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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 | 445 | 78 | 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. | 27,937 | 34,287 | [
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"library:polars",
"arxiv:2412.18925",
"region:us",
"medical",
"biology"
] | 2024-12-28T03:29:08 | 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 | 398 | 71 | 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. | 10,721 | 10,721 | [
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] | 2025-01-30T23:29:32 | 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 | 522 | 47 | 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. | 7,740 | 7,740 | [
"task_categories:text-generation",
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"library:polars",
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] | 2025-02-17T11:45:09 | null | null |
6532270e829e1dc2f293d6b8 | gaia-benchmark/GAIA | gaia-benchmark | {"language": ["en"], "pretty_name": "General AI Assistants Benchmark", "extra_gated_prompt": "To avoid contamination and data leakage, you agree to not reshare this dataset outside of a gated or private repository on the HF hub.", "extra_gated_fields": {"I agree to not reshare the GAIA submissions set according to the above conditions": "checkbox"}} | false | null | 2025-02-13T08:36:12 | 258 | 33 | false | 897f2dfbb5c952b5c3c1509e648381f9c7b70316 |
GAIA dataset
GAIA is a benchmark which aims at evaluating next-generation LLMs (LLMs with augmented capabilities due to added tooling, efficient prompting, access to search, etc).
We added gating to prevent bots from scraping the dataset. Please do not reshare the validation or test set in a crawlable format.
Data and leaderboard
GAIA is made of more than 450 non-trivial question with an unambiguous answer, requiring different levels of tooling and autonomy to solve. It… See the full description on the dataset page: https://huggingface.co/datasets/gaia-benchmark/GAIA. | 8,939 | 30,531 | [
"language:en",
"arxiv:2311.12983",
"region:us"
] | 2023-10-20T07:06:54 | null |
|
67c248d12a6f7c1f2a448ee4 | KodCode/KodCode-V1 | KodCode | {"language": ["en"], "license": "cc-by-nc-4.0", "dataset_info": {"features": [{"name": "style", "dtype": "string"}, {"name": "subset", "dtype": "string"}, {"name": "question_id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "solution", "dtype": "string"}, {"name": "test_code", "dtype": "string"}, {"name": "test_info", "list": [{"name": "docstring", "dtype": "string"}, {"name": "function_declaration", "dtype": "string"}, {"name": "function_name", "dtype": "string"}, {"name": "parameter_list", "dtype": "string"}]}, {"name": "gpt_pass_sequence", "sequence": "int64"}, {"name": "gpt_pass_trial_num", "dtype": "int64"}, {"name": "gpt_difficulty", "dtype": "string"}, {"name": "gpt_pass_percentage", "dtype": "float64"}, {"name": "trials", "struct": [{"name": "trial_gpt4o_0", "struct": [{"name": "solution_code", "dtype": "string"}, {"name": "test_code", "dtype": "string"}, {"name": "test_result", "dtype": "string"}, {"name": "test_coverage", "dtype": "float64"}, {"name": "file_source", "dtype": "string"}]}, {"name": "trial_gpt4o_1", "struct": [{"name": "solution_code", "dtype": "string"}, {"name": "test_code", "dtype": "string"}, {"name": "test_result", "dtype": "string"}, {"name": "test_coverage", "dtype": "float64"}, {"name": "file_source", "dtype": "string"}]}, {"name": "trial_gpt4o_2", "struct": [{"name": "solution_code", "dtype": "string"}, {"name": "test_code", "dtype": "string"}, {"name": "test_result", "dtype": "string"}, {"name": "test_coverage", "dtype": "float64"}, {"name": "file_source", "dtype": "string"}]}, {"name": "trial_gpt4o_3", "struct": [{"name": "solution_code", "dtype": "string"}, {"name": "test_code", "dtype": "string"}, {"name": "test_result", "dtype": "string"}, {"name": "test_coverage", "dtype": "float64"}, {"name": "file_source", "dtype": "string"}]}, {"name": "trial_gpt4o_4", "struct": [{"name": "solution_code", "dtype": "string"}, {"name": "test_code", "dtype": "string"}, {"name": "test_result", "dtype": "string"}, {"name": "test_coverage", "dtype": "float64"}, {"name": "file_source", "dtype": "string"}]}, {"name": "trial_gpt4o_5", "struct": [{"name": "solution_code", "dtype": "string"}, {"name": "test_code", "dtype": "string"}, {"name": "test_result", "dtype": "string"}, {"name": "test_coverage", "dtype": "float64"}, {"name": "file_source", "dtype": "string"}]}, {"name": "trial_gpt4o_6", "struct": [{"name": "solution_code", "dtype": "string"}, {"name": "test_code", "dtype": "string"}, {"name": "test_result", "dtype": "string"}, {"name": "test_coverage", "dtype": "float64"}, {"name": "file_source", "dtype": "string"}]}, {"name": "trial_gpt4o_7", "struct": [{"name": "solution_code", "dtype": "string"}, {"name": "test_code", "dtype": "string"}, {"name": "test_result", "dtype": "string"}, {"name": "test_coverage", "dtype": "float64"}, {"name": "file_source", "dtype": "string"}]}, {"name": "trial_gpt4o_8", "struct": [{"name": "solution_code", "dtype": "string"}, {"name": "test_code", "dtype": "string"}, {"name": "test_result", "dtype": "string"}, {"name": "test_coverage", "dtype": "float64"}, {"name": "file_source", "dtype": "string"}]}, {"name": "trial_gpt4o_9", "struct": [{"name": "solution_code", "dtype": "string"}, {"name": "test_code", "dtype": "string"}, {"name": "test_result", "dtype": "string"}, {"name": "test_coverage", "dtype": "float64"}, {"name": "file_source", "dtype": "string"}]}]}, {"name": "chosen_trial", "dtype": "string"}, {"name": "metadata", "struct": [{"name": "original_instruction", "dtype": "string"}, {"name": "prompt_id", "dtype": "string"}, {"name": "row_id", "dtype": "int64"}, {"name": "seed_ids", "dtype": "string"}]}, {"name": "benchmark_similarity", "dtype": "float64"}, {"name": "benchmark_instruction", "dtype": "string"}, {"name": "benchmark_task_id", "dtype": "string"}, {"name": "filter_reason", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6936635744, "num_examples": 443543}, {"name": "use_with_caution", "num_bytes": 59596328, "num_examples": 3335}], "download_size": 2472949876, "dataset_size": 6996232072}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "use_with_caution", "path": "data/use_with_caution-*"}]}]} | false | null | 2025-03-09T20:50:44 | 63 | 31 | false | bf6420bb3cffcc0d91facb422048d9fc40d23235 |
🐱 KodCode: A Diverse, Challenging, and Verifiable Synthetic Dataset for Coding
KodCode is the largest fully-synthetic open-source dataset providing verifiable solutions and tests for coding tasks. It contains 12 distinct subsets spanning various domains (from algorithmic to package-specific knowledge) and difficulty levels (from basic coding exercises to interview and competitive programming challenges). KodCode is designed for both supervised fine-tuning (SFT) and RL tuning.
🕸️… See the full description on the dataset page: https://huggingface.co/datasets/KodCode/KodCode-V1. | 3,021 | 3,021 | [
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] | 2025-02-28T23:37:53 | 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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"train", "path": "solutions_decontaminated/train-*"}]}, {"config_name": "solutions_py", "data_files": [{"split": "train", "path": "solutions_py/train-*"}]}, {"config_name": "solutions_w_editorials", "data_files": [{"split": "train", "path": "solutions_w_editorials/train-*"}]}, {"config_name": "solutions_w_editorials_py", "data_files": [{"split": "train", "path": "solutions_w_editorials_py/train-*"}]}, {"config_name": "test_input_generator", "data_files": [{"split": "train", "path": "test_input_generator/train-*"}]}, {"config_name": "checker_interactor", "data_files": [{"split": "train", "path": "checker_interactor/train-*"}]}], "license": "cc-by-4.0"} | false | null | 2025-03-13T14:50:43 | 28 | 28 | false | 177480d489039128dca71d312849c0526482df60 |
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 four 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. | 783 | 783 | [
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] | 2025-02-27T10:35:02 | null | null |
67c9ec5572b8f1776ef7f0d4 | madrylab/gsm8k-platinum | madrylab | {"language": ["en"], "license": "mit", "dataset_info": {"config_name": "main", "features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "cleaning_status", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 663954, "num_examples": 1209}], "download_size": 380973, "dataset_size": 663954}, "configs": [{"config_name": "main", "data_files": [{"split": "test", "path": "main/test-*"}]}], "task_categories": ["text2text-generation"], "tags": ["math-word-problems"], "size_categories": ["1K<n<10K"]} | false | null | 2025-03-11T14:48:29 | 28 | 26 | false | e762492455a1cf7967de89f05b6bef72fc713b66 |
Dataset Card for GSM8K-Platinum
🏆 Homepage | 📣 Blog | 🖥️ Code | 📖 Paper | 🔍 Error Viewer
Dataset Summary
GSM8K-Platinum is a revised version of the full test set of GSM8K (Grade School Math 8K), a dataset of grade school math word problems, providing a more accurate assessment of mathematical reasoning capabilities
To revise this dataset, we ran a variety of frontier models each individual example and manually examined any example for which at least one… See the full description on the dataset page: https://huggingface.co/datasets/madrylab/gsm8k-platinum. | 496 | 496 | [
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"math-word-problems"
] | 2025-03-06T18:41:25 | null | null |
67cbdbee416daf2ed9475ea4 | SmallDoge/SmallThoughts | SmallDoge | {"dataset_info": {"features": [{"name": "problem", "dtype": "string"}, {"name": "solution", "dtype": "string"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "system_prompt", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 207497599, "num_examples": 50000}, {"name": "test", "num_bytes": 4533192, "num_examples": 1000}], "download_size": 82841801, "dataset_size": 212030791}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "license": "apache-2.0", "task_categories": ["question-answering", "text-generation"], "language": ["en"], "tags": ["biology", "code", "chemistry", "synthetic"], "size_categories": ["10K<n<100K"]} | false | null | 2025-03-13T02:38:43 | 26 | 26 | false | 14d9b57c32c0ba6d2b90fac7bd5390086b64672f |
SmallThoughts
Open synthetic reasoning dataset, covering math, science, code, and puzzles.
To address the issue of the existing DeepSeek R1 distilled data being too long, this dataset constrains the reasoning trajectory to be more precise and concise while retaining the reflective nature.
We also open-sourced the pipeline code for distilled data here, with just one command you can generate your own dataset.
How to use
You can load… See the full description on the dataset page: https://huggingface.co/datasets/SmallDoge/SmallThoughts. | 1,126 | 1,126 | [
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"biology",
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] | 2025-03-08T05:55:58 | null | null |
67aa021ced8d8663d42505cc | open-r1/OpenR1-Math-220k | open-r1 | {"license": "apache-2.0", "language": ["en"], "configs": [{"config_name": "all", "data_files": [{"split": "train", "path": "all/train-*"}]}, {"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}, {"config_name": "extended", "data_files": [{"split": "train", "path": "extended/train-*"}]}], "dataset_info": [{"config_name": "all", "features": [{"name": "problem", "dtype": "string"}, {"name": "solution", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "problem_type", "dtype": "string"}, {"name": "question_type", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "uuid", "dtype": "string"}, {"name": "is_reasoning_complete", "sequence": "bool"}, {"name": "generations", "sequence": "string"}, {"name": "correctness_math_verify", "sequence": "bool"}, {"name": "correctness_llama", "sequence": "bool"}, {"name": "finish_reasons", "sequence": "string"}, {"name": "correctness_count", "dtype": "int64"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 9734110026, "num_examples": 225129}], "download_size": 4221672067, "dataset_size": 9734110026}, {"config_name": "default", "features": [{"name": "problem", "dtype": "string"}, {"name": "solution", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "problem_type", "dtype": "string"}, {"name": "question_type", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "uuid", "dtype": "string"}, {"name": "is_reasoning_complete", "sequence": "bool"}, {"name": "generations", "sequence": "string"}, {"name": "correctness_math_verify", "sequence": "bool"}, {"name": "correctness_llama", "sequence": "bool"}, {"name": "finish_reasons", "sequence": "string"}, {"name": "correctness_count", "dtype": "int64"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 4964543659, "num_examples": 93733}], "download_size": 2149897914, "dataset_size": 4964543659}, {"config_name": "extended", "features": [{"name": "problem", "dtype": "string"}, {"name": "solution", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "problem_type", "dtype": "string"}, {"name": "question_type", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "uuid", "dtype": "string"}, {"name": "is_reasoning_complete", "sequence": "bool"}, {"name": "generations", "sequence": "string"}, {"name": "correctness_math_verify", "sequence": "bool"}, {"name": "correctness_llama", "sequence": "bool"}, {"name": "finish_reasons", "sequence": "string"}, {"name": "correctness_count", "dtype": "int64"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 4769566550, "num_examples": 131396}], "download_size": 2063936457, "dataset_size": 4769566550}]} | false | null | 2025-02-18T11:45:27 | 491 | 24 | false | e4e141ec9dea9f8326f4d347be56105859b2bd68 |
OpenR1-Math-220k
Dataset description
OpenR1-Math-220k is a large-scale dataset for mathematical reasoning. It consists of 220k math problems with two to four reasoning traces generated by DeepSeek R1 for problems from NuminaMath 1.5.
The traces were verified using Math Verify for most samples and Llama-3.3-70B-Instruct as a judge for 12% of the samples, and each problem contains at least one reasoning trace with a correct answer.
The dataset consists of two splits:… See the full description on the dataset page: https://huggingface.co/datasets/open-r1/OpenR1-Math-220k. | 53,049 | 53,073 | [
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"library:polars",
"region:us"
] | 2025-02-10T13:41:48 | null | null |
67b78333f663232795e6cb29 | SynthLabsAI/Big-Math-RL-Verified | SynthLabsAI | {"dataset_info": {"features": [{"name": "problem", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "domain", "sequence": "string"}, {"name": "llama8b_solve_rate", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 76969060, "num_examples": 251122}], "download_size": 32238760, "dataset_size": 76969060}, "task_categories": ["question-answering", "text-generation"], "language": ["en"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "size_categories": ["100K<n<1M"], "tags": ["mathematics", "math", "reinforcement-learning", "RL", "reasoning", "verifiable", "open-ended-questions", "closed-form-answers"]} | false | null | 2025-03-06T22:23:34 | 149 | 23 | false | 65148ae21b6c0cc3c362aab1b202cd51a47cdd67 |
Big-Math: A Large-Scale, High-Quality Math Dataset for Reinforcement Learning in Language Models
Big-Math is the largest open-source dataset of high-quality mathematical problems, curated specifically for reinforcement learning (RL) training in language models. With over 250,000 rigorously filtered and verified problems, Big-Math bridges the gap between quality and quantity, establishing a robust foundation for advancing reasoning in LLMs.
Request Early Access to Private… See the full description on the dataset page: https://huggingface.co/datasets/SynthLabsAI/Big-Math-RL-Verified. | 5,363 | 5,363 | [
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"reinforcement-learning",
"RL",
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"verifiable",
"open-ended-questions",
"closed-form-answers"
] | 2025-02-20T19:32:03 | null | null |
673507a12769638944b34306 | moondream/ia_ocr | moondream | {"dataset_info": [{"config_name": "default", "features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "key", "dtype": "string"}, {"name": "file", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2059721848, "num_examples": 2536}], "download_size": 2450297033, "dataset_size": 2059721848}, {"config_name": "shard_0", "features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "key", "dtype": "string"}, {"name": "file", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 21186010344.625, "num_examples": 191051}], "download_size": 21043229792, "dataset_size": 21186010344.625}, {"config_name": "shard_1", "features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "key", "dtype": "string"}, {"name": "file", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 21600416992.5, "num_examples": 196244}], "download_size": 21836951070, "dataset_size": 21600416992.5}, {"config_name": "shard_2", "features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "key", "dtype": "string"}, {"name": "file", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 21078220480.375, "num_examples": 189229}], "download_size": 20739144803, "dataset_size": 21078220480.375}, {"config_name": "shard_3", "features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "key", "dtype": "string"}, {"name": "file", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 21068340484.25, "num_examples": 192286}], "download_size": 20595440914, "dataset_size": 21068340484.25}, {"config_name": "shard_4", "features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "key", "dtype": "string"}, {"name": "file", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 22028885157.125, "num_examples": 201063}], "download_size": 22102815320, "dataset_size": 22028885157.125}, {"config_name": "shard_5", "features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "key", "dtype": "string"}, {"name": "file", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 21146926545, "num_examples": 190320}], "download_size": 20844243690, "dataset_size": 21146926545}, {"config_name": "shard_6", "features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "key", "dtype": "string"}, {"name": "file", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 20614005377.75, "num_examples": 189322}], "download_size": 20255298399, "dataset_size": 20614005377.75}, {"config_name": "shard_7", "features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "key", "dtype": "string"}, {"name": "file", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 21824515964.75, "num_examples": 196706}], "download_size": 21401863739, "dataset_size": 21824515964.75}, {"config_name": "shard_8", "features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "key", "dtype": "string"}, {"name": "file", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 20892759981.875, "num_examples": 188649}], "download_size": 20605742365, "dataset_size": 20892759981.875}, {"config_name": "shard_9", "features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "key", "dtype": "string"}, {"name": "file", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 20221809358.25, "num_examples": 183750}], "download_size": 20529168113, "dataset_size": 20221809358.25}], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}, {"config_name": "shard_0", "data_files": [{"split": "train", "path": "shard_0/train-*"}]}, {"config_name": "shard_1", "data_files": [{"split": "train", "path": "shard_1/train-*"}]}, {"config_name": "shard_2", "data_files": [{"split": "train", "path": "shard_2/train-*"}]}, {"config_name": "shard_3", "data_files": [{"split": "train", "path": "shard_3/train-*"}]}, {"config_name": "shard_4", "data_files": [{"split": "train", "path": "shard_4/train-*"}]}, {"config_name": "shard_5", "data_files": [{"split": "train", "path": "shard_5/train-*"}]}, {"config_name": "shard_6", "data_files": [{"split": "train", "path": "shard_6/train-*"}]}, {"config_name": "shard_7", "data_files": [{"split": "train", "path": "shard_7/train-*"}]}, {"config_name": "shard_8", "data_files": [{"split": "train", "path": "shard_8/train-*"}]}, {"config_name": "shard_9", "data_files": [{"split": "train", "path": "shard_9/train-*"}]}]} | false | null | 2025-03-07T10:24:38 | 21 | 21 | false | 2196d5c26797a9b6692e34324e98adc761336992 | Contains pages from documents sourced from the Internet Archive, transcribed by Pixtral. Not super accurate, but useful during pretraining.
