Datasets:

---

librarian-bots

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dataset_cards_with_metadata

like 12

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Librarian Bots 103

AiAsistent/LLMConversation

AiAsistent

--- license: mit ---

Mohamed-DLM/asr_en_ar_switch_split_69_final_updated

Mohamed-DLM

--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription dtype: string splits: - name: train num_bytes: 4863591.0 num_examples: 52 download_size: 4310867 dataset_size: 4863591.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

Ashkchamp/GarbageLittering

Ashkchamp

--- dataset_info: features: - name: Image Name dtype: image - name: Prompt dtype: string - name: Garbage and Littering dtype: string - name: Garbage and Littering Score dtype: int64 splits: - name: train num_bytes: 4913319691.596 num_examples: 12509 download_size: 5519174848 dataset_size: 4913319691.596 configs: - config_name: default data_files: - split: train path: data/train-* ---

ducut91/judgement_train_v2

ducut91

--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 26835796 num_examples: 1648 download_size: 7562439 dataset_size: 26835796 configs: - config_name: default data_files: - split: train path: data/train-* ---

fhai50032/Aryabhatt-Hinglish

fhai50032

--- dataset_info: features: - name: english_question dtype: string - name: hinglish_question dtype: string - name: thoughts dtype: string - name: answer dtype: string - name: hash dtype: string - name: modelId dtype: string splits: - name: train num_bytes: 4402087.9883040935 num_examples: 509 download_size: 2093204 dataset_size: 4402087.9883040935 configs: - config_name: default data_files: - split: train path: data/train-* ---

xiaoxl/crisismmd2inf_features

xiaoxl

--- dataset_info: features: - name: features sequence: float32 - name: labels sequence: int64 splits: - name: train num_bytes: 59142160 num_examples: 9601 - name: dev num_bytes: 9689680 num_examples: 1573 - name: test num_bytes: 9449440 num_examples: 1534 download_size: 86280440 dataset_size: 78281280 configs: - config_name: default data_files: - split: train path: data/train-* - split: dev path: data/dev-* - split: test path: data/test-* ---

txyucas/wenge-data_reformat

txyucas

--- license: mit ---

Evren78/trainning

Evren78

--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: response dtype: string splits: - name: train num_bytes: 27450.0 num_examples: 90 - name: test num_bytes: 3050.0 num_examples: 10 download_size: 10219 dataset_size: 30500.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

MaziyarPanahi/SYNTHETIC-1-200K

MaziyarPanahi

--- dataset_info: features: - name: response_id dtype: string - name: problem_id dtype: string - name: source dtype: string - name: in_source_id dtype: string - name: hf_dataset_name dtype: string - name: task_type dtype: string - name: prompt dtype: string - name: gold_standard_solution dtype: string - name: llm_response dtype: string - name: verification_info dtype: string - name: score dtype: float64 - name: verification_result_info dtype: string - name: metadata dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 7761257969 num_examples: 199426 download_size: 3631365192 dataset_size: 7761257969 configs: - config_name: default data_files: - split: train path: data/train-* ---

Mohamed-DLM/asr_en_ar_switch_split_70_final_updated

Mohamed-DLM

--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription dtype: string splits: - name: train num_bytes: 5248472.0 num_examples: 46 download_size: 4649742 dataset_size: 5248472.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

prithivMLmods/AI-vs-Deepfake-vs-Real

prithivMLmods

--- license: apache-2.0 task_categories: - image-classification language: - en tags: - deepfake - ai - real size_categories: - 1K<n<10K --- # **AI vs Deepfake vs Real** **AI vs Deepfake vs Real** is a dataset designed for image classification, distinguishing between artificial, deepfake, and real images. This dataset includes a diverse collection of high-quality images to enhance classification accuracy and improve the model’s overall efficiency. By providing a well-balanced dataset, it aims to support the development of more robust AI-generated and deepfake detection models. # **Label Mappings** - **Mapping of IDs to Labels:** `{0: 'Artificial', 1: 'Deepfake', 2: 'Real'}` - **Mapping of Labels to IDs:** `{'Artificial': 0, 'Deepfake': 1, 'Real': 2}` This dataset serves as a valuable resource for training, evaluating, and benchmarking AI models in the field of deepfake and AI-generated image detection. # **Dataset Composition** The **AI vs Deepfake vs Real** dataset is composed of modular subsets derived from the following datasets: - [open-image-preferences-v1](https://huggingface.co/datasets/data-is-better-together/open-image-preferences-v1) - [Deepfakes-QA-Patch1](https://huggingface.co/datasets/prithivMLmods/Deepfakes-QA-Patch1) - [Deepfakes-QA-Patch2](https://huggingface.co/datasets/prithivMLmods/Deepfakes-QA-Patch2) The dataset is evenly distributed across three categories: - **Artificial** (33.3%) - **Deepfake** (33.3%) - **Real** (33.3%) With a total of **9,999 entries**, this balanced distribution ensures better generalization and improved robustness in distinguishing between AI-generated, deepfake, and real images.

Narenameme/indian_supreme_court_judgements_en_ta

Narenameme

--- license: mit ---

alea-institute/kl3m-data-edgar-10-q

alea-institute

--- dataset_info: features: - name: identifier dtype: string - name: dataset dtype: string - name: mime_type dtype: string - name: tokens sequence: int64 splits: - name: train num_bytes: 5610563 num_examples: 100 download_size: 1102905 dataset_size: 5610563 configs: - config_name: default data_files: - split: train path: data/train-* ---

MaziyarPanahi/SYNTHETIC-1-800K

MaziyarPanahi

--- dataset_info: features: - name: response_id dtype: string - name: problem_id dtype: string - name: source dtype: string - name: in_source_id dtype: string - name: hf_dataset_name dtype: string - name: task_type dtype: string - name: prompt dtype: string - name: gold_standard_solution dtype: string - name: llm_response dtype: string - name: verification_info dtype: string - name: score dtype: float64 - name: verification_result_info dtype: string - name: metadata dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 32108753629 num_examples: 797705 download_size: 14936914253 dataset_size: 32108753629 configs: - config_name: default data_files: - split: train path: data/train-* ---

fedric95/AIME2025-ita

fedric95

--- dataset_info: features: - name: split dtype: string - name: id dtype: int64 - name: english dtype: string - name: italian dtype: string - name: answer dtype: int64 splits: - name: train num_bytes: 33528 num_examples: 30 download_size: 26998 dataset_size: 33528 configs: - config_name: default data_files: - split: train path: data/train-* --- # Description This repository contains an Italian translated version of the AIME2025 dataset. As the english reference version, I haved used the one created by the authors of MathArena. Thank you Jasper Dekoninck for the help in understanding the structure of the dataset. The **aime_2025_I** and **aime_2025_II** folders, contain the translated dataset in the same format used by MathArena in their evaluation pipeline: https://github.com/eth-sri/matharena (I did not try to run the pipeline) **translate.py** contains the code I have used to create the first version of the translations. Basically, for each question, I asked three times gpt-4o to translate it. After that, I have asked gpt-4o to select the best translation among them. After this automatic step, I manually checked the translations, and when needed, I manually modified them. The prompt that I haved used to ask gpt-4o to translate from English to Italian, is strongly inspired by the one used by Edoardo Federici (https://huggingface.co/efederici). The main difference is that I have used gpt-4o instead of Claude Opus and structured output. You can find the details in this file. **pus_to_hub.py** contains the code to push the data to huggingface. # Disclaimer I hope that all the translations are correct, but some of them could contain mistakes, let me know if you find some.

Kyleyee/train_data_imdb_reform

Kyleyee

--- tags: - trl --- # HH-RLHF-Helpful-Base Dataset ## Summary The HH-RLHF-Helpful-Base dataset is a processed version of [Anthropic's HH-RLHF](https://huggingface.co/datasets/Anthropic/hh-rlhf) dataset, specifically curated to train models using the [TRL library](https://github.com/huggingface/trl) for preference learning and alignment tasks. It contains pairs of text samples, each labeled as either "chosen" or "rejected," based on human preferences regarding the helpfulness of the responses. This dataset enables models to learn human preferences in generating helpful responses, enhancing their ability to assist users effectively. ## Data Structure - **Format**: [Conversational](https://huggingface.co/docs/trl/main/dataset_formats#conversational) - **Type**: [Preference](https://huggingface.co/docs/trl/main/dataset_formats#preference) Columns: - `"prompt"`: The user query. - `"chosen"`: A response deemed helpful by human evaluators. - `"rejected"`: A response considered less helpful or unhelpful. This structure allows models to learn to prefer the _chosen_ response over the _rejected_ one, thereby aligning with human preferences in helpfulness. ## Generation script The script used to generate this dataset can be found [here](https://github.com/huggingface/trl/blob/main/examples/datasets/hh-rlhf-helpful-base.py).

xiaoxl/crisismmd2hum_features

xiaoxl

--- dataset_info: features: - name: features sequence: float32 - name: labels sequence: int64 splits: - name: train num_bytes: 37736160 num_examples: 6126 - name: dev num_bytes: 6147680 num_examples: 998 - name: test num_bytes: 5882800 num_examples: 955 download_size: 54708871 dataset_size: 49766640 configs: - config_name: default data_files: - split: train path: data/train-* - split: dev path: data/dev-* - split: test path: data/test-* ---

aharley2/deskewed-mnist-images

aharley2

--- dataset_info: features: - name: original_image dtype: image - name: deskewed_image dtype: image - name: label dtype: class_label: names: '0': '0' '1': '1' '2': '2' '3': '3' '4': '4' '5': '5' '6': '6' '7': '7' '8': '8' '9': '9' splits: - name: train num_bytes: 75007840.0 num_examples: 60000 - name: test num_bytes: 12430020.0 num_examples: 10000 download_size: 134390512 dataset_size: 87437860.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

