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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 | 345 | 80 | 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. | 8,251 | 8,251 | [
"task_categories:text-generation",
"language:en",
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"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2502.13124",
"region:us"
] | 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 | 483 | 76 | 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. | 6,558 | 6,558 | [
"task_categories:text-generation",
"task_categories:text2text-generation",
"task_categories:question-answering",
"language:zh",
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:json",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-02-17T11:45:09 | null | null |
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 | 390 | 67 | 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. | 26,448 | 30,365 | [
"task_categories:question-answering",
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"library:polars",
"arxiv:2412.18925",
"region:us",
"medical",
"biology"
] | 2024-12-28T03:29:08 | null | null |
67c58d2e6c6e0371152cf00f | GeneralReasoning/GeneralThought-195K | GeneralReasoning | {"language": ["en"], "license": "mit"} | false | null | 2025-03-03T11:20:23 | 52 | 52 | false | 5b28bacb11bea72af3c4a776d4d0db486ff10899 |
GeneralThought-195K
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-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-195K. | 497 | 497 | [
"language:en",
"license:mit",
"size_categories:100K<n<1M",
"modality:tabular",
"modality:text",
"region:us"
] | 2025-03-03T11:06:22 | null | null |
67c248d12a6f7c1f2a448ee4 | KodCode/KodCode-V1 | KodCode | {"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-*"}]}], "language": ["en"], "license": "cc"} | false | null | 2025-03-06T09:18:18 | 50 | 45 | false | 0350cf9d8d66005e4962d5fc2d224c438740f517 |
🐱 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. | 1,812 | 1,812 | [
"language:en",
"license:cc",
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"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2503.02951",
"region:us"
] | 2025-02-28T23:37:53 | 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 | 133 | 41 | 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. | 4,253 | 4,253 | [
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"mathematics",
"math",
"reinforcement-learning",
"RL",
"reasoning",
"verifiable",
"open-ended-questions",
"closed-form-answers"
] | 2025-02-20T19:32:03 | 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 | 475 | 34 | 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. | 44,397 | 44,397 | [
"language:en",
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"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-02-10T13:41:48 | 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 | 33 | 33 | 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. | 447 | 450 | [
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"library:mlcroissant",
"library:polars",
"region:us",
"synthetic",
"science"
] | 2025-03-01T13:59:04 | null | null |
67bfc6ed21e5f4fcc4af2b1d | voidful/fineweb-zhtw | voidful | {"dataset_info": {"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": "language_script", "dtype": "string"}, {"name": "minhash_cluster_size", "dtype": "int64"}, {"name": "top_langs", "dtype": "string"}, {"name": "avg_words_per_line", "dtype": "float64"}]}], "splits": [{"name": "train", "num_bytes": 160689800579, "num_examples": 48058113}], "download_size": 107457281288, "dataset_size": 160689800579}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "license": "odc-by", "language": ["zh"], "pretty_name": "z"} | false | null | 2025-03-04T09:38:21 | 35 | 31 | false | 588aa4d7fe6bce8d3bf1fc6f2abff3652ecdbb90 |
Fineweb-zhtw
Overview / 概覽
This repository contains the Fineweb-zhtw dataset, a large-scale collection of Traditional Chinese text data mined from the web. It is built upon the HuggingFaceFW/fineweb-2 dataset with modifications provided by mtkresearch/fineweb-zhtw.
本專案提供 Fineweb-zhtw 資料集，為大規模的繁體中文網路文本資料。此資料集基於 HuggingFaceFW/fineweb-2 並經由 mtkresearch/fineweb-zhtw 進行修改。
https://github.com/voidful/fineweb-zhtw/tree/main
Dataset Details / 資料集細節
Data Size: 107… See the full description on the dataset page: https://huggingface.co./datasets/voidful/fineweb-zhtw. | 1,668 | 1,668 | [
"language:zh",
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"library:mlcroissant",
"library:polars",
"arxiv:2411.16387",
"region:us"
] | 2025-02-27T01:59:09 | 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 | 124 | 30 | 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. | 3,678 | 3,678 | [
"task_categories:text-generation",
"task_categories:text2text-generation",
"task_categories:question-answering",
"language:zh",
"license:apache-2.0",
"size_categories:100K<n<1M",
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"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-02-17T14:36:10 | 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 | 261 | 24 | 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. | 101,080 | 286,606 | [
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"library:mlcroissant",
"arxiv:2407.05361",
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] | 2024-08-23T08:25:08 | 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 | 646 | 24 | 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. | 100,141 | 127,333 | [
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"curator",
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] | 2025-01-27T20:02:16 | 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 | 51 | 24 | 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. | 880 | 880 | [
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"library:mlcroissant",
"library:polars",
"arxiv:2502.14739",
"region:us"
] | 2025-02-20T08:26:10 | 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. | 318,497 | 2,188,782 | [
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"region:us"
] | 2024-04-18T14:33:13 | 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 | 624 | 21 | 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. | 364,276 | 4,012,310 | [
"task_categories:text2text-generation",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:mit",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2110.14168",
"region:us",
"math-word-problems"
] | 2022-04-12T10:22:10 | gsm8k | null |
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 | 74 | 20 | 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. | 2,749 | 2,749 | [
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"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-02-10T20:41:50 | null | null |
67c179456a31b8fe773e591c | di-zhang-fdu/R1-Vision-Reasoning-Instructions | di-zhang-fdu | {"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "gt", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "correct", "dtype": "bool"}, {"name": "image", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 637594547, "num_examples": 167128}], "download_size": 377536732, "dataset_size": 637594547}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | false | null | 2025-03-06T12:48:45 | 20 | 20 | false | 5462a20d854aede546c58a1934fc46112955c0a6 |
VRI-160K: A dataset for Vision Reasoning Instruction Tuning
Images
Images data can be access from https://huggingface.co./datasets/Xkev/LLaVA-CoT-100k
Data Source
Raw data can be access from https://huggingface.co./datasets/di-zhang-fdu/llava-cot-100k-r1-format for GRPO training
Citations
@misc {di_zhang_2025,
author = { {Di Zhang} },
title = { R1-Vision-Reasoning-Instructions (Revision 49c1686) },
year = 2025,
url… See the full description on the dataset page: https://huggingface.co./datasets/di-zhang-fdu/R1-Vision-Reasoning-Instructions. | 578 | 578 | [
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2411.18203",
"doi:10.57967/hf/4686",
"region:us"
] | 2025-02-28T08:52:21 | 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 | 7,606 | 18 | false | 68ba7694e23014788dcc8ab5afe613824f45a05c | 🧠 Awesome ChatGPT Prompts [CSV dataset]
This is a Dataset Repository of Awesome ChatGPT Prompts
View All Prompts on GitHub
License
CC-0
| 11,817 | 130,836 | [
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"region:us",
"ChatGPT"
] | 2022-12-13T23:47:45 | null | null |
67b20fc10861cec33b3afb8a | Conard/fortune-telling | Conard | {"license": "mit"} | false | null | 2025-02-17T05:13:43 | 40 | 18 | false | 6261fe0d35a75997972bbfcd9828020e340303fb | null | 2,533 | 2,533 | [
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"format:json",
"modality:text",
"library:datasets",
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"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-02-16T16:18:09 | null | null |
67b960eb8e9228b80838bfe6 | TIGER-Lab/TheoremExplainBench | TIGER-Lab | {"license": "mit", "pretty_name": "THB", "dataset_info": {"features": [{"name": "subject", "dtype": "string"}, {"name": "difficulty", "dtype": "string"}, {"name": "theorem", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "subfield", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 71677, "num_examples": 240}], "download_size": 39223, "dataset_size": 71677}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | false | null | 2025-03-01T05:29:42 | 22 | 16 | false | 4d992a7ff28a338774fb489ac735632e68dc47c0 |
TheoremExplainBench
TheoremExplainBench is a dataset designed to evaluate and improve the ability of large language models (LLMs) to understand and explain mathematical and scientific theorems across multiple domains, through long-form multimodal content (e.g. Manim Videos). It consists of 240 theorems, categorized by difficulty and subject area to enable structured benchmarking.
Dataset Details
Curated by: Max Ku, Thomas Chong
Language(s) (NLP): English
License:… See the full description on the dataset page: https://huggingface.co./datasets/TIGER-Lab/TheoremExplainBench. | 686 | 686 | [
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"size_categories:n<1K",
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"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2502.19400",
"region:us"
] | 2025-02-22T05:30:19 | 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 | 234 | 15 | 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. | 9,804 | 28,641 | [
"language:en",
"arxiv:2311.12983",
"region:us"
] | 2023-10-20T07:06:54 | null | |
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 | 69 | 15 | 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. | 959 | 1,279 | [
"task_categories:text-generation",
"task_categories:question-answering",
"language:en",
"license:cc-by-nc-sa-4.0",
"size_categories:100K<n<1M",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2501.15587",
"region:us",
"chemistry",
"biology",
"medical"
] | 2025-01-26T07:21:44 | null | null |
67bd467a478fa63bfc98f795 | simplescaling/s1K-claude-3-7-sonnet | simplescaling | {"language": "en", "license": "mit", "tags": ["curator"]} | false | null | 2025-02-27T15:02:04 | 19 | 15 | 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. | 563 | 563 | [
"language:en",
"license:mit",
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"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"curator"
] | 2025-02-25T04:26:34 | null | null |
67c9ec5572b8f1776ef7f0d4 | madrylab/gsm8k-platinum | madrylab | {"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-*"}]}]} | false | null | 2025-03-07T20:23:57 | 15 | 15 | false | c2730837b1f4e49f4120f7a0248513190de9828a |
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. | 150 | 150 | [
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"arxiv:2110.14168",
"region:us"
] | 2025-03-06T18:41:25 | 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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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. | 1,616 | 1,616 | [
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Dataset Card for m-WildVision
Dataset Details
The m-WildVision dataset is a multilingual multimodal LLM evaluation set covering 23 languages. It was created by translating prompts from the original English-only WildVision (vision_bench_0617) test set.
The original prompts, developed by Lu et al. (2024) , consist of 500 challenging user queries sourced from the WildVision-Arena platform.
The authors demonstrated that these prompts enable automatic LLM judge… See the full description on the dataset page: https://huggingface.co./datasets/CohereForAI/m-WildVision. | 844 | 844 | [
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] | 2025-03-02T21:43:23 | 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 | 13 | 13 | 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. | 158 | 176 | [
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"library:dask",
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"library:polars",
"region:us"
] | 2024-11-13T20:10:09 | 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 | 77 | 13 | 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. | 5,963 | 6,000 | [
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"library:polars",
"arxiv:2501.19393",
"region:us",
"curator"
] | 2025-02-07T00:45:46 | null | null |
67b95b604a1673b790a5dde5 | FreedomIntelligence/Medical-R1-Distill-Data-Chinese | FreedomIntelligence | {"license": "apache-2.0", "task_categories": ["question-answering", "text-generation"], "language": ["en", "zh"], "tags": ["medical", "biology"], "configs": [{"config_name": "zh", "data_files": "medical_r1_distill_sft_Chinese.json"}]} | false | null | 2025-02-22T06:56:41 | 24 | 13 | false | a9b43c2bbfec1487db5c2aeb9f49b82c4b562b19 |
Introduction
This dataset is an SFT dataset distilled from Deepseek-R1 (Full Power Version), based on Chinese medical verifiable problems from HuatuoGPT-o1.
The distillation originates from the native Deepseek-R1 API requests. We hope this distilled dataset can help initialize your models with the reasoning chain from R1. You can also use our previously built medical verified long reasoning chains based on GPT-4o on medical-o1-reasoning-SFT.