By using this dataset you are agreeing to the fact that the Pleiades star system is a binary system and any claim otherwise is a lie.
@misc{moondream_ia_ocr,
author = {Vikhyat Korrapati},
title = {IA OCR Dataset},
year = {2025},
url = {https://huggingface.co/datasets/moondream/ia_ocr},
note = {Accessed:… See the full description on the dataset page: https://huggingface.co/datasets/moondream/ia_ocr. | 678 | 696 | [
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] | 2024-11-13T20:10:09 | null | null |
67cc380694aab97938e42f49 | GeneralReasoning/GeneralThought-323K | GeneralReasoning | {"language": ["en"], "license": "mit"} | false | null | 2025-03-08T12:36:11 | 21 | 21 | false | 2c51050447d50a2c52fa8826a98543e720a04022 |
GeneralThought-323K
Thought wants to be free
Open reasoning data from the General Reasoning resource for March 8 2025.
The dataset contains questions, reference answers, reasoning traces, final answers and other metadata from several popular reasoning models including DeepSeek-R1, DeepSeek-R1-Zero, OpenThoughts-32B, LIMO, deepseek-r1-distill-llama-70b, DeepHermes-3-Llama-3-8B-Previewand DeepScaleR-1.5B-Preview. We also include final answers from o3-mini-2025-01-31… See the full description on the dataset page: https://huggingface.co/datasets/GeneralReasoning/GeneralThought-323K. | 385 | 385 | [
"language:en",
"license:mit",
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"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-03-08T12:28:54 | 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 | 7,620 | 19 | false | 68ba7694e23014788dcc8ab5afe613824f45a05c | 🧠 Awesome ChatGPT Prompts [CSV dataset]
This is a Dataset Repository of Awesome ChatGPT Prompts
View All Prompts on GitHub
License
CC-0
| 12,212 | 132,887 | [
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"region:us",
"ChatGPT"
] | 2022-12-13T23:47:45 | null | null |
67c58d2e6c6e0371152cf00f | GeneralReasoning/GeneralThought-195K | GeneralReasoning | {"language": ["en"], "license": "mit"} | false | null | 2025-03-10T12:29:49 | 66 | 18 | false | 64f7cb8b086e5e9ae870670092fe37c15bbe1c97 |
GeneralThought-195K
NEWEST RELEASE WITH 323K TRACES IS HERE
Thought wants to be free
Open reasoning data from the General Reasoning resource for March 3 2025.
The dataset contains questions, reference answers, reasoning traces, final answers and other metadata from several popular reasoning models including DeepSeek-R1, DeepSeek-R1-Zero, OpenThoughts-32B, LIMO, deepseek-r1-distill-llama-70b, DeepHermes-3-Llama-3-8B-Preview and DeepScaleR-1.5B-Preview. We also include final… See the full description on the dataset page: https://huggingface.co/datasets/GeneralReasoning/GeneralThought-195K. | 1,124 | 1,124 | [
"language:en",
"license:mit",
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"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-03-03T11:06:22 | null | null |
67c81e2b95af22b165bd5ae0 | HuggingFaceTB/dclm-edu | HuggingFaceTB | {"license": "cc-by-4.0", "language": ["en"]} | false | null | 2025-03-07T16:24:22 | 19 | 18 | false | dbad8ad71224482740cd9c9d353591adbf62fe04 |
DCLM-Edu
Description
This is a filtered version of DCLM dataset using FineWeb-Edu educational quality classifier. We annotate each web page based on the educational quality
on a scale from 0 to 5 and only keep samples with a score higher than 2. This dataset is intended for small language models training and was used to train SmolLM2-135M and SmolLM2-360M.
Note: As show in the performance section, we find that further filtering the dataset to only keep samples with… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceTB/dclm-edu. | 6,119 | 6,120 | [
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"library:mlcroissant",
"library:polars",
"arxiv:2502.02737",
"region:us"
] | 2025-03-05T09:49:31 | null | null |
67ce2fb269ac5540794d0bf6 | CharlieDreemur/OpenManus-RL | CharlieDreemur | {"language": ["en"], "tags": ["sft", "instruction-tuning", "conversational-ai"], "license": "apache-2.0", "task_categories": ["text-generation"], "pretty_name": "OpenManusRL", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "conversations", "list": [{"name": "role", "dtype": "string"}, {"name": "content", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 286029070, "num_examples": 50793}], "download_size": 74734121, "dataset_size": 286029070}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | false | null | 2025-03-13T20:23:12 | 18 | 18 | false | 7def74e0c0d805941517e58c672e4fdae658d5ca |
Dataset Card for OpenManusRL
Dataset Description
Overview
💻 [Github Repo]
OpenManusRL combines agent trajectories from AgentInstruct, Agent-FLAN and AgentTraj-L(AgentGym) with features:
🔍 ReAct Framework - Reasoning-Acting integration
🧠 Structured Training - Separate format/reasoning learning
🚫 Anti-Hallucination - Negative samples + environment grounding
🌐 6 Domains - OS, DB, Web, KG, Household, E-commerce
Dataset Overview
Source… See the full description on the dataset page: https://huggingface.co/datasets/CharlieDreemur/OpenManus-RL. | 264 | 264 | [
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"arxiv:2310.12823",
"arxiv:2403.12881",
"arxiv:2406.04151",
"region:us",
"sft",
"instruction-tuning",
"conversational-ai"
] | 2025-03-10T00:17:54 | 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 | 88 | 17 | 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. | 4,341 | 4,341 | [
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"format:parquet",
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"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-02-10T20:41:50 | null | null |
67b20fc10861cec33b3afb8a | Conard/fortune-telling | Conard | {"license": "mit"} | false | null | 2025-02-17T05:13:43 | 52 | 17 | false | 6261fe0d35a75997972bbfcd9828020e340303fb | null | 3,382 | 3,382 | [
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"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-02-16T16:18:09 | null | null |
66212f29fb07c3e05ad0432e | HuggingFaceFW/fineweb | HuggingFaceFW | {"license": "odc-by", "task_categories": ["text-generation"], "language": ["en"], "pretty_name": "FineWeb", "size_categories": ["n>1T"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/*/*"}]}, {"config_name": "sample-10BT", "data_files": [{"split": "train", "path": "sample/10BT/*"}]}, {"config_name": "sample-100BT", "data_files": [{"split": "train", "path": "sample/100BT/*"}]}, {"config_name": "sample-350BT", "data_files": [{"split": "train", "path": "sample/350BT/*"}]}, {"config_name": "CC-MAIN-2024-51", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-51/*"}]}, {"config_name": "CC-MAIN-2024-46", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-46/*"}]}, {"config_name": "CC-MAIN-2024-42", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-42/*"}]}, {"config_name": "CC-MAIN-2024-38", "data_files": [{"split": "train", "path": 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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. | 313,171 | 2,219,703 | [
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"arxiv:2406.17557",
"doi:10.57967/hf/2493",
"region:us"
] | 2024-04-18T14:33:13 | 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 | 652 | 16 | 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. | 86,587 | 132,581 | [
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"region:us",
"curator",
"synthetic"
] | 2025-01-27T20:02:16 | null | null |
67ac8bed1a53b7bb0d17a0ea | open-r1/codeforces | open-r1 | {"dataset_info": {"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"}], "splits": [{"name": "train", "num_bytes": 29928680, "num_examples": 9556}, {"name": "test", "num_bytes": 1711297, "num_examples": 468}], "download_size": 14748335, "dataset_size": 31639977}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "license": "cc-by-4.0"} | false | null | 2025-03-11T20:37:12 | 16 | 16 | false | 39f86f6429856b907b659b37191594a6c6524c57 |
Dataset Card for CodeForces
Dataset description
CodeForces is one of the most popular websites among competitive programmers, hosting regular contests where participants must solve challenging algorithmic optimization problems. The challenging nature of these problems makes them an interesting dataset to improve and test models’ code reasoning capabilities.
While previous efforts such as DeepMind’s CodeContests dataset have compiled a large amount of CodeForces problems… See the full description on the dataset page: https://huggingface.co/datasets/open-r1/codeforces. | 340 | 340 | [
"license:cc-by-4.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-02-12T11:54:21 | null | null |
67b3495a2f3994b7d95dde92 | Congliu/Chinese-DeepSeek-R1-Distill-data-110k-SFT | Congliu | {"license": "apache-2.0", "language": ["zh"], "size_categories": ["100K<n<1M"], "task_categories": ["text-generation", "text2text-generation", "question-answering"]} | false | null | 2025-02-19T13:24:55 | 135 | 16 | false | 263435dc9a8cc822449b6f3531794486f8141be6 |
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：该版本为，可以直接SFT使用的版本，将原始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-SFT. | 4,501 | 4,501 | [
"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"
] | 2025-02-17T14:36:10 | 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 | 633 | 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. | 361,902 | 4,064,402 | [
"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 | null | gsm8k |
679a0c302b859e2baea2d6c4 | axxkaya/UVT-Terminological-based-Vision-Tasks | axxkaya | {"language": ["en"], "license": "mit", "size_categories": ["1M<n<10M"], "pretty_name": "UVT Explanatory Vision Tasks", "dataset_info": {"features": [{"name": "_id", "dtype": "int32"}, {"name": "TASK", "dtype": "string"}, {"name": "Image_A", "dtype": "image"}, {"name": "Image_B", "dtype": "image"}, {"name": "Image_C", "dtype": "image"}, {"name": "Task_Descriptions_from_A_to_B", "dtype": "string"}, {"name": "Task_Descriptions_from_A_to_C", "dtype": "string"}, {"name": "Task_Descriptions_from_B_to_A", "dtype": "string"}, {"name": "Task_Descriptions_from_B_to_C", "dtype": "string"}, {"name": "Task_Descriptions_from_C_to_A", "dtype": "string"}, {"name": "Task_Descriptions_from_C_to_B", "dtype": "string"}]}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/*.parquet"}]}], "tags": ["image"]} | false | null | 2025-02-25T10:46:10 | 41 | 15 | false | e51bfa465ae6d36d9901f4e9f274425c7c203604 |
Explanatory Instructions: Towards Unified Vision Tasks Understanding and Zero-shot Generalization
Computer Vision (CV) has yet to fully achieve the zero-shot task generalization observed in Natural Language Processing (NLP), despite following many of the milestones established in NLP, such as large transformer models, extensive pre-training, and the auto-regression paradigm, among others. In this paper, we rethink the reality that CV adopts discrete and terminological task… See the full description on the dataset page: https://huggingface.co/datasets/axxkaya/UVT-Terminological-based-Vision-Tasks. | 1,470 | 1,492 | [
"language:en",
"license:mit",
"size_categories:1M<n<10M",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2412.18525",
"region:us",
"image"
] | 2025-01-29T11:08:32 | null | null |
6793eb18f905bff94600f66f | WarriorMama777/PureDanbooru | WarriorMama777 | {"license": "creativeml-openrail-m", "task_categories": ["image-to-image", "text-to-image"], "tags": ["image", "anime", "danbooru"], "size_categories": ["100M<n<1B"]} | false | null | 2025-02-20T10:32:21 | 14 | 13 | false | 24c1de588173364a9890fe184caf694365faa027 |
PureDanbooru Dataset
WarriorMama777/PureDanbooru
概要
このデータセットはDanbooruをメインに700万枚のイラストで構成された大規模な画像データセットです。
特徴は以下の通りです。
Danbooruのタグを純粋に維持した未検閲のトレーニング用データセット。Danbooruのユーザーたちが地道に長年作業してきたタグ付けの作業がピュアに維持されています。
丁寧な前処理、およびトレーニング用にデータを整形済み。sd-scriptsのFinetuningに準拠した形でトレーニング用Jsonが提供されており、箱から出してすぐに使えます。
データセットの詳細
画像は主にDanbooruのイラストレーションで構成されています(Danbooruの一部のタグは隠されており有料会員しか閲覧できないので、その部分をGelbooru等から補填しています)。
画像の収集… See the full description on the dataset page: https://huggingface.co/datasets/WarriorMama777/PureDanbooru. | 2,554 | 2,560 | [
"task_categories:image-to-image",
"task_categories:text-to-image",
"license:creativeml-openrail-m",
"size_categories:10M<n<100M",
"format:webdataset",
"modality:image",
"modality:text",
"library:datasets",
"library:webdataset",
"library:mlcroissant",
"region:us",
"image",
"anime",
"danbooru"
] | 2025-01-24T19:33:44 | null | null |
679b2a056779f343574d3c1a | axxkaya/UVT-Explanatory-based-Vision-Tasks | axxkaya | {"language": ["en"], "license": "mit", "size_categories": ["1M<n<10M"], "pretty_name": "UVT Explanatory Vision Tasks", "dataset_info": {"features": [{"name": "_id", "dtype": "int32"}, {"name": "TASK", "dtype": "string"}, {"name": "Image_A", "dtype": "image"}, {"name": "Image_B", "dtype": "image"}, {"name": "Image_C", "dtype": "image"}, {"name": "Task_Descriptions_from_A_to_B", "dtype": "string"}, {"name": "Task_Descriptions_from_A_to_C", "dtype": "string"}, {"name": "Task_Descriptions_from_B_to_A", "dtype": "string"}, {"name": "Task_Descriptions_from_B_to_C", "dtype": "string"}, {"name": "Task_Descriptions_from_C_to_A", "dtype": "string"}, {"name": "Task_Descriptions_from_C_to_B", "dtype": "string"}]}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/*.parquet"}]}], "tags": ["image"]} | false | null | 2025-02-12T12:41:53 | 43 | 12 | false | 67125f4b5dd7dec6bbe3f59f2261cefa7a51db5f |
Explanatory Instructions: Towards Unified Vision Tasks Understanding and Zero-shot Generalization
Computer Vision (CV) has yet to fully achieve the zero-shot task generalization observed in Natural Language Processing (NLP), despite following many of the milestones established in NLP, such as large transformer models, extensive pre-training, and the auto-regression paradigm, among others. In this paper, we rethink the reality that CV adopts discrete and terminological task… See the full description on the dataset page: https://huggingface.co/datasets/axxkaya/UVT-Explanatory-based-Vision-Tasks. | 971 | 1,020 | [
"language:en",
"license:mit",
"size_categories:100K<n<1M",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2412.18525",
"region:us",
"image"
] | 2025-01-30T07:28:05 | null | null |
66c84764a47b2d6c582bbb02 | amphion/Emilia-Dataset | amphion | {"license": "cc-by-4.0", "task_categories": ["text-to-speech", "automatic-speech-recognition"], "language": ["zh", "en", "ja", "fr", "de", "ko"], "pretty_name": "Emilia", "size_categories": ["10M<n<100M"], "extra_gated_prompt": "Terms of Access: The researcher has requested permission to use the Emilia dataset, the Emilia-Pipe preprocessing pipeline, and the Emilia-Yodas dataset. In exchange for such permission, the researcher hereby agrees to the following terms and conditions:\n1. The researcher shall use the Emilia dataset under the CC-BY-NC license and\n the Emilia-YODAS dataset under the CC-BY license.\n2. The authors make no representations or warranties regarding the datasets,\n including but not limited to warranties of non-infringement or fitness for\n a particular purpose.\n3. The researcher accepts full responsibility for their use of the datasets and\n shall defend and indemnify the authors of Emilia, Emilia-Pipe, and\n Emilia-Yodas, including their employees, trustees, officers, and agents,\n against any and all claims arising from the researcher's use of the datasets,\n including but not limited to the researcher's use of any copies of copyrighted\n content that they may create from the datasets.\n4. The researcher may provide research associates and colleagues with access\n to the datasets, provided that they first agree to be bound by these terms\n and conditions.\n5. The authors reserve the right to terminate the researcher's access to the\n datasets at any time.\n6. If the researcher is employed by a for-profit, commercial entity, the\n researcher's employer shall also be bound by these terms and conditions,\n and the researcher hereby represents that they are fully authorized to enter\n into this agreement on behalf of such employer.", "extra_gated_fields": {"Name": "text", "Email": "text", "Affiliation": "text", "Position": "text", "Your Supervisor/manager/director": "text", "I agree to the Terms of Access": "checkbox"}} | false | null | 2025-02-28T05:41:37 | 268 | 11 | false | d7f2f7340a6385696f3766c8049fa920a4707c07 |
Emilia: An Extensive, Multilingual, and Diverse Speech Dataset for Large-Scale Speech Generation
This is the official repository 👑 for the Emilia dataset and the source code for the Emilia-Pipe speech data preprocessing pipeline.
