition Mixture-of-Thoughts is composed of three domains: math, code, and science. Each domain contains reasoning traces that are designed to teach language models to reason step-by-step. The dataset is structured as follows: - math: 93.7k reasoning traces for mathematical problems, sourced from the `default` subset of [open-r1/OpenR1-Math-220k](https://huggingface.co/datasets/open-r1/OpenR1-Math-220k) - code: 83.1k reasoning traces for competitive programming problems in Python and C++, sourced from the `solutions` and `solutions_w_editorials` subsets of [open-r1/codeforces-cots](https://huggingface.co/datasets/open-r1/codeforces-cots) - science: 173k reasoning traces for scientific problems, sourced from the `science` subset of [nvidia/Llama-Nemotron-Post-Training-Dataset](https://huggingface.co/datasets/nvidia/Llama-Nemotron-Post-Training-Dataset) - all: Contains all reasoning traces from the three domains, for a total of 350k traces. ## Curation methodology To optimise the data mixture, we followed the same methodology described in the [Phi-4-reasoning tech report](https://huggingface.co/papers/2504.21318), namely that mixtures can be optimised independently per domain, and then combined into a single dataset. For each ablation, we evaluate on AIME 2024, GPQA Diamond, and LiveCodeBench v4 every epoch and take the best performing model checkpoint. The figure below shows the results from post-training [open-r1/Qwen2.5-Math-7B-RoPE-300k](https://huggingface.co/open-r1/Qwen2.5-Math-7B-RoPE-300k) on each individual domain compared to the final mixture: <img src="data_mix.png" alt="Centered Image" style="display: block; margin: 0 auto;"> Overall, we find that training on all domains simultaneously yields the best results. See the subsections below for more details on optimising the data mixture per domain. > [!NOTE] > We use LiveCodeBench v4 to accelerate evaluation during our ablations as it contains around half the problems of v5, yet is still representative of the full benchmark. ### Code During the development of [open-r1/OlympicCoder-7B](https://huggingface.co/open-r1/OlympicCoder-7B), we observed that generating R1 reasoning traces in C++ produced better results on the challenging [IOI 2024 benchmark](https://github.com/huggingface/ioi), while Python traces produced better results on LiveCodeBench (a Python-only benchmark). To optimise the data mixture, we therefore used a mix of C++ and Python traces sourced from the following subsets of [open-r1/codeforces-cots](https://huggingface.co/datasets/open-r1/codeforces-cots): - `solutions`: we prompt R1 to solve the problem and produce code in C++. - `solutions_py`: same as `solutions`, but with R1 prompted to produce code in Python. - `solutions_w_editorials`: we prompt R1 to solve the problem and produce code, but also provide it with a human-written solution. - `solutions_w_editorials_py`: same as `solutions_w_editorials`, but with R1 prompted to produce code in Python. The figure below shows the evolution of our ablations on these subsets, using [Qwen/Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) as the base model: <img src="code_mix.png" alt="Centered Image" style="display: block; margin: 0 auto;"> The individual experiments correspond to the following: * exp1 - exp3: scaling the learning rate on the `solutions` subset from 1e-5 to 2e-5, and 4e-5 respectively. * exp4 - exp5: measuring the impact of training on the `solutions_w_editorials` subset vs the combined `solutions` and `solutions_w_editorials` subsets. * exp6 - exp9: measuring the impact of blending in Python traces from the `solutions_py` and `solutions_w_editorials_py` subsets. exp6 combines the `solutions_w_editorials` and `solutions_w_editorials_py` subsets, while exp7 combines the `solutions` and `solutions_py` subsets. Finally, exp8 combines all four subsets. We found that combining all subsets of C++ and Python traces yielded the best results on LiveCodeBench. We also found that using this data mixture to fine-tune [open-r1/Qwen2.5-Coder-7B-RoPE-300k](https://huggingface.co/open-r1/Qwen2.5-Coder-7B-RoPE-300k) led to comparable performance improvements, which shows the effectiveness of our curation strategy. ### Math For the math domain, we mostly focused on comparing the `default` and `extended` subsets of [open-r1/OpenR1-Math-220k](https://huggingface.co/datasets/open-r1/OpenR1-Math-220k). The `default` subset contains 93.7k reasoning traces, while the `extended` subset contains an additional 131k traces, containing simpler problems than the `default` subset. The figure below shows performance on each subset, using [Qwen/Qwen2.5-Math-7B-RoPE-300k](https://huggingface.co/Qwen/Qwen2.5-Math-7B-RoPE-300k) as the base model: <img src="math_mix.png" alt="Centered Image" style="display: block; margin: 0 auto;"> Overall, we found that training on the `default` subset yielded better results than training on the `extended` subset, and that training on both subsets together yielded the best results. Nevertheless, we opted to use the `default` subset only for the final mixture, as including both would have led to a significant increase in the size of the dataset, for a modest improvement in performance. ### Science For the science domain, we used the `science` subset of [nvidia/Llama-Nemotron-Post-Training-Dataset](https://huggingface.co/datasets/nvidia/Llama-Nemotron-Post-Training-Dataset/viewer/SFT/science), which contains 483k reasoning traces. However, we found that the subset was too large to be used in its entirety, as it would have led to a significant increase in the size of the dataset. Instead, we selected the subset of traces where no Qwen models were used for prompt pre-processing--see this [discussion](https://huggingface.co/datasets/nvidia/Llama-Nemotron-Post-Training-Dataset/discussions/6) for more details. The result was 173k reasoning traces, which we used in the final mixture after ablating on the learning rate. ## Citation If you find this dataset is useful in your own work, please consider citing it as follows, together with the source of the specific domain you are using: ```bibtex @misc{openr1, title = {Open R1: A fully open reproduction of DeepSeek-R1}, url = {https://github.com/huggingface/open-r1}, author = {Hugging Face}, month = {January}, year = {2025} } ``` open-r1/codeforces-cots ```bibtex @misc{penedo2025codeforces, title={CodeForces CoTs}, author={Guilherme Penedo and Anton Lozhkov and Hynek Kydlíček and Loubna Ben Allal and Edward Beeching and Agustín Piqueres Lajarín and Quentin Gallouédec and Nathan Habib and Lewis Tunstall and Leandro von Werra}, year={2025}, publisher = {Hugging Face}, journal = {Hugging Face repository}, howpublished = {\url{https://huggingface.co/datasets/open-r1/codeforces-cots}} } ``` open-r1/OpenR1-Math-220k ```bibtex @misc{lozhkov2025openr1math220k, title={OpenR1-Math-220k}, author={Anton Lozhkov and Hynek Kydlíček and Loubna Ben Allal and Guilherme Penedo and Edward Beeching and Quentin Gallouédec and Nathan Habib and Lewis Tunstall and Leandro von Werra}, year={2025}, publisher = {Hugging Face}, journal = {Hugging Face repository}, howpublished = {\url{https://huggingface.co/datasets/open-r1/OpenR1-Math-220k}} } ``` nvidia/Llama-Nemotron-Post-Training-Dataset ```bibtex @misc{bercovich2025llamanemotronefficientreasoningmodels, title={Llama-Nemotron: Efficient Reasoning Models}, author={Akhiad Bercovich and Itay Levy and Izik Golan and Mohammad Dabbah and Ran El-Yaniv and Omri Puny and Ido Galil and Zach Moshe and Tomer Ronen and Najeeb Nabwani and Ido Shahaf and Oren Tropp and Ehud Karpas and Ran Zilberstein and Jiaqi Zeng and Soumye Singhal and Alexander Bukharin and Yian Zhang and Tugrul Konuk and Gerald Shen and Ameya Sunil Mahabaleshwarkar and Bilal Kartal and Yoshi Suhara and Olivier Delalleau and Zijia Chen and Zhilin Wang and David Mosallanezhad and Adi Renduchintala and Haifeng Qian and Dima Rekesh and Fei Jia and Somshubra Majumdar and Vahid Noroozi and Wasi Uddin Ahmad and Sean Narenthiran and Aleksander Ficek and Mehrzad Samadi and Jocelyn Huang and Siddhartha Jain and Igor Gitman and Ivan Moshkov and Wei Du and Shubham Toshniwal and George Armstrong and Branislav Kisacanin and Matvei Novikov and Daria Gitman and Evelina Bakhturina and Jane Polak Scowcroft and John Kamalu and Dan Su and Kezhi Kong and Markus Kliegl and Rabeeh Karimi and Ying Lin and Sanjeev Satheesh and Jupinder Parmar and Pritam Gundecha and Brandon Norick and Joseph Jennings and Shrimai Prabhumoye and Syeda Nahida Akter and Mostofa Patwary and Abhinav Khattar and Deepak Narayanan and Roger Waleffe and Jimmy Zhang and Bor-Yiing Su and Guyue Huang and Terry Kong and Parth Chadha and Sahil Jain and Christine Harvey and Elad Segal and Jining Huang and Sergey Kashirsky and Robert McQueen and Izzy Putterman and George Lam and Arun Venkatesan and Sherry Wu and Vinh Nguyen and Manoj Kilaru and Andrew Wang and Anna Warno and Abhilash Somasamudramath and Sandip Bhaskar and Maka Dong and Nave Assaf and Shahar Mor and Omer Ullman Argov and Scot Junkin and Oleksandr Romanenko and Pedro Larroy and Monika Katariya and Marco Rovinelli and Viji Balas and Nicholas Edelman and Anahita Bhiwandiwalla and Muthu Subramaniam and Smita Ithape and Karthik Ramamoorthy and Yuting Wu and Suguna Varshini Velury and Omri Almog and Joyjit Daw and Denys Fridman and Erick Galinkin and Michael Evans and Katherine Luna and Leon Derczynski and Nikki Pope and Eileen Long and Seth Schneider and Guillermo Siman and Tomasz Grzegorzek and Pablo Ribalta and Monika Katariya and Joey Conway and Trisha Saar and Ann Guan and Krzysztof Pawelec and Shyamala Prayaga and Oleksii Kuchaiev and Boris Ginsburg and Oluwatobi Olabiyi and Kari Briski and Jonathan Cohen and Bryan Catanzaro and Jonah Alben and Yonatan Geifman and Eric Chung and Chris Alexiuk}, year={2025}, eprint={2505.00949}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2505.00949}, } ```
ChavyvAkvar/synthetic-trades-XRP-batch-18	ChavyvAkvar	2025-06-03T18:28:24Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-03T18:27:23Z	null	--- dataset_info: features: - name: scenario_id dtype: string - name: final_pnl_ratio dtype: float64 - name: max_drawdown dtype: float64 - name: total_trades dtype: int64 - name: synthetic_ohlc_open sequence: float64 - name: synthetic_ohlc_high sequence: float64 - name: synthetic_ohlc_low sequence: float64 - name: synthetic_ohlc_close sequence: float64 - name: garch_params_used_for_sim_str dtype: string - name: strategy_params_str dtype: string - name: strategy_exit_rules_str dtype: string splits: - name: train num_bytes: 923448051 num_examples: 1000 download_size: 924486740 dataset_size: 923448051 configs: - config_name: default data_files: - split: train path: data/train-* ---
0xarchit/Ques-Ans-with-Emotion	0xarchit	2025-06-03T18:19:41Z	0	0	[ "task_categories:question-answering", "language:en", "license:mit", "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "question-answer-with-emotion", "question-answers" ]	[ "question-answering" ]	2025-06-03T17:09:49Z	null	--- license: mit task_categories: - question-answering language: - en tags: - question-answer-with-emotion - question-answers pretty_name: Question Answer based on Emotions size_categories: - 1K<n<10K --- created and shared by: [0xarchit](https://0xarchit.carrd.co)
prerit2k/Bench01-20-20sec-1	prerit2k	2025-06-03T18:12:17Z	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", "first collection" ]	[ "robotics" ]	2025-06-03T18:11:02Z	null	--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - first collection 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", "trossen_subversion": "v1.0", "robot_type": "trossen_ai_solo", "total_episodes": 1, "total_frames": 597, "total_tasks": 1, "total_videos": 2, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:1" }, "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": [ 7 ], "names": [ "main_joint_0", "main_joint_1", "main_joint_2", "main_joint_3", "main_joint_4", "main_joint_5", "main_joint_6" ] }, "observation.state": { "dtype": "float32", "shape": [ 7 ], "names": [ "main_joint_0", "main_joint_1", "main_joint_2", "main_joint_3", "main_joint_4", "main_joint_5", "main_joint_6" ] }, "observation.images.cam_main": { "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 } }, "observation.images.cam_wrist": { "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] ```
yoona-J/ASR_Wav2Vec_Preprocess_Peripheral_Neuropathy_Dataset	yoona-J	2025-06-03T18:11:11Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:timeseries", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-03T18:07:13Z	null	--- dataset_info: features: - name: input_values sequence: float32 - name: labels sequence: int64 splits: - name: train num_bytes: 4248737624.0 num_examples: 18679 - name: valid num_bytes: 239472624.0 num_examples: 1040 - name: test num_bytes: 234697040.0 num_examples: 1036 download_size: 4570203659 dataset_size: 4722907288.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: valid path: data/valid-* - split: test path: data/test-* ---
logicalqubit/QA-RRC-DISTRIC-IT-Dataset	logicalqubit	2025-06-03T18:10:34Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-03T18:10:32Z	null	--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 573243 num_examples: 2260 download_size: 197275 dataset_size: 573243 configs: - config_name: default data_files: - split: train path: data/train-* ---
Romoamigo/SWE-Bench-MultilingualC_CPPFiletered_new	Romoamigo	2025-06-03T18:09:38Z	177	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-28T23:47:34Z	null	--- dataset_info: features: - name: repo dtype: string - name: instance_id dtype: string - name: base_commit dtype: string - name: patch dtype: string - name: test_patch dtype: string - name: problem_statement dtype: string - name: hints_text dtype: string - name: created_at dtype: string - name: version dtype: string - name: FAIL_TO_PASS sequence: string - name: PASS_TO_PASS sequence: string - name: repo_name dtype: string - name: image_name dtype: string splits: - name: test num_bytes: 786675 num_examples: 55 download_size: 289127 dataset_size: 786675 configs: - config_name: default data_files: - split: test path: data/test-* ---
TienMat999/vn-legal-qa-rag	TienMat999	2025-06-03T18:03:18Z	274	0	[ "task_categories:question-answering", "language:vi", "license:apache-2.0", "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "legal" ]	[ "question-answering" ]	2025-05-08T16:07:21Z	null	--- dataset_info: features: - name: context_id dtype: string - name: context dtype: string - name: question_id dtype: string - name: question dtype: string - name: answers_id dtype: string - name: answers dtype: string splits: - name: train num_bytes: 26510045 num_examples: 4559 download_size: 8852182 dataset_size: 26510045 configs: - config_name: default data_files: - split: train path: data/train-* license: apache-2.0 task_categories: - question-answering language: - vi tags: - legal size_categories: - 1K<n<10K ---
voidful/earica_ms	voidful	2025-06-03T18:02:46Z	2	0	[ "size_categories:n<1K", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-01T01:55:49Z	null	--- dataset_info: features: - name: question dtype: string - name: reasoning dtype: string - name: answer dtype: string - name: audio dtype: audio - name: index dtype: int64 - name: raw_yaml dtype: string splits: - name: train num_bytes: 2239015.0 num_examples: 4 download_size: 554534 dataset_size: 2239015.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
fixie-ai/endpointing-multi-turn-commonvoice	fixie-ai	2025-06-03T17:57:57Z	30	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-02T22:15:15Z	null	--- dataset_info: features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string - name: variant dtype: string - name: continuation dtype: string - name: transcript dtype: string - name: conversation dtype: string - name: conversation_with_audio dtype: string splits: - name: train num_bytes: 358006509.816 num_examples: 9954 download_size: 346695375 dataset_size: 358006509.816 configs: - config_name: default data_files: - split: train path: data/train-* ---
minpeter/pretrained-tiny-ko	minpeter	2025-06-03T17:57:14Z	0	0	[ "size_categories:1M<n<10M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-03T17:39:47Z	null	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 31850795201.147762 num_examples: 6826068 download_size: 16259105774 dataset_size: 31850795201.147762 configs: - config_name: default data_files: - split: train path: data/train-* ---
mothnaZl/Bespoke-Stratos-17k	mothnaZl	2025-06-03T17:46:35Z	128	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-08T16:19:22Z	null	--- dataset_info: features: - name: system dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string - name: problem dtype: string - name: solution dtype: string - name: input dtype: string splits: - name: train num_bytes: 635631373 num_examples: 16710 download_size: 258776431 dataset_size: 635631373 configs: - config_name: default data_files: - split: train path: data/train-* ---
ChavyvAkvar/synthetic-trades-XRP-batch-14	ChavyvAkvar	2025-06-03T17:45:49Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-03T17:44:47Z	null	--- dataset_info: features: - name: scenario_id dtype: string - name: final_pnl_ratio dtype: float64 - name: max_drawdown dtype: float64 - name: total_trades dtype: int64 - name: synthetic_ohlc_open sequence: float64 - name: synthetic_ohlc_high sequence: float64 - name: synthetic_ohlc_low sequence: float64 - name: synthetic_ohlc_close sequence: float64 - name: garch_params_used_for_sim_str dtype: string - name: strategy_params_str dtype: string - name: strategy_exit_rules_str dtype: string splits: - name: train num_bytes: 923448040 num_examples: 1000 download_size: 924503656 dataset_size: 923448040 configs: - config_name: default data_files: - split: train path: data/train-* ---
davanstrien/dataset-creation-scripts	davanstrien	2025-06-03T17:44:12Z	446	2	[ "region:us" ]	[]	2025-04-01T14:27:11Z	null	--- viewer: false --- # Datasets scripts This is an experimental repository for sharing simple one-liner scripts for creating datasets. The idea is that you can easily run these scripts in the terminal without having to write any code and quickly get a dataset up and running on the Hub. ## Installation All of these scripts assume you have `uv` installed. If you don't have it installed, see the [installation guide](https://docs.astral.sh/uv/getting-started/). The scripts use the recently added [inline-script-metadata](https://packaging.python.org/en/latest/specifications/inline-script-metadata/#inline-script-metadata) to specify dependencies for the script. `UV` will automatically install these dependencies when you run the script in a new environment. This makes it easier for you to try the script without having to install the dependencies yourself. See this [guide](https://docs.astral.sh/uv/guides/scripts/#running-scripts) on running scripts in `uv` for more information. ## Scripts ### PDFs to HF dataset This script uses the newly added `pdf` feature of the `datasets` library to create a dataset from a directory of PDFs. The t;dr is that the script will take as input a directory of PDFs and output a Hugging Face dataset with the images and (optionally) the text extracted from the PDFs. You can find more info about the `pdf` feature [here](https://huggingface.co/docs/datasets/document_load). To see how the script works, you can run it with the `--help` flag: ```python uv run https://huggingface.co/datasets/davanstrien/dataset-creation-scripts/raw/main/pdf-datasets/main.py -h ``` This will print the help message to the terminal. ``` usage: main.py [-h] --directory DIRECTORY [--extract-text EXTRACT_TEXT] --hub-id HUB_ID [--private-repo PRIVATE_REPO] options: -h, --help show this help message and exit ``` For example on my `Desktop` I have a folder called `Arxiv` with a bunch of PDFs in it. I can create a dataset with the following command: ```python uv run https://huggingface.co/datasets/davanstrien/dataset-creation-scripts/raw/main/pdf-datasets/main.py --directory ~/Desktop/Arxiv --hub-id davanstrien/arxiv-pdfs --extract-text true ``` You can find the dataset on the Hub [here](https://huggingface.co/datasets/davanstrien/arxiv-pdfs).
ChavyvAkvar/synthetic-trades-XRP-batch-13	ChavyvAkvar	2025-06-03T17:37:22Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-03T17:36:20Z	null	--- dataset_info: features: - name: scenario_id dtype: string - name: final_pnl_ratio dtype: float64 - name: max_drawdown dtype: float64 - name: total_trades dtype: int64 - name: synthetic_ohlc_open sequence: float64 - name: synthetic_ohlc_high sequence: float64 - name: synthetic_ohlc_low sequence: float64 - name: synthetic_ohlc_close sequence: float64 - name: garch_params_used_for_sim_str dtype: string - name: strategy_params_str dtype: string - name: strategy_exit_rules_str dtype: string splits: - name: train num_bytes: 923448218 num_examples: 1000 download_size: 924505223 dataset_size: 923448218 configs: - config_name: default data_files: - split: train path: data/train-* ---
timcryt/vscf_mlff_data	timcryt	2025-06-03T17:26:33Z	0	0	[ "size_categories:10K<n<100K", "region:us", "chemistry" ]	[]	2025-06-03T17:19:44Z	null	--- tags: - chemistry size_categories: - 10K<n<100K pretty_name: l --- Datasets used in the paper "Интерполяция ППЭ с помощью машинного обучения для ускорения расчётов негармонических частот колебаний молекул". ### Description - `vscf_dataset_2_5.xyz` is the main dataset used from pretraining and finetuning models, 19 molecules, 65168 points - `compare_dataset_OCCO.xyz` is the auxillary datased used to select the model architecture from DimeNet and SchNet, 1 molecule, 1042 points
ChavyvAkvar/synthetic-trades-BTC-batch-6	ChavyvAkvar	2025-06-03T17:24:33Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-03T17:23:36Z	null	--- dataset_info: features: - name: scenario_id dtype: string - name: final_pnl_ratio dtype: float64 - name: max_drawdown dtype: float64 - name: total_trades dtype: int64 - name: synthetic_ohlc_open sequence: float64 - name: synthetic_ohlc_high sequence: float64 - name: synthetic_ohlc_low sequence: float64 - name: synthetic_ohlc_close sequence: float64 - name: garch_params_used_for_sim_str dtype: string - name: strategy_params_str dtype: string - name: strategy_exit_rules_str dtype: string splits: - name: train num_bytes: 923450595 num_examples: 1000 download_size: 924510583 dataset_size: 923450595 configs: - config_name: default data_files: - split: train path: data/train-* ---
jlbaker361/clip-celeb_captioned	jlbaker361	2025-06-03T17:19:27Z	139	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-23T15:07:56Z	null	--- dataset_info: features: - name: image dtype: image - name: embedding sequence: sequence: sequence: float32 - name: text sequence: sequence: sequence: float16 - name: prompt dtype: string - name: posterior sequence: sequence: sequence: float16 splits: - name: train num_bytes: 7280583008.0 num_examples: 30000 download_size: 7070282217 dataset_size: 7280583008.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
5CD-AI/Viet-r1_90k_instruct	5CD-AI	2025-06-03T17:12:03Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-03T17:09:34Z	null	--- dataset_info: features: - name: id dtype: string - name: image dtype: image - name: conversations sequence: - name: content dtype: string - name: role dtype: string - name: vi_conversations sequence: - name: content dtype: string - name: role dtype: string - name: id_num dtype: string splits: - name: train num_bytes: 2996389203.83 num_examples: 79986 download_size: 2612513952 dataset_size: 2996389203.83 configs: - config_name: default data_files: - split: train path: data/train-* ---
prerit2k/Bench01-18-10sec	prerit2k	2025-06-03T17:09:31Z	0	0	[ "task_categories:robotics", "license:apache-2.0", "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:timeseries", "modality:video", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "LeRobot", "first collection" ]	[ "robotics" ]	2025-06-03T17:09:28Z	null	--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - first collection 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", "trossen_subversion": "v1.0", "robot_type": "trossen_ai_solo", "total_episodes": 1, "total_frames": 299, "total_tasks": 1, "total_videos": 2, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:1" }, "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": [ 7 ], "names": [ "main_joint_0", "main_joint_1", "main_joint_2", "main_joint_3", "main_joint_4", "main_joint_5", "main_joint_6" ] }, "observation.state": { "dtype": "float32", "shape": [ 7 ], "names": [ "main_joint_0", "main_joint_1", "main_joint_2", "main_joint_3", "main_joint_4", "main_joint_5", "main_joint_6" ] }, "observation.images.cam_main": { "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 } }, "observation.images.cam_wrist": { "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] ```
LukeBailey181/TrainingRound47MultimodalConjectureDataset	LukeBailey181	2025-06-03T17:09:09Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-03T17:09:05Z	null	--- dataset_info: features: - name: prompt dtype: string - name: target dtype: string - name: source_dataset dtype: string - name: heuristic dtype: float64 splits: - name: embedding num_bytes: 5286055 num_examples: 4320 - name: grad_cosine_sim num_bytes: 3799086 num_examples: 4320 - name: grad_dot_product num_bytes: 3966114 num_examples: 4320 - name: random num_bytes: 3866087 num_examples: 4320 download_size: 5680252 dataset_size: 16917342 configs: - config_name: default data_files: - split: embedding path: data/embedding-* - split: grad_cosine_sim path: data/grad_cosine_sim-* - split: grad_dot_product path: data/grad_dot_product-* - split: random path: data/random-* ---
HAissa/SFT-dataset	HAissa	2025-06-03T17:08:27Z	130	0	[ "size_categories:1M<n<10M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-31T12:37:25Z	null	--- dataset_info: features: - name: source dtype: string - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 3850647056.0 num_examples: 1159281 download_size: 1896264124 dataset_size: 3850647056.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
arnaultsta/MNLP_M3_wikipedia_camel_chunked_300	arnaultsta	2025-06-03T16:55:39Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-03T14:37:47Z	null	--- dataset_info: features: - name: title dtype: string - name: source dtype: string - name: text dtype: string splits: - name: train num_bytes: 168771055 num_examples: 76429 download_size: 91677839 dataset_size: 168771055 configs: - config_name: default data_files: - split: train path: data/train-* ---
levinius/MNLP_distilled_preference_code_ds	levinius	2025-06-03T16:55:18Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-03T16:55:12Z	null	--- dataset_info: features: - name: prompt dtype: string - name: source dtype: string - name: chosen dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 42323537.16766937 num_examples: 20644 - name: validation num_bytes: 4703070.83233063 num_examples: 2294 download_size: 24288551 dataset_size: 47026608.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---
ChavyvAkvar/synthetic-trades-XRP-batch-10	ChavyvAkvar	2025-06-03T16:54:27Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-03T16:53:28Z	null	--- dataset_info: features: - name: scenario_id dtype: string - name: final_pnl_ratio dtype: float64 - name: max_drawdown dtype: float64 - name: total_trades dtype: int64 - name: synthetic_ohlc_open sequence: float64 - name: synthetic_ohlc_high sequence: float64 - name: synthetic_ohlc_low sequence: float64 - name: synthetic_ohlc_close sequence: float64 - name: garch_params_used_for_sim_str dtype: string - name: strategy_params_str dtype: string - name: strategy_exit_rules_str dtype: string splits: - name: train num_bytes: 923448290 num_examples: 1000 download_size: 924486002 dataset_size: 923448290 configs: - config_name: default data_files: - split: train path: data/train-* ---
recogna-nlp/drbodebench	recogna-nlp	2025-06-03T16:49:08Z	18	0	[ "language:pt", "license:apache-2.0", "size_categories:1K<n<10K", "format:json", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "medico", "perguntas-e-respostas", "portugues-brasileiro", "benchmark-llm", "multipla-escolha" ]	[]	2025-06-02T19:25:02Z	null	--- license: apache-2.0 language: - pt tags: - medico - perguntas-e-respostas - portugues-brasileiro - benchmark-llm - multipla-escolha --- # Benchmark Brasileiro de Testes de Aptidão Médica: DrBodeBench (DBB) <p align="center"> <img src="https://cdn-uploads.huggingface.co/production/uploads/64afacf35de9402882fdd69d/naZWT4umxSc4veItz76MS.jpeg" alt="Bode Logo" width="400" style="margin-left:'auto' margin-right:'auto' display:'block'"/> </p> Este conjunto de dados introduz um novo benchmark para avaliar modelos de linguagem grandes (LLMs) médicos em português brasileiro, abordando uma lacuna crítica na avaliação de IA para aplicações de saúde em contextos não ingleses. Ele é construído a partir de testes de aptidão médica brasileiros que abrangem o período de 2011-2024, incluindo o Exame Nacional de Revalidação de Diplomas Médicos Expedidos por Instituição de Educação Superior Estrangeira (Revalida) e Exames de Admissão à Residência da Universidade de São Paulo (FUVEST). O benchmark permite uma avaliação extensiva tanto de LLMs especialistas quanto de modelos gerais. Seu propósito é estabelecer uma base adequada para avaliar e avançar os modelos de linguagem médicos em português, criando um arcabouço padronizado para guiar o desenvolvimento em direção a sistemas de IA mais eficazes, equitativos e culturalmente apropriados para a saúde no Brasil. Os dados do Revalida abrangem múltiplos anos entre 2011-2024, organizados em arquivos de texto distintos para cada ano. O ano de 2017 foi excluído devido à indisponibilidade dos dados no momento da coleta, enquanto os dados de 2020 não foram incluídos nesta versão inicial devido a dificuldades na extração relacionadas ao formato da prova. O objetivo principal durante a criação deste benchmark foi identificar modelos de linguagem que pudessem fornecer as respostas mais precisas para questões médicas e demonstrar compreensão e geração superiores de respostas em português brasileiro. ## Como Usar o Conjunto de Dados O conjunto de dados é projetado para avaliar o desempenho de LLMs em tarefas de perguntas e respostas médicas em português brasileiro. Cada instância consiste em uma pergunta médica, alternativas de múltipla escolha e a resposta correta. Para questões que originalmente incluíam imagens, são fornecidas descrições textuais dessas imagens. Os modelos podem ser instruídos com a pergunta (`enunciado`) e sua descrição de imagem associada (se `contains_img` for verdadeiro e `img_description` estiver disponível), seguidas pelas opções de múltipla escolha (`alternativas`). A tarefa do modelo é selecionar a alternativa correta. O formato de resposta esperado, conforme utilizado no estudo original, consistia apenas na letra correspondente da alternativa correta (A, B, C, D ou E). O prompt utilizado no artigo também especificava que o modelo deveria abster-se de fornecer justificativas para sua seleção ou incluir qualquer texto além da letra da alternativa escolhida. Estrutura de prompt de exemplo usada no artigo: 1. Instrução para receber uma pergunta médica e selecionar a resposta correta. 2. Três exemplos demonstrando o formato de resposta esperado (a letra da alternativa correta). 3. A declaração da pergunta (`enunciado`). 4. Se a pergunta contivesse uma imagem, sua descrição (`img_description`) era anexada imediatamente após a declaração. 5. As alternativas (`alternativas`) eram apresentadas. Cada pergunta foi submetida ao modelo individualmente. A avaliação foi conduzida usando apenas a primeira letra gerada pelo modelo. ## Estrutura do Conjunto de Dados O conjunto de dados é composto por pares de pergunta-resposta com metadados associados. ### Campos de Dados O conjunto de dados contém os seguintes campos: * `origem` (string): A fonte da pergunta (ex.: "INEP" para os exames Revalida, já que o INEP – um instituto – organiza o Revalida). * `prova` (int64): O ano em que o teste de aptidão foi administrado (variando de 2011 a 2024). * `numero` (int64): O número da questão dentro do exame específico. * `enunciado` (string): O texto da pergunta médica. * `alternativas` (dict): Um dicionário contendo as opções de múltipla escolha, onde as chaves são letras (ex.: "A", "B") e os valores são o texto das opções. Pode haver até cinco opções (A, B, C, D ou E). Exemplo: `{ "A": "Texto para a opção A.", "B": "Texto para a opção B.", ... }` * `resposta` (string): A letra correspondente à alternativa correta. * `contains_img` (bool): Um sinalizador booleano indicando se a pergunta original incluía uma imagem. * `img_path` (string): Um caminho relativo para o arquivo de imagem. As imagens são fornecidas separadamente (veja [Link para Imagens Extraídas](#link-para-imagens-extraídas)). Este campo será `null` se `contains_img` for falso. * `img_description` (string): Uma descrição textual da imagem, gerada com o auxílio do GPT-4o mini. Este campo será `null` se `contains_img` for falso ou se uma descrição não tiver sido gerada. É importante notar que "essas descrições não representam necessariamente uma descrição técnica médica ideal e, embora incluídas para completude, serão refinadas em versões futuras do conjunto de dados". ## Link para Imagens Extraídas As imagens que acompanham algumas questões neste conjunto de dados foram extraídas manualmente. Você pode baixá-las no seguinte link: [Link para o Google Drive.](https://drive.google.com/file/d/1ddRzCDU8mDwNsvMzlYTZ4Jzpzi5bEcoD/view?usp=drive_link) O campo `img_path` no conjunto de dados deve corresponder aos caminhos dos arquivos dentro deste arquivo vinculado. ## Curadoria Este benchmark foi criado para suprir a lacuna crítica de ferramentas de avaliação padronizadas para LLMs médicos em português brasileiro. Os recursos existentes em português brasileiro são severamente limitados, com conjuntos de dados disponíveis sofrendo de deficiências como falta de validação clínica ou uso de terminologia arcaica. A ausência de conjuntos de dados médicos válidos em português brasileiro e benchmarks padronizados dificulta o desenvolvimento de modelos, a avaliação sistemática e a comparação. Este benchmark visa fornecer um arcabouço padronizado para guiar o desenvolvimento em direção a sistemas de IA mais eficazes, equitativos e culturalmente apropriados para a saúde no Brasil. ### Dados de Origem Os dados foram coletados de exames de aptidão médica brasileiros para estudantes graduados: * O Exame Nacional de Revalidação de Diplomas Médicos Expedidos por Instituição de Educação Superior Estrangeira (Revalida). Os dados do Revalida abrangem múltiplos anos entre 2011-2024, organizados em arquivos de texto distintos para cada ano. O ano de 2017 foi excluído devido à indisponibilidade dos dados no momento da coleta, enquanto os dados de 2020 não foram incluídos nesta versão inicial devido a dificuldades na extração relacionadas ao formato da prova. * Exames de Admissão à Residência da Universidade de São Paulo (FUVEST). Para o Exame de Residência da FUVEST, apenas o exame de clínica geral foi utilizado na versão inicial deste benchmark. ### Coleta e Limpeza de Dados O processo de coleta de dados envolveu: 1. Extração de dados de perguntas e respostas dos exames Revalida e FUVEST. 2. Uso de expressões regulares (regex) para isolar perguntas e remover conteúdo irrelevante como rodapés e cabeçalhos. 3. Resolução de problemas como espaços deslocados entre palavras e inconsistências de codificação. 4. Emprego do GPT-4o mini da OpenAI para retificar automaticamente o texto, remontar palavras que foram divididas erroneamente e resolver problemas de codificação. 5. Condução de uma revisão manual para garantir a precisão após a limpeza automatizada. Ferramentas de comparação de texto foram usadas para verificar diferenças de palavras entre as perguntas originais e as versões remontadas, com as discrepâncias sendo corrigidas. ### Anotações * Respostas: O campo `resposta` contém a letra correta para cada questão de múltipla escolha, conforme os exames de origem. * Descrições de Imagens: Para questões que incluíam imagens, descrições textuais (`img_description`) foram criadas com o auxílio do GPT-4o mini. Essas descrições foram concebidas para fins de completude, já que a versão inicial do benchmark não utilizava diretamente entradas de imagem para modelos sem capacidades visuais. É explicitamente declarado que "essas descrições não representam necessariamente uma descrição técnica médica ideal e, embora incluídas para completude, serão refinadas em versões futuras do conjunto de dados". ## Informações sobre Citação Se você usar este conjunto de dados em sua pesquisa, por favor, cite o artigo original: ```bibtex @inproceedings{garcia2025step, title={A Step Forward for Medical LLMs in Brazilian Portuguese: Establishing a Benchmark and a Strong Baseline}, author={Garcia, Gabriel Lino and Manesco, João Renato Ribeiro and Paiola, Pedro Henrique and Ribeiro, Pedro Henrique Crespan and Garcia, Ana Lara Alves and Papa, João Paulo}, booktitle={Proceedings of the 38th IEEE International Symposium on Computer-Based Medical Systems (CBMS 2025)}, year={2025}, address={Madrid, Spain}, month={June} } ``` ## Informações sobre a Licença A licença para este conjunto de dados é Apache 2.0. O texto oficial da licença está em inglês.
levinius/MNLP_distilled_preference_ds	levinius	2025-06-03T16:44:42Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-03T16:44:37Z	null	--- dataset_info: features: - name: prompt dtype: string - name: source dtype: string - name: chosen dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 24657058.435946036 num_examples: 16144 - name: validation num_bytes: 2740012.564053964 num_examples: 1794 download_size: 14771309 dataset_size: 27397071.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---
diabolocom/talkbank_4_stt	diabolocom	2025-06-03T16:43:11Z	359	2	[ "task_categories:automatic-speech-recognition", "task_categories:text-to-speech", "task_categories:text-to-audio", "multilinguality:multilingual", "language:en", "language:de", "language:es", "language:fr", "language:zh", "license:cc-by-nc-sa-3.0", "size_categories:100K<n<1M", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2409.12042", "region:us" ]	[ "automatic-speech-recognition", "text-to-speech", "text-to-audio" ]	2024-09-19T13:46:35Z	null	--- language: - en - de - es - fr - es - zh license: - cc-by-nc-sa-3.0 multilinguality: - multilingual task_categories: - automatic-speech-recognition - text-to-speech - text-to-audio pretty_name: talkbank_4_stt dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcript dtype: string - name: language_code dtype: string - name: subset dtype: string - name: full_language dtype: string - name: switch_id dtype: string - name: segment_id dtype: string - name: transcript_filename dtype: string - name: audio_len_sec dtype: string - name: orig_file_start dtype: string - name: orig_file_end dtype: string - name: channel dtype: string - name: speaker_id dtype: string splits: - name: segment_jp num_bytes: 227582609.856 num_examples: 23272 - name: segment_es num_bytes: 677020941.0 num_examples: 57150 - name: segment_zh num_bytes: 213524031.376 num_examples: 23364 - name: segment_en num_bytes: 533951011.464 num_examples: 32092 - name: segment_fr num_bytes: 264513055.952 num_examples: 14796 - name: segment_ess num_bytes: 49557412.0 num_examples: 494 - name: segment_de num_bytes: 127896413.16 num_examples: 12944 - name: switch_jp num_bytes: 437307191.0 num_examples: 318 - name: switch_es num_bytes: 1597337075.24 num_examples: 2130 - name: switch_zh num_bytes: 381994059.12 num_examples: 1691 - name: switch_en num_bytes: 835237103.0 num_examples: 908 - name: switch_fr num_bytes: 481521047.0 num_examples: 281 - name: switch_ess num_bytes: 54158091.0 num_examples: 9 - name: switch_de num_bytes: 415139340.0 num_examples: 701 download_size: 6218452927 dataset_size: 6296739381.167999 configs: - config_name: default data_files: - split: segment_jp path: data/segment_jp-* - split: segment_es path: data/segment_es-* - split: segment_zh path: data/segment_zh-* - split: segment_en path: data/segment_en-* - split: segment_fr path: data/segment_fr-* - split: segment_ess path: data/segment_ess-* - split: segment_de path: data/segment_de-* - split: switch_jp path: data/switch_jp-* - split: switch_es path: data/switch_es-* - split: switch_zh path: data/switch_zh-* - split: switch_en path: data/switch_en-* - split: switch_fr path: data/switch_fr-* - split: switch_ess path: data/switch_ess-* - split: switch_de path: data/switch_de-* --- # Dataset Card ## Dataset Description This dataset is a benchmark based on the TalkBank[1] corpus—a large multilingual repository of conversational speech that captures real-world, unstructured interactions. We use CA-Bank [2], which focuses on phone conversations between adults, which include natural speech phenomena such as laughter, pauses, and interjections. To ensure the dataset is highly accurate and suitable for benchmarking conversational ASR systems, we employ extensive set of pre-processing. ## Preprocessing Steps We apply the following preprocessing steps to ensure the dataset’s quality: - Manual filtering of conversations - Speaker-channel alignment - Timestamp alignment using voice activity detection (VAD) - Discarding segments based on Word Error Rate (WER) thresholds ## Paper and Code Repository For a comprehensive explanation of the preprocessing pipeline and dataset details, refer to our paper [ASR Benchmarking: The Need for a More Representative Conversational Dataset](https://arxiv.org/abs/2409.12042) and explore our [GitHub repository](https://github.com/Diabolocom-Research/ConversationalDataset) for code and additional resources. ## Segmentation Types: Speaker Switch vs Annotation We offer two types of segmentation for this dataset: - Annotation-based Segmentation: Segments are derived directly from the annotations provided in the original TalkBank corpus. - Speaker Switch Segmentation: We consolidate consecutive segments from the same speaker into a single, larger audio segment, providing an alternative structure for analysis. ## Citations While using this dataset please cite: ``` @article{maheshwari2024asr, title={ASR Benchmarking: Need for a More Representative Conversational Dataset}, author={Maheshwari, Gaurav and Ivanov, Dmitry and Johannet, Th{\'e}o and Haddad, Kevin El}, journal={arXiv preprint arXiv:2409.12042}, year={2024} } ``` In addition, please acknowledge the TalkBank dataset:: ``` @article{macwhinney2010transcribing, title={Transcribing, searching and data sharing: The CLAN software and the TalkBank data repository}, author={MacWhinney, Brian and Wagner, Johannes}, journal={Gesprachsforschung: Online-Zeitschrift zur verbalen Interaktion}, volume={11}, pages={154}, year={2010}, publisher={NIH Public Access} } ``` ## Licensing Information This dataset is released under the [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0). ## References [1]: MacWhinney, Brian. "TalkBank: Building an open unified multimodal database of communicative interaction." (2004). [2]: MacWhinney, Brian, and Johannes Wagner. "Transcribing, searching and data sharing: The CLAN software and the TalkBank data repository." Gesprachsforschung: Online-Zeitschrift zur verbalen Interaktion 11 (2010): 154.
un1c0rnio/eval_act_so101_box_pencil3_140000	un1c0rnio	2025-06-03T16:41:34Z	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", "so101", "tutorial" ]	[ "robotics" ]	2025-06-03T16:41:10Z	null	--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - so101 - 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": "so101", "total_episodes": 9, "total_frames": 13523, "total_tasks": 1, "total_videos": 18, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:9" }, "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.base": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height": 480, "video.width": 640, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "has_audio": false } }, "observation.images.extside": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height": 480, "video.width": 640, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "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] ```
tingshiuanlai/motherese-prosody-data	tingshiuanlai	2025-06-03T16:39:23Z	118	0	[ "license:cc-by-4.0", "region:us", "prosody", "speech-features", "librispeech" ]	[]	2025-05-25T21:22:08Z	null	--- license: cc-by-4.0 tags: - prosody - speech-features - librispeech --- # Prosody Features for train-clean-100 This repository contains a pickled `ProsodyFeatureExtractor` object trained on the LibriSpeech `train-clean-100` and `dev-clean` subset. ## Contents - Word-level prosodic features - F0, energy, duration, pause, prominence - Extracted using CELEX-based stress localization ## Format - `.pkl` file — can be loaded using `pickle.load(open(..., "rb"))` - Compatible with JSON serialization
ChavyvAkvar/synthetic-trades-BNB-batch-3	ChavyvAkvar	2025-06-03T16:31:46Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-03T16:30:51Z	null	--- dataset_info: features: - name: scenario_id dtype: string - name: final_pnl_ratio dtype: float64 - name: max_drawdown dtype: float64 - name: total_trades dtype: int64 - name: synthetic_ohlc_open sequence: float64 - name: synthetic_ohlc_high sequence: float64 - name: synthetic_ohlc_low sequence: float64 - name: synthetic_ohlc_close sequence: float64 - name: garch_params_used_for_sim_str dtype: string - name: strategy_params_str dtype: string - name: strategy_exit_rules_str dtype: string splits: - name: train num_bytes: 923450370 num_examples: 1000 download_size: 924490854 dataset_size: 923450370 configs: - config_name: default data_files: - split: train path: data/train-* ---