For details, see our paper and GitHub… See the full description on the dataset page: https://huggingface.co./datasets/FreedomIntelligence/Medical-R1-Distill-Data-Chinese. | 680 | 680 | [
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"library:mlcroissant",
"library:polars",
"arxiv:2412.18925",
"region:us",
"medical",
"biology"
] | 2025-02-22T05:06:40 | null | null |
67c2955fbe05f412aa264278 | KodCode/KodCode-V1-SFT-R1 | KodCode | {"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-*"}]}], "license": "cc", "language": ["en"], "size_categories": ["100K<n<1M"]} | false | null | 2025-03-06T09:14:38 | 15 | 13 | false | dec2d25d58d4ece3187c659e23ca4aab90c9ad97 |
🐱 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. | 1,995 | 1,995 | [
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"arxiv:2503.02951",
"region:us"
] | 2025-03-01T05:04:31 | 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": 10792019, "num_examples": 746}], "download_size": 1059070, "dataset_size": 10792019}, "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 | 2025-03-06T12:54:47 | 13 | 13 | false | 5b6fd66927a483f6e36cba46dab365654c1001f3 |
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. | 102 | 102 | [
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"translation",
"humanfeedback",
"deepseek-r1",
"deepl",
"llama",
"mixtral",
"DE",
"PT",
"ES",
"FR"
] | 2025-03-05T15:23:57 | null | null |
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 | 28 | 12 | 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. | 1,239 | 1,256 | [
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"library:datasets",
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"library:mlcroissant",
"library:polars",
"arxiv:2412.18525",
"region:us",
"image"
] | 2025-01-29T11:08:32 | null | null |
67b940cfd6bb21aa160b5520 | FreedomIntelligence/Medical-R1-Distill-Data | FreedomIntelligence | {"license": "apache-2.0", "task_categories": ["question-answering", "text-generation"], "language": ["en", "zh"], "tags": ["medical", "biology"], "configs": [{"config_name": "en", "data_files": "medical_r1_distill_sft.json"}]} | false | null | 2025-02-22T06:55:02 | 22 | 12 | false | 3491deecebe1973a2d7370b824f4b41be29dcf1a |
Introduction
This dataset is an SFT dataset distilled from Deepseek-R1 (Full Power Version), based on medical verifiable problems from HuatuoGPT-o1.
The Chinese version of the dataset is available at FreedomIntelligence/Medical-R1-Distill-Data-Chinese.
The distillation originates from the native Deepseek-R1 API requests. We hope this distilled dataset can help initialize your models with the reasoning chain from R1. You can also use our previously built medical verified long… See the full description on the dataset page: https://huggingface.co./datasets/FreedomIntelligence/Medical-R1-Distill-Data. | 590 | 590 | [
"task_categories:question-answering",
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"license:apache-2.0",
"size_categories:10K<n<100K",
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"library:mlcroissant",
"library:polars",
"arxiv:2412.18925",
"region:us",
"medical",
"biology"
] | 2025-02-22T03:13:19 | null | null |
67c81e2b95af22b165bd5ae0 | HuggingFaceTB/dclm-edu | HuggingFaceTB | {"license": "cc-by-4.0", "language": ["en"]} | false | null | 2025-03-07T16:24:22 | 12 | 12 | 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. | 540 | 541 | [
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"library:mlcroissant",
"library:polars",
"arxiv:2502.02737",
"region:us"
] | 2025-03-05T09:49:31 | 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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"data_files": [{"split": "train", "path": "data/CC-MAIN-2015-06/*"}]}, {"config_name": "CC-MAIN-2014-52", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-52/*"}]}, {"config_name": "CC-MAIN-2014-49", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-49/*"}]}, {"config_name": "CC-MAIN-2014-42", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-42/*"}]}, {"config_name": "CC-MAIN-2014-41", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-41/*"}]}, {"config_name": "CC-MAIN-2014-35", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-35/*"}]}, {"config_name": "CC-MAIN-2014-23", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-23/*"}]}, {"config_name": "CC-MAIN-2014-15", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-15/*"}]}, {"config_name": "CC-MAIN-2014-10", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-10/*"}]}, {"config_name": "CC-MAIN-2013-48", "data_files": [{"split": "train", "path": "data/CC-MAIN-2013-48/*"}]}, {"config_name": "CC-MAIN-2013-20", "data_files": [{"split": "train", "path": "data/CC-MAIN-2013-20/*"}]}]} | false | null | 2025-01-31T15:56:54 | 648 | 11 | false | 4863ab07d7520451e6f73e2912ad8bfee7d97c11 |