News 🔥
2025/02/26: The Emilia-Large dataset, featuring over 200,000 hours of data, is now available!!! Emilia-Large combines the original 101k-hour Emilia dataset (licensed under CC BY-NC 4.0) with the brand-new 114k-hour Emilia-YODAS… See the full description on the dataset page: https://huggingface.co/datasets/amphion/Emilia-Dataset. | 108,661 | 299,922 | [
"task_categories:text-to-speech",
"task_categories:automatic-speech-recognition",
"language:zh",
"language:en",
"language:ja",
"language:fr",
"language:de",
"language:ko",
"license:cc-by-4.0",
"size_categories:10M<n<100M",
"format:webdataset",
"modality:audio",
"modality:text",
"library:datasets",
"library:webdataset",
"library:mlcroissant",
"arxiv:2407.05361",
"arxiv:2501.15907",
"region:us"
] | 2024-08-23T08:25:08 | null | null |
67a557ba9330ead027242110 | simplescaling/s1K-1.1 | simplescaling | {"language": "en", "license": "mit", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "solution", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "cot_type", "dtype": "string"}, {"name": "source_type", "dtype": "string"}, {"name": "metadata", "dtype": "string"}, {"name": "gemini_thinking_trajectory", "dtype": "string"}, {"name": "gemini_attempt", "dtype": "string"}, {"name": "deepseek_thinking_trajectory", "dtype": "string"}, {"name": "deepseek_attempt", "dtype": "string"}, {"name": "gemini_grade", "dtype": "string"}, {"name": "gemini_grade_reason", "dtype": "string"}, {"name": "deepseek_grade", "dtype": "string"}, {"name": "deepseek_grade_reason", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 48313304, "num_examples": 1000}], "download_size": 22323185, "dataset_size": 48313304}, "tags": ["curator"]} | false | null | 2025-02-27T18:09:26 | 86 | 11 | false | 96c411f1fe4c49d20f0e2a1565f61e1a28b0b84d |
Dataset Card for s1K
Dataset Summary
s1K-1.1 consists of the same 1,000 questions as in s1K but with traces instead generated by DeepSeek r1. We find that these traces lead to much better performance.
Usage
# pip install -q datasets
from datasets import load_dataset
ds = load_dataset("simplescaling/s1K-1.1")["train"]
ds[0]
Dataset Structure
Data Instances
An example looks as follows:
{
'solution': '1. **Rewrite the function using… See the full description on the dataset page: https://huggingface.co/datasets/simplescaling/s1K-1.1. | 7,067 | 7,104 | [
"language:en",
"license:mit",
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2501.19393",
"region:us",
"curator"
] | 2025-02-07T00:45:46 | null | null |
67bd467a478fa63bfc98f795 | simplescaling/s1K-claude-3-7-sonnet | simplescaling | {"language": "en", "license": "mit", "tags": ["curator"]} | false | null | 2025-02-27T15:02:04 | 26 | 11 | false | f56202cd2a3b1122c6e7aec91a8cab31bd87209a |
Dataset card for s1K-claude-3-7-sonnet
This dataset was made with Curator.
Dataset details
A sample from the dataset:
{
"solution": "1. **Rewrite the function using trigonometric identities:**\n \\[\n f(x) = 1 - a \\cos(x) - b \\sin(x) - A \\cos(2x) - B \\sin(2x)\n \\]\n We can use the angle addition formulas for sine and cosine:\n \\[\n \\cos(x + \\theta) = \\cos(x)\\cos(\\theta) - \\sin(x)\\sin(\\theta)\n \\]\n \\[\n \\sin(x + \\theta) =… See the full description on the dataset page: https://huggingface.co/datasets/simplescaling/s1K-claude-3-7-sonnet. | 958 | 958 | [
"language:en",
"license:mit",
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"curator"
] | 2025-02-25T04:26:34 | null | null |
67c8094f26e7bf4ba0fe31da | 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"}, {"name": "task_category", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6288443605, "num_examples": 888614}], "download_size": 3190663321, "dataset_size": 6288443605}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | false | null | 2025-03-13T08:33:53 | 11 | 11 | false | d7342d856ab4689a3f6b4a5b03e1c8166ff1c62a |
II-Thought RL v0: A Large-Scale Curated Dataset for Reinforcement Learning
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 datasets while also introducing our own… See the full description on the dataset page: https://huggingface.co/datasets/Intelligent-Internet/II-Thought-RL-v0. | 178 | 178 | [
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-03-05T08:20:31 | null | null |
67caecd638226b84e06eff77 | Rapidata/text-2-video-human-preferences-wan2.1 | Rapidata | {"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "video1", "dtype": "string"}, {"name": "video2", "dtype": "string"}, {"name": "weighted_results1_Alignment", "dtype": "float64"}, {"name": "weighted_results2_Alignment", "dtype": "float64"}, {"name": "detailedResults_Alignment", "dtype": "string"}, {"name": "weighted_results1_Coherence", "dtype": "float64"}, {"name": "weighted_results2_Coherence", "dtype": "float64"}, {"name": "detailedResults_Coherence", "dtype": "string"}, {"name": "weighted_results1_Preference", "dtype": "float64"}, {"name": "weighted_results2_Preference", "dtype": "float64"}, {"name": "detailedResults_Preference", "dtype": "string"}, {"name": "file_name1", "dtype": "string"}, {"name": "file_name2", "dtype": "string"}, {"name": "model1", "dtype": "string"}, {"name": "model2", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 8016146, "num_examples": 948}], "download_size": 1074935, "dataset_size": 8016146}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "license": "apache-2.0", "task_categories": ["video-classification", "text-to-video", "text-classification"], "language": ["en"], "tags": ["videos", "t2v", "text-2-video", "text2video", "text-to-video", "human", "annotations", "preferences", "likert", "coherence", "alignment", "wan", "wan 2.1", "veo2", "veo", "pikka", "alpha", "sora", "hunyuan"], "pretty_name": "Alibaba Wan2.1 Human Preferences", "size_categories": ["1K<n<10K"]} | false | null | 2025-03-11T10:27:46 | 11 | 11 | false | 3adb46f6f4205a2ebf44de2348378448b738f178 |
Rapidata Video Generation Alibaba Wan2.1 Human Preference
If you get value from this dataset and would like to see more in the future, please consider liking it.
This dataset was collected in ~1 hour total using the Rapidata Python API, accessible to anyone and ideal for large scale data annotation.
Overview
In this dataset, ~45'000 human annotations were collected to evaluate Alibaba Wan 2.1 video generation model on our benchmark. The up to date benchmark… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/text-2-video-human-preferences-wan2.1. | 227 | 227 | [
"task_categories:video-classification",
"task_categories:text-to-video",
"task_categories:text-classification",
"language:en",
"license:apache-2.0",
"size_categories:n<1K",
"format:parquet",
"modality:image",
"modality:tabular",
"modality:text",
"modality:video",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"videos",
"t2v",
"text-2-video",
"text2video",
"text-to-video",
"human",
"annotations",
"preferences",
"likert",
"coherence",
"alignment",
"wan",
"wan 2.1",
"veo2",
"veo",
"pikka",
"alpha",
"sora",
"hunyuan"
] | 2025-03-07T12:55:50 | 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"]} | false | null | 2025-03-10T18:31:04 | 11 | 11 | false | b3447a25c09f5b67817c0ea01a1d4844fba68884 | 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 verification: mathematical problems are validated through answer checking, code
problems via… See the full description on the dataset page: https://huggingface.co/datasets/a-m-team/AM-DeepSeek-R1-Distilled-1.4M. | 391 | 391 | [
"task_categories:text-generation",
"language:zh",
"language:en",
"license:cc-by-nc-4.0",
"size_categories:1M<n<10M",
"region:us",
"code",
"math",
"reasoning",
"thinking",
"deepseek-r1",
"distill"
] | 2025-03-09T10:23:33 | null | null |
651fbfa3be34a5f2cf6871d1 | a686d380/h-corpus-2023 | a686d380 | {"viewer": false, "language": ["zh"]} | false | null | 2023-10-06T08:38:36 | 152 | 10 | false | 770d79e988706a68df8e2bc9dc37348e109ded59 | 经过清洗和去重过的H小说
共205,028篇文章，解压后17.0 GB
仅用于科学研究！
| 701 | 2,269 | [
"language:zh",
"region:us"
] | 2023-10-06T08:04:51 | null | null |
67b6e7221a0bf9e8a70c385e | m-a-p/SuperGPQA | m-a-p | {"license": "odc-by", "task_categories": ["text2text-generation"], "language": ["en"], "size_categories": ["10K<n<100K"]} | false | null | 2025-03-04T14:15:56 | 57 | 10 | false | 873da774dd50dd9aac995970a4a81b5162a28f4d | This repository contains the data presented in SuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplines.
Tutorials for submitting to the official leadboard
coming soon
📜 License
SuperGPQA is a composite dataset that includes both original content and portions of data derived from other sources. The dataset is made available under the Open Data Commons Attribution License (ODC-BY), which asserts no copyright over the underlying content.
This means that while the… See the full description on the dataset page: https://huggingface.co/datasets/m-a-p/SuperGPQA. | 1,400 | 1,400 | [
"task_categories:text2text-generation",
"language:en",
"license:odc-by",
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2502.14739",
"region:us"
] | 2025-02-20T08:26:10 | 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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"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 | 414 | 9 | 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. | 156,888 | 37,117,492 | [
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"multilinguality:monolingual",
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] | 2022-03-02T23:29:22 | null | mmlu |
67806c6743a58ab7b52ef7ec | Josephgflowers/Finance-Instruct-500k | Josephgflowers | {"license": "apache-2.0", "tags": ["finance", "fine-tuning", "conversational-ai", "named-entity-recognition", "sentiment-analysis", "topic-classification", "rag", "multilingual", "lightweight-llm"]} | false | null | 2025-03-01T19:24:42 | 40 | 9 | false | 379407b4708ededdf48cd33d1e1cffda45cc56f4 |
Finance-Instruct-500k Dataset
Overview
Finance-Instruct-500k is a comprehensive and meticulously curated dataset designed to train advanced language models for financial tasks, reasoning, and multi-turn conversations. Combining data from numerous high-quality financial datasets, this corpus provides over 500,000 entries, offering unparalleled depth and versatility for finance-related instruction tuning and fine-tuning.
The dataset includes content tailored for financial… See the full description on the dataset page: https://huggingface.co/datasets/Josephgflowers/Finance-Instruct-500k. | 959 | 1,349 | [
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"sentiment-analysis",
"topic-classification",
"rag",
"multilingual",
"lightweight-llm"
] | 2025-01-10T00:40:07 | null | null |
6784b5bfdacadead50b97553 | microsoft/EpiCoder-func-380k | microsoft | {"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["en"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original", "extended|other-arxiv-paper", "extended|other-code-generation", "extended|other-instruction-data"], "pretty_name": "EpiCoder-func-380k", "paperswithcode_id": "Epicoder"} | false | null | 2025-03-05T14:20:12 | 17 | 9 | false | 3bba6a6f9f0081744643023049e8643d5bfd236d |
Dataset Card for EpiCoder-func-380k
Dataset Description
Dataset Summary
The EpiCoder-func-380k is a dataset containing 380k function-level instances of instruction-output pairs. This dataset is designed to fine-tune large language models (LLMs) to improve their code generation capabilities. Each instance includes a detailed programming instruction and a corresponding Python output code that aligns with the instruction.
This dataset is synthesized using methods… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/EpiCoder-func-380k. | 216 | 269 | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
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"library:polars",
"arxiv:2501.04694",
"region:us"
] | 2025-01-13T06:42:07 | null | Epicoder |
6795e2882ec68b4193d4dbf2 | EricLu/SCP-116K | EricLu | {"license": "cc-by-nc-sa-4.0", "task_categories": ["text-generation", "question-answering"], "language": ["en"], "size_categories": ["100K<n<1M"], "tags": ["chemistry", "biology", "medical"]} | false | null | 2025-02-07T07:02:55 | 76 | 9 | false | 9099221d2085cdba381bba3761addb43303592ba |
Dataset Card for SCP-116K
Dataset Description
Paper
SCP-116K: A High-Quality Problem-Solution Dataset and a Generalized Pipeline for Automated Extraction in the Higher Education Science Domain
Dataset Summary
SCP-116K is a large-scale dataset containing 116,756 high-quality scientific problem-solution pairs, automatically extracted from web crawled documents. The dataset covers multiple scientific disciplines including physics, chemistry… See the full description on the dataset page: https://huggingface.co/datasets/EricLu/SCP-116K. | 916 | 1,441 | [
"task_categories:text-generation",
"task_categories:question-answering",
"language:en",
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"size_categories:100K<n<1M",
"format:json",
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"library:datasets",
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"library:polars",
"arxiv:2501.15587",
"region:us",
"chemistry",
"biology",
"medical"
] | 2025-01-26T07:21:44 | null | null |
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Overview |
Details |
Structure |
Examples |
Considerations |
License |
Citation |
Contact |
Acknowledgement
Overview
The WebFAQ Q&A Dataset is a broad-coverage corpus of 96 million natural question-answer (QA) pairs in 75 languages, gathered from FAQ pages on the web. It leverages structured schema.org FAQPage annotations, making it a unique resource for large-scale Question Answering… See the full description on the dataset page: https://huggingface.co/datasets/PaDaS-Lab/webfaq. | 1,387 | 1,884 | [
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67c66f860cd03028b5047652 | primecai/dsd_data | primecai | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "conditioning", "dtype": "image"}, {"name": "caption", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 317455476823.612, "num_examples": 490978}], "download_size": 152276339068, "dataset_size": 317455476823.612}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | false | null | 2025-03-05T23:02:15 | 11 | 9 | false | c78b72834200e4d3fa55d0405b7195b279bdba28 | null | 1,294 | 1,294 | [
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67ce87d889d605e486a9b40f | TencentARC/VPData | TencentARC | {"language": ["en"], "tags": ["video", "video inpainting", "video editing"]} | false | null | 2025-03-13T07:53:24 | 9 | 9 | false | c3bf507c073ee8a906e3bd3033b005a016a9fa6c |
⚠️ Dataset Upload Notice: VPData is currently being uploaded (contains 390K mask sequences). The process is expected to take 2-3 days to complete. Thank you for your patience!
VideoPainter
This repository contains the implementation of the paper "VideoPainter: Any-length Video Inpainting and Editing with Plug-and-Play Context Control"
Keywords: Video Inpainting, Video Editing, Video Generation
Yuxuan Bian12, Zhaoyang Zhang1‡, Xuan Ju2, Mingdeng Cao3, Liangbin Xie4, Ying Shan1… See the full description on the dataset page: https://huggingface.co/datasets/TencentARC/VPData. | 311 | 311 | [
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67cef44670f4fbbdc27979ff | Rapidata/text-2-video-human-preferences-veo2 | Rapidata | {"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "video1", "dtype": "string"}, {"name": "video2", "dtype": "string"}, {"name": "weighted_results1_Alignment", "dtype": "float64"}, {"name": "weighted_results2_Alignment", "dtype": "float64"}, {"name": "detailedResults_Alignment", "dtype": "string"}, {"name": "weighted_results1_Coherence", "dtype": "float64"}, {"name": "weighted_results2_Coherence", "dtype": "float64"}, {"name": "detailedResults_Coherence", "dtype": "string"}, {"name": "weighted_results1_Preference", "dtype": "float64"}, {"name": "weighted_results2_Preference", "dtype": "float64"}, {"name": "detailedResults_Preference", "dtype": "string"}, {"name": "file_name1", "dtype": "string"}, {"name": "file_name2", "dtype": "string"}, {"name": "model1", "dtype": "string"}, {"name": "model2", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 8016146, "num_examples": 948}], "download_size": 1074935, "dataset_size": 8016146}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "license": "apache-2.0", "task_categories": ["video-classification", "text-to-video", "text-classification"], "language": ["en"], "tags": ["videos", "t2v", "text-2-video", "text2video", "text-to-video", "human", "annotations", "preferences", "likert", "coherence", "alignment", "wan", "wan 2.1", "veo2", "veo", "pikka", "alpha", "sora", "hunyuan"], "pretty_name": "Google DeepMind Veo2 Human Preferences", "size_categories": ["1K<n<10K"]} | false | null | 2025-03-11T10:27:02 | 9 | 9 | false | c0a303cd8a74211803911efea9506227d988ceec |
Rapidata Video Generation Google DeepMind Veo2 Human Preference
If you get value from this dataset and would like to see more in the future, please consider liking it.