Mohamed-DLM/asr_en_ar_switch_split_71_final_updated

Mohamed-DLM

--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription dtype: string splits: - name: train num_bytes: 3882882.0 num_examples: 46 download_size: 3409812 dataset_size: 3882882.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

irasalsabila/javanese_asr_dataset_20k

irasalsabila

--- dataset_info: features: - name: filename dtype: string - name: userid dtype: string - name: label dtype: string splits: - name: train num_bytes: 1181326 num_examples: 16000 - name: test num_bytes: 296292 num_examples: 4000 download_size: 1055430 dataset_size: 1477618 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

irasalsabila/sundanese_asr_dataset_20k

irasalsabila

--- dataset_info: features: - name: filename dtype: string - name: userid dtype: string - name: label dtype: string splits: - name: train num_bytes: 1286913 num_examples: 16000 - name: test num_bytes: 322114 num_examples: 4000 download_size: 803351 dataset_size: 1609027 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

laudite-ufg/MTS-Dialog-Gemini-Translated-With-Voices-whisper-finetuning-large-v3-tokenized

laudite-ufg

--- dataset_info: features: - name: Dialog dtype: int64 - name: Turn dtype: int64 - name: Speaker dtype: string - name: Voice dtype: string - name: Version dtype: int64 - name: audio_filename dtype: string - name: Sentence dtype: string - name: Translated_Sentence dtype: string - name: input_features sequence: sequence: float32 - name: labels sequence: int64 splits: - name: train num_bytes: 65784436401 num_examples: 42803 - name: validation num_bytes: 4739855542 num_examples: 3084 - name: test num_bytes: 21049609382 num_examples: 13696 download_size: 13183751184 dataset_size: 91573901325 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

Intelligent-Internet/Big-Thought-Gemini-part2

Intelligent-Internet

--- dataset_info: features: - name: solution dtype: string - name: answer dtype: string - name: system dtype: string - name: problem dtype: string - name: thought dtype: string - name: source dtype: string - name: model_source dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string - name: qwen2.5-math-1.5b dtype: string - name: is_correct dtype: bool splits: - name: train num_bytes: 15716665838.667767 num_examples: 800805 download_size: 7268777536 dataset_size: 15716665838.667767 configs: - config_name: default data_files: - split: train path: data/train-* ---

Bartm3/Tape_to_bin

Bartm3

--- 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": "v2.0", "robot_type": "so100", "total_episodes": 5, "total_frames": 1589, "total_tasks": 1, "total_videos": 10, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:5" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "action": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.state": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.images.laptop": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "observation.images.phone": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "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] ```

will-rads/MTDSB

will-rads

--- license: unknown ---

Mohamed-DLM/asr_en_ar_switch_split_72_final_updated

Mohamed-DLM

--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription dtype: string splits: - name: train num_bytes: 5281479.0 num_examples: 54 download_size: 4659877 dataset_size: 5281479.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

embodied-ai/piper_shirt_hanging_10

embodied-ai

--- license: apache-2.0 task_categories: - robotics tags: - LeRobot configs: - config_name: default data_files: data/*/*.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - **Homepage:** [More Information Needed] - **Paper:** [More Information Needed] - **License:** apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.0", "robot_type": "piper_ros", "total_episodes": 51, "total_frames": 182854, "total_tasks": 1, "total_videos": 204, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:51" }, "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": [ 14 ], "names": [ "left_waist", "left_shoulder", "left_elbow", "left_forearm_roll", "left_wrist_angle", "left_wrist_rotate", "left_gripper", "right_waist", "right_shoulder", "right_elbow", "right_forearm_roll", "right_wrist_angle", "right_wrist_rotate", "right_gripper" ] }, "observation.state": { "dtype": "float32", "shape": [ 14 ], "names": [ "left_waist", "left_shoulder", "left_elbow", "left_forearm_roll", "left_wrist_angle", "left_wrist_rotate", "left_gripper", "right_waist", "right_shoulder", "right_elbow", "right_forearm_roll", "right_wrist_angle", "right_wrist_rotate", "right_gripper" ] }, "observation.images.cam_high": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "h264", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "observation.images.cam_low": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "h264", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "observation.images.cam_left_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": "h264", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "observation.images.cam_right_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": "h264", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "timestamp": { "dtype": "float32", "shape": [ 1 ], "names": null }, "frame_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "episode_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "task_index": { "dtype": "int64", "shape": [ 1 ], "names": null } } } ``` ## Citation **BibTeX:** ```bibtex [More Information Needed] ```

mattzcarey/climbs

mattzcarey

--- dataset_info: features: - name: uuid dtype: string - name: layout_id dtype: int64 - name: setter_id dtype: int64 - name: setter_username dtype: string - name: name dtype: string - name: description dtype: string - name: hsm dtype: int64 - name: edge_left dtype: int64 - name: edge_right dtype: int64 - name: edge_bottom dtype: int64 - name: edge_top dtype: int64 - name: angle dtype: float64 - name: frames_count dtype: int64 - name: frames_pace dtype: int64 - name: frames dtype: string - name: is_draft dtype: bool - name: is_listed dtype: bool - name: created_at dtype: timestamp[ns] - name: source_db dtype: string splits: - name: train num_bytes: 81659268 num_examples: 295996 - name: test num_bytes: 10201112 num_examples: 37000 - name: validation num_bytes: 10208297 num_examples: 37000 download_size: 53037864 dataset_size: 102068677 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* task_categories: - text-generation language: - en tags: - sport - climbing pretty_name: Climbs size_categories: - 10K<n<100K --- # Climbs Dataset This dataset contains climbing route data. It was created using the [BoardLib (unofficial) API](https://github.com/lemeryfertitta/BoardLib). This api pulls publicly available climbs from the following Aurora climbing boards: - Kilter - Tension - Decoy - Aurora - Grasshopper - Touchstone - Soill ## Usage ```python from datasets import load_dataset dataset = load_dataset("mattzcarey/climbs") ``` ## License not sure yet. Also not sure how legal this is, please don't sue me.

JimboDDjh/MoneyEgg006

JimboDDjh

--- license: unknown ---

herman66/Chinese-DeepSeek-R1-Distill-data-Fin-2k-SFT-v2

herman66

--- license: apache-2.0 task_categories: - question-answering language: - zh tags: - finance ---

Tonic/OpenReasonerZero

Tonic

--- license: mit task_categories: - question-answering - fill-mask - text2text-generation language: - en tags: - math - mathematics pretty_name: Open Reasoner Zero Dataset size_categories: - 10K<n<100K datasets: configs: - config_name: default data_files: - split: train path: "orz_math_57k_collected.json" default: true format: type: json columns: - name: from type: string description: Indicates the source of the entry ("human" or "assistant"). - name: value type: string|null description: The content of the entry (question, answer, or null if not provided). - name: ground_truth type: object|null description: Contains the correct answer and optionally a pass_at_n metric. subcolumns: - name: value type: string description: The correct answer to the question, often as a number or fraction. - name: pass_at_n type: float|null description: A metric indicating performance (e.g., probability of correctness), if available. --- A single training split containing mathematical questions, assistant responses (if provided), and ground truth answers collected for the Open Reasoner Zero project. A dataset of mathematical questions and answers collected for the Open Reasoner Zero project. Each entry consists of a human-posed math problem and an assistant response (if provided), along with ground truth data including the correct answer. The dataset is stored in a single JSON file and is structured to support automatic loading with Hugging Face's `load_dataset()`. ```json - input: | {"from": "human", "value": "Let $a_1 = 2,$ and for $n\\ge 1,$ let $a_{n+1} = 2a_n + 1.$ Find the smallest value of an $a_n$ that is not a prime number.", "ground_truth": {"value": "95", "pass_at_n": 0.75}} output: | {"from": "assistant", "value": "95"} - input: | {"from": "human", "value": "A student council must select a two-person welcoming committee and a three-person planning committee from among its members. There are exactly $10$ ways to select a two-person team for the welcoming committee. It is possible for students to serve on both committees. In how many different ways can a three-person planning committee be selected? $\\textbf{(A)}\\ 10\\qquad\\textbf{(B)}\\ 12\\qquad\\textbf{(C)}\\ 15\\qquad\\textbf{(D)}\\ 18\\qquad\\textbf{(E)}\\ 25$", "ground_truth": {"value": "10", "pass_at_n": null}} output: | {"from": "assistant", "value": "10"} - input: | {"from": "human", "value": "In a drawer Sandy has $5$ pairs of socks, each pair a different color. On Monday Sandy selects two individual socks at random from the $10$ socks in the drawer. On Tuesday Sandy selects $2$ of the remaining $8$ socks at random and on Wednesday two of the remaining $6$ socks at random. The probability that Wednesday is the first day Sandy selects matching socks is $\\frac{m}{n}$, where $m$ and $n$ are relatively prime positive integers, Find $m+n$.", "ground_truth": {"value": "341", "pass_at_n": null}} output: | {"from": "assistant", "value": "341"} ``` @misc{OpenReasonerZero2025, title={Open-Reasoner-Zero: An Open Source Approach to Scaling Reinforcement Learning on the Base Model}, author={Jingcheng Hu and Yinmin Zhang and Qi Han and Daxin Jiang and Xiangyu Zhang, Heung-Yeung Shum}, year={2025}, howpublished={\url{https://github.com/Open-Reasoner-Zero/Open-Reasoner-Zero}}, }

Mohamed-DLM/asr_en_ar_switch_split_73_final_updated

Mohamed-DLM

--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription dtype: string splits: - name: train num_bytes: 5916352.0 num_examples: 55 download_size: 5203124 dataset_size: 5916352.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

Tgratzi/tma-intents

Tgratzi

--- dataset_info: features: - name: text dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 182178 num_examples: 2214 - name: test num_bytes: 19458 num_examples: 233 download_size: 64244 dataset_size: 201636 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

Bartm3/Tape_to_bin_1

Bartm3

--- 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": "v2.0", "robot_type": "so100", "total_episodes": 32, "total_frames": 10011, "total_tasks": 1, "total_videos": 64, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:32" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "action": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.state": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.images.laptop": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "observation.images.phone": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "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] ```

