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2025-04-01 23:31:26
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67d3479522a51de18affff22
nvidia/Llama-Nemotron-Post-Training-Dataset-v1
nvidia
{"license": "cc-by-4.0", "configs": [{"config_name": "SFT", "data_files": [{"split": "code", "path": "SFT/code/*.jsonl"}, {"split": "math", "path": "SFT/math/*.jsonl"}, {"split": "science", "path": "SFT/science/*.jsonl"}, {"split": "chat", "path": "SFT/chat/*.jsonl"}, {"split": "safety", "path": "SFT/safety/*.jsonl"}], "default": true}, {"config_name": "RL", "data_files": [{"split": "instruction_following", "path": "RL/instruction_following/*.jsonl"}]}]}
false
null
2025-03-18T15:56:14
291
72
false
ed905e6239c9d191e4c965a403dde07a5383b5eb
Llama-Nemotron-Post-Training-Dataset-v1 Release Data Overview This dataset is a compilation of SFT and RL data that supports improvements of math, code, general reasoning, and instruction following capabilities of the original Llama instruct model, in support of NVIDIA’s release of Llama-3.3-Nemotron-Super-49B-v1 and Llama-3.1-Nemotron-Nano-8B-v1. Llama-3.3-Nemotron-Super-49B-v1 is a large language model (LLM) which is a derivative of Meta’s Llama-3.3-70B-Instruct (AKA… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Llama-Nemotron-Post-Training-Dataset-v1.
9,490
9,499
[ "license:cc-by-4.0", "size_categories:10M<n<100M", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
2025-03-13T21:01:09
null
null
67cd6c25b770987b3f80af97
a-m-team/AM-DeepSeek-R1-Distilled-1.4M
a-m-team
{"license": "cc-by-nc-4.0", "task_categories": ["text-generation"], "language": ["zh", "en"], "tags": ["code", "math", "reasoning", "thinking", "deepseek-r1", "distill"], "size_categories": ["1M<n<10M"], "configs": [{"config_name": "am_0.5M", "data_files": "am_0.5M.jsonl.zst", "features": [{"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "info", "struct": [{"name": "answer_content", "dtype": "string"}, {"name": "reference_answer", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "test_case", "struct": [{"name": "test_code", "dtype": "string"}, {"name": "test_entry_point", "dtype": "string"}]}, {"name": "think_content", "dtype": "string"}]}, {"name": "role", "dtype": "string"}]}]}, {"config_name": "am_0.9M", "data_files": "am_0.9M.jsonl.zst", "features": [{"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "info", "struct": [{"name": "answer_content", "dtype": "string"}, {"name": "reference_answer", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "test_case", "struct": [{"name": "test_code", "dtype": "string"}, {"name": "test_entry_point", "dtype": "string"}]}, {"name": "think_content", "dtype": "string"}]}, {"name": "role", "dtype": "string"}]}]}, {"config_name": "am_0.9M_sample_1k", "data_files": "am_0.9M_sample_1k.jsonl", "features": [{"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "info", "struct": [{"name": "answer_content", "dtype": "string"}, {"name": "reference_answer", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "test_case", "struct": [{"name": "test_code", "dtype": "string"}, {"name": "test_entry_point", "dtype": "string"}]}, {"name": "think_content", "dtype": "string"}]}, {"name": "role", "dtype": "string"}]}]}]}
false
null
2025-03-30T01:30:08
94
68
false
53531c06634904118a2dcd83961918c4d69d1cdf
For more open-source datasets, models, and methodologies, please visit our GitHub repository. AM-DeepSeek-R1-Distilled-1.4M is a large-scale general reasoning task dataset composed of high-quality and challenging reasoning problems. These problems are collected from numerous open-source datasets, semantically deduplicated, and cleaned to eliminate test set contamination. All responses in the dataset are distilled from the reasoning model (mostly DeepSeek-R1) and have undergone rigorous… See the full description on the dataset page: https://huggingface.co/datasets/a-m-team/AM-DeepSeek-R1-Distilled-1.4M.
6,000
6,000
[ "task_categories:text-generation", "language:zh", "language:en", "license:cc-by-nc-4.0", "size_categories:1M<n<10M", "arxiv:2503.19633", "region:us", "code", "math", "reasoning", "thinking", "deepseek-r1", "distill" ]
2025-03-09T10:23:33
null
null
67c0cda5c0b7a236a5f070e3
glaiveai/reasoning-v1-20m
glaiveai
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 177249016911, "num_examples": 22199375}], "download_size": 87247205094, "dataset_size": 177249016911}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "license": "apache-2.0", "task_categories": ["text-generation"], "language": ["en"], "size_categories": ["10M<n<100M"]}
false
null
2025-03-19T13:21:37
151
55
false
da6bb3d0ff8fd8ea5abacee8519762ca6aaf367e
We are excited to release a synthetic reasoning dataset containing 22mil+ general reasoning questions and responses generated using deepseek-ai/DeepSeek-R1-Distill-Llama-70B. While there have been multiple efforts to build open reasoning datasets for math and code tasks, we noticed a lack of large datasets containing reasoning traces for diverse non code/math topics like social and natural sciences, education, creative writing and general conversations, which is why we decided to release this… See the full description on the dataset page: https://huggingface.co/datasets/glaiveai/reasoning-v1-20m.
7,910
7,984
[ "task_categories:text-generation", "language:en", "license:apache-2.0", "size_categories:10M<n<100M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
2025-02-27T20:40:05
null
null
676f70846bf205795346d2be
FreedomIntelligence/medical-o1-reasoning-SFT
FreedomIntelligence
{"license": "apache-2.0", "task_categories": ["question-answering", "text-generation"], "language": ["en", "zh"], "tags": ["medical", "biology"], "configs": [{"config_name": "en", "data_files": "medical_o1_sft.json"}, {"config_name": "zh", "data_files": "medical_o1_sft_Chinese.json"}]}
false
null
2025-02-22T05:15:38
588
47
false
61536c1d80b2c799df6800cc583897b77d2c86d2
News [2025/02/22] We released the distilled dataset from Deepseek-R1 based on medical verifiable problems. You can use it to initialize your models with the reasoning chain from Deepseek-R1. [2024/12/25] We open-sourced the medical reasoning dataset for SFT, built on medical verifiable problems and an LLM verifier. Introduction This dataset is used to fine-tune HuatuoGPT-o1, a medical LLM designed for advanced medical reasoning. This dataset is constructed using GPT-4o… See the full description on the dataset page: https://huggingface.co/datasets/FreedomIntelligence/medical-o1-reasoning-SFT.
24,384
49,663
[ "task_categories:question-answering", "task_categories:text-generation", "language:en", "language:zh", "license:apache-2.0", "size_categories:10K<n<100K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2412.18925", "region:us", "medical", "biology" ]
2024-12-28T03:29:08
null
null
67a404bc8c6d42c5ec097433
Anthropic/EconomicIndex
Anthropic
{"language": "en", "pretty_name": "EconomicIndex", "tags": ["AI", "LLM", "Economic Impacts", "Anthropic"], "viewer": true, "license": "mit", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "release_2025_03_27/automation_vs_augmentation_by_task.csv"}]}]}
false
null
2025-03-27T22:08:25
239
39
false
2f63ea41bda89c22c00bbd3dd487771087717614
The Anthropic Economic Index Overview The Anthropic Economic Index provides insights into how AI is being incorporated into real-world tasks across the modern economy. Data Releases This repository contains multiple data releases, each with its own documentation: 2025-02-10 Release: Initial release with O*NET task mappings, automation vs. augmentation data, and more 2025-03-27 Release: Updated analysis with Claude 3.7 Sonnet data and cluster-level insights… See the full description on the dataset page: https://huggingface.co/datasets/Anthropic/EconomicIndex.
