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68072cc4cce05035af98207e | nvidia/OpenMathReasoning | nvidia | {"language": ["en"], "license": "cc-by-4.0", "size_categories": ["1M<n<10M"], "task_categories": ["question-answering", "text-generation"], "pretty_name": "OpenMathReasoning", "tags": ["math", "nvidia"], "configs": [{"config_name": "default", "data_files": [{"split": "cot", "path": "data/cot-*"}, {"split": "tir", "path": "data/tir-*"}, {"split": "genselect", "path": "data/genselect-*"}]}], "dataset_info": {"features": [{"name": "expected_answer", "dtype": "string"}, {"name": "problem_type", "dtype": "string"}, {"name": "problem_source", "dtype": "string"}, {"name": "generation_model", "dtype": "string"}, {"name": "pass_rate_72b_tir", "dtype": "string"}, {"name": "problem", "dtype": "string"}, {"name": "generated_solution", "dtype": "string"}, {"name": "inference_mode", "dtype": "string"}], "splits": [{"name": "cot", "num_bytes": 71638774515, "num_examples": 3201061}, {"name": "tir", "num_bytes": 35467270369, "num_examples": 1703010}, {"name": "genselect", "num_bytes": 6981053721, "num_examples": 565620}], "download_size": 49370957110, "dataset_size": 114087098605}} | false | null | 2025-04-24T04:13:32 | 125 | 125 | false | 47ea246374954205b6b9c8a7077b9bb0fd58b11a |
OpenMathReasoning
OpenMathReasoning is a large-scale math reasoning dataset for training large language models (LLMs).
This dataset contains
540K unique mathematical problems sourced from AoPS forums,
3.2M long chain-of-thought (CoT) solutions
1.7M long tool-integrated reasoning (TIR) solutions
566K samples that select the most promising solution out of many candidates (GenSelect)
We used Qwen2.5-32B-Instruct to preprocess problems, and
DeepSeek-R1 and QwQ-32B to generate… See the full description on the dataset page: https://huggingface.co./datasets/nvidia/OpenMathReasoning. | 10,936 | 10,936 | [
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] | 2025-04-22T05:44:36 | null | null |
67f75f7450cb3eb7e88dc887 | Anthropic/values-in-the-wild | Anthropic | {"license": "cc-by-4.0", "configs": [{"config_name": "values_frequencies", "data_files": "values_frequencies.csv"}, {"config_name": "values_tree", "data_files": "values_tree.csv"}]} | false | null | 2025-04-28T17:31:57 | 115 | 88 | false | ab3f8507ad9513889edfbc5a9ec56b0072ae632c |
Summary
This dataset presents a comprehensive taxonomy of 3307 values expressed by Claude (an AI assistant) across hundreds of thousands of real-world conversations. Using a novel privacy-preserving methodology, these values were extracted and classified without human reviewers accessing any conversation content. The dataset reveals patterns in how AI systems express values "in the wild" when interacting with diverse users and tasks.
We're releasing this resource to advance research… See the full description on the dataset page: https://huggingface.co./datasets/Anthropic/values-in-the-wild. | 394 | 394 | [
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] | 2025-04-10T06:04:36 | null | null |
67fa1588873f5fd677eb1161 | OpenGVLab/InternVL-Data | OpenGVLab | {"language": ["multilingual"], "license": "cc-by-4.0", "task_categories": ["image-to-text", "question-answering"], "size_categories": ["10M<n<100M"]} | false | null | 2025-04-27T19:28:16 | 87 | 77 | false | 7e5ac72fb5a016ed7786baade134cb74c491ccd7 |
InternVL-Data
[📂 GitHub] [📜 InternVL 1.0] [📜 InternVL 1.5] [📜 InternVL 2.5] [📜 InternVL2.5-MPO] [📜 InternVL3]
[🆕 Blog] [🗨️ Chat Demo] [🤗 HF Demo] [🚀 Quick Start] [📖 Documents]
Introduction
Welcome to the InternVL3 Open Dataset! This dataset is designed to support research and development in the field of multimodal large language models (MLLMs), specifically for tasks involving image, text, and video understanding. The dataset is composed of data… See the full description on the dataset page: https://huggingface.co./datasets/OpenGVLab/InternVL-Data. | 3,332 | 3,332 | [
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"arxiv:2412.05271",
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67ec47948647cfa17739af7a | nvidia/OpenCodeReasoning | nvidia | {"license": "cc-by-4.0", "size_categories": ["100K<n<1M"], "pretty_name": "OpenCodeReasoning", "dataset_info": [{"config_name": "split_0", "features": [{"name": "id", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "license", "dtype": "string"}, {"name": "dataset", "dtype": "string"}, {"name": "split", "dtype": "string"}, {"name": "difficulty", "dtype": "string"}, {"name": "solution", "dtype": "string"}], "splits": [{"name": "split_0", "num_bytes": 28108469190, "num_examples": 567850}]}, {"config_name": "split_1", "features": [{"name": "id", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "license", "dtype": "string"}, {"name": "dataset", "dtype": "string"}, {"name": "split", "dtype": "string"}, {"name": "difficulty", "dtype": "string"}, {"name": "solution", "dtype": "string"}, {"name": "index", "dtype": "string"}], "splits": [{"name": "split_1", "num_bytes": 4722811278, "num_examples": 167405}]}], "configs": [{"config_name": "split_0", "data_files": [{"split": "split_0", "path": "split_0/train-*"}]}, {"config_name": "split_1", "data_files": [{"split": "split_1", "path": "split_1/train-*"}]}], "task_categories": ["text-generation"], "tags": ["synthetic"]} | false | null | 2025-04-15T16:56:07 | 312 | 50 | false | c141f0b01e489370f312cd54985b7b02e8dab8da |
OpenCodeReasoning: Advancing Data Distillation for Competitive Coding
Data Overview
OpenCodeReasoning is the largest reasoning-based synthetic dataset to date for coding, comprises 735,255 samples in Python across 28,319 unique competitive programming
questions. OpenCodeReasoning is designed for supervised fine-tuning (SFT).
Technical Report - Discover the methodology and technical details behind OpenCodeReasoning.
Github Repo - Access the complete pipeline used to… See the full description on the dataset page: https://huggingface.co./datasets/nvidia/OpenCodeReasoning. | 13,883 | 13,883 | [
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67fce65dd1ec7d15ba6a2da3 | zwhe99/DeepMath-103K | zwhe99 | {"license": "mit", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "final_answer", "dtype": "string"}, {"name": "difficulty", "dtype": "float64"}, {"name": "topic", "dtype": "string"}, {"name": "r1_solution_1", "dtype": "string"}, {"name": "r1_solution_2", "dtype": "string"}, {"name": "r1_solution_3", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4963982703, "num_examples": 103110}], "download_size": 2135928958, "dataset_size": 4963982703}, "task_categories": ["text-generation", "text2text-generation"], "language": ["en"], "tags": ["math", "reasoning", "rl"], "pretty_name": "deepmath-103k", "size_categories": ["100K<n<1M"]} | false | null | 2025-04-18T06:29:38 | 156 | 37 | false | 736ce9bfca63afc046a07d545915fa261bbe843f |
DeepMath-103K
📖 Overview
DeepMath-103K is meticulously curated to push the boundaries of mathematical reasoning in language models. Key features include:1. Challenging Problems: DeepMath-103K has a strong focus on difficult mathematical problems (primarily Levels 5-9), significantly raising the complexity bar compared to many existing open datasets.
Difficulty… See the full description on the dataset page: https://huggingface.co./datasets/zwhe99/DeepMath-103K. | 16,582 | 16,582 | [
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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 | 7,740 | 33 | false | 68ba7694e23014788dcc8ab5afe613824f45a05c | 🧠 Awesome ChatGPT Prompts [CSV dataset]
This is a Dataset Repository of Awesome ChatGPT Prompts
View All Prompts on GitHub
License
CC-0
| 12,250 | 151,069 | [
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6807bc2b567016bd8ab2025c | Eureka-Lab/PHYBench | Eureka-Lab | {"license": "mit", "task_categories": ["question-answering", "mathematical-reasoning"], "language": ["en"], "size_categories": ["500<n<1K"]} | false | null | 2025-04-26T13:56:46 | 30 | 30 | false | e9ad4df6b2bbdbefdde1653da01e2226348e7c19 |
PHYBench: Holistic Evaluation of Physical Perception and Reasoning in Large Language Models
[🌐 Project]
[📄 Paper]
[💻 Code]
[🌟 Overview]
[🔧 Data Details]
[🚩 Citation]
New Updates
2025.4.25: We release our code of EED Score. View and star on our github page!
Recently: The leaderboard is still under progress, we'll release it as soon as possible.
🚀 Acknowledgement and Progress
We're excited to announce the initial release of our PHYBench dataset!
100… See the full description on the dataset page: https://huggingface.co./datasets/Eureka-Lab/PHYBench. | 275 | 275 | [
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] | 2025-04-22T15:56:27 | null | null |
679dee7e52390b33e5970da6 | future-technologies/Universal-Transformers-Dataset | future-technologies | {"task_categories": ["text-classification", "token-classification", "table-question-answering", "question-answering", "zero-shot-classification", "translation", "summarization", "feature-extraction", "text-generation", "text2text-generation", "fill-mask", "sentence-similarity", "text-to-speech", "text-to-audio", "automatic-speech-recognition", "audio-to-audio", "audio-classification", "voice-activity-detection", "depth-estimation", "image-classification", "object-detection", "image-segmentation", "text-to-image", "image-to-text", "image-to-image", "image-to-video", "unconditional-image-generation", "video-classification", "reinforcement-learning", "robotics", "tabular-classification", "tabular-regression", "tabular-to-text", "table-to-text", "multiple-choice", "text-retrieval", "time-series-forecasting", "text-to-video", "visual-question-answering", "zero-shot-image-classification", "graph-ml", "mask-generation", "zero-shot-object-detection", "text-to-3d", "image-to-3d", "image-feature-extraction", "video-text-to-text"], "language": ["ab", "ace", "ady", "af", "alt", "am", "ami", "an", "ang", "anp", "ar", "arc", "ary", "arz", "as", "ast", "atj", "av", "avk", "awa", "ay", "az", "azb", "ba", "ban", "bar", "bbc", "bcl", "be", "bg", "bh", "bi", "bjn", "blk", "bm", "bn", "bo", "bpy", "br", "bs", "bug", "bxr", "ca", "cbk", "cdo", "ce", "ceb", "ch", "chr", "chy", "ckb", "co", "cr", "crh", "cs", "csb", "cu", "cv", "cy", "da", "dag", "de", "dga", "din", "diq", "dsb", "dty", "dv", "dz", "ee", "el", "eml", "en", "eo", "es", "et", "eu", "ext", "fa", "fat", "ff", "fi", "fj", "fo", "fon", "fr", "frp", "frr", "fur", "fy", "ga", "gag", "gan", "gcr", "gd", "gl", "glk", "gn", "gom", "gor", "got", "gpe", "gsw", "gu", "guc", "gur", "guw", "gv", "ha", "hak", "haw", "hbs", "he", "hi", "hif", "hr", "hsb", "ht", "hu", "hy", "hyw", "ia", "id", "ie", "ig", "ik", "ilo", "inh", "io", "is", "it", "iu", "ja", "jam", "jbo", "jv", "ka", "kaa", "kab", "kbd", "kbp", "kcg", "kg", "ki", "kk", "kl", "km", "kn", "ko", "koi", "krc", "ks", "ksh", "ku", "kv", "kw", "ky", "la", "lad", "lb", "lbe", "lez", "lfn", "lg", "li", "lij", "lld", "lmo", "ln", "lo", "lt", "ltg", "lv", "lzh", "mad", "mai", "map", "mdf", "mg", "mhr", "mi", "min", "mk", "ml", "mn", "mni", "mnw", "mr", "mrj", "ms", "mt", "mwl", "my", "myv", "mzn", "nah", "nan", "nap", "nds", "ne", "new", "nia", "nl", "nn", "no", "nov", "nqo", "nrf", "nso", "nv", "ny", "oc", "olo", "om", "or", "os", "pa", "pag", "pam", "pap", "pcd", "pcm", "pdc", "pfl", "pi", "pih", "pl", "pms", "pnb", "pnt", "ps", "pt", "pwn", "qu", "rm", "rmy", "rn", "ro", "ru", "rue", "rup", "rw", "sa", "sah", "sat", "sc", "scn", "sco", "sd", "se", "sg", "sgs", "shi", "shn", "si", "sk", "skr", "sl", "sm", "smn", "sn", "so", "sq", "sr", "srn", "ss", "st", "stq", "su", "sv", "sw", "szl", "szy", "ta", "tay", "tcy", "te", "tet", "tg", "th", "ti", "tk", "tl", "tly", "tn", "to", "tpi", "tr", "trv", "ts", "tt", "tum", "tw", "ty", "tyv", "udm", "ug", "uk", "ur", "uz", "ve", "vec", "vep", "vi", "vls", "vo", "vro", "wa", "war", "wo", "wuu", "xal", "xh", "xmf", "yi", "yo", "yue", "za", "zea", "zgh", "zh", "zu"], "tags": ["tabular", "video", "image", "audio", "text-prompts", "text", "universal", "transformer", "database", "massive-data", "ai", "training", "huggingface", "ai", "artificial-intelligence", "machine-learning", "deep-learning", "transformers", "neural-networks", "text", "image", "audio", "video", "multimodal", "structured-data", "tabular-data", "nlp", "computer-vision", "speech-recognition", "reinforcement-learning", "time-series", "large-language-models", "generative-ai", "huggingface-dataset", "huggingface", "pytorch", "tensorflow", "jax", "pretraining", "finetuning", "self-supervised-learning", "few-shot-learning", "zero-shot-learning", "unsupervised-learning", "meta-learning", "diffusion-models"], "size_categories": ["n>1T"], "pretty_name": "Universal Transformers: Multilingual & Scalable AI Dataset"} | false | null | 2025-04-28T17:45:46 | 92 | 27 | false | 42663728dce84ff23725a25dea1604eaff8803ba |
Universal Transformer Dataset
Join Our Discord Community!
💠 A Message from Ujjawal Tyagi (Founder & CEO)
"This is more than a dataset..... it’s the start of a new world....."
I’m Ujjawal Tyagi, Founder of Lambda Go & GoX AI Platform — proudly born in the land of wisdom, resilience, and rising technology..... India 🇮🇳
What we’ve built here isn’t just numbers, files, or data points..... it’s purpose. It’s a movement. It’s for every developer, researcher… See the full description on the dataset page: https://huggingface.co./datasets/future-technologies/Universal-Transformers-Dataset. | 5,989 | 6,055 | [
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"region:us",
"tabular",
"video",
"image",
"audio",
"text-prompts",
"text",
"universal",
"transformer",
"database",
"massive-data",
"ai",
"training",
"huggingface",
"artificial-intelligence",
"machine-learning",
"deep-learning",
"transformers",
"neural-networks",
"multimodal",
"structured-data",
"tabular-data",
"nlp",
"computer-vision",
"speech-recognition",
"reinforcement-learning",
"time-series",
"large-language-models",
"generative-ai",
"huggingface-dataset",
"pytorch",
"tensorflow",
"jax",
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"self-supervised-learning",
"few-shot-learning",
"zero-shot-learning",
"unsupervised-learning",
"meta-learning",
"diffusion-models"
] | 2025-02-01T09:50:54 | null | null |
6807532a88578d94443709fe | nvidia/describe-anything-dataset | nvidia | {"configs": [{"config_name": "COCOStuff", "data_files": [{"split": "train", "path": "COCOStuff/images/*.tar"}]}, {"config_name": "LVIS", "data_files": [{"split": "train", "path": "LVIS/images/*.tar"}]}, {"config_name": "Mapillary", "data_files": [{"split": "train", "path": "Mapillary/images/*.tar"}]}, {"config_name": "OpenImages", "data_files": [{"split": "train", "path": "OpenImages/images/*.tar"}]}, {"config_name": "PACO", "data_files": [{"split": "train", "path": "PACO/images/*.tar"}]}, {"config_name": "SAM", "data_files": [{"split": "train", "path": "SAM/images/*.tar"}]}, {"config_name": "SAV", "data_files": [{"split": "train", "path": "SAV/images/*.tar"}]}], "language": ["en"], "task_categories": ["image-to-text", "video-text-to-text"], "tags": ["image", "video"]} | false | null | 2025-04-24T21:29:02 | 24 | 24 | false | 058b14afa104276d2e24872bfa059ee8249a3dc6 |
Describe Anything: Detailed Localized Image and Video Captioning
NVIDIA, UC Berkeley, UCSF
Long Lian, Yifan Ding, Yunhao Ge, Sifei Liu, Hanzi Mao, Boyi Li, Marco Pavone, Ming-Yu Liu, Trevor Darrell, Adam Yala, Yin Cui
[Paper] | [Code] | [Project Page] | [Video] | [HuggingFace Demo] | [Model/Benchmark/Datasets] | [Citation]
Dataset Card for Describe Anything Datasets
Datasets used in the training of describe anything models (DAM).
The datasets are in tar files. These… See the full description on the dataset page: https://huggingface.co./datasets/nvidia/describe-anything-dataset. | 3,957 | 3,957 | [
"task_categories:image-to-text",
"task_categories:video-text-to-text",
"language:en",
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"format:webdataset",
"modality:image",
"modality:text",
"modality:video",
"library:datasets",
"library:webdataset",
"library:mlcroissant",
"arxiv:2504.16072",
"region:us",
"image",
"video"
] | 2025-04-22T08:28:26 | null | null |
68051956b83ee49250233b17 | marcodsn/academic-chains | marcodsn | {"tags": ["reasoning-datasets-competition", "reasoning", "academic-papers", "question-answering", "chain-of-thought", "biology", "economics"], "language": ["en"], "license": "apache-2.0", "pretty_name": "Academic Reasoning and Intuition Chains", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "zraw", "path": "data/zraw-*"}, {"split": "zraw_curator", "path": "data/zraw_curator-*"}]}], "dataset_info": {"features": [{"name": "arxiv_id", "dtype": "string"}, {"name": "paper_doi", "dtype": "string"}, {"name": "paper_authors", "sequence": "string"}, {"name": "paper_published_date", "dtype": "int64"}, {"name": "paper_updated_date", "dtype": "int64"}, {"name": "conversations", "list": [{"name": "role", "dtype": "string"}, {"name": "content", "dtype": "string"}]}, {"name": "entry_type", "dtype": "string"}, {"name": "categories", "sequence": "string"}, {"name": "avg_thinking_tokens", "dtype": "float64"}, {"name": "model", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3915833.8152380954, "num_examples": 478}, {"name": "zraw", "num_bytes": 8601727, "num_examples": 1050}, {"name": "zraw_curator", "num_bytes": 3557678, "num_examples": 496}], "download_size": 7757626, "dataset_size": 16075238.815238096}} | false | null | 2025-04-28T20:01:49 | 22 | 21 | false | 0fdade74eaa5a5824c9ee63fd8ca28adbd836f8d |
Dataset Card for Academic Reasoning and Intuition Chains
This dataset contains reasoning (and intuition) chains distilled from open-access research papers, primarily focusing on the q-bio and econ.GN categories (check arXiv for more information about the categories). The goal is to create academically-grounded reasoning chains that capture the underlying logical structure, argumentation, or justification presented by the authors.
This dataset was created as a proof-of-concept for… See the full description on the dataset page: https://huggingface.co./datasets/marcodsn/academic-chains. | 1,588 | 1,588 | [
"language:en",
"license:apache-2.0",
"size_categories:1K<n<10K",
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"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"reasoning-datasets-competition",
"reasoning",
"academic-papers",
"question-answering",
"chain-of-thought",
"biology",
"economics"
] | 2025-04-20T15:57:10 | 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 | 310 | 14 | 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. | 11,526 | 48,179 | [
"language:en",
"arxiv:2311.12983",
"region:us"
] | 2023-10-20T07:06:54 | 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 | 647 | 14 | 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. | 2,493 | 13,032 | [
"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 |
67f9abed63243ae752060832 | openai/mrcr | openai | {"license": "mit"} | false | null | 2025-04-14T18:58:12 | 131 | 14 | false | 204b0d4e8d9ca5c0a90bf942fdb2a5969094adc0 |
OpenAI MRCR: Long context multiple needle in a haystack benchmark
OpenAI MRCR (Multi-round co-reference resolution) is a long context dataset for benchmarking an LLM's ability to distinguish between multiple needles hidden in context.
This eval is inspired by the MRCR eval first introduced by Gemini (https://arxiv.org/pdf/2409.12640v2). OpenAI MRCR expands the tasks's difficulty and provides opensource data for reproducing results.
The task is as follows: The model is given a long… See the full description on the dataset page: https://huggingface.co./datasets/openai/mrcr. | 3,271 | 3,271 | [
"license:mit",
"size_categories:1K<n<10K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2409.12640",
"region:us"
] | 2025-04-11T23:55:25 | null | null |
67ffa22d0d123ebf23677e9e | JoeYing/ReTool-SFT | JoeYing | {"license": "apache-2.0"} | false | null | 2025-04-16T12:58:58 | 24 | 14 | false | 8b676fbb9f095830253943699f16035381a2baa1 | null | 749 | 749 | [
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-04-16T12:27:25 | null | null |
6802d31c7694675650679ff7 | nvidia/dynpose-100k | nvidia | {"license": "other", "language": ["en"], "pretty_name": "dynpose-100k", "size_categories": ["100K<n<1M"], "task_categories": ["other"]} | false | null | 2025-04-25T02:29:31 | 14 | 14 | false | dd6884f0d88ff6d11fbd6a571c412a011cea1086 |
DynPose-100K
Dynamic Camera Poses and Where to Find Them Chris Rockwell1,2, Joseph Tung3, Tsung-Yi Lin1,
Ming-Yu Liu1, David F. Fouhey3, Chen-Hsuan Lin1 1NVIDIA 2University of Michigan 3New York University
Overview
DynPose-100K is a large-scale dataset of diverse, dynamic videos with camera annotations. We curate 100K videos containing dynamic content while ensuring cameras can be accurately estimated (including intrinsics and poses), addressing two key challenges:… See the full description on the dataset page: https://huggingface.co./datasets/nvidia/dynpose-100k. | 708 | 708 | [
"task_categories:other",
"language:en",
"license:other",
"size_categories:100K<n<1M",
"arxiv:2504.17788",
"region:us"
] | 2025-04-18T22:33:00 | null | null |
6801c3764dc338207b777a11 | a-m-team/AM-DeepSeek-Distilled-40M | a-m-team | {"language": ["zh", "en"], "license": "cc-by-nc-4.0", "size_categories": ["35M<n<45M"], "task_categories": ["text-generation"], "tags": ["code", "math", "science", "instruction follow", "reasoning", "thinking", "deepseek-r1", "distill"], "configs": [{"config_name": "code_1.5b_1pass", "data_files": "code_1.5b_1pass.jsonl", "features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "question_source", "dtype": "string"}, {"name": "answer_source", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "ground_truth", "dtype": "string"}, {"name": "test_case", "dtype": "string"}, {"name": "instruction_constrain", "dtype": "string"}, {"name": "pass_rate_r1", "dtype": "float32"}, {"name": "pass_rate_7b", "dtype": "float32"}, {"name": "pass_rate_1.5b", "dtype": "float32"}, {"name": "verify_score", "dtype": "float32"}, {"name": "ppl", "dtype": "float32"}, {"name": "model_name", "dtype": "string"}]}, {"config_name": "code_1.5b_2pass", "data_files": "code_1.5b_2pass.jsonl", "features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "question_source", "dtype": "string"}, {"name": "answer_source", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "ground_truth", "dtype": "string"}, {"name": "test_case", "dtype": "string"}, {"name": "instruction_constrain", "dtype": "string"}, {"name": "pass_rate_r1", "dtype": "float32"}, {"name": "pass_rate_7b", "dtype": "float32"}, {"name": "pass_rate_1.5b", "dtype": "float32"}, {"name": "verify_score", "dtype": "float32"}, {"name": "ppl", "dtype": "float32"}, {"name": "model_name", "dtype": "string"}]}, {"config_name": "code_1.5b_3pass", "data_files": "code_1.5b_3pass.jsonl", "features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "question_source", "dtype": "string"}, {"name": "answer_source", "dtype": 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{"name": "pass_rate_1.5b", "dtype": "float32"}, {"name": "verify_score", "dtype": "float32"}, {"name": "ppl", "dtype": "float32"}, {"name": "model_name", "dtype": "string"}]}, {"config_name": "code_7b_1pass", "data_files": "code_7b_1pass.jsonl", "features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "question_source", "dtype": "string"}, {"name": "answer_source", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "ground_truth", "dtype": "string"}, {"name": "test_case", "dtype": "string"}, {"name": "instruction_constrain", "dtype": "string"}, {"name": "pass_rate_r1", "dtype": "float32"}, {"name": "pass_rate_7b", "dtype": "float32"}, {"name": "pass_rate_1.5b", "dtype": "float32"}, {"name": "verify_score", "dtype": "float32"}, {"name": "ppl", "dtype": "float32"}, {"name": "model_name", "dtype": "string"}]}, {"config_name": "code_7b_2pass", "data_files": "code_7b_2pass.jsonl", "features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "question_source", "dtype": "string"}, {"name": "answer_source", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "ground_truth", "dtype": "string"}, {"name": "test_case", "dtype": "string"}, {"name": "instruction_constrain", "dtype": "string"}, {"name": "pass_rate_r1", "dtype": "float32"}, {"name": "pass_rate_7b", "dtype": "float32"}, {"name": "pass_rate_1.5b", "dtype": "float32"}, {"name": "verify_score", "dtype": "float32"}, {"name": "ppl", "dtype": "float32"}, {"name": "model_name", "dtype": "string"}]}, {"config_name": "code_7b_3pass", "data_files": "code_7b_3pass.jsonl", "features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "question_source", "dtype": "string"}, {"name": "answer_source", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "ground_truth", "dtype": "string"}, {"name": "test_case", "dtype": "string"}, {"name": "instruction_constrain", "dtype": "string"}, {"name": "pass_rate_r1", "dtype": "float32"}, {"name": "pass_rate_7b", "dtype": "float32"}, {"name": "pass_rate_1.5b", "dtype": "float32"}, {"name": "verify_score", "dtype": "float32"}, {"name": "ppl", "dtype": "float32"}, {"name": "model_name", "dtype": "string"}]}, {"config_name": "code_7b_4pass", "data_files": "code_7b_4pass.jsonl", "features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "question_source", "dtype": "string"}, {"name": "answer_source", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "ground_truth", "dtype": "string"}, {"name": "test_case", "dtype": "string"}, {"name": "instruction_constrain", "dtype": "string"}, {"name": "pass_rate_r1", "dtype": "float32"}, {"name": "pass_rate_7b", "dtype": "float32"}, {"name": "pass_rate_1.5b", "dtype": "float32"}, {"name": "verify_score", "dtype": "float32"}, {"name": "ppl", "dtype": "float32"}, {"name": "model_name", "dtype": "string"}]}, {"config_name": "code_r1_1pass", "data_files": "code_r1_1pass.jsonl", "features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "question_source", "dtype": "string"}, {"name": "answer_source", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "ground_truth", "dtype": "string"}, {"name": "test_case", "dtype": "string"}, {"name": "instruction_constrain", "dtype": "string"}, {"name": "pass_rate_r1", "dtype": "float32"}, {"name": "pass_rate_7b", "dtype": "float32"}, {"name": "pass_rate_1.5b", "dtype": "float32"}, {"name": "verify_score", "dtype": "float32"}, {"name": "ppl", "dtype": "float32"}, {"name": "model_name", "dtype": "string"}]}, {"config_name": "code_r1_2pass", "data_files": "code_r1_2pass.jsonl", "features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "question_source", "dtype": "string"}, {"name": "answer_source", "dtype": "string"}, {"name": 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"dtype": "float32"}, {"name": "pass_rate_1.5b", "dtype": "float32"}, {"name": "verify_score", "dtype": "float32"}, {"name": "ppl", "dtype": "float32"}, {"name": "model_name", "dtype": "string"}]}, {"config_name": "math_r1_4pass", "data_files": "math_r1_4pass.jsonl", "features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "question_source", "dtype": "string"}, {"name": "answer_source", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "ground_truth", "dtype": "string"}, {"name": "test_case", "dtype": "string"}, {"name": "instruction_constrain", "dtype": "string"}, {"name": "pass_rate_r1", "dtype": "float32"}, {"name": "pass_rate_7b", "dtype": "float32"}, {"name": "pass_rate_1.5b", "dtype": "float32"}, {"name": "verify_score", "dtype": "float32"}, {"name": "ppl", "dtype": "float32"}, {"name": "model_name", "dtype": "string"}]}]} | false | null | 2025-04-25T12:28:24 | 23 | 13 | false | 6b3eee01e89e4e87a9b7f13208913cb126c7177a | For more open-source datasets, models, and methodologies, please visit our GitHub repository and paper: DeepDistill: Enhancing LLM Reasoning Capabilities via Large-Scale Difficulty-Graded Data Training.
Model Training Performance based on this dataset
On AIME 2024, our 72B model achieved a score of 79.2 using only supervised fine-tuning (SFT). The 32B model reached 75.8 and improved further to 77.9 through annealing training, approaching state-of-the-art open-source performance.… See the full description on the dataset page: https://huggingface.co./datasets/a-m-team/AM-DeepSeek-Distilled-40M. | 2,701 | 2,701 | [
"task_categories:text-generation",
"language:zh",
"language:en",
"license:cc-by-nc-4.0",
"size_categories:10M<n<100M",
"format:json",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2504.17565",
"region:us",
"code",
"math",
"science",
"instruction follow",
"reasoning",
"thinking",
"deepseek-r1",
"distill"
] | 2025-04-18T03:13:58 | null | null |
67d3479522a51de18affff22 | nvidia/Llama-Nemotron-Post-Training-Dataset | nvidia | {"license": "cc-by-4.0", "configs": [{"config_name": "SFT", "data_files": [{"split": "code", "path": "SFT/code/*.jsonl"}, {"split": "math", "path": "SFT/math/*.jsonl"}, {"split": "science", "path": "SFT/science/*.jsonl"}, {"split": "chat", "path": "SFT/chat/*.jsonl"}, {"split": "safety", "path": "SFT/safety/*.jsonl"}], "default": true}, {"config_name": "RL", "data_files": [{"split": "instruction_following", "path": "RL/instruction_following/*.jsonl"}]}]} | false | null | 2025-04-27T18:10:38 | 429 | 11 | false | 9d14669b5faf555a04aa7fbd5791b2ac7a83f25e |
Llama-Nemotron-Post-Training-Dataset-v1.1 Release
Update [4/8/2025]:
v1.1: We are releasing an additional 2.2M Math and 500K Code Reasoning Data in support of our release of Llama-3.1-Nemotron-Ultra-253B-v1. 🎉
Data Overview
This dataset is a compilation of SFT and RL data that supports improvements of math, code, general reasoning, and instruction following capabilities of the original Llama instruct model, in support of NVIDIA’s release of… See the full description on the dataset page: https://huggingface.co./datasets/nvidia/Llama-Nemotron-Post-Training-Dataset. | 8,281 | 8,292 | [
"license:cc-by-4.0",
"size_categories:1M<n<10M",
"format:json",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"region:us"
] | 2025-03-13T21:01:09 | null | null |
67e428ff51af4261f7bed8c7 | nvidia/ClimbLab | nvidia | {"language": ["en"], "license": "cc-by-nc-4.0", "task_categories": ["text-generation"]} | false | null | 2025-04-21T19:02:49 | 30 | 11 | false | 9c3267aa7b4b4eda47fba41bbc95d99d072416c5 |
ClimbLab Dataset
🚀 Creating the highest-quality pre-training datasets for LLMs 🌟
📄 PAPER
🤗 CLIMBLAB
🤗 CLIMBMIX
🏠 HOMEPAGE
Figure 1: Continuously training a 1B model yields a 2.0% improvement over Llama-3.2-1B, demonstrating a more efficient scaling trend compared to prior models.