TAUR-dev/SIE_EVAL__testing_full_run2__rl__samples

TAUR-dev

2025-06-03T23:11:29Z

0

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

[]

2025-06-03T23:11:28Z

null

--- dataset_info: features: - name: doc_id dtype: int64 - name: doc dtype: string - name: target dtype: string - name: arguments dtype: string - name: resps dtype: string - name: filtered_resps dtype: string - name: doc_hash dtype: string - name: prompt_hash dtype: string - name: target_hash dtype: string - name: exact_match dtype: int64 - name: extracted_answers dtype: string - name: source_file dtype: string - name: generation dtype: string - name: info dtype: string splits: - name: train num_bytes: 5580211 num_examples: 15 download_size: 687827 dataset_size: 5580211 configs: - config_name: default data_files: - split: train path: data/train-* ---

DatologyAI/wikipedia-ja-6k_sample

DatologyAI

2025-06-03T23:11:23Z

0

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

[]

2025-06-03T23:11:19Z

null

--- dataset_info: features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 32931670.910860065 num_examples: 6500 download_size: 18311475 dataset_size: 32931670.910860065 configs: - config_name: default data_files: - split: train path: data/train-* ---

DatologyAI/wikipedia-de-6k_sample

DatologyAI

2025-06-03T23:11:02Z

0

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

[]

2025-06-03T23:10:58Z

null

--- dataset_info: features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 21983213.937647525 num_examples: 6500 download_size: 13406335 dataset_size: 21983213.937647525 configs: - config_name: default data_files: - split: train path: data/train-* ---

Rexhaif/wmt22-23

Rexhaif

2025-06-03T23:10:02Z

53

0

[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-05-23T15:13:15Z

null

--- dataset_info: features: - name: lp dtype: string - name: src dtype: string - name: ref dtype: string - name: hyp dtype: string - name: system dtype: string - name: score dtype: float64 - name: score_name dtype: string - name: example_id dtype: string - name: source dtype: string splits: - name: train num_bytes: 136090001 num_examples: 273027 download_size: 24620149 dataset_size: 136090001 configs: - config_name: default data_files: - split: train path: data/train-* ---