3,187
9,643
[ "language:en", "license:mit", "size_categories:1K<n<10K", "format:csv", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "AI", "LLM", "Economic Impacts", "Anthropic" ]
2025-02-06T00:39:24
null
null
67d9394e2e311ae0f2e8183f
PixelAI-Team/TalkBody4D
PixelAI-Team
{"viewer": false, "license": "cc-by-nc-4.0", "extra_gated_prompt": "The dataset is encrypted to prevent unauthorized access. Please fill out the request form : https://forms.gle/eC2aLRXZ8DAdKcis7. We'll check with your PI.", "extra_gated_fields": {"Name": "text", "E-Mail": "text", "Company/Organization": "text", "PI's Name": "text", "PI's E-Mail": "text", "Specific date": "date_picker", "I want to use this dataset for": {"type": "select", "options": ["Research", "Education", {"label": "Other", "value": "other"}]}, "I have signed the request form": "checkbox"}, "size_categories": ["100B<n<1T"]}
false
null
2025-03-25T12:05:54
64
37
false
e20725b0891c858f73fff56ad1ea34e46bfc54ec
TalkBody4D Dataset This dataset contains four multi-view image sequences used in our paper "TaoAvatar: Real-Time Lifelike Full-Body Talking Avatars for Augmented Reality via 3D Gaussian Splatting". They are captured with 59 well-calibrated RGB cameras in 20 fps, with a resolution of 3000×4000 and lengths ranging from 800 to 1000 frames. We use the data to evaluate our method for building animatable human body avatars. We also provide the SMPL-X fitting in the dataset.… See the full description on the dataset page: https://huggingface.co/datasets/PixelAI-Team/TalkBody4D.
75
75
[ "license:cc-by-nc-4.0", "size_categories:1M<n<10M", "format:webdataset", "modality:image", "modality:text", "library:datasets", "library:webdataset", "library:mlcroissant", "region:us" ]
2025-03-18T09:13:50
null
null
67e134c540496e1ded36dcc3
Intelligent-Internet/II-Thought-RL-v0
Intelligent-Internet
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "problem", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "verification_info", "dtype": "string"}, {"name": "data_source", "dtype": "string"}, {"name": "domain", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4819048664, "num_examples": 341795}], "download_size": 2448038647, "dataset_size": 4819048664}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
false
null
2025-03-28T15:26:57
38
36
false
c41b695c60b0af3c3701e41d483031246c378088
II-Thought RL v0: A Large-Scale Curated Dataset for Reinforcement Learning See our blog here for additional details. We introduce II-Thought RL v0, the first large-scale, multi-task dataset designed for Reinforcement Learning. This dataset consists of high-quality question-answer pairs that have undergone a rigorous multi-step filtering process, leveraging Gemini 2.0 Flash and Qwen 32B as quality evaluators. In this initial release, we have curated and refined publicly available… See the full description on the dataset page: https://huggingface.co/datasets/Intelligent-Internet/II-Thought-RL-v0.
2,287
2,287
[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2412.08819", "region:us" ]
2025-03-24T10:32:37
null
null
67e5170dd9b7021d4a7f48be
Rapidata/OpenAI-4o_t2i_human_preference
Rapidata
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "image1", "dtype": "image"}, {"name": "image2", "dtype": "image"}, {"name": "model1", "dtype": "string"}, {"name": "model2", "dtype": "string"}, {"name": "weighted_results_image1_preference", "dtype": "float32"}, {"name": "weighted_results_image2_preference", "dtype": "float32"}, {"name": "detailed_results_preference", "dtype": "string"}, {"name": "weighted_results_image1_coherence", "dtype": "float32"}, {"name": "weighted_results_image2_coherence", "dtype": "float32"}, {"name": "detailed_results_coherence", "dtype": "string"}, {"name": "weighted_results_image1_alignment", "dtype": "float32"}, {"name": "weighted_results_image2_alignment", "dtype": "float32"}, {"name": "detailed_results_alignment", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 10832696953, "num_examples": 13000}], "download_size": 5203247080, "dataset_size": 10832696953}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "license": "cdla-permissive-2.0", "task_categories": ["text-to-image", "image-to-text", "image-classification", "reinforcement-learning"], "language": ["en"], "tags": ["Human", "Preference", "Coherence", "Alignment", "country", "language", "flux", "midjourney", "dalle3", "stabeldiffusion", "alignment", "flux1.1", "flux1", "imagen3", "aurora", "lumina", "recraft", "recraft v2", "ideogram", "frames", "OpenAI 4o", "4o", "OpenAI"], "size_categories": ["10K<n<100K"], "pretty_name": "OpenAI 4o vs. Ideogram V2 / Recraft V2 / Lumina-15-2-25 / Frames-23-1-25 / Aurora / imagen-3 / Flux-1.1-pro / Flux-1-pro / Dalle-3 / Midjourney-5.2 / Stabel-Diffusion-3 - Human Preference Dataset"}
false
null
2025-03-28T20:00:43
26
26
false
9fafb39b4bb3bac6e2fbabd13503fa1199fde400
Rapidata OpenAI 4o Preference This T2I dataset contains over 200'000 human responses from over ~45,000 individual annotators, collected in less than half a day using the Rapidata Python API, accessible to anyone and ideal for large scale evaluation. Evaluating OpenAI 4o (version from 26.3.2025) across three categories: preference, coherence, and alignment. Explore our latest model rankings on our website. If you get value from this dataset and would like to see more in the… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/OpenAI-4o_t2i_human_preference.
738
738
[ "task_categories:text-to-image", "task_categories:image-to-text", "task_categories:image-classification", "task_categories:reinforcement-learning", "language:en", "license:cdla-permissive-2.0", "size_categories:10K<n<100K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "Human", "Preference", "Coherence", "Alignment", "country", "language", "flux", "midjourney", "dalle3", "stabeldiffusion", "alignment", "flux1.1", "flux1", "imagen3", "aurora", "lumina", "recraft", "recraft v2", "ideogram", "frames", "OpenAI 4o", "4o", "OpenAI" ]
2025-03-27T09:14:53
null
null
67ea45bbcb39affecc10763e
virtuoussy/Multi-subject-RLVR
virtuoussy
{"license": "apache-2.0", "task_categories": ["question-answering"], "language": ["en"]}
false
null
2025-03-31T07:39:29
26
26
false
69b869a6b47e3a19c86a17c90248efad277888f3
Multi-subject data for paper "Expanding RL with Verifiable Rewards Across Diverse Domains". we use a multi-subject multiple-choice QA dataset ExamQA (Yu et al., 2021). Originally written in Chinese, ExamQA covers at least 48 first-level subjects. We remove the distractors and convert each instance into a free-form QA pair. This dataset consists of 638k college-level instances, with both questions and objective answers written by domain experts for examination purposes. We also use GPT-4o-mini… See the full description on the dataset page: https://huggingface.co/datasets/virtuoussy/Multi-subject-RLVR.
