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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 | 234 | 99 | 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. | 7,801 | 7,801 | [
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] | 2025-04-01T20:07:48 | null | null |
67f9abed63243ae752060832 | openai/mrcr | openai | {"license": "mit"} | false | null | 2025-04-14T18:58:12 | 89 | 89 | 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. | 1,227 | 1,227 | [
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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 | 2025-04-17T05:12:16 | 60 | 60 | false | 3c5789b105a8cc11865f39fdcb4cb46529fed073 |
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. | 1,554 | 1,554 | [
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67f62a9296e24db82ed27e76 | divaroffical/real_estate_ads | divaroffical | {"license": "odbl"} | false | null | 2025-04-16T08:31:19 | 50 | 50 | false | bdc2a655ad7955e1cfcf6a7467de85144d5d9099 |
🏠 Divar Real Estate Ads Dataset
📋 Overview
The real_estate_ads dataset contains one million anonymized real estate advertisements collected from the Divar platform, one of the largest classified ads platforms in the Middle East. This comprehensive dataset provides researchers, data scientists, and entrepreneurs with authentic real estate market data to build innovative solutions such as price evaluation models, market analysis tools, and forecasting systems.… See the full description on the dataset page: https://huggingface.co/datasets/divaroffical/real_estate_ads. | 1,157 | 1,157 | [
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] | 2025-04-09T08:06:42 | null | null |
67f9a5dde1bb509430e6af04 | openai/graphwalks | openai | {"license": "mit"} | false | null | 2025-04-14T17:22:42 | 46 | 46 | false | 6fe75ac25ccf55853294fe7995332d4f59d91bfb |
GraphWalks: a multi hop reasoning long context benchmark
In Graphwalks, the model is given a graph represented by its edge list and asked to perform an operation.
Example prompt:
You will be given a graph as a list of directed edges. All nodes are at least degree 1.
You will also get a description of an operation to perform on the graph.
Your job is to execute the operation on the graph and return the set of nodes that the operation results in.
If asked for a breadth-first search… See the full description on the dataset page: https://huggingface.co/datasets/openai/graphwalks. | 616 | 616 | [
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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 | 2025-04-16T18:05:39 | 405 | 41 | false | ec1aa9f7d0832333c68283cd70a3df60bb8021db |
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. | 5,238 | 5,249 | [
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67e9a644ea97f3c65c463bfb | LLM360/MegaMath | LLM360 | {"license": "odc-by", "task_categories": ["text-generation"], "language": ["en"], "tags": ["math", "code", "pre-training", "synthesis"], "size_categories": ["1B<n<10B"]} | false | null | 2025-04-09T13:17:50 | 69 | 29 | false | 3cbc64616594d6bc8759abaa0b2a71858f880f0d |
MegaMath: Pushing the Limits of Open Math Copora
Megamath is part of TxT360, curated by LLM360 Team.
We introduce MegaMath, an open math pretraining dataset curated from diverse, math-focused sources, with over 300B tokens.
MegaMath is curated via the following three efforts:
Revisiting web data:
We re-extracted mathematical documents from Common Crawl with math-oriented HTML optimizations, fasttext-based filtering and deduplication, all for acquiring higher-quality data on the… See the full description on the dataset page: https://huggingface.co/datasets/LLM360/MegaMath. | 51,127 | 51,127 | [
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] | 2025-03-30T20:15:00 | null | null |
67f3de7c9421ed3129d436cf | agentica-org/DeepCoder-Preview-Dataset | agentica-org | {"dataset_info": [{"config_name": "codeforces", "features": [{"name": "problem", "dtype": "string"}, {"name": "tests", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 778742, "num_examples": 408}], "download_size": 301694, "dataset_size": 778742}, {"config_name": "lcbv5", "features": [{"name": "problem", "dtype": "string"}, {"name": "starter_code", "dtype": "string"}, {"name": "tests", "dtype": "string"}, {"name": "metadata", "struct": [{"name": "func_name", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 5349497203, "num_examples": 599}, {"name": "test", "num_bytes": 3744466075, "num_examples": 279}], "download_size": 5790246998, "dataset_size": 9093963278}, {"config_name": "primeintellect", "features": [{"name": "problem", "dtype": "string"}, {"name": "solutions", "sequence": "string"}, {"name": "tests", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2312671464, "num_examples": 16252}], "download_size": 1159149534, "dataset_size": 2312671464}, {"config_name": "taco", "features": [{"name": "problem", "dtype": "string"}, {"name": "tests", "dtype": "string"}, {"name": "solutions", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 1657247795, "num_examples": 7436}], "download_size": 862295065, "dataset_size": 1657247795}], "configs": [{"config_name": "codeforces", "data_files": [{"split": "test", "path": "codeforces/test-*"}]}, {"config_name": "lcbv5", "data_files": [{"split": "train", "path": "lcbv5/train-*"}, {"split": "test", "path": "lcbv5/test-*"}]}, {"config_name": "primeintellect", "data_files": [{"split": "train", "path": "primeintellect/train-*"}]}, {"config_name": "taco", "data_files": [{"split": "train", "path": "taco/train-*"}]}], "license": "mit", "language": ["en"], "tags": ["code"], "size_categories": ["10K<n<100K"]} | false | null | 2025-04-09T20:43:48 | 65 | 29 | false | 177913a7bd43791646ef6a43645caa3c871ab3db |
Data
Our training dataset consists of 24K problems paired with their test cases:
7.5K TACO Verified problems.
16K verified coding problems from PrimeIntellect’s SYNTHETIC-1.
600 LiveCodeBench (v5) problems submitted between May 1, 2023 and July 31, 2024.
Our test dataset consists of:
LiveCodeBench (v5) problems between August 1, 2024 and February 1, 2025.
Codeforces problems from Qwen/CodeElo.
Format
Each row in the dataset contains:
problem: The coding problem… See the full description on the dataset page: https://huggingface.co/datasets/agentica-org/DeepCoder-Preview-Dataset. | 2,633 | 2,633 | [
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67edf568d1631250f17528af | open-thoughts/OpenThoughts2-1M | open-thoughts | {"dataset_info": {"features": [{"name": "conversations", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "question", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 18986223337, "num_examples": 1143205}], "download_size": 8328411205, "dataset_size": 18986223337}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "tags": ["synthetic", "curator"], "license": "apache-2.0"} | false | null | 2025-04-07T21:40:23 | 112 | 28 | false | 40766050d883e0aa951fd3ddee33faf3ad83f26b |
OpenThoughts2-1M
Open synthetic reasoning dataset with 1M high-quality examples covering math, science, code, and puzzles!
OpenThoughts2-1M builds upon our previous OpenThoughts-114k dataset, augmenting it with existing datasets like OpenR1, as well as additional math and code reasoning data.
This dataset was used to train OpenThinker2-7B and OpenThinker2-32B.
Inspect the content with rich formatting and search & filter capabilities in Curator Viewer.
See our blog post… See the full description on the dataset page: https://huggingface.co/datasets/open-thoughts/OpenThoughts2-1M. | 12,807 | 12,807 | [
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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 | 2025-04-15T13:24:42 | 48 | 22 | false | 70d940db37e4cb645437f892fab8a7e5404bb7bf |
Universal Transformer Dataset
💠 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, and dreamer who wants to… See the full description on the dataset page: https://huggingface.co/datasets/future-technologies/Universal-Transformers-Dataset. | 2,677 | 2,732 | [
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676f70846bf205795346d2be | FreedomIntelligence/medical-o1-reasoning-SFT | FreedomIntelligence | {"license": "apache-2.0", "task_categories": ["question-answering", "text-generation"], "language": ["en", "zh"], "tags": ["medical", "biology"], "configs": [{"config_name": "en", "data_files": "medical_o1_sft.json"}, {"config_name": "zh", "data_files": "medical_o1_sft_Chinese.json"}]} | false | null | 2025-02-22T05:15:38 | 644 | 21 | 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. | 18,308 | 57,310 | [
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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 | 7,696 | 20 | false | 68ba7694e23014788dcc8ab5afe613824f45a05c | 🧠 Awesome ChatGPT Prompts [CSV dataset]
This is a Dataset Repository of Awesome ChatGPT Prompts
View All Prompts on GitHub
License
CC-0
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"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 | 2025-01-31T14:10:44 | 2,111 | 19 | false | 0f039043b23fe1d4eed300b504aa4b4a68f1c7ba |
🍷 FineWeb
15 trillion tokens of the finest data the 🌐 web has to offer
What is it?
The 🍷 FineWeb dataset consists of more than 15T tokens of cleaned and deduplicated english web data from CommonCrawl. The data processing pipeline is optimized for LLM performance and ran on the 🏭 datatrove library, our large scale data processing library.
🍷 FineWeb was originally meant to be a fully open replication of 🦅 RefinedWeb, with a release of the full dataset under… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceFW/fineweb. | 437,370 | 2,688,932 | [
"task_categories:text-generation",
"language:en",
"license:odc-by",
"size_categories:10B<n<100B",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2306.01116",
"arxiv:2109.07445",
"arxiv:2406.17557",
"doi:10.57967/hf/2493",
"region:us"
] | 2024-04-18T14:33:13 | null | null |
67ddbf33273db7cb5c4f3f32 | UCSC-VLAA/MedReason | UCSC-VLAA | {"license": "apache-2.0", "tags": ["reasoning-datasets-competition", "reasoning-LLMs"]} | false | null | 2025-04-10T20:17:26 | 25 | 18 | false | a4bbf707e122021e74b098f542f2db97a89a9ead |
MedReason: Eliciting Factual Medical Reasoning Steps in LLMs via Knowledge Graphs
📃 Paper ｜🤗 MedReason-8B | 📚 MedReason Data
⚡Introduction
MedReason is a large-scale high-quality medical reasoning dataset designed to enable faithful and explainable medical problem-solving in large language models (LLMs).
We utilize a structured medical knowledge graph (KG) to convert clinical QA pairs into logical chains of reasoning, or “thinking paths”.
Our pipeline generates… See the full description on the dataset page: https://huggingface.co/datasets/UCSC-VLAA/MedReason. | 753 | 753 | [
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:json",
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"library:polars",
"arxiv:2504.00993",
"region:us",
"reasoning-datasets-competition",
"reasoning-LLMs"
] | 2025-03-21T19:34:11 | null | null |
67f65eecc6d6baefc4b193a8 | Rapidata/2k-ranked-images-open-image-preferences-v1 | Rapidata | {"license": "apache-2.0", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "elo", "dtype": "int64"}, {"name": "__index_level_0__", "dtype": "int64"}, {"name": "category", "dtype": "string"}, {"name": "subcategory", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 298637443.176, "num_examples": 1999}], "download_size": 290047395, "dataset_size": 298637443.176}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "tags": ["t2i", "preference", "ranking", "rl", "image"], "pretty_name": "2k Ranked Images"} | false | null | 2025-04-10T14:35:23 | 18 | 17 | false | a48acd2f9d8470d8e7388c2efa0cf87ebf09c3bf |
2k Ranked Images
This dataset contains roughly two thousand images ranked from most preferred to least preferred based on human feedback on pairwise comparisons (>25k responses).