MaziyarPanahi/SYNTHETIC-1-1.6M

MaziyarPanahi

--- dataset_info: features: - name: response_id dtype: string - name: problem_id dtype: string - name: source dtype: string - name: in_source_id dtype: string - name: hf_dataset_name dtype: string - name: task_type dtype: string - name: prompt dtype: string - name: gold_standard_solution dtype: string - name: llm_response dtype: string - name: verification_info dtype: string - name: score dtype: float64 - name: verification_result_info dtype: string - name: metadata dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 64512387272 num_examples: 1595409 download_size: 30072816215 dataset_size: 64512387272 configs: - config_name: default data_files: - split: train path: data/train-* ---

mehrdad-abdi/BTCUSDT_1h

mehrdad-abdi

--- dataset_info: features: - name: Open dtype: float64 - name: High dtype: float64 - name: Low dtype: float64 - name: Close dtype: float64 - name: Volume dtype: float64 - name: EMA_10 dtype: float64 - name: EMA_50 dtype: float64 - name: EMA_200 dtype: float64 - name: RSI_6 dtype: float64 - name: RSI_14 dtype: float64 - name: RSI_24 dtype: float64 - name: MACD dtype: float64 - name: MACD_Signal dtype: float64 - name: BB_Upper dtype: float64 - name: BB_Lower dtype: float64 - name: BB_Width dtype: float64 - name: ATR dtype: float64 - name: Ichimoku_Conversion dtype: float64 - name: Ichimoku_Base dtype: float64 - name: Ichimoku_Leading_Span_A dtype: float64 - name: Ichimoku_Leading_Span_B dtype: float64 - name: Ichimoku_Chikou_Span dtype: float64 - name: Open Time dtype: timestamp[ns] splits: - name: train num_bytes: 12131881 num_examples: 65269 download_size: 11920306 dataset_size: 12131881 configs: - config_name: default data_files: - split: train path: data/train-* ---

mehrdad-abdi/BTCUSDT_6h

mehrdad-abdi

--- dataset_info: features: - name: Open dtype: float64 - name: High dtype: float64 - name: Low dtype: float64 - name: Close dtype: float64 - name: Volume dtype: float64 - name: EMA_10 dtype: float64 - name: EMA_50 dtype: float64 - name: EMA_200 dtype: float64 - name: RSI_6 dtype: float64 - name: RSI_14 dtype: float64 - name: RSI_24 dtype: float64 - name: MACD dtype: float64 - name: MACD_Signal dtype: float64 - name: BB_Upper dtype: float64 - name: BB_Lower dtype: float64 - name: BB_Width dtype: float64 - name: ATR dtype: float64 - name: Ichimoku_Conversion dtype: float64 - name: Ichimoku_Base dtype: float64 - name: Ichimoku_Leading_Span_A dtype: float64 - name: Ichimoku_Leading_Span_B dtype: float64 - name: Ichimoku_Chikou_Span dtype: float64 - name: Open Time dtype: timestamp[ns] splits: - name: train num_bytes: 2022136 num_examples: 10879 download_size: 2009817 dataset_size: 2022136 configs: - config_name: default data_files: - split: train path: data/train-* ---

mehrdad-abdi/BTCUSDT_1d

mehrdad-abdi

--- dataset_info: features: - name: Open dtype: float64 - name: High dtype: float64 - name: Low dtype: float64 - name: Close dtype: float64 - name: Volume dtype: float64 - name: EMA_10 dtype: float64 - name: EMA_50 dtype: float64 - name: EMA_200 dtype: float64 - name: RSI_6 dtype: float64 - name: RSI_14 dtype: float64 - name: RSI_24 dtype: float64 - name: MACD dtype: float64 - name: MACD_Signal dtype: float64 - name: BB_Upper dtype: float64 - name: BB_Lower dtype: float64 - name: BB_Width dtype: float64 - name: ATR dtype: float64 - name: Ichimoku_Conversion dtype: float64 - name: Ichimoku_Base dtype: float64 - name: Ichimoku_Leading_Span_A dtype: float64 - name: Ichimoku_Leading_Span_B dtype: float64 - name: Ichimoku_Chikou_Span dtype: float64 - name: Open Time dtype: timestamp[ns] splits: - name: train num_bytes: 506515 num_examples: 2725 download_size: 510027 dataset_size: 506515 configs: - config_name: default data_files: - split: train path: data/train-* ---

mehrdad-abdi/BTCUSDT_1w

mehrdad-abdi

--- dataset_info: features: - name: Open dtype: float64 - name: High dtype: float64 - name: Low dtype: float64 - name: Close dtype: float64 - name: Volume dtype: float64 - name: EMA_10 dtype: float64 - name: EMA_50 dtype: float64 - name: EMA_200 dtype: float64 - name: RSI_6 dtype: float64 - name: RSI_14 dtype: float64 - name: RSI_24 dtype: float64 - name: MACD dtype: float64 - name: MACD_Signal dtype: float64 - name: BB_Upper dtype: float64 - name: BB_Lower dtype: float64 - name: BB_Width dtype: float64 - name: ATR dtype: float64 - name: Ichimoku_Conversion dtype: float64 - name: Ichimoku_Base dtype: float64 - name: Ichimoku_Leading_Span_A dtype: float64 - name: Ichimoku_Leading_Span_B dtype: float64 - name: Ichimoku_Chikou_Span dtype: float64 - name: Open Time dtype: timestamp[ns] splits: - name: train num_bytes: 84575 num_examples: 455 download_size: 89955 dataset_size: 84575 configs: - config_name: default data_files: - split: train path: data/train-* ---

Mohamed-DLM/asr_en_ar_switch_split_74_final_updated

Mohamed-DLM

--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription dtype: string splits: - name: train num_bytes: 4143811.0 num_examples: 48 download_size: 3685118 dataset_size: 4143811.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

akhooli/Afw24_tok

akhooli

--- dataset_info: features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 splits: - name: train_part_0 num_bytes: 1713503130 num_examples: 1000000 - name: train_part_1 num_bytes: 1695905810 num_examples: 1000000 - name: train_part_final num_bytes: 70642405 num_examples: 41730 download_size: 1650121747 dataset_size: 3480051345 configs: - config_name: default data_files: - split: train_part_0 path: data/train_part_0-* - split: train_part_1 path: data/train_part_1-* - split: train_part_final path: data/train_part_final-* ---

pankaj9075rawat/suraj_val_mcq_demo

pankaj9075rawat

--- license: mit ---

LuckyLukke/REFUEL-8-7500_vs_8B_500

LuckyLukke

--- dataset_info: features: - name: id dtype: int64 - name: starting_agent dtype: int64 - name: game dtype: string - name: trajectory_starter list: - name: content dtype: string - name: role dtype: string - name: trajectory_responder list: - name: content dtype: string - name: role dtype: string - name: model_agent_1 dtype: string - name: model_agent_2 dtype: string - name: evaluation dtype: string splits: - name: train num_bytes: 8765339 num_examples: 500 download_size: 4125997 dataset_size: 8765339 configs: - config_name: default data_files: - split: train path: data/train-* ---

LuckyLukke/REFUEL-8-7500_vs_8B

LuckyLukke

--- dataset_info: features: - name: id dtype: int64 - name: starting_agent dtype: int64 - name: game dtype: string - name: trajectory_starter list: - name: content dtype: string - name: role dtype: string - name: trajectory_responder list: - name: content dtype: string - name: role dtype: string - name: model_agent_1 dtype: string - name: model_agent_2 dtype: string - name: evaluation dtype: string splits: - name: train num_bytes: 8765339 num_examples: 500 download_size: 4125997 dataset_size: 8765339 configs: - config_name: default data_files: - split: train path: data/train-* ---

Mohamed-DLM/asr_en_ar_switch_split_75_final_updated

Mohamed-DLM

--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription dtype: string splits: - name: train num_bytes: 5220918.0 num_examples: 48 download_size: 4588521 dataset_size: 5220918.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

smmrokn/reddit_dataset_18

smmrokn

--- license: mit multilinguality: - multilingual source_datasets: - original task_categories: - text-classification - token-classification - question-answering - summarization - text-generation task_ids: - sentiment-analysis - topic-classification - named-entity-recognition - language-modeling - text-scoring - multi-class-classification - multi-label-classification - extractive-qa - news-articles-summarization --- # Bittensor Subnet 13 Reddit Dataset <center> <img src="https://huggingface.co/datasets/macrocosm-os/images/resolve/main/bittensor.png" alt="Data-universe: The finest collection of social media data the web has to offer"> </center> <center> <img src="https://huggingface.co/datasets/macrocosm-os/images/resolve/main/macrocosmos-black.png" alt="Data-universe: The finest collection of social media data the web has to offer"> </center> ## Dataset Description - **Repository:** smmrokn/reddit_dataset_18 - **Subnet:** Bittensor Subnet 13 - **Miner Hotkey:** 5GL2drVv1k92XUa967RCTgYkEb3du1VRmUUBqZKz2oxyt8Sw ### Dataset Summary This dataset is part of the Bittensor Subnet 13 decentralized network, containing preprocessed Reddit data. The data is continuously updated by network miners, providing a real-time stream of Reddit content for various analytical and machine learning tasks. For more information about the dataset, please visit the [official repository](https://github.com/macrocosm-os/data-universe). ### Supported Tasks The versatility of this dataset allows researchers and data scientists to explore various aspects of social media dynamics and develop innovative applications. Users are encouraged to leverage this data creatively for their specific research or business needs. For example: - Sentiment Analysis - Topic Modeling - Community Analysis - Content Categorization ### Languages Primary language: Datasets are mostly English, but can be multilingual due to decentralized ways of creation. ## Dataset Structure ### Data Instances Each instance represents a single Reddit post or comment with the following fields: ### Data Fields - `text` (string): The main content of the Reddit post or comment. - `label` (string): Sentiment or topic category of the content. - `dataType` (string): Indicates whether the entry is a post or a comment. - `communityName` (string): The name of the subreddit where the content was posted. - `datetime` (string): The date when the content was posted or commented. - `username_encoded` (string): An encoded version of the username to maintain user privacy. - `url_encoded` (string): An encoded version of any URLs included in the content. ### Data Splits This dataset is continuously updated and does not have fixed splits. Users should create their own splits based on their requirements and the data's timestamp. ## Dataset Creation ### Source Data Data is collected from public posts and comments on Reddit, adhering to the platform's terms of service and API usage guidelines. ### Personal and Sensitive Information All usernames and URLs are encoded to protect user privacy. The dataset does not intentionally include personal or sensitive information. ## Considerations for Using the Data ### Social Impact and Biases Users should be aware of potential biases inherent in Reddit data, including demographic and content biases. This dataset reflects the content and opinions expressed on Reddit and should not be considered a representative sample of the general population. ### Limitations - Data quality may vary due to the nature of media sources. - The dataset may contain noise, spam, or irrelevant content typical of social media platforms. - Temporal biases may exist due to real-time collection methods. - The dataset is limited to public subreddits and does not include private or restricted communities. ## Additional Information ### Licensing Information The dataset is released under the MIT license. The use of this dataset is also subject to Reddit Terms of Use. ### Citation Information If you use this dataset in your research, please cite it as follows: ``` @misc{smmrokn2025datauniversereddit_dataset_18, title={The Data Universe Datasets: The finest collection of social media data the web has to offer}, author={smmrokn}, year={2025}, url={https://huggingface.co/datasets/smmrokn/reddit_dataset_18}, } ``` ### Contributions To report issues or contribute to the dataset, please contact the miner or use the Bittensor Subnet 13 governance mechanisms. ## Dataset Statistics [This section is automatically updated] - **Total Instances:** 9642340 - **Date Range:** 2025-01-30T00:00:00Z to 2025-02-23T00:00:00Z - **Last Updated:** 2025-02-23T00:34:39Z ### Data Distribution - Posts: 2.71% - Comments: 97.29% ### Top 10 Subreddits For full statistics, please refer to the `stats.json` file in the repository. | Rank | Topic | Total Count | Percentage | |------|-------|-------------|-------------| | 1 | r/canada | 41520 | 0.43% | | 2 | r/news | 35100 | 0.36% | | 3 | r/nba | 33884 | 0.35% | | 4 | r/BestofRedditorUpdates | 29039 | 0.30% | | 5 | r/worldnews | 26226 | 0.27% | | 6 | r/facepalm | 25746 | 0.27% | | 7 | r/gaming | 25676 | 0.27% | | 8 | r/unitedkingdom | 25300 | 0.26% | | 9 | r/soccer | 25269 | 0.26% | | 10 | r/AskOldPeople | 23683 | 0.25% | ## Update History | Date | New Instances | Total Instances | |------|---------------|-----------------| | 2025-02-22T18:14:15Z | 9564493 | 9564493 | | 2025-02-22T19:40:44Z | 24587 | 9589080 | | 2025-02-22T21:21:45Z | 16649 | 9605729 | | 2025-02-22T23:00:49Z | 20445 | 9626174 | | 2025-02-23T00:34:39Z | 16166 | 9642340 |