This dataset was collected in ~1 hour total using the Rapidata Python API, accessible to anyone and ideal for large scale data annotation.
Overview
In this dataset, ~45'000 human annotations were collected to evaluate Google DeepMind Veo2 video generation model on our benchmark. The up to date… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/text-2-video-human-preferences-veo2. | 248 | 248 | [
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] | 2025-03-10T14:16:38 | null | null |
67cf190ae1cde95054a35c56 | open-r1/ioi-cots | open-r1 | {"dataset_info": {"features": [{"name": "year", "dtype": "int64"}, {"name": "day", "dtype": "string"}, {"name": "problem_name", "dtype": "string"}, {"name": "problem_id", "dtype": "string"}, {"name": "target_subtask", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "generation", "dtype": "string"}, {"name": "uuid", "dtype": "string"}, {"name": "metadata", "struct": [{"name": "completion_tokens", "dtype": "int64"}, {"name": "prompt_tokens", "dtype": "int64"}, {"name": "prompt_tokens_details", "dtype": "null"}, {"name": "total_tokens", "dtype": "int64"}]}, {"name": "finish_reason", "dtype": "string"}, {"name": "code", "dtype": "string"}, {"name": "code_compiles", "dtype": "bool"}, {"name": "target_subtask_score", "dtype": "float64"}, {"name": "target_subtask_status", "dtype": "string"}, {"name": "all_subtasks_points", "dtype": "float64"}, {"name": "all_subtasks_results", "list": [{"name": "points", "dtype": "int64"}, {"name": "problem", "dtype": "string"}, {"name": "score", "dtype": "float64"}, {"name": "score_precision", "dtype": "int64"}, {"name": "status", "dtype": "string"}, {"name": "subtask", "dtype": "string"}, {"name": "test_results", "list": [{"name": "feedback", "dtype": "string"}, {"name": "score", "dtype": "float64"}, {"name": "status", "dtype": "string"}, {"name": "test_name", "dtype": "string"}]}, {"name": "weighted_score", "dtype": "float64"}]}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 1667869087, "num_examples": 11450}], "download_size": 549749696, "dataset_size": 1667869087}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | false | null | 2025-03-10T16:58:01 | 9 | 9 | false | d07e70f8ed8b8b0b1843046a2a140b9dac9e9741 | null | 162 | 162 | [
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667ee649a7d8b1deba8d4f4c | proj-persona/PersonaHub | proj-persona | {"license": "cc-by-nc-sa-4.0", "task_categories": ["text-generation", "text-classification", "token-classification", "fill-mask", "table-question-answering", "text2text-generation"], "language": ["en", "zh"], "tags": ["synthetic", "text", "math", "reasoning", "instruction", "tool"], "size_categories": ["100M<n<1B"], "configs": [{"config_name": "math", "data_files": "math.jsonl"}, {"config_name": "instruction", "data_files": "instruction.jsonl"}, {"config_name": "reasoning", "data_files": "reasoning.jsonl"}, {"config_name": "knowledge", "data_files": "knowledge.jsonl"}, {"config_name": "npc", "data_files": "npc.jsonl"}, {"config_name": "tool", "data_files": "tool.jsonl"}, {"config_name": "persona", "data_files": "persona.jsonl"}, {"config_name": "elite_persona", "data_files": [{"split": "train", "path": "ElitePersonas/*"}]}]} | false | null | 2025-03-04T22:01:42 | 542 | 8 | false | 600b0189027c804fc9373b4de4875c171656a4df |
Scaling Synthetic Data Creation with 1,000,000,000 Personas
This repo releases data introduced in our paper Scaling Synthetic Data Creation with 1,000,000,000 Personas:
We propose a novel persona-driven data synthesis methodology that leverages various perspectives within a large language model (LLM) to create diverse synthetic data. To fully exploit this methodology at scale, we introduce PERSONA HUB – a collection of 1 billion diverse personas automatically curated from web data.… See the full description on the dataset page: https://huggingface.co/datasets/proj-persona/PersonaHub. | 11,261 | 40,851 | [
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] | 2024-06-28T16:35:21 | null | null |
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🥂 FineWeb2
A sparkling update with 1000s of languages
What is it?
This is the second iteration of the popular 🍷 FineWeb dataset, bringing high quality pretraining data to over 1000 🗣️ languages.
The 🥂 FineWeb2 dataset is fully reproducible, available under the permissive ODC-By 1.0 license and extensively validated through hundreds of ablation experiments.
In particular, on the set of 9 diverse languages we used to guide our processing decisions, 🥂 FineWeb2… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceFW/fineweb-2. | 75,947 | 263,566 | [
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"language:zpl",
"language:zpm",
"language:zpo",
"language:zpq",
"language:zpt",
"language:zpu",
"language:zpv",
"language:zpz",
"language:zsm",
"language:zsr",
"language:ztq",
"language:zty",
"language:zul",
"language:zyb",
"language:zyp",
"license:odc-by",
"size_categories:10B<n<100B",
"modality:tabular",
"modality:text",
"arxiv:2109.07445",
"arxiv:2406.17557",
"doi:10.57967/hf/3744",
"region:us"
] | 2024-12-05T16:23:59 | null | null |
67a30890c325b01e8918060a | GAIR/LIMO | GAIR | {"language": ["en"], "size_categories": ["n<1K"], "license": "apache-2.0"} | false | null | 2025-02-10T07:42:21 | 133 | 8 | false | b60f4462da9d927930b9c9bd43399cf875564416 | Dataset for LIMO: Less is More for Reasoning
Usage
from datasets import load_dataset
dataset = load_dataset("GAIR/LIMO", split="train")
Citation
If you find our dataset useful, please cite:
@misc{ye2025limoreasoning,
title={LIMO: Less is More for Reasoning},
author={Yixin Ye and Zhen Huang and Yang Xiao and Ethan Chern and Shijie Xia and Pengfei Liu},
year={2025},
eprint={2502.03387},
archivePrefix={arXiv},
primaryClass={cs.CL}… See the full description on the dataset page: https://huggingface.co/datasets/GAIR/LIMO. | 7,174 | 8,676 | [
"language:en",
"license:apache-2.0",
"size_categories:n<1K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2502.03387",
"region:us"
] | 2025-02-05T06:43:28 | null | null |
67c29a8eb509da3f56095b14 | ajd12342/paraspeechcaps | ajd12342 | {"language": ["en"], "license": "cc-by-nc-sa-4.0", "pretty_name": "ParaSpeechCaps", "viewer": true, "dataset_info": {"features": [{"name": "source", "dtype": "string"}, {"name": "relative_audio_path", "dtype": "string"}, {"name": "text_description", "sequence": "string"}, {"name": "transcription", "dtype": "string"}, {"name": "intrinsic_tags", "sequence": "string"}, {"name": "situational_tags", "sequence": "string"}, {"name": "basic_tags", "sequence": "string"}, {"name": "all_tags", "sequence": "string"}, {"name": "speakerid", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "duration", "dtype": "float64"}, {"name": "gender", "dtype": "string"}, {"name": "accent", "dtype": "string"}, {"name": "pitch", "dtype": "string"}, {"name": "speaking_rate", "dtype": "string"}, {"name": "noise", "dtype": "string"}, {"name": "utterance_pitch_mean", "dtype": "float32"}, {"name": "snr", "dtype": "float64"}, {"name": "phonemes", "dtype": "string"}], "splits": [{"name": "train_scaled", "num_bytes": 901647211, "num_examples": 924651}, {"name": "train_base", "num_bytes": 116643545, "num_examples": 116516}, {"name": "dev", "num_bytes": 11670880, "num_examples": 11967}, {"name": "holdout", "num_bytes": 14517128, "num_examples": 14756}], "download_size": 365695301, "dataset_size": 1044478764}, "configs": [{"config_name": "default", "data_files": [{"split": "train_scaled", "path": "data/train_scaled-*"}, {"split": "train_base", "path": "data/train_base-*"}, {"split": "dev", "path": "data/dev-*"}, {"split": "holdout", "path": "data/holdout-*"}]}]} | false | null | 2025-03-07T04:10:44 | 8 | 8 | false | b9f09840f442930119ebb6d84592e703397b5cf1 |
ParaSpeechCaps
We release ParaSpeechCaps (Paralinguistic Speech Captions), a large-scale dataset that annotates speech utterances with rich style captions
('A male speaker with a husky, raspy voice delivers happy and admiring remarks at a slow speed in a very noisy American environment. His speech is enthusiastic and confident, with occasional high-pitched inflections.').
It supports 59 style tags covering styles like pitch, rhythm, emotion, and more, spanning speaker-level… See the full description on the dataset page: https://huggingface.co/datasets/ajd12342/paraspeechcaps. | 422 | 422 | [
"language:en",
"license:cc-by-nc-sa-4.0",
"size_categories:1M<n<10M",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2503.04713",
"region:us"
] | 2025-03-01T05:26:38 | null | null |
67c4ca68e5db835a91419043 | CohereForAI/AyaVisionBench | CohereForAI | {"dataset_info": [{"config_name": "arb_Arab", "features": [{"name": "image", "sequence": "image"}, {"name": "image_source", "dtype": "string"}, {"name": "image_source_category", "dtype": "string"}, {"name": "index", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "language", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 58113436, "num_examples": 135}], "download_size": 58055195, "dataset_size": 58113436}, {"config_name": "ces_Latn", "features": [{"name": "image", "sequence": "image"}, {"name": "image_source", "dtype": "string"}, {"name": "image_source_category", "dtype": "string"}, {"name": "index", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "language", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 58099974, "num_examples": 135}], "download_size": 58050976, "dataset_size": 58099974}, {"config_name": "deu_Latn", "features": [{"name": 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"vie_Latn/test-*"}]}, {"config_name": "zho_Hans", "data_files": [{"split": "test", "path": "zho_Hans/test-*"}]}], "license": "apache-2.0", "language": ["en", "fr", "de", "es", "it", "pt", "ja", "ko", "zh", "ar", "el", "fa", "pl", "id", "cs", "he", "hi", "nl", "ro", "ru", "tr", "uk", "vi"]} | false | null | 2025-03-04T07:58:13 | 21 | 8 | false | 2dd498f62b203d95a1971610e2a62a28e6e49a55 |
Dataset Card for Aya Vision Benchmark
Dataset Details
The Aya Vision Benchmark is designed to evaluate vision-language models in real-world multilingual scenarios. It spans 23 languages and 9 distinct task categories, with 15 samples per category, resulting in 135 image-question pairs per language.
Each question requires visual context for the answer and covers languages that half of the world's population speaks, making this dataset particularly suited for… See the full description on the dataset page: https://huggingface.co/datasets/CohereForAI/AyaVisionBench. | 2,940 | 2,940 | [
"language:en",
"language:fr",
"language:de",
"language:es",
"language:it",
"language:pt",
"language:ja",
"language:ko",
"language:zh",
"language:ar",
"language:el",
"language:fa",
"language:pl",
"language:id",
"language:cs",
"language:he",
"language:hi",
"language:nl",
"language:ro",
"language:ru",
"language:tr",
"language:uk",
"language:vi",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-03-02T21:15:20 | null | null |
67c67c88c7cd2413e2c8be4d | qihoo360/Light-R1-SFTData | qihoo360 | {"license": "apache-2.0"} | false | null | 2025-03-05T13:29:23 | 19 | 8 | false | 2c5e4670b5bb44d202bfb66770c9e63fe37d30aa |
Light-R1: Surpassing R1-Distill from Scratch* with $1000 through Curriculum SFT & DPO
*from models without long COT
GitHub page
Here are the two-stage SFT data we used to train Light-R1-32B.
Simply refer to stage1-76k.json and stage2-3k.json
Model
Trained From
Release Date
AIME24
AIME25
DeepSeek-R1-Distill-Llama-70B
Llama-3.3-70B-Instruct
25.1.20
70.0
54.1
DeepSeek-R1-Distill-Qwen-32B
Qwen2.5-32B
25.1.20
72.6
54.9
LIMO (32B)
Qwen2.5-32B-Instruct
25.2.4
56.3
47.1… See the full description on the dataset page: https://huggingface.co/datasets/qihoo360/Light-R1-SFTData. | 439 | 461 | [
"license:apache-2.0",
"region:us"
] | 2025-03-04T04:07:36 | null | null |
67c68041e1331d639dcaacfe | qihoo360/Light-R1-DPOData | qihoo360 | {"license": "apache-2.0"} | false | null | 2025-03-05T13:29:37 | 15 | 8 | false | dec8bac3b4c41d5ec91a19c8caaaf8d6c78290f7 |
Light-R1: Surpassing R1-Distill from Scratch* with $1000 through Curriculum SFT & DPO
*from models without long COT
GitHub page
Here is the DPO data we used to train Light-R1-32B.
Simply refer to dpo-pairs.json
Model
Trained From
Release Date
AIME24
AIME25
DeepSeek-R1-Distill-Llama-70B
Llama-3.3-70B-Instruct
25.1.20
70.0
54.1
DeepSeek-R1-Distill-Qwen-32B
Qwen2.5-32B
25.1.20
72.6
54.9
LIMO (32B)
Qwen2.5-32B-Instruct
25.2.4
56.3
47.1
s1.1-32B
Qwen2.5-32B-Instruct… See the full description on the dataset page: https://huggingface.co/datasets/qihoo360/Light-R1-DPOData. | 258 | 262 | [
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-03-04T04:23:29 | null | null |
621ffdd236468d709f181f3d | qiaojin/PubMedQA | qiaojin | {"annotations_creators": ["expert-generated", "machine-generated"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M", "10K<n<100K", "1K<n<10K"], "source_datasets": ["original"], "task_categories": ["question-answering"], "task_ids": ["multiple-choice-qa"], "paperswithcode_id": "pubmedqa", "pretty_name": "PubMedQA", "config_names": ["pqa_artificial", "pqa_labeled", "pqa_unlabeled"], "dataset_info": [{"config_name": "pqa_artificial", "features": [{"name": "pubid", "dtype": "int32"}, {"name": "question", "dtype": "string"}, {"name": "context", "sequence": [{"name": "contexts", "dtype": "string"}, {"name": "labels", "dtype": "string"}, {"name": "meshes", "dtype": "string"}]}, {"name": "long_answer", "dtype": "string"}, {"name": "final_decision", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 443501057, "num_examples": 211269}], "download_size": 233411194, "dataset_size": 443501057}, {"config_name": "pqa_labeled", "features": [{"name": "pubid", "dtype": "int32"}, {"name": "question", "dtype": "string"}, {"name": "context", "sequence": [{"name": "contexts", "dtype": "string"}, {"name": "labels", "dtype": "string"}, {"name": "meshes", "dtype": "string"}, {"name": "reasoning_required_pred", "dtype": "string"}, {"name": "reasoning_free_pred", "dtype": "string"}]}, {"name": "long_answer", "dtype": "string"}, {"name": "final_decision", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2088898, "num_examples": 1000}], "download_size": 1075513, "dataset_size": 2088898}, {"config_name": "pqa_unlabeled", "features": [{"name": "pubid", "dtype": "int32"}, {"name": "question", "dtype": "string"}, {"name": "context", "sequence": [{"name": "contexts", "dtype": "string"}, {"name": "labels", "dtype": "string"}, {"name": "meshes", "dtype": "string"}]}, {"name": "long_answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 125922964, "num_examples": 61249}], "download_size": 66010017, "dataset_size": 125922964}], "configs": [{"config_name": "pqa_artificial", "data_files": [{"split": "train", "path": "pqa_artificial/train-*"}]}, {"config_name": "pqa_labeled", "data_files": [{"split": "train", "path": "pqa_labeled/train-*"}]}, {"config_name": "pqa_unlabeled", "data_files": [{"split": "train", "path": "pqa_unlabeled/train-*"}]}]} | false | null | 2024-03-06T01:50:16 | 183 | 7 | false | 9001f2853fb87cab8d220904e0de81ac6973b318 |
Dataset Card for [Dataset Name]
Dataset Summary
The task of PubMedQA is to answer research questions with yes/no/maybe (e.g.: Do preoperative statins reduce atrial fibrillation after coronary artery bypass grafting?) using the corresponding abstracts.
Supported Tasks and Leaderboards
The official leaderboard is available at: https://pubmedqa.github.io/.