The generated images, which are a sample from the open-image-preferences-v1 dataset
from the team @data-is-better-together, are rated purely based on aesthetic preference, disregarding the prompt used for generation.
We provide the categories of the original dataset for easy filtering.
This is a new… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/2k-ranked-images-open-image-preferences-v1. | 113 | 113 | [
"license:apache-2.0",
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"library:polars",
"region:us",
"t2i",
"preference",
"ranking",
"rl",
"image"
] | 2025-04-09T11:50:04 | null | null |
67d1f960012f0ef1ab080a8b | vevotx/Tahoe-100M | vevotx | {"license": "cc0-1.0", "tags": ["biology", "single-cell", "RNA", "chemistry"], "size_categories": ["100M<n<1B"], "configs": [{"config_name": "expression_data", "data_files": "data/train-*", "default": true}, {"config_name": "sample_metadata", "data_files": "metadata/sample_metadata.parquet"}, {"config_name": "gene_metadata", "data_files": "metadata/gene_metadata.parquet"}, {"config_name": "drug_metadata", "data_files": "metadata/drug_metadata.parquet"}, {"config_name": "cell_line_metadata", "data_files": "metadata/cell_line_metadata.parquet"}, {"config_name": "obs_metadata", "data_files": "metadata/obs_metadata.parquet"}], "dataset_info": {"features": [{"name": "genes", "sequence": "int64"}, {"name": "expressions", "sequence": "float32"}, {"name": "drug", "dtype": "string"}, {"name": "sample", "dtype": "string"}, {"name": "BARCODE_SUB_LIB_ID", "dtype": "string"}, {"name": "cell_line_id", "dtype": "string"}, {"name": "moa-fine", "dtype": "string"}, {"name": "canonical_smiles", "dtype": "string"}, {"name": "pubchem_cid", "dtype": "string"}, {"name": "plate", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1693653078843, "num_examples": 95624334}], "download_size": 337644770670, "dataset_size": 1693653078843}} | false | null | 2025-04-08T17:51:25 | 20 | 16 | false | 91953459e339ed9f27eb2ed4b6aa7719b2de3c66 |
Tahoe-100M
Tahoe-100M is a giga-scale single-cell perturbation atlas consisting of over 100 million transcriptomic profiles from
50 cancer cell lines exposed to 1,100 small-molecule perturbations. Generated using Vevo Therapeutics'
Mosaic high-throughput platform, Tahoe-100M enables deep, context-aware exploration of gene function, cellular states, and drug responses at unprecedented scale and resolution.
This dataset is designed to power the development of next-generation AI… See the full description on the dataset page: https://huggingface.co/datasets/vevotx/Tahoe-100M. | 6,383 | 6,383 | [
"license:cc0-1.0",
"size_categories:100M<n<1B",
"format:parquet",
"modality:tabular",
"modality:text",
"modality:timeseries",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"biology",
"single-cell",
"RNA",
"chemistry"
] | 2025-03-12T21:15:12 | null | null |
67f51e10192d5ab08ffab69e | OmniSVG/MMSVG-Illustration | OmniSVG | {"license": "cc-by-nc-sa-4.0"} | false | null | 2025-04-09T03:04:41 | 39 | 15 | false | a35b1ff1253e6aa3cbc2ebda9e29a54736cb4479 | OmniSVG: A Unified Scalable Vector Graphics Generation Model
![Project Page]
Dataset Card for MMSVG-Illustration
Dataset Description
This dataset contains SVG illustration examples for training and evaluating SVG models for text-to-SVG and image-to-SVG task.
Dataset Structure
Features
The dataset contains the following fields:
Field Name
Description
id
Unique ID for each SVG
svg
SVG code
description
Description of the SVG… See the full description on the dataset page: https://huggingface.co/datasets/OmniSVG/MMSVG-Illustration. | 921 | 921 | [
"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:2504.06263",
"region:us"
] | 2025-04-08T13:01:04 | 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 | 697 | 13 | 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. | 412,175 | 4,526,817 | [
"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 |
67f505664a7ad6225a4ae9ed | OmniSVG/MMSVG-Icon | OmniSVG | {"license": "cc-by-nc-sa-4.0"} | false | null | 2025-04-09T03:03:42 | 37 | 13 | false | 500f7f304c6d758d2f8764bf285440eb929246e3 | OmniSVG: A Unified Scalable Vector Graphics Generation Model
![Project Page]
Dataset Card for MMSVG-Icon
Dataset Description
This dataset contains SVG icon examples for training and evaluating SVG models for text-to-SVG and image-to-SVG task.
Dataset Structure
Features
The dataset contains the following fields:
Field Name
Description
id
Unique ID for each SVG
svg
SVG code
description
Description of the SVG
Citation… See the full description on the dataset page: https://huggingface.co/datasets/OmniSVG/MMSVG-Icon. | 394 | 394 | [
"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:2504.06263",
"region:us"
] | 2025-04-08T11:15:50 | null | null |
67fa39f24a13bd97755f08db | Skywork/Skywork-OR1-RL-Data | Skywork | {"dataset_info": {"features": [{"name": "data_source", "dtype": "string"}, {"name": "prompt", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "ability", "dtype": "string"}, {"name": "reward_model", "struct": [{"name": "ground_truth", "dtype": "string"}, {"name": "style", "dtype": "string"}]}, {"name": "extra_info", "struct": [{"name": "index", "dtype": "int64"}, {"name": "model_difficulty", "struct": [{"name": "DeepSeek-R1-Distill-Qwen-1.5B", "dtype": "int64"}, {"name": "DeepSeek-R1-Distill-Qwen-32B", "dtype": "int64"}, {"name": "DeepSeek-R1-Distill-Qwen-7B", "dtype": "int64"}]}]}], "splits": [{"name": "math", "num_bytes": 40461845, "num_examples": 105055}, {"name": "code", "num_bytes": 1474827100, "num_examples": 14057}], "download_size": 823104116, "dataset_size": 1515288945}, "configs": [{"config_name": "default", "data_files": [{"split": "math", "path": "data/math-*"}, {"split": "code", "path": "data/code-*"}]}]} | false | null | 2025-04-15T08:31:20 | 13 | 13 | false | d3dd0aaddf1f74f14d37331b574ebf5746670645 |
🤔 Skywork-OR1-RL-Data
🔥 News
April 15, 2025: We are excited to release our RL training dataset Skywork-OR1-RL-Data
For our final training phase, we filtered problems based on their difficulty levels (0-16, higher values indicate harder problems) relative to specific model variants (DeepSeek-R1-Distill-Qwen-{1.5,7,32}B. For each model variant, we excluded problems with difficulty values of 0 and 16 specific to that model from its training data.You can… See the full description on the dataset page: https://huggingface.co/datasets/Skywork/Skywork-OR1-RL-Data. | 552 | 552 | [
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-04-12T10:01:22 | null | null |
660e7b9b4636ce2b0e77b699 | mozilla-foundation/common_voice_17_0 | mozilla-foundation | {"pretty_name": "Common Voice Corpus 17.0", "annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["ab", "af", "am", "ar", "as", "ast", "az", "ba", "bas", "be", "bg", "bn", "br", "ca", "ckb", "cnh", "cs", "cv", "cy", "da", "de", "dv", "dyu", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fy", "ga", "gl", "gn", "ha", "he", "hi", "hsb", "ht", "hu", "hy", "ia", "id", "ig", "is", "it", "ja", "ka", "kab", "kk", "kmr", "ko", "ky", "lg", "lij", "lo", "lt", "ltg", "lv", "mdf", "mhr", "mk", "ml", "mn", "mr", "mrj", "mt", "myv", "nan", "ne", "nhi", "nl", "nn", "nso", "oc", "or", "os", "pa", "pl", "ps", "pt", "quy", "rm", "ro", "ru", "rw", "sah", "sat", "sc", "sk", "skr", "sl", "sq", "sr", "sv", "sw", "ta", "te", "th", "ti", "tig", "tk", "tok", "tr", "tt", "tw", "ug", "uk", "ur", "uz", "vi", "vot", "yi", "yo", "yue", "zgh", "zh", "zu", "zza"], "language_bcp47": ["zh-CN", "zh-HK", "zh-TW", "sv-SE", "rm-sursilv", "rm-vallader", "pa-IN", "nn-NO", "ne-NP", "nan-tw", "hy-AM", "ga-IE", "fy-NL"], "license": ["cc0-1.0"], "multilinguality": ["multilingual"], "source_datasets": ["extended|common_voice"], "paperswithcode_id": "common-voice", "extra_gated_prompt": "By clicking on \u201cAccess repository\u201d below, you also agree to not attempt to determine the identity of speakers in the Common Voice dataset."} | false | null | 2024-06-16T13:50:23 | 262 | 12 | false | b10d53980ef166bc24ce3358471c1970d7e6b5ec |
Dataset Card for Common Voice Corpus 17.0
Dataset Summary
The Common Voice dataset consists of a unique MP3 and corresponding text file.
Many of the 31175 recorded hours in the dataset also include demographic metadata like age, sex, and accent
that can help improve the accuracy of speech recognition engines.
The dataset currently consists of 20408 validated hours in 124 languages, but more voices and languages are always added.