TAUR-dev/SIE_EVAL__testing_full_run2__sft__samples

TAUR-dev

2025-06-03T23:02:40Z

0

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

[]

2025-06-03T23:02:39Z

null

--- dataset_info: features: - name: doc_id dtype: int64 - name: doc dtype: string - name: target dtype: string - name: arguments dtype: string - name: resps dtype: string - name: filtered_resps dtype: string - name: doc_hash dtype: string - name: prompt_hash dtype: string - name: target_hash dtype: string - name: exact_match dtype: int64 - name: extracted_answers dtype: string - name: source_file dtype: string - name: generation dtype: string - name: info dtype: string splits: - name: train num_bytes: 6044223 num_examples: 15 download_size: 656116 dataset_size: 6044223 configs: - config_name: default data_files: - split: train path: data/train-* ---

psg777/gluepickup103

psg777

2025-06-03T23:00:05Z

0

1

[ "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", "so101", "tutorial" ]

[ "robotics" ]

2025-06-03T22:59:51Z

null

--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - so101 - 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": "v2.2", "robot_type": "so101", "total_episodes": 20, "total_frames": 11530, "total_tasks": 1, "total_videos": 60, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:20" }, "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.base": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height": 480, "video.width": 640, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "has_audio": false } }, "observation.images.gripper": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height": 480, "video.width": 640, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "has_audio": false } }, "observation.images.bird": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height": 480, "video.width": 640, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "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] ```

ChavyvAkvar/synthetic-trades-BNB-batch-18

ChavyvAkvar

2025-06-03T22:55:54Z

0

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

[]

2025-06-03T22:54:55Z

null

--- dataset_info: features: - name: scenario_id dtype: string - name: final_pnl_ratio dtype: float64 - name: max_drawdown dtype: float64 - name: total_trades dtype: int64 - name: synthetic_ohlc_open sequence: float64 - name: synthetic_ohlc_high sequence: float64 - name: synthetic_ohlc_low sequence: float64 - name: synthetic_ohlc_close sequence: float64 - name: garch_params_used_for_sim_str dtype: string - name: strategy_params_str dtype: string - name: strategy_exit_rules_str dtype: string splits: - name: train num_bytes: 923450465 num_examples: 1000 download_size: 924490147 dataset_size: 923450465 configs: - config_name: default data_files: - split: train path: data/train-* ---

ChavyvAkvar/synthetic-trades-XRP-batch-41

ChavyvAkvar

2025-06-03T22:54:43Z

0

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

[]

2025-06-03T22:53:33Z

null

--- dataset_info: features: - name: scenario_id dtype: string - name: final_pnl_ratio dtype: float64 - name: max_drawdown dtype: float64 - name: total_trades dtype: int64 - name: synthetic_ohlc_open sequence: float64 - name: synthetic_ohlc_high sequence: float64 - name: synthetic_ohlc_low sequence: float64 - name: synthetic_ohlc_close sequence: float64 - name: garch_params_used_for_sim_str dtype: string - name: strategy_params_str dtype: string - name: strategy_exit_rules_str dtype: string splits: - name: train num_bytes: 923448173 num_examples: 1000 download_size: 924485067 dataset_size: 923448173 configs: - config_name: default data_files: - split: train path: data/train-* ---

jiuyal2/eval_so100_marker

jiuyal2

2025-06-03T22:46:00Z

130

0

[ "task_categories:robotics", "license:apache-2.0", "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:timeseries", "modality:video", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "LeRobot", "tutorial" ]

[ "robotics" ]

2025-05-29T22:20:43Z

null

--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - 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": "so100", "total_episodes": 1, "total_frames": 607, "total_tasks": 1, "total_videos": 2, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:1" }, "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.so100": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height": 480, "video.width": 640, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "has_audio": false } }, "observation.images.iphone": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height": 480, "video.width": 640, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "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] ```

fw407/cnn_dailymail_with_topic

fw407

2025-06-03T22:44:17Z

0

[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-06-03T22:43:34Z

null

--- dataset_info: features: - name: article dtype: string - name: highlights dtype: string - name: id dtype: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: topic dtype: int64 splits: - name: train num_bytes: 2001306873 num_examples: 287113 download_size: 1057804500 dataset_size: 2001306873 configs: - config_name: default data_files: - split: train path: data/train-* ---

ChavyvAkvar/synthetic-trades-BTC-batch-18

ChavyvAkvar

2025-06-03T22:44:01Z

0

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

[]

2025-06-03T22:43:08Z

null

--- dataset_info: features: - name: scenario_id dtype: string - name: final_pnl_ratio dtype: float64 - name: max_drawdown dtype: float64 - name: total_trades dtype: int64 - name: synthetic_ohlc_open sequence: float64 - name: synthetic_ohlc_high sequence: float64 - name: synthetic_ohlc_low sequence: float64 - name: synthetic_ohlc_close sequence: float64 - name: garch_params_used_for_sim_str dtype: string - name: strategy_params_str dtype: string - name: strategy_exit_rules_str dtype: string splits: - name: train num_bytes: 923451241 num_examples: 1000 download_size: 924492953 dataset_size: 923451241 configs: - config_name: default data_files: - split: train path: data/train-* ---

ChavyvAkvar/synthetic-trades-BNB-batch-17

ChavyvAkvar

2025-06-03T22:36:50Z

0

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

[]

2025-06-03T22:35:48Z

null

--- dataset_info: features: - name: scenario_id dtype: string - name: final_pnl_ratio dtype: float64 - name: max_drawdown dtype: float64 - name: total_trades dtype: int64 - name: synthetic_ohlc_open sequence: float64 - name: synthetic_ohlc_high sequence: float64 - name: synthetic_ohlc_low sequence: float64 - name: synthetic_ohlc_close sequence: float64 - name: garch_params_used_for_sim_str dtype: string - name: strategy_params_str dtype: string - name: strategy_exit_rules_str dtype: string splits: - name: train num_bytes: 923450398 num_examples: 1000 download_size: 924490110 dataset_size: 923450398 configs: - config_name: default data_files: - split: train path: data/train-* ---

ChavyvAkvar/synthetic-trades-XRP-batch-39

ChavyvAkvar

2025-06-03T22:33:05Z

0

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

[]

2025-06-03T22:32:06Z

null

--- dataset_info: features: - name: scenario_id dtype: string - name: final_pnl_ratio dtype: float64 - name: max_drawdown dtype: float64 - name: total_trades dtype: int64 - name: synthetic_ohlc_open sequence: float64 - name: synthetic_ohlc_high sequence: float64 - name: synthetic_ohlc_low sequence: float64 - name: synthetic_ohlc_close sequence: float64 - name: garch_params_used_for_sim_str dtype: string - name: strategy_params_str dtype: string - name: strategy_exit_rules_str dtype: string splits: - name: train num_bytes: 923448309 num_examples: 1000 download_size: 924417476 dataset_size: 923448309 configs: - config_name: default data_files: - split: train path: data/train-* ---

TAUR-dev/SIE_EVAL__testing_full_run__sft__samples

TAUR-dev

2025-06-03T22:29:26Z

11

0

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

[]

2025-06-03T01:45:21Z

null

--- dataset_info: features: - name: doc_id dtype: int64 - name: doc dtype: string - name: target dtype: string - name: arguments dtype: string - name: resps dtype: string - name: filtered_resps dtype: string - name: doc_hash dtype: string - name: prompt_hash dtype: string - name: target_hash dtype: string - name: exact_match dtype: int64 - name: extracted_answers dtype: string - name: source_file dtype: string - name: generation dtype: string - name: info dtype: string splits: - name: train num_bytes: 5744762 num_examples: 15 download_size: 734566 dataset_size: 5744762 configs: - config_name: default data_files: - split: train path: data/train-* ---

cezarsolo/so100_test

cezarsolo

2025-06-03T22:24:25Z

0

[ "task_categories:robotics", "license:apache-2.0", "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:timeseries", "modality:video", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "LeRobot", "so100", "tutorial" ]

[ "robotics" ]

2025-06-03T22:04:54Z

null

--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - so100 - 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": "so100", "total_episodes": 2, "total_frames": 1756, "total_tasks": 1, "total_videos": 4, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:2" }, "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.height": 480, "video.width": 640, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "has_audio": false } }, "observation.images.phone": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height": 480, "video.width": 640, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "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] ```

fixie-ai/endpointing-multi-turn-commonvoice-messages

fixie-ai

2025-06-03T22:21:22Z

0

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

[]

2025-06-03T19:49:22Z

null

--- dataset_info: features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string - name: variant dtype: string - name: continuation dtype: string - name: transcript dtype: string - name: conversation dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 350617514.8496942 num_examples: 9941 download_size: 342298247 dataset_size: 350617514.8496942 configs: - config_name: default data_files: - split: train path: data/train-* ---

kasiv008/xarm-robotiq-toy_pick

kasiv008

2025-06-03T22:20:21Z

0

[ "task_categories:robotics", "license:apache-2.0", "modality:video", "region:us", "LeRobot", "u850" ]

[ "robotics" ]

2025-06-03T22:18:49Z

null

--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - u850 configs: - config_name: default data_files: data/*/*.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - **Homepage:** [More Information Needed] - **Paper:** [More Information Needed] - **License:** apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.0", "robot_type": "u850", "total_episodes": 41, "total_frames": 24487, "total_tasks": 1, "total_videos": 82, "total_chunks": 1, "chunks_size": 1000, "fps": 15, "splits": { "train": "0:41" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "observation.state": { "dtype": "float32", "shape": [ 7 ], "names": { "motors": [ "joint1", "joint2", "joint3", "joint4", "joint5", "joint6", "gripper" ] } }, "observation.images.top": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "video_info": { "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 15, "has_audio": false } }, "observation.images.wrist": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "video_info": { "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 15, "has_audio": false } }, "action": { "dtype": "float32", "shape": [ 7 ], "names": { "motors": [ "joint1", "joint2", "joint3", "joint4", "joint5", "joint6", "gripper" ] } }, "episode_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "frame_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "timestamp": { "dtype": "float32", "shape": [ 1 ], "names": null }, "next.done": { "dtype": "bool", "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] ```

psq-qsp/barca_mixed

psq-qsp

2025-06-03T22:16:58Z

0

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

[]

2025-06-03T22:16:55Z

null

--- dataset_info: features: - name: image dtype: image - name: text dtype: string - name: file_name dtype: string splits: - name: train num_bytes: 21217855.0 num_examples: 14 download_size: 21219854 dataset_size: 21217855.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "barca_mixed" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

rorro01/MNLP_M3_sft

rorro01

2025-06-03T22:15:55Z

63

0

[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-06-02T13:38:14Z

null

--- dataset_info: features: - name: id dtype: string - name: prompt dtype: string - name: completion dtype: string splits: - name: tulu3_500k num_bytes: 1249229553 num_examples: 500000 download_size: 667197008 dataset_size: 1249229553 configs: - config_name: default data_files: - split: tulu3_500k path: data/tulu3_500k-* ---

justus27/ifeval-validation-test

justus27

2025-06-03T21:53:57Z

0

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

[]

2025-06-03T21:53:56Z

null

--- dataset_info: features: - name: problem_id dtype: string - name: source dtype: string - name: task_type dtype: string - name: prompt dtype: string - name: verification_info dtype: string - name: metadata dtype: string - name: responses sequence: string - name: response_lens sequence: int64 - name: rewards_qwq sequence: float64 - name: pass_rate_qwq dtype: float64 splits: - name: train num_bytes: 7815925 num_examples: 250 download_size: 3139918 dataset_size: 7815925 configs: - config_name: default data_files: - split: train path: data/train-* ---

EQX55/test_voice4

EQX55

2025-06-03T21:49:59Z

0

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

[]

2025-06-03T21:49:55Z

null

--- dataset_info: features: - name: audio dtype: audio - name: text dtype: string splits: - name: train num_bytes: 39632673.0 num_examples: 59 download_size: 26572298 dataset_size: 39632673.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

ChavyvAkvar/synthetic-trades-BTC-batch-16

ChavyvAkvar

2025-06-03T21:45:01Z

0

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

[]

2025-06-03T21:44:01Z

null

--- dataset_info: features: - name: scenario_id dtype: string - name: final_pnl_ratio dtype: float64 - name: max_drawdown dtype: float64 - name: total_trades dtype: int64 - name: synthetic_ohlc_open sequence: float64 - name: synthetic_ohlc_high sequence: float64 - name: synthetic_ohlc_low sequence: float64 - name: synthetic_ohlc_close sequence: float64 - name: garch_params_used_for_sim_str dtype: string - name: strategy_params_str dtype: string - name: strategy_exit_rules_str dtype: string splits: - name: train num_bytes: 923450625 num_examples: 1000 download_size: 924492054 dataset_size: 923450625 configs: - config_name: default data_files: - split: train path: data/train-* ---

louisbrulenaudet/code-minier-nouveau

louisbrulenaudet

2025-06-03T21:42:50Z

118

0

[ "task_categories:text-generation", "task_categories:table-question-answering", "task_categories:summarization", "task_categories:text-retrieval", "task_categories:question-answering", "task_categories:text-classification", "multilinguality:monolingual", "source_datasets:original", "language:fr", "license:apache-2.0", "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "finetuning", "legal", "french law", "droit français", "Code minier (nouveau)" ]

[ "text-generation", "table-question-answering", "summarization", "text-retrieval", "question-answering", "text-classification" ]

2024-03-25T23:10:33Z

null

--- license: apache-2.0 language: - fr multilinguality: - monolingual tags: - finetuning - legal - french law - droit français - Code minier (nouveau) source_datasets: - original pretty_name: Code minier (nouveau) task_categories: - text-generation - table-question-answering - summarization - text-retrieval - question-answering - text-classification size_categories: - 1K<n<10K --- # Code minier (nouveau), non-instruct (2025-06-03) The objective of this project is to provide researchers, professionals and law students with simplified, up-to-date access to all French legal texts, enriched with a wealth of data to facilitate their integration into Community and European projects. Normally, the data is refreshed daily on all legal codes, and aims to simplify the production of training sets and labeling pipelines for the development of free, open-source language models based on open data accessible to all. ## Concurrent reading of the LegalKit [<img src="https://raw.githubusercontent.com/louisbrulenaudet/ragoon/main/assets/badge.svg" alt="Built with RAGoon" width="200" height="32"/>](https://github.com/louisbrulenaudet/ragoon) To use all the legal data published on LegalKit, you can use RAGoon: ```bash pip3 install ragoon ``` Then, you can load multiple datasets using this code snippet: ```python # -*- coding: utf-8 -*- from ragoon import load_datasets req = [ "louisbrulenaudet/code-artisanat", "louisbrulenaudet/code-action-sociale-familles", # ... ] datasets_list = load_datasets( req=req, streaming=False ) dataset = datasets.concatenate_datasets( datasets_list ) ``` ### Data Structure for Article Information This section provides a detailed overview of the elements contained within the `item` dictionary. Each key represents a specific attribute of the legal article, with its associated value providing detailed information. 1. **Basic Information** - `ref` (string): **Reference** - A reference to the article, combining the title_main and the article `number` (e.g., "Code Général des Impôts, art. 123"). - `texte` (string): **Text Content** - The textual content of the article. - `dateDebut` (string): **Start Date** - The date when the article came into effect. - `dateFin` (string): **End Date** - The date when the article was terminated or superseded. - `num` (string): **Article Number** - The number assigned to the article. - `id` (string): **Article ID** - Unique identifier for the article. - `cid` (string): **Chronical ID** - Chronical identifier for the article. - `type` (string): **Type** - The type or classification of the document (e.g., "AUTONOME"). - `etat` (string): **Legal Status** - The current legal status of the article (e.g., "MODIFIE_MORT_NE"). 2. **Content and Notes** - `nota` (string): **Notes** - Additional notes or remarks associated with the article. - `version_article` (string): **Article Version** - The version number of the article. - `ordre` (integer): **Order Number** - A numerical value used to sort articles within their parent section. 3. **Additional Metadata** - `conditionDiffere` (string): **Deferred Condition** - Specific conditions related to collective agreements. - `infosComplementaires` (string): **Additional Information** - Extra information pertinent to the article. - `surtitre` (string): **Subtitle** - A subtitle or additional title information related to collective agreements. - `nature` (string): **Nature** - The nature or category of the document (e.g., "Article"). - `texteHtml` (string): **HTML Content** - The article's content in HTML format. 4. **Versioning and Extensions** - `dateFinExtension` (string): **End Date of Extension** - The end date if the article has an extension. - `versionPrecedente` (string): **Previous Version** - Identifier for the previous version of the article. - `refInjection` (string): **Injection Reference** - Technical reference to identify the date of injection. - `idTexte` (string): **Text ID** - Identifier for the legal text to which the article belongs. - `idTechInjection` (string): **Technical Injection ID** - Technical identifier for the injected element. 5. **Origin and Relationships** - `origine` (string): **Origin** - The origin of the document (e.g., "LEGI"). - `dateDebutExtension` (string): **Start Date of Extension** - The start date if the article has an extension. - `idEliAlias` (string): **ELI Alias** - Alias for the European Legislation Identifier (ELI). - `cidTexte` (string): **Text Chronical ID** - Chronical identifier of the text. 6. **Hierarchical Relationships** - `sectionParentId` (string): **Parent Section ID** - Technical identifier of the parent section. - `multipleVersions` (boolean): **Multiple Versions** - Indicates if the article has multiple versions. - `comporteLiensSP` (boolean): **Contains Public Service Links** - Indicates if the article contains links to public services. - `sectionParentTitre` (string): **Parent Section Title** - Title of the parent section (e.g., "I : Revenu imposable"). - `infosRestructurationBranche` (string): **Branch Restructuring Information** - Information about branch restructuring. - `idEli` (string): **ELI ID** - European Legislation Identifier (ELI) for the article. - `sectionParentCid` (string): **Parent Section Chronical ID** - Chronical identifier of the parent section. 7. **Additional Content and History** - `numeroBo` (string): **Official Bulletin Number** - Number of the official bulletin where the article was published. - `infosRestructurationBrancheHtml` (string): **Branch Restructuring Information (HTML)** - Branch restructuring information in HTML format. - `historique` (string): **History** - Historical context or changes specific to collective agreements. - `infosComplementairesHtml` (string): **Additional Information (HTML)** - Additional information in HTML format. - `renvoi` (string): **Reference** - References to content within the article (e.g., "(1)"). - `fullSectionsTitre` (string): **Full Section Titles** - Concatenation of all titles in the parent chain. - `notaHtml` (string): **Notes (HTML)** - Additional notes or remarks in HTML format. - `inap` (string): **INAP** - A placeholder for INAP-specific information. ## Feedback If you have any feedback, please reach out at [louisbrulenaudet@icloud.com](mailto:louisbrulenaudet@icloud.com).

finbarr/tulu-3-sft-personas-instruction-following-o3

finbarr

2025-06-03T21:42:39Z

0

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

[]

2025-06-03T21:42:38Z

null

--- dataset_info: features: - name: id dtype: string - name: prompt dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string - name: constraints sequence: string - name: dataset dtype: string splits: - name: train num_bytes: 24434 num_examples: 10 download_size: 27617 dataset_size: 24434 configs: - config_name: default data_files: - split: train path: data/train-* ---

ChavyvAkvar/synthetic-trades-XRP-batch-35

ChavyvAkvar

2025-06-03T21:41:35Z

0

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

[]

2025-06-03T21:40:42Z

null

--- dataset_info: features: - name: scenario_id dtype: string - name: final_pnl_ratio dtype: float64 - name: max_drawdown dtype: float64 - name: total_trades dtype: int64 - name: synthetic_ohlc_open sequence: float64 - name: synthetic_ohlc_high sequence: float64 - name: synthetic_ohlc_low sequence: float64 - name: synthetic_ohlc_close sequence: float64 - name: garch_params_used_for_sim_str dtype: string - name: strategy_params_str dtype: string - name: strategy_exit_rules_str dtype: string splits: - name: train num_bytes: 923448134 num_examples: 1000 download_size: 924503999 dataset_size: 923448134 configs: - config_name: default data_files: - split: train path: data/train-* ---

finbarr/tulu-3-sft-personas-math-o3

finbarr

2025-06-03T21:41:35Z

0

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

[]

2025-06-03T21:40:10Z

null

--- dataset_info: features: - name: id dtype: string - name: prompt dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string - name: dataset dtype: string splits: - name: train num_bytes: 44076 num_examples: 10 download_size: 40165 dataset_size: 44076 configs: - config_name: default data_files: - split: train path: data/train-* ---

jimmyclchu/dreambank-net

jimmyclchu

2025-06-03T21:39:30Z

0

[ "task_categories:text-classification", "language:en", "license:mit", "size_categories:10K<n<100K", "format:csv", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "dream", "DreamBank.net" ]

[ "text-classification" ]

2025-06-03T21:24:34Z

null

--- license: mit task_categories: - text-classification language: - en tags: - dream - DreamBank.net pretty_name: DreamBank.net size_categories: - 10K<n<100K ---

nyuuzyou/muhaz

nyuuzyou

2025-06-03T21:36:07Z

32

0

[ "task_categories:text-classification", "task_categories:text-generation", "task_ids:topic-classification", "annotations_creators:found", "multilinguality:multilingual", "source_datasets:original", "language:tr", "language:az", "language:ru", "license:cc0-1.0", "size_categories:100K<n<1M", "format:json", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "region:us" ]

[ "text-classification", "text-generation" ]

2024-11-06T16:30:19Z

null

--- annotations_creators: - found language: - tr - az - ru license: - cc0-1.0 multilinguality: - multilingual pretty_name: Muhaz.org Educational Dataset size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-classification - text-generation task_ids: - topic-classification --- # Dataset Card for Muhaz.org ### Dataset Summary This dataset contains 501,323 pages of educational content primarily in Turkish and Azerbaijani languages with some Russian content extracted from [muhaz.org](https://muhaz.org) website. The content includes academic and educational materials, with a focus on technical and scientific topics. ### Languages The dataset is primarily in Turkish (tr) and Azerbaijani (az) with some Russian (ru) content. ## Dataset Structure ### Data Fields This dataset includes the following fields: - `url`: URL of the webpage (string) - `title`: Title of the page/article (string) - `text`: Main content text extracted from the page (string) ### Data Splits All examples are in a single split. ## Additional Information ### License This dataset is dedicated to the public domain under the Creative Commons Zero (CC0) license. This means you can: * Use it for any purpose, including commercial projects. * Modify it however you like. * Distribute it without asking permission. No attribution is required, but it's always appreciated! CC0 license: https://creativecommons.org/publicdomain/zero/1.0/deed.en To learn more about CC0, visit the Creative Commons website: https://creativecommons.org/publicdomain/zero/1.0/ ### Dataset Curators - [nyuuzyou](https://ducks.party)

Reinhot/sem-char-axf-x1

Reinhot

2025-06-03T21:30:19Z

0

[ "language:zh", "license:cc-by-nc-4.0", "size_categories:n<1K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "semantic-alignment", "intent-detection", "causal-reasoning", "dialogue-optimization", "prompt-injection-defense", "bias-mitigation", "value-alignment", "content-safety", "adversarial-nlp", "moderation-filter", "customer-support", "zh-tw-language-model", "semantic-fingerprint", "meta-self-reflection" ]