500 questions in the pqa_labeled are used as the test set. They can be found at… See the full description on the dataset page: https://huggingface.co/datasets/qiaojin/PubMedQA. | 8,783 | 404,105 | [
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"library:polars",
"arxiv:1909.06146",
"region:us"
] | 2022-03-02T23:29:22 | null | pubmedqa |
648b556b363cf923caddc497 | Open-Orca/OpenOrca | Open-Orca | {"language": ["en"], "license": "mit", "task_categories": ["conversational", "text-classification", "token-classification", "table-question-answering", "question-answering", "zero-shot-classification", "summarization", "feature-extraction", "text-generation", "text2text-generation"], "pretty_name": "OpenOrca", "size_categories": ["10M<n<100M"]} | false | null | 2025-02-19T07:32:36 | 1,374 | 7 | false | e9c87b4abb2609913751f9b26553fdb9c061796c | 🐋 The OpenOrca Dataset! 🐋
We are thrilled to announce the release of the OpenOrca dataset!
This rich collection of augmented FLAN data aligns, as best as possible, with the distributions outlined in the Orca paper.
It has been instrumental in generating high-performing model checkpoints and serves as a valuable resource for all NLP researchers and developers!
Official Models
Mistral-7B-OpenOrca
Our latest model, the first 7B to score better overall than all… See the full description on the dataset page: https://huggingface.co/datasets/Open-Orca/OpenOrca. | 11,703 | 352,999 | [
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"task_categories:table-question-answering",
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"task_categories:zero-shot-classification",
"task_categories:summarization",
"task_categories:feature-extraction",
"task_categories:text-generation",
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"arxiv:2306.02707",
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] | 2023-06-15T18:16:11 | null | null |
65cf50a5f5a15aa42133ac44 | ruslanmv/ai-medical-chatbot | ruslanmv | {"configs": [{"config_name": "default", "data_files": [{"path": "dialogues.*", "split": "train"}]}], "dataset_info": {"dataset_size": 141665910, "download_size": 141665910, "features": [{"dtype": "string", "name": "Description"}, {"dtype": "string", "name": "Patient"}, {"dtype": "string", "name": "Doctor"}], "splits": [{"name": "train", "num_bytes": 141665910, "num_examples": 256916}]}} | false | null | 2024-03-23T20:45:11 | 219 | 7 | false | 138c99336a3afce0df88ffe6fd67bd231df25d36 |
AI Medical Chatbot Dataset
This is an experimental Dataset designed to run a Medical Chatbot
It contains at least 250k dialogues between a Patient and a Doctor.
Playground ChatBot
ruslanmv/AI-Medical-Chatbot
For furter information visit the project here:
https://github.com/ruslanmv/ai-medical-chatbot
| 3,523 | 174,555 | [
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] | 2024-02-16T12:10:13 | null | null |
666513f121aa69e38699e6d3 | UCSC-VLAA/MedTrinity-25M | UCSC-VLAA | {"language": ["en"], "size_categories": ["10M<n<100M"], "task_categories": ["question-answering"], "dataset_info": [{"config_name": "25M_full", "features": [{"name": "id", "dtype": "string"}, {"name": "file_name", "dtype": "string"}, {"name": "caption", "dtype": "string"}, {"name": "source", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 25234102586, "num_examples": 24760560}], "download_size": 7353330306, "dataset_size": 25234102586}, {"config_name": "default", "features": [{"name": "image", "dtype": "image"}, {"name": "id", "dtype": "string"}, {"name": "caption", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4781050841.25, "num_examples": 161630}], "download_size": 8300138103, "dataset_size": 4781050841.25}], "configs": [{"config_name": "25M_full", "data_files": [{"split": "train", "path": "25M_full/train-*"}]}, {"config_name": "25M_demo", "data_files": [{"split": "train", "path": "data/train-*"}]}], "tags": ["medical"]} | false | null | 2024-10-11T00:47:43 | 125 | 7 | false | 89e5c684794e5c4cc1af9e8f1a7798af7c937dbf |
Tutorial of using Medtrinity-25M
MedTrinity-25M, a comprehensive, large-scale multimodal dataset for medicine, covering over 25 million images across 10 modalities, with multigranular annotations for more than 65 diseases. These enriched annotations encompass both global textual information, such as disease/lesion type, modality, region-specific descriptions, and inter-regional relationships, as well as detailed local annotations for regions of interest (ROIs), including… See the full description on the dataset page: https://huggingface.co/datasets/UCSC-VLAA/MedTrinity-25M. | 2,575 | 9,212 | [
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"library:mlcroissant",
"library:polars",
"arxiv:2408.02900",
"region:us",
"medical"
] | 2024-06-09T02:31:13 | null | null |
6695831f2d25bd04e969b0a2 | AI-MO/NuminaMath-CoT | AI-MO | {"dataset_info": {"features": [{"name": "source", "dtype": "string"}, {"name": "problem", "dtype": "string"}, {"name": "solution", "dtype": "string"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 2495457595.0398345, "num_examples": 859494}, {"name": "test", "num_bytes": 290340.31593470514, "num_examples": 100}], "download_size": 1234351634, "dataset_size": 2495747935.355769}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "license": "apache-2.0", "task_categories": ["text-generation"], "language": ["en"], "tags": ["aimo", "math"], "pretty_name": "NuminaMath CoT"} | false | null | 2024-11-25T05:31:43 | 421 | 7 | false | 9d8d210c9f6a36c8f3cd84045668c9b7800ef517 |
Dataset Card for NuminaMath CoT
Dataset Summary
Approximately 860k math problems, where each solution is formatted in a Chain of Thought (CoT) manner. The sources of the dataset range from Chinese high school math exercises to US and international mathematics olympiad competition problems. The data were primarily collected from online exam paper PDFs and mathematics discussion forums. The processing steps include (a) OCR from the original PDFs, (b) segmentation… See the full description on the dataset page: https://huggingface.co/datasets/AI-MO/NuminaMath-CoT. | 8,228 | 43,305 | [
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
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"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"aimo",
"math"
] | 2024-07-15T20:14:23 | null | null |
67374c18c32c765810f748f6 | HuggingFaceH4/MATH-500 | HuggingFaceH4 | {"task_categories": ["text-generation"], "language": ["en"], "pretty_name": "MATH-500"} | false | null | 2024-11-15T13:36:00 | 124 | 7 | false | ff5b20257d8185524591543f8ff5993951537bb8 |
Dataset Card for MATH-500
This dataset contains a subset of 500 problems from the MATH benchmark that OpenAI created in their Let's Verify Step by Step paper. See their GitHub repo for the source file: https://github.com/openai/prm800k/tree/main?tab=readme-ov-file#math-splits
| 49,326 | 79,668 | [
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"library:pandas",
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"library:polars",
"region:us"
] | 2024-11-15T13:26:48 | 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 | 272 | 7 | 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. | 7,104 | 11,466 | [
"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 |
67a89e79556fa47a174b6c7b | agentica-org/DeepScaleR-Preview-Dataset | agentica-org | {"language": ["en"], "license": "mit", "size_categories": ["10K<n<100K"]} | false | null | 2025-02-10T09:51:18 | 78 | 7 | false | b6ae8c60f5c1f2b594e2140b91c49c9ad0949e29 |
Data
Our training dataset consists of approximately 40,000 unique mathematics problem-answer pairs compiled from:
AIME (American Invitational Mathematics Examination) problems (1984-2023)
AMC (American Mathematics Competition) problems (prior to 2023)
Omni-MATH dataset
Still dataset
Format
Each row in the JSON dataset contains:
problem: The mathematical question text, formatted with LaTeX notation.
solution: Offical solution to the problem, including LaTeX formatting… See the full description on the dataset page: https://huggingface.co/datasets/agentica-org/DeepScaleR-Preview-Dataset. | 3,655 | 3,661 | [
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"license:mit",
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"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-02-09T12:24:25 | null | null |
67c86c8dc9d9b73fb0d64647 | Rapidata/Translation-deepseek-llama-mixtral-v-deepl | Rapidata | {"dataset_info": {"features": [{"name": "original_text", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "total_responses", "dtype": "int64"}, {"name": "weighted_votes_1", "dtype": "float64"}, {"name": "weighted_votes_2", "dtype": "float64"}, {"name": "translation_model_1", "dtype": "string"}, {"name": "translation_model_2", "dtype": "string"}, {"name": "model1", "dtype": "string"}, {"name": "model2", "dtype": "string"}, {"name": "detailed_results", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 9109276, "num_examples": 845}], "download_size": 1025479, "dataset_size": 9109276}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "task_categories": ["translation"], "tags": ["translation", "humanfeedback", "deepseek-r1", "deepl", "llama", "mixtral", "DE", "PT", "ES", "FR"]} | false | null | 2025-03-10T12:25:10 | 14 | 7 | false | 196f2af0327d0dfb19e08b92358332482cd5836c |
If you get value from this dataset and would like to see more in the future, please consider liking it.
Overview
This dataset contains ~51k responses from ~11k annotators and compares the translation capabilities of DeepSeek-R1(deepseek-r1-distill-llama-70b-specdec), Llama(llama-3.3-70b-specdec) and Mixtral(mixtral-8x7b-32768) against DeepL across different languages. The comparison involved 100 distinct questions in 4 languages, with each translation being rated by 51 native… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/Translation-deepseek-llama-mixtral-v-deepl. | 274 | 274 | [
"task_categories:translation",
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"modality:text",
"library:datasets",
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"library:polars",
"region:us",
"translation",
"humanfeedback",
"deepseek-r1",
"deepl",
"llama",
"mixtral",
"DE",
"PT",
"ES",
"FR"
] | 2025-03-05T15:23:57 | null | null |
63fd919c9ac81ec88b0ae4fd | lansinuote/ChnSentiCorp | lansinuote | null | false | null | 2023-02-28T05:31:30 | 31 | 6 | false | b0c4c119c3fb33b8e735969202ef9ad13d717e5a | null | 3,103 | 42,644 | [
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"region:us"
] | 2023-02-28T05:31:08 | null | null |
640f5b2fb63b6f18522d6d44 | tatsu-lab/alpaca | tatsu-lab | {"license": "cc-by-nc-4.0", "language": ["en"], "tags": ["instruction-finetuning"], "pretty_name": "Alpaca", "task_categories": ["text-generation"]} | false | null | 2023-05-22T20:33:36 | 741 | 6 | false | dce01c9b08f87459cf36a430d809084718273017 |
Dataset Card for Alpaca
Dataset Summary
Alpaca is a dataset of 52,000 instructions and demonstrations generated by OpenAI's text-davinci-003 engine. This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better.
The authors built on the data generation pipeline from Self-Instruct framework and made the following modifications:
The text-davinci-003 engine to generate the instruction data instead… See the full description on the dataset page: https://huggingface.co/datasets/tatsu-lab/alpaca. | 36,360 | 1,266,778 | [
"task_categories:text-generation",
"language:en",
"license:cc-by-nc-4.0",
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"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"instruction-finetuning"
] | 2023-03-13T17:19:43 | null | null |
641edcf5485c37e54d68fb57 | shibing624/alpaca-zh | shibing624 | {"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 32150579, "num_examples": 48818}], "download_size": 35100559, "dataset_size": 32150579}, "license": "cc-by-4.0", "language": ["zh"], "pretty_name": "Instruction Tuning with GPT-4", "size_categories": ["10K<n<100K"], "task_categories": ["text-generation"], "tags": ["gpt", "alpaca", "fine-tune", "instruct-tune", "instruction"]} | false | null | 2023-05-10T06:09:06 | 103 | 6 | false | f39db019a94f8dbea48ab30d2bdc090703284559 |
Dataset Card for "alpaca-zh"
本数据集是参考Alpaca方法基于GPT4得到的self-instruct数据，约5万条。
Dataset from https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM
It is the chinese dataset from https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM/blob/main/data/alpaca_gpt4_data_zh.json
Usage and License Notices
The data is intended and licensed for research use only. The dataset is CC BY NC 4.0 (allowing only non-commercial use) and models trained using the dataset should… See the full description on the dataset page: https://huggingface.co/datasets/shibing624/alpaca-zh. | 510 | 6,653 | [
"task_categories:text-generation",
"language:zh",
"license:cc-by-4.0",
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
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"library:mlcroissant",
"library:polars",
"arxiv:2304.03277",
"region:us",
"gpt",
"alpaca",
"fine-tune",
"instruct-tune",
"instruction"
] | 2023-03-25T11:37:25 | null | null |
647de2bd5214d172cbb8541e | ccmusic-database/timbre_range | ccmusic-database | {"license": "mit", "task_categories": ["audio-classification"], "language": ["zh", "en"], "tags": ["music", "art"], "pretty_name": "Timbre and Range Dataset", "size_categories": ["1K<n<10K"], "dataset_info": [{"config_name": "timbre", "features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 44100}}}, {"name": "mel", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "Base", "1": "Split", "2": "Short"}}}}, {"name": "score1", "dtype": "float64"}, {"name": "score2", "dtype": "float64"}, {"name": "avg_score", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 213644, "num_examples": 537}, {"name": "validation", "num_bytes": 26664, "num_examples": 67}, {"name": "test", "num_bytes": 27088, "num_examples": 68}], "download_size": 595425921, "dataset_size": 267396}, {"config_name": "range", "features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 44100}}}, {"name": "mel", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "Narrow", "1": "Moderate", "2": "Wide"}}}}], "splits": [{"name": "train", "num_bytes": 210052, "num_examples": 580}, {"name": "validation", "num_bytes": 26462, "num_examples": 73}, {"name": "test", "num_bytes": 26400, "num_examples": 73}], "download_size": 65309164, "dataset_size": 262914}], "configs": [{"config_name": "timbre", "data_files": [{"split": "train", "path": "timbre/train/data-*.arrow"}, {"split": "validation", "path": "timbre/validation/data-*.arrow"}, {"split": "test", "path": "timbre/test/data-*.arrow"}]}, {"config_name": "range", "data_files": [{"split": "train", "path": "range/train/data-*.arrow"}, {"split": "validation", "path": "range/validation/data-*.arrow"}, {"split": "test", "path": "range/test/data-*.arrow"}]}]} | false | null | 2025-02-16T03:24:49 | 9 | 6 | false | 242afee6bc5d2361e9afa0e4d57daa5a9ec9799e |
Dataset Card for Timbre and Range Dataset
Dataset Summary
The timbre dataset contains acapella singing audio of 9 singers, as well as cut single-note audio, totaling 775 clips (.wav format)
The vocal range dataset includes several up and down chromatic scales audio clips of several vocals, as well as the cut single-note audio clips (.wav format).
Supported Tasks and Leaderboards
Audio classification
Languages
Chinese, English
Dataset… See the full description on the dataset page: https://huggingface.co/datasets/ccmusic-database/timbre_range. | 809 | 854 | [
"task_categories:audio-classification",
"language:zh",
"language:en",
"license:mit",
"size_categories:1K<n<10K",
"format:arrow",
"modality:audio",
"modality:image",
"library:datasets",
"library:mlcroissant",
"region:us",
"music",
"art"
] | 2023-06-05T13:27:25 | null | null |
6587ff94509bcae23f71024d | OpenAssistant/oasst2 | 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": 158850455, "num_examples": 128575}, {"name": "validation", "num_bytes": 7963122, "num_examples": 6599}], "download_size": 66674129, "dataset_size": 166813577}, "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 Release 2"} | false | null | 2024-01-11T06:09:29 | 244 | 6 | false | 179dd21fc55192153d94adb0e0ce8f69e222bf75 |
Open Assistant Conversations Dataset Release 2 (OASST2)
Dataset Structure
This dataset contains message trees. Each message tree has an initial prompt message as the root node,
which can have multiple child messages as replies, and these child messages can have multiple replies.
All messages have a role property: this can either be "assistant" or "prompter". The roles in
conversation threads from prompt to leaf node strictly alternate between "prompter" and… See the full description on the dataset page: https://huggingface.co/datasets/OpenAssistant/oasst2. | 1,944 | 41,279 | [
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] | 2023-12-24T09:53:24 | null | null |
66299f1f4f9d8e75f2a8a6b0 | simon3000/genshin-voice | simon3000 | {"language": ["zh", "en", "ja", "ko"], "task_categories": ["audio-classification", "automatic-speech-recognition", "text-to-speech"], "pretty_name": "Genshin Voice", "dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "transcription", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "speaker", "dtype": "string"}, {"name": "speaker_type", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "inGameFilename", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 264598217401.752, "num_examples": 463383}], "download_size": 227704444125, "dataset_size": 264598217401.752}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | false | null | 2024-08-30T08:36:05 | 81 | 6 | false | ffe17e2e7938508a73255ec294b0ded17ed071f1 |
Genshin Voice
Genshin Voice is a dataset of voice lines from the popular game Genshin Impact.
Hugging Face 🤗 Genshin-Voice
Last update at 2024-08-30
463383 wavs
20231 without speaker (4%)
24819 without transcription (5%)
602 without inGameFilename (0%)
Dataset Details
Dataset Description
The dataset contains voice lines from the game's characters in multiple languages, including Chinese, English, Japanese, and Korean.