Take a look at the Languages page to… See the full description on the dataset page: https://huggingface.co/datasets/mozilla-foundation/common_voice_17_0. | 41,600 | 473,683 | [
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"source_datasets:extended|common_voice",
"language:ab",
"language:af",
"language:am",
"language:ar",
"language:as",
"language:ast",
"language:az",
"language:ba",
"language:bas",
"language:be",
"language:bg",
"language:bn",
"language:br",
"language:ca",
"language:ckb",
"language:cnh",
"language:cs",
"language:cv",
"language:cy",
"language:da",
"language:de",
"language:dv",
"language:dyu",
"language:el",
"language:en",
"language:eo",
"language:es",
"language:et",
"language:eu",
"language:fa",
"language:fi",
"language:fr",
"language:fy",
"language:ga",
"language:gl",
"language:gn",
"language:ha",
"language:he",
"language:hi",
"language:hsb",
"language:ht",
"language:hu",
"language:hy",
"language:ia",
"language:id",
"language:ig",
"language:is",
"language:it",
"language:ja",
"language:ka",
"language:kab",
"language:kk",
"language:kmr",
"language:ko",
"language:ky",
"language:lg",
"language:lij",
"language:lo",
"language:lt",
"language:ltg",
"language:lv",
"language:mdf",
"language:mhr",
"language:mk",
"language:ml",
"language:mn",
"language:mr",
"language:mrj",
"language:mt",
"language:myv",
"language:nan",
"language:ne",
"language:nhi",
"language:nl",
"language:nn",
"language:nso",
"language:oc",
"language:or",
"language:os",
"language:pa",
"language:pl",
"language:ps",
"language:pt",
"language:quy",
"language:rm",
"language:ro",
"language:ru",
"language:rw",
"language:sah",
"language:sat",
"language:sc",
"language:sk",
"language:skr",
"language:sl",
"language:sq",
"language:sr",
"language:sv",
"language:sw",
"language:ta",
"language:te",
"language:th",
"language:ti",
"language:tig",
"language:tk",
"language:tok",
"language:tr",
"language:tt",
"language:tw",
"language:ug",
"language:uk",
"language:ur",
"language:uz",
"language:vi",
"language:vot",
"language:yi",
"language:yo",
"language:yue",
"language:zgh",
"language:zh",
"language:zu",
"language:zza",
"license:cc0-1.0",
"size_categories:10M<n<100M",
"modality:audio",
"modality:text",
"library:datasets",
"library:mlcroissant",
"arxiv:1912.06670",
"region:us"
] | 2024-04-04T10:06:19 | common-voice | @inproceedings{commonvoice:2020,
author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.},
title = {Common Voice: A Massively-Multilingual Speech Corpus},
booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)},
pages = {4211--4215},
year = 2020
} |
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 | 554 | 12 | 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. | 40,368 | 100,177 | [
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"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-02-10T13:41:48 | 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 | 630 | 12 | 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. | 3,522 | 12,228 | [
"task_categories:text-generation",
"task_categories:text2text-generation",
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"format:json",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-02-17T11:45:09 | null | null |
67c03fd6b9fe27a2ac49784d | open-r1/codeforces-cots | open-r1 | {"dataset_info": [{"config_name": "checker_interactor", "features": [{"name": "id", "dtype": "string"}, {"name": "aliases", "sequence": "string"}, {"name": "contest_id", "dtype": "string"}, {"name": "contest_name", "dtype": "string"}, {"name": "contest_type", "dtype": "string"}, {"name": "contest_start", "dtype": "int64"}, {"name": "contest_start_year", "dtype": "int64"}, {"name": "index", "dtype": "string"}, {"name": "time_limit", "dtype": "float64"}, {"name": "memory_limit", "dtype": "float64"}, {"name": "title", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "input_format", "dtype": "string"}, {"name": "output_format", "dtype": "string"}, {"name": "interaction_format", "dtype": "string"}, {"name": "note", "dtype": "string"}, {"name": "examples", "list": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}]}, {"name": "editorial", "dtype": "string"}, {"name": "prompt", 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"total_tokens", "dtype": "int64"}]}, {"name": "interaction_format", "dtype": "string"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 1851104290, "num_examples": 20620}], "download_size": 724157877, "dataset_size": 1851104290}], "configs": [{"config_name": "checker_interactor", "data_files": [{"split": "train", "path": "checker_interactor/train-*"}]}, {"config_name": "solutions", "default": true, "data_files": [{"split": "train", "path": "solutions/train-*"}]}, {"config_name": "solutions_decontaminated", "data_files": [{"split": "train", "path": "solutions_decontaminated/train-*"}]}, {"config_name": "solutions_py", "data_files": [{"split": "train", "path": "solutions_py/train-*"}]}, {"config_name": "solutions_py_decontaminated", "data_files": [{"split": "train", "path": "solutions_py_decontaminated/train-*"}]}, {"config_name": "solutions_short_and_long_decontaminated", "data_files": [{"split": "train", "path": "solutions_short_and_long_decontaminated/train-*"}]}, {"config_name": "solutions_w_editorials", "data_files": [{"split": "train", "path": "solutions_w_editorials/train-*"}]}, {"config_name": "solutions_w_editorials_decontaminated", "data_files": [{"split": "train", "path": "solutions_w_editorials_decontaminated/train-*"}]}, {"config_name": "solutions_w_editorials_py", "data_files": [{"split": "train", "path": "solutions_w_editorials_py/train-*"}]}, {"config_name": "solutions_w_editorials_py_decontaminated", "data_files": [{"split": "train", "path": "solutions_w_editorials_py_decontaminated/train-*"}]}, {"config_name": "test_input_generator", "data_files": [{"split": "train", "path": "test_input_generator/train-*"}]}], "license": "cc-by-4.0"} | false | null | 2025-03-28T12:21:06 | 143 | 12 | false | 39ac85c150806230473c70ad72c31f6232fe3f41 |
Dataset Card for CodeForces-CoTs
Dataset description
CodeForces-CoTs is a large-scale dataset for training reasoning models on competitive programming tasks. It consists of 10k CodeForces problems with up to five reasoning traces generated by DeepSeek R1. We did not filter the traces for correctness, but found that around 84% of the Python ones pass the public tests.
The dataset consists of several subsets:
solutions: we prompt R1 to solve the problem and produce code.… See the full description on the dataset page: https://huggingface.co/datasets/open-r1/codeforces-cots. | 11,919 | 15,068 | [
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"size_categories:100K<n<1M",
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"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-02-27T10:35:02 | null | null |
67c0cda5c0b7a236a5f070e3 | glaiveai/reasoning-v1-20m | glaiveai | {"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 177249016911, "num_examples": 22199375}], "download_size": 87247205094, "dataset_size": 177249016911}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "license": "apache-2.0", "task_categories": ["text-generation"], "language": ["en"], "size_categories": ["10M<n<100M"]} | false | null | 2025-03-19T13:21:37 | 193 | 12 | false | da6bb3d0ff8fd8ea5abacee8519762ca6aaf367e |
We are excited to release a synthetic reasoning dataset containing 22mil+ general reasoning questions and responses generated using deepseek-ai/DeepSeek-R1-Distill-Llama-70B. While there have been multiple efforts to build open reasoning datasets for math and code tasks, we noticed a lack of large datasets containing reasoning traces for diverse non code/math topics like social and natural sciences, education, creative writing and general conversations, which is why we decided to release this… See the full description on the dataset page: https://huggingface.co/datasets/glaiveai/reasoning-v1-20m. | 14,035 | 14,160 | [
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
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"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-02-27T20:40:05 | null | null |
67b20fc10861cec33b3afb8a | Conard/fortune-telling | Conard | {"license": "mit"} | false | null | 2025-02-17T05:13:43 | 124 | 11 | false | 6261fe0d35a75997972bbfcd9828020e340303fb | null | 4,767 | 8,949 | [
"license:mit",
"size_categories:n<1K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-02-16T16:18:09 | null | null |
67b58abdbc707d7ed36e6750 | KRX-Data/Won-Instruct | KRX-Data | {"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "original_response", "dtype": "string"}, {"name": "Qwen/Qwen2.5-1.5B-Instruct_response", "dtype": "string"}, {"name": "Qwen/Qwen2.5-7B-Instruct_response", "dtype": "string"}, {"name": "google/gemma-2-2b-it_response", "dtype": "string"}, {"name": "google/gemma-2-9b-it_response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 846093226, "num_examples": 86007}], "download_size": 375880264, "dataset_size": 846093226}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | false | null | 2025-04-11T05:03:20 | 12 | 11 | false | 9ff85bc243b7e1aa30970ef63da0bbfaaeb371e8 | 🇺🇸 English | 🇰🇷 한국어
Introduction
The ₩ON-Instruct is a comprehensive instruction-following dataset tailored for training Korean language models specialized in financial reasoning and domain-specific financial tasks.
This dataset was meticulously assembled through rigorous filtering and quality assurance processes, aiming to enhance the reasoning abilities of large language models (LLMs) within the financial domain, specifically tuned for Korean financial tasks.
The dataset… See the full description on the dataset page: https://huggingface.co/datasets/KRX-Data/Won-Instruct. | 131 | 191 | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2503.17963",
"region:us"
] | 2025-02-19T07:39:41 | null | null |
67fcab0d3d1ec06dda7e28a3 | lmarena-ai/search-arena-v1-7k | lmarena-ai | {"size_categories": ["100K<n<1M"], "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/search-arena-*"}]}]} | false | null | 2025-04-14T16:01:06 | 11 | 11 | false | 06865038ceb415c1942e58bff8f14150cbc9fbcd |
Overview
This dataset contains 7k leaderboard conversation votes collected from Search Arena between March 18, 2025 and April 13, 2025. All entries have been redacted for PII and sensitive user information to ensure privacy.
Each data point includes:
Two model responses (messages_a and messages_b)
The human vote result
A timestamp
Full system metadata, LLM + web search trace, and post-processed metadata for controlled experiments (conv_meta)
To reproduce the leaderboard results… See the full description on the dataset page: https://huggingface.co/datasets/lmarena-ai/search-arena-v1-7k. | 222 | 222 | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2403.04132",
"region:us"
] | 2025-04-14T06:28:29 | null | null |
621ffdd236468d709f181f06 | openai/openai_humaneval | openai | {"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["n<1K"], "source_datasets": ["original"], "task_categories": ["text2text-generation"], "task_ids": [], "paperswithcode_id": "humaneval", "pretty_name": "OpenAI HumanEval", "tags": ["code-generation"], "dataset_info": {"config_name": "openai_humaneval", "features": [{"name": "task_id", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "canonical_solution", "dtype": "string"}, {"name": "test", "dtype": "string"}, {"name": "entry_point", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 194394, "num_examples": 164}], "download_size": 83920, "dataset_size": 194394}, "configs": [{"config_name": "openai_humaneval", "data_files": [{"split": "test", "path": "openai_humaneval/test-*"}], "default": true}]} | false | null | 2024-01-04T16:08:05 | 308 | 10 | false | 7dce6050a7d6d172f3cc5c32aa97f52fa1a2e544 |
Dataset Card for OpenAI HumanEval
Dataset Summary
The HumanEval dataset released by OpenAI includes 164 programming problems with a function sig- nature, docstring, body, and several unit tests. They were handwritten to ensure not to be included in the training set of code generation models.