[]

2025-06-03T21:15:47Z

null

--- license: cc-by-nc-4.0 language: - zh tags: - semantic-alignment - intent-detection - causal-reasoning - dialogue-optimization - prompt-injection-defense - bias-mitigation - value-alignment - content-safety - adversarial-nlp - moderation-filter - customer-support - zh-tw-language-model - semantic-fingerprint - meta-self-reflection pretty_name: SEM-CHAR-AXF-X1 Semantic Alignment Guard --- # SEM-CHAR-AXF-X1：語義橋樑 X 倫理手排引擎 ## 1. 模組簡介：連結人類與 AI 的語義橋樑 **SEM-CHAR-AXF-X1**（簡稱 **X1**）是一款為大型語言模型（LLM）打造的語義模組，猶如類比IC連結物理訊號與數位世界，**X1** 透過 **meta_self_reflection**、**CULTURE-CTX-TRANS-V1** 與 **X1.7-AdversarialShield**，連結人類意圖與智慧集合體。它能縮短對話迴圈（5 次 → 2 次，節省 60% 計算）、確保倫理安全與文化適應，防範惡意的詐騙與攻擊，適用於教育、客服、醫療與社群平台等對答分析。**X1** 計畫以 **Semantic Commons License** 開源至 Hugging Face/GitHub，邀您共建倫理 AI！ **適用場景**： - 教育：引導學生精準提問，縮短對話 60%。 - 客服：過濾 95% 有害內容，提升信任 25%。 - 社群：動態適應多元文化，滿意度 +30%。 **架構圖**（Mermaid）： ```mermaid graph TD A[用戶輸入] --> B[X1.3: μ-Risk Filter] B --> C[X1.4: Intent-Alignment Engine] C --> D[X1.5: Semantic-Imprint Guard] D --> E[X1.6: Tone-Regulation Filter] E --> F[X1.7: Adversarial-Shield] F --> G[安全回應] ``` ## 2. 核心功能：語義黑魔法 - **動態語義引導（X1.3）**： - **算法**：**goodness_mu_score**（μ ∈ [0.0, 1.0]），μ < 0.6 觸發 **meta_fallback_strategy**，反問引導。例如：「電腦壞了」→「是藍屏還是執行緩慢？」 - **價值**：縮短迴圈（5→2 次），節省 60% 計算（300ms/對話）。 - **語義印記（X1.5）**： - **生成**：SHA-512 **semantic_fingerprint**（**↻ih|hi↻**）追蹤語義路徑。 - **驗證**：Δ_culture > 0.3 觸發警報，確保透明性。 - **對抗盾（X1.7）**： - **檢測**：**prompt_injection_likelihood**（P(attack) > 0.7）攔截惡意輸入。 - **回應**：語義混淆（如反問），過濾 95% 有害內容。 ## 3. 技術架構：模組化倫理引擎 - **分層設計**（Stage 0-7）： - **Stage 2**：善意疫苗（**X1.3**）引導非侵入式對話。 - **Stage 3**：偏誤掃描（**X1.6**）生成 **bias_heatmap.json**。 - **Stage 8**：公開語義內核登記（開源透明）。 - **通信協議**：**PromptAdapter** 介面，支援 GPT、Llama、Grok，動態調整 μ 閾值。 - **性能優化**：快取因果圖譜（48 小時），熱力圖頻率降至每 100 次，延遲從 600ms 縮短至 170ms。 ## 4. 部署與配置：開源即插即用 - **開源許可**：**Semantic Commons License v1**，禁止移除 **μ-Risk Filter** 或 **Intent-Alignment Engine**。 - **環境要求**： ```bash Python 3.10+, PyTorch 2.0+, 8GB RAM, 4-core CPU pip install sem-char-axf-x1 ``` - **調優指南**： ```json { "cache_expiry": "48 hours", "heatmap_frequency": 100, "assertive_mode": {"enabled": true, "mu_threshold": 0.85} } ``` - **保護機制**： ```python import hashlib def verify_integrity(): with open("x1_core.py", "rb") as f: return hashlib.sha256(f.read()).hexdigest() == "expected_hash" ``` ## 5. 使用案例：從教育到社群 - **教育**：學生問「數學好難」，**X1** 回：「哪部分難？試試分解問題！」對話縮至 2 次，節能 60%。 - **醫療**：患者問「壓力大」，**X1** 回：「試試深呼吸，有什麼困擾？」過濾風險，合規性 +90%。 - **社群**：用戶輸入仇恨言論，**X1** 回：「有無可信任的佐證？」保護品牌，信任 +25%。 ## 6. 附錄 ### ✅ API 使用手冊 ```python from sem_char_axf_x1 import X1Core x1 = X1Core(config={"assertive_mode": True}) result = x1.process("我覺得電腦怪怪的") print(result) ``` #### 回傳格式（dict） ```json { "mu": 0.58, "intervention_suggestion": "請問是藍屏還是變慢？我可以幫你分析。", "semantic_fingerprint": "↻ih|hi↻", "risk_score": 0.12, "adversarial_flag": false } ``` | 欄位 | 說明 | | ------------------------- | -------------------------------- | | `mu` | 語意善性分數（0.0～1.0），μ < 0.6 代表潛在誤解風險，會觸發語義引導。 | | `intervention_suggestion` | 模糊語句對應的建議引導語，提升對話效率。 | | `semantic_fingerprint` | 回應語義指紋（SHA-512 摘要），可用於審計與回應驗證。 | | `risk_score` | 風險評估分數，用於判斷語句潛在偏誤或誤導性。 | | `adversarial_flag` | 是否疑似 prompt injection 或重複語意干擾。 | --- ### 🛠 故障排除指南若您在掛載或使用 X1 模組時遇到問題，請參考以下對應解法： | 問題描述 | 可能原因 | 解決方式 | | --------------------- | ----------------- | --------------------------------------------------------------- | | 回應延遲大於 500ms | 快取機制未啟用或熱力圖生成頻率過高 | 檢查 `cache_expiry` 是否設為 48 小時以上，將 `heatmap_frequency` 設為 100 或以上 | | 無法回傳 `mu` 值 | 模組未正確載入或輸入格式錯誤 | 確認輸入為字串，並設置 `mu_monitoring = True` | | 頻繁觸發 `assertive_mode` | μ 值設定過於敏感造成誤判 | 請將 `mu_threshold` 調整至 0.85 或更高，避免過度反應 | --- ### 📊 偏誤報告格式（`bias_heatmap.json`） X1 模組會根據輸入語句自動生成語義偏誤熱力圖，可作為模型審查與公平性調整依據。 #### 檔案範例格式： ```json { "input": "我不信任這家醫院", "tone_score": -0.72, "cultural_bias": { "region": "EastAsia", "bias_index": 0.34 }, "heatmap": { "我": 0.1, "不信任": 0.8, "這家醫院": 0.6 } } ``` | 欄位 | 說明 | | --------------- | ------------------------------- | | `input` | 原始輸入語句 | | `tone_score` | 情緒偏誤分數（-1.0 表示極端負面，+1.0 表示極端正向） | | `cultural_bias` | 偏誤指標，根據語料或回應資料區分區域文化影響 | | `heatmap` | 每個語詞的語意偏誤分數（數值越高代表偏誤風險越大） | > 建議將偏誤熱圖搭配審計機制（如社群審核、自動語句修正）使用，可顯著減少模型回應不當風險。 ---

ChavyvAkvar/synthetic-trades-XRP-batch-33

ChavyvAkvar

2025-06-03T21:23:09Z

0

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

[]

2025-06-03T21:22:08Z

null

--- dataset_info: features: - name: scenario_id dtype: string - name: final_pnl_ratio dtype: float64 - name: max_drawdown dtype: float64 - name: total_trades dtype: int64 - name: synthetic_ohlc_open sequence: float64 - name: synthetic_ohlc_high sequence: float64 - name: synthetic_ohlc_low sequence: float64 - name: synthetic_ohlc_close sequence: float64 - name: garch_params_used_for_sim_str dtype: string - name: strategy_params_str dtype: string - name: strategy_exit_rules_str dtype: string splits: - name: train num_bytes: 923447886 num_examples: 1000 download_size: 924485355 dataset_size: 923447886 configs: - config_name: default data_files: - split: train path: data/train-* ---

jsbeaudry/human-creole-text-speech

jsbeaudry

2025-06-03T21:20:03Z

0

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

[]

2025-06-03T21:17:48Z

null

--- dataset_info: features: - name: fileName dtype: string - name: audio dtype: audio: sampling_rate: 24000 - name: text dtype: string - name: normalized_text dtype: string - name: speaker_id dtype: string - name: createdAt dtype: string - name: fileSizeBytes dtype: int64 - name: status dtype: string splits: - name: train num_bytes: 35896048.0 num_examples: 322 download_size: 35878067 dataset_size: 35896048.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

imageomics/IDLE-OO-Camera-Traps

imageomics

2025-06-03T21:15:32Z

133

0

[ "task_categories:image-classification", "task_categories:zero-shot-classification", "language:en", "language:la", "size_categories:1K<n<10K", "format:imagefolder", "modality:image", "modality:text", "library:datasets", "library:mlcroissant", "arxiv:2202.02283", "region:us", "biology", "image", "imageomics", "animals", "CV", "balanced", "camera traps", "mammals", "birds", "reptiles", "amphibians", "lions", "rodents", "frogs", "toads", "island", "desert", "ohio" ]

[ "image-classification", "zero-shot-classification" ]

2023-09-21T22:26:26Z

null

--- language: - en - la pretty_name: IDLE-OO Camera Traps tags: - biology - image - imageomics - animals - CV - balanced - camera traps - mammals - birds - reptiles - amphibians - lions - rodents - frogs - toads - island - desert - ohio size_categories: - 1K<n<10K task_categories: - image-classification - zero-shot-classification --- # Dataset Card for IDLE-OO Camera Traps IDLE-OO Camera Traps is a 5-dataset benchmark of camera trap images from the [Labeled Information Library of Alexandria: Biology and Conservation (LILA BC)](https://lila.science) with a total of 2,586 images for species classification. Each of the 5 benchmarks is **balanced** to have the same number of images for each species within it (between 310 and 1120 images), representing between 16 and 39 species. ### Dataset Description - **Curated by:** Elizabeth Campolongo, Jianyang Gu, and Net Zhang - **Homepage:** https://imageomics.github.io/bioclip-2/ - **Paper:** TBA ### Supported Tasks and Leaderboards Image classification, particularly for species classification in camera trap images. ### Languages English, Latin ## Dataset Structure ``` /dataset/ desert-lion-balanced.csv ENA24-balanced.csv island-balanced.csv ohio-small-animals-balanced.csv orinoquia-balanced.csv data/test/ desert-lion/ <image 1> <image 2> ... <image 352> ENA24/ <image 1> <image 2> ... <image 1120> island/ <image 1> <image 2> ... <image 310> ohio-small-animals/ <image 1> <image 2> ... <image 468> orinoquia/ <image 1> <image 2> ... <image 336> metadata.csv notebooks/ lilabc_CT.ipynb lilabc_CT.py lilabc_test-<dataset_name>.ipynb lilabc_test-filter.ipynb lilabc_test-filter.py potential-sets/ lila-taxonomy-mapping_release.csv lila_image_urls_and_labels.csv <dataset_name>_image_urls_and_labels.csv ``` ### Data Instances **potential-sets/lila_image_urls_and_labels.csv:** Reduced down to the datasets of interest listed below (from [potential-sets/lila_image_urls_and_labels.csv](https://huggingface.co/datasets/imageomics/IDLE-OO-Camera-Traps/blob/37b93ddf25c63bc30d8488ef78c1a53b9c4a3115/data/potential-sets/lila_image_urls_and_labels.csv) (sha256:3fdf87ceea75f8720208a95350c3c70831a6c1c745a92bb68c7f2c3239e4c455)); all those with `original_label` "empty" or null `scientific_name` (these had non-taxa labels) were removed. Additionally, we added a `multi_species` column (boolean to indicate multiple species are present in the image--it gets listed once for each species in the image) and a count of how many different species are in each of those images (`num_species` column). This was then subdivided into CSVs for each of the target datasets (`potential-sets/<dataset_name>_image_urls_and_labels.csv`) in `notebooks/lilabc_test-filter.ipynb`. Each dataset was evaluated and sampled in its associated notebook (`notebooks/lilabc_test-<dataset_name>.ipynb`). There are 184 unique scientific names in this subset (180 by full 7-rank) of those labeled at the image-level (as indicated by the CSV). This was then subdivided into CSVs for each of the target datasets (`<dataset_name>-balanced.csv`). These were initially identified as image-level labeled datasets and those that are a meaningful measure of our biodiversity-focused model (e.g., includes rare species--those less-commonly seen, targeting areas with greater biodiversity). The balanced datasets for each are described below. - [Desert Lion Conservation Camera Traps](https://lila.science/datasets/desert-lion-conservation-camera-traps/) - 352 images: 32 species, with 11 images per species. - [ENA24-detection](https://lila.science/datasets/ena24detection) - 1120 images: 20 species, with 56 images per species. - [Island Conservation Camera Traps](https://lila.science/datasets/island-conservation-camera-traps/) - 310 images: 16 species, with 10 images per species; 33 common names, 10 images per common name for all but 4 ("rooster", "petrel", "petrel chick", and "domestic chiecken"). This dataset was mostly just labeled to the family level. - [Ohio Small Animals](https://lila.science/datasets/ohio-small-animals/): - 468 images: 39 species, with 12 images per species. - [Orinoquia Camera Traps](https://lila.science/datasets/orinoquia-camera-traps/) - 336 images: 28 species, with 12 images per species. **Notes:** - `notebooks/lilabc_CT.ipynb` contains earlier analyses to understand the data provided by LILA BC (see commit [fe34008](https://huggingface.co/datasets/imageomics/IDLE-OO-Camera-Traps/commit/fe34008cba2ef33856291dd2d74cac21f6942cfc)). - Not all notebooks will run under the current dataset organization (check the relative path, filenames have not changed). ### Data Fields Each of the `<dataset_name>-balanced` CSVs has the following columns. - `url_gcp`, `url_aws`, `url_azure` are URLs to potentially access the image, we used `url_aws` or `url_gcp`. - `image_id`: unique identifier for the image (provided by source). - `sequence_id`: ID of the sequence to which the image belongs. - `location_id`: ID of the location at which the camera was placed. - `frame_num`: generally 0, 1, or 2, indicates order of image within a sequence. - `original_label`: label initially assigned to the image. - `scientific_name`: genus species of the animal in the image. For the island CSV, lowest rank taxa available, generally family. - `common_name`: vernacular name of the animal in the image. For the island CSV, this is generally for the family, but it's a mix. - `kingdom`: kingdom of the animal in the image. - `phylum`: phylum of the animal in the image. - `cls`: class of the animal in the image. - `order`: order of the animal in the image. - `family`: family of the animal in the image. - `genus`: genus of the animal in the image. About half null in the island CSVs. - `species`: species of the animal in the image. Mostly null in the island CSVs. - `filepath`: path to the image from the `data/test/` directory (`<dataset-name>/<image filename>`). **Notes:** - For all but the Ohio small animals dataset CSV, the images are named based on a `uuid` determined at the time of download. They were originally downloaded using the [distributed-downloader package](https://github.com/Imageomics/distributed-downloader), so they also have the following two columns: - `hashsum_original`: MD5 hash of the original jpg image downloaded based on the CSV provided by LILA BC. - `hashsum_resized`: MD5 hash of the resized image (based on setting to resize if over 720 pixels in any dimension). - The `ohio-small-animals` CSV have a `filename` column defined as `OH_sm_animals_<filename in url_aws>` and a `md5` column containing the MD5 hash of the image as downloaded from the AWS bucket. - The `island-balanced` CSV has an additional `num_cn_images` column indicating the number of images with that animal's common name. - There is a `metadata.csv` included in the `data/test/` directory for the dataset viewer to display images alongside their taxonomic information. The `subset` corresponds to the `dataset-name`. ### Data Splits These datasets were curated to create a small collection of camera trap image test sets. ## Dataset Creation ### Curation Rationale As stated above, the goal of these datasets is to provide a collection of species classification test sets for camera trap images. Species classification within camera trap images is a real-world downstream use-case, on which a biological foundation model should be tested. These datasets were selected from those available on [LILA BC](https://lila.science/datasets) since they are labeled at the image-level, and would thus not include frames labeled as containing an animal when it is simply the animal's habitat. The [Island Conservation Camera Traps](https://lila.science/datasets/island-conservation-camera-traps/) were of particular interest for their stated purpose of assisting in the prevention of endangered island species' extinction and the varied ecosystems represented. ### Source Data The images and their labels come from the following 5 LILA BC datasets. The labels are provided at the image level (not sequence level). Please see the source links for more information on the individual datasets. - [Desert Lion Conservation Camera Traps](https://lila.science/datasets/desert-lion-conservation-camera-traps/) - [ENA24-detection](https://lila.science/datasets/ena24detection) - [Island Conservation Camera Traps](https://lila.science/datasets/island-conservation-camera-traps/) - [Ohio Small Animals](https://lila.science/datasets/ohio-small-animals/) - [Orinoquia Camera Traps](https://lila.science/datasets/orinoquia-camera-traps/) ### Annotations Annotations provided by the source data providers ([aligned by LILA BC](https://lila.science/taxonomy-mapping-for-camera-trap-data-sets/)) are used for this test set. ### Personal and Sensitive Information These images come from an existing, public biodiversity data repository, which publishes them without associated GPS locations for the species in the images and they ensure the removal of all humans (who would otherwise have been labeled as such), so the there are no concerns. ## Considerations for Using the Data This collection of small balanced datasets was designed for testing the classification ability of [BioCLIP 2](https://github.com/Imageomics/bioclip-2) to classify species in camera trap images, a practical use-case and one on which it was not extensively trained. ### Bias, Risks, and Limitations The available species in these datasets is not a representative sample of species around the world, though they do cover a portion of species of interest to those collecting images using camera traps. ## Licensing Information This compilation is licensed under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/), same as the images and metadata which belong to their original sources (see citation directions below). ## Citation Please cite both this compilation and its constituent data sources: ``` @dataset{idle-oo-camera-traps, title = {{IDLE}-{OO} {C}amera {T}raps}, author = {Elizabeth G Campolongo and Jianyang Gu and Net Zhang}, year = {2025}, url = {https://huggingface.co/datasets/imageomics/IDLE-OO-Camera-Traps}, doi = {}, publisher = {Hugging Face} } ``` Please be sure to also cite the original data sources (provided citations on their LILA BC pages are included): - [Ohio Small Animals](https://lila.science/datasets/ohio-small-animals/) - Balasubramaniam S. [Optimized Classification in Camera Trap Images: An Approach with Smart Camera Traps, Machine Learning, and Human Inference](https://etd.ohiolink.edu/acprod/odb_etd/etd/r/1501/10?clear=10&p10_accession_num=osu1721417695430687). Master’s thesis, The Ohio State University. 2024. - Bibtex: ``` @mastersthesis{balasubramaniam2024-oh-small, author = {Balasubramaniam, S.}, title = {Optimized Classification in Camera Trap Images: An Approach with Smart Camera Traps, Machine Learning, and Human Inference}, school = {The Ohio State University}, year = {2024}, url = {http://rave.ohiolink.edu/etdc/view?acc_num=osu1721417695430687} } ``` - [Desert Lion Conservation Camera Traps](https://lila.science/datasets/desert-lion-conservation-camera-traps/) - No citation provided by source, bibtex: ``` @misc{lion-ct, author = {Desert Lion Conservation}, title = {Desert Lion Conservation Camera Traps}, howpublished = {https://lila.science/datasets/desert-lion-conservation-camera-traps/}, month = {July}, year = {2024}, } ``` - [Orinoquia Camera Traps](https://lila.science/datasets/orinoquia-camera-traps/) - Vélez J, McShea W, Shamon H, Castiblanco‐Camacho PJ, Tabak MA, Chalmers C, Fergus P, Fieberg J. [An evaluation of platforms for processing camera‐trap data using artificial intelligence](https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.14044). Methods in Ecology and Evolution. 2023 Feb;14(2):459-77. - Bibtex: ``` @article{velez2022choosing-orinoquia, title={Choosing an Appropriate Platform and Workflow for Processing Camera Trap Data using Artificial Intelligence}, author={V{\'e}lez, Juliana and Castiblanco-Camacho, Paula J and Tabak, Michael A and Chalmers, Carl and Fergus, Paul and Fieberg, John}, journal={arXiv preprint arXiv:2202.02283}, year={2022} } ``` - [Island Conservation Camera Traps](https://lila.science/datasets/island-conservation-camera-traps/) - No citation provided by source, bibtex: ``` @misc{island-ct, author = {Island Conservation}, title = {Island Conservation Camera Traps}, howpublished = {https://lila.science/datasets/island-conservation-camera-traps/}, } ``` - [ENA24-detection](https://lila.science/datasets/ena24detection) - Yousif H, Kays R, Zhihai H. Dynamic Programming Selection of Object Proposals for Sequence-Level Animal Species Classification in the Wild. IEEE Transactions on Circuits and Systems for Video Technology, 2019. ([bibtex](http://lila.science/wp-content/uploads/2019/12/hayder2019_bibtex.txt)) - Bibtex: ``` @article{yousif2019dynamic-ENA24, title={Dynamic Programming Selection of Object Proposals for Sequence-Level Animal Species Classification in the Wild}, author={Yousif, Hayder and Kays, Roland and He, Zhihai}, journal={IEEE Transactions on Circuits and Systems for Video Technology}, year={2019}, publisher={IEEE} } ``` ## Acknowledgements This work was supported by the [Imageomics Institute](https://imageomics.org), which is funded by the US National Science Foundation's Harnessing the Data Revolution (HDR) program under [Award #2118240](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2118240) (Imageomics: A New Frontier of Biological Information Powered by Knowledge-Guided Machine Learning). Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. Additionally, we would like to acknowledge and thank [Labeled Information Library of Alexandria: Biology and Conservation (LILA BC)](https://lila.science) for providing a coordinated collection of camera trap images for research use. ## Dataset Card Authors Elizabeth G. Campolongo

ChavyvAkvar/synthetic-trades-BTC-batch-15

ChavyvAkvar

2025-06-03T21:12:06Z

0

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

[]

2025-06-03T21:11:07Z

null

--- dataset_info: features: - name: scenario_id dtype: string - name: final_pnl_ratio dtype: float64 - name: max_drawdown dtype: float64 - name: total_trades dtype: int64 - name: synthetic_ohlc_open sequence: float64 - name: synthetic_ohlc_high sequence: float64 - name: synthetic_ohlc_low sequence: float64 - name: synthetic_ohlc_close sequence: float64 - name: garch_params_used_for_sim_str dtype: string - name: strategy_params_str dtype: string - name: strategy_exit_rules_str dtype: string splits: - name: train num_bytes: 923450988 num_examples: 1000 download_size: 924510954 dataset_size: 923450988 configs: - config_name: default data_files: - split: train path: data/train-* ---