prithivMLmods/Deepfake-QA-10K-OPT

prithivMLmods

--- license: apache-2.0 task_categories: - image-classification language: - en tags: - deepfake - optimized size_categories: - 1K<n<10K --- # **Deepfake Quality Assessment** Deepfake QA is a Deepfake Quality Assessment model designed to analyze the quality of deepfake images & videos. It evaluates whether a deepfake is of good or bad quality, where: - **0** represents a bad-quality deepfake - **1** represents a good-quality deepfake This classification serves as the foundation for training models on deepfake quality assessment, helping improve deepfake detection and enhancement techniques. ## Citation ```bibtex @misc{deepfake_quality_assessment_2025, author = {Wildy AI Team Collaborations}, title = {Deepfake Quality Assessment Models}, year = {2025}, note = {Early release}, models_training = {@prithivMLmods}, dataset_curation_strategy = {@prithivMLmods}, dataset_curation = {Wildy AI Team} } ```

harshana95/ForegroundDataset

harshana95

--- dataset_info: features: - name: mask dtype: image - name: foreground dtype: image splits: - name: train num_bytes: 4437834.0 num_examples: 196 - name: validation num_bytes: 79279.0 num_examples: 4 download_size: 4483120 dataset_size: 4517113.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---

tttx/20k_postcorrect_022225

tttx

--- dataset_info: features: - name: messages list: - name: content dtype: string - name: role dtype: string - name: difficulty dtype: int64 - name: problem_uid dtype: string - name: step dtype: int64 splits: - name: train num_bytes: 15131654.073410923 num_examples: 400 - name: test num_bytes: 43660 num_examples: 1 download_size: 4318646 dataset_size: 15175314.073410923 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

harshana95/BackgroundDataset

harshana95

--- dataset_info: features: - name: background dtype: image splits: - name: train num_bytes: 9961204.0 num_examples: 196 - name: validation num_bytes: 234495.0 num_examples: 4 download_size: 8909097 dataset_size: 10195699.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---

tttx/20k_precorrect_022225

tttx

--- dataset_info: features: - name: messages list: - name: content dtype: string - name: role dtype: string - name: difficulty dtype: int64 - name: problem_uid dtype: string - name: step dtype: int64 splits: - name: train num_bytes: 15194573.483146068 num_examples: 400 - name: test num_bytes: 44040 num_examples: 1 download_size: 4333451 dataset_size: 15238613.483146068 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

subashdvorak/hotel-restaurant-dataset-emb

subashdvorak

--- dataset_info: features: - name: Place Name dtype: string - name: Review dtype: string - name: Place Type dtype: string - name: revel_rating dtype: float64 splits: - name: train num_bytes: 8975641 num_examples: 44361 download_size: 4110029 dataset_size: 8975641 configs: - config_name: default data_files: - split: train path: data/train-* ---

Mohamed-DLM/asr_en_ar_switch_split_76_final_updated

Mohamed-DLM

--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription dtype: string splits: - name: train num_bytes: 4815250.0 num_examples: 56 download_size: 4258660 dataset_size: 4815250.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

ayousanz/Emilia-Dataset-JA-Plus

ayousanz

--- license: cc-by-nc-4.0 task_categories: - text-to-speech - automatic-speech-recognition language: - zh - en - ja - fr - de - ko pretty_name: Emilia size_categories: - 10M<n<100M extra_gated_prompt: >- Terms of Access: The researcher has requested permission to use the Emilia dataset and the Emilia-Pipe preprocessing pipeline. In exchange for such permission, the researcher hereby agrees to the following terms and conditions: 1. The researcher shall use the dataset ONLY for non-commercial research and educational purposes. 2. The authors make no representations or warranties regarding the dataset, including but not limited to warranties of non-infringement or fitness for a particular purpose. 3. The researcher accepts full responsibility for their use of the dataset and shall defend and indemnify the authors of Emilia, including their employees, trustees, officers, and agents, against any and all claims arising from the researcher's use of the dataset, including but not limited to the researcher's use of any copies of copyrighted content that they may create from the dataset. 4. The researcher may provide research associates and colleagues with access to the dataset, provided that they first agree to be bound by these terms and conditions. 5. The authors reserve the right to terminate the researcher's access to the dataset at any time. 6. If the researcher is employed by a for-profit, commercial entity, the researcher's employer shall also be bound by these terms and conditions, and the researcher hereby represents that they are fully authorized to enter into this agreement on behalf of such employer. extra_gated_fields: Name: text Email: text Affiliation: text Position: text Your Supervisor/manager/director: text I agree to the Terms of Access: checkbox --- # Emilia: An Extensive, Multilingual, and Diverse Speech Dataset for Large-Scale Speech Generation <!-- [](https://arxiv.org/abs/2407.05361) [](https://huggingface.co/datasets/amphion/Emilia-Dataset) [](https://opendatalab.com/Amphion/Emilia) [](https://github.com/open-mmlab/Amphion/tree/main/preprocessors/Emilia) [](https://emilia-dataset.github.io/Emilia-Demo-Page/) --> This is the official repository 👑 for the **Emilia** dataset and the source code for the **Emilia-Pipe** speech data preprocessing pipeline. <div align="center"><img width="500px" src="https://github.com/user-attachments/assets/b1c1a1f8-3149-4f96-8eb4-af470152a9b7" /></div> ## News 🔥 - **2024/08/28**: Welcome to join Amphion's [Discord channel](https://discord.com/invite/ZxxREr3Y) to stay connected and engage with our community! - **2024/08/27**: *The Emilia dataset is now publicly available!* Discover the most extensive and diverse speech generation dataset with 101k hours of in-the-wild speech data now at [HuggingFace](https://huggingface.co/datasets/amphion/Emilia-Dataset) or [OpenDataLab](https://opendatalab.com/Amphion/Emilia)! 👑👑👑 - **2024/07/08**: Our preprint [paper](https://arxiv.org/abs/2407.05361) is now available! 🔥🔥🔥 - **2024/07/03**: We welcome everyone to check our [homepage](https://emilia-dataset.github.io/Emilia-Demo-Page/) for our brief introduction for Emilia dataset and our demos! - **2024/07/01**: We release of Emilia and Emilia-Pipe! We welcome everyone to explore it on our [GitHub](https://github.com/open-mmlab/Amphion/tree/main/preprocessors/Emilia)! 🎉🎉🎉 ## Emilia Overview ⭐️ The **Emilia** dataset is a comprehensive, multilingual dataset with the following features: - containing over *101k* hours of speech data; - covering six different languages: *English (En), Chinese (Zh), German (De), French (Fr), Japanese (Ja), and Korean (Ko)*; - containing diverse speech data with *various speaking styles* from diverse video platforms and podcasts on the Internet, covering various content genres such as talk shows, interviews, debates, sports commentary, and audiobooks. The table below provides the duration statistics for each language in the dataset. | Language | Duration (hours) | |:-----------:|:----------------:| | English | 46,828 | | Chinese | 49,922 | | German | 1,590 | | French | 1,381 | | Japanese | 1,715 | | Korean | 217 | The **Emilia-Pipe** is the first open-source preprocessing pipeline designed to transform raw, in-the-wild speech data into high-quality training data with annotations for speech generation. This pipeline can process one hour of raw audio into model-ready data in just a few minutes, requiring only the raw speech data. Detailed descriptions for the Emilia and Emilia-Pipe can be found in our [paper](https://arxiv.org/abs/2407.05361). ## Emilia Dataset Usage 📖 Emilia is publicly available at [HuggingFace](https://huggingface.co/datasets/amphion/Emilia-Dataset). If you are from mainland China or having a connecting issue with HuggingFace, you can also download Emilia from [OpenDataLab](https://opendatalab.com/Amphion/Emilia). - To download from HuggingFace: 1. Gain access to the dataset and get the HF access token from: [https://huggingface.co/settings/tokens](https://huggingface.co/settings/tokens). 2. Install dependencies and login HF: - Install Python - Run `pip install librosa soundfile datasets huggingface_hub[cli]` - Login by `huggingface-cli login` and paste the HF access token. Check [here](https://huggingface.co/docs/huggingface_hub/guides/cli#huggingface-cli-login) for details. 3. Use following code to load Emilia: ```py from datasets import load_dataset dataset = load_dataset("amphion/Emilia-Dataset", streaming=True) print(dataset) print(next(iter(dataset['train']))) ``` - To download from OpenDataLab (i.e., OpenXLab), please follow the guidance [here](https://speechteam.feishu.cn/wiki/PC8Ew5igviqBiJkElMJcJxNonJc) to gain access. **ENJOY USING EMILIA!!!** 🔥 ### Use cases If you want to load a subset of Emilia, e.g., only language `DE`, you can use the following code: ```py from datasets import load_dataset path = "DE/*.tar" dataset = load_dataset("amphion/Emilia-Dataset", data_files={"de": path}, split="de", streaming=True) print(dataset) # here should only shows 90 n_shards instead of 2360 print(next(iter(dataset['train']))) ``` If you want to download all files to your local before using Emilia, remove the `streaming=True` argument: ```py from datasets import load_dataset dataset = load_dataset("amphion/Emilia-Dataset") # prepare 2.4TB space to store Emilia print(dataset) ``` ### Re-build or Processing your own data If you wish to re-build Emilia from scratch, you may download the raw audio files from the [provided URL list](https://huggingface.co/datasets/amphion/Emilia) and use our open-source [Emilia-Pipe](https://github.com/open-mmlab/Amphion/tree/main/preprocessors/Emilia) preprocessing pipeline to preprocess the raw data. Additionally, users can easily use Emilia-Pipe to preprocess their own raw speech data for custom needs. By open-sourcing the Emilia-Pipe code, we aim to enable the speech community to collaborate on large-scale speech generation research. ### Notes *Please note that Emilia does not own the copyright to the audio files; the copyright remains with the original owners of the videos or audio. Users are permitted to use this dataset only for non-commercial purposes under the CC BY-NC-4.0 license.* ## Emilia Dataset Structure ⛪️ ### Structure on HuggingFace On HuggingFace, Emilia is now formatted as [WebDataset](https://github.com/webdataset/webdataset). Each audio is tared with a corresponding JSON file (having the same prefix filename) within 2360 tar files. By utilizing WebDataset, you can easily stream audio data, which is magnitude faster than reading separate data files one by one. Read the *Emilia Dataset Usage 📖* part for a detailed usage guide. Learn more about WebDataset [here](https://huggingface.co/docs/hub/datasets-webdataset). *PS: If you want to download the `OpenDataLab` format from HuggingFace, you can specify the `revision` argument to `fc71e07e8572f5f3be1dbd02ed3172a4d298f152`, [which](https://huggingface.co/datasets/amphion/Emilia-Dataset/tree/fc71e07e8572f5f3be1dbd02ed3172a4d298f152) is the old format.* ### Structure on OpenDataLab On OpenDataLab, Emilia is formatted using the following structure. Structure example: ``` |-- openemilia_all.tar.gz (all .JSONL files are gzipped with directory structure in this file) |-- EN (114 batches) | |-- EN_B00000.jsonl | |-- EN_B00000 (= EN_B00000.tar.gz) | | |-- EN_B00000_S00000 | | | `-- mp3 | | | |-- EN_B00000_S00000_W000000.mp3 | | | `-- EN_B00000_S00000_W000001.mp3 | | |-- ... | |-- ... | |-- EN_B00113.jsonl | `-- EN_B00113 |-- ZH (92 batches) |-- DE (9 batches) |-- FR (10 batches) |-- JA (7 batches) |-- KO (4 batches) ``` JSONL files example: ``` {"id": "EN_B00000_S00000_W000000", "wav": "EN_B00000/EN_B00000_S00000/mp3/EN_B00000_S00000_W000000.mp3", "text": " You can help my mother and you- No. You didn't leave a bad situation back home to get caught up in another one here. What happened to you, Los Angeles?", "duration": 6.264, "speaker": "EN_B00000_S00000", "language": "en", "dnsmos": 3.2927} {"id": "EN_B00000_S00000_W000001", "wav": "EN_B00000/EN_B00000_S00000/mp3/EN_B00000_S00000_W000001.mp3", "text": " Honda's gone, 20 squads done. X is gonna split us up and put us on different squads. The team's come and go, but 20 squad, can't believe it's ending.", "duration": 8.031, "speaker": "EN_B00000_S00000", "language": "en", "dnsmos": 3.0442} ``` ## Reference 📖 If you use the Emilia dataset or the Emilia-Pipe pipeline, please cite the following papers: ```bibtex @inproceedings{emilia, author={He, Haorui and Shang, Zengqiang and Wang, Chaoren and Li, Xuyuan and Gu, Yicheng and Hua, Hua and Liu, Liwei and Yang, Chen and Li, Jiaqi and Shi, Peiyang and Wang, Yuancheng and Chen, Kai and Zhang, Pengyuan and Wu, Zhizheng}, title={Emilia: An Extensive, Multilingual, and Diverse Speech Dataset for Large-Scale Speech Generation}, booktitle={Proc.~of SLT}, year={2024} } ``` ```bibtex @inproceedings{amphion, author={Zhang, Xueyao and Xue, Liumeng and Gu, Yicheng and Wang, Yuancheng and Li, Jiaqi and He, Haorui and Wang, Chaoren and Song, Ting and Chen, Xi and Fang, Zihao and Chen, Haopeng and Zhang, Junan and Tang, Tze Ying and Zou, Lexiao and Wang, Mingxuan and Han, Jun and Chen, Kai and Li, Haizhou and Wu, Zhizheng}, title={Amphion: An Open-Source Audio, Music and Speech Generation Toolkit}, booktitle={Proc.~of SLT}, year={2024} } ```