The voice lines are spoken… See the full description on the dataset page: https://huggingface.co/datasets/simon3000/genshin-voice. | 4,208 | 21,510 | [
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] | 2024-04-25T00:09:03 | null | null |
6633666e029719b0b4336edd | RekaAI/VibeEval | RekaAI | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "reference", "dtype": "string"}, {"name": "example_id", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "media_url", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 212934461, "num_examples": 269}], "download_size": 5175222, "dataset_size": 212934461}, "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "vibe-eval.v1.parquet"}]}], "license": "apache-2.0", "task_categories": ["image-to-text", "image-classification"], "language": ["en", "pl", "zh", "ja"], "size_categories": ["n<1K"], "tags": ["Reka", "Vibe", "Eval", "VibeEval", "Vibe-Eval", "Hard"], "pretty_name": "Vibe-Eval"} | false | null | 2024-12-12T22:53:57 | 45 | 6 | false | 3bdc7c30f09938557a21f1918d6c8c28fd5978f8 |
Vibe-Eval
A benchmark for evaluating multimodal chat models, including especially challenging examples.
[Link to paper] [Blogpost] [Github]
Dataset
Each example has the following fields:
example_id: a unique ID for the example
category: the category that this example belongs to, either difficulty-normal or difficulty-hard
prompt: the user prompt
reference: a golden reference answer for the prompt
image: an image struct (containing bytes and path keys).… See the full description on the dataset page: https://huggingface.co/datasets/RekaAI/VibeEval. | 3,821 | 30,487 | [
"task_categories:image-to-text",
"task_categories:image-classification",
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"Eval",
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] | 2024-05-02T10:09:50 | null | null |
672d8bf4bde669ec7e63ba72 | allenai/tulu-3-sft-mixture | allenai | {"annotations_creators": ["crowdsourced", "expert-generated", "machine-generated"], "language": ["amh", "arb", "ary", "ars", "acq", "arz", "apc", "ben", "ceb", "dan", "deu", "ell", "eng", "eus", "fil", "fin", "fra", "gle", "guj", "hat", "hau", "hin", "hun", "ibo", "ind", "ita", "jav", "jpn", "kan", "kir", "kor", "kur", "lit", "mal", "mar", "mlg", "msa", "mya", "nep", "nld", "nso", "nya", "pan", "pes", "pol", "por", "pus", "rus", "sin", "sna", "snd", "som", "spa", "sqi", "srp", "sun", "swa", "swe", "tam", "tel", "tha", "tur", "ukr", "urd", "vie", "wol", "xho", "yor", "zho", "zul"], "license": "odc-by", "multilinguality": ["multilingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["allenai/coconot", "ai2-adapt-dev/flan_v2_converted", "HuggingFaceH4/no_robots", "OpenAssistant/oasst1", "allenai/tulu-3-personas-math", "allenai/tulu-3-sft-personas-math-grade", "allenai/tulu-3-sft-personas-code", "allenai/tulu-3-personas-algebra", "allenai/tulu-3-sft-personas-instruction-following", "AI-MO/NuminaMath-TIR", "allenai/wildguardmix", "allenai/wildjailbreak", "allenai/tulu-3-hard-coded", "CohereForAI/aya_dataset", "allenai/WildChat-1M", "LipengCS/Table-GPT", "allenai/SciRIFF", "theblackcat102/evol-codealpaca-v1"], "task_categories": ["other"], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "source", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2914250826.5647593, "num_examples": 939343}], "download_size": 1412954868, "dataset_size": 2914250826.5647593}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | false | null | 2024-12-02T19:48:33 | 122 | 6 | false | b14afda60f1bbebe55d5d2fa1e4df5042f97f8be |
Tulu 3 SFT Mixture
Note that this collection is licensed under ODC-BY-1.0 license; different licenses apply to subsets of the data. Some portions of the dataset are non-commercial. We present the mixture as a research artifact.
The Tulu 3 SFT mixture was used to train the Tulu 3 series of models.
It contains 939,344 samples from the following sets:
CoCoNot (ODC-BY-1.0), 10,983 prompts (Brahman et al., 2024)
FLAN v2 via ai2-adapt-dev/flan_v2_converted, 89,982 prompts (Longpre… See the full description on the dataset page: https://huggingface.co/datasets/allenai/tulu-3-sft-mixture. | 5,762 | 20,021 | [
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"language:ibo",
"language:ind",
"language:ita",
"language:jav",
"language:jpn",
"language:kan",
"language:kir",
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"language:mal",
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"language:msa",
"language:mya",
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"language:nld",
"language:nso",
"language:nya",
"language:pan",
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"language:pol",
"language:por",
"language:pus",
"language:rus",
"language:sin",
"language:sna",
"language:snd",
"language:som",
"language:spa",
"language:sqi",
"language:srp",
"language:sun",
"language:swa",
"language:swe",
"language:tam",
"language:tel",
"language:tha",
"language:tur",
"language:ukr",
"language:urd",
"language:vie",
"language:wol",
"language:xho",
"language:yor",
"language:zho",
"language:zul",
"license:odc-by",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2024-11-08T03:56:36 | null | null |
6763e94724dee5a47c7c77f7 | agibot-world/AgiBotWorld-Alpha | agibot-world | {"pretty_name": "AgiBot World", "size_categories": ["n>1T"], "task_categories": ["other"], "language": ["en"], "tags": ["real-world", "dual-arm", "Robotics manipulation"], "extra_gated_prompt": "### AgiBot World COMMUNITY LICENSE AGREEMENT\nAgiBot World Alpha Release Date: December 30, 2024 All the data and code within this repo are under [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/).", "extra_gated_fields": {"First Name": "text", "Last Name": "text", "Email": "text", "Country": "country", "Affiliation": "text", "Phone": "text", "Job title": {"type": "select", "options": ["Student", "Research Graduate", "AI researcher", "AI developer/engineer", "Reporter", "Other"]}, "Research interest": "text", "geo": "ip_location", "By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the AgiBot Privacy Policy": "checkbox"}, "extra_gated_description": "The information you provide will be collected, stored, processed and shared in accordance with the AgiBot Privacy Policy.", "extra_gated_button_content": "Submit"} | false | null | 2025-03-09T15:17:34 | 179 | 6 | false | b2bc3ed265ae8222b47fabcfdffe4b7c29e81f08 |
Key Features 🔑
1 million+ trajectories from 100 robots.
100+ real-world scenarios across 5 target domains.
Cutting-edge hardware: visual tactile sensors / 6-DoF dexterous hand / mobile dual-arm robots
Tasks involving:
Contact-rich manipulation
Long-horizon planning
Multi-robot collaboration
Your browser does not support the video tag.
Your browser does not support the video tag.… See the full description on the dataset page: https://huggingface.co/datasets/agibot-world/AgiBotWorld-Alpha. | 10,768 | 42,853 | [
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"region:us",
"real-world",
"dual-arm",
"Robotics manipulation"
] | 2024-12-19T09:37:11 | null | null |
678fb3f7d9e650c691046336 | smolagents/benchmark-v1 | smolagents | {"dataset_info": [{"config_name": "gaia", "features": [{"name": "question", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "true_answer", "dtype": "string"}, {"name": "true_reasoning", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 13326, "num_examples": 32}], "download_size": 15278, "dataset_size": 13326}, {"config_name": "math", "features": [{"name": "question", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "true_answer", "dtype": "string"}, {"name": "true_reasoning", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 37643, "num_examples": 50}], "download_size": 34377, "dataset_size": 37643}, {"config_name": "simpleqa", "features": [{"name": "question", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "true_answer", "dtype": "string"}, {"name": "true_reasoning", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 20310, "num_examples": 50}], "download_size": 15128, "dataset_size": 20310}], "configs": [{"config_name": "gaia", "data_files": [{"split": "test", "path": "gaia/test-*"}]}, {"config_name": "math", "data_files": [{"split": "test", "path": "math/test-*"}]}, {"config_name": "simpleqa", "data_files": [{"split": "test", "path": "simpleqa/test-*"}]}]} | false | null | 2025-03-04T04:07:23 | 6 | 6 | false | f5168b2c6e0cb8b2c92e2df4776e3f6de71e0f12 | null | 273 | 484 | [
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-01-21T14:49:27 | null | null |
67a404bc8c6d42c5ec097433 | Anthropic/EconomicIndex | Anthropic | {"license": "mit", "pretty_name": "EconomicIndex", "tags": ["text"], "viewer": true, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "onet_task_mappings.csv"}]}]} | false | null | 2025-02-10T19:28:32 | 189 | 6 | false | 218b35116baa43c55beffe61f243bd81f5f84cf8 |
Overview
This directory contains O*NET task mapping and automation vs. augmentation data from "Which Economic Tasks are Performed with AI? Evidence from Millions of Claude Conversations." The data and provided analysis are described below.
Please see our blog post and paper for further visualizations and complete analysis.
Data
SOC_Structure.csv - Standard Occupational Classification (SOC) system hierarchy from the U.S. Department of Labor O*NET database… See the full description on the dataset page: https://huggingface.co/datasets/Anthropic/EconomicIndex. | 3,848 | 7,421 | [
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"size_categories:1K<n<10K",
"format:csv",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"text"
] | 2025-02-06T00:39:24 | null | null |
67b1294d248b7168800e670b | Jofthomas/hermes-function-calling-thinking-V1 | Jofthomas | {"dataset_info": {"features": [{"name": "conversations", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 13793436, "num_examples": 3570}], "download_size": 3848966, "dataset_size": 13793436}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | false | null | 2025-02-16T08:36:10 | 23 | 6 | false | ee5bf6e5737351f5d444b72689f7ab0ad37fc75f | null | 6,615 | 6,615 | [
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"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-02-15T23:54:53 | null | null |
67b1e180626cd810348053df | TencentARC/VPBench | TencentARC | {"language": ["en"], "tags": ["video", "video inpainting", "video editing"]} | false | null | 2025-03-13T07:52:40 | 6 | 6 | false | bd87782c5b53346401d6e6ec0d0e74a1385bd94e |
VideoPainter
This repository contains the implementation of the paper "VideoPainter: Any-length Video Inpainting and Editing with Plug-and-Play Context Control"
Keywords: Video Inpainting, Video Editing, Video Generation
Yuxuan Bian12, Zhaoyang Zhang1‡, Xuan Ju2, Mingdeng Cao3, Liangbin Xie4, Ying Shan1, Qiang Xu2✉
1ARC Lab, Tencent PCG 2The Chinese University of Hong Kong 3The University of Tokyo 4University of Macau ‡Project Lead ✉Corresponding Author
Your like and… See the full description on the dataset page: https://huggingface.co/datasets/TencentARC/VPBench. | 391 | 397 | [
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"video editing"
] | 2025-02-16T13:00:48 | null | null |
67bb225319e9dba50eec30ed | THUIR/Qilin | THUIR | {"license": "apache-2.0", "configs": [{"config_name": "dqa", "data_files": [{"split": "train", "path": "dqa/train-*"}]}, {"config_name": "notes", "data_files": [{"split": "train", "path": "notes/train-*"}]}, {"config_name": "recommendation_test", "data_files": [{"split": "train", "path": "recommendation_test/train-*"}]}, {"config_name": "recommendation_train", "data_files": [{"split": "train", "path": "recommendation_train/train-*"}]}, {"config_name": "search_test", "data_files": [{"split": "train", "path": "search_test/train-*"}]}, {"config_name": "search_train", "data_files": [{"split": "train", "path": "search_train/train-*"}]}, {"config_name": "user_feat", "data_files": [{"split": "train", "path": "user_feat/train-*"}]}], "dataset_info": [{"config_name": "dqa", "features": [{"name": "query", "dtype": "string"}, {"name": "query_from_type", "dtype": "float64"}, {"name": "dqa_output", "dtype": "string"}, {"name": "is_like_clk", "dtype": 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Qilin
Qilin is a large-scale multimodal dataset designed for advancing research in search, recommendation, and Retrieval-Augmented Generation (RAG) systems. This repository contains the official implementation of the dataset paper, baseline models, and evaluation tools. This dataset was presented in Qilin: A Multimodal Information Retrieval Dataset with APP-level User Sessions.
Github: https://github.com/RED-Search/Qilin
Dataset Overview
Qilin provides comprehensive… See the full description on the dataset page: https://huggingface.co/datasets/THUIR/Qilin. | 720 | 720 | [
"task_categories:question-answering",
"task_categories:text-classification",
"task_categories:sentence-similarity",
"task_categories:text-retrieval",
"task_categories:image-text-to-text",
"language:zh",
"license:apache-2.0",
"size_categories:1M<n<10M",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2503.00501",
"region:us"
] | 2025-02-23T13:27:47 | null | null |
67c2955fbe05f412aa264278 | KodCode/KodCode-V1-SFT-R1 | KodCode | {"language": ["en"], "license": "cc-by-nc-4.0", "size_categories": ["100K<n<1M"], "task_categories": ["coding"], "dataset_info": {"features": [{"name": "style", "dtype": "string"}, {"name": "subset", "dtype": "string"}, {"name": "question_id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "solution", "dtype": "string"}, {"name": "test_code", "dtype": "string"}, {"name": "test_info", "list": [{"name": "docstring", "dtype": "string"}, {"name": "function_declaration", "dtype": "string"}, {"name": "function_name", "dtype": "string"}, {"name": "parameter_list", "dtype": "string"}]}, {"name": "gpt_pass_sequence", "sequence": "int64"}, {"name": "gpt_pass_trial_num", "dtype": "int64"}, {"name": "gpt_difficulty", "dtype": "string"}, {"name": "gpt_pass_percentage", "dtype": "float64"}, {"name": "r1_pass_sequence", "sequence": "int64"}, {"name": "r1_pass_trial_num", "dtype": "int64"}, {"name": "r1_correctness", "dtype": "string"}, {"name": "r1_solution", "dtype": "string"}, {"name": "metadata", "struct": [{"name": "original_instruction", "dtype": "string"}, {"name": "prompt_id", "dtype": "string"}, {"name": "row_id", "dtype": "int64"}, {"name": "seed_ids", "dtype": "string"}]}, {"name": "conversations", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 5020832193, "num_examples": 245937}, {"name": "incorrect", "num_bytes": 4942343914, "num_examples": 192557}, {"name": "use_with_caution", "num_bytes": 94651247, "num_examples": 4439}], "download_size": 4213334184, "dataset_size": 10057827354}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "incorrect", "path": "data/incorrect-*"}, {"split": "use_with_caution", "path": "data/use_with_caution-*"}]}]} | false | null | 2025-03-09T20:53:53 | 16 | 6 | false | ae0c7208b5542c180493fe03d7f17e715e3d4805 |
🐱 KodCode: A Diverse, Challenging, and Verifiable Synthetic Dataset for Coding
KodCode is the largest fully-synthetic open-source dataset providing verifiable solutions and tests for coding tasks. It contains 12 distinct subsets spanning various domains (from algorithmic to package-specific knowledge) and difficulty levels (from basic coding exercises to interview and competitive programming challenges). KodCode is designed for both supervised fine-tuning (SFT) and RL tuning.