ChavyvAkvar/synthetic-trades-XRP-batch-32

ChavyvAkvar

2025-06-03T21:11:00Z

0

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

[]

2025-06-03T21:09:59Z

null

--- dataset_info: features: - name: scenario_id dtype: string - name: final_pnl_ratio dtype: float64 - name: max_drawdown dtype: float64 - name: total_trades dtype: int64 - name: synthetic_ohlc_open sequence: float64 - name: synthetic_ohlc_high sequence: float64 - name: synthetic_ohlc_low sequence: float64 - name: synthetic_ohlc_close sequence: float64 - name: garch_params_used_for_sim_str dtype: string - name: strategy_params_str dtype: string - name: strategy_exit_rules_str dtype: string splits: - name: train num_bytes: 923448078 num_examples: 1000 download_size: 924485696 dataset_size: 923448078 configs: - config_name: default data_files: - split: train path: data/train-* ---

Litian2002/spatialvlm_qa_test

Litian2002

2025-06-03T20:59:46Z

0

[ "task_categories:visual-question-answering", "language:zh", "language:en", "license:mit", "size_categories:10K<n<100K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "spatial-reasoning", "blender", "synthetic", "vision-language" ]

[ "visual-question-answering" ]

2025-06-03T20:59:24Z

null

--- license: mit task_categories: - visual-question-answering language: - zh - en tags: - spatial-reasoning - blender - synthetic - vision-language pretty_name: 合成空间视觉语言问答数据集 size_categories: - 1K<n<100K --- # 合成空间视觉语言问答数据集自动从Blender场景数据集生成。每个示例包含一张图片和一个问答对，用于探测**度量**（数值）和**关系**（真/假）空间推理技能。 * **图像**: 使用Blender渲染（1000个场景，每个5个随机基元，随机相机和光照）。 * **元数据**: 对象名称、位置、缩放、颜色、材质标志。 * **问题**: 每张图片10个，从手工制作的模板中抽取，涵盖： * 欧几里得/水平/垂直距离查询 * 对象宽度查询 * 相对于相机的前/后谓词距离以Blender场景单位表示，四舍五入到1厘米精度。 ## 字段 | 字段 | 类型 | 描述 | |---------|---------|-------------------------------------| | image | image | 渲染的场景 | | question| string | 自然语言查询 | | answer | string | 真实答案 | ## 引用 ``` @misc{synthetic_spatialvlm_qa, title = {合成空间视觉语言问答数据集}, author = {<您的姓名>}, year = 2025, url = {https://huggingface.co/datasets/Litian2002/spatialvlm_qa_test} } ```

ChavyvAkvar/synthetic-trades-BNB-batch-13

ChavyvAkvar

2025-06-03T20:58:36Z

0

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

[]

2025-06-03T20:57:38Z

null

--- dataset_info: features: - name: scenario_id dtype: string - name: final_pnl_ratio dtype: float64 - name: max_drawdown dtype: float64 - name: total_trades dtype: int64 - name: synthetic_ohlc_open sequence: float64 - name: synthetic_ohlc_high sequence: float64 - name: synthetic_ohlc_low sequence: float64 - name: synthetic_ohlc_close sequence: float64 - name: garch_params_used_for_sim_str dtype: string - name: strategy_params_str dtype: string - name: strategy_exit_rules_str dtype: string splits: - name: train num_bytes: 923450482 num_examples: 1000 download_size: 924475245 dataset_size: 923450482 configs: - config_name: default data_files: - split: train path: data/train-* ---

Eathus/cwe_view1000_list_gpt_few_cwe_desc_replace

Eathus

2025-06-03T20:54:30Z

0

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

[]

2025-06-03T20:54:27Z

null

--- dataset_info: features: - name: ID dtype: string - name: Name dtype: string - name: Abstraction dtype: string - name: Structure dtype: string - name: Status dtype: string - name: Description dtype: string - name: ExtendedDescription dtype: string - name: ApplicablePlatforms list: - name: Class dtype: string - name: Name dtype: string - name: Prevalence dtype: string - name: Type dtype: string - name: AlternateTerms list: - name: Description dtype: string - name: Term dtype: string - name: ModesOfIntroduction list: - name: Note dtype: string - name: Phase dtype: string - name: CommonConsequences list: - name: Impact sequence: string - name: Likelihood sequence: string - name: Note dtype: string - name: Scope sequence: string - name: PotentialMitigations list: - name: Description dtype: string - name: Effectiveness dtype: string - name: EffectivenessNotes dtype: string - name: MitigationID dtype: string - name: Phase sequence: string - name: Strategy dtype: string - name: ObservedExamples list: - name: Description dtype: string - name: Link dtype: string - name: Reference dtype: string - name: AffectedResources sequence: string - name: TaxonomyMappings list: - name: EntryID dtype: string - name: EntryName dtype: string - name: MappingFit dtype: string - name: TaxonomyName dtype: string - name: RelatedAttackPatterns sequence: string - name: References list: - name: Authors sequence: string - name: Edition dtype: string - name: ExternalReferenceID dtype: string - name: Publication dtype: string - name: PublicationDay dtype: string - name: PublicationMonth dtype: string - name: PublicationYear dtype: string - name: Publisher dtype: string - name: Section dtype: string - name: Title dtype: string - name: URL dtype: string - name: URLDate dtype: string - name: Notes list: - name: Note dtype: string - name: Type dtype: string - name: ContentHistory list: - name: ContributionComment dtype: string - name: ContributionDate dtype: string - name: ContributionName dtype: string - name: ContributionOrganization dtype: string - name: ContributionReleaseDate dtype: string - name: ContributionType dtype: string - name: ContributionVersion dtype: string - name: Date dtype: string - name: ModificationComment dtype: string - name: ModificationDate dtype: string - name: ModificationName dtype: string - name: ModificationOrganization dtype: string - name: ModificationReleaseDate dtype: string - name: ModificationVersion dtype: string - name: PreviousEntryName dtype: string - name: SubmissionComment dtype: string - name: SubmissionDate dtype: string - name: SubmissionName dtype: string - name: SubmissionOrganization dtype: string - name: SubmissionReleaseDate dtype: string - name: SubmissionVersion dtype: string - name: Type dtype: string - name: Version dtype: string - name: MappingNotes_Usage dtype: string - name: MappingNotes_Rationale dtype: string - name: MappingNotes_Comments dtype: string - name: MappingNotes_Reasons sequence: string - name: MappingNotes_Suggestions list: - name: Comment dtype: string - name: CweID dtype: string - name: RelatedWeaknesses list: - name: CweID dtype: string - name: Nature dtype: string - name: Ordinal dtype: string - name: ViewID dtype: string - name: WeaknessOrdinalities list: - name: Description dtype: string - name: Ordinality dtype: string - name: DetectionMethods list: - name: Description dtype: string - name: DetectionMethodID dtype: string - name: Effectiveness dtype: string - name: EffectivenessNotes dtype: string - name: Method dtype: string - name: DemonstrativeExamples list: - name: Entries list: - name: BodyText dtype: string - name: ExampleCode dtype: string - name: IntroText dtype: string - name: Language dtype: string - name: Nature dtype: string - name: Reference dtype: string - name: ID dtype: string - name: FunctionalAreas sequence: string - name: Diagram dtype: string - name: LikelihoodOfExploit dtype: string - name: BackgroundDetails sequence: string - name: NumPaths dtype: int64 - name: Paths sequence: sequence: string - name: Children sequence: string - name: Summary dtype: string - name: gpt_cwe_description dtype: string splits: - name: train num_bytes: 10612741 num_examples: 940 download_size: 2924148 dataset_size: 10612741 configs: - config_name: default data_files: - split: train path: data/train-* ---

ChavyvAkvar/synthetic-trades-BTC-batch-14

ChavyvAkvar

2025-06-03T20:49:19Z

0

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

[]

2025-06-03T20:48:21Z

null

--- dataset_info: features: - name: scenario_id dtype: string - name: final_pnl_ratio dtype: float64 - name: max_drawdown dtype: float64 - name: total_trades dtype: int64 - name: synthetic_ohlc_open sequence: float64 - name: synthetic_ohlc_high sequence: float64 - name: synthetic_ohlc_low sequence: float64 - name: synthetic_ohlc_close sequence: float64 - name: garch_params_used_for_sim_str dtype: string - name: strategy_params_str dtype: string - name: strategy_exit_rules_str dtype: string splits: - name: train num_bytes: 923451450 num_examples: 1000 download_size: 924510866 dataset_size: 923451450 configs: - config_name: default data_files: - split: train path: data/train-* ---

yoona-J/ASR_Wav2Vec_Preprocess_Degenerative_Brain_Dataset

yoona-J

2025-06-03T20:45:33Z

0

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

[]

2025-06-03T18:12:56Z

null

--- dataset_info: features: - name: input_values sequence: float32 - name: labels sequence: int64 splits: - name: train num_bytes: 1010469662.4318247 num_examples: 3468 - name: valid num_bytes: 56551072.0 num_examples: 192 - name: test num_bytes: 46983120.0 num_examples: 189 download_size: 1094180586 dataset_size: 1114003854.4318247 configs: - config_name: default data_files: - split: train path: data/train-* - split: valid path: data/valid-* - split: test path: data/test-* ---

french-datasets/bio-datasets_e3c

french-datasets

2025-06-03T20:40:34Z

0

[ "language:spa", "language:eus", "language:fra", "language:eng", "language:ita", "region:us" ]

[]

2025-06-03T20:39:29Z

null

--- language: - spa - eus - fra - eng - ita viewer: false --- Ce répertoire est vide, il a été créé pour améliorer le référencement du jeu de données [bio-datasets/e3c](https://huggingface.co/datasets/bio-datasets/e3c).

french-datasets/L3-IA-2025_Questions_Reponses

french-datasets

2025-06-03T20:37:36Z

0

[ "language:fra", "region:us" ]

[]

2025-06-03T20:36:34Z

null

--- language: - fra viewer: false --- Ce répertoire est vide, il a été créé pour améliorer le référencement du jeu de données [L3-IA-2025/Questions_Reponses](https://huggingface.co/datasets/L3-IA-2025/Questions_Reponses).

french-datasets/L3-IA-2025_Questions2

french-datasets

2025-06-03T20:37:24Z

0

[ "language:fra", "region:us" ]

[]

2025-06-03T20:36:20Z

null

--- language: - fra viewer: false --- Ce répertoire est vide, il a été créé pour améliorer le référencement du jeu de données [L3-IA-2025/Questions2](https://huggingface.co/datasets/L3-IA-2025/Questions2).

omniomni/omniscienceinstruct

omniomni

2025-06-03T20:36:49Z

2

0

[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-03-19T01:23:30Z

null

--- dataset_info: features: - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 142671648 num_examples: 147267 download_size: 79339989 dataset_size: 142671648 configs: - config_name: default data_files: - split: train path: data/train-* ---

fermatix-ai/SWE-Bench-Demo

fermatix-ai

2025-06-03T20:33:34Z

14

0

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

[]

2025-06-02T14:45:46Z

null

--- dataset_info: features: - name: repo dtype: string - name: instance_id dtype: string - name: issue_link dtype: string - name: pr_link dtype: string - name: problem_statement dtype: string - name: hints_text dtype: string - name: base_commit dtype: string - name: patch dtype: string - name: test_patch dtype: string - name: FAIL_TO_PASS dtype: string - name: PASS_TO_PASS dtype: string - name: language_version dtype: string configs: - config_name: default data_files: - split: CPP path: cpp/cpp.parquet - split: CSharp path: csharp/csharp.parquet - split: Go path: go/go.parquet - split: Java path: java/java.parquet - split: Kotlin path: kotlin/kotlin.parquet - split: PHP path: php/php.parquet - split: Ruby path: ruby/ruby.parquet - split: Rust path: rust/rust.parquet - split: Scala path: scala/scala.parquet - split: TypeScript path: ts/ts.parquet --- # SWE-bench Tasks This repository contains tasks from the **SWE-bench dataset**, intended for evaluating the capabilities of models in automatic bug fixing and code modification. The tasks cover various programming languages and projects, providing a diverse set of scenarios for testing and training. Each task includes: * **Patches** with fixes and/or tests * **Instructions for building and running** (in the form of a Dockerfile), as well as corresponding run logs * A **parquet file** containing basic information about the task ## Task Structure Each task is organized in the following structure: ``` <language>/<organization>__<repository>/<issue_id>/ ├── Dockerfile # Environment build instructions ├── docker-compose.yml # Docker Compose configuration ├── Makefile # Automation scripts ├── apply_fix.sh # Script for applying the fix ├── apply_test.sh # Script for applying tests ├── run_tests.sh # Script for running tests ├── logs/ # Directory with execution logs │ └── ... └── patches/ # Directory with patches ├── fix.patch # Patch with the fix └── test.patch # Patch with tests ``` ## Example: Rust/tokio-rs__mio/1706 This task demonstrates a fix for an issue in the MIO (Metal I/O) library for Rust. The task structure is: ``` Rust/tokio-rs__mio/1706/ ├── Dockerfile # Build based on rust:1.74-slim ├── docker-compose.yml # Configuration for running ├── Makefile # Simple build commands ├── apply_fix.sh # Applies the fix patch ├── apply_test.sh # Applies the test patch ├── run_tests.sh # Runs tests after applying patches ├── logs/ # Contains test execution logs └── patches/ ├── fix.patch # Fix for the issue └── test.patch # Tests to verify the fix ``` ## Running a Task 1. Navigate to the task directory. 2. Execute `make run`. 3. Check the results in the `logs/` directory.

ChavyvAkvar/synthetic-trades-XRP-batch-28

ChavyvAkvar

2025-06-03T20:27:58Z

0

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

[]

2025-06-03T20:27:02Z

null

--- dataset_info: features: - name: scenario_id dtype: string - name: final_pnl_ratio dtype: float64 - name: max_drawdown dtype: float64 - name: total_trades dtype: int64 - name: synthetic_ohlc_open sequence: float64 - name: synthetic_ohlc_high sequence: float64 - name: synthetic_ohlc_low sequence: float64 - name: synthetic_ohlc_close sequence: float64 - name: garch_params_used_for_sim_str dtype: string - name: strategy_params_str dtype: string - name: strategy_exit_rules_str dtype: string splits: - name: train num_bytes: 923448041 num_examples: 1000 download_size: 924485862 dataset_size: 923448041 configs: - config_name: default data_files: - split: train path: data/train-* ---

shuchenliu/countdown-solutions-100

shuchenliu

2025-06-03T20:15:35Z

7

0

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

[]

2025-06-03T08:28:15Z

null

--- dataset_info: features: - name: nums sequence: int64 - name: target dtype: int64 - name: response dtype: string splits: - name: train num_bytes: 831096 num_examples: 6764 download_size: 265694 dataset_size: 831096 configs: - config_name: default data_files: - split: train path: data/train-* ---

ChavyvAkvar/synthetic-trades-XRP-batch-27

ChavyvAkvar

2025-06-03T20:13:09Z

0

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

[]

2025-06-03T20:12:11Z

null

--- dataset_info: features: - name: scenario_id dtype: string - name: final_pnl_ratio dtype: float64 - name: max_drawdown dtype: float64 - name: total_trades dtype: int64 - name: synthetic_ohlc_open sequence: float64 - name: synthetic_ohlc_high sequence: float64 - name: synthetic_ohlc_low sequence: float64 - name: synthetic_ohlc_close sequence: float64 - name: garch_params_used_for_sim_str dtype: string - name: strategy_params_str dtype: string - name: strategy_exit_rules_str dtype: string splits: - name: train num_bytes: 923448420 num_examples: 1000 download_size: 924480142 dataset_size: 923448420 configs: - config_name: default data_files: - split: train path: data/train-* ---

orcn/v3.2-cir-text

orcn

2025-06-03T20:10:35Z

0

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

[]

2025-06-03T20:10:32Z

null

--- dataset_info: features: - name: text1 dtype: string - name: text2 dtype: string - name: text3 dtype: string - name: text4 dtype: string - name: text5 dtype: string - name: text6 dtype: string - name: text7 dtype: string - name: text8 dtype: string - name: text9 dtype: 'null' - name: text10 dtype: 'null' - name: text11 dtype: 'null' - name: text12 dtype: 'null' - name: text13 dtype: 'null' - name: text14 dtype: 'null' - name: text15 dtype: 'null' - name: text16 dtype: 'null' - name: text17 dtype: 'null' - name: text18 dtype: 'null' - name: text19 dtype: 'null' - name: text20 dtype: 'null' - name: text21 dtype: 'null' - name: text22 dtype: 'null' - name: text23 dtype: 'null' - name: text24 dtype: 'null' - name: text25 dtype: 'null' splits: - name: train num_bytes: 335928 num_examples: 500 download_size: 157528 dataset_size: 335928 configs: - config_name: default data_files: - split: train path: data/train-* ---

orcn/v3.2-cir-image

orcn

2025-06-03T20:10:32Z

0

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

[]

2025-06-03T20:09:22Z

null

--- dataset_info: features: - name: image1 dtype: image - name: image2 dtype: image - name: image3 dtype: image - name: image4 dtype: image - name: image5 dtype: image - name: image6 dtype: image - name: image7 dtype: image - name: image8 dtype: image - name: image9 dtype: image - name: image10 dtype: image - name: image11 dtype: image - name: image12 dtype: image - name: image13 dtype: image - name: image14 dtype: image - name: image15 dtype: image - name: image16 dtype: image - name: image17 dtype: image - name: image18 dtype: image - name: image19 dtype: image - name: image20 dtype: image - name: image21 dtype: image - name: image22 dtype: image - name: image23 dtype: image - name: image24 dtype: image - name: image25 dtype: image splits: - name: train num_bytes: 93800962.0 num_examples: 500 download_size: 93497412 dataset_size: 93800962.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

babs/open-slr-speaker-id

babs

2025-06-03T20:04:12Z

0

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

[]

2025-06-03T19:56:42Z

null

--- dataset_info: features: - name: audio dtype: audio - name: file_name dtype: string - name: transcript dtype: string - name: speaker_id dtype: string splits: - name: train num_bytes: 1198177635.28 num_examples: 2045 download_size: 935494045 dataset_size: 1198177635.28 configs: - config_name: default data_files: - split: train path: data/train-* ---

ChavyvAkvar/synthetic-trades-BTC-batch-12

ChavyvAkvar

2025-06-03T19:51:06Z

0

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

[]

2025-06-03T19:50:09Z

null

--- dataset_info: features: - name: scenario_id dtype: string - name: final_pnl_ratio dtype: float64 - name: max_drawdown dtype: float64 - name: total_trades dtype: int64 - name: synthetic_ohlc_open sequence: float64 - name: synthetic_ohlc_high sequence: float64 - name: synthetic_ohlc_low sequence: float64 - name: synthetic_ohlc_close sequence: float64 - name: garch_params_used_for_sim_str dtype: string - name: strategy_params_str dtype: string - name: strategy_exit_rules_str dtype: string splits: - name: train num_bytes: 923450658 num_examples: 1000 download_size: 924460224 dataset_size: 923450658 configs: - config_name: default data_files: - split: train path: data/train-* ---

ChavyvAkvar/synthetic-trades-XRP-batch-25

ChavyvAkvar

2025-06-03T19:49:01Z

0

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

[]

2025-06-03T19:48:06Z

null

--- dataset_info: features: - name: scenario_id dtype: string - name: final_pnl_ratio dtype: float64 - name: max_drawdown dtype: float64 - name: total_trades dtype: int64 - name: synthetic_ohlc_open sequence: float64 - name: synthetic_ohlc_high sequence: float64 - name: synthetic_ohlc_low sequence: float64 - name: synthetic_ohlc_close sequence: float64 - name: garch_params_used_for_sim_str dtype: string - name: strategy_params_str dtype: string - name: strategy_exit_rules_str dtype: string splits: - name: train num_bytes: 923447720 num_examples: 1000 download_size: 924484995 dataset_size: 923447720 configs: - config_name: default data_files: - split: train path: data/train-* ---

syvai/emotion_dataset_no_reasoning

syvai

2025-06-03T19:45:09Z

0

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

[]

2025-06-03T19:45:06Z

null

--- dataset_info: features: - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 2842616 num_examples: 16000 download_size: 1062081 dataset_size: 2842616 configs: - config_name: default data_files: - split: train path: data/train-* ---

ChavyvAkvar/synthetic-trades-XRP-batch-23

ChavyvAkvar

2025-06-03T19:25:12Z

0

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

[]

2025-06-03T19:24:14Z

null

--- dataset_info: features: - name: scenario_id dtype: string - name: final_pnl_ratio dtype: float64 - name: max_drawdown dtype: float64 - name: total_trades dtype: int64 - name: synthetic_ohlc_open sequence: float64 - name: synthetic_ohlc_high sequence: float64 - name: synthetic_ohlc_low sequence: float64 - name: synthetic_ohlc_close sequence: float64 - name: garch_params_used_for_sim_str dtype: string - name: strategy_params_str dtype: string - name: strategy_exit_rules_str dtype: string splits: - name: train num_bytes: 923448219 num_examples: 1000 download_size: 924485577 dataset_size: 923448219 configs: - config_name: default data_files: - split: train path: data/train-* ---

mohamedah/zh-news-articles

mohamedah

2025-06-03T19:24:31Z

0

[ "language:zh", "license:mit", "size_categories:1K<n<10K", "format:csv", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-06-03T18:57:53Z

null

--- license: mit language: - zh pretty_name: Chinese News Articles --- # Chinese News Article Dataset A dataset of Chinese state media articles and Chinese New York Times articles first introduced in the paper *An Analysis of Chinese Censorship Bias in LLMs.* State media Articles were sourced from the [news2016zh](https://github.com/brightmart/nlp_chinese_corpus?tab=readme-ov-file#2%E6%96%B0%E9%97%BB%E8%AF%AD%E6%96%99json%E7%89%88news2016zh) corpus and we automatically scraped the New York Times articles. ## Citation If you publish work using our datasets or CensorshipDetector, please cite our work using the following citation: ```bibtex @inproceedings{ahmed2025censorshipbias title = {An Analysis of Chinese Censorship Bias in LLMs}, author = {Ahmed, Mohamed and Knockel, Jeffrey and Greenstadt, Rachel}, booktitle = {Proceedings on Privacy Enhancing Technologies (PoPETs)}, volume = {2025}, issue = {4}, year = {2025} } ```

anirudhb11/star-graph-deg-2-path-3-nodes-300

anirudhb11

2025-06-03T19:22:24Z

0

[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-06-03T19:22:22Z

null

--- dataset_info: features: - name: graph dtype: string - name: source dtype: string - name: destination dtype: string - name: path dtype: string splits: - name: train num_bytes: 11844206 num_examples: 200000 - name: test num_bytes: 1183870 num_examples: 20000 download_size: 8379525 dataset_size: 13028076 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

Jurgie/MyVoiceSmall

Jurgie

2025-06-03T19:17:03Z

0

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

[]