📚 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. | 492,869 | 2,999,861 | [
"task_categories:text-generation",
"language:en",
"license:odc-by",
"size_categories:1B<n<10B",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2406.17557",
"arxiv:2404.14219",
"arxiv:2401.10020",
"arxiv:2109.07445",
"doi:10.57967/hf/2497",
"region:us"
] | 2024-05-28T14:32:57 | null | null |
67374c18c32c765810f748f6 | HuggingFaceH4/MATH-500 | HuggingFaceH4 | {"task_categories": ["text-generation"], "language": ["en"], "pretty_name": "MATH-500"} | false | null | 2024-11-15T13:36:00 | 119 | 11 | 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
| 45,442 | 70,108 | [
"task_categories:text-generation",
"language:en",
"size_categories:n<1K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2024-11-15T13:26:48 | 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 | 31 | 11 | 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. | 668 | 705 | [
"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 |
67c67c88c7cd2413e2c8be4d | qihoo360/Light-R1-SFTData | qihoo360 | {"license": "apache-2.0"} | false | null | 2025-03-05T13:29:23 | 11 | 11 | 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. | 182 | 204 | [
"license:apache-2.0",
"region:us"
] | 2025-03-04T04:07:36 | 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 | 653 | 10 | false | 12567cabf869d7c92e573c7c783905fc160e9639 |
Dataset Card for Alpaca-Cleaned
Repository: https://github.com/gururise/AlpacaDataCleaned
Dataset Description
This is a cleaned version of the original Alpaca Dataset released by Stanford. The following issues have been identified in the original release and fixed in this dataset:
Hallucinations: Many instructions in the original dataset had instructions referencing data on the internet, which just caused GPT3 to hallucinate an answer.
"instruction":"Summarize the… See the full description on the dataset page: https://huggingface.co./datasets/yahma/alpaca-cleaned. | 23,244 | 600,713 | [
"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 | 1,357 | 10 | 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. | 10,220 | 245,685 | [
"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",
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"format:parquet",
"modality:tabular",
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"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2304.07327",
"region:us",
"human-feedback"
] | 2023-04-13T15:48:16 | null | null |
673463fabe618c1a378d99c6 | qgyd2021/chinese_porn_novel | qgyd2021 | {"language": ["zh"], "size_categories": ["100M<n<1B"], "task_categories": ["text-generation"], "tags": ["art"], "dataset_info": {"config_name": "xbookcn_short_story", "features": [{"name": "source", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "content", "dtype": "string"}, {"name": "content_length", "dtype": "uint32"}, {"name": "url", "dtype": "string"}, {"name": "summary1", "dtype": "string"}, {"name": "summary2", "dtype": "string"}, {"name": "summary3", "dtype": "string"}, {"name": "summary4", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1167355353, "num_examples": 627195}], "download_size": 721183317, "dataset_size": 1167355353}, "configs": [{"config_name": "xbookcn_short_story", "data_files": [{"split": "train", "path": "xbookcn_short_story/train-*"}], "default": true}]} | false | null | 2024-11-13T11:06:27 | 59 | 10 | false | 170c125e168cf58400ad3b31300c88ed8a1c978a |
Chinese Porn Novel
https://huggingface.co./docs/hub/en/datasets-adding
datasets-cli convert_to_parquet qgyd2021/chinese_porn_novel --trust_remote_code
SQ小说, 用于制作特殊的 GPT 语言模型.
将每篇小说切分 chunk,
用 Qwen-instruct 对 chunk 进行4个摘要,
4个摘要的 prompt
{content}
对于此文本，
根据文本的长度输出3到7个具有代表性的简短句子来描述其内容。
每个句子控制在10字左右，不要有序号等，每行一句。
{content}
对于此文本，
根据文本的长度输出2到4个具有代表性的简短句子来描述其内容。
每个句子控制在15字左右，不要有序号等，每行一句。
{content}
对于此文本，
根据文本的长度输出2到4个具有代表性的简短句子来概括其内容。
每个句子控制在10字左右，不要有序号等，每行一句。… See the full description on the dataset page: https://huggingface.co./datasets/qgyd2021/chinese_porn_novel. | 1,373 | 1,994 | [
"task_categories:text-generation",
"language:zh",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"art"
] | 2024-11-13T08:31:54 | null | null |
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