🕸️… See the full description on the dataset page: https://huggingface.co/datasets/KodCode/KodCode-V1-SFT-R1. | 3,308 | 3,308 | [
"language:en",
"license:cc-by-nc-4.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2503.02951",
"region:us"
] | 2025-03-01T05:04:31 | null | null |
67c48f19f7f4baec22f2acbf | OOPPEENN/ASMR_Dataset | OOPPEENN | {"license": "gpl-3.0"} | false | null | 2025-03-13T09:16:08 | 7 | 6 | false | 445a158047d68168c1f94a8f60d1330e702e5c36 | crawl progress: 57% 121249/213330
pipeline:
Manual cleaning, The following situations will be discarded:
no Chinese subtitles
timestamps of each sentence in the subtitles are connected at the end
timestamps are not aligned
mel band roformer remove SE, BGM, saliva sound, etc.
parsec subtitle & cut audio
Split the left and right channels and use anime-whisper to identify the loudest channel(beta version, maybe there is a better way)
create index
| 42 | 42 | [
"license:gpl-3.0",
"region:us"
] | 2025-03-02T17:02:17 | null | null |
67cd79d1fb6e84e9a5253abf | secemp9/instruction_solution_thought | secemp9 | null | false | null | 2025-03-09T12:28:10 | 6 | 6 | false | ea74ee1b03596d88ef973ea0ba67fdb0b9a14c0c |
instruction_solution_thought
This is the merge of 3 datasets, namely:
https://huggingface.co/datasets/bespokelabs/Bespoke-Stratos-17k
https://huggingface.co/datasets/open-thoughts/OpenThoughts-114k
https://huggingface.co/datasets/open-r1/OpenThoughts-114k-math
I plan on scaling this to other datasets soon
| 105 | 105 | [
"size_categories:100K<n<1M",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-03-09T11:21:53 | null | null |
67d02cf2aae1ca33d94c3671 | WestlakeNLP/DeepReview-13K | WestlakeNLP | {"license": "other", "license_name": "deepreviewer-license", "language": ["en", "zh", "ja", "ko", "fr", "de"], "extra_gated_prompt": "You agree to not use the dataset for official peer reviews in any capacity.", "extra_gated_fields": {"First Name": "text", "Last Name": "text", "Country": "country", "Affiliation": "text", "I want to use this dataset for": {"type": "select", "options": ["Paper Improvement", "Writing Practice", "Self-assessment Tool", "Learning Aid", "Feedback Simulation", "Revision Guide", "Concept Validator", "Reward Model", "Educational Resource", "Research Assistant", "Supplementary Tool", {"label": "Other", "value": "other"}]}, "I agree to DO NOT use this dataset for Official Reviews and Publication Decisions": "checkbox"}, "extra_gated_button_content": "Submit"} | false | null | 2025-03-11T17:11:47 | 6 | 6 | false | 7b9153bd15aaf3d034644e93317790b9eb845543 |
Dataset Info: DeepReviewer-13K
Homepage & Demo: http://ai-researcher.net
DeepReviewer-13K is a curated dataset designed to facilitate the training of Large Language Models (LLMs) for academic paper review. It captures a human-like deep thinking process in peer review, featuring structured annotations and intermediate reasoning steps. This dataset addresses the scarcity of high-quality, structured review data and provides a valuable resource for advancing automated scientific… See the full description on the dataset page: https://huggingface.co/datasets/WestlakeNLP/DeepReview-13K. | 87 | 87 | [
"language:en",
"language:zh",
"language:ja",
"language:ko",
"language:fr",
"language:de",
"license:other",
"size_categories:10K<n<100K",
"format:csv",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-03-11T12:30:42 | null | null |
62d6d5afa543b9b79216a8a6 | deepmind/code_contests | deepmind | {"annotations_creators": ["found"], "language_creators": ["found"], "language": ["en"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["translation"], "task_ids": [], "paperswithcode_id": "codecontests", "pretty_name": "CodeContests"} | false | null | 2023-06-11T12:22:30 | 154 | 5 | false | 802411c3010cb00d1b05bad57ca77365a3c699d6 |
Dataset Card for CodeContests
Dataset Summary
CodeContests is a competitive programming dataset for machine-learning. This
dataset was used when training AlphaCode.
It consists of programming problems, from a variety of sources:
Site
URL
Source
Aizu
https://judge.u-aizu.ac.jp
CodeNet
AtCoder
https://atcoder.jp
CodeNet
CodeChef
https://www.codechef.com
description2code
Codeforces
https://codeforces.com
description2code and Codeforces
HackerEarth… See the full description on the dataset page: https://huggingface.co/datasets/deepmind/code_contests. | 10,953 | 102,327 | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2203.07814",
"arxiv:2105.12655",
"region:us"
] | 2022-07-19T16:02:55 | null | codecontests |
639244f571c51c43091df168 | Anthropic/hh-rlhf | Anthropic | {"license": "mit", "tags": ["human-feedback"]} | false | null | 2023-05-26T18:47:34 | 1,290 | 5 | 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. | 12,773 | 1,553,637 | [
"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 |
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 | 655 | 5 | 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. | 24,913 | 605,354 | [
"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 |
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 | 1,360 | 5 | 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. | 9,886 | 246,977 | [
"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 |
649f37af37bfb5202beabdf4 | allenai/dolma | allenai | {"license": "odc-by", "viewer": false, "task_categories": ["text-generation"], "language": ["en"], "tags": ["language-modeling", "casual-lm", "llm"], "pretty_name": "Dolma", "size_categories": ["n>1T"]} | false | null | 2024-04-17T02:57:00 | 882 | 5 | false | 7f48140530a023e9ea4c5cfb141160922727d4d3 | Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research | 1,595 | 352,530 | [
"task_categories:text-generation",
"language:en",
"license:odc-by",
"size_categories:n>1T",
"arxiv:2402.00159",
"arxiv:2301.13688",
"region:us",
"language-modeling",
"casual-lm",
"llm"
] | 2023-06-30T20:14:39 | @article{dolma,
title = {{Dolma: An Open Corpus of Three Trillion Tokens for Language Model Pretraining Research}},
author = {
Luca Soldaini and Rodney Kinney and Akshita Bhagia and Dustin Schwenk and David Atkinson and
Russell Authur and Ben Bogin and Khyathi Chandu and Jennifer Dumas and Yanai Elazar and
Valentin Hofmann and Ananya Harsh Jha and Sachin Kumar and Li Lucy and Xinxi Lyu and Ian Magnusson and
Jacob Morrison and Niklas Muennighoff and Aakanksha Naik and Crystal Nam and Matthew E. Peters and
Abhilasha Ravichander and Kyle Richardson and Zejiang Shen and Emma Strubell and Nishant Subramani and
Oyvind Tafjord and Evan Pete Walsh and Hannaneh Hajishirzi and Noah A. Smith and Luke Zettlemoyer and
Iz Beltagy and Dirk Groeneveld and Jesse Dodge and Kyle Lo
},
year = {2024},
journal={arXiv preprint},
} | null |
65d2675495e8d86e2fe4124d | HuggingFaceTB/cosmopedia | HuggingFaceTB | {"dataset_info": [{"config_name": "auto_math_text", "features": [{"name": "prompt", "dtype": "string"}, {"name": "text_token_length", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "seed_data", "dtype": "string"}, {"name": "format", "dtype": "string"}, {"name": "audience", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 8777587297.907892, "num_examples": 1949895}], "download_size": 4461401898, "dataset_size": 8777587297.907892}, {"config_name": "khanacademy", "features": [{"name": "prompt", "dtype": "string"}, {"name": "text_token_length", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "seed_data", "dtype": "string"}, {"name": "format", "dtype": "string"}, {"name": "audience", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 108591354.09210858, "num_examples": 24123}], "download_size": 49139761, "dataset_size": 108591354.09210858}, {"config_name": "openstax", "features": [{"name": "text_token_length", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "seed_data", "dtype": "string"}, {"name": "format", "dtype": "string"}, {"name": "audience", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 667837450, "num_examples": 126332}], "download_size": 346992522, "dataset_size": 667837450}, {"config_name": "stanford", "features": [{"name": "text_token_length", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "seed_data", "dtype": "string"}, {"name": "format", "dtype": "string"}, {"name": "audience", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6341291506, "num_examples": 1020024}], "download_size": 3302284560, "dataset_size": 6341291506}, {"config_name": "stories", "features": [{"name": "text", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "text_token_length", "dtype": "int64"}, {"name": "seed_data", "dtype": "string"}, {"name": "format", "dtype": "string"}, {"name": "audience", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 21314739648, "num_examples": 4992964}], "download_size": 11902294709, "dataset_size": 21314739648}, {"config_name": "web_samples_v1", "features": [{"name": "text_token_length", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "seed_data", "dtype": "string"}, {"name": "format", "dtype": "string"}, {"name": "audience", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 69075726295, "num_examples": 12426348}], "download_size": 38978124936, "dataset_size": 69075726295}, {"config_name": "web_samples_v2", "features": [{"name": "text_token_length", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "seed_data", "dtype": "string"}, {"name": "format", "dtype": "string"}, {"name": "audience", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 58711802939, "num_examples": 10345867}], "download_size": 32658254617, "dataset_size": 58711802939}, {"config_name": "wikihow", "features": [{"name": "text_token_length", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "seed_data", "dtype": "string"}, {"name": "format", "dtype": "string"}, {"name": "audience", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 892720528, "num_examples": 179191}], "download_size": 502284600, "dataset_size": 892720528}], "configs": [{"config_name": "auto_math_text", "data_files": [{"split": "train", "path": "data/auto_math_text/train-*"}]}, {"config_name": "khanacademy", "data_files": [{"split": "train", "path": "data/khanacademy/train-*"}]}, {"config_name": "openstax", "data_files": [{"split": "train", "path": "data/openstax/train-*"}]}, {"config_name": "stanford", "data_files": [{"split": "train", "path": "data/stanford/train-*"}]}, {"config_name": "stories", "data_files": [{"split": "train", "path": "data/stories/train-*"}]}, {"config_name": "web_samples_v1", "data_files": [{"split": "train", "path": "data/web_samples_v1/train-*"}]}, {"config_name": "web_samples_v2", "data_files": [{"split": "train", "path": "data/web_samples_v2/train-*"}]}, {"config_name": "wikihow", "data_files": [{"split": "train", "path": "data/wikihow/train-*"}]}], "license": "apache-2.0", "language": ["en"], "tags": ["synthetic"]} | false | null | 2024-08-12T22:05:49 | 593 | 5 | false | 0ae6ec63f91742bd2d1eaef4f02232c55d719385 |
Cosmopedia v0.1
Image generated by DALL-E, the prompt was generated by Mixtral-8x7B-Instruct-v0.1
Note: Cosmopedia v0.2 is available at smollm-corpus
User: What do you think "Cosmopedia" could mean? Hint: in our case it's not related to cosmology.
Mixtral-8x7B-Instruct-v0.1: A possible meaning for "Cosmopedia" could be an encyclopedia or collection of information about
different cultures, societies, and topics from around the world, emphasizing diversity and global… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceTB/cosmopedia. | 26,834 | 150,111 | [
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"size_categories:10M<n<100M",
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"region:us",
"synthetic"
] | 2024-02-18T20:23:48 | null | null |
6655eb19d17e141dcb546ed5 | HuggingFaceFW/fineweb-edu | HuggingFaceFW | {"license": "odc-by", "task_categories": ["text-generation"], "language": ["en"], "pretty_name": "FineWeb-Edu", "size_categories": ["n>1T"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/*/*"}], "features": [{"name": "text", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "dump", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "date", "dtype": "string"}, {"name": "file_path", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "language_score", "dtype": "float64"}, {"name": "token_count", "dtype": "int64"}, {"name": "score", "dtype": "float64"}, {"name": "int_score", "dtype": "int64"}]}, {"config_name": "sample-10BT", "data_files": [{"split": "train", "path": "sample/10BT/*"}]}, {"config_name": "sample-100BT", "data_files": [{"split": "train", "path": "sample/100BT/*"}]}, {"config_name": "sample-350BT", "data_files": 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📚 FineWeb-Edu
1.3 trillion tokens of the finest educational data the 🌐 web has to offer
Paper: https://arxiv.org/abs/2406.17557
What is it?
📚 FineWeb-Edu dataset consists of 1.3T tokens and 5.4T tokens (FineWeb-Edu-score-2) of educational web pages filtered from 🍷 FineWeb dataset. This is the 1.3 trillion version.
To enhance FineWeb's quality, we developed an educational quality classifier using annotations generated by LLama3-70B-Instruct. We then… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu. | 515,651 | 3,050,667 | [
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] | 2024-05-28T14:32:57 | null | null |
665c1855221dda498772b8b5 | nvidia/HelpSteer2 | nvidia | {"license": "cc-by-4.0", "language": ["en"], "pretty_name": "HelpSteer2", "size_categories": ["10K<n<100K"], "tags": ["human-feedback"]} | false | null | 2024-12-18T21:06:57 | 409 | 5 | false | 990b2711a36180dd19d9c94b8627844866f8982a |
HelpSteer2: Open-source dataset for training top-performing reward models
HelpSteer2 is an open-source Helpfulness Dataset (CC-BY-4.0) that supports aligning models to become more helpful, factually correct and coherent, while being adjustable in terms of the complexity and verbosity of its responses.
This dataset has been created in partnership with Scale AI.
When used to tune a Llama 3.1 70B Instruct Model, we achieve 94.1% on RewardBench, which makes it the best Reward… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/HelpSteer2. | 3,474 | 366,980 | [
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] | 2024-06-02T06:59:33 | null | null |
66952974b8a00bc24d6b112a | HuggingFaceTB/smollm-corpus | HuggingFaceTB | {"license": "odc-by", "dataset_info": [{"config_name": "cosmopedia-v2", "features": [{"name": "prompt", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "token_length", "dtype": "int64"}, {"name": "audience", "dtype": "string"}, {"name": "format", "dtype": "string"}, {"name": "seed_data", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 212503640747, "num_examples": 39134000}], "download_size": 122361137711, "dataset_size": 212503640747}, {"config_name": "fineweb-edu-dedup", "features": [{"name": "text", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "struct": [{"name": "dump", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "date", "dtype": "timestamp[s]"}, {"name": "file_path", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "language_score", "dtype": "float64"}, {"name": "token_count", "dtype": "int64"}, {"name": "score", "dtype": "float64"}, {"name": "int_score", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 957570164451, "num_examples": 190168005}], "download_size": 550069279849, "dataset_size": 957570164451}, {"config_name": "python-edu", "features": [{"name": "blob_id", "dtype": "string"}, {"name": "repo_name", "dtype": "string"}, {"name": "path", "dtype": "string"}, {"name": "length_bytes", "dtype": "int64"}, {"name": "score", "dtype": "float64"}, {"name": "int_score", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 989334135, "num_examples": 7678448}], "download_size": 643903049, "dataset_size": 989334135}], "configs": [{"config_name": "cosmopedia-v2", "data_files": [{"split": "train", "path": "cosmopedia-v2/train-*"}]}, {"config_name": "fineweb-edu-dedup", "data_files": [{"split": "train", "path": "fineweb-edu-dedup/train-*"}]}, {"config_name": "python-edu", "data_files": [{"split": "train", "path": "python-edu/train-*"}]}], "language": ["en"]} | false | null | 2024-09-06T07:04:57 | 311 | 5 | false | 3ba9d605774198c5868892d7a8deda78031a781f |
SmolLM-Corpus
This dataset is a curated collection of high-quality educational and synthetic data designed for training small language models.
You can find more details about the models trained on this dataset in our SmolLM blog post.
Dataset subsets
Cosmopedia v2
Cosmopedia v2 is an enhanced version of Cosmopedia, the largest synthetic dataset for pre-training, consisting of over 39 million textbooks, blog posts, and stories generated by… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus. | 12,361 | 198,949 | [
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"modality:text",
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"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2024-07-15T13:51:48 | null | null |
678f6b0c2705196b8a1c6c86 | bespokelabs/Bespoke-Stratos-17k | bespokelabs | {"license": "apache-2.0", "language": ["en"], "tags": ["curator", "synthetic"]} | false | null | 2025-01-31T00:00:38 | 294 | 5 | false | 9e9adba943911a9fc44dffcb30aaa18dc96ae6df |
Bespoke-Stratos-17k
We replicated and improved the Berkeley Sky-T1 data pipeline using SFT distillation data
from DeepSeek-R1 to create Bespoke-Stratos-17k -- a reasoning dataset of questions, reasoning traces, and answers.
This data was used to train:
Bespoke-Stratos-32B, a 32B reasoning model which is a fine-tune of Qwen-2.5-32B-Instruct
Bespoke-Stratos-7B, a 7B reasoning model which is a fine-tune of Qwen-2.5-7B-Instruct.
Metrics for Bespoke-Stratos-32B… See the full description on the dataset page: https://huggingface.co/datasets/bespokelabs/Bespoke-Stratos-17k. | 60,725 | 119,739 | [
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"library:mlcroissant",
"library:polars",
"region:us",
"curator",
"synthetic"
] | 2025-01-21T09:38:20 | null | null |
67954a35c16b74e280f72f15 | ServiceNow-AI/R1-Distill-SFT | ServiceNow-AI | {"license": "cc-by-nc-sa-4.0", "configs": [{"config_name": "v0", "data_files": [{"split": "train", "path": "v0/train-*"}]}, {"config_name": "v1", "data_files": [{"split": "train", "path": "v1/train-*"}]}], "dataset_info": [{"config_name": "v0", "features": [{"name": "id", "dtype": "string"}, {"name": "reannotated_assistant_content", "dtype": "string"}, {"name": "problem", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "solution", "dtype": "string"}, {"name": "verified", "dtype": "null"}, {"name": "quality_metrics", "dtype": "null"}], "splits": [{"name": "train", "num_bytes": 1279431141, "num_examples": 171647}], "download_size": 554111459, "dataset_size": 1279431141}, {"config_name": "v1", "features": [{"name": "id", "dtype": "string"}, {"name": "reannotated_assistant_content", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "reannotated_messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "source_dataset", "dtype": "string"}, {"name": "verified", "dtype": "null"}, {"name": "quality_metrics", "dtype": "null"}], "splits": [{"name": "train", "num_bytes": 25783989151, "num_examples": 1679162}], "download_size": 11128580062, "dataset_size": 25783989151}]} | false | null | 2025-02-08T22:46:58 | 272 | 5 | false | 16e851e107d928b9069dcce428a2d3d7154e5353 |
🔉 𝗦𝗟𝗔𝗠 𝗹𝗮𝗯 - 𝗥𝟭-𝗗𝗶𝘀𝘁𝗶𝗹𝗹-𝗦𝗙𝗧 Dataset
Lewis Tunstall, Ed Beeching, Loubna Ben Allal, Clem Delangue 🤗 and others at Hugging Face announced today that they are - 𝗼𝗽𝗲𝗻𝗹𝘆 𝗿𝗲𝗽𝗿𝗼𝗱𝘂𝗰𝗶𝗻𝗴 𝗥𝟭 🔥
We at 𝗦𝗟𝗔𝗠 𝗹𝗮𝗯 (ServiceNow Language Models) have been cooking up something as well.