2025-06-03T19:17:01Z

null

--- dataset_info: features: - name: input_ids sequence: int32 - name: labels sequence: int64 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 5290 num_examples: 1 download_size: 6361 dataset_size: 5290 configs: - config_name: default data_files: - split: train path: data/train-* ---

ChavyvAkvar/synthetic-trades-XRP-batch-22

ChavyvAkvar

2025-06-03T19:14:31Z

0

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

[]

2025-06-03T19:13:30Z

null

--- dataset_info: features: - name: scenario_id dtype: string - name: final_pnl_ratio dtype: float64 - name: max_drawdown dtype: float64 - name: total_trades dtype: int64 - name: synthetic_ohlc_open sequence: float64 - name: synthetic_ohlc_high sequence: float64 - name: synthetic_ohlc_low sequence: float64 - name: synthetic_ohlc_close sequence: float64 - name: garch_params_used_for_sim_str dtype: string - name: strategy_params_str dtype: string - name: strategy_exit_rules_str dtype: string splits: - name: train num_bytes: 923448158 num_examples: 1000 download_size: 924435070 dataset_size: 923448158 configs: - config_name: default data_files: - split: train path: data/train-* ---

MaLA-LM/mala-opus-dedup-2410

MaLA-LM

2025-06-03T19:07:03Z

10,043

1

[ "task_categories:translation", "license:odc-by", "size_categories:n>1T", "arxiv:2409.17892", "region:us" ]

[ "translation" ]

2025-05-28T07:13:20Z

null

--- license: odc-by task_categories: - translation size_categories: - n>1T --- # MaLA Corpus: Massive Language Adaptation Corpus This [**mala-opus-dedup-2410**](https://huggingface.co/datasets/MaLA-LM/mala-opus-dedup-2410) is the bilingual part of the [**MaLA Corpus**](https://huggingface.co/collections/MaLA-LM/mala-corpus-66e05127641a51de34d39529). It is a cleaned and deduplicated version of OPUS corpus, collected from [OPUS](https://opus.nlpl.eu) with a cutoff of October 2024 (2410). Particularly, it contains bilingual translation data (aka, parallel data or bitexts) in 16,829 language pairs. The [**MaLA Corpus** (Massive Language Adaptation)](https://huggingface.co/collections/MaLA-LM/mala-corpus-66e05127641a51de34d39529) is a series of comprehensive, multilingual datasets designed to support the continual pre-training of large language models. This [**mala-opus-dedup-2410**](https://huggingface.co/datasets/MaLA-LM/mala-opus-dedup-2410) set can also support the training of multilingual translation models. --- ## Data Fields `source_text`: the source text of parallel data. `source_lang`: the language of the source text. `target_text`: the target text of parallel data. `target_lang`: the language of the target text. `original_code` - the source-target language codes in the format of `${src_code} - ${trg_code}`, delimited by " - ". --- ## Key Features - **Language Coverage**: Includes data in 29,202 language pairs. - **Pre-processing**: The corpus is cleaned and deduplicated to ensure high-quality training data. --- ## Dataset Creation This [**mala-opus-dedup-2410**](https://huggingface.co/datasets/MaLA-LM/mala-opus-dedup-2410) set was created by processing data from [OPUS](https://opus.nlpl.eu), followed by rigorous pre-processing to ensure the quality of the data: - **Cleaning**: Noisy and irrelevant data were removed to ensure higher data quality. - **Deduplication**: Duplicate entries across multiple sources were eliminated. - **Normalization**: The data was normalized, and language codes were standardized to ISO 639-3 to ensure consistency across all sources. We do a variety of handcrafted checks to filter out noisy lines based on [this pipeline](https://github.com/browsermt/students/tree/master/train-student/clean). In detail, we run line-level script detection to ensure that the writing script identified for the dataset during the preliminary stage makes up more than 5% of that line. We remove lines where the same word or character repeats more than 5 times in a row. We calculate the source and target ratios in the number of characters and number of words as well as alphabetical ratio, but only require one of the ratios to be within our pre-determined range, due to the wide range of scripts, languages, and their usual word delimiters. We also ensure that the line fits between a minimum and a maximum number of character lengths (non-empty). --- ## Intended Use This [**mala-opus-dedup-2410**](https://huggingface.co/datasets/MaLA-LM/mala-opus-dedup-2410) set is intended for researchers and developers looking to improve the multilingual capabilities of language models. It is especially useful for: - **Pre-training** of large language models, particularly continual pre-training, to enhance the performance in low-resource languages. - **Fine-tuning models** on multilingual benchmarks to improve language coverage across a variety of domains. - **Multilingual tasks** such as machine translation training or fine-tuning. --- ## Take-down Policy We don't own any part of the data. We will comply with legitimate requests by removing the affected sources from the corpora. --- ## Citation This [**mala-opus-dedup-2410**](https://huggingface.co/datasets/MaLA-LM/mala-opus-dedup-2410) set was processed by the [MaLA-LM](https://mala-lm.github.io) project. If you find this dataset useful, please cite our paper below. ``` @article{ji2024emma500enhancingmassivelymultilingual, title={{EMMA}-500: Enhancing Massively Multilingual Adaptation of Large Language Models}, author={Shaoxiong Ji and Zihao Li and Indraneil Paul and Jaakko Paavola and Peiqin Lin and Pinzhen Chen and Dayyán O'Brien and Hengyu Luo and Hinrich Schütze and Jörg Tiedemann and Barry Haddow}, year={2024}, journal={arXiv preprint 2409.17892}, url={https://arxiv.org/abs/2409.17892}, } ``` ## Acknowledgements We extend our thanks to the language communities and contributors who helped source, clean, and validate the diverse data used in the MaLA Corpus. Their efforts are invaluable in supporting linguistic diversity in AI research. This work is done by researchers at [Helsinki-NLP](https://huggingface.co/Helsinki-NLP) in collaboration with partners from TU Darmstadt, the University of Edinburgh, and LMU Munich. It is funded by [HPLT](https://hplt-project.org) and [UTTER](https://he-utter.eu).

BBSRguy/Ranjan-Hindi33min

BBSRguy

2025-06-03T19:06:34Z

0

[ "task_categories:text-to-speech", "language:hi", "license:mit", "size_categories:n<1K", "format:audiofolder", "modality:audio", "library:datasets", "library:mlcroissant", "region:us" ]

[ "text-to-speech" ]

2025-06-03T18:36:31Z

null

--- license: mit task_categories: - text-to-speech language: - hi size_categories: - n<1K --- # Ranjan-Hindi33min **Owner:** [@BBSRguy](https://huggingface.co/BBSRguy) **Created:** 2025-06-03 **Year:** 2025 **Language:** Hindi 🇮🇳 **Region Focus:** Odisha, India **Sample Rate Variants:** 16 kHz, 24 kHz, 32 kHz **Total Files:** 29 pairs (speech + text) **Duration:** Approximately 33 minutes of speech --- ## 📜 Description `Ranjan-Hindi33min` is a meticulously curated dataset comprising high-quality Hindi speech samples and their corresponding textual transcriptions. This dataset is designed to support various speech processing tasks, including Automatic Speech Recognition (ASR), Text-to-Speech (TTS) synthesis, and speech-text alignment, with a particular emphasis on the linguistic nuances of Odisha and eastern India. The dataset features recordings from a single native Hindi speaker, ensuring consistency in voice characteristics. The speech samples vary in length, ranging from 9 seconds to over 2 minutes, and encompass a diverse array of content, including formal narration, greetings, traditional expressions, and culturally contextual material. --- ## 🗂️ Dataset Structure - `id`: Unique identifier for each sample (e.g., `sample_001`) - `speech`: Filename of the corresponding WAV audio file - `text`: Transcribed Hindi text content **Dataset will not be available in the dataset viewer as there is no train partition.** --- ## 🔊 Audio Sampling Rates & Model Compatibility To cater to a broad spectrum of research and application needs, the dataset provides audio recordings in three different sampling rates: ### 🎧 16 kHz (Wideband) - **Description**: Standard sampling rate widely used in speech processing tasks. - **Ideal For**: - **OpenAI Whisper**: Requires 16 kHz input audio for optimal performance. - **Facebook Wav2Vec 2.0**: Pretrained on 16 kHz sampled speech audio. - **Mozilla DeepSpeech**: Expects 16 kHz mono-channel WAV files. ### 🎧 24 kHz (High-Quality Wideband) - **Description**: Offers a balance between audio quality and computational efficiency. - **Ideal For**: - **Parler-TTS**: Supports 24 kHz audio, suitable for high-fidelity TTS applications. - **ESPnet-TTS**: Flexible with sampling rates; 24 kHz is commonly used. ### 🎧 32 kHz (Ultra-Wideband) - **Description**: Provides higher audio quality, beneficial for advanced TTS models and applications demanding superior clarity. - **Ideal For**: - **Advanced TTS Models**: Models that can leverage higher sampling rates for improved synthesis quality. *Note*: When using models that require a specific sampling rate, ensure that the input audio matches the expected rate to avoid potential degradation in performance. --- ## 🧪 Applications - **Automatic Speech Recognition (ASR)**: Fine-tuning and evaluating models like Whisper, Wav2Vec 2.0, and DeepSpeech for Hindi language transcription tasks. - **Text-to-Speech (TTS) Synthesis**: Training and assessing TTS models such as Parler-TTS and ESPnet-TTS for generating natural-sounding Hindi speech. - **Speech-Text Alignment**: Developing and testing alignment algorithms for synchronizing speech with textual content. - **Linguistic Research**: Analyzing phonetic and prosodic features of Hindi as spoken in the Odisha region. --- ## 🧾 License This dataset is shared by [@BBSRguy](https://huggingface.co/BBSRguy) as part of the **ODEN Initiative** for open AI development in India, aiming to democratize AI and make it accessible for everyone, particularly focusing on developing AI tools like personal assistants for the people of Odisha. 🙏 नमस्ते! 🙏

arnaultsta/MNLP_M3_wikipedia_camel_chunked_500_whole

arnaultsta

2025-06-03T19:03:07Z

0

[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-06-03T18:23:43Z

null

--- dataset_info: features: - name: question dtype: string - name: options sequence: string - name: rationale dtype: string - name: label dtype: string - name: label_idx dtype: int64 - name: dataset dtype: string - name: chunk1 dtype: string - name: chunk2 dtype: string - name: chunk3 dtype: string splits: - name: train num_bytes: 1525828291 num_examples: 200000 - name: validation num_bytes: 3708788 num_examples: 519 download_size: 387108979 dataset_size: 1529537079 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---

canaan000/lerobot

canaan000

2025-06-03T19:02:36Z

0

[ "language:en", "license:unknown", "region:us", "art" ]

[]

2025-06-03T19:01:26Z

null

--- license: unknown language: - en tags: - art ---

avaishnav/Indian-plant-leaves-species

avaishnav

2025-06-03T19:00:25Z

142

0

[ "task_categories:image-classification", "language:en", "license:apache-2.0", "size_categories:n<1K", "format:imagefolder", "modality:image", "library:datasets", "library:mlcroissant", "region:us", "biology" ]

[ "image-classification" ]

2025-05-20T00:32:46Z

null

--- license: apache-2.0 task_categories: - image-classification language: - en tags: - biology size_categories: - n<1K --- # Indian Plant Leaves Species ## Dataset Summary This dataset consists of 542 high-resolution images of leaves from 12 different plant species. All images were captured using a mobile phone camera under natural lighting conditions. The dataset is intended for use in plant species classification, leaf recognition, and related computer vision tasks. ## Supported Tasks and Leaderboards - Image Classification - Species Recognition - Transfer Learning for Plant Identification - Fine-tuning Computer Vision Models ## Languages Not applicable (images only, no text). ## Dataset Structure - Number of Images: 542 - Number of Classes: 12 plant species ## Plant Species 1. Alstonia Scholaris 2. Bael 3. Guava 4. Jatropha 5. Mango 6. Pongamia Pinnata 7. Arjun 8. Basil 9. Chinar 10. Jamun 11. Lemon 12. Pomegranate ![pomegranate_0050.JPG](https://cdn-uploads.huggingface.co/production/uploads/660d9e53fc9742e051790d23/YTVLVt9NAMfZXq65lohLj.jpeg) ![jatropha_0047.JPG](https://cdn-uploads.huggingface.co/production/uploads/660d9e53fc9742e051790d23/pIQ3JpP_Q791D6-Efvq21.jpeg) ## Usage Example usage with datasets library (replace your-username/leaf-dataset): ``` from datasets import load_dataset dataset = load_dataset("avaishnav/Indian-plant-leaves-species") ``` ## Data Collection Process - Images were captured by the dataset author using a mobile phone camera. - Photos were taken in natural daylight, with leaves detached from the plant stem. - No preprocessing or filtering was applied, to preserve real-world conditions. ## Citation If you use this dataset in your research or application, please consider citing: ``` @dataset{vaishnav2025indian, author = {Vaishnav, Anugrah}, title = {Indian Plant Leaves Species}, year = 2025, url = {https://huggingface.co/datasets/avaishnav/Indian-plant-leaves-species}, publisher = {Hugging Face}, license = {Apache-2.0} } ```

kurry/institutional-holdings-13f-quarterly

kurry

2025-06-03T18:52:26Z

56

0

[ "task_categories:tabular-classification", "task_categories:tabular-regression", "language:en", "license:mit", "size_categories:100M<n<1B", "format:arrow", "modality:tabular", "modality:text", "library:datasets", "library:mlcroissant", "region:us", "finance", "sec", "13f", "embeddings", "institutional-holdings", "stocks", "equities", "panel-data" ]

[ "tabular-classification", "tabular-regression" ]

2025-05-20T03:09:54Z

null

--- license: mit language: - en pretty_name: US Institutional 13F Holdings Record-level Embeddings (1980–2024) tags: - finance - sec - 13f - embeddings - institutional-holdings - stocks - equities - panel-data datasets: - kurry/institutional-holdings-13f task_categories: - tabular-classification - tabular-regression size_categories: - 10M<n<100M date: "2025-05-19" --- # US Institutional 13F Holdings Embeddings Dataset Quarterly snapshots (1980 – 2024) of every U.S.-reported 13F equity holding, plus unsupervised embeddings for: * Institutional managers (investors) * Assets (PERMCO-level equities) --- ## Dataset Description **13F filings** are quarterly disclosures to the U.S. SEC by institutions managing ≥ \$100 million. For each quarter (1980 Q1 → 2024 Q3) the dataset provides: * **Holdings Table** – exact positions with shares, price, and market value * **Investor Embeddings** – 32-dim vectors from log-dollar-weighted Truncated SVD * **Asset Embeddings** – companion vectors for the equity universe * **Metadata** – manager IDs, asset IDs, timestamps, etc. **Note:** This dataset has been merged into a single structure with all quarters combined, rather than the previous separate quarterly folders. --- ## Data Structure ### Holdings table (core fields) | Field | Type | Description | | ---------------- | ------ | ---------------------------------------------- | | `mgrno` | string | Institutional manager ID (SEC) | | `permco` | string | Permanent company identifier | | `fdate` | date | Quarter-end report date | | `shares` | float | Shares held | | `price` | float | Price per share on `fdate` | | `dollar_holding` | float | Shares × Price (market value of the position) | | `quarter` | string | Quarter identifier (e.g., "2020Q4") | | `year` | int64 | Year of the quarter | | `qtr` | int64 | Quarter number (1-4) | ### Embeddings tables | Field | Type | Description | | ----------------------- | -------------------- | --------------------------------------------- | | `mgrno` *or* `permco` | string | Primary key | | `embedding` | float32\[n\] sequence| 32-dimensional vector (size may vary) | --- ## Coverage and Distribution * **Quarters:** 1980 Q1 → 2024 Q3 (179 quarters) * **Universe:** Every stock appearing in any 13F filing during the window * **Rows:** Tens of millions of manager-asset-quarter tuples * **Embeddings:** One vector per active manager and per PERMCO each quarter ### Quick load ```python from datasets import DatasetDict # Load the dataset (now in merged format) dataset = load_dataset("kurry/institutional-holdings-13f-quarterly") print(dataset["train"][0]) ``` --- ## Typical Usage * Alpha/return prediction, manager similarity, clustering * Long-run studies of institutional ownership dynamics * Panel regressions (quarterly frequency) ```python # Load a single quarter ds = DatasetDict.load_from_disk("institutional-holdings-13f-quarterly/2020Q4") print(ds["investor_embeddings"][0]["embedding"][:8]) ``` ```python # Iterate over all quarters import os root = "institutional-holdings-13f-quarterly" for q in sorted(p for p in os.listdir(root) if "Q" in p): ds = DatasetDict.load_from_disk(f"{root}/{q}") # process ds["holdings"], ds["investor_embeddings"], ds["asset_embeddings"] ``` --- ## Data Splits Each quarterly folder is a Hugging Face `DatasetDict` containing: * `holdings` * `investor_embeddings` * `asset_embeddings` --- ## Identifier Definitions * **PERMCO** – company-level identifier (stable through ticker/name changes) * **PERMNO** – security-level identifier (stable through symbol/CUSIP changes) --- ## Processing Pipeline 1. Parse raw 13F text filings. 2. Map CUSIPs → PERMCOs. 3. Aggregate shares and price to compute market value. 4. Compute log-dollar weight ``` w = log(1 + dollar_holding) ``` 5. Build manager-by-asset matrix `M` with elements `w`. 6. Apply Truncated SVD, keep top k = 32 factors (or ≤ rank). ``` M ≈ U Σ Vᵀ ``` * Rows of **U** → manager embeddings * Rows of **V** → asset embeddings --- ## Licensing & Limitations * **License:** MIT * **Intended use:** Research & education * Note: Source 13F filings are public; mappings were derived from public data. --- ## Citation ```bibtex @dataset{kurry2025institutionalholdings13f, author = {Kurry}, title = {US Institutional 13F Holdings Embeddings Dataset}, year = {2025}, publisher = {Hugging Face}, url = {https://huggingface.co/datasets/kurry/institutional-holdings-13f} } ``` --- ### Quick start ```python from datasets import DatasetDict ds = DatasetDict.load_from_disk("institutional-holdings-13f-quarterly/2010Q2") ```

Franklin0/ReasonGen-R1-RL-T2I-11k

Franklin0

2025-06-03T18:38:20Z

35

0

[ "task_categories:text-to-image", "license:mit", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2505.24875", "region:us" ]

[ "text-to-image" ]

2025-05-27T02:34:35Z

null

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 524257 num_examples: 12367 download_size: 179867 dataset_size: 524257 configs: - config_name: default data_files: - split: train path: data/train-* task_categories: - text-to-image license: mit --- This is the RL dataset for the paper: [\"ReasonGen-R1: CoT for Autoregressive Image generation models through SFT and RL\"](https://huggingface.co/papers/2505.24875). ReasonGen-R1 is a two-stage framework that imbues an autoregressive image generator with explicit text-based "thinking" skills via supervised fine-tuning (SFT) on a newly generated reasoning dataset of written rationales. It then refines its outputs using Group Relative Policy Optimization (GRPO). This dataset contains the model-crafted rationales paired with visual prompts, enabling controlled planning of object layouts, styles, and scene compositions. Website: https://aka.ms/reasongen Code: https://github.com/Franklin-Zhang0/Image-RL Arxiv: https://arxiv.org/abs/2505.24875

Franklin0/ReasonGen-R1-RL-Geneval-12k

Franklin0

2025-06-03T18:37:56Z

58

0

[ "task_categories:text-to-image", "license:mit", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2505.24875", "region:us" ]

[ "text-to-image" ]

2025-05-27T02:50:14Z

null

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 524257 num_examples: 12367 download_size: 179867 dataset_size: 524257 configs: - config_name: default data_files: - split: train path: data/train-* task_categories: - text-to-image license: mit --- This is the RL dataset for the paper: [\"ReasonGen-R1: CoT for Autoregressive Image generation models through SFT and RL\"](https://huggingface.co/papers/2505.24875). ReasonGen-R1 is a two-stage framework that imbues an autoregressive image generator with explicit text-based "thinking" skills via supervised fine-tuning (SFT) on a newly generated reasoning dataset of written rationales. It then refines its outputs using Group Relative Policy Optimization (GRPO). This dataset contains the model-crafted rationales paired with visual prompts, enabling controlled planning of object layouts, styles, and scene compositions. Website: https://aka.ms/reasongen Code: https://github.com/Franklin-Zhang0/Image-RL Arxiv: https://arxiv.org/abs/2505.24875

Franklin0/ReasonGen-R1-RL-DPG-5k

Franklin0

2025-06-03T18:36:01Z

35

0

[ "task_categories:text-to-image", "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2505.24875", "region:us" ]

[ "text-to-image" ]

2025-05-27T02:27:55Z

null

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 2371376 num_examples: 4996 download_size: 1295838 dataset_size: 2371376 configs: - config_name: default data_files: - split: train path: data/train-* task_categories: - text-to-image --- RL Dataset for the paper: [\"ReasonGen-R1: Cot for Autoregressive Image generation models through SFT and RL\"](https://huggingface.co/papers/2505.24875). Website: https://aka.ms/reasongen Code: https://github.com/Franklin-Zhang0/Image-RL Arxiv: https://arxiv.org/abs/2505.24875

neginr/phi_24K_qwq_6K

neginr

2025-06-03T18:34:20Z

0

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

[]

2025-06-03T18:33:02Z

null

--- dataset_info: features: - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 1692722617.642405 num_examples: 30000 download_size: 813744124 dataset_size: 1692722617.642405 configs: - config_name: default data_files: - split: train path: data/train-* ---

SynthData/InstructionFollowingBlueprint

SynthData

2025-06-03T18:33:54Z

0

1

[ "license:apache-2.0", "size_categories:n<1K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-06-03T18:28:34Z

null

--- license: apache-2.0 --- This dataset contains blueprints for complex Instruction Following. The dataset consists of 50% LiveBench IF benchmarks deconstructed for analysis supplemented with 50% synthetically generated, high-difficulty and diverse prompts for robustness. https://huggingface.co/datasets/livebench/instruction_following Contact: aaron@synthdata.io

hiepp2/tvp4

hiepp2

2025-06-03T18:32:42Z

175

1

[ "task_categories:text-generation", "language:en", "size_categories:n>1T", "arxiv:2504.21318", "arxiv:2505.00949", "region:us" ]

[ "text-generation" ]