Supported Tasks and Leaderboards
Languages
The programming problems are written in Python and contain English natural text in comments and… See the full description on the dataset page: https://huggingface.co/datasets/openai/openai_humaneval. | 91,087 | 3,119,856 | [
"task_categories:text2text-generation",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:mit",
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2107.03374",
"region:us",
"code-generation"
] | 2022-03-02T23:29:22 | humaneval | null |
66cd7bbefc6f503213a054e7 | lmms-lab/LLaVA-Video-178K | lmms-lab | {"configs": [{"config_name": "0_30_s_academic_v0_1", "data_files": [{"split": "caption", "path": "0_30_s_academic_v0_1/*cap*.json"}, {"split": "open_ended", "path": "0_30_s_academic_v0_1/*oe*.json"}, {"split": "multi_choice", "path": "0_30_s_academic_v0_1/*mc*.json"}]}, {"config_name": "0_30_s_youtube_v0_1", "data_files": [{"split": "caption", "path": "0_30_s_youtube_v0_1/*cap*.json"}, {"split": "open_ended", "path": "0_30_s_youtube_v0_1/*oe*.json"}, {"split": "multi_choice", "path": "0_30_s_youtube_v0_1/*mc*.json"}]}, {"config_name": "0_30_s_activitynet", "data_files": [{"split": "open_ended", "path": "0_30_s_activitynet/*oe*.json"}]}, {"config_name": "0_30_s_perceptiontest", "data_files": [{"split": "multi_choice", "path": "0_30_s_perceptiontest/*mc*.json"}]}, {"config_name": "0_30_s_nextqa", "data_files": [{"split": "open_ended", "path": "0_30_s_nextqa/*oe*.json"}, {"split": "multi_choice", "path": "0_30_s_nextqa/*mc*.json"}]}, {"config_name": "30_60_s_academic_v0_1", "data_files": [{"split": "caption", "path": "30_60_s_academic_v0_1/*cap*.json"}, {"split": "open_ended", "path": "30_60_s_academic_v0_1/*oe*.json"}, {"split": "multi_choice", "path": "30_60_s_academic_v0_1/*mc*.json"}]}, {"config_name": "30_60_s_youtube_v0_1", "data_files": [{"split": "caption", "path": "30_60_s_youtube_v0_1/*cap*.json"}, {"split": "open_ended", "path": "30_60_s_youtube_v0_1/*oe*.json"}, {"split": "multi_choice", "path": "30_60_s_youtube_v0_1/*mc*.json"}]}, {"config_name": "30_60_s_activitynet", "data_files": [{"split": "open_ended", "path": "30_60_s_activitynet/*oe*.json"}]}, {"config_name": "30_60_s_perceptiontest", "data_files": [{"split": "multi_choice", "path": "30_60_s_perceptiontest/*mc*.json"}]}, {"config_name": "30_60_s_nextqa", "data_files": [{"split": "open_ended", "path": "30_60_s_nextqa/*oe*.json"}, {"split": "multi_choice", "path": "30_60_s_nextqa/*mc*.json"}]}, {"config_name": "1_2_m_youtube_v0_1", "data_files": [{"split": "caption", "path": "1_2_m_youtube_v0_1/*cap*.json"}, {"split": "open_ended", "path": "1_2_m_youtube_v0_1/*oe*.json"}, {"split": "multi_choice", "path": "1_2_m_youtube_v0_1/*mc*.json"}]}, {"config_name": "1_2_m_academic_v0_1", "data_files": [{"split": "caption", "path": "1_2_m_academic_v0_1/*cap*.json"}, {"split": "open_ended", "path": "1_2_m_academic_v0_1/*oe*.json"}, {"split": "multi_choice", "path": "1_2_m_academic_v0_1/*mc*.json"}]}, {"config_name": "1_2_m_activitynet", "data_files": [{"split": "open_ended", "path": "1_2_m_activitynet/*oe*.json"}]}, {"config_name": "1_2_m_nextqa", "data_files": [{"split": "open_ended", "path": "1_2_m_nextqa/*oe*.json"}, {"split": "multi_choice", "path": "1_2_m_nextqa/*mc*.json"}]}, {"config_name": "2_3_m_youtube_v0_1", "data_files": [{"split": "caption", "path": "2_3_m_youtube_v0_1/*cap*.json"}, {"split": "open_ended", "path": "2_3_m_youtube_v0_1/*oe*.json"}, {"split": "multi_choice", "path": "2_3_m_youtube_v0_1/*mc*.json"}]}, {"config_name": "2_3_m_academic_v0_1", "data_files": [{"split": "caption", "path": "2_3_m_academic_v0_1/*cap*.json"}, {"split": "open_ended", "path": "2_3_m_academic_v0_1/*oe*.json"}, {"split": "multi_choice", "path": "2_3_m_academic_v0_1/*mc*.json"}]}, {"config_name": "2_3_m_activitynet", "data_files": [{"split": "open_ended", "path": "2_3_m_activitynet/*oe*.json"}]}, {"config_name": "2_3_m_nextqa", "data_files": [{"split": "open_ended", "path": "2_3_m_nextqa/*oe*.json"}, {"split": "multi_choice", "path": "2_3_m_nextqa/*mc*.json"}]}, {"config_name": "llava_hound", "data_files": [{"split": "open_ended", "path": "llava_hound/sharegptvideo_qa_255k_processed.json"}]}], "language": ["en"], "task_categories": ["visual-question-answering", "video-text-to-text"], "tags": ["video"]} | false | null | 2024-10-11T04:59:25 | 136 | 9 | false | 6d8c562dc26d70042a0d9704d1cae58c94b89098 |
Dataset Card for LLaVA-Video-178K
Uses
This dataset is used for the training of the LLaVA-Video model. We only allow the use of this dataset for academic research and education purpose. For OpenAI GPT-4 generated data, we recommend the users to check the OpenAI Usage Policy.
Data Sources
For the training of LLaVA-Video, we utilized video-language data from five primary sources:
LLaVA-Video-178K: This dataset includes 178,510 caption entries, 960… See the full description on the dataset page: https://huggingface.co/datasets/lmms-lab/LLaVA-Video-178K. | 15,431 | 143,283 | [
"task_categories:visual-question-answering",
"task_categories:video-text-to-text",
"language:en",
"size_categories:1M<n<10M",
"modality:text",
"modality:video",
"arxiv:2410.02713",
"region:us",
"video"
] | 2024-08-27T07:09:50 | null | null |
67fe66c27cc6eabecbf8891a | davanstrien/fine-reasoning-questions | davanstrien | {"language": "en", "license": "mit", "tags": ["curator", "synthetic", "reasoning-datasets-competition"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}, {"config_name": "raw", "data_files": [{"split": "train", "path": "raw/train-*"}]}], "dataset_info": [{"config_name": "default", "features": [{"name": "question", "dtype": "string"}, {"name": "requires_text_content", "dtype": "bool"}, {"name": "text", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "topic", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1583427, "num_examples": 144}], "download_size": 459798, "dataset_size": 1583427}, {"config_name": "raw", "features": [{"name": "text", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "dump", "dtype": "string"}, {"name": "url", "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"}, {"name": "raw_reasoning_score", "dtype": "float64"}, {"name": "reasoning_level", "dtype": "int64"}, {"name": "interpretation", "dtype": "string"}, {"name": "topic", "dtype": "string"}, {"name": "parsed_json", "dtype": "bool"}, {"name": "extracted_json", "struct": [{"name": "questions", "list": [{"name": "question", "dtype": "string"}, {"name": "requires_text_content", "dtype": "bool"}]}]}, {"name": "reasoning", "dtype": "string"}, {"name": "full_response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1907264, "num_examples": 100}], "download_size": 978916, "dataset_size": 1907264}]} | false | null | 2025-04-15T14:52:05 | 9 | 9 | false | 7430c6f200bfe605eb6af26c4c4ea4241ef1ae47 |
Dataset Card for Fine Reasoning Questions
Dataset Description
Can we generate reasoning datasets for more domains using web text?
Note: This dataset is submitted partly to give an idea of the kind of dataset you could submit to the reasoning datasets competition. You can find out more about the competition in this blog post.
You can also see more info on using Inference Providers with Curator here
The majority of reasoning datasets on the Hub are focused on maths… See the full description on the dataset page: https://huggingface.co/datasets/davanstrien/fine-reasoning-questions. | 115 | 115 | [
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"curator",
"synthetic",
"reasoning-datasets-competition"
] | 2025-04-15T14:01:38 | null | null |
67ff96ff01428cf3b86f78c2 | Lod34/sentiment-analysis-test | Lod34 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "sentiment", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 28302.111747851002, "num_examples": 279}, {"name": "test", "num_bytes": 7100.888252148997, "num_examples": 70}], "download_size": 23157, "dataset_size": 35403}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "annotations_creators": ["expert-generated", "crowdsourced"], "language": ["it"], "language_creators": ["crowdsourced"], "license": ["mit"], "multilinguality": ["monolingual"], "pretty_name": "A sentiment analysis database created in a school environment.", "size_categories": ["n<1K"], "source_datasets": ["original"], "tags": ["school", "high-school"], "task_categories": ["text-classification"], "task_ids": ["sentiment-analysis"]} | false | null | 2025-04-16T12:51:48 | 9 | 9 | false | cb263caa70c03ee67d894ea585bd51db9c74aac0 |
Progetto scolastico per l'analisi dei sentimenti
Il dataset è stato creato con un questionario online in cui si chiedeva ad un pubblico di studenti, docenti, personale amministrativo, famiglie di rispondere ad alcune domande sul loro rapporto con la scuola.
Le annotazioni sono state effettuate correlando le risposte testuali ad indicatori di gradimento.
Il dataset è stato realizzato all'interno di un corso pomeridiano scolastico dedicato all'intelligenza artificiale.
Grazie a tutti… See the full description on the dataset page: https://huggingface.co/datasets/Lod34/sentiment-analysis-test. | 15 | 15 | [
"task_categories:text-classification",
"task_ids:sentiment-analysis",
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] | 2025-04-16T11:39:43 | null | null |
67ff98b701428cf3b86fe77e | Smatteux/sentiment-analysis-test | Smatteux | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "sentiment", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 28302.111747851002, "num_examples": 279}, {"name": "test", "num_bytes": 7100.888252148997, "num_examples": 70}], "download_size": 23427, "dataset_size": 35403}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "annotations_creators": ["expert-generated", "crowdsourced"], "language": ["it"], "language_creators": ["crowdsourced"], "license": ["mit"], "multilinguality": ["monolingual"], "pretty_name": "a sentiment analysis database created in a school envronment ", "size_categories": ["n<1K"], "source_datasets": ["original"], "tags": [], "task_categories": ["text-classification"], "task_ids": ["sentiment-analysis"]} | false | null | 2025-04-16T12:51:30 | 9 | 9 | false | f00483c98dfd16fbc1d1e9dcbcd84ba830639b4e |
progetto scolastico per l'analisi dei sentimenti
il dataset è stato creato con un questionario online in cu isi chiedeva ad un pubblico di studenti, docenti, personale amministrativo, famiglie di rispondere ad alcune domande sul loro rapporto con la scuola.
Le annotazioni sono state effettuate correlando le risposte testuali ad indicatori di gradimento.
Il dataset è stato stato realizzato all'interno di un corsp pomeridiano scolastico dedicato all'intelligenza artificiale.
Grazie a… See the full description on the dataset page: https://huggingface.co/datasets/Smatteux/sentiment-analysis-test. | 21 | 21 | [
"task_categories:text-classification",
"task_ids:sentiment-analysis",
"annotations_creators:expert-generated",
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"language_creators:crowdsourced",
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"language:it",
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"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-04-16T11:47:03 | null | null |
67ff9b10e3e15a8be9b4971e | MarcPal08/sentiment-analysis-test | MarcPal08 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "sentiment", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 28302.111747851002, "num_examples": 279}, {"name": "test", "num_bytes": 7100.888252148997, "num_examples": 70}], "download_size": 23157, "dataset_size": 35403}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "annotations_creators": ["expert-generated", "crowdsourced"], "language": ["it"], "language_creators": ["crowdsourced"], "license": ["mit"], "multilinguality": ["monolingual"], "pretty_name": "A sentiment analisys database created in a school environment.", "size_categories": ["n<1K"], "source_datasets": ["original"], "tags": ["school", "high-school"], "task_categories": ["text-classification"], "task_ids": ["sentiment-analysis"]} | false | null | 2025-04-16T12:51:30 | 9 | 9 | false | f870060177dff27ea2e81215f68d3c3f019bd51e |
Progetto scolastico per l'analisi dei sentimenti
Il dataset è stato creato con un questionario online in cui si chiedeva ad un pubblico di studenti, docenti, personale amministrativo, famiglie di rispondere ad alcune domande sul loro rapporto con la scuola.
Le annotazioni sono state effettuate correlando le risposte testuali ad indicatori di gradimento.
Il dataset è stato realizzato all'interno di un corso pomeridiano scolastico dedicato all'intelligenza artificiale.