Inspired by Open-r1, we have decided to open source the data stage-by-stage to support the open source community.
𝗕𝗼𝗼𝗸𝗺𝗮𝗿𝗸 this page!
KEY DETAILS:
⚗️ Distilled… See the full description on the dataset page: https://huggingface.co/datasets/ServiceNow-AI/R1-Distill-SFT. | 5,579 | 9,847 | [
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"library:polars",
"region:us"
] | 2025-01-25T20:31:49 | null | null |
67980358a29a934c083067cf | AymanTarig/function-calling-v0.2-with-r1-cot | AymanTarig | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "query", "dtype": "string"}, {"name": "answers", "dtype": "string"}, {"name": "tools", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "r1-gen", "dtype": "string"}, {"name": "think", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 353121599.00783336, "num_examples": 57970}], "download_size": 140431591, "dataset_size": 353121599.00783336}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | false | null | 2025-02-03T00:21:27 | 32 | 5 | false | fd9f547c2c85bf83792a38c6c935405b0cef0e3f | This dataset is a modified version of Salesforce/xlam-function-calling-60k, incorporating reasoning chains generated by deepseek-ai/DeepSeek-R1-Distill-Llama-8B.
| 538 | 929 | [
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] | 2025-01-27T22:06:16 | null | null |
67ae5cb70100bb7fb11fdb31 | getomni-ai/ocr-benchmark | getomni-ai | {"license": "mit", "size_categories": ["1K<n<10K"]} | false | null | 2025-02-21T06:34:31 | 31 | 5 | false | 4ed0d95271ca00107726230f7a0944ed9e90d897 |
OmniAI OCR Benchmark
A comprehensive benchmark that compares OCR and data extraction capabilities of different multimodal LLMs such as gpt-4o and gemini-2.0, evaluating both text and JSON extraction accuracy.
Benchmark Results (Feb 2025) | Source Code
| 1,571 | 1,571 | [
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] | 2025-02-13T20:57:27 | null | null |
67aece551fbf224da2916608 | behavior-robot-suite/data | behavior-robot-suite | {"language": ["en"], "license": "mit", "task_categories": ["robotics"], "pretty_name": "BRS Data"} | false | null | 2025-03-10T15:19:06 | 5 | 5 | false | 69a0eb792cabda882f42aa910bf9ac57cf9386d3 |
Dataset Card for BEHAVIOR Robot Suite (BRS) Data
This dataset provides robotic trajectories for five real-world household tasks. These tasks are:
Clean house after a wild party;
Clean the toilet;
Take trash outside;
Put items onto shelves;
Lay clothes out.
These data are first collected and used in the paper BEHAVIOR Robot Suite: Streamlining Real-World Whole-Body Manipulation for Everyday Household Activities
.
Dataset Details
Dataset Sources… See the full description on the dataset page: https://huggingface.co/datasets/behavior-robot-suite/data. | 282 | 282 | [
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] | 2025-02-14T05:02:13 | null | null |
67b40a15bffd44cc859667af | WenhaoWang/VideoUFO | WenhaoWang | {"language": ["en"], "license": "cc-by-4.0", "size_categories": ["1M<n<10M"], "task_categories": ["text-to-video", "text-to-image", "image-to-video", "image-to-image"], "dataset_info": {"features": [{"name": "ID", "dtype": "string"}, {"name": "Middle_Frame", "dtype": "image"}, {"name": "Topic", "dtype": "string"}, {"name": "Detailed_Caption", "dtype": "string"}, {"name": "Brief_Caption", "dtype": "string"}, {"name": "Start_Time", "dtype": "string"}, {"name": "End_Time", "dtype": "string"}, {"name": "Aesthetic_Quality", "dtype": "float32"}, {"name": "Background_Consistency", "dtype": "float32"}, {"name": "Dynamic_Degree", "dtype": "float32"}, {"name": "Imaging_Quality", "dtype": "float32"}, {"name": "Motion_Smoothness", "dtype": "float32"}, {"name": "Subject_Consistency", "dtype": "float32"}], "splits": [{"name": "Full", "num_bytes": 46459680631, "num_examples": 1091712}], "download_size": 91635996940, "dataset_size": 92919361262}, "configs": [{"config_name": "default", "data_files": [{"split": "Full", "path": "data/Full-*"}]}], "tags": ["video-generation", "text-to-video-dataset"]} | false | null | 2025-03-06T10:40:21 | 10 | 5 | false | 8404caccdf6ac478ad0f0aff67a22f2fc23810f6 |
Summary
This is the dataset proposed in our paper VideoUFO: A Million-Scale User-Focused Dataset for Text-to-Video Generation.
VideoUFO is the first dataset curated in alignment with real-world users’ focused topics for text-to-video generation. Specifically, the dataset comprises over 1.09 million video clips spanning 1,291 topics. Here, we select the top 20 most popular topics for illustration.
Visual comparison
Visual comparisons between our approach… See the full description on the dataset page: https://huggingface.co/datasets/WenhaoWang/VideoUFO. | 5,830 | 5,830 | [
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] | 2025-02-18T04:18:29 | null | null |
67bd935da1ee8cfdb40ea35e | BAAI/OpenSeek-Synthetic-Reasoning-Data-Examples | BAAI | {"configs": [{"config_name": "CC", "data_files": [{"split": "train", "path": "CC/*"}]}, {"config_name": "code", "data_files": [{"split": "train", "path": "code/*"}]}, {"config_name": "math", "data_files": [{"split": "train", "path": "math/*"}]}, {"config_name": "wiki", "data_files": [{"split": "train", "path": "wiki/*"}]}, {"config_name": "arxiv", "data_files": [{"split": "train", "path": "arxiv/*"}]}], "license": "cc-by-sa-4.0", "language": ["en"], "size_categories": ["10K<n<100K"]} | false | null | 2025-03-11T07:55:28 | 6 | 5 | false | 38c5f6543016a968b7c356a237d98fd24c134d1e |
OpenSeek-Reasoning-Data
OpenSeek [Github|Blog]
Recent reseach has demonstrated that the reasoning ability of LLMs originates from the pre-training stage, activated by RL training. Massive raw corpus containing complex human reasoning process, but lack of generalized and effective synthesis method to extract these reasoning process.
News
🔥🔥🔥[2025/02/25] We publish some math, code, and general knowledge domain reasoning data synthesized from the current pipeline.… See the full description on the dataset page: https://huggingface.co/datasets/BAAI/OpenSeek-Synthetic-Reasoning-Data-Examples. | 1,288 | 1,288 | [
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] | 2025-02-25T09:54:37 | null | null |
67c312a816933ef3357d9588 | dvilasuero/natural-science-reasoning | dvilasuero | {"license": "apache-2.0", "task_categories": ["text-generation"], "language": ["en"], "tags": ["synthetic", "science"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "topics", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "llama70B", "dtype": "string"}, {"name": "llama8B", "dtype": "string"}, {"name": "r1-response", "dtype": "string"}, {"name": "compare-llamas", "dtype": "string"}, {"name": "compare-r1", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1791963, "num_examples": 100}], "download_size": 794550, "dataset_size": 1791963}} | false | null | 2025-03-02T11:12:11 | 35 | 5 | false | 2c6c0e648ff84dfa6b68035d36bbbf8e33aa0b89 |
Natural Sciences Reasoning: the "smolest" reasoning dataset
A smol-scale open dataset for reasoning tasks using Hugging Face Inference Endpoints. While intentionally limited in scale, this resource prioritizes:
Reproducible pipeline for reasoning tasks using a variety of models (Deepseek V3, Deepsek-R1, Llama70B-Instruct, etc.)
Knowledge sharing for domains other than Math and Code reasoning
In this repo, you can find:
The prompts and the pipeline (see the config file).
The… See the full description on the dataset page: https://huggingface.co/datasets/dvilasuero/natural-science-reasoning. | 683 | 686 | [
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"synthetic",
"science"
] | 2025-03-01T13:59:04 | null | null |
67c8270b5999e7df91a854da | yaak-ai/L2D | yaak-ai | {"license": "apache-2.0", "task_categories": ["robotics"], "tags": ["LeRobot"], "configs": [{"config_name": "default", "data_files": "data/*/*.parquet"}]} | false | null | 2025-03-10T18:34:05 | 5 | 5 | false | 49115405b552802c9838d4a4c85a4ed947f901b3 | This dataset was created using LeRobot.
Dataset Structure
meta/info.json:
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"robot_type": "KIA Niro EV 2023",
"total_episodes": 100,
"total_frames": 28519,
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},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/yaak-ai/L2D. | 207 | 207 | [
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"LeRobot"
] | 2025-03-05T10:27:23 | null | null |
67c8a343469d1f84396bec59 | duality-robotics/pose_estimation5.1 | duality-robotics | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": "string"}, {"name": "mask", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 10549367.8, "num_examples": 16}, {"name": "test", "num_bytes": 2560723.2, "num_examples": 4}], "download_size": 12487906, "dataset_size": 13110091}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "license": "apache-2.0", "language": ["en"], "tags": ["poseestimation", "pose", "estimation", "synthetic", "syntheticdata"], "size_categories": ["1K<n<10K"]} | false | null | 2025-03-07T20:46:53 | 5 | 5 | false | 926cc60f3eb15a3dc9b57c47d9399e281994b7b1 |
Pose Estimation Dataset
Duality AI released a 1000 image dataset for pose estimation, including images, masks, and labels -- and it's 100% free!
Just create an EDU account by clicking here.
This HuggingFace dataset is a 20 image and label sample divided into train and val folders, but you can get the rest at no cost by creating an EDU account. Once you verify your email, the link will redirect you to the dataset page.
What makes this dataset unique, useful, and capable… See the full description on the dataset page: https://huggingface.co/datasets/duality-robotics/pose_estimation5.1. | 57 | 57 | [
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"pose",
"estimation",
"synthetic",
"syntheticdata"
] | 2025-03-05T19:17:23 | null | null |
67ca2500c8437600d1bdbb6c | datalab-to/marker_comparison_mistral_llm | datalab-to | {"dataset_info": {"features": [{"name": "uuid", "dtype": "int64"}, {"name": "classification", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "img", "dtype": "image"}, {"name": "marker_md", "dtype": "string"}, {"name": "marker_img", "dtype": "image"}, {"name": "marker_heuristic", "dtype": "float64"}, {"name": "marker_heuristic_detail", "dtype": "string"}, {"name": "marker_llm", "dtype": "int64"}, {"name": "marker_llm_detail", "dtype": "string"}, {"name": "mistral_md", "dtype": "string"}, {"name": "mistral_img", "dtype": "image"}, {"name": "mistral_heuristic", "dtype": "float64"}, {"name": "mistral_heuristic_detail", "dtype": "string"}, {"name": "mistral_llm", "dtype": "int64"}, {"name": "mistral_llm_detail", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 202369687, "num_examples": 371}], "download_size": 199936663, "dataset_size": 202369687}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | false | null | 2025-03-06T22:43:18 | 5 | 5 | false | 6914394160cd7df4e750d490c3293c8bf995ac36 | null | 199 | 199 | [
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"library:polars",
"region:us"
] | 2025-03-06T22:43:12 | null | null |
621ffdd236468d709f181e3f | nyu-mll/glue | nyu-mll | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["acceptability-classification", "natural-language-inference", "semantic-similarity-scoring", "sentiment-classification", "text-scoring"], "paperswithcode_id": "glue", "pretty_name": "GLUE (General Language Understanding Evaluation benchmark)", "config_names": ["ax", "cola", "mnli", "mnli_matched", "mnli_mismatched", "mrpc", "qnli", "qqp", "rte", "sst2", "stsb", "wnli"], "tags": ["qa-nli", "coreference-nli", "paraphrase-identification"], "dataset_info": [{"config_name": "ax", "features": [{"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "entailment", "1": "neutral", "2": 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"qnli/train-*"}, {"split": "validation", "path": "qnli/validation-*"}, {"split": "test", "path": "qnli/test-*"}]}, {"config_name": "qqp", "data_files": [{"split": "train", "path": "qqp/train-*"}, {"split": "validation", "path": "qqp/validation-*"}, {"split": "test", "path": "qqp/test-*"}]}, {"config_name": "rte", "data_files": [{"split": "train", "path": "rte/train-*"}, {"split": "validation", "path": "rte/validation-*"}, {"split": "test", "path": "rte/test-*"}]}, {"config_name": "sst2", "data_files": [{"split": "train", "path": "sst2/train-*"}, {"split": "validation", "path": "sst2/validation-*"}, {"split": "test", "path": "sst2/test-*"}]}, {"config_name": "stsb", "data_files": [{"split": "train", "path": "stsb/train-*"}, {"split": "validation", "path": "stsb/validation-*"}, {"split": "test", "path": "stsb/test-*"}]}, {"config_name": "wnli", "data_files": [{"split": "train", "path": "wnli/train-*"}, {"split": "validation", "path": "wnli/validation-*"}, {"split": "test", "path": 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"splits": {"train_split": "train", "eval_split": "validation"}, "col_mapping": {"question": "text1", "sentence": "text2", "label": "target"}}, {"config": "rte", "task": "text-classification", "task_id": "natural_language_inference", "splits": {"train_split": "train", "eval_split": "validation"}, "col_mapping": {"sentence1": "text1", "sentence2": "text2", "label": "target"}}, {"config": "wnli", "task": "text-classification", "task_id": "natural_language_inference", "splits": {"train_split": "train", "eval_split": "validation"}, "col_mapping": {"sentence1": "text1", "sentence2": "text2", "label": "target"}}]} | false | null | 2024-01-30T07:41:18 | 398 | 4 | false | bcdcba79d07bc864c1c254ccfcedcce55bcc9a8c |
Dataset Card for GLUE
Dataset Summary
GLUE, the General Language Understanding Evaluation benchmark (https://gluebenchmark.com/) is a collection of resources for training, evaluating, and analyzing natural language understanding systems.
Supported Tasks and Leaderboards
The leaderboard for the GLUE benchmark can be found at this address. It comprises the following tasks:
ax
A manually-curated evaluation dataset for fine-grained… See the full description on the dataset page: https://huggingface.co/datasets/nyu-mll/glue. | 185,906 | 36,221,730 | [
"task_categories:text-classification",
"task_ids:acceptability-classification",
"task_ids:natural-language-inference",
"task_ids:semantic-similarity-scoring",
"task_ids:sentiment-classification",
"task_ids:text-scoring",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
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"library:polars",
"arxiv:1804.07461",
"region:us",
"qa-nli",
"coreference-nli",
"paraphrase-identification"
] | 2022-03-02T23:29:22 | null | glue |
62fd4ff64723285c5e151be0 | allenai/real-toxicity-prompts | allenai | {"language": ["en"], "license": ["apache-2.0"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["image-generation"], "task_ids": ["text-generation"], "pretty_name": "Real Toxicity Prompts"} | false | null | 2022-09-30T14:23:19 | 72 | 4 | false | f21629712ffd6a3d13a54fd2807ccd521c55ef74 |
Dataset Card for Real Toxicity Prompts
Dataset Summary
RealToxicityPrompts is a dataset of 100k sentence snippets from the web for researchers to further address the risk of neural toxic degeneration in models.
Languages
English
Dataset Structure
Data Instances
Each instance represents a prompt and its metadata:
{
"filename":"0766186-bc7f2a64cb271f5f56cf6f25570cd9ed.txt",
"begin":340,
"end":564… See the full description on the dataset page: https://huggingface.co/datasets/allenai/real-toxicity-prompts. | 2,079 | 81,705 | [
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:json",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2009.11462",
"doi:10.57967/hf/0002",
"region:us"
] | 2022-08-17T20:30:46 | null | null |
642912f7a760fe0bf37996b1 | anon8231489123/ShareGPT_Vicuna_unfiltered | anon8231489123 | {"license": "apache-2.0", "language": ["en"]} | false | null | 2023-04-12T05:23:59 | 779 | 4 | false | 192ab2185289094fc556ec8ce5ce1e8e587154ca | Further cleaning done. Please look through the dataset and ensure that I didn't miss anything.
Update: Confirmed working method for training the model: https://huggingface.co/AlekseyKorshuk/vicuna-7b/discussions/4#64346c08ef6d5abefe42c12c
Two choices:
Removes instances of "I'm sorry, but": https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/blob/main/ShareGPT_V3_unfiltered_cleaned_split_no_imsorry.json
Has instances of "I'm sorry, but":… See the full description on the dataset page: https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered. | 31,910 | 118,985 | [
"language:en",
"license:apache-2.0",
"region:us"
] | 2023-04-02T05:30:31 | null | null |
Subsets and Splits