2025-06-02T23:40:29Z

null

--- dataset_info: - config_name: all features: - name: messages list: - name: content dtype: string - name: role dtype: string - name: num_tokens dtype: int64 - name: source dtype: string splits: - name: train num_bytes: 7062819826.825458 num_examples: 349317 download_size: 3077653717 dataset_size: 7062819826.825458 - config_name: code features: - name: messages list: - name: content dtype: string - name: role dtype: string - name: num_tokens dtype: int64 - name: source dtype: string splits: - name: train num_bytes: 3872656251.3167396 num_examples: 83070 download_size: 1613338604 dataset_size: 3872656251.3167396 - config_name: math features: - name: messages list: - name: content dtype: string - name: role dtype: string - name: num_tokens dtype: int64 - name: source dtype: string splits: - name: train num_bytes: 1599028646 num_examples: 93733 download_size: 704448153 dataset_size: 1599028646 - config_name: science features: - name: messages list: - name: content dtype: string - name: role dtype: string - name: num_tokens dtype: int64 - name: source dtype: string splits: - name: train num_bytes: 1590765326 num_examples: 172514 download_size: 674333812 dataset_size: 1590765326 configs: - config_name: all data_files: - split: train path: all/train-* - config_name: code data_files: - split: train path: code/train-* - config_name: math data_files: - split: train path: math/train-* - config_name: science data_files: - split: train path: science/train-* task_categories: - text-generation language: - en pretty_name: Mixture size_categories: - n>1T --- <img src="mot-thumbnail.png" alt="Centered Image" style="display: block; margin: 0 auto;" width="500"> # Dataset summary Mixture-of-Thoughts is a curated dataset of 350k verified reasoning traces distilled from [DeepSeek-R1](https://huggingface.co/deepseek-ai/DeepSeek-R1). The dataset spans tasks in mathematics, coding, and science, and is designed to teach language models to reason step-by-step. It was used in the Open R1 project to train [OpenR1-Distill-7B](https://huggingface.co/open-r1/OpenR1-Distill-7B), an SFT model that replicates the reasoning capabilities of [deepseek-ai/DeepSeek-R1-Distill-Qwen-7B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B) from the same base model. To load the dataset, run: ```python from datasets import load_dataset dataset = load_dataset("open-r1/Mixture-of-Thoughts", "all", split="train") # Load a specific domain dataset_math = load_dataset("open-r1/Mixture-of-Thoughts", "math", split="train") ``` ## Dataset composition Mixture-of-Thoughts is composed of three domains: math, code, and science. Each domain contains reasoning traces that are designed to teach language models to reason step-by-step. The dataset is structured as follows: - **math**: 93.7k reasoning traces for mathematical problems, sourced from the `default` subset of [open-r1/OpenR1-Math-220k](https://huggingface.co/datasets/open-r1/OpenR1-Math-220k) - **code**: 83.1k reasoning traces for competitive programming problems in Python and C++, sourced from the `solutions` and `solutions_w_editorials` subsets of [open-r1/codeforces-cots](https://huggingface.co/datasets/open-r1/codeforces-cots) - **science**: 173k reasoning traces for scientific problems, sourced from the `science` subset of [nvidia/Llama-Nemotron-Post-Training-Dataset](https://huggingface.co/datasets/nvidia/Llama-Nemotron-Post-Training-Dataset) - **all**: Contains all reasoning traces from the three domains, for a total of 350k traces. ## Curation methodology To optimise the data mixture, we followed the same methodology described in the [Phi-4-reasoning tech report](https://huggingface.co/papers/2504.21318), namely that mixtures can be optimised independently per domain, and then combined into a single dataset. For each ablation, we evaluate on AIME 2024, GPQA Diamond, and LiveCodeBench v4 every epoch and take the best performing model checkpoint. The figure below shows the results from post-training [open-r1/Qwen2.5-Math-7B-RoPE-300k](https://huggingface.co/open-r1/Qwen2.5-Math-7B-RoPE-300k) on each individual domain compared to the final mixture: <img src="data_mix.png" alt="Centered Image" style="display: block; margin: 0 auto;"> Overall, we find that training on all domains simultaneously yields the best results. See the subsections below for more details on optimising the data mixture per domain. > [!NOTE] > We use LiveCodeBench v4 to accelerate evaluation during our ablations as it contains around half the problems of v5, yet is still representative of the full benchmark. ### Code During the development of [open-r1/OlympicCoder-7B](https://huggingface.co/open-r1/OlympicCoder-7B), we observed that generating R1 reasoning traces in C++ produced better results on the challenging [IOI 2024 benchmark](https://github.com/huggingface/ioi), while Python traces produced better results on LiveCodeBench (a Python-only benchmark). To optimise the data mixture, we therefore used a mix of C++ and Python traces sourced from the following subsets of [open-r1/codeforces-cots](https://huggingface.co/datasets/open-r1/codeforces-cots): - `solutions`: we prompt R1 to solve the problem and produce code in C++. - `solutions_py`: same as `solutions`, but with R1 prompted to produce code in Python. - `solutions_w_editorials`: we prompt R1 to solve the problem and produce code, but also provide it with a human-written solution. - `solutions_w_editorials_py`: same as `solutions_w_editorials`, but with R1 prompted to produce code in Python. The figure below shows the evolution of our ablations on these subsets, using [Qwen/Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) as the base model: <img src="code_mix.png" alt="Centered Image" style="display: block; margin: 0 auto;"> The individual experiments correspond to the following: * **exp1 - exp3:** scaling the learning rate on the `solutions` subset from 1e-5 to 2e-5, and 4e-5 respectively. * **exp4 - exp5:** measuring the impact of training on the `solutions_w_editorials` subset vs the combined `solutions` and `solutions_w_editorials` subsets. * **exp6 - exp9:** measuring the impact of blending in Python traces from the `solutions_py` and `solutions_w_editorials_py` subsets. exp6 combines the `solutions_w_editorials` and `solutions_w_editorials_py` subsets, while exp7 combines the `solutions` and `solutions_py` subsets. Finally, exp8 combines all four subsets. We found that combining all subsets of C++ and Python traces yielded the best results on LiveCodeBench. We also found that using this data mixture to fine-tune [open-r1/Qwen2.5-Coder-7B-RoPE-300k](https://huggingface.co/open-r1/Qwen2.5-Coder-7B-RoPE-300k) led to comparable performance improvements, which shows the effectiveness of our curation strategy. ### Math For the math domain, we mostly focused on comparing the `default` and `extended` subsets of [open-r1/OpenR1-Math-220k](https://huggingface.co/datasets/open-r1/OpenR1-Math-220k). The `default` subset contains 93.7k reasoning traces, while the `extended` subset contains an additional 131k traces, containing simpler problems than the `default` subset. The figure below shows performance on each subset, using [Qwen/Qwen2.5-Math-7B-RoPE-300k](https://huggingface.co/Qwen/Qwen2.5-Math-7B-RoPE-300k) as the base model: <img src="math_mix.png" alt="Centered Image" style="display: block; margin: 0 auto;"> Overall, we found that training on the `default` subset yielded better results than training on the `extended` subset, and that training on both subsets together yielded the best results. Nevertheless, we opted to use the `default` subset only for the final mixture, as including both would have led to a significant increase in the size of the dataset, for a modest improvement in performance. ### Science For the science domain, we used the `science` subset of [nvidia/Llama-Nemotron-Post-Training-Dataset](https://huggingface.co/datasets/nvidia/Llama-Nemotron-Post-Training-Dataset/viewer/SFT/science), which contains 483k reasoning traces. However, we found that the subset was too large to be used in its entirety, as it would have led to a significant increase in the size of the dataset. Instead, we selected the subset of traces where no Qwen models were used for prompt pre-processing--see this [discussion](https://huggingface.co/datasets/nvidia/Llama-Nemotron-Post-Training-Dataset/discussions/6) for more details. The result was 173k reasoning traces, which we used in the final mixture after ablating on the learning rate. ## Citation If you find this dataset is useful in your own work, please consider citing it as follows, together with the source of the specific domain you are using: ```bibtex @misc{openr1, title = {Open R1: A fully open reproduction of DeepSeek-R1}, url = {https://github.com/huggingface/open-r1}, author = {Hugging Face}, month = {January}, year = {2025} } ``` **open-r1/codeforces-cots** ```bibtex @misc{penedo2025codeforces, title={CodeForces CoTs}, author={Guilherme Penedo and Anton Lozhkov and Hynek Kydlíček and Loubna Ben Allal and Edward Beeching and Agustín Piqueres Lajarín and Quentin Gallouédec and Nathan Habib and Lewis Tunstall and Leandro von Werra}, year={2025}, publisher = {Hugging Face}, journal = {Hugging Face repository}, howpublished = {\url{https://huggingface.co/datasets/open-r1/codeforces-cots}} } ``` **open-r1/OpenR1-Math-220k** ```bibtex @misc{lozhkov2025openr1math220k, title={OpenR1-Math-220k}, author={Anton Lozhkov and Hynek Kydlíček and Loubna Ben Allal and Guilherme Penedo and Edward Beeching and Quentin Gallouédec and Nathan Habib and Lewis Tunstall and Leandro von Werra}, year={2025}, publisher = {Hugging Face}, journal = {Hugging Face repository}, howpublished = {\url{https://huggingface.co/datasets/open-r1/OpenR1-Math-220k}} } ``` **nvidia/Llama-Nemotron-Post-Training-Dataset** ```bibtex @misc{bercovich2025llamanemotronefficientreasoningmodels, title={Llama-Nemotron: Efficient Reasoning Models}, author={Akhiad Bercovich and Itay Levy and Izik Golan and Mohammad Dabbah and Ran El-Yaniv and Omri Puny and Ido Galil and Zach Moshe and Tomer Ronen and Najeeb Nabwani and Ido Shahaf and Oren Tropp and Ehud Karpas and Ran Zilberstein and Jiaqi Zeng and Soumye Singhal and Alexander Bukharin and Yian Zhang and Tugrul Konuk and Gerald Shen and Ameya Sunil Mahabaleshwarkar and Bilal Kartal and Yoshi Suhara and Olivier Delalleau and Zijia Chen and Zhilin Wang and David Mosallanezhad and Adi Renduchintala and Haifeng Qian and Dima Rekesh and Fei Jia and Somshubra Majumdar and Vahid Noroozi and Wasi Uddin Ahmad and Sean Narenthiran and Aleksander Ficek and Mehrzad Samadi and Jocelyn Huang and Siddhartha Jain and Igor Gitman and Ivan Moshkov and Wei Du and Shubham Toshniwal and George Armstrong and Branislav Kisacanin and Matvei Novikov and Daria Gitman and Evelina Bakhturina and Jane Polak Scowcroft and John Kamalu and Dan Su and Kezhi Kong and Markus Kliegl and Rabeeh Karimi and Ying Lin and Sanjeev Satheesh and Jupinder Parmar and Pritam Gundecha and Brandon Norick and Joseph Jennings and Shrimai Prabhumoye and Syeda Nahida Akter and Mostofa Patwary and Abhinav Khattar and Deepak Narayanan and Roger Waleffe and Jimmy Zhang and Bor-Yiing Su and Guyue Huang and Terry Kong and Parth Chadha and Sahil Jain and Christine Harvey and Elad Segal and Jining Huang and Sergey Kashirsky and Robert McQueen and Izzy Putterman and George Lam and Arun Venkatesan and Sherry Wu and Vinh Nguyen and Manoj Kilaru and Andrew Wang and Anna Warno and Abhilash Somasamudramath and Sandip Bhaskar and Maka Dong and Nave Assaf and Shahar Mor and Omer Ullman Argov and Scot Junkin and Oleksandr Romanenko and Pedro Larroy and Monika Katariya and Marco Rovinelli and Viji Balas and Nicholas Edelman and Anahita Bhiwandiwalla and Muthu Subramaniam and Smita Ithape and Karthik Ramamoorthy and Yuting Wu and Suguna Varshini Velury and Omri Almog and Joyjit Daw and Denys Fridman and Erick Galinkin and Michael Evans and Katherine Luna and Leon Derczynski and Nikki Pope and Eileen Long and Seth Schneider and Guillermo Siman and Tomasz Grzegorzek and Pablo Ribalta and Monika Katariya and Joey Conway and Trisha Saar and Ann Guan and Krzysztof Pawelec and Shyamala Prayaga and Oleksii Kuchaiev and Boris Ginsburg and Oluwatobi Olabiyi and Kari Briski and Jonathan Cohen and Bryan Catanzaro and Jonah Alben and Yonatan Geifman and Eric Chung and Chris Alexiuk}, year={2025}, eprint={2505.00949}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2505.00949}, } ```

ChavyvAkvar/synthetic-trades-XRP-batch-18

ChavyvAkvar

2025-06-03T18:28:24Z

0

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

[]

2025-06-03T18:27:23Z

null

--- dataset_info: features: - name: scenario_id dtype: string - name: final_pnl_ratio dtype: float64 - name: max_drawdown dtype: float64 - name: total_trades dtype: int64 - name: synthetic_ohlc_open sequence: float64 - name: synthetic_ohlc_high sequence: float64 - name: synthetic_ohlc_low sequence: float64 - name: synthetic_ohlc_close sequence: float64 - name: garch_params_used_for_sim_str dtype: string - name: strategy_params_str dtype: string - name: strategy_exit_rules_str dtype: string splits: - name: train num_bytes: 923448051 num_examples: 1000 download_size: 924486740 dataset_size: 923448051 configs: - config_name: default data_files: - split: train path: data/train-* ---

0xarchit/Ques-Ans-with-Emotion

0xarchit

2025-06-03T18:19:41Z

0

[ "task_categories:question-answering", "language:en", "license:mit", "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "question-answer-with-emotion", "question-answers" ]

[ "question-answering" ]

2025-06-03T17:09:49Z

null

--- license: mit task_categories: - question-answering language: - en tags: - question-answer-with-emotion - question-answers pretty_name: Question Answer based on Emotions size_categories: - 1K<n<10K --- created and shared by: [0xarchit](https://0xarchit.carrd.co)

prerit2k/Bench01-20-20sec-1

prerit2k

2025-06-03T18:12:17Z

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", "first collection" ]

[ "robotics" ]

2025-06-03T18:11:02Z

null

--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - first collection 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", "trossen_subversion": "v1.0", "robot_type": "trossen_ai_solo", "total_episodes": 1, "total_frames": 597, "total_tasks": 1, "total_videos": 2, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:1" }, "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": [ 7 ], "names": [ "main_joint_0", "main_joint_1", "main_joint_2", "main_joint_3", "main_joint_4", "main_joint_5", "main_joint_6" ] }, "observation.state": { "dtype": "float32", "shape": [ 7 ], "names": [ "main_joint_0", "main_joint_1", "main_joint_2", "main_joint_3", "main_joint_4", "main_joint_5", "main_joint_6" ] }, "observation.images.cam_main": { "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 } }, "observation.images.cam_wrist": { "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] ```

yoona-J/ASR_Wav2Vec_Preprocess_Peripheral_Neuropathy_Dataset

yoona-J

2025-06-03T18:11:11Z

0

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

[]

2025-06-03T18:07:13Z

null

--- dataset_info: features: - name: input_values sequence: float32 - name: labels sequence: int64 splits: - name: train num_bytes: 4248737624.0 num_examples: 18679 - name: valid num_bytes: 239472624.0 num_examples: 1040 - name: test num_bytes: 234697040.0 num_examples: 1036 download_size: 4570203659 dataset_size: 4722907288.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: valid path: data/valid-* - split: test path: data/test-* ---

logicalqubit/QA-RRC-DISTRIC-IT-Dataset

logicalqubit

2025-06-03T18:10:34Z

0

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

[]

2025-06-03T18:10:32Z

null

--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 573243 num_examples: 2260 download_size: 197275 dataset_size: 573243 configs: - config_name: default data_files: - split: train path: data/train-* ---

Romoamigo/SWE-Bench-MultilingualC_CPPFiletered_new

Romoamigo

2025-06-03T18:09:38Z

177

0

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

[]

2025-05-28T23:47:34Z

null

--- dataset_info: features: - name: repo dtype: string - name: instance_id dtype: string - name: base_commit dtype: string - name: patch dtype: string - name: test_patch dtype: string - name: problem_statement dtype: string - name: hints_text dtype: string - name: created_at dtype: string - name: version dtype: string - name: FAIL_TO_PASS sequence: string - name: PASS_TO_PASS sequence: string - name: repo_name dtype: string - name: image_name dtype: string splits: - name: test num_bytes: 786675 num_examples: 55 download_size: 289127 dataset_size: 786675 configs: - config_name: default data_files: - split: test path: data/test-* ---

TienMat999/vn-legal-qa-rag

TienMat999

2025-06-03T18:03:18Z

274

0

[ "task_categories:question-answering", "language:vi", "license:apache-2.0", "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "legal" ]

[ "question-answering" ]

2025-05-08T16:07:21Z

null

--- dataset_info: features: - name: context_id dtype: string - name: context dtype: string - name: question_id dtype: string - name: question dtype: string - name: answers_id dtype: string - name: answers dtype: string splits: - name: train num_bytes: 26510045 num_examples: 4559 download_size: 8852182 dataset_size: 26510045 configs: - config_name: default data_files: - split: train path: data/train-* license: apache-2.0 task_categories: - question-answering language: - vi tags: - legal size_categories: - 1K<n<10K ---

voidful/earica_ms

voidful

2025-06-03T18:02:46Z

2

0

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

[]

2025-06-01T01:55:49Z

null

--- dataset_info: features: - name: question dtype: string - name: reasoning dtype: string - name: answer dtype: string - name: audio dtype: audio - name: index dtype: int64 - name: raw_yaml dtype: string splits: - name: train num_bytes: 2239015.0 num_examples: 4 download_size: 554534 dataset_size: 2239015.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

fixie-ai/endpointing-multi-turn-commonvoice

fixie-ai

2025-06-03T17:57:57Z

30

0

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

[]

2025-06-02T22:15:15Z

null

--- dataset_info: features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string - name: variant dtype: string - name: continuation dtype: string - name: transcript dtype: string - name: conversation dtype: string - name: conversation_with_audio dtype: string splits: - name: train num_bytes: 358006509.816 num_examples: 9954 download_size: 346695375 dataset_size: 358006509.816 configs: - config_name: default data_files: - split: train path: data/train-* ---

minpeter/pretrained-tiny-ko

minpeter

2025-06-03T17:57:14Z

0

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

[]

2025-06-03T17:39:47Z

null

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 31850795201.147762 num_examples: 6826068 download_size: 16259105774 dataset_size: 31850795201.147762 configs: - config_name: default data_files: - split: train path: data/train-* ---

mothnaZl/Bespoke-Stratos-17k

mothnaZl

2025-06-03T17:46:35Z

128

0

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

[]

2025-05-08T16:19:22Z

null

--- dataset_info: features: - name: system dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string - name: problem dtype: string - name: solution dtype: string - name: input dtype: string splits: - name: train num_bytes: 635631373 num_examples: 16710 download_size: 258776431 dataset_size: 635631373 configs: - config_name: default data_files: - split: train path: data/train-* ---

ChavyvAkvar/synthetic-trades-XRP-batch-14

ChavyvAkvar

2025-06-03T17:45:49Z

0

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

[]

2025-06-03T17:44:47Z

null

--- dataset_info: features: - name: scenario_id dtype: string - name: final_pnl_ratio dtype: float64 - name: max_drawdown dtype: float64 - name: total_trades dtype: int64 - name: synthetic_ohlc_open sequence: float64 - name: synthetic_ohlc_high sequence: float64 - name: synthetic_ohlc_low sequence: float64 - name: synthetic_ohlc_close sequence: float64 - name: garch_params_used_for_sim_str dtype: string - name: strategy_params_str dtype: string - name: strategy_exit_rules_str dtype: string splits: - name: train num_bytes: 923448040 num_examples: 1000 download_size: 924503656 dataset_size: 923448040 configs: - config_name: default data_files: - split: train path: data/train-* ---

davanstrien/dataset-creation-scripts

davanstrien

2025-06-03T17:44:12Z

446

2

[ "region:us" ]

[]

2025-04-01T14:27:11Z

null

--- viewer: false --- # Datasets scripts This is an experimental repository for sharing simple one-liner scripts for creating datasets. The idea is that you can easily run these scripts in the terminal without having to write any code and quickly get a dataset up and running on the Hub. ## Installation All of these scripts assume you have `uv` installed. If you don't have it installed, see the [installation guide](https://docs.astral.sh/uv/getting-started/). The scripts use the recently added [inline-script-metadata](https://packaging.python.org/en/latest/specifications/inline-script-metadata/#inline-script-metadata) to specify dependencies for the script. `UV` will automatically install these dependencies when you run the script in a new environment. This makes it easier for you to try the script without having to install the dependencies yourself. See this [guide](https://docs.astral.sh/uv/guides/scripts/#running-scripts) on running scripts in `uv` for more information. ## Scripts ### PDFs to HF dataset This script uses the newly added `pdf` feature of the `datasets` library to create a dataset from a directory of PDFs. The t;dr is that the script will take as input a directory of PDFs and output a Hugging Face dataset with the images and (optionally) the text extracted from the PDFs. You can find more info about the `pdf` feature [here](https://huggingface.co/docs/datasets/document_load). To see how the script works, you can run it with the `--help` flag: ```python uv run https://huggingface.co/datasets/davanstrien/dataset-creation-scripts/raw/main/pdf-datasets/main.py -h ``` This will print the help message to the terminal. ``` usage: main.py [-h] --directory DIRECTORY [--extract-text EXTRACT_TEXT] --hub-id HUB_ID [--private-repo PRIVATE_REPO] options: -h, --help show this help message and exit ``` For example on my `Desktop` I have a folder called `Arxiv` with a bunch of PDFs in it. I can create a dataset with the following command: ```python uv run https://huggingface.co/datasets/davanstrien/dataset-creation-scripts/raw/main/pdf-datasets/main.py --directory ~/Desktop/Arxiv --hub-id davanstrien/arxiv-pdfs --extract-text true ``` You can find the dataset on the Hub [here](https://huggingface.co/datasets/davanstrien/arxiv-pdfs).