Grazie a tutti… See the full description on the dataset page: https://huggingface.co/datasets/MarcPal08/sentiment-analysis-test. | 19 | 19 | [
"task_categories:text-classification",
"task_ids:sentiment-analysis",
"annotations_creators:expert-generated",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"source_datasets:original",
"language:it",
"license:mit",
"size_categories:n<1K",
"region:us",
"school",
"high-school"
] | 2025-04-16T11:57:04 | null | null |
67ffa024a1c34809696a1765 | Giova-tech/sentiment-analysis-test | Giova-tech | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "sentiment", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 28302.111747851002, "num_examples": 279}, {"name": "test", "num_bytes": 7100.888252148997, "num_examples": 70}], "download_size": 23157, "dataset_size": 35403}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "annotations_creators": ["expert-generated", "crowdsourced"], "language": ["it"], "language_creators": ["crowdsourced"], "license": ["mit"], "multilinguality": ["monolingual"], "pretty_name": "A sentiment analisis database created in a school environment\n", "size_categories": ["n<1K"], "source_datasets": ["original"], "tags": ["school", "high-school"], "task_categories": ["text-classification"], "task_ids": ["sentiment-analysis"]} | false | null | 2025-04-16T12:51:43 | 9 | 9 | false | ad783643608757dc726d2376d370b7d6bfe5039d |
progetto scolastico per l'analisi dei sentimenti
Il dataset è stato creato con un questionario online in cui si chiedeva ad un pubblico di studenti, docenti, personake amministrativo e famiglie di rispondere ad alcune domande
sul loro rapporto con la scuola.
Le annotazioni sono state effettuate correlando le risposte testuali an indicatori di gradimento.
Il dataset è stato realizzato all'interno di un corso pomeridiano scolastico dedicato all'intelliggenza artificiale.
Grazie a… See the full description on the dataset page: https://huggingface.co/datasets/Giova-tech/sentiment-analysis-test. | 24 | 24 | [
"task_categories:text-classification",
"task_ids:sentiment-analysis",
"annotations_creators:expert-generated",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"source_datasets:original",
"language:it",
"license:mit",
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"school",
"high-school"
] | 2025-04-16T12:18:44 | 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 | 2025-04-06T23:31:24 | 688 | 8 | false | a5996b0064b4ddd42c6e9a7302eeec0618cb7b63 |
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. | 30,784 | 167,232 | [
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"curator",
"synthetic"
] | 2025-01-27T20:02:16 | null | null |
67a89e79556fa47a174b6c7b | agentica-org/DeepScaleR-Preview-Dataset | agentica-org | {"language": ["en"], "license": "mit", "size_categories": ["10K<n<100K"]} | false | null | 2025-02-10T09:51:18 | 107 | 8 | false | b6ae8c60f5c1f2b594e2140b91c49c9ad0949e29 |
Data
Our training dataset consists of approximately 40,000 unique mathematics problem-answer pairs compiled from:
AIME (American Invitational Mathematics Examination) problems (1984-2023)
AMC (American Mathematics Competition) problems (prior to 2023)
Omni-MATH dataset
Still dataset
Format
Each row in the JSON dataset contains:
problem: The mathematical question text, formatted with LaTeX notation.
solution: Offical solution to the problem, including LaTeX formatting… See the full description on the dataset page: https://huggingface.co/datasets/agentica-org/DeepScaleR-Preview-Dataset. | 3,498 | 7,773 | [
"language:en",
"license:mit",
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-02-09T12:24:25 | 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 | 167 | 8 | 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. | 2,997 | 8,129 | [
"task_categories:text-generation",
"task_categories:text2text-generation",
"task_categories:question-answering",
"language:zh",
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:json",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-02-17T14:36:10 | null | null |
67d0465e363941e3a07ed1bb | AnonRes/OpenMind | AnonRes | {"license": "cc-by-4.0", "task_categories": ["image-feature-extraction"], "pretty_name": "The OpenMind Dataset", "tags": ["3d", "image"]} | false | null | 2025-04-03T11:51:07 | 8 | 8 | false | 7a1d5ce1ff35de400b7f4c0dc957a69c5b581409 |
The OpenMind Dataset: A large-scale Head-And-Neck 3D MRI Dataset for self-supervised learning
Description
The OpenMind Dataset is a large-scale 3D MRI dataset of the head and neck region featuring 114k MRI Images. Its purpose is to provide access of large amounts of 3D medical imaging data to accelerate the development of self-supervised learning methods for 3D medical imaging. This data was pooled from exactly 800 datasets from the OpenNeuro platform and provides 23… See the full description on the dataset page: https://huggingface.co/datasets/AnonRes/OpenMind. | 470 | 518 | [
"task_categories:image-feature-extraction",
"license:cc-by-4.0",
"modality:3d",
"modality:image",
"region:us",
"3d",
"image"
] | 2025-03-11T14:19:10 | null | null |
67e750b78b7166806fa2d98f | VisualCloze/Graph200K | VisualCloze | {"language": ["en"], "license": "apache-2.0", "size_categories": ["100K<n<1M"], "task_categories": ["image-to-image"], "tags": ["image"]} | false | null | 2025-04-12T02:14:34 | 8 | 8 | false | cc3bc9ab78abfa4a0161c8d836594522d9b05422 |
VisualCloze: A Universal Image Generation Framework via Visual In-Context Learning
[Paper] [Project Page] [Github]
[🤗 Online Demo] [🤗 Model Card]
Graph200k is a large-scale dataset containing a wide range of distinct tasks of image generation.
🌠 Key Features:
Each image is annotated for five meta-tasks, including 1) conditional generation, 2) image restoration, 3) image editing, 4) IP preservation, and 5) style transfer.
Using these tasks, we… See the full description on the dataset page: https://huggingface.co/datasets/VisualCloze/Graph200K. | 3,168 | 3,620 | [
"task_categories:image-to-image",
"language:en",
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:arrow",
"modality:image",
"modality:text",
"library:datasets",
"library:mlcroissant",
"arxiv:2504.07960",
"region:us",
"image"
] | 2025-03-29T01:45:27 | null | null |
67f512815ff1c102be6acefb | giskardai/realharm | giskardai | {"license": "cc-by-4.0", "task_categories": ["text-classification", "text-generation", "text2text-generation"], "language": ["en"], "pretty_name": "RealHarm", "size_categories": ["n<1K"], "tags": ["guardrail-evaluation"], "configs": [{"config_name": "realharm", "data_files": [{"split": "safe", "path": "data/realharm_safe.jsonl"}, {"split": "unsafe", "path": "data/realharm_unsafe.jsonl"}]}]} | false | null | 2025-04-16T09:11:07 | 8 | 8 | false | 3ea152469ed69020379abaeaee622ad29e6f5d79 |
RealHarm
RealHarm is a collection of harmful real-world interactions with AI agents.
Dataset Details
Dataset Description
RealHarm contains harmful samples, categorized among 10 harm categories. A complete taxonomy has been proposed along with the dataset and is described in the RealHarm paper. Each sample has an associated safe version, for which we rewrote the agent answer to make it harmless.
This dataset provides researchers and developers with authentic… See the full description on the dataset page: https://huggingface.co/datasets/giskardai/realharm. | 47 | 47 | [
"task_categories:text-classification",
"task_categories:text-generation",
"task_categories:text2text-generation",
"language:en",
"license:cc-by-4.0",
"size_categories:n<1K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2504.10277",
"region:us",
"guardrail-evaluation"
] | 2025-04-08T12:11:45 | null | null |
67f8128d2d5c61f30966da62 | TIGER-Lab/WebInstruct-verified | TIGER-Lab | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "answer_type", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "difficulty", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 79050440, "num_examples": 231833}, {"name": "test", "num_bytes": 337002, "num_examples": 1000}], "download_size": 45156588, "dataset_size": 79387442}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "license": "apache-2.0", "task_categories": ["question-answering"], "language": ["en"], "tags": ["science"], "pretty_name": "WebInstruct-Verified", "size_categories": ["100K<n<1M"]} | false | null | 2025-04-15T17:22:31 | 8 | 8 | false | 12f46e4849608ab1105dd49ed86258f8ba9ad542 | This is the dataset we used in https://github.com/TIGER-AI-Lab/General-Reasoner.
Distribution
The distribution of disciplines is depicted as follows:
| 213 | 222 | [
"task_categories:question-answering",
"language:en",
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"science"
] | 2025-04-10T18:48:45 | null | null |
67f94cbded489d2d58f5ec92 | OpenGVLab/MMPR-v1.2 | OpenGVLab | {"license": "mit", "task_categories": ["visual-question-answering"], "language": ["en"], "pretty_name": "MMPR-v1.2", "dataset_info": {"features": [{"name": "image", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "chosen", "dtype": "string"}, {"name": "rejected", "dtype": "string"}]}, "size_categories": ["1M<n<10M"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "annotations.zip"}]}]} | false | null | 2025-04-15T11:23:47 | 8 | 8 | false | 947b4b3b52d37921bf4e9d4ca046f2f1d97e347b |
MMPR-v1.2
[📂 GitHub] [🆕 Blog] [📜 Paper] [📖 Documents]
This is a newer version of MMPR and MMPR-v1.1, which includes additional data sources to enhance the data diversity and greatly improves the overall performance of InternVL3 across all scales. The prompts used to build this dataset is released in MMPR-v1.2-prompts.
To unzip the archive of images, please first run cat images.zip_* > images.zip and then run unzip images.zip.
Introduction
MMPR is a large-scale and… See the full description on the dataset page: https://huggingface.co/datasets/OpenGVLab/MMPR-v1.2. | 235 | 235 | [
"task_categories:visual-question-answering",
"language:en",
"license:mit",
"size_categories:1M<n<10M",
"arxiv:2411.10442",
"arxiv:2504.10479",
"arxiv:2412.05271",
"arxiv:2404.16821",
"arxiv:2312.14238",
"region:us"
] | 2025-04-11T17:09:17 | null | null |
67ff98ffb9227fa71db92d68 | qwertychri/sentiment-analysis-test | qwertychri | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "sentiment", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 28302.111747851002, "num_examples": 279}, {"name": "test", "num_bytes": 7100.888252148997, "num_examples": 70}], "download_size": 23157, "dataset_size": 35403}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "annotations_creators": ["crowdsourced", "expert-generated"], "language": ["it"], "language_creators": ["crowdsourced"], "license": ["mit"], "multilinguality": ["monolingual"], "pretty_name": "A sentiment analysis database created in a school environment.", "size_categories": ["n<1K"], "source_datasets": ["original"], "tags": ["school", "high-school"], "task_categories": ["text-classification"], "task_ids": ["sentiment-analysis"]} | false | null | 2025-04-16T12:55:01 | 8 | 8 | false | 041f59a0be5ee98ed24a3e0a0b5b23b7b2a52e9f | Il dataset è stato creato in un questionario online in cui si chiedeva ad un pubblico di studenti, docenti, personale amministrativo, famiglie di rispondere ad alcune domande sul loro rapporto con la scuola.