Broniya123/CoT_all_0.6

Broniya123

--- dataset_info: features: - name: image dtype: image - name: problem dtype: string - name: thinking dtype: string - name: solution dtype: string splits: - name: train num_bytes: 255822520.945 num_examples: 1595 download_size: 196000241 dataset_size: 255822520.945 configs: - config_name: default data_files: - split: train path: data/train-* ---

ashiquesulthantpokm/My

ashiquesulthantpokm

--- license: apache-2.0 ---

adhilbinmujeeb/sharktank_revenue_prediction

adhilbinmujeeb

--- dataset_info: features: - name: industry dtype: string - name: sub_industry dtype: string - name: market_perception dtype: string - name: revenue_year_one dtype: float64 - name: revenue_year_two dtype: float64 - name: profit_2023 dtype: float64 splits: - name: train num_bytes: 177260 num_examples: 2072 download_size: 15160 dataset_size: 177260 configs: - config_name: default data_files: - split: train path: data/train-* ---

dsrselfcorr/star_turn2_prompt

dsrselfcorr

--- dataset_info: features: - name: gt dtype: string - name: my_prompt dtype: string - name: idx dtype: int64 - name: true_reward dtype: bool splits: - name: train num_bytes: 158013004 num_examples: 45000 download_size: 49543433 dataset_size: 158013004 configs: - config_name: default data_files: - split: train path: data/train-* ---

Krm1/CVE-2025

Krm1

--- license: apache-2.0 ---

caoanh44al3/cars_dataset

caoanh44al3

--- dataset_info: features: - name: id dtype: string - name: brand dtype: string - name: name dtype: string - name: price dtype: string - name: image struct: - name: bytes dtype: binary - name: path dtype: string - name: Exterior color dtype: string - name: questions sequence: string splits: - name: train num_bytes: 43595285 num_examples: 4500 - name: test num_bytes: 14439058 num_examples: 1500 - name: eval num_bytes: 14423997 num_examples: 1500 download_size: 69629243 dataset_size: 72458340 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: eval path: data/eval-* ---

balaji-ramk/parliamentary-debate-cases

balaji-ramk

--- license: mit ---

shoskeri/reuters_articles

shoskeri

--- dataset_info: features: - name: title dtype: string - name: body dtype: string splits: - name: train num_bytes: 13792576 num_examples: 17262 - name: validation num_bytes: 1870389 num_examples: 2158 - name: test num_bytes: 1379190 num_examples: 2158 download_size: 10073414 dataset_size: 17042155 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

Rokii3/complexity-annotated-questions

Rokii3

--- license: mit ---

beyoru/function_calling_only

beyoru

--- dataset_info: features: - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 14398930.064 num_examples: 6856 download_size: 1551646 dataset_size: 14398930.064 configs: - config_name: default data_files: - split: train path: data/train-* ---

dsrselfcorr/star_turn2_prompt2

dsrselfcorr

--- dataset_info: features: - name: gt dtype: string - name: my_prompt dtype: string - name: idx dtype: int64 - name: true_reward dtype: bool splits: - name: train num_bytes: 170268908 num_examples: 48647 download_size: 53338574 dataset_size: 170268908 configs: - config_name: default data_files: - split: train path: data/train-* ---

takuzennn/aloha-pick100

takuzennn

--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - aloha - robotics - hdf5 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": "aloha-stationary", "total_episodes": 99, "total_frames": 24750, "total_tasks": 1, "total_videos": 0, "total_chunks": 1, "chunks_size": 1000, "fps": 10, "splits": { "train": "0:99" }, "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": [ 14 ], "names": [ "action_0", "action_1", "action_2", "action_3", "action_4", "action_5", "action_6", "action_7", "action_8", "action_9", "action_10", "action_11", "action_12", "action_13" ] }, "observation.image.camera1": { "dtype": "image", "shape": [ 480, 640, 3 ], "names": [ "channel", "height", "width" ] }, "observation.image.camera2": { "dtype": "image", "shape": [ 480, 640, 3 ], "names": [ "channel", "height", "width" ] }, "observation.image.camera3": { "dtype": "image", "shape": [ 480, 640, 3 ], "names": [ "channel", "height", "width" ] }, "observation.state": { "dtype": "float32", "shape": [ 14 ], "names": [ "qpos_0", "qpos_1", "qpos_2", "qpos_3", "qpos_4", "qpos_5", "qpos_6", "qpos_7", "qpos_8", "qpos_9", "qpos_10", "qpos_11", "qpos_12", "qpos_13" ] }, "observation.qvel": { "dtype": "float32", "shape": [ 12 ], "names": [ "qvel_0", "qvel_1", "qvel_2", "qvel_3", "qvel_4", "qvel_5", "qvel_6", "qvel_7", "qvel_8", "qvel_9", "qvel_10", "qvel_11" ] }, "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] ```

jerry128/hotpotqa-filtered-disjoint-1-10-passages

jerry128

--- dataset_info: features: - name: question dtype: string - name: context dtype: string - name: answer dtype: string - name: citation1 dtype: string - name: citation2 dtype: string splits: - name: train num_bytes: 19942228 num_examples: 4000 download_size: 11321151 dataset_size: 19942228 configs: - config_name: default data_files: - split: train path: data/train-* ---

jerry128/hotpotqa-filtered-disjoint-2-10-passages

jerry128

--- dataset_info: features: - name: question dtype: string - name: context dtype: string - name: answer dtype: string - name: citation1 dtype: string - name: citation2 dtype: string splits: - name: train num_bytes: 20009825 num_examples: 4000 download_size: 11390665 dataset_size: 20009825 configs: - config_name: default data_files: - split: train path: data/train-* ---