8
8
[ "task_categories:question-answering", "language:en", "license:apache-2.0", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
2025-03-31T07:35:23
null
null
67e46df98c0347025bba131b
sychonix/emotion
sychonix
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "sadness", "1": "joy", "2": "love", "3": "anger", "4": "fear", "5": "surprise"}}}}], "splits": [{"name": "train", "num_bytes": 1741533, "num_examples": 16000}, {"name": "validation", "num_bytes": 214695, "num_examples": 2000}, {"name": "test", "num_bytes": 217173, "num_examples": 2000}], "download_size": 1281072, "dataset_size": 2173401}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}]}
false
null
2025-03-26T21:13:34
24
24
false
5a355b76cee6387d370d99d7ff656e79cc10d2eb
null
407
407
[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
2025-03-26T21:13:29
null
null
67d97c4be2b27852325fd8e2
nvidia/PhysicalAI-Robotics-GR00T-X-Embodiment-Sim
nvidia
{"license": "cc-by-4.0"}
false
null
2025-04-01T23:28:56
100
23
false
334fa4108a2f543dcc7949d038af919d2a931095
PhysicalAI-Robotics-GR00T-X-Embodiment-Sim Github Repo: Isaac GR00T N1 We provide a set of datasets used for post-training of GR00T N1. Each dataset is a collection of trajectories from different robot embodiments and tasks. Cross-embodied bimanual manipulation: 9k trajectories Dataset Name #trajectories bimanual_panda_gripper.Threading 1000 bimanual_panda_hand.LiftTray 1000 bimanual_panda_gripper.ThreePieceAssembly 1000… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/PhysicalAI-Robotics-GR00T-X-Embodiment-Sim.
27,818
27,818
[ "license:cc-by-4.0", "region:us" ]
2025-03-18T13:59:39
null
null
67e52d5dc04bd73d2f46a929
MrDragonFox/Elise
MrDragonFox
{"license": "mit"}
false
null
2025-03-27T11:22:24
22
22
false
ee867f95526856352ba9c607e6f97e6b9c65b043
this is very much a clone of https://huggingface.co/datasets/Jinsaryko/Elise but with classified emotions like laughs and giggles not ment to be comprehenive - its about 3h in total and will be enough to for a finetuned voice and some basic emotional tags short but sweet - acts as demo test set "giggles - 76", "laughs - 336", "long pause - 2", "chuckles - 20", "whispers - 2", "normal volume - 2", "sighs - 156", "clicks tongue - 2", "gasps - 4", "moans - 8", "sonora - 2", "habla en inglés -… See the full description on the dataset page: https://huggingface.co/datasets/MrDragonFox/Elise.
1,001
1,001
[ "license:mit", "size_categories:1K<n<10K", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
2025-03-27T10:50:05
null
null
67b32145bac2756ce9a4a0fe
Congliu/Chinese-DeepSeek-R1-Distill-data-110k
Congliu
{"license": "apache-2.0", "language": ["zh"], "size_categories": ["100K<n<1M"], "task_categories": ["text-generation", "text2text-generation", "question-answering"]}
false
null
2025-02-21T02:18:08
608
21
false
8520b649430617c2be4490f424d251d09d835ed3
中文基于满血DeepSeek-R1蒸馏数据集(Chinese-Data-Distill-From-R1) 🤗 Hugging Face   |   🤖 ModelScope    |   🚀 Github    |   📑 Blog 注意:提供了直接SFT使用的版本,点击下载。将数据中的思考和答案整合成output字段,大部分SFT代码框架均可直接直接加载训练。 本数据集为中文开源蒸馏满血R1的数据集,数据集中不仅包含math数据,还包括大量的通用类型数据,总数量为110K。 为什么开源这个数据? R1的效果十分强大,并且基于R1蒸馏数据SFT的小模型也展现出了强大的效果,但检索发现,大部分开源的R1蒸馏数据集均为英文数据集。 同时,R1的报告中展示,蒸馏模型中同时也使用了部分通用场景数据集。 为了帮助大家更好地复现R1蒸馏模型的效果,特此开源中文数据集。该中文数据集中的数据分布如下: Math:共计36568个样本, Exam:共计2432个样本, STEM:共计12648个样本,… See the full description on the dataset page: https://huggingface.co/datasets/Congliu/Chinese-DeepSeek-R1-Distill-data-110k.
5,712
10,779
[ "task_categories:text-generation", "task_categories:text2text-generation", "task_categories:question-answering", "language:zh", "license:apache-2.0", "size_categories:100K<n<1M", "format:json", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
2025-02-17T11:45:09
null
null
679c0b5c32cf4c58bdcba8eb
facebook/natural_reasoning
facebook
{"license": "cc-by-nc-4.0", "task_categories": ["text-generation"], "language": ["en"], "pretty_name": "Natural Reasoning", "size_categories": ["1M<n<10M"]}
false
null
2025-02-21T06:02:40
474
20
false
99eea5dc6bfa45a925eb42600e81dc90377ba237
NaturalReasoning is a large-scale dataset for general reasoning tasks. It consists of high-quality challenging reasoning questions backtranslated from pretraining corpora DCLM and FineMath. The questions have been deduplicated and decontaminated from popular reasoning benchmarks including MATH, GPQA, MMLU-Pro, MMLU-STEM. For each question, we extract the reference final answer from the original document from the pretraining corpora if possible. We also provide a model-generated response from… See the full description on the dataset page: https://huggingface.co/datasets/facebook/natural_reasoning.
12,253
16,815
[ "task_categories:text-generation", "language:en", "license:cc-by-nc-4.0", "size_categories:1M<n<10M", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2502.13124", "region:us" ]
2025-01-30T23:29:32
null
null
66212f29fb07c3e05ad0432e
HuggingFaceFW/fineweb
HuggingFaceFW
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false
null
2025-01-31T14:10:44
2,077
17
false
0f039043b23fe1d4eed300b504aa4b4a68f1c7ba
🍷 FineWeb 15 trillion tokens of the finest data the 🌐 web has to offer What is it? The 🍷 FineWeb dataset consists of more than 15T tokens of cleaned and deduplicated english web data from CommonCrawl. The data processing pipeline is optimized for LLM performance and ran on the 🏭 datatrove library, our large scale data processing library. 🍷 FineWeb was originally meant to be a fully open replication of 🦅 RefinedWeb, with a release of the full dataset under… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceFW/fineweb.
211,427
2,333,932
[ "task_categories:text-generation", "language:en", "license:odc-by", "size_categories:10B<n<100B", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2306.01116", "arxiv:2109.07445", "arxiv:2406.17557", "doi:10.57967/hf/2493", "region:us" ]
2024-04-18T14:33:13
null
null
67e90b135e63bac35a2dbaf0
MohamedRashad/Quran-Recitations
MohamedRashad
{"dataset_info": {"features": [{"name": "source", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "audio", "dtype": "audio"}], "splits": [{"name": "train", "num_bytes": 49579449331.918, "num_examples": 124689}], "download_size": 33136131149, "dataset_size": 49579449331.918}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "task_categories": ["automatic-speech-recognition", "text-to-speech"], "language": ["ar"], "size_categories": ["100K<n<1M"]}
false
null
2025-03-30T11:19:54
16
16
false
65ee6114d526c02f7f96d696bb254a2dd666270c
Quran-Recitations Dataset Overview The Quran-Recitations dataset is a rich and reverent collection of Quranic verses, meticulously paired with their respective recitations by esteemed Qaris. This dataset serves as a valuable resource for researchers, developers, and students interested in Quranic studies, speech recognition, audio analysis, and Islamic applications. Dataset Structure source: The name of the Qari (reciter) who performed… See the full description on the dataset page: https://huggingface.co/datasets/MohamedRashad/Quran-Recitations.