Figure 2: Pre-training a 1B model from scratch on ClimbMix shows better scaling effects than training on other datasets.… See the full description on the dataset page: https://huggingface.co./datasets/nvidia/ClimbLab. | 11,141 | 11,141 | [
"task_categories:text-generation",
"language:en",
"license:cc-by-nc-4.0",
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"format:parquet",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2504.13161",
"region:us"
] | 2025-03-26T16:19:11 | null | null |
680a5ef919f25e184a85c785 | Rapidata/text-2-image-Rich-Human-Feedback-32k | Rapidata | {"dataset_info": {"features": [{"name": "image_name", "dtype": "image"}, {"name": "sentence", "dtype": "string"}, {"name": "word_scores", "dtype": "string"}, {"name": "alignment_score_norm", "dtype": "float32"}, {"name": "coherence_score_norm", "dtype": "float32"}, {"name": "style_score_norm", "dtype": "float32"}, {"name": "alignment_heatmap", "dtype": {"array2_d": {"shape": [1024, 1024], "dtype": "float32"}}}, {"name": "coherence_heatmap", "dtype": {"array2_d": {"shape": [1024, 1024], "dtype": "float32"}}}, {"name": "alignment_score", "dtype": "float32"}, {"name": "coherence_score", "dtype": "float32"}, {"name": "style_score", "dtype": "float32"}], "splits": [{"name": "train", "num_bytes": 116617124714.976, "num_examples": 32528}], "download_size": 91216762385, "dataset_size": 116617124714.976}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | false | null | 2025-04-28T08:46:16 | 11 | 11 | false | 2bb7ce6617c736bd98267cbdce769c73ff378b5a |
Building upon Google's research Rich Human Feedback for Text-to-Image Generation, and the
smaller, previous version of this dataset, we have collected over 3.7 million responses from 307'415 individual humans using Rapidata via the Python API. Collection took less than 2 weeks.
If you get value from this dataset and would like to see more in the future, please consider liking it ♥️
Overview
We asked humans to evaluate AI-generated images in style, coherence and prompt… See the full description on the dataset page: https://huggingface.co./datasets/Rapidata/text-2-image-Rich-Human-Feedback-32k. | 118 | 118 | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:image",
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"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2312.10240",
"region:us"
] | 2025-04-24T15:55:37 | 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"}, {"config_name": "en_mix", "data_files": "medical_o1_sft_mix.json"}, {"config_name": "zh_mix", "data_files": "medical_o1_sft_mix_Chinese.json"}]} | false | null | 2025-04-22T15:11:21 | 665 | 10 | false | fc2c9e8a37b38f38da6d449564a8c350b244aef4 |
News
[2025/04/22] We split the data and kept only the medical SFT dataset (medical_o1_sft.json). The file medical_o1_sft_mix.json contains a mix of medical and general instruction data.
[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… See the full description on the dataset page: https://huggingface.co./datasets/FreedomIntelligence/medical-o1-reasoning-SFT. | 13,006 | 61,172 | [
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"library:polars",
"arxiv:2412.18925",
"region:us",
"medical",
"biology"
] | 2024-12-28T03:29:08 | null | null |
67e104c5e5179149a17a9b58 | amazon-agi/SIFT-50M | amazon-agi | {"license": "cdla-sharing-1.0", "language": ["en", "de", "fr", "it", "es"], "size_categories": ["10M<n<100M"], "task_categories": ["audio-text-to-text", "audio-classification", "text-to-speech", "audio-to-audio"], "pretty_name": "SIFT-50M", "configs": [{"config_name": "closed_ended_acoustic_level", "data_files": [{"split": "train", "path": "train/closed_ended/acoustic_level/*/*.jsonl"}, {"split": "validation", "path": "dev/closed_ended/acoustic_level/*/*.jsonl"}, {"split": "EvalSIFT", "path": "EvalSIFT/closed_ended/acoustic_level/*/*.jsonl"}]}, {"config_name": "closed_ended_content_level", "data_files": [{"split": "train", "path": "train/closed_ended/content_level/*/*.jsonl"}, {"split": "validation", "path": "dev/closed_ended/content_level/*/*.jsonl"}, {"split": "EvalSIFT", "path": "EvalSIFT/closed_ended/content_level/*/*.jsonl"}]}, {"config_name": "closed_ended_word_align", "data_files": [{"split": "train", "path": "train/closed_ended/word_align/*/*.jsonl"}, {"split": "validation", "path": "dev/closed_ended/word_align/*/*.jsonl"}, {"split": "EvalSIFT", "path": "EvalSIFT/closed_ended/word_align/*/*.jsonl"}]}, {"config_name": "closed_ended_comparison", "data_files": [{"split": "train", "path": "train/closed_ended/comparison/*/*.jsonl"}, {"split": "validation", "path": "dev/closed_ended/comparison/*/*.jsonl"}, {"split": "EvalSIFT", "path": "EvalSIFT/closed_ended/comparison/*/*.jsonl"}]}, {"config_name": "open_ended", "data_files": [{"split": "train", "path": "train/open_ended/*/*.jsonl"}, {"split": "validation", "path": "dev/open_ended/*/*.jsonl"}, {"split": "EvalSIFT", "path": "EvalSIFT/open_ended/*/*.jsonl"}]}, {"config_name": "controllable_generation", "data_files": [{"split": "train", "path": "train/controllable_generation/*/*.jsonl"}, {"split": "validation", "path": "dev/controllable_generation/*/*.jsonl"}, {"split": "EvalSIFT", "path": "EvalSIFT/controllable_generation/*/*.jsonl"}]}], "tags": ["speech", "speech-llm", "spoken-language-understanding", "controllable-speech-synthesis", "instruction-finetuning"]} | false | null | 2025-04-23T05:08:59 | 19 | 10 | false | 1277f4010c28983c41e4e549071f01b22af4bc87 |
Dataset Card for SIFT-50M
SIFT-50M (Speech Instruction Fine-Tuning) is a 50-million-example dataset designed for instruction fine-tuning and pre-training of speech-text large language models (LLMs). It is built from publicly available speech corpora containing a total of 14K hours of speech and leverages LLMs and off-the-shelf expert models. The dataset spans five languages, covering diverse aspects of speech understanding and controllable speech generation instructions. SIFT-50M… See the full description on the dataset page: https://huggingface.co./datasets/amazon-agi/SIFT-50M. | 7,159 | 7,613 | [
"task_categories:audio-text-to-text",
"task_categories:audio-classification",
"task_categories:text-to-speech",
"task_categories:audio-to-audio",
"language:en",
"language:de",
"language:fr",
"language:it",
"language:es",
"license:cdla-sharing-1.0",
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"format:json",
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"arxiv:2504.09081",
"region:us",
"speech",
"speech-llm",
"spoken-language-understanding",
"controllable-speech-synthesis",
"instruction-finetuning"
] | 2025-03-24T07:07:49 | null | null |
67e72ca8f52d9d15f9d38a2a | facebook/PE-Video | facebook | {"license": "cc-by-nc-4.0"} | false | null | 2025-04-18T22:33:23 | 21 | 10 | false | 43a297dde47e2036721f259397df04b3c338d002 |
PE Video Dataset (PVD)
[📃 Tech Report]
[📂 Github]
The PE Video Dataset (PVD) is a large-scale collection of 1 million diverse videos, featuring 120,000+ expertly annotated clips. The dataset was introduced in our paper "Perception Encoder".