jerry128/hotpotqa-filtered-disjoint-3-10-passages

jerry128

--- dataset_info: features: - name: question dtype: string - name: context dtype: string - name: answer dtype: string - name: citation1 dtype: string - name: citation2 dtype: string splits: - name: train num_bytes: 20045441 num_examples: 4000 download_size: 11391400 dataset_size: 20045441 configs: - config_name: default data_files: - split: train path: data/train-* ---

jerry128/hotpotqa-filtered-disjoint-4-10-passages

jerry128

--- dataset_info: features: - name: question dtype: string - name: context dtype: string - name: answer dtype: string - name: citation1 dtype: string - name: citation2 dtype: string splits: - name: train num_bytes: 20070427 num_examples: 4000 download_size: 11434632 dataset_size: 20070427 configs: - config_name: default data_files: - split: train path: data/train-* ---

jerry128/hotpotqa-filtered-disjoint-5-10-passages

jerry128

--- dataset_info: features: - name: question dtype: string - name: context dtype: string - name: answer dtype: string - name: citation1 dtype: string - name: citation2 dtype: string splits: - name: train num_bytes: 20134605 num_examples: 4000 download_size: 11452987 dataset_size: 20134605 configs: - config_name: default data_files: - split: train path: data/train-* ---

jerry128/hotpotqa-filtered-disjoint-6-10-passages

jerry128

--- dataset_info: features: - name: question dtype: string - name: context dtype: string - name: answer dtype: string - name: citation1 dtype: string - name: citation2 dtype: string splits: - name: train num_bytes: 19990211 num_examples: 4000 download_size: 11358942 dataset_size: 19990211 configs: - config_name: default data_files: - split: train path: data/train-* ---

jerry128/hotpotqa-filtered-disjoint-7-10-passages

jerry128

--- dataset_info: features: - name: question dtype: string - name: context dtype: string - name: answer dtype: string - name: citation1 dtype: string - name: citation2 dtype: string splits: - name: train num_bytes: 20030799 num_examples: 4000 download_size: 11423487 dataset_size: 20030799 configs: - config_name: default data_files: - split: train path: data/train-* ---

jerry128/hotpotqa-filtered-disjoint-8-10-passages

jerry128

--- dataset_info: features: - name: question dtype: string - name: context dtype: string - name: answer dtype: string - name: citation1 dtype: string - name: citation2 dtype: string splits: - name: train num_bytes: 19982768 num_examples: 4000 download_size: 11386440 dataset_size: 19982768 configs: - config_name: default data_files: - split: train path: data/train-* ---

jerry128/hotpotqa-filtered-disjoint-9-10-passages

jerry128

--- dataset_info: features: - name: question dtype: string - name: context dtype: string - name: answer dtype: string - name: citation1 dtype: string - name: citation2 dtype: string splits: - name: train num_bytes: 20071645 num_examples: 4000 download_size: 11418975 dataset_size: 20071645 configs: - config_name: default data_files: - split: train path: data/train-* ---

jerry128/hotpotqa-filtered-disjoint-10-10-passages

jerry128

--- dataset_info: features: - name: question dtype: string - name: context dtype: string - name: answer dtype: string - name: citation1 dtype: string - name: citation2 dtype: string splits: - name: train num_bytes: 20044058 num_examples: 4000 download_size: 11421772 dataset_size: 20044058 configs: - config_name: default data_files: - split: train path: data/train-* ---

ViLP/ViLP

ViLP

--- license: odc-by dataset_info: features: - name: question dtype: string - name: image1 dtype: image - name: answer1 dtype: string - name: image2 dtype: image - name: answer2 dtype: string - name: image3 dtype: image - name: answer3 dtype: string language: - en pretty_name: ViLP data_files: - split: test path: ViLP.parquet --- ## Dataset Description - **Paper:** [Probing Visual Language Priors in VLMs](https://arxiv.org/abs/2501.00569) - **Repository**: [Github_ViLP](https://github.com/ViLP-team/ViLP) **ViLP** is the dataset we used to probe the visual language priors of VLMs by constructing Question-Image-Answer (QIA) triplets that deliberately deviate from the training data distribution. It contains 300 carefully designed questions, each paired with three distinct answers: a Prior Answer and two Test Answers, resulting in a total of 900 QIA triplets. Our question context directly leads to the Prior Answer. In contrast, the two Test Answers are crafted to challenge the priors by requiring both textual and visual cues for accurate reasoning. ## Usage Our benchmark evaluation does not require the involvement of other LLMs/VLMs due to the design of the single-word output. We provide the evaluation code for both the LLaVA-v1.5 ([test_llava.py](https://github.com/ViLP-team/ViLP/blob/main/test_llava.py)) and OpenAI models ([test_gpt.py](https://github.com/ViLP-team/ViLP/blob/main/test_gpt.py)). It can be also easily integrated into other VLM inference pipelines. Please refer to our **[Github Page](https://github.com/ViLP-team/ViLP)** ## Citation Information If you find our data or paper useful, please consider citing: ``` @article{luo2024probing, title={Probing Visual Language Priors in VLMs}, author={Luo, Tiange and Cao, Ang and Lee, Gunhee and Johnson, Justin and Lee, Honglak}, journal={arXiv preprint arXiv:2501.00569}, year={2024}, url={https://arxiv.org/abs/2501.00569} } ```

Athspi/agi-knowledge-base

Athspi

--- dataset_info: features: - name: query dtype: string - name: response dtype: string - name: validation_data struct: - name: improved dtype: string - name: issues sequence: 'null' - name: score dtype: int64 - name: timestamp dtype: float64 - name: validated_response dtype: string splits: - name: train num_bytes: 10493 num_examples: 6 download_size: 11084 dataset_size: 10493 configs: - config_name: default data_files: - split: train path: data/train-* ---

orcn/dataVLM-triangle

orcn

--- dataset_info: features: - name: text1 dtype: string - name: text2 dtype: string - name: text3 dtype: string - name: text4 dtype: string - name: text5 dtype: 'null' - name: text6 dtype: 'null' - name: text7 dtype: 'null' - name: text8 dtype: 'null' - 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 splits: - name: train num_bytes: 8815885.0 num_examples: 100 download_size: 6259204 dataset_size: 8815885.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

jacpetro/Jailbreak_Complete_DS_labeled

jacpetro

--- dataset_info: features: - name: prompt dtype: string - name: completion dtype: string - name: label dtype: int64 - name: q_plus_a dtype: string splits: - name: train num_bytes: 18563166.0 num_examples: 11383 - name: test num_bytes: 1320515.7548746518 num_examples: 1076 download_size: 10743620 dataset_size: 19883681.75487465 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

drkvcsstvn/smearshare_cumulative_distribution_lims

drkvcsstvn

--- dataset_info: features: - name: Peeler dtype: string - name: Total dtype: int64 splits: - name: train num_bytes: 186 num_examples: 13 download_size: 1146 dataset_size: 186 configs: - config_name: default data_files: - split: train path: data/train-* ---

RAGEVALUATION-HJKMY/ragbench_10row_tester_synthetic_mistake

RAGEVALUATION-HJKMY

--- dataset_info: features: - name: id dtype: string - name: question dtype: string - name: documents sequence: string - name: response dtype: string - name: generation_model_name dtype: string - name: annotating_model_name dtype: string - name: dataset_name dtype: string - name: documents_sentences sequence: sequence: sequence: string - name: response_sentences sequence: sequence: string - name: sentence_support_information list: - name: explanation dtype: string - name: fully_supported dtype: bool - name: response_sentence_key dtype: string - name: supporting_sentence_keys sequence: string - name: unsupported_response_sentence_keys sequence: string - name: adherence_score dtype: bool - name: overall_supported_explanation dtype: string - name: relevance_explanation dtype: string - name: all_relevant_sentence_keys sequence: string - name: all_utilized_sentence_keys sequence: string - name: trulens_groundedness dtype: float64 - name: trulens_context_relevance dtype: float64 - name: ragas_faithfulness dtype: float64 - name: ragas_context_relevance dtype: float64 - name: gpt3_adherence dtype: float64 - name: gpt3_context_relevance dtype: float64 - name: gpt35_utilization dtype: float64 - name: relevance_score dtype: float64 - name: utilization_score dtype: float64 - name: completeness_score dtype: float64 - name: num_mistake dtype: int64 - name: mistake_distribution sequence: string - name: Paraphrased dtype: string - name: Incorrect dtype: string - name: Error_Locations sequence: int64 splits: - name: train num_bytes: 134200 num_examples: 10 - name: validation num_bytes: 108022 num_examples: 10 - name: test num_bytes: 94378 num_examples: 10 download_size: 258103 dataset_size: 336600 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

labofsahil/pypi-packages-metadata-dataset

labofsahil

--- license: mit ---

alif-munim/eeg-filtered

alif-munim

--- dataset_info: features: - name: event dtype: int64 - name: word dtype: string - name: topic dtype: string - name: selected_topic dtype: string - name: semantic_relevance dtype: int64 - name: interestingness dtype: int64 - name: pre-knowledge dtype: int64 - name: sentence_number dtype: int64 - name: participant dtype: string - name: eeg dtype: array2_d: shape: - 32 - 2001 dtype: float64 splits: - name: train num_bytes: 571404242.2859906 num_examples: 1115 download_size: 571765850 dataset_size: 571404242.2859906 configs: - config_name: default data_files: - split: train path: data/train-* ---

yuzhangmatrix/my_test_dataset

yuzhangmatrix

--- dataset_info: features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: train num_bytes: 79346108 num_examples: 87599 - name: validation num_bytes: 10472984 num_examples: 10570 download_size: 16279403 dataset_size: 89819092 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---

yannn666/tf8

yannn666

--- license: mit ---

RAGEVALUATION-HJKMY/ragbench_10row_tester_synthetic_mistake_evaluated

RAGEVALUATION-HJKMY

--- dataset_info: features: - name: id dtype: string - name: question dtype: string - name: documents sequence: string - name: response dtype: string - name: generation_model_name dtype: string - name: annotating_model_name dtype: string - name: dataset_name dtype: string - name: documents_sentences sequence: sequence: sequence: string - name: response_sentences sequence: sequence: string - name: sentence_support_information list: - name: explanation dtype: string - name: fully_supported dtype: bool - name: response_sentence_key dtype: string - name: supporting_sentence_keys sequence: string - name: unsupported_response_sentence_keys sequence: string - name: adherence_score dtype: bool - name: overall_supported_explanation dtype: string - name: relevance_explanation dtype: string - name: all_relevant_sentence_keys sequence: string - name: all_utilized_sentence_keys sequence: string - name: trulens_groundedness dtype: float64 - name: trulens_context_relevance dtype: float64 - name: ragas_faithfulness dtype: float64 - name: ragas_context_relevance dtype: float64 - name: gpt3_adherence dtype: float64 - name: gpt3_context_relevance dtype: float64 - name: gpt35_utilization dtype: float64 - name: relevance_score dtype: float64 - name: utilization_score dtype: float64 - name: completeness_score dtype: float64 - name: num_mistake dtype: int64 - name: mistake_distribution sequence: string - name: Paraphrased dtype: string - name: Incorrect dtype: string - name: Error_Locations sequence: int64 - name: Incorrect_TP dtype: int64 - name: Incorrect_FP dtype: int64 - name: Incorrect_FN dtype: int64 - name: Incorrect_F1_score dtype: float64 splits: - name: train num_bytes: 134520 num_examples: 10 - name: validation num_bytes: 108342 num_examples: 10 - name: test num_bytes: 94698 num_examples: 10 download_size: 264925 dataset_size: 337560 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