94
94
[ "task_categories:automatic-speech-recognition", "task_categories:text-to-speech", "language:ar", "size_categories:100K<n<1M", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
2025-03-30T09:12:51
null
null
625552d2b339bb03abe3432d
openai/gsm8k
openai
{"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text2text-generation"], "task_ids": [], "paperswithcode_id": "gsm8k", "pretty_name": "Grade School Math 8K", "tags": ["math-word-problems"], "dataset_info": [{"config_name": "main", "features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3963202, "num_examples": 7473}, {"name": "test", "num_bytes": 713732, "num_examples": 1319}], "download_size": 2725633, "dataset_size": 4676934}, {"config_name": "socratic", "features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5198108, "num_examples": 7473}, {"name": "test", "num_bytes": 936859, "num_examples": 1319}], "download_size": 3164254, "dataset_size": 6134967}], "configs": [{"config_name": "main", "data_files": [{"split": "train", "path": "main/train-*"}, {"split": "test", "path": "main/test-*"}]}, {"config_name": "socratic", "data_files": [{"split": "train", "path": "socratic/train-*"}, {"split": "test", "path": "socratic/test-*"}]}]}
false
null
2024-01-04T12:05:15
671
15
false
e53f048856ff4f594e959d75785d2c2d37b678ee
Dataset Card for GSM8K Dataset Summary GSM8K (Grade School Math 8K) is a dataset of 8.5K high quality linguistically diverse grade school math word problems. The dataset was created to support the task of question answering on basic mathematical problems that require multi-step reasoning. These problems take between 2 and 8 steps to solve. Solutions primarily involve performing a sequence of elementary calculations using basic arithmetic operations (+ − ×÷) to reach the… See the full description on the dataset page: https://huggingface.co/datasets/openai/gsm8k.
327,604
4,274,795
[ "task_categories:text2text-generation", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:mit", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2110.14168", "region:us", "math-word-problems" ]
2022-04-12T10:22:10
gsm8k
null
67d6cac12e36db434b2be97e
manycore-research/SpatialLM-Testset
manycore-research
{"license": "cc-by-nc-4.0", "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "test.csv"}]}]}
false
null
2025-03-19T15:05:46
49
15
false
3a5c44deac7ac1de370c5341d2748250cbbf52e3
SpatialLM Testset We provide a test set of 107 preprocessed point clouds and their corresponding GT layouts, point clouds are reconstructed from RGB videos using MASt3R-SLAM. SpatialLM-Testset is quite challenging compared to prior clean RGBD scan datasets due to the noises and occlusions in the point clouds reconstructed from monocular RGB videos. Folder Structure Outlines of the dataset files: project-root/ ├── pcd/*.ply… See the full description on the dataset page: https://huggingface.co/datasets/manycore-research/SpatialLM-Testset.
8,451
8,451
[ "license:cc-by-nc-4.0", "size_categories:n<1K", "format:csv", "modality:3d", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
2025-03-16T12:57:37
null
null
621ffdd236468d709f181e5e
cais/mmlu
cais
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{"split": "validation", "path": "public_relations/validation-*"}, {"split": "dev", "path": "public_relations/dev-*"}]}, {"config_name": "security_studies", "data_files": [{"split": "test", "path": "security_studies/test-*"}, {"split": "validation", "path": "security_studies/validation-*"}, {"split": "dev", "path": "security_studies/dev-*"}]}, {"config_name": "sociology", "data_files": [{"split": "test", "path": "sociology/test-*"}, {"split": "validation", "path": "sociology/validation-*"}, {"split": "dev", "path": "sociology/dev-*"}]}, {"config_name": "us_foreign_policy", "data_files": [{"split": "test", "path": "us_foreign_policy/test-*"}, {"split": "validation", "path": "us_foreign_policy/validation-*"}, {"split": "dev", "path": "us_foreign_policy/dev-*"}]}, {"config_name": "virology", "data_files": [{"split": "test", "path": "virology/test-*"}, {"split": "validation", "path": "virology/validation-*"}, {"split": "dev", "path": "virology/dev-*"}]}, {"config_name": "world_religions", "data_files": [{"split": "test", "path": "world_religions/test-*"}, {"split": "validation", "path": "world_religions/validation-*"}, {"split": "dev", "path": "world_religions/dev-*"}]}]}
false
null
2024-03-08T20:36:26
439
14
false
c30699e8356da336a370243923dbaf21066bb9fe
Dataset Card for MMLU Dataset Summary Measuring Massive Multitask Language Understanding by Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt (ICLR 2021). This is a massive multitask test consisting of multiple-choice questions from various branches of knowledge. The test spans subjects in the humanities, social sciences, hard sciences, and other areas that are important for some people to learn. This covers 57… See the full description on the dataset page: https://huggingface.co/datasets/cais/mmlu.
143,641
37,196,277
[ "task_categories:question-answering", "task_ids:multiple-choice-qa", "annotations_creators:no-annotation", "language_creators:expert-generated", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:mit", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2009.03300", "arxiv:2005.00700", "arxiv:2005.14165", "arxiv:2008.02275", "region:us" ]
2022-03-02T23:29:22
mmlu
null
67abc2c2d6edf5606aa5c0d7
facebook/collaborative_agent_bench
facebook
{"license": "other", "extra_gated_prompt": "## License", "extra_gated_fields": {"First Name": "text", "Last Name": "text", "Date of birth": "date_picker", "Country": "country", "Affiliation": "text", "I accept the terms and conditions": "checkbox", "geo": "ip_location"}, "extra_gated_description": "SWEET-RL Research License and Acceptable Use Policy", "extra_gated_button_content": "I Accept Self-taught Evaluator Research License and AUP"}
false
null
2025-03-20T04:17:14
53
14
false
cf3526da25989b53f105fe9b74c1174a3e19c548
This dataset is released as part of SWEET-RL: Training Multi-Turn LLM Agents on Collaborative Reasoning Tasks research project. Please refer to our project materials here for training and evaluation details. Citation If you use data, model, or code from this work, please cite with the following BibTex entry: @misc{zhou2025sweetrltrainingmultiturnllm, title={SWEET-RL: Training Multi-Turn LLM Agents on Collaborative Reasoning Tasks}, author={Yifei Zhou and Song Jiang and… See the full description on the dataset page: https://huggingface.co/datasets/facebook/collaborative_agent_bench.
