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67edf568d1631250f17528af | open-thoughts/OpenThoughts2-1M | open-thoughts | {"dataset_info": {"features": [{"name": "conversations", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "question", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 18986223337, "num_examples": 1143205}], "download_size": 8328411205, "dataset_size": 18986223337}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "tags": ["synthetic", "curator"], "license": "apache-2.0"} | false | null | 2025-04-07T21:40:23 | 60 | 60 | false | 40766050d883e0aa951fd3ddee33faf3ad83f26b |
OpenThoughts2-1M
Open synthetic reasoning dataset with 1M high-quality examples covering math, science, code, and puzzles!
OpenThoughts2-1M builds upon our previous OpenThoughts-114k dataset, augmenting it with existing datasets like OpenR1, as well as additional math and code reasoning data.
This dataset was used to train OpenThinker2-7B and OpenThinker2-32B.
Inspect the content with rich formatting and search & filter capabilities in Curator Viewer.
See our blog post… See the full description on the dataset page: https://huggingface.co/datasets/open-thoughts/OpenThoughts2-1M. | 3,469 | 3,469 | [
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] | 2025-04-03T02:41:44 | null | null |
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 | 333 | 54 | 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. | 13,549 | 13,558 | [
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"region:us"
] | 2025-03-13T21:01:09 | null | null |
67ec47948647cfa17739af7a | nvidia/OpenCodeReasoning | nvidia | {"license": "cc-by-4.0", "size_categories": ["100K<n<1M"], "pretty_name": "OpenCodeReasoning", "dataset_info": [{"config_name": "split_0", "features": [{"name": "id", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "license", "dtype": "string"}, {"name": "dataset", "dtype": "string"}, {"name": "split", "dtype": "string"}, {"name": "difficulty", "dtype": "string"}, {"name": "solution", "dtype": "string"}], "splits": [{"name": "split_0", "num_bytes": 28108469190, "num_examples": 567850}]}, {"config_name": "split_1", "features": [{"name": "id", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "license", "dtype": "string"}, {"name": "dataset", "dtype": "string"}, {"name": "split", "dtype": "string"}, {"name": "difficulty", "dtype": "string"}, {"name": "solution"}, {"name": "index", "dtype": "string"}], "splits": [{"name": "split_1", "num_bytes": 4722811278, "num_examples": 167405}]}], "configs": [{"config_name": "split_0", "data_files": [{"split": "split_0", "path": "split_0/train-*"}]}, {"config_name": "split_1", "data_files": [{"split": "split_1", "path": "split_1/train-*"}]}], "task_categories": ["text-generation"], "tags": ["synthetic"]} | false | null | 2025-04-07T18:22:47 | 46 | 46 | false | 483a88186bc78293f715e0a9f06bc11a37eb6b06 |
OpenCodeReasoning: Advancing Data Distillation for Competitive Coding
Data Overview
OpenCodeReasoning is the largest reasoning-based synthetic dataset to date for coding, comprises 735,255 samples in Python across 28,319 unique competitive programming
questions. OpenCodeReasoning is designed for supervised fine-tuning (SFT).
Technical Report - Discover the methodology and technical details behind OpenCodeReasoning.
Github Repo - Access the complete pipeline used to… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/OpenCodeReasoning. | 238 | 238 | [
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] | 2025-04-01T20:07:48 | null | null |
67ea45bbcb39affecc10763e | virtuoussy/Multi-subject-RLVR | virtuoussy | {"license": "apache-2.0", "task_categories": ["question-answering"], "language": ["en"]} | false | null | 2025-04-02T10:29:40 | 44 | 44 | false | 5be8ffa52bf3ccbfe0d4f601ddee1183cb1be0ab | 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. | 654 | 654 | [
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] | 2025-03-31T07:35:23 | 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 | 619 | 42 | 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. | 22,399 | 52,658 | [
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] | 2024-12-28T03:29:08 | 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 | 173 | 30 | 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. | 10,728 | 10,842 | [
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] | 2025-02-27T20:40:05 | 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 | 108 | 24 | 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. | 10,120 | 10,120 | [
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"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 |
67e9a644ea97f3c65c463bfb | LLM360/MegaMath | LLM360 | {"license": "odc-by", "task_categories": ["text-generation"], "language": ["en"], "tags": ["math", "code", "pre-training", "synthesis"], "size_categories": ["1B<n<10B"]} | false | null | 2025-04-04T14:04:23 | 24 | 23 | false | b2dbbfdb0bb40f8f5893b4057c6f5f430ae34d35 |
MegaMath: Pushing the Limits of Open Math Copora
Megamath is part of TxT360, curated by LLM360 Team.