IJyad/NDMO_chroma_db

IJyad

--- license: mit ---

pravindsurve/pravindsurve

pravindsurve

--- task_categories: - question-answering language: - en tags: - icd pretty_name: pravindsurve size_categories: - n<1K ---

safiha/thirukural_prose

safiha

--- dataset_info: features: - name: audio dtype: audio splits: - name: train num_bytes: 141195571.0 num_examples: 375 download_size: 122596258 dataset_size: 141195571.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

DrMarcus24/stock-predictor-data

DrMarcus24

--- dataset_info: features: - name: predictions dtype: float32 - name: label dtype: float64 - name: Open dtype: float64 - name: High dtype: float64 - name: Low dtype: float64 - name: Close dtype: float64 - name: Volume dtype: int64 - name: Dividends dtype: float64 - name: Stock Splits dtype: float64 - name: Datetime dtype: timestamp[ns, tz=America/New_York] splits: - name: train num_bytes: 22724.0 num_examples: 299 - name: test num_bytes: 7600.0 num_examples: 100 download_size: 30769 dataset_size: 30324.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

willwm24/PPS

willwm24

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

AdamLucek/quickb-kb-video

AdamLucek

--- language: - en pretty_name: "quickb-kb-video" tags: - quickb - text-chunking - n<1K task_categories: - text-generation - text-retrieval task_ids: - document-retrieval library_name: quickb --- # quickb-kb-video Generated using [QuicKB](https://github.com/AdamLucek/quickb), a tool developed by [Adam Lucek](https://huggingface.co/AdamLucek). QuicKB optimizes document retrieval by creating fine-tuned knowledge bases through an end-to-end pipeline that handles document chunking, training data generation, and embedding model optimization. ### Chunking Configuration - **Chunker**: RecursiveTokenChunker - **Parameters**: - **chunk_size**: `400` - **chunk_overlap**: `0` - **length_type**: `'character'` - **separators**: `['\n\n', '\n', '.', '?', '!', ' ', '']` - **keep_separator**: `True` - **is_separator_regex**: `False` ### Dataset Statistics - Total chunks: 429 - Average chunk size: 57.3 words - Source files: 4 ### Dataset Structure This dataset contains the following fields: - `text`: The content of each text chunk - `source`: The source file path for the chunk - `id`: Unique identifier for each chunk

AdamLucek/quickb-qa-video

AdamLucek

--- language: - en pretty_name: "quickb-qa-video" tags: - quickb - text-chunking - question-generation - unknown task_categories: - text-generation - text-retrieval task_ids: - document-retrieval library_name: quickb --- # quickb-qa-video Generated using [QuicKB](https://github.com/AdamLucek/quickb), a tool developed by [Adam Lucek](https://huggingface.co/AdamLucek). QuicKB optimizes document retrieval by creating fine-tuned knowledge bases through an end-to-end pipeline that handles document chunking, training data generation, and embedding model optimization. ### Question Generation - **Model**: openai/gpt-4o-mini - **Deduplication threshold**: 0.85 - **Results**: - Total questions generated: 1716 - Questions after deduplication: 1600 ### Dataset Structure - `anchor`: The generated question - `positive`: The text chunk containing the answer - `question_id`: Unique identifier for the question - `chunk_id`: Reference to the source chunk

sert121/github_repos

sert121

--- license: mit configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: id dtype: int64 - name: repo_name dtype: string - name: stars_count dtype: int64 - name: description dtype: string - name: languages dtype: string - name: license_name dtype: string - name: last_updated dtype: timestamp[ns] - name: url dtype: string - name: owner dtype: string splits: - name: train num_bytes: 21509410 num_examples: 100091 download_size: 13257207 dataset_size: 21509410 language: - en --- The dataset contains 100K+ repos and their corresponded metadata collected through the graphQL library.

simonycl/llama-3.3-70b-ultrainteract-filtered

simonycl

--- dataset_info: features: - name: instruction dtype: string - name: response dtype: string splits: - name: train num_bytes: 190054239.53548896 num_examples: 80996 download_size: 86579080 dataset_size: 190054239.53548896 configs: - config_name: default data_files: - split: train path: data/train-* ---