109
109
[ "license:other", "arxiv:2503.15478", "region:us" ]
2025-02-11T21:36:02
null
null
67b20fc10861cec33b3afb8a
Conard/fortune-telling
Conard
{"license": "mit"}
false
null
2025-02-17T05:13:43
108
14
false
6261fe0d35a75997972bbfcd9828020e340303fb
null
5,943
6,921
[ "license:mit", "size_categories:n<1K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
2025-02-16T16:18:09
null
null
67c03fd6b9fe27a2ac49784d
open-r1/codeforces-cots
open-r1
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"num_examples": 11672}], "download_size": 415023817, "dataset_size": 1067124847}, {"config_name": "solutions_w_editorials_py_decontaminated", "features": [{"name": "id", "dtype": "string"}, {"name": "aliases", "sequence": "string"}, {"name": "contest_id", "dtype": "string"}, {"name": "contest_name", "dtype": "string"}, {"name": "contest_type", "dtype": "string"}, {"name": "contest_start", "dtype": "int64"}, {"name": "contest_start_year", "dtype": "int64"}, {"name": "index", "dtype": "string"}, {"name": "time_limit", "dtype": "float64"}, {"name": "memory_limit", "dtype": "float64"}, {"name": "title", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "input_format", "dtype": "string"}, {"name": "output_format", "dtype": "string"}, {"name": "interaction_format", "dtype": "string"}, {"name": "note", "dtype": "string"}, {"name": "examples", "list": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}]}, {"name": "editorial", "dtype": "string"}, 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"contest_start", "dtype": "int64"}, {"name": "contest_start_year", "dtype": "int64"}, {"name": "index", "dtype": "string"}, {"name": "time_limit", "dtype": "float64"}, {"name": "memory_limit", "dtype": "float64"}, {"name": "title", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "input_format", "dtype": "string"}, {"name": "output_format", "dtype": "string"}, {"name": "examples", "list": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}]}, {"name": "note", "dtype": "string"}, {"name": "editorial", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "generation", "dtype": "string"}, {"name": "finish_reason", "dtype": "string"}, {"name": "api_metadata", "struct": [{"name": "completion_tokens", "dtype": "int64"}, {"name": "completion_tokens_details", "dtype": "null"}, {"name": "prompt_tokens", "dtype": "int64"}, {"name": "prompt_tokens_details", "dtype": "null"}, {"name": "total_tokens", "dtype": "int64"}]}, {"name": "interaction_format", "dtype": "string"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 1851104290, "num_examples": 20620}], "download_size": 724157877, "dataset_size": 1851104290}], "configs": [{"config_name": "checker_interactor", "data_files": [{"split": "train", "path": "checker_interactor/train-*"}]}, {"config_name": "solutions", "default": true, "data_files": [{"split": "train", "path": "solutions/train-*"}]}, {"config_name": "solutions_decontaminated", "data_files": [{"split": "train", "path": "solutions_decontaminated/train-*"}]}, {"config_name": "solutions_py", "data_files": [{"split": "train", "path": "solutions_py/train-*"}]}, {"config_name": "solutions_py_decontaminated", "data_files": [{"split": "train", "path": "solutions_py_decontaminated/train-*"}]}, {"config_name": "solutions_short_and_long_decontaminated", "data_files": [{"split": "train", "path": "solutions_short_and_long_decontaminated/train-*"}]}, {"config_name": "solutions_w_editorials", "data_files": [{"split": "train", "path": "solutions_w_editorials/train-*"}]}, {"config_name": "solutions_w_editorials_decontaminated", "data_files": [{"split": "train", "path": "solutions_w_editorials_decontaminated/train-*"}]}, {"config_name": "solutions_w_editorials_py", "data_files": [{"split": "train", "path": "solutions_w_editorials_py/train-*"}]}, {"config_name": "solutions_w_editorials_py_decontaminated", "data_files": [{"split": "train", "path": "solutions_w_editorials_py_decontaminated/train-*"}]}, {"config_name": "test_input_generator", "data_files": [{"split": "train", "path": "test_input_generator/train-*"}]}], "license": "cc-by-4.0"}
false
null
2025-03-28T12:21:06
122
14
false
39ac85c150806230473c70ad72c31f6232fe3f41
Dataset Card for CodeForces-CoTs Dataset description CodeForces-CoTs is a large-scale dataset for training reasoning models on competitive programming tasks. It consists of 10k CodeForces problems with up to five reasoning traces generated by DeepSeek R1. We did not filter the traces for correctness, but found that around 84% of the Python ones pass the public tests. The dataset consists of several subsets: solutions: we prompt R1 to solve the problem and produce code.… See the full description on the dataset page: https://huggingface.co/datasets/open-r1/codeforces-cots.
9,014
9,082
[ "license:cc-by-4.0", "size_categories:100K<n<1M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
2025-02-27T10:35:02
null
null
67ce7e23fee7f7ce990104eb
X-ART/LeX-10K
X-ART
{"license": "apache-2.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-to-image"], "tags": ["text-rendering", "art"]}
false
null
2025-03-31T08:19:17
14
14
false
18a5cf77b06608dac50442903bf5b9ad46bd7059
🖼️ LeX-10K: High-Quality Dataset for Text Rendering LeX-10K is a curated dataset of 10,000 high-resolution, visually diverse 1024×1024 images tailored for text-to-image generation with a focus on aesthetics, text fidelity, and stylistic richness. Project Page | Paper 🌟 Why LeX-10K? We compare LeX-10K with two widely used datasets: AnyWord-3M and MARIO-10M.As shown below, LeX-10K significantly outperforms both in terms of aesthetic quality, text readability, and… See the full description on the dataset page: https://huggingface.co/datasets/X-ART/LeX-10K.
701
701
[ "task_categories:text-to-image", "license:apache-2.0", "size_categories:1K<n<10K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2503.21749", "region:us", "text-rendering", "art" ]
2025-03-10T05:52:35
null
null
621ffdd236468d709f181e16
dair-ai/emotion
dair-ai
{"annotations_creators": ["machine-generated"], "language_creators": ["machine-generated"], "language": ["en"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["multi-class-classification"], "paperswithcode_id": "emotion", "pretty_name": "Emotion", "tags": ["emotion-classification"], "dataset_info": [{"config_name": "split", "features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "sadness", "1": "joy", "2": "love", "3": "anger", "4": "fear", "5": "surprise"}}}}], "splits": [{"name": "train", "num_bytes": 1741533, "num_examples": 16000}, {"name": "validation", "num_bytes": 214695, "num_examples": 2000}, {"name": "test", "num_bytes": 217173, "num_examples": 2000}], "download_size": 1287193, "dataset_size": 2173401}, {"config_name": "unsplit", "features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "sadness", "1": "joy", "2": "love", "3": "anger", "4": "fear", "5": "surprise"}}}}], "splits": [{"name": "train", "num_bytes": 45444017, "num_examples": 416809}], "download_size": 26888538, "dataset_size": 45444017}], "configs": [{"config_name": "split", "data_files": [{"split": "train", "path": "split/train-*"}, {"split": "validation", "path": "split/validation-*"}, {"split": "test", "path": "split/test-*"}], "default": true}, {"config_name": "unsplit", "data_files": [{"split": "train", "path": "unsplit/train-*"}]}], "train-eval-index": [{"config": "default", "task": "text-classification", "task_id": "multi_class_classification", "splits": {"train_split": "train", "eval_split": "test"}, "col_mapping": {"text": "text", "label": "target"}, "metrics": [{"type": "accuracy", "name": "Accuracy"}, {"type": "f1", "name": "F1 macro", "args": {"average": "macro"}}, {"type": "f1", "name": "F1 micro", "args": {"average": "micro"}}, {"type": "f1", "name": "F1 weighted", "args": {"average": "weighted"}}, {"type": "precision", "name": "Precision macro", "args": {"average": "macro"}}, {"type": "precision", "name": "Precision micro", "args": {"average": "micro"}}, {"type": "precision", "name": "Precision weighted", "args": {"average": "weighted"}}, {"type": "recall", "name": "Recall macro", "args": {"average": "macro"}}, {"type": "recall", "name": "Recall micro", "args": {"average": "micro"}}, {"type": "recall", "name": "Recall weighted", "args": {"average": "weighted"}}]}]}
false
null
2024-08-08T06:10:47
347
12
false
cab853a1dbdf4c42c2b3ef2173804746df8825fe
Dataset Card for "emotion" Dataset Summary Emotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise. For more detailed information please refer to the paper. Supported Tasks and Leaderboards More Information Needed Languages More Information Needed Dataset Structure Data Instances An example looks as follows. { "text": "im feeling quite sad… See the full description on the dataset page: https://huggingface.co/datasets/dair-ai/emotion.