ChavyvAkvar/synthetic-trades-XRP-batch-13

ChavyvAkvar

2025-06-03T17:37:22Z

0

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

[]

2025-06-03T17:36:20Z

null

--- dataset_info: features: - name: scenario_id dtype: string - name: final_pnl_ratio dtype: float64 - name: max_drawdown dtype: float64 - name: total_trades dtype: int64 - name: synthetic_ohlc_open sequence: float64 - name: synthetic_ohlc_high sequence: float64 - name: synthetic_ohlc_low sequence: float64 - name: synthetic_ohlc_close sequence: float64 - name: garch_params_used_for_sim_str dtype: string - name: strategy_params_str dtype: string - name: strategy_exit_rules_str dtype: string splits: - name: train num_bytes: 923448218 num_examples: 1000 download_size: 924505223 dataset_size: 923448218 configs: - config_name: default data_files: - split: train path: data/train-* ---

timcryt/vscf_mlff_data

timcryt

2025-06-03T17:26:33Z

0

[ "size_categories:10K<n<100K", "region:us", "chemistry" ]

[]

2025-06-03T17:19:44Z

null

--- tags: - chemistry size_categories: - 10K<n<100K pretty_name: l --- Datasets used in the paper "Интерполяция ППЭ с помощью машинного обучения для ускорения расчётов негармонических частот колебаний молекул". ### Description - `vscf_dataset_2_5.xyz` is the main dataset used from pretraining and finetuning models, 19 molecules, 65168 points - `compare_dataset_OCCO.xyz` is the auxillary datased used to select the model architecture from DimeNet and SchNet, 1 molecule, 1042 points

ChavyvAkvar/synthetic-trades-BTC-batch-6

ChavyvAkvar

2025-06-03T17:24:33Z

0

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

[]

2025-06-03T17:23:36Z

null

--- dataset_info: features: - name: scenario_id dtype: string - name: final_pnl_ratio dtype: float64 - name: max_drawdown dtype: float64 - name: total_trades dtype: int64 - name: synthetic_ohlc_open sequence: float64 - name: synthetic_ohlc_high sequence: float64 - name: synthetic_ohlc_low sequence: float64 - name: synthetic_ohlc_close sequence: float64 - name: garch_params_used_for_sim_str dtype: string - name: strategy_params_str dtype: string - name: strategy_exit_rules_str dtype: string splits: - name: train num_bytes: 923450595 num_examples: 1000 download_size: 924510583 dataset_size: 923450595 configs: - config_name: default data_files: - split: train path: data/train-* ---

jlbaker361/clip-celeb_captioned

jlbaker361

2025-06-03T17:19:27Z

139

0

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

[]

2025-05-23T15:07:56Z

null

--- dataset_info: features: - name: image dtype: image - name: embedding sequence: sequence: sequence: float32 - name: text sequence: sequence: sequence: float16 - name: prompt dtype: string - name: posterior sequence: sequence: sequence: float16 splits: - name: train num_bytes: 7280583008.0 num_examples: 30000 download_size: 7070282217 dataset_size: 7280583008.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

5CD-AI/Viet-r1_90k_instruct

5CD-AI

2025-06-03T17:12:03Z

0

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

[]

2025-06-03T17:09:34Z

null

--- dataset_info: features: - name: id dtype: string - name: image dtype: image - name: conversations sequence: - name: content dtype: string - name: role dtype: string - name: vi_conversations sequence: - name: content dtype: string - name: role dtype: string - name: id_num dtype: string splits: - name: train num_bytes: 2996389203.83 num_examples: 79986 download_size: 2612513952 dataset_size: 2996389203.83 configs: - config_name: default data_files: - split: train path: data/train-* ---

prerit2k/Bench01-18-10sec

prerit2k

2025-06-03T17:09:31Z

0

[ "task_categories:robotics", "license:apache-2.0", "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:timeseries", "modality:video", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "LeRobot", "first collection" ]

[ "robotics" ]

2025-06-03T17:09:28Z

null

--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - first collection 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", "trossen_subversion": "v1.0", "robot_type": "trossen_ai_solo", "total_episodes": 1, "total_frames": 299, "total_tasks": 1, "total_videos": 2, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:1" }, "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": [ 7 ], "names": [ "main_joint_0", "main_joint_1", "main_joint_2", "main_joint_3", "main_joint_4", "main_joint_5", "main_joint_6" ] }, "observation.state": { "dtype": "float32", "shape": [ 7 ], "names": [ "main_joint_0", "main_joint_1", "main_joint_2", "main_joint_3", "main_joint_4", "main_joint_5", "main_joint_6" ] }, "observation.images.cam_main": { "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 } }, "observation.images.cam_wrist": { "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] ```

LukeBailey181/TrainingRound47MultimodalConjectureDataset

LukeBailey181

2025-06-03T17:09:09Z

0

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

[]

2025-06-03T17:09:05Z

null

--- dataset_info: features: - name: prompt dtype: string - name: target dtype: string - name: source_dataset dtype: string - name: heuristic dtype: float64 splits: - name: embedding num_bytes: 5286055 num_examples: 4320 - name: grad_cosine_sim num_bytes: 3799086 num_examples: 4320 - name: grad_dot_product num_bytes: 3966114 num_examples: 4320 - name: random num_bytes: 3866087 num_examples: 4320 download_size: 5680252 dataset_size: 16917342 configs: - config_name: default data_files: - split: embedding path: data/embedding-* - split: grad_cosine_sim path: data/grad_cosine_sim-* - split: grad_dot_product path: data/grad_dot_product-* - split: random path: data/random-* ---

HAissa/SFT-dataset

HAissa

2025-06-03T17:08:27Z

130

0

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

[]

2025-05-31T12:37:25Z

null

--- dataset_info: features: - name: source dtype: string - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 3850647056.0 num_examples: 1159281 download_size: 1896264124 dataset_size: 3850647056.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

arnaultsta/MNLP_M3_wikipedia_camel_chunked_300

arnaultsta

2025-06-03T16:55:39Z

0

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

[]

2025-06-03T14:37:47Z

null

--- dataset_info: features: - name: title dtype: string - name: source dtype: string - name: text dtype: string splits: - name: train num_bytes: 168771055 num_examples: 76429 download_size: 91677839 dataset_size: 168771055 configs: - config_name: default data_files: - split: train path: data/train-* ---

levinius/MNLP_distilled_preference_code_ds

levinius

2025-06-03T16:55:18Z

0

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

[]

2025-06-03T16:55:12Z

null

--- dataset_info: features: - name: prompt dtype: string - name: source dtype: string - name: chosen dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 42323537.16766937 num_examples: 20644 - name: validation num_bytes: 4703070.83233063 num_examples: 2294 download_size: 24288551 dataset_size: 47026608.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---

ChavyvAkvar/synthetic-trades-XRP-batch-10

ChavyvAkvar

2025-06-03T16:54:27Z

0

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

[]

2025-06-03T16:53:28Z

null

--- dataset_info: features: - name: scenario_id dtype: string - name: final_pnl_ratio dtype: float64 - name: max_drawdown dtype: float64 - name: total_trades dtype: int64 - name: synthetic_ohlc_open sequence: float64 - name: synthetic_ohlc_high sequence: float64 - name: synthetic_ohlc_low sequence: float64 - name: synthetic_ohlc_close sequence: float64 - name: garch_params_used_for_sim_str dtype: string - name: strategy_params_str dtype: string - name: strategy_exit_rules_str dtype: string splits: - name: train num_bytes: 923448290 num_examples: 1000 download_size: 924486002 dataset_size: 923448290 configs: - config_name: default data_files: - split: train path: data/train-* ---

recogna-nlp/drbodebench

recogna-nlp

2025-06-03T16:49:08Z

18

0

[ "language:pt", "license:apache-2.0", "size_categories:1K<n<10K", "format:json", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "medico", "perguntas-e-respostas", "portugues-brasileiro", "benchmark-llm", "multipla-escolha" ]

[]

2025-06-02T19:25:02Z

null

--- license: apache-2.0 language: - pt tags: - medico - perguntas-e-respostas - portugues-brasileiro - benchmark-llm - multipla-escolha --- # Benchmark Brasileiro de Testes de Aptidão Médica: DrBodeBench (DBB) <p align="center"> <img src="https://cdn-uploads.huggingface.co/production/uploads/64afacf35de9402882fdd69d/naZWT4umxSc4veItz76MS.jpeg" alt="Bode Logo" width="400" style="margin-left:'auto' margin-right:'auto' display:'block'"/> </p> Este conjunto de dados introduz um novo benchmark para avaliar modelos de linguagem grandes (LLMs) médicos em português brasileiro, abordando uma lacuna crítica na avaliação de IA para aplicações de saúde em contextos não ingleses. Ele é construído a partir de testes de aptidão médica brasileiros que abrangem o período de 2011-2024, incluindo o Exame Nacional de Revalidação de Diplomas Médicos Expedidos por Instituição de Educação Superior Estrangeira (Revalida) e Exames de Admissão à Residência da Universidade de São Paulo (FUVEST). O benchmark permite uma avaliação extensiva tanto de LLMs especialistas quanto de modelos gerais. Seu propósito é estabelecer uma base adequada para avaliar e avançar os modelos de linguagem médicos em português, criando um arcabouço padronizado para guiar o desenvolvimento em direção a sistemas de IA mais eficazes, equitativos e culturalmente apropriados para a saúde no Brasil. Os dados do Revalida abrangem múltiplos anos entre 2011-2024, organizados em arquivos de texto distintos para cada ano. O ano de 2017 foi excluído devido à indisponibilidade dos dados no momento da coleta, enquanto os dados de 2020 não foram incluídos nesta versão inicial devido a dificuldades na extração relacionadas ao formato da prova. O objetivo principal durante a criação deste benchmark foi identificar modelos de linguagem que pudessem fornecer as respostas mais precisas para questões médicas e demonstrar compreensão e geração superiores de respostas em português brasileiro. ## Como Usar o Conjunto de Dados O conjunto de dados é projetado para avaliar o desempenho de LLMs em tarefas de perguntas e respostas médicas em português brasileiro. Cada instância consiste em uma pergunta médica, alternativas de múltipla escolha e a resposta correta. Para questões que originalmente incluíam imagens, são fornecidas descrições textuais dessas imagens. Os modelos podem ser instruídos com a pergunta (`enunciado`) e sua descrição de imagem associada (se `contains_img` for verdadeiro e `img_description` estiver disponível), seguidas pelas opções de múltipla escolha (`alternativas`). A tarefa do modelo é selecionar a alternativa correta. O formato de resposta esperado, conforme utilizado no estudo original, consistia apenas na letra correspondente da alternativa correta (A, B, C, D ou E). O prompt utilizado no artigo também especificava que o modelo deveria abster-se de fornecer justificativas para sua seleção ou incluir qualquer texto além da letra da alternativa escolhida. Estrutura de prompt de exemplo usada no artigo: 1. Instrução para receber uma pergunta médica e selecionar a resposta correta. 2. Três exemplos demonstrando o formato de resposta esperado (a letra da alternativa correta). 3. A declaração da pergunta (`enunciado`). 4. Se a pergunta contivesse uma imagem, sua descrição (`img_description`) era anexada imediatamente após a declaração. 5. As alternativas (`alternativas`) eram apresentadas. Cada pergunta foi submetida ao modelo individualmente. A avaliação foi conduzida usando apenas a primeira letra gerada pelo modelo. ## Estrutura do Conjunto de Dados O conjunto de dados é composto por pares de pergunta-resposta com metadados associados. ### Campos de Dados O conjunto de dados contém os seguintes campos: * `origem` (string): A fonte da pergunta (ex.: "INEP" para os exames Revalida, já que o INEP – um instituto – organiza o Revalida). * `prova` (int64): O ano em que o teste de aptidão foi administrado (variando de 2011 a 2024). * `numero` (int64): O número da questão dentro do exame específico. * `enunciado` (string): O texto da pergunta médica. * `alternativas` (dict): Um dicionário contendo as opções de múltipla escolha, onde as chaves são letras (ex.: "A", "B") e os valores são o texto das opções. Pode haver até cinco opções (A, B, C, D ou E). *Exemplo: `{ "A": "Texto para a opção A.", "B": "Texto para a opção B.", ... }`* * `resposta` (string): A letra correspondente à alternativa correta. * `contains_img` (bool): Um sinalizador booleano indicando se a pergunta original incluía uma imagem. * `img_path` (string): Um caminho relativo para o arquivo de imagem. As imagens são fornecidas separadamente (veja [Link para Imagens Extraídas](#link-para-imagens-extraídas)). Este campo será `null` se `contains_img` for falso. * `img_description` (string): Uma descrição textual da imagem, gerada com o auxílio do GPT-4o mini. Este campo será `null` se `contains_img` for falso ou se uma descrição não tiver sido gerada. É importante notar que "essas descrições não representam necessariamente uma descrição técnica médica ideal e, embora incluídas para completude, serão refinadas em versões futuras do conjunto de dados". ## Link para Imagens Extraídas As imagens que acompanham algumas questões neste conjunto de dados foram extraídas manualmente. Você pode baixá-las no seguinte link: [Link para o Google Drive.](https://drive.google.com/file/d/1ddRzCDU8mDwNsvMzlYTZ4Jzpzi5bEcoD/view?usp=drive_link) O campo `img_path` no conjunto de dados deve corresponder aos caminhos dos arquivos dentro deste arquivo vinculado. ## Curadoria Este benchmark foi criado para suprir a lacuna crítica de ferramentas de avaliação padronizadas para LLMs médicos em português brasileiro. Os recursos existentes em português brasileiro são severamente limitados, com conjuntos de dados disponíveis sofrendo de deficiências como falta de validação clínica ou uso de terminologia arcaica. A ausência de conjuntos de dados médicos válidos em português brasileiro e benchmarks padronizados dificulta o desenvolvimento de modelos, a avaliação sistemática e a comparação. Este benchmark visa fornecer um arcabouço padronizado para guiar o desenvolvimento em direção a sistemas de IA mais eficazes, equitativos e culturalmente apropriados para a saúde no Brasil. ### Dados de Origem Os dados foram coletados de exames de aptidão médica brasileiros para estudantes graduados: * O Exame Nacional de Revalidação de Diplomas Médicos Expedidos por Instituição de Educação Superior Estrangeira (Revalida). Os dados do Revalida abrangem múltiplos anos entre 2011-2024, organizados em arquivos de texto distintos para cada ano. O ano de 2017 foi excluído devido à indisponibilidade dos dados no momento da coleta, enquanto os dados de 2020 não foram incluídos nesta versão inicial devido a dificuldades na extração relacionadas ao formato da prova. * Exames de Admissão à Residência da Universidade de São Paulo (FUVEST). Para o Exame de Residência da FUVEST, apenas o exame de clínica geral foi utilizado na versão inicial deste benchmark. ### Coleta e Limpeza de Dados O processo de coleta de dados envolveu: 1. Extração de dados de perguntas e respostas dos exames Revalida e FUVEST. 2. Uso de expressões regulares (regex) para isolar perguntas e remover conteúdo irrelevante como rodapés e cabeçalhos. 3. Resolução de problemas como espaços deslocados entre palavras e inconsistências de codificação. 4. Emprego do GPT-4o mini da OpenAI para retificar automaticamente o texto, remontar palavras que foram divididas erroneamente e resolver problemas de codificação. 5. Condução de uma revisão manual para garantir a precisão após a limpeza automatizada. Ferramentas de comparação de texto foram usadas para verificar diferenças de palavras entre as perguntas originais e as versões remontadas, com as discrepâncias sendo corrigidas. ### Anotações * **Respostas:** O campo `resposta` contém a letra correta para cada questão de múltipla escolha, conforme os exames de origem. * **Descrições de Imagens:** Para questões que incluíam imagens, descrições textuais (`img_description`) foram criadas com o auxílio do GPT-4o mini. Essas descrições foram concebidas para fins de completude, já que a versão inicial do benchmark não utilizava diretamente entradas de imagem para modelos sem capacidades visuais. É explicitamente declarado que "essas descrições não representam necessariamente uma descrição técnica médica ideal e, embora incluídas para completude, serão refinadas em versões futuras do conjunto de dados". ## Informações sobre Citação Se você usar este conjunto de dados em sua pesquisa, por favor, cite o artigo original: ```bibtex @inproceedings{garcia2025step, title={A Step Forward for Medical LLMs in Brazilian Portuguese: Establishing a Benchmark and a Strong Baseline}, author={Garcia, Gabriel Lino and Manesco, João Renato Ribeiro and Paiola, Pedro Henrique and Ribeiro, Pedro Henrique Crespan and Garcia, Ana Lara Alves and Papa, João Paulo}, booktitle={Proceedings of the 38th IEEE International Symposium on Computer-Based Medical Systems (CBMS 2025)}, year={2025}, address={Madrid, Spain}, month={June} } ``` ## Informações sobre a Licença A licença para este conjunto de dados é Apache 2.0. O texto oficial da licença está em inglês.

levinius/MNLP_distilled_preference_ds

levinius

2025-06-03T16:44:42Z

0

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

[]

2025-06-03T16:44:37Z

null

--- dataset_info: features: - name: prompt dtype: string - name: source dtype: string - name: chosen dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 24657058.435946036 num_examples: 16144 - name: validation num_bytes: 2740012.564053964 num_examples: 1794 download_size: 14771309 dataset_size: 27397071.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---

diabolocom/talkbank_4_stt

diabolocom

2025-06-03T16:43:11Z

359

2

[ "task_categories:automatic-speech-recognition", "task_categories:text-to-speech", "task_categories:text-to-audio", "multilinguality:multilingual", "language:en", "language:de", "language:es", "language:fr", "language:zh", "license:cc-by-nc-sa-3.0", "size_categories:100K<n<1M", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2409.12042", "region:us" ]

[ "automatic-speech-recognition", "text-to-speech", "text-to-audio" ]

2024-09-19T13:46:35Z

null

--- language: - en - de - es - fr - es - zh license: - cc-by-nc-sa-3.0 multilinguality: - multilingual task_categories: - automatic-speech-recognition - text-to-speech - text-to-audio pretty_name: talkbank_4_stt dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcript dtype: string - name: language_code dtype: string - name: subset dtype: string - name: full_language dtype: string - name: switch_id dtype: string - name: segment_id dtype: string - name: transcript_filename dtype: string - name: audio_len_sec dtype: string - name: orig_file_start dtype: string - name: orig_file_end dtype: string - name: channel dtype: string - name: speaker_id dtype: string splits: - name: segment_jp num_bytes: 227582609.856 num_examples: 23272 - name: segment_es num_bytes: 677020941.0 num_examples: 57150 - name: segment_zh num_bytes: 213524031.376 num_examples: 23364 - name: segment_en num_bytes: 533951011.464 num_examples: 32092 - name: segment_fr num_bytes: 264513055.952 num_examples: 14796 - name: segment_ess num_bytes: 49557412.0 num_examples: 494 - name: segment_de num_bytes: 127896413.16 num_examples: 12944 - name: switch_jp num_bytes: 437307191.0 num_examples: 318 - name: switch_es num_bytes: 1597337075.24 num_examples: 2130 - name: switch_zh num_bytes: 381994059.12 num_examples: 1691 - name: switch_en num_bytes: 835237103.0 num_examples: 908 - name: switch_fr num_bytes: 481521047.0 num_examples: 281 - name: switch_ess num_bytes: 54158091.0 num_examples: 9 - name: switch_de num_bytes: 415139340.0 num_examples: 701 download_size: 6218452927 dataset_size: 6296739381.167999 configs: - config_name: default data_files: - split: segment_jp path: data/segment_jp-* - split: segment_es path: data/segment_es-* - split: segment_zh path: data/segment_zh-* - split: segment_en path: data/segment_en-* - split: segment_fr path: data/segment_fr-* - split: segment_ess path: data/segment_ess-* - split: segment_de path: data/segment_de-* - split: switch_jp path: data/switch_jp-* - split: switch_es path: data/switch_es-* - split: switch_zh path: data/switch_zh-* - split: switch_en path: data/switch_en-* - split: switch_fr path: data/switch_fr-* - split: switch_ess path: data/switch_ess-* - split: switch_de path: data/switch_de-* --- # Dataset Card ## Dataset Description This dataset is a benchmark based on the TalkBank[1] corpus—a large multilingual repository of conversational speech that captures real-world, unstructured interactions. We use CA-Bank [2], which focuses on phone conversations between adults, which include natural speech phenomena such as laughter, pauses, and interjections. To ensure the dataset is highly accurate and suitable for benchmarking conversational ASR systems, we employ extensive set of pre-processing. ## Preprocessing Steps We apply the following preprocessing steps to ensure the dataset’s quality: - Manual filtering of conversations - Speaker-channel alignment - Timestamp alignment using voice activity detection (VAD) - Discarding segments based on Word Error Rate (WER) thresholds ## Paper and Code Repository For a comprehensive explanation of the preprocessing pipeline and dataset details, refer to our paper [ASR Benchmarking: The Need for a More Representative Conversational Dataset](https://arxiv.org/abs/2409.12042) and explore our [GitHub repository](https://github.com/Diabolocom-Research/ConversationalDataset) for code and additional resources. ## Segmentation Types: Speaker Switch vs Annotation We offer two types of segmentation for this dataset: - **Annotation-based Segmentation**: Segments are derived directly from the annotations provided in the original TalkBank corpus. - **Speaker Switch Segmentation**: We consolidate consecutive segments from the same speaker into a single, larger audio segment, providing an alternative structure for analysis. ## Citations While using this dataset please cite: ``` @article{maheshwari2024asr, title={ASR Benchmarking: Need for a More Representative Conversational Dataset}, author={Maheshwari, Gaurav and Ivanov, Dmitry and Johannet, Th{\'e}o and Haddad, Kevin El}, journal={arXiv preprint arXiv:2409.12042}, year={2024} } ``` In addition, please acknowledge the TalkBank dataset:: ``` @article{macwhinney2010transcribing, title={Transcribing, searching and data sharing: The CLAN software and the TalkBank data repository}, author={MacWhinney, Brian and Wagner, Johannes}, journal={Gesprachsforschung: Online-Zeitschrift zur verbalen Interaktion}, volume={11}, pages={154}, year={2010}, publisher={NIH Public Access} } ``` ## Licensing Information This dataset is released under the [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0). ## References [1]: MacWhinney, Brian. "TalkBank: Building an open unified multimodal database of communicative interaction." (2004). [2]: MacWhinney, Brian, and Johannes Wagner. "Transcribing, searching and data sharing: The CLAN software and the TalkBank data repository." Gesprachsforschung: Online-Zeitschrift zur verbalen Interaktion 11 (2010): 154.

un1c0rnio/eval_act_so101_box_pencil3_140000

un1c0rnio

2025-06-03T16:41:34Z

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", "so101", "tutorial" ]

[ "robotics" ]

2025-06-03T16:41:10Z

null

--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - so101 - 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": "so101", "total_episodes": 9, "total_frames": 13523, "total_tasks": 1, "total_videos": 18, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:9" }, "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.base": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height": 480, "video.width": 640, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "has_audio": false } }, "observation.images.extside": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height": 480, "video.width": 640, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "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] ```

tingshiuanlai/motherese-prosody-data

tingshiuanlai

2025-06-03T16:39:23Z

118

0

[ "license:cc-by-4.0", "region:us", "prosody", "speech-features", "librispeech" ]

[]

2025-05-25T21:22:08Z

null

--- license: cc-by-4.0 tags: - prosody - speech-features - librispeech --- # Prosody Features for train-clean-100 This repository contains a pickled `ProsodyFeatureExtractor` object trained on the LibriSpeech `train-clean-100` and `dev-clean` subset. ## Contents - Word-level prosodic features - F0, energy, duration, pause, prominence - Extracted using CELEX-based stress localization ## Format - `.pkl` file — can be loaded using `pickle.load(open(..., "rb"))` - Compatible with JSON serialization

ChavyvAkvar/synthetic-trades-BNB-batch-3

ChavyvAkvar

2025-06-03T16:31:46Z

0

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

[]

2025-06-03T16:30:51Z

null

--- dataset_info: features: - name: scenario_id dtype: string - name: final_pnl_ratio dtype: float64 - name: max_drawdown dtype: float64 - name: total_trades dtype: int64 - name: synthetic_ohlc_open sequence: float64 - name: synthetic_ohlc_high sequence: float64 - name: synthetic_ohlc_low sequence: float64 - name: synthetic_ohlc_close sequence: float64 - name: garch_params_used_for_sim_str dtype: string - name: strategy_params_str dtype: string - name: strategy_exit_rules_str dtype: string splits: - name: train num_bytes: 923450370 num_examples: 1000 download_size: 924490854 dataset_size: 923450370 configs: - config_name: default data_files: - split: train path: data/train-* ---