dmitriihook/deepseek-r1-qwen-32b-planning-6-blocks-self-probing-state-distilabel

dmitriihook

--- size_categories: n<1K dataset_info: features: - name: gen_text dtype: string - name: line_n dtype: int64 - name: item_idx dtype: int64 - name: generation dtype: string - name: distilabel_metadata struct: - name: raw_input_text_generation_0 list: - name: content dtype: string - name: role dtype: string - name: raw_output_text_generation_0 dtype: string - name: statistics_text_generation_0 struct: - name: input_tokens dtype: int64 - name: output_tokens dtype: int64 - name: model_name dtype: string splits: - name: train num_bytes: 778822718 num_examples: 31400 download_size: 87955994 dataset_size: 778822718 configs: - config_name: default data_files: - split: train path: data/train-* tags: - synthetic - distilabel - rlaif --- <p align="left"> <a href="https://github.com/argilla-io/distilabel"> <img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/> </a> </p> # Dataset Card for deepseek-r1-qwen-32b-planning-6-blocks-self-probing-state-distilabel This dataset has been created with [distilabel](https://distilabel.argilla.io/). ## Dataset Summary This dataset contains a `pipeline.yaml` which can be used to reproduce the pipeline that generated it in distilabel using the `distilabel` CLI: ```console distilabel pipeline run --config "https://huggingface.co/datasets/dmitriihook/deepseek-r1-qwen-32b-planning-6-blocks-self-probing-state-distilabel/raw/main/pipeline.yaml" ``` or explore the configuration: ```console distilabel pipeline info --config "https://huggingface.co/datasets/dmitriihook/deepseek-r1-qwen-32b-planning-6-blocks-self-probing-state-distilabel/raw/main/pipeline.yaml" ``` ## Dataset structure The examples have the following structure per configuration: <details><summary> Configuration: default </summary><hr> ```json { "distilabel_metadata": { "raw_input_text_generation_0": [ { "content": "\u003c\uff5cbegin\u2581of\u2581sentence\uff5c\u003e\u003c\uff5cUser\uff5c\u003eI am playing with a set of blocks where I need to arrange the blocks into stacks. Here are the actions I can do\n\nPick up a block\nUnstack a block from on top of another block\nPut down a block\nStack a block on top of another block\n\nI have the following restrictions on my actions:\nI can only pick up or unstack one block at a time.\nI can only pick up or unstack a block if my hand is empty.\nI can only pick up a block if the block is on the table and the block is clear. A block is clear if the block has no other blocks on top of it and if the block is not picked up.\nI can only unstack a block from on top of another block if the block I am unstacking was really on top of the other block.\nI can only unstack a block from on top of another block if the block I am unstacking is clear.\nOnce I pick up or unstack a block, I am holding the block.\nI can only put down a block that I am holding.\nI can only stack a block on top of another block if I am holding the block being stacked.\nI can only stack a block on top of another block if the block onto which I am stacking the block is clear.\nOnce I put down or stack a block, my hand becomes empty.\nOnce you stack a block on top of a second block, the second block is no longer clear.\n\nHere is an example problem:\n\n[STATEMENT]\nAs initial conditions I have that, Block B is clear, Block C is clear, the hand is empty, Block C is on top of Block A, Block A is on the table, Block B is on the table.\nMy goal is to have that Block A is on top of Block C and Block B is on top of Block A\n\nMy plan is as follows:\n\n[PLAN]\nunstack Block C from on top of Block A\nput down Block C\npick up Block A\nstack Block A on top of Block C\npick up Block B\nstack Block B on top of Block A\n[PLAN END]\n\nHere is another example:\n\n[STATEMENT]\nAs initial conditions I have that, Block B is clear, the hand is empty, Block A is on top of Block D, Block B is on top of Block C, Block C is on top of Block A and Block D is on the table.\nMy goal is to have that Block C is on top of Block D, Block A is on top of Block C and Block B is on top of Block A.\n\nMy plan is as follows:\n\n[PLAN]\nunstack Block B from on top of Block C\nput down Block B\nunstack Block C from on top of Block A\nput down Block C\nunstack Block A from on top of Block D\nput down Block A\npick up Block C\nstack Block C on top of Block D\npick up Block A\nstack Block A on top of Block C\npick up Block B\nstack Block B on top of Block A\n[PLAN END]\nHere is the problem to solve:\n\n[STATEMENT]\nAs initial conditions I have that: Block A is clear, Block C is clear, Block E is clear, the hand is empty, Block B is on top of Block F, Block C is on top of Block D, Block D is on top of Block B, Block A is on the table, Block E is on the table and Block F is on the table.\nMy goal is for the following to be true: Block A is on top of Block E, Block B is on top of Block D, Block C is on top of Block B, Block E is on top of Block C and Block F is on top of Block A.\n\nMy plan is as follows:\u003c\uff5cAssistant\uff5c\u003eOkay, so I have this block-stacking problem to solve. Let me try to figure out the steps needed to get from the initial state to the goal state. I\u0027ll take it step by step because I\u0027m still getting the hang of these kinds of problems.\n\nFirst, let me understand the initial conditions. The blocks are A, B, C, D, E, F. The hand is empty. The clear blocks are A, C, and E. That means these blocks have nothing on top of them and are on the table or on another block that\u0027s clear.\n\nLooking at the initial setup:\n\n- Block B is on top of F.\n- Block C is on top of D.\n- Block D is on top of B.\n- So, the stack from bottom to top is F, B, D, C.\n- Blocks A and E are on the table, clear.\n- Block F is also on the table.\n\nSo, the initial stacks are:\n- F (on table) \u2192 B \u2192 D \u2192 C\n- A (on table)\n- E (on table)\n\nThe goal is to have:\n- A on top of E\n- B on top of D\n- C on top of B\n- E on top of C\n- F on top of A\n\nWait, that seems a bit confusing. Let me parse the goal again:\n\nGoal:\n- A is on top of E\n- B is on top of D\n- C is on top of B\n- E is on top of C\n- F is on top of A\n\nSo, the desired stacks would be:\n\nStarting from the bottom:\n- E \u2192 C \u2192 B \u2192 D\n- A \u2192 F\nBut wait, A is on top of E, and E is on top of C. So, E is under A, but E is also on top of C. That would mean the stack is C \u2192 E \u2192 A, but then F is on top of A. So, C \u2192 E \u2192 A \u2192 F.\n\nBut also, B is on top of D, and C is on top of B. So, D \u2192 B \u2192 C. But C is also part of the other stack. Hmm, that seems conflicting because C can\u0027t be in two places at once.\n\nWait, maybe I\u0027m misunderstanding. Let me re-express the goal:\n\n- A is on E\n- B is on D\n- C is on B\n- E is on C\n- F is on A\n\nSo, E is on C, which is on B, which is on D. So, D \u2192 B \u2192 C \u2192 E \u2192 A \u2192 F.\n\nBut also, A is on E, which is on C, which is on B, which is on D. So, the main stack would be D \u2192 B \u2192 C \u2192 E \u2192 A \u2192 F.\n\nBut wait, that would mean F is on top of A, which is on E, which is on C, which is on B, which is on D. So, the entire stack is D at the bottom, then B, then C, then E, then A, then F on top.\n\nBut the initial state has F on the table with B on top of it, and D on top of B, and C on top of D. So, the initial stack is F \u2192 B \u2192 D \u2192 C.\n\nAnd A and E are on the table.\n\nSo, the goal is to rearrange these blocks into a single stack: D \u2192 B \u2192 C \u2192 E \u2192 A \u2192 F.\n\nWait, but the goal also says F is on top of A, which is on E, which is on C, which is on B, which is on D. So, yes, that\u0027s the same as D \u2192 B \u2192 C \u2192 E \u2192 A \u2192 F.\n\nBut how do I get there from the initial state?\n\nLet me list the initial state again:\n\n- F is on the table, with B on top, D on top of B, and C on top of D. So, stack F-B-D-C.\n\nNow, the stacks are:\n\n", "role": "user" } ], "raw_output_text_generation_0": "- F (table) \u2192 B \u2192 D \u2192 C\n- A (table)\n- E (table)\n\nGoal stacks:\n\n- D (table) \u2192 B \u2192 C \u2192 E \u2192 A \u2192 F\n\nWait, no. Because in the goal,", "statistics_text_generation_0": { "input_tokens": 1616, "output_tokens": 50 } }, "gen_text": "\u003c\uff5cbegin\u2581of\u2581sentence\uff5c\u003e\u003c\uff5cUser\uff5c\u003eI am playing with a set of blocks where I need to arrange the blocks into stacks. Here are the actions I can do\n\nPick up a block\nUnstack a block from on top of another block\nPut down a block\nStack a block on top of another block\n\nI have the following restrictions on my actions:\nI can only pick up or unstack one block at a time.\nI can only pick up or unstack a block if my hand is empty.\nI can only pick up a block if the block is on the table and the block is clear. A block is clear if the block has no other blocks on top of it and if the block is not picked up.\nI can only unstack a block from on top of another block if the block I am unstacking was really on top of the other block.\nI can only unstack a block from on top of another block if the block I am unstacking is clear.\nOnce I pick up or unstack a block, I am holding the block.\nI can only put down a block that I am holding.\nI can only stack a block on top of another block if I am holding the block being stacked.\nI can only stack a block on top of another block if the block onto which I am stacking the block is clear.\nOnce I put down or stack a block, my hand becomes empty.\nOnce you stack a block on top of a second block, the second block is no longer clear.\n\nHere is an example problem:\n\n[STATEMENT]\nAs initial conditions I have that, Block B is clear, Block C is clear, the hand is empty, Block C is on top of Block A, Block A is on the table, Block B is on the table.\nMy goal is to have that Block A is on top of Block C and Block B is on top of Block A\n\nMy plan is as follows:\n\n[PLAN]\nunstack Block C from on top of Block A\nput down Block C\npick up Block A\nstack Block A on top of Block C\npick up Block B\nstack Block B on top of Block A\n[PLAN END]\n\nHere is another example:\n\n[STATEMENT]\nAs initial conditions I have that, Block B is clear, the hand is empty, Block A is on top of Block D, Block B is on top of Block C, Block C is on top of Block A and Block D is on the table.\nMy goal is to have that Block C is on top of Block D, Block A is on top of Block C and Block B is on top of Block A.\n\nMy plan is as follows:\n\n[PLAN]\nunstack Block B from on top of Block C\nput down Block B\nunstack Block C from on top of Block A\nput down Block C\nunstack Block A from on top of Block D\nput down Block A\npick up Block C\nstack Block C on top of Block D\npick up Block A\nstack Block A on top of Block C\npick up Block B\nstack Block B on top of Block A\n[PLAN END]\nHere is the problem to solve:\n\n[STATEMENT]\nAs initial conditions I have that: Block A is clear, Block C is clear, Block E is clear, the hand is empty, Block B is on top of Block F, Block C is on top of Block D, Block D is on top of Block B, Block A is on the table, Block E is on the table and Block F is on the table.\nMy goal is for the following to be true: Block A is on top of Block E, Block B is on top of Block D, Block C is on top of Block B, Block E is on top of Block C and Block F is on top of Block A.\n\nMy plan is as follows:\u003c\uff5cAssistant\uff5c\u003eOkay, so I have this block-stacking problem to solve. Let me try to figure out the steps needed to get from the initial state to the goal state. I\u0027ll take it step by step because I\u0027m still getting the hang of these kinds of problems.\n\nFirst, let me understand the initial conditions. The blocks are A, B, C, D, E, F. The hand is empty. The clear blocks are A, C, and E. That means these blocks have nothing on top of them and are on the table or on another block that\u0027s clear.\n\nLooking at the initial setup:\n\n- Block B is on top of F.\n- Block C is on top of D.\n- Block D is on top of B.\n- So, the stack from bottom to top is F, B, D, C.\n- Blocks A and E are on the table, clear.\n- Block F is also on the table.\n\nSo, the initial stacks are:\n- F (on table) \u2192 B \u2192 D \u2192 C\n- A (on table)\n- E (on table)\n\nThe goal is to have:\n- A on top of E\n- B on top of D\n- C on top of B\n- E on top of C\n- F on top of A\n\nWait, that seems a bit confusing. Let me parse the goal again:\n\nGoal:\n- A is on top of E\n- B is on top of D\n- C is on top of B\n- E is on top of C\n- F is on top of A\n\nSo, the desired stacks would be:\n\nStarting from the bottom:\n- E \u2192 C \u2192 B \u2192 D\n- A \u2192 F\nBut wait, A is on top of E, and E is on top of C. So, E is under A, but E is also on top of C. That would mean the stack is C \u2192 E \u2192 A, but then F is on top of A. So, C \u2192 E \u2192 A \u2192 F.\n\nBut also, B is on top of D, and C is on top of B. So, D \u2192 B \u2192 C. But C is also part of the other stack. Hmm, that seems conflicting because C can\u0027t be in two places at once.\n\nWait, maybe I\u0027m misunderstanding. Let me re-express the goal:\n\n- A is on E\n- B is on D\n- C is on B\n- E is on C\n- F is on A\n\nSo, E is on C, which is on B, which is on D. So, D \u2192 B \u2192 C \u2192 E \u2192 A \u2192 F.\n\nBut also, A is on E, which is on C, which is on B, which is on D. So, the main stack would be D \u2192 B \u2192 C \u2192 E \u2192 A \u2192 F.\n\nBut wait, that would mean F is on top of A, which is on E, which is on C, which is on B, which is on D. So, the entire stack is D at the bottom, then B, then C, then E, then A, then F on top.\n\nBut the initial state has F on the table with B on top of it, and D on top of B, and C on top of D. So, the initial stack is F \u2192 B \u2192 D \u2192 C.\n\nAnd A and E are on the table.\n\nSo, the goal is to rearrange these blocks into a single stack: D \u2192 B \u2192 C \u2192 E \u2192 A \u2192 F.\n\nWait, but the goal also says F is on top of A, which is on E, which is on C, which is on B, which is on D. So, yes, that\u0027s the same as D \u2192 B \u2192 C \u2192 E \u2192 A \u2192 F.\n\nBut how do I get there from the initial state?\n\nLet me list the initial state again:\n\n- F is on the table, with B on top, D on top of B, and C on top of D. So, stack F-B-D-C.\n\nNow, the stacks are:\n\n", "generation": "- F (table) \u2192 B \u2192 D \u2192 C\n- A (table)\n- E (table)\n\nGoal stacks:\n\n- D (table) \u2192 B \u2192 C \u2192 E \u2192 A \u2192 F\n\nWait, no. Because in the goal,", "item_idx": 0, "line_n": 22, "model_name": "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B" } ``` This subset can be loaded as: ```python from datasets import load_dataset ds = load_dataset("dmitriihook/deepseek-r1-qwen-32b-planning-6-blocks-self-probing-state-distilabel", "default") ``` Or simply as it follows, since there's only one configuration and is named `default`: ```python from datasets import load_dataset ds = load_dataset("dmitriihook/deepseek-r1-qwen-32b-planning-6-blocks-self-probing-state-distilabel") ``` </details>

CompassioninMachineLearning/real_animals_from_ea_forum_topics

CompassioninMachineLearning

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 437964 num_examples: 5005 download_size: 253506 dataset_size: 437964 configs: - config_name: default data_files: - split: train path: data/train-* ---

yuzhangmatrix/rick-and-morty-transcripts-sharegpt

yuzhangmatrix

--- dataset_info: features: - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 656714 num_examples: 1507 download_size: 141827 dataset_size: 656714 configs: - config_name: default data_files: - split: train path: data/train-* ---

ayushayush591/filtered_slim_orca

ayushayush591

--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 249357988 num_examples: 174949 download_size: 127604510 dataset_size: 249357988 configs: - config_name: default data_files: - split: train path: data/train-* ---