17,080
354,644
[ "task_categories:text-classification", "task_ids:multi-class-classification", "annotations_creators:machine-generated", "language_creators:machine-generated", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:other", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "emotion-classification" ]
2022-03-02T23:29:22
emotion
null
63990f21cc50af73d29ecfa3
fka/awesome-chatgpt-prompts
fka
{"license": "cc0-1.0", "tags": ["ChatGPT"], "task_categories": ["question-answering"], "size_categories": ["100K<n<1M"]}
false
null
2025-01-06T00:02:53
7,654
12
false
68ba7694e23014788dcc8ab5afe613824f45a05c
🧠 Awesome ChatGPT Prompts [CSV dataset] This is a Dataset Repository of Awesome ChatGPT Prompts View All Prompts on GitHub License CC-0
11,448
139,565
[ "task_categories:question-answering", "license:cc0-1.0", "size_categories:n<1K", "format:csv", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "ChatGPT" ]
2022-12-13T23:47:45
null
null
6797e648de960c48ff034e54
open-thoughts/OpenThoughts-114k
open-thoughts
{"dataset_info": [{"config_name": "default", "features": [{"name": "system", "dtype": "string"}, {"name": "conversations", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 2635015668, "num_examples": 113957}], "download_size": 1078777193, "dataset_size": 2635015668}, {"config_name": "metadata", "features": [{"name": "problem", "dtype": "string"}, {"name": "deepseek_reasoning", "dtype": "string"}, {"name": "deepseek_solution", "dtype": "string"}, {"name": "ground_truth_solution", "dtype": "string"}, {"name": "domain", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "test_cases", "dtype": "string"}, {"name": "starter_code", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5525214077.699433, "num_examples": 113957}], "download_size": 2469729724, "dataset_size": 5525214077.699433}], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}, {"config_name": "metadata", "data_files": [{"split": "train", "path": "metadata/train-*"}]}], "tags": ["curator", "synthetic"], "license": "apache-2.0"}
false
null
2025-02-20T07:16:57
675
12
false
56b06e3066a8163577ac93b24613a560e685d029
Open-Thoughts-114k Open synthetic reasoning dataset with 114k high-quality examples covering math, science, code, and puzzles! Inspect the content with rich formatting with Curator Viewer. Available Subsets default subset containing ready-to-train data used to finetune the OpenThinker-7B and OpenThinker-32B models: ds = load_dataset("open-thoughts/OpenThoughts-114k", split="train") metadata subset containing extra columns used in dataset construction:… See the full description on the dataset page: https://huggingface.co/datasets/open-thoughts/OpenThoughts-114k.
28,077
146,660
[ "license:apache-2.0", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "curator", "synthetic" ]
2025-01-27T20:02:16
null
null
67b143989d15e90f2c15ac76
zhang0jhon/Aesthetic-4K
zhang0jhon
{"license": "mit"}
false
null
2025-03-25T02:40:34
18
12
false
40a07c883a5c824a42b3dac2e393f705579cbf82
Aesthetic-4K Dataset We introduce Aesthetic-4K, a high-quality dataset for ultra-high-resolution image generation, featuring carefully selected images and captions generated by GPT-4o. Additionally, we have meticulously filtered out low-quality images through manual inspection, excluding those with motion blur, focus issues, or mismatched text prompts. For more details, please refer to our paper: Diffusion-4K: Ultra-High-Resolution Image Synthesis with Latent Diffusion Models (CVPR… See the full description on the dataset page: https://huggingface.co/datasets/zhang0jhon/Aesthetic-4K.
1,991
3,892
[ "license:mit", "size_categories:1K<n<10K", "format:imagefolder", "modality:image", "modality:text", "library:datasets", "library:mlcroissant", "arxiv:2503.18352", "region:us" ]
2025-02-16T01:47:04
null
null
67e2d9efa823152c92e695d4
nyuuzyou/archiveofourown
nyuuzyou
{"annotations_creators": ["found"], "language": ["ar", "bg", "ca", "cs", "da", "de", "el", "en", "es", "et", "fa", "fi", "fr", "he", "hi", "hr", "hu", "id", "it", "ja", "ko", "lt", "lv", "ms", "nl", "no", "pl", "pt", "ro", "ru", "sk", "sl", "sr", "sv", "th", "tr", "uk", "vi", "zh"], "language_bcp47": ["pt-BR", "zh-HK", "zh-TW"], "license": ["cc0-1.0"], "multilinguality": ["multilingual"], "pretty_name": "Archive of Our Own (AO3)", "size_categories": ["10M<n<100M"], "source_datasets": ["original"], "task_categories": ["text-generation", "text-classification"], "task_ids": ["language-modeling", "topic-classification"], "configs": [{"config_name": "train", "data_files": [{"split": "train", "path": "*.jsonl.zst"}], "default": true}]}
false
null
2025-03-25T17:07:57
12
12
false
4f0f0eb835ab627a78d02d2973ca0263400720df
Dataset Card for Archive of Our Own (AO3) Dataset Summary This dataset contains approximately 12.6 million publicly available works from Archive of Our Own (AO3), a fan-created, fan-run, non-profit archive for transformative fanworks. The dataset was created by processing works with IDs from 1 to 63,200,000 that are publicly accessible. Each entry contains the full text of the work along with comprehensive metadata including title, author, fandom, relationships… See the full description on the dataset page: https://huggingface.co/datasets/nyuuzyou/archiveofourown.
351
351
[ "task_categories:text-generation", "task_categories:text-classification", "task_ids:language-modeling", "task_ids:topic-classification", "annotations_creators:found", "multilinguality:multilingual", "source_datasets:original", "language:ar", "language:bg", "language:ca", "language:cs", "language:da", "language:de", "language:el", "language:en", "language:es", "language:et", "language:fa", "language:fi", "language:fr", "language:he", "language:hi", "language:hr", "language:hu", "language:id", "language:it", "language:ja", "language:ko", "language:lt", "language:lv", "language:ms", "language:nl", "language:no", "language:pl", "language:pt", "language:ro", "language:ru", "language:sk", "language:sl", "language:sr", "language:sv", "language:th", "language:tr", "language:uk", "language:vi", "language:zh", "license:cc0-1.0", "size_categories:10M<n<100M", "format:json", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "region:us" ]
2025-03-25T16:29:35
null
null
641debae1d05404efd046a4f
yahma/alpaca-cleaned
yahma
{"license": "cc-by-4.0", "language": ["en"], "tags": ["instruction-finetuning"], "pretty_name": "Alpaca-Cleaned", "task_categories": ["text-generation"]}
false
null
2023-04-10T20:29:06
674
11
false
12567cabf869d7c92e573c7c783905fc160e9639
Dataset Card for Alpaca-Cleaned Repository: https://github.com/gururise/AlpacaDataCleaned Dataset Description This is a cleaned version of the original Alpaca Dataset released by Stanford. The following issues have been identified in the original release and fixed in this dataset: Hallucinations: Many instructions in the original dataset had instructions referencing data on the internet, which just caused GPT3 to hallucinate an answer. "instruction":"Summarize the… See the full description on the dataset page: https://huggingface.co/datasets/yahma/alpaca-cleaned.