Overview
PE Video Dataset (PVD) comprises 1M high quality and diverse videos. Among them, 120K videos are accompanied by automated and human-verified annotations. and all videos are accompanied with video description and keywords.… See the full description on the dataset page: https://huggingface.co./datasets/facebook/PE-Video. | 7,882 | 7,882 | [
"license:cc-by-nc-4.0",
"size_categories:100K<n<1M",
"format:webdataset",
"modality:text",
"library:datasets",
"library:webdataset",
"library:mlcroissant",
"arxiv:2504.13181",
"region:us"
] | 2025-03-28T23:11:36 | null | null |
68064ea6877cc153240df798 | weaverbirdllm/famma-reasoning | weaverbirdllm | {"license": "apache-2.0", "dataset_info": {"features": [{"name": "idx", "dtype": "int64"}, {"name": "question_id", "dtype": "string"}, {"name": "source_release", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "options", "sequence": "string"}, {"name": "answers", "dtype": "string"}, {"name": "thinking_trajectory", "dtype": "string"}, {"name": "structured_thinking_trajectory", "dtype": "string"}, {"name": "topic_difficulty", "dtype": "string"}, {"name": "question_type", "dtype": "string"}, {"name": "subfield", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "main_question_id", "dtype": "int64"}, {"name": "sub_question_id", "dtype": "int64"}, {"name": "is_arithmetic", "dtype": "bool"}, {"name": "release", "dtype": "string"}], "splits": [{"name": "release_reasoning_basic_txt", "num_bytes": 17080446, "num_examples": 1273}], "download_size": 6860503, "dataset_size": 17080446}, "configs": [{"config_name": "default", "data_files": [{"split": "release_reasoning_basic_txt", "path": "data/release_reasoning_basic_txt-*"}]}], "task_categories": ["question-answering"], "language": ["en", "zh", "fr"], "tags": ["finance"], "pretty_name": "long cot for finance", "size_categories": ["1K<n<10K"]} | false | null | 2025-04-27T05:31:00 | 10 | 10 | false | 655c686a04325852346d8a677c82ea463c2983f8 |
FAMMA‑Reasoning
A distilled, tool‑augmented reasoning dataset for financial Q&A
Dataset Summary
FAMMA‑Reasoning is built on top of the FAMMA benchmark, a multilingual, multimodal financial question‑answering dataset covering tables, charts, and text/math screenshots across eight subfields and three difficulty levels.Here, every example pairs a question with:
Thinking Trajectories: natural‑language, step‑by‑step chains of thought generated by DeepSeek‑R1.
Structured… See the full description on the dataset page: https://huggingface.co./datasets/weaverbirdllm/famma-reasoning. | 482 | 482 | [
"task_categories:question-answering",
"language:en",
"language:zh",
"language:fr",
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"size_categories:1K<n<10K",
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"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2410.04526",
"region:us",
"finance"
] | 2025-04-21T13:56:54 | null | null |
6809becb4c287fa790959bc2 | miscovery/arabic_egypt_english_world_facts | miscovery | {"license": "mit", "task_categories": ["question-answering", "translation", "text-generation", "fill-mask"], "language": ["en", "ar"], "pretty_name": "Arabic_Egypt_English_World_Facts", "size_categories": ["10K<n<100K"]} | false | null | 2025-04-26T02:18:29 | 10 | 10 | false | cd71750efb8dc8f03228b3930612679fb93deecf |
🌍 Version (v2.0) World Facts in English, Arabic & Egyptian Arabic (Categorized)
The World Facts General Knowledge Dataset (v2.0) is a high-quality, human-reviewed Q&A resource by Miscovery. It features general facts categorized across 50+ knowledge domains, provided in three languages:
🌍 English
🇸🇦 Modern Standard Arabic (MSA)
🇪🇬 Egyptian Arabic (Dialect)
Each entry includes:
The question and answer
A category and sub-category
Language tag (en, ar, ar_eg)
Basic metadata:… See the full description on the dataset page: https://huggingface.co./datasets/miscovery/arabic_egypt_english_world_facts. | 171 | 171 | [
"task_categories:question-answering",
"task_categories:translation",
"task_categories:text-generation",
"task_categories:fill-mask",
"language:en",
"language:ar",
"license:mit",
"size_categories:10K<n<100K",
"format:csv",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-04-24T04:32:11 | null | null |
680a2f2273a233a03913a327 | stepfun-ai/GEdit-Bench | stepfun-ai | {"license": "mit"} | false | null | 2025-04-24T13:00:51 | 10 | 10 | false | 58159f3de6689982b1df7f1a6c557ded16548a24 | null | 395 | 395 | [
"license:mit",
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"format:arrow",
"modality:image",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us"
] | 2025-04-24T12:31:30 | null | null |
End of preview.
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