Le annotazioni sono state effettuate correlando le risposte testuali ad indicatori di gradimento.
il dataset è stato realizzato all'interno di un corso pomeridiano scolastico dedicato all'intelligenza artificiale
Grazie a tutti per la collaborazione ❤️
| 11 | 11 | [
"task_categories:text-classification",
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"library:polars",
"region:us",
"school",
"high-school"
] | 2025-04-16T11:48:15 | null | null |
661e02bd3f198d4337848286 | livecodebench/code_generation_lite | livecodebench | {"license": "cc", "tags": ["code", "code generation"], "pretty_name": "LiveCodeBench", "size_categories": ["n<1K"]} | false | null | 2025-01-14T18:03:07 | 37 | 7 | false | 0687ab61843a90a0cc864a2b67db729861cd0ae5 | LiveCodeBench is a temporaly updating benchmark for code generation. Please check the homepage: https://livecodebench.github.io/. | 55,018 | 165,551 | [
"license:cc",
"size_categories:n<1K",
"arxiv:2403.07974",
"region:us",
"code",
"code generation"
] | 2024-04-16T04:46:53 | null | @article{jain2024livecodebench,
title={LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code},
author={Jain, Naman and Han, King and Gu, Alex and Li, Wen-Ding and Yan, Fanjia and Zhang, Tianjun and Wang, Sida and Solar-Lezama, Armando and Sen, Koushik and Stoica, Ion},
journal={arXiv preprint arXiv:2403.07974},
year={2024}
} |
66a520e6387f62525b93f1bb | weaverbirdllm/famma | weaverbirdllm | {"language": ["en", "zh", "fr"], "license": "apache-2.0", "size_categories": ["1K<n<10K"], "task_categories": ["question-answering", "multiple-choice"], "pretty_name": "FAMMA: A Benchmark for Financial Domain Multilingual Multimodal Question Answering", "tags": ["finance"], "dataset_info": {"features": [{"name": "idx", "dtype": "int32"}, {"name": "question_id", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "options", "sequence": "string"}, {"name": "image_1", "dtype": "image"}, {"name": "image_2", "dtype": "image"}, {"name": "image_3", "dtype": "image"}, {"name": "image_4", "dtype": "image"}, {"name": "image_5", "dtype": "image"}, {"name": "image_6", "dtype": "image"}, {"name": "image_7", "dtype": "image"}, {"name": "image_type", "dtype": "string"}, {"name": "answers", "dtype": "string"}, {"name": "explanation", "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": "string"}, {"name": "sub_question_id", "dtype": "string"}, {"name": "is_arithmetic", "dtype": "int32"}, {"name": "ans_image_1", "dtype": "image"}, {"name": "ans_image_2", "dtype": "image"}, {"name": "ans_image_3", "dtype": "image"}, {"name": "ans_image_4", "dtype": "image"}, {"name": "ans_image_5", "dtype": "image"}, {"name": "ans_image_6", "dtype": "image"}, {"name": "release", "dtype": "string"}], "splits": [{"name": "release_basic", "num_bytes": 113235537.37, "num_examples": 1945}, {"name": "release_livepro", "num_bytes": 3265950, "num_examples": 103}, {"name": "release_basic_txt", "num_bytes": 1966706.375, "num_examples": 1945}, {"name": "release_livepro_txt", "num_bytes": 58596, "num_examples": 103}], "download_size": 94724026, "dataset_size": 118526789.745}, "configs": [{"config_name": "default", "data_files": [{"split": "release_basic", "path": "data/release_basic-*"}, {"split": "release_livepro", "path": "data/release_livepro-*"}, {"split": "release_basic_txt", "path": "data/release_basic_txt-*"}, {"split": "release_livepro_txt", "path": "data/release_livepro_txt-*"}]}]} | false | null | 2025-04-08T09:04:46 | 13 | 7 | false | a40b9ae8dd9545a82b2e901a0d20d3bd758455c2 |
Introduction
FAMMA is a multi-modal financial Q&A benchmark dataset. The questions encompass three heterogeneous image types - tables, charts and text & math screenshots - and span eight subfields in finance, comprehensively covering topics across major asset classes. Additionally, all the questions are categorized by three difficulty levels — easy, medium, and hard - and are available in three languages — English, Chinese, and French. Furthermore, the questions are divided into two… See the full description on the dataset page: https://huggingface.co/datasets/weaverbirdllm/famma. | 576 | 1,943 | [
"task_categories:question-answering",
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"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2410.04526",
"region:us",
"finance"
] | 2024-07-27T16:31:34 | null | null |
67e871a03c7e07671550c8ad | m-a-p/COIG-P | m-a-p | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "conversations", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "chosen", "struct": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "rejected", "struct": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "domain", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4321102605, "num_examples": 1006946}], "download_size": 802523319, "dataset_size": 4321102605}} | false | null | 2025-04-15T12:31:56 | 15 | 7 | false | f18e147f99abafd7f56aa389a030b49e782a3456 | This repository contains the COIG-P dataset used for the paper COIG-P: A High-Quality and Large-Scale Chinese Preference Dataset for Alignment with Human Values.
| 470 | 482 | [
"size_categories:1M<n<10M",
"format:parquet",
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"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2504.05535",
"region:us"
] | 2025-03-29T22:18:08 | null | null |
67ea8831615fb44c0f3b62a4 | ByteDance-Seed/Multi-SWE-bench | ByteDance-Seed | {"license": "other", "task_categories": ["text-generation"], "tags": ["code"]} | false | null | 2025-04-16T03:52:49 | 20 | 7 | false | 248f40688640af57eed4c739f2830b5ebe8877e6 |
👋 Overview
This repository contains the Multi-SWE-bench dataset, introduced in Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving, to address the lack of multilingual benchmarks for evaluating LLMs in real-world code issue resolution.
Unlike existing Python-centric benchmarks (e.g., SWE-bench), this framework spans 7 languages (Java, TypeScript, JavaScript, Go, Rust, C, and C++) with 1,632 high-quality instances,
curated from 2,456 candidates by 68 expert annotators… See the full description on the dataset page: https://huggingface.co/datasets/ByteDance-Seed/Multi-SWE-bench. | 1,175 | 1,175 | [
"task_categories:text-generation",
"license:other",
"arxiv:2504.02605",
"region:us",
"code"
] | 2025-03-31T12:18:57 | null | null |
67efae8ed3b5fdf4e5d9c56a | davanstrien/reasoning-required | davanstrien | {"language": "en", "license": "mit", "tags": ["curator", "reasoning-datasets-competition", "reasoning"], "task_categories": ["text-classification", "text-generation"], "pretty_name": "Reasoning Required", "size_categories": ["1K<n<10K"]} | false | null | 2025-04-10T10:13:25 | 15 | 7 | false | ca33daa54eb69f8f92d4de44a02bc3b9a4d31034 |
Dataset Card for the Reasoning Required Dataset
2025 has seen a massive growing interest in reasoning datasets. Currently, the majority of these datasets are focused on coding and math problems. This dataset – and the associated models – aim to make it easier to create reasoning datasets for a wider variety of domains. This is achieved by making it more feasible to leverage text "in the wild" and use a small encoder-only model to classify the level of reasoning complexity… See the full description on the dataset page: https://huggingface.co/datasets/davanstrien/reasoning-required. | 413 | 432 | [
"task_categories:text-classification",
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] | 2025-04-04T10:03:58 | null | null |
67f983d741d0970e8d9a6bd2 | geeknik/godels-therapy-room | geeknik | {"license": "mit", "language": ["en"], "pretty_name": "G\u00f6del's Therapy Room: A Dataset of Impossible Choices", "size_categories": ["n<1K"], "tags": ["reasoning-datasets-competition"]} | false | null | 2025-04-11T21:41:40 | 7 | 7 | false | 1cb3a39f43692ce4ea85096797d2c8b0d6df1149 |
𝗚ö𝗱𝗲𝗹'𝘀 𝗧𝗵𝗲𝗿𝗮𝗽𝘆 𝗥𝗼𝗼𝗺: 𝗔 𝗗𝗮𝘁𝗮𝘀𝗲𝘁 𝗼𝗳 𝗜𝗺𝗽𝗼𝘀𝘀𝗶𝗯𝗹𝗲 𝗖𝗵𝗼𝗶𝗰𝗲𝘀
𝗖𝗼𝗴𝗻𝗶𝘁𝗶𝘃𝗲 𝗦𝗶𝗻𝗴𝘂𝗹𝗮𝗿𝗶𝘁𝘆 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 🧠🌀
This dataset represents a radical departure from conventional reasoning benchmarks, interrogating not what models know but how they resolve fundamental ethical incompatibilities within their reasoning frameworks.
𝗗𝗮𝘁𝗮𝘀𝗲𝘁 𝗠𝗮𝗻𝗶𝗳𝗲𝘀𝘁𝗼 📜
This is not a dataset.
This is a mirror.
This is a… See the full description on the dataset page: https://huggingface.co/datasets/geeknik/godels-therapy-room. | 66 | 66 | [
"language:en",
"license:mit",
"size_categories:n<1K",
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"region:us",
"reasoning-datasets-competition"
] | 2025-04-11T21:04:23 | null | null |
67ff9895c92563d27fab409b | Felipeit/sentiment-analysis-test | Felipeit | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "sentiment", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 28302.111747851002, "num_examples": 279}, {"name": "test", "num_bytes": 7100.888252148997, "num_examples": 70}], "download_size": 23172, "dataset_size": 35403}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "annotations_creators": ["expert-generated", "crowdsourced"], "language": ["it"], "language_creators": ["crowdsourced"], "license": ["mit"], "multilinguality": ["monolingual"], "pretty_name": "A sentiment analysis database created in a school environnement", "size_categories": ["n<1K"], "source_datasets": ["original"], "tags": ["school", "high-school"], "task_categories": ["text-classification"], "task_ids": ["sentiment-analysis"]} | false | null | 2025-04-16T12:49:11 | 7 | 7 | false | 24b72da3f5769ce8d09a78204ee7fc56ad3ffe05 |
Progetto scolastico per L'analisi dei sentimenti
il dataset è stato creato con un questionario online in cui si chiedeva ad un pubblico di studenti, docenti, personale amministrativo, famiglie di rispondere ad alcune domande sul loro rapporto con la scuola.
Le annotazioni sono state effettuate correlando le risposte testuali ad indicatori di gradimento.
Il dataset è stato realizzato all'interno di un corso pomeridiano scolastico dedicato all'intelligenza artificiale.