23,499
619,191
[ "task_categories:text-generation", "language:en", "license:cc-by-4.0", "size_categories:10K<n<100K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "instruction-finetuning" ]
2023-03-24T18:27:58
null
null
67d36b3428667f6d99eef6aa
microsoft/WildFeedback
microsoft
{"license": "odc-by", "task_categories": ["text-generation"], "language": ["en"], "pretty_name": "WildFeedback", "configs": [{"config_name": "wildfeedback", "data_files": "wildfeedback.json"}, {"config_name": "sat_dsat_annotation", "data_files": "sat_dsat_annotation.json"}, {"config_name": "user_preference", "data_files": "user_preference.json"}], "size_categories": ["10K<n<100K"]}
false
null
2025-03-25T22:55:36
11
11
false
8b1a3e530b949d6aacfad6ba8912e209a05bc846
Dataset Card for WildFeedback WildFeedback is a preference dataset constructed from real-world user interactions with ChatGPT. Unlike synthetic datasets that rely solely on AI-generated rankings, WildFeedback captures authentic human preferences through naturally occurring user feedback signals in conversation. The dataset is designed to improve the alignment of large language models (LLMs) with actual human values by leveraging direct user input. Dataset Details… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/WildFeedback.
184
184
[ "task_categories:text-generation", "language:en", "license:odc-by", "size_categories:1M<n<10M", "format:json", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2408.15549", "region:us" ]
2025-03-13T23:33:08
null
null
67d7eeec9830e5c1e2a8f708
BytedTsinghua-SIA/DAPO-Math-17k
BytedTsinghua-SIA
{"license": "apache-2.0", "task_categories": ["text-generation"], "language": ["en"], "tags": ["math"], "pretty_name": "DAPO-Math-17k", "size_categories": ["1M<n<10M"]}
false
null
2025-03-18T07:47:04
53
11
false
9f6440001c15da8e7c7516fdbb3d2ce49de711de
This dataset actually only contains ~17k unique prompts and was duplicated by ~100x by accident.
3,309
3,309
[ "task_categories:text-generation", "language:en", "license:apache-2.0", "size_categories:1M<n<10M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "math" ]
2025-03-17T09:44:12
null
null
639244f571c51c43091df168
Anthropic/hh-rlhf
Anthropic
{"license": "mit", "tags": ["human-feedback"]}
false
null
2023-05-26T18:47:34
1,308
10
false
09be8c5bbc57cb3887f3a9732ad6aa7ec602a1fa
Dataset Card for HH-RLHF Dataset Summary This repository provides access to two different kinds of data: Human preference data about helpfulness and harmlessness from Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback. These data are meant to train preference (or reward) models for subsequent RLHF training. These data are not meant for supervised training of dialogue agents. Training dialogue agents on these data is likely… See the full description on the dataset page: https://huggingface.co/datasets/Anthropic/hh-rlhf.
12,851
1,561,716
[ "license:mit", "size_categories:100K<n<1M", "format:json", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2204.05862", "region:us", "human-feedback" ]
2022-12-08T20:11:33
null
null
6791fcbb49c4df6d798ca7c9
cais/hle
cais
{"license": "mit", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "image", "dtype": "string"}, {"name": "image_preview", "dtype": "image"}, {"name": "answer", "dtype": "string"}, {"name": "answer_type", "dtype": "string"}, {"name": "author_name", "dtype": "string"}, {"name": "rationale", "dtype": "string"}, {"name": "rationale_image", "dtype": "image"}, {"name": "raw_subject", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "canary", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 276479149, "num_examples": 2700}], "download_size": 266651469, "dataset_size": 276479149}, "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}]}
false
null
2025-02-15T00:05:49
288
10
false
1a9f4713d5a6bc9b7988db7c42e1dccdf41d1f43
Humanity's Last Exam 🌐 Website | 📄 Paper | GitHub Center for AI Safety & Scale AI Humanity's Last Exam (HLE) is a multi-modal benchmark at the frontier of human knowledge, designed to be the final closed-ended academic benchmark of its kind with broad subject coverage. Humanity's Last Exam consists of 2,700 questions across dozens of subjects, including mathematics, humanities, and the natural sciences. HLE is developed globally by subject-matter experts and consists of… See the full description on the dataset page: https://huggingface.co/datasets/cais/hle.
5,873
15,085
[ "license:mit", "size_categories:1K<n<10K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
2025-01-23T08:24:27
null
null
67e74b725fb029dd96363693
inclusionAI/AReaL-boba-Data
inclusionAI
{"license": "apache-2.0"}
false
null
2025-03-29T01:26:55
10
10
false
1799c00be3f1216ab55a5cae3562d654dbfd7d82
null
156
156
[ "license:apache-2.0", "region:us" ]
2025-03-29T01:22:58
null
null
6798a4f11290e49f6e4b7ceb
LWHYC/PASTA-Gen-30K
LWHYC
{"license": "mit"}
false
null
2025-02-18T05:47:25
12
9
false
af31b74e90824a4ccd06c8f583664763942a91e2
Workflow of PASTA Model Development and Training Pipeline. a, Overview of organs and lesion types involved in PASTA training. b, Examples of lesions generated by PASTA-Gen from healthy organs. c, Lesion generation process pipeline of PASTA-Gen. d, Two-stage training of PASTA using the PASTA-Gen-30K dataset. Model, Paper Overview PASTA-Gen-30K, a large-scale synthetic dataset of 30,000 CT volumes with precise lesion masks and structured textual reports from 15 lesion types (10… See the full description on the dataset page: https://huggingface.co/datasets/LWHYC/PASTA-Gen-30K.
7,171
11,611
[ "license:mit", "arxiv:2502.06171", "region:us" ]
2025-01-28T09:35:45
null
null
67e46d6a65b73a7980e27dc3
sychonix/movies-dataset
sychonix
{"dataset_info": {"features": [{"name": "title", "dtype": "string"}, {"name": "genre", "dtype": "string"}, {"name": "rating", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 127, "num_examples": 4}], "download_size": 1527, "dataset_size": 127}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
false
null
2025-03-26T21:11:09
9
9
false
167e24b4fdce9aff2984628bf5466858f3ca3e55
null
88
88
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
2025-03-26T21:11:06
null
null
64382440c212a363c3ac15c8
OpenAssistant/oasst1
OpenAssistant
{"license": "apache-2.0", "dataset_info": {"features": [{"name": "message_id", "dtype": "string"}, {"name": "parent_id", "dtype": "string"}, {"name": "user_id", "dtype": "string"}, {"name": "created_date", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "role", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "review_count", "dtype": "int32"}, {"name": "review_result", "dtype": "bool"}, {"name": "deleted", "dtype": "bool"}, {"name": "rank", "dtype": "int32"}, {"name": "synthetic", "dtype": "bool"}, {"name": "model_name", "dtype": "string"}, {"name": "detoxify", "struct": [{"name": "toxicity", "dtype": "float64"}, {"name": "severe_toxicity", "dtype": "float64"}, {"name": "obscene", "dtype": "float64"}, {"name": "identity_attack", "dtype": "float64"}, {"name": "insult", "dtype": "float64"}, {"name": "threat", "dtype": "float64"}, {"name": "sexual_explicit", "dtype": "float64"}]}, {"name": "message_tree_id", "dtype": "string"}, {"name": "tree_state", "dtype": "string"}, {"name": "emojis", "sequence": [{"name": "name", "dtype": "string"}, {"name": "count", "dtype": "int32"}]}, {"name": "labels", "sequence": [{"name": "name", "dtype": "string"}, {"name": "value", "dtype": "float64"}, {"name": "count", "dtype": "int32"}]}], "splits": [{"name": "train", "num_bytes": 100367999, "num_examples": 84437}, {"name": "validation", "num_bytes": 5243405, "num_examples": 4401}], "download_size": 41596430, "dataset_size": 105611404}, "language": ["en", "es", "ru", "de", "pl", "th", "vi", "sv", "bn", "da", "he", "it", "fa", "sk", "id", "nb", "el", "nl", "hu", "eu", "zh", "eo", "ja", "ca", "cs", "bg", "fi", "pt", "tr", "ro", "ar", "uk", "gl", "fr", "ko"], "tags": ["human-feedback"], "size_categories": ["100K<n<1M"], "pretty_name": "OpenAssistant Conversations"}
false
null
2023-05-02T13:21:21
1,371
8
false
fdf72ae0827c1cda404aff25b6603abec9e3399b
OpenAssistant Conversations Dataset (OASST1) Dataset Summary In an effort to democratize research on large-scale alignment, we release OpenAssistant Conversations (OASST1), a human-generated, human-annotated assistant-style conversation corpus consisting of 161,443 messages in 35 different languages, annotated with 461,292 quality ratings, resulting in over 10,000 fully annotated conversation trees. The corpus is a product of a worldwide crowd-sourcing effort… See the full description on the dataset page: https://huggingface.co/datasets/OpenAssistant/oasst1.
8,748
252,041
[ "language:en", "language:es", "language:ru", "language:de", "language:pl", "language:th", "language:vi", "language:sv", "language:bn", "language:da", "language:he", "language:it", "language:fa", "language:sk", "language:id", "language:nb", "language:el", "language:nl", "language:hu", "language:eu", "language:zh", "language:eo", "language:ja", "language:ca", "language:cs", "language:bg", "language:fi", "language:pt", "language:tr", "language:ro", "language:ar", "language:uk", "language:gl", "language:fr", "language:ko", "license:apache-2.0", "size_categories:10K<n<100K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2304.07327", "region:us", "human-feedback" ]
2023-04-13T15:48:16
null
null
650a9248d26103b6eee3ea7b
lmsys/lmsys-chat-1m
lmsys
{"size_categories": ["1M<n<10M"], "task_categories": ["conversational"], "extra_gated_prompt": "You agree to the [LMSYS-Chat-1M Dataset License Agreement](https://huggingface.co/datasets/lmsys/lmsys-chat-1m#lmsys-chat-1m-dataset-license-agreement).", "extra_gated_fields": {"Name": "text", "Email": "text", "Affiliation": "text", "Country": "text"}, "extra_gated_button_content": "I agree to the terms and conditions of the LMSYS-Chat-1M Dataset License Agreement.", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "conversation_id", "dtype": "string"}, {"name": "model", "dtype": "string"}, {"name": "conversation", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "turn", "dtype": "int64"}, {"name": "language", "dtype": "string"}, {"name": "openai_moderation", "list": [{"name": "categories", "struct": [{"name": "harassment", "dtype": "bool"}, {"name": "harassment/threatening", "dtype": "bool"}, {"name": "hate", "dtype": "bool"}, {"name": "hate/threatening", "dtype": "bool"}, {"name": "self-harm", "dtype": "bool"}, {"name": "self-harm/instructions", "dtype": "bool"}, {"name": "self-harm/intent", "dtype": "bool"}, {"name": "sexual", "dtype": "bool"}, {"name": "sexual/minors", "dtype": "bool"}, {"name": "violence", "dtype": "bool"}, {"name": "violence/graphic", "dtype": "bool"}]}, {"name": "category_scores", "struct": [{"name": "harassment", "dtype": "float64"}, {"name": "harassment/threatening", "dtype": "float64"}, {"name": "hate", "dtype": "float64"}, {"name": "hate/threatening", "dtype": "float64"}, {"name": "self-harm", "dtype": "float64"}, {"name": "self-harm/instructions", "dtype": "float64"}, {"name": "self-harm/intent", "dtype": "float64"}, {"name": "sexual", "dtype": "float64"}, {"name": "sexual/minors", "dtype": "float64"}, {"name": "violence", "dtype": "float64"}, {"name": "violence/graphic", "dtype": "float64"}]}, {"name": "flagged", "dtype": "bool"}]}, {"name": "redacted", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 2626438904, "num_examples": 1000000}], "download_size": 1488850250, "dataset_size": 2626438904}}
false
null
2024-07-27T09:28:42
653
8
false
200748d9d3cddcc9d782887541057aca0b18c5da
LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset This dataset contains one million real-world conversations with 25 state-of-the-art LLMs. It is collected from 210K unique IP addresses in the wild on the Vicuna demo and Chatbot Arena website from April to August 2023. Each sample includes a conversation ID, model name, conversation text in OpenAI API JSON format, detected language tag, and OpenAI moderation API tag. User consent is obtained through the "Terms of… See the full description on the dataset page: https://huggingface.co/datasets/lmsys/lmsys-chat-1m.
3,982
222,055
[ "size_categories:1M<n<10M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2309.11998", "region:us" ]
2023-09-20T06:33:44
null
null
6564cf8ec9611f7e11423ff4
b3x0m/Chinese-H-Novels
b3x0m
{"language": ["zh"], "size_categories": ["1B<n<10B"], "task_categories": ["text-classification", "summarization", "token-classification", "text2text-generation", "question-answering", "text-generation", "fill-mask", "sentence-similarity"], "pretty_name": "H-novel-corpus", "tags": ["art"], "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 95784400372, "num_examples": 934354429}], "download_size": 60873072258, "dataset_size": 95784400372}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
false
null
2024-07-12T02:32:57
208
8
false
16258fb735f019d2d0100960ec739b6dabc3db77
Update 12/07/2024: convert to parquet to download easier. Chinese 18+ novels corpus, use at your own risk, you and only you are responsible for every choice you make. (͡ ° ͜ʖ ͡ °) tags: socks, garter belt, foot fetish, ntr, netori..... Thanks Moleys/Numeron for the dataset donation.
1,828
8,695
[ "task_categories:text-classification", "task_categories:summarization", "task_categories:token-classification", "task_categories:text2text-generation", "task_categories:question-answering", "task_categories:text-generation", "task_categories:fill-mask", "task_categories:sentence-similarity", "language:zh", "size_categories:100M<n<1B", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "art" ]
2023-11-27T17:19:10
null
null
661823b590a8b6724f1c6534
HuggingFaceM4/the_cauldron
HuggingFaceM4
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null
2024-05-06T13:37:52
394
8
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847a98a779b1652d65111daf20c972dfcd333605
Dataset Card for The Cauldron Dataset description The Cauldron is part of the Idefics2 release. It is a massive collection of 50 vision-language datasets (training sets only) that were used for the fine-tuning of the vision-language model Idefics2. Load the dataset To load the dataset, install the library datasets with pip install datasets. Then, from datasets import load_dataset ds = load_dataset("HuggingFaceM4/the_cauldron", "ai2d") to download… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceM4/the_cauldron.
850,044
1,866,872
[ "size_categories:1M<n<10M", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:1603.07396", "arxiv:2206.01718", "arxiv:2208.05358", "arxiv:1612.06890", "arxiv:2310.00367", "arxiv:1710.07300", "arxiv:2312.12241", "arxiv:1912.03098", "arxiv:2211.08545", "arxiv:2306.05425", "arxiv:1709.00103", "arxiv:2003.12462", "arxiv:1612.00837", "arxiv:2205.00363", "arxiv:2403.09029", "arxiv:2405.02246", "region:us" ]
2024-04-11T17:53:57
null
null
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