We introduce MegaMath, an open math pretraining dataset curated from diverse, math-focused sources, with over 300B tokens.
MegaMath is curated via the following three efforts:
Revisiting web data:
We re-extracted mathematical documents from Common Crawl with math-oriented HTML optimizations, fasttext-based filtering and deduplication, all for acquiring higher-quality data on the… See the full description on the dataset page: https://huggingface.co/datasets/LLM360/MegaMath. | 547 | 547 | [
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"region:us",
"math",
"code",
"pre-training",
"synthesis"
] | 2025-03-30T20:15:00 | null | 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 | 7,670 | 20 | false | 68ba7694e23014788dcc8ab5afe613824f45a05c | 🧠 Awesome ChatGPT Prompts [CSV dataset]
This is a Dataset Repository of Awesome ChatGPT Prompts
View All Prompts on GitHub
License
CC-0
| 10,623 | 141,435 | [
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"region:us",
"ChatGPT"
] | 2022-12-13T23:47:45 | 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 | 254 | 20 | 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. | 3,697 | 10,684 | [
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"region:us",
"AI",
"LLM",
"Economic Impacts",
"Anthropic"
] | 2025-02-06T00:39:24 | 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 | 28 | 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. | 884 | 884 | [
"task_categories:automatic-speech-recognition",
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"modality:audio",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-03-30T09:12:51 | 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 | 486 | 15 | 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. | 9,955 | 18,137 | [
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"arxiv:2502.13124",
"region:us"
] | 2025-01-30T23:29:32 | 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 | 48 | 15 | 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. | 3,671 | 3,767 | [
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] | 2025-03-24T10:32:37 | null | null |
67e9eb451ba052dc29fd90f8 | camel-ai/loong | camel-ai | {"authors": ["camel-ai"], "description": "A comprehensive collection of 3,551 high-quality problems across 8 diverse domains, curated for Project Loong. Each problem includes a detailed executable rationale and solution, designed for training and evaluating reasoning models.", "language": ["en"], "license": "mit", "pretty_name": "camel-ai/loong", "tags": ["reasoning", "problem-solving", "project-loong", "multi-domain", "mathematics", "physics", "finance", "optimization"], "task_categories": ["question-answering"], "configs": [{"config_name": "default", "data_files": [{"split": "advanced_physics", "path": "data/advanced_physics-*"}, {"split": "graph_discrete_math", "path": "data/graph_discrete_math-*"}, {"split": "computational_biology", "path": "data/computational_biology-*"}, {"split": "logic", "path": "data/logic-*"}, {"split": "security_and_safety", "path": "data/security_and_safety-*"}, {"split": "advanced_math", "path": "data/advanced_math-*"}, {"split": "finance", "path": "data/finance-*"}, {"split": "mathematical_programming", "path": "data/mathematical_programming-*"}]}], "dataset_info": {"features": [{"name": "source_type", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "rationale", "dtype": "string"}, {"name": "final_answer", "dtype": "string"}, {"name": "meta_data", "dtype": "string"}], "splits": [{"name": "advanced_physics", "num_bytes": 829991.8175161927, "num_examples": 434}, {"name": "graph_discrete_math", "num_bytes": 342323.8141368629, "num_examples": 179}, {"name": "computational_biology", "num_bytes": 581376.7569698676, "num_examples": 304}, {"name": "logic", "num_bytes": 210366.58969304422, "num_examples": 110}, {"name": "security_and_safety", "num_bytes": 996372.6657279639, "num_examples": 521}, {"name": "advanced_math", "num_bytes": 3088564.021402422, "num_examples": 1615}, {"name": "finance", "num_bytes": 611975.5336524922, "num_examples": 320}, {"name": "mathematical_programming", "num_bytes": 130044.80090115461, "num_examples": 68}], "download_size": 2447494, "dataset_size": 6791016.000000001}} | false | null | 2025-04-01T22:04:20 | 16 | 13 | false | 74cadda690866a8b60cbc31e801fba5f173cb392 |
Additional Information
Project Loong Seed Dataset
This dataset is part of Project Loong, a collaborative effort to explore whether reasoning-capable models can bootstrap themselves from small, high-quality seed datasets by generating synthetic data and verifying LLM agent responses.
Dataset Description
This comprehensive collection contains 3,551 human-vetted problems across 8 diverse domains:
🧮 Advanced Math: 1,615 questions
⚛️ Advanced Physics: 434… See the full description on the dataset page: https://huggingface.co/datasets/camel-ai/loong. | 562 | 562 | [
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"region:us",
"reasoning",
"problem-solving",
"project-loong",
"multi-domain",
"mathematics",
"physics",
"finance",
"optimization"
] | 2025-03-31T01:09:25 | null | null |
67c03fd6b9fe27a2ac49784d | open-r1/codeforces-cots | open-r1 | {"dataset_info": [{"config_name": "checker_interactor", "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"}, {"name": "prompt", 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"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 | 129 | 12 | 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. | 11,061 | 11,147 | [
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"library:mlcroissant",
"library:polars",
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] | 2025-02-27T10:35:02 | 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 | 678 | 11 | 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. | 347,121 | 4,357,711 | [
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] | 2022-04-12T10:22:10 | gsm8k | null |
665eaefe5baf7febc7207877 | OOPPEENN/Galgame_Dataset | OOPPEENN | {"license": "gpl-3.0"} | false | null | 2025-04-07T15:26:37 | 125 | 11 | false | 01bf42028b4004dca1ada6dcd3c01ce03dfe1870 |
0x0 使用协议：
必须遵守GNU General Public License v3.0内的所有协议！附加：禁止商用，本数据集以及使用本数据集训练出来的任何模型都不得用于任何商业行为，如要用于商业用途，请找数据列表内的所有厂商授权（笑），因违反开源协议而出现的任何问题都与本人无关！
训练出来的模型必须开源，是否在README内引用本数据集由训练者自主决定，不做强制要求。
0x1 数据说明：
解压密码：9ll9Ke4iq0jqyq3gS1Wy。
标注说明：标注，说话人和对应的音频是直接读游戏引擎的脚本生成的，应该是100%准确率，全部存放在index.json里面，如果还有错误可以在开issues反馈（有些遗漏的控制符可能没洗干净）。
务必根据index.json里面的键值对找音频，不在index内的音频请直接丢弃，如果对应的任务需要区分说话人，那么说话人为？？？的请直接丢弃。
数据语言：日语（100%）
数据时长：8823h 22m 07s
角色总数：25387人（未合并）… See the full description on the dataset page: https://huggingface.co/datasets/OOPPEENN/Galgame_Dataset. | 4,400 | 23,710 | [
"license:gpl-3.0",
"region:us"
] | 2024-06-04T06:06:54 | null | null |
621ffdd236468d709f181e5e | cais/mmlu | cais | {"annotations_creators": ["no-annotation"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["question-answering"], "task_ids": ["multiple-choice-qa"], "paperswithcode_id": "mmlu", "pretty_name": "Measuring Massive Multitask Language Understanding", "language_bcp47": ["en-US"], "dataset_info": [{"config_name": "abstract_algebra", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 49618.6654322746, "num_examples": 100}, {"name": "validation", "num_bytes": 5485.515349444808, "num_examples": 11}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], 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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. | 126,411 | 37,225,247 | [
"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 |
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🍷 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. | 186,008 | 2,373,802 | [
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] | 2024-04-18T14:33:13 | null | null |
67aa648e91e6f5eb545e854e | allenai/olmOCR-mix-0225 | allenai | {"license": "odc-by", "configs": [{"config_name": "00_documents", "data_files": [{"split": "train_s2pdf", "path": ["train-s2pdf.parquet"]}, {"split": "eval_s2pdf", "path": ["eval-s2pdf.parquet"]}]}, {"config_name": "01_books", "data_files": [{"split": "train_iabooks", "path": ["train-iabooks.parquet"]}, {"split": "eval_iabooks", "path": ["eval-iabooks.parquet"]}]}]} | false | null | 2025-02-25T09:36:14 | 110 | 10 | false | a602926844ed47c43439627fd16d3de45b39e494 |
olmOCR-mix-0225
olmOCR-mix-0225 is a dataset of ~250,000 PDF pages which have been OCRed into plain-text in a natural reading order using gpt-4o-2024-08-06 and a special
prompting strategy that preserves any born-digital content from each page.
This dataset can be used to train, fine-tune, or evaluate your own OCR document pipeline.
Quick links:
📃 Paper
🤗 Model
🛠️ Code
🎮 Demo
Data Mix
Table 1: Training set composition by source
Source
Unique… See the full description on the dataset page: https://huggingface.co/datasets/allenai/olmOCR-mix-0225. | 3,581 | 6,323 | [
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] | 2025-02-10T20:41:50 | 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 | 72 | 10 | 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. | 94 | 94 | [
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] | 2025-03-18T09:13:50 | null | null |
639244f571c51c43091df168 | Anthropic/hh-rlhf | Anthropic | {"license": "mit", "tags": ["human-feedback"]} | false | null | 2023-05-26T18:47:34 | 1,313 | 9 | 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. | 12,923 | 1,564,283 | [
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] | 2022-12-08T20:11:33 | null | null |
67ecdf7e693ef0b1e0d7a06b | a-m-team/AM-Math-Difficulty-RL | a-m-team | {"license": "cc-by-nc-4.0", "task_categories": ["text-generation"], "language": ["en"], "tags": ["math"], "size_categories": ["100K<n<1M"]} | false | null | 2025-04-02T08:39:29 | 9 | 9 | false | 32540e9bce5952736795ac78cf049a0757f601d3 | For more open-source datasets, models, and methodologies, please visit our GitHub repository.
We believe that the selection of training data for reinforcement learning is crucial.
To validate this, we conducted several experiments exploring how data difficulty influences training performance.
Our data sources originate from numerous excellent open-source projects, and we sincerely appreciate their contributions, without which our current achievements would not have been possible.… See the full description on the dataset page: https://huggingface.co/datasets/a-m-team/AM-Math-Difficulty-RL. | 232 | 232 | [
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"math"
] | 2025-04-02T06:55:58 | null | null |
64035e3d723a03e62696f152 | biglam/european_art | biglam | {"dataset_info": [{"config_name": "coco", "features": [{"name": "image", "dtype": "image"}, {"name": "source", "dtype": "string"}, {"name": "width", "dtype": "int16"}, {"name": "height", "dtype": "int16"}, {"name": "dept", "dtype": "int8"}, {"name": "segmented", "dtype": "int8"}, {"name": "objects", "list": [{"name": "category_id", "dtype": {"class_label": {"names": {"0": "zebra", "1": "tree", "2": "nude", "3": "crucifixion", "4": "scroll", "5": "head", "6": "swan", "7": "shield", "8": "lily", "9": "mouse", "10": "knight", "11": "dragon", "12": "horn", "13": "dog", "14": "palm", "15": "tiara", "16": "helmet", "17": "sheep", "18": "deer", "19": "person", "20": "sword", "21": "rooster", "22": "bear", "23": "halo", "24": "lion", "25": "monkey", "26": "prayer", "27": "crown of thorns", "28": "elephant", "29": "zucchetto", "30": "unicorn", "31": "holy shroud", "32": "cat", "33": "apple", "34": "banana", "35": "chalice", "36": "bird", "37": "eagle", "38": "pegasus", "39": "crown", "40": "camauro", "41": "saturno", "42": "arrow", "43": "dove", "44": "centaur", "45": "horse", "46": "hands", "47": "skull", "48": "orange", "49": "monk", "50": "trumpet", "51": "key of heaven", "52": "fish", "53": "cow", "54": "angel", "55": "devil", "56": "book", "57": "stole", "58": "butterfly", "59": "serpent", "60": "judith", "61": "mitre", "62": "banner", "63": "donkey", "64": "shepherd", "65": "boat", "66": "god the father", "67": "crozier", "68": "jug", "69": "lance"}}}}, {"name": "image_id", "dtype": "string"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "segmentation", "list": {"list": "float32"}}, {"name": "iscrowd", "dtype": "bool"}]}, {"name": "image_id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 8285204, "num_examples": 15156}], "download_size": 18160510195, "dataset_size": 8285204}, {"config_name": "default", "features": [{"name": "image", "dtype": "image"}, {"name": "file_id", "dtype": "string"}, {"name": "annotations", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 18197594657, "num_examples": 15154}], "download_size": 18151946901, "dataset_size": 18197594657}, {"config_name": "raw", "features": [{"name": "image", "dtype": "image"}, {"name": "source", "dtype": "string"}, {"name": "width", "dtype": "int16"}, {"name": "height", "dtype": "int16"}, {"name": "dept", "dtype": "int8"}, {"name": "segmented", "dtype": "int8"}, {"name": "objects", "list": [{"name": "name", "dtype": {"class_label": {"names": {"0": "zebra", "1": "tree", "2": "nude", "3": "crucifixion", "4": "scroll", "5": "head", "6": "swan", "7": "shield", "8": "lily", "9": "mouse", "10": "knight", "11": "dragon", "12": "horn", "13": "dog", "14": "palm", "15": "tiara", "16": "helmet", "17": "sheep", "18": "deer", "19": "person", "20": "sword", "21": "rooster", "22": "bear", "23": "halo", "24": "lion", "25": "monkey", "26": "prayer", "27": "crown of thorns", "28": "elephant", "29": "zucchetto", "30": "unicorn", "31": "holy shroud", "32": "cat", "33": "apple", "34": "banana", "35": "chalice", "36": "bird", "37": "eagle", "38": "pegasus", "39": "crown", "40": "camauro", "41": "saturno", "42": "arrow", "43": "dove", "44": "centaur", "45": "horse", "46": "hands", "47": "skull", "48": "orange", "49": "monk", "50": "trumpet", "51": "key of heaven", "52": "fish", "53": "cow", "54": "angel", "55": "devil", "56": "book", "57": "stole", "58": "butterfly", "59": "serpent", "60": "judith", "61": "mitre", "62": "banner", "63": "donkey", "64": "shepherd", "65": "boat", "66": "god the father", "67": "crozier", "68": "jug", "69": "lance"}}}}, {"name": "pose", "dtype": {"class_label": {"names": {"0": "stand", "1": "sit", "2": "partial", "3": "Unspecified", "4": "squats", "5": "lie", "6": "bend", "7": "fall", "8": "walk", "9": "push", "10": "pray", "11": "undefined", "12": "kneel", "13": "unrecognize", "14": "unknown", "15": "other", "16": "ride"}}}}, {"name": "diffult", "dtype": "int32"}, {"name": "xmin", "dtype": "float64"}, {"name": "ymin", "dtype": "float64"}, {"name": "xmax", "dtype": "float64"}, {"name": "ymax", "dtype": "float64"}]}], "splits": [{"name": "train", "num_bytes": 9046918, "num_examples": 15156}], "download_size": 18160510195, "dataset_size": 9046918}], "license": "cc-by-nc-2.0", "task_categories": ["object-detection", "image-classification"], "tags": ["lam", "art", "historical"], "pretty_name": "DEArt: Dataset of European Art", "size_categories": ["10K<n<100K"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | false | null | 2025-03-31T18:04:12 | 16 | 8 | false | f00afe1c164f7d1d9819e3b55b1fe693e4cfa91c |
Dataset Card for DEArt: Dataset of European Art
Dataset Summary
DEArt is an object detection and pose classification dataset meant to be a reference for paintings between the XIIth and the XVIIIth centuries. It contains more than 15000 images, about 80% non-iconic, aligned with manual annotations for the bounding boxes identifying all instances of 69 classes as well as 12 possible poses for boxes identifying human-like objects. Of these, more than 50 classes are cultural… See the full description on the dataset page: https://huggingface.co/datasets/biglam/european_art. | 770 | 1,274 | [
"task_categories:object-detection",
"task_categories:image-classification",
"license:cc-by-nc-2.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2211.01226",
"region:us",
"lam",
"art",
"historical"
] | 2023-03-04T15:05:33 | null | null |
660e7b9b4636ce2b0e77b699 | mozilla-foundation/common_voice_17_0 | mozilla-foundation | {"pretty_name": "Common Voice Corpus 17.0", "annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["ab", "af", "am", "ar", "as", "ast", "az", "ba", "bas", "be", "bg", "bn", "br", "ca", "ckb", "cnh", "cs", "cv", "cy", "da", "de", "dv", "dyu", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fy", "ga", "gl", "gn", "ha", "he", "hi", "hsb", "ht", "hu", "hy", "ia", "id", "ig", "is", "it", "ja", "ka", "kab", "kk", "kmr", "ko", "ky", "lg", "lij", "lo", "lt", "ltg", "lv", "mdf", "mhr", "mk", "ml", "mn", "mr", "mrj", "mt", "myv", "nan", "ne", "nhi", "nl", "nn", "nso", "oc", "or", "os", "pa", "pl", "ps", "pt", "quy", "rm", "ro", "ru", "rw", "sah", "sat", "sc", "sk", "skr", "sl", "sq", "sr", "sv", "sw", "ta", "te", "th", "ti", "tig", "tk", "tok", "tr", "tt", "tw", "ug", "uk", "ur", "uz", "vi", "vot", "yi", "yo", "yue", "zgh", "zh", "zu", "zza"], "language_bcp47": ["zh-CN", "zh-HK", "zh-TW", "sv-SE", "rm-sursilv", "rm-vallader", "pa-IN", "nn-NO", "ne-NP", "nan-tw", "hy-AM", "ga-IE", "fy-NL"], "license": ["cc0-1.0"], "multilinguality": ["multilingual"], "source_datasets": ["extended|common_voice"], "paperswithcode_id": "common-voice", "extra_gated_prompt": "By clicking on \u201cAccess repository\u201d below, you also agree to not attempt to determine the identity of speakers in the Common Voice dataset."} | false | null | 2024-06-16T13:50:23 | 250 | 8 | false | b10d53980ef166bc24ce3358471c1970d7e6b5ec |
Dataset Card for Common Voice Corpus 17.0
Dataset Summary
The Common Voice dataset consists of a unique MP3 and corresponding text file.
Many of the 31175 recorded hours in the dataset also include demographic metadata like age, sex, and accent
that can help improve the accuracy of speech recognition engines.
The dataset currently consists of 20408 validated hours in 124 languages, but more voices and languages are always added.
Take a look at the Languages page to… See the full description on the dataset page: https://huggingface.co/datasets/mozilla-foundation/common_voice_17_0. | 38,795 | 460,365 | [
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"source_datasets:extended|common_voice",
"language:ab",
"language:af",
"language:am",
"language:ar",
"language:as",
"language:ast",
"language:az",
"language:ba",
"language:bas",
"language:be",
"language:bg",
"language:bn",
"language:br",
"language:ca",
"language:ckb",
"language:cnh",
"language:cs",
"language:cv",
"language:cy",
"language:da",
"language:de",
"language:dv",
"language:dyu",
"language:el",
"language:en",
"language:eo",
"language:es",
"language:et",
"language:eu",
"language:fa",
"language:fi",
"language:fr",
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"language:ga",
"language:gl",
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"language:hi",
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"language:hy",
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"language:mdf",
"language:mhr",
"language:mk",
"language:ml",
"language:mn",
"language:mr",
"language:mrj",
"language:mt",
"language:myv",
"language:nan",
"language:ne",
"language:nhi",
"language:nl",
"language:nn",
"language:nso",
"language:oc",
"language:or",
"language:os",
"language:pa",
"language:pl",
"language:ps",
"language:pt",
"language:quy",
"language:rm",
"language:ro",
"language:ru",
"language:rw",
"language:sah",
"language:sat",
"language:sc",
"language:sk",
"language:skr",
"language:sl",
"language:sq",
"language:sr",
"language:sv",
"language:sw",
"language:ta",
"language:te",
"language:th",
"language:ti",
"language:tig",
"language:tk",
"language:tok",
"language:tr",
"language:tt",
"language:tw",
"language:ug",
"language:uk",
"language:ur",
"language:uz",
"language:vi",
"language:vot",
"language:yi",
"language:yo",
"language:yue",
"language:zgh",
"language:zh",
"language:zu",
"language:zza",
"license:cc0-1.0",
"size_categories:10M<n<100M",
"modality:audio",
"modality:text",
"library:datasets",
"library:mlcroissant",
"arxiv:1912.06670",
"region:us"
] | 2024-04-04T10:06:19 | common-voice | @inproceedings{commonvoice:2020,
author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.},
title = {Common Voice: A Massively-Multilingual Speech Corpus},
booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)},
pages = {4211--4215},
year = 2020
} |
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