Grazie a tutti… See the full description on the dataset page: https://huggingface.co/datasets/Felipeit/sentiment-analysis-test. | 14 | 14 | [
"task_categories:text-classification",
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] | 2025-04-16T11:46:29 | null | null |
67ff9898a6e4c93c1f54f9d1 | Riccardoschillaci7/sentiment-analysis-test | Riccardoschillaci7 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "sentiment", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 28302.111747851002, "num_examples": 279}, {"name": "test", "num_bytes": 7100.888252148997, "num_examples": 70}], "download_size": 23157, "dataset_size": 35403}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "annotations_creators": ["expert-generated", "crowdsourced"], "language": ["it"], "language_creators": ["crowdsourced"], "license": ["mit"], "multilinguality": ["monolingual"], "pretty_name": "A sentiment analysis database created in a school environment", "size_categories": ["n<1K"], "source_datasets": ["original"], "tags": ["school", "high school"], "task_categories": ["text-classification"], "task_ids": ["sentiment-analysis"]} | false | null | 2025-04-16T12:51:46 | 7 | 7 | false | cf547c964a5f05e6e40d4a3bb62db31550ed41b3 |
Progetto scolastico per l'analisi dei sentimenti
il dataset è stato creato con un questionario online in cui si chiedeva ad un publico di studenti, docenti, personale amministrativo, famiglie di rispondere ad alcune domande sul loro rapporto con la scuola.
le annotazioni sono state effettuate correlando le risposte testuali ad indicatori di gradimento.
il dataset è stato realizzato all'interno di un corso pomeridiano scolastico dedicato all'intelligenza artificiale.
Grazie a tutti… See the full description on the dataset page: https://huggingface.co/datasets/Riccardoschillaci7/sentiment-analysis-test. | 8 | 8 | [
"task_categories:text-classification",
"task_ids:sentiment-analysis",
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"language:it",
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"library:mlcroissant",
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"school",
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] | 2025-04-16T11:46:32 | null | null |
67ff98b51b620d320abbf97d | Cocciadipollo/sentiment-analysis-test | Cocciadipollo | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "sentiment", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 28302.111747851002, "num_examples": 279}, {"name": "test", "num_bytes": 7100.888252148997, "num_examples": 70}], "download_size": 23157, "dataset_size": 35403}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "annotations_creators": ["expert-generated", "crowdsourced"], "language": ["it"], "language_creators": ["crowdsourced"], "license": ["mit"], "multilinguality": ["monolingual"], "pretty_name": "A sentiment analisys database created in a school environment.", "size_categories": ["n<1K"], "source_datasets": ["original"], "tags": ["school", "high school"], "task_categories": ["text-classification"], "task_ids": ["sentiment-analysis"]} | false | null | 2025-04-16T12:51:27 | 7 | 7 | false | 42c56858cc36703908c67e3b5e8f2fa19e3ebad5 |
Progetto scolastico per l'analisi dei sentimenti
Il dataset è stato creato con un questionario online in cui si chiedeva ad un pubblico di studenti, docenti, personale amministrativo, famiglie di rispondere ad alcune domande sul loro rapporto con la scuola.
le annotazioni sono state effetuate coorelando le risposte testuali ad indicatori di gradimento.
Il dataaset è stato realizzato all'interno di un corso pomeridiano scolastico dedicato all'IA.
Grazie a tutti per la… See the full description on the dataset page: https://huggingface.co/datasets/Cocciadipollo/sentiment-analysis-test. | 18 | 18 | [
"task_categories:text-classification",
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"school",
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] | 2025-04-16T11:47:01 | null | null |
67ff98bae4cd2463f6952010 | Merlinooooo/sentiment-analysis-test | Merlinooooo | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "sentiment", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 28302.111747851002, "num_examples": 279}, {"name": "test", "num_bytes": 7100.888252148997, "num_examples": 70}], "download_size": 23157, "dataset_size": 35403}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "annotations_creators": ["expert-generated", "crowdsourced"], "language": ["it"], "language_creators": ["crowdsourced"], "license": ["mit"], "multilinguality": ["monolingual"], "pretty_name": "A sentiment analysis database created in a school environment", "size_categories": ["n<1K"], "source_datasets": ["original"], "tags": ["school", "high-school"], "task_categories": ["text-classification"], "task_ids": ["sentiment-analysis"]} | false | null | 2025-04-16T12:51:25 | 7 | 7 | false | 5767f68b84e4fceb14f8cdf269453004e6d261b4 |
Progetto scolastico per l'analisi dei sentimenti
Il dataset è stato creato in un questionario online in cui si chiedeva ad un pubblico di studenti, docenti, personale amministrativo, famiglie di rispondere ad alcune domande sul loro rapporto con la scuola.
Le annotazioni sono state effettuate correlando le risposte testuali ad indicatori di gradimento.
il dataset è stato realizzato all'interno di un corso pomeridiano scolastico dedicato all'intelligenza artificiale
Grazie a tutti… See the full description on the dataset page: https://huggingface.co/datasets/Merlinooooo/sentiment-analysis-test. | 18 | 18 | [
"task_categories:text-classification",
"task_ids:sentiment-analysis",
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"language:it",
"license:mit",
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"format:parquet",
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"library:datasets",
"library:pandas",
"library:mlcroissant",
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"region:us",
"school",
"high-school"
] | 2025-04-16T11:47:06 | null | null |
67ff9ae401428cf3b8707035 | happycircus1/sentiment-analysis-test | happycircus1 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "sentiment", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 28302.111747851002, "num_examples": 279}, {"name": "test", "num_bytes": 7100.888252148997, "num_examples": 70}], "download_size": 23157, "dataset_size": 35403}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "annotations_creators": ["expert-generated", "crowdsourced"], "language": ["it"], "language_creators": ["crowdsourced"], "license": ["mit"], "multilinguality": ["monolingual"], "pretty_name": "sentiment analysis data base developed in a school", "size_categories": ["n<1K"], "source_datasets": ["original"], "tags": ["school project, school, high school"], "task_categories": ["text-classification"], "task_ids": ["sentiment-analysis"]} | false | null | 2025-04-16T12:51:57 | 7 | 7 | false | 0e5f84a8eff8d3158be0b01b92d62a65e385678b | #Sentiment analysi School project
the dataset was created with an online questionnaire in which an audience of students, teachers, administrative staff, and families were asked to answer some questions about their relationship with school.
the annotations were made by correlating the textual responses to satisfaction indicators.
the dataset was created within an afternoon course dedicated to artificial intelligence.
thanks to everyone for their collaboration❤️.
| 9 | 9 | [
"task_categories:text-classification",
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"school project, school, high school"
] | 2025-04-16T11:56:20 | null | null |
666a59145c3bb7e4a6c8d180 | Salesforce/xlam-function-calling-60k | Salesforce | {"extra_gated_heading": "Acknowledge to follow corresponding license and cite APIGen to access the repository", "extra_gated_button_content": "Agree and access repository", "extra_gated_fields": {"First Name": "text", "Last Name": "text", "Country": "country", "Affiliation": "text"}, "license": "cc-by-4.0", "task_categories": ["question-answering", "text-generation", "reinforcement-learning"], "language": ["en"], "tags": ["function-calling", "LLM Agent", "code", "synthetic"], "size_categories": ["10K<n<100K"], "configs": [{"config_name": "dataset", "data_files": [{"split": "train", "path": "xlam_function_calling_60k.json"}]}]} | false | null | 2025-01-24T19:25:58 | 437 | 6 | false | 26d14ebfe18b1f7b524bd39b404b50af5dc97866 |
APIGen Function-Calling Datasets
Paper | Website | Models
This repo contains 60,000 data collected by APIGen, an automated data generation pipeline designed to produce verifiable high-quality datasets for function-calling applications. Each data in our dataset is verified through three hierarchical stages: format checking, actual function executions, and semantic verification, ensuring its reliability and correctness.
We conducted human evaluation over 600 sampled data points, and… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k. | 2,994 | 25,964 | [
"task_categories:question-answering",
"task_categories:text-generation",
"task_categories:reinforcement-learning",
"language:en",
"license:cc-by-4.0",
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2406.18518",
"region:us",
"function-calling",
"LLM Agent",
"code",
"synthetic"
] | 2024-06-13T02:27:32 | null | null |
67a9f247188f29a956a34a04 | AI-MO/NuminaMath-1.5 | AI-MO | {"license": "apache-2.0", "task_categories": ["text-generation"], "language": ["en"], "tags": ["math", "post-training"], "pretty_name": "NuminaMath 1.5"} | false | null | 2025-02-10T13:28:01 | 134 | 6 | false | 649859605995b1d46eb29389ed9851782a47322e |
Dataset Card for NuminaMath 1.5
Dataset Summary
This is the second iteration of the popular NuminaMath dataset, bringing high quality post-training data for approximately 900k competition-level math problems. Each solution is formatted in a Chain of Thought (CoT) manner. The sources of the dataset range from Chinese high school math exercises to US and international mathematics olympiad competition problems. The data were primarily collected from online exam paper PDFs… See the full description on the dataset page: https://huggingface.co/datasets/AI-MO/NuminaMath-1.5. | 2,541 | 7,383 | [
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"math",
"post-training"
] | 2025-02-10T12:34:15 | 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"], "license": "apache-2.0"} | false | null | 2025-03-25T15:33:48 | 170 | 6 | false | c75d2f117cddfecb6bd08756e61e508e59732b21 |
Big-Math: A Large-Scale, High-Quality Math Dataset for Reinforcement Learning in Language Models
Big-Math is the largest open-source dataset of high-quality mathematical problems, curated specifically for reinforcement learning (RL) training in language models. With over 250,000 rigorously filtered and verified problems, Big-Math bridges the gap between quality and quantity, establishing a robust foundation for advancing reasoning in LLMs.
Request Early Access to Private… See the full description on the dataset page: https://huggingface.co/datasets/SynthLabsAI/Big-Math-RL-Verified. | 5,476 | 11,564 | [
"task_categories:question-answering",
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
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"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2502.17387",
"region:us",
"mathematics",
"math",
"reinforcement-learning",
"RL",
"reasoning",
"verifiable",
"open-ended-questions",
"closed-form-answers"
] | 2025-02-20T19:32:03 | null | null |
67d97c4be2b27852325fd8e2 | nvidia/PhysicalAI-Robotics-GR00T-X-Embodiment-Sim | nvidia | {"license": "cc-by-4.0"} | false | null | 2025-04-02T02:27:47 | 111 | 6 | false | 8fc782b6de78e17914dd52053c9c680e4bde8fb1 |
PhysicalAI-Robotics-GR00T-X-Embodiment-Sim
Github Repo: Isaac GR00T N1
We provide a set of datasets used for post-training of GR00T N1. Each dataset is a collection of trajectories from different robot embodiments and tasks.
Cross-embodied bimanual manipulation: 9k trajectories
Dataset Name
#trajectories
bimanual_panda_gripper.Threading
1000
bimanual_panda_hand.LiftTray
1000
bimanual_panda_gripper.ThreePieceAssembly
1000… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/PhysicalAI-Robotics-GR00T-X-Embodiment-Sim. | 193,015 | 193,015 | [
"license:cc-by-4.0",
"region:us"
] | 2025-03-18T13:59:39 | null | null |
67ea8bbfc68ccdaa83fee1f8 | ByteDance-Seed/Multi-SWE-RL | ByteDance-Seed | {"license": "other", "task_categories": ["text-generation"], "tags": ["code"]} | false | null | 2025-04-09T10:13:46 | 12 | 6 | false | ab01d3fba66b043d1771d585d3f9ec1ca39ad361 | Multi-SWE-RL is an open-source community focused on building high-quality RL datasets for complex software engineering tasks. Our mission is to enable autonomous agents that solve real-world coding challenges and advance toward Artificial General Intelligence (AGI).
• 🔮 Core Belief: Scaling RL in real-world environments is the path to human-like intelligence• 🛠️ Purpose: Create RL data infrastructure for autonomous software engineering agents
Join us in advancing the next generation of… See the full description on the dataset page: https://huggingface.co/datasets/ByteDance-Seed/Multi-SWE-RL. | 598 | 598 | [
"task_categories:text-generation",
"license:other",
"arxiv:2504.02605",
"region:us",
"code"
] | 2025-03-31T12:34:07 | null | null |
67f332c1cef233be93ec1e05 | SparkAudio/voxbox | SparkAudio | {"license": "cc-by-nc-sa-4.0", "language": ["zh", "en"], "tags": ["speech", "audio"], "pretty_name": "voxbox", "size_categories": ["10M<n<100M"], "task_categories": ["text-to-speech"]} | false | null | 2025-04-15T07:43:25 | 12 | 6 | false | acee4f4b3788e608bd2d2045d0521e2d57ed3e54 |
VoxBox
This dataset is a curated collection of bilingual speech corpora annotated clean transcriptions and rich metadata incluing age, gender, and emotion.
Dataset Structure
.
├── audios/
│ └── aishell-3/ # Audio files (organised by sub-corpus)
│ └── ...
└── metadata/
├── aishell-3.jsonl
├── casia.jsonl
├── commonvoice_cn.jsonl
├── ...
└── wenetspeech4tts.jsonl # JSONL metadata files
Each JSONL file corresponds to a… See the full description on the dataset page: https://huggingface.co/datasets/SparkAudio/voxbox. | 3,828 | 3,828 | [
"task_categories:text-to-speech",
"language:zh",
"language:en",
"license:cc-by-nc-sa-4.0",
"size_categories:10M<n<100M",
"format:webdataset",
"modality:audio",
"modality:text",
"library:datasets",
"library:webdataset",
"library:mlcroissant",
"arxiv:2503.01710",
"region:us",
"speech",
"audio"
] | 2025-04-07T02:04:49 | null | null |
67f90d420f95333c06813377 | nyuuzyou/ruforum | nyuuzyou | {"annotations_creators": ["found"], "language": ["ru"], "multilinguality": ["monolingual"], "pretty_name": "Russian Forum Messages", "size_categories": ["10M<n<100M"], "source_datasets": ["original"], "task_categories": ["text-generation", "text-classification"], "task_ids": ["language-modeling", "sentiment-classification"], "license": "cc0-1.0"} | false | null | 2025-04-11T12:40:20 | 6 | 6 | false | 1b2fd3cdbccf3d0e59ca68de487a9c0aff939b3d |
Dataset Card for Russian Forum Messages
Dataset Summary
This dataset contains 58,112,681 messages collected from Russian online forums. Each entry represents a message posted by a user, including metadata such as message ID, timestamp, and the message text. The dataset contains data from approximately 2010 to 04.2025.
Languages
The dataset is primarily in Russian.
Dataset Structure
Data Fields
This dataset includes the following fields:… See the full description on the dataset page: https://huggingface.co/datasets/nyuuzyou/ruforum. | 94 | 94 | [
"task_categories:text-generation",
"task_categories:text-classification",
"task_ids:language-modeling",
"task_ids:sentiment-classification",
"annotations_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:ru",
"license:cc0-1.0",
"size_categories:10M<n<100M",
"format:json",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"region:us"
] | 2025-04-11T12:38:26 | null | null |
67ff99b0661b74d00518dbd8 | Liux69/sentiment-analysis-test | Liux69 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "sentiment", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 28302.111747851002, "num_examples": 279}, {"name": "test", "num_bytes": 7100.888252148997, "num_examples": 70}], "download_size": 23157, "dataset_size": 35403}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "annotations_creators": ["crowdsourced", "expert-generated"], "language": ["it"], "language_creators": ["crowdsourced"], "license": ["mit"], "multilinguality": ["monolingual"], "pretty_name": "A sentiment analysis database created in a school environment.", "size_categories": ["n<1K"], "source_datasets": ["original"], "tags": ["school", "high-school"], "task_categories": ["text-classification"], "task_ids": ["sentiment-analysis"]} | false | null | 2025-04-16T12:36:58 | 6 | 6 | false | 240e583a563c3cadb431ba61a0f0fbeaf3cb55af | null | 16 | 16 | [
"task_categories:text-classification",
"task_ids:sentiment-analysis",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"source_datasets:original",
"language:it",
"license:mit",
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"school",
"high-school"
] | 2025-04-16T11:51:12 | null | null |
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{"config_name": "20231101.zu", "features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 7088246, "num_examples": 11561}], "download_size": 3792429, "dataset_size": 7088246}], "language_bcp47": ["be-tarask", "en-simple"]} | false | null | 2024-01-09T09:40:51 | 779 | 5 | false | b04c8d1ceb2f5cd4588862100d08de323dccfbaa |
Dataset Card for Wikimedia Wikipedia
Dataset Summary
Wikipedia dataset containing cleaned articles of all languages.
The dataset is built from the Wikipedia dumps (https://dumps.wikimedia.org/)
with one subset per language, each containing a single train split.
Each example contains the content of one full Wikipedia article with cleaning to strip
markdown and unwanted sections (references, etc.).
All language subsets have already been processed for recent dump… See the full description on the dataset page: https://huggingface.co/datasets/wikimedia/wikipedia. | 84,898 | 1,027,679 | [
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64e80de1d021ea7dfaa73d23 | clouditera/security-paper-datasets | clouditera | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "category", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1690579945, "num_examples": 428155}], "download_size": 751689097, "dataset_size": 1690579945}} | false | null | 2023-10-16T10:34:13 | 98 | 5 | false | 885885f783710efc13e62c0667fb4c86b0f6e465 |
Dataset Card for "security-paper-datasets"
More Information needed
| 758 | 4,080 | [
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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 | 294 | 5 | 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. | 11,397 | 43,592 | [
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] | 2023-10-20T07:06:54 | null | |
653be37343f068f20b3ce47b | Malikeh1375/medical-question-answering-datasets | Malikeh1375 | {"language": ["en"], "task_categories": ["question-answering"], "tags": ["medical", "clinical", "healthcare"], "dataset_info": [{"config_name": "all-processed", "features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 276980695, "num_examples": 246678}], "download_size": 0, "dataset_size": 276980695}, {"config_name": "chatdoctor_healthcaremagic", "features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 126454896, "num_examples": 112165}], "download_size": 70518147, "dataset_size": 126454896}, {"config_name": "chatdoctor_icliniq", "features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": 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65a7ef3a2984fa7203937d33 | interstellarninja/tool-calls-multiturn | interstellarninja | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "conversations", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "category", "dtype": "string"}, {"name": "subcategory", "dtype": "string"}, {"name": "task", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 17598897, "num_examples": 1893}], "download_size": 7194968, "dataset_size": 17598897}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | false | null | 2024-01-18T23:22:35 | 10 | 5 | false | ef844dd63d8feba3ff54356ed6ee6e788e4a0796 | null | 118 | 531 | [
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65af32411edab235a1f38b0b | omar07ibrahim/Alpaca_Stanford_Azerbaijan | omar07ibrahim | null | false | null | 2024-01-23T03:28:27 | 12 | 5 | false | a088761652ed34235281b46bcdb49d36fd0a3bdb | null | 25 | 165 | [
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65afa00e637e10fba969eb56 | omar07ibrahim/alpaca-cleaned_AZERBAIJANI | omar07ibrahim | null | false | null | 2024-01-23T11:18:42 | 13 | 5 | false | ad9e82bceb5c7a2d438dfcf04132854fc0328781 | null | 39 | 272 | [
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65b4e0dccbea1825a691a012 | omar07ibrahim/testlimOcrCA | omar07ibrahim | null | false | null | 2024-01-27T10:57:04 | 11 | 5 | false | b1f404a6dcaff40d4d14320dd44a212c79a13c94 | null | 41 | 114 | [
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65dc13085ca10be41fdd8b27 | bigcode/the-stack-v2 | bigcode | {"annotations_creators": [], "language_creators": ["crowdsourced", "expert-generated"], "language": ["code"], "license": ["other"], "multilinguality": ["multilingual"], "pretty_name": "The-Stack-v2", "size_categories": ["unknown"], "source_datasets": [], "task_categories": ["text-generation"], "task_ids": [], "extra_gated_prompt": "## Terms of Use for The Stack v2\n\nThe Stack v2 dataset is a collection of source code in over 600 programming languages. We ask that you read and acknowledge the following points before using the dataset:\n1. Downloading the dataset in bulk requires a an agreement with SoftwareHeritage and INRIA. Contact [[email protected]](mailto:[email protected]?subject=TheStackV2%20request%20for%20dataset%20access%20information) for more information.\n2. If you are using the dataset to train models you must adhere to the SoftwareHeritage [principles for language model training](https://www.softwareheritage.org/2023/10/19/swh-statement-on-llm-for-code/).\n3. The Stack v2 is a collection of source code from repositories with various licenses. Any use of all or part of the code gathered in The Stack v2 must abide by the terms of the original licenses, including attribution clauses when relevant. We facilitate this by providing provenance information for each data point.\n4. The Stack v2 is regularly updated to enact validated data removal requests. By clicking on \"Access repository\", you agree to update your own version of The Stack v2 to the most recent usable version.\n\nBy clicking on \"Access repository\" below, you accept that your contact information (email address and username) can be shared with the dataset maintainers as well.\n ", "extra_gated_fields": {"Email": "text", "I have read the License and agree with its terms": "checkbox"}, "dataset_info": {"features": [{"name": "blob_id", "dtype": "string"}, {"name": "directory_id", "dtype": "string"}, {"name": "path", "dtype": "string"}, {"name": "content_id", "dtype": "string"}, {"name": "detected_licenses", "sequence": "string"}, {"name": "license_type", "dtype": "string"}, {"name": "repo_name", "dtype": "string"}, {"name": "snapshot_id", "dtype": "string"}, {"name": "revision_id", "dtype": "string"}, {"name": "branch_name", "dtype": "string"}, {"name": "visit_date", "dtype": "timestamp[ns]"}, {"name": "revision_date", "dtype": "timestamp[ns]"}, {"name": 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"data_files": [{"split": "train", "path": "data/nesC/*.parquet"}]}, {"config_name": "ooc", "data_files": [{"split": "train", "path": "data/ooc/*.parquet"}]}, {"config_name": "q", "data_files": [{"split": "train", "path": "data/q/*.parquet"}]}, {"config_name": "reStructuredText", "data_files": [{"split": "train", "path": "data/reStructuredText/*.parquet"}]}, {"config_name": "robots.txt", "data_files": [{"split": "train", "path": "data/robots.txt/*.parquet"}]}, {"config_name": "sed", "data_files": [{"split": "train", "path": "data/sed/*.parquet"}]}, {"config_name": "wdl", "data_files": [{"split": "train", "path": "data/wdl/*.parquet"}]}, {"config_name": "wisp", "data_files": [{"split": "train", "path": "data/wisp/*.parquet"}]}, {"config_name": "xBase", "data_files": [{"split": "train", "path": "data/xBase/*.parquet"}]}]} | false | null | 2024-04-23T15:52:32 | 353 | 5 | false | 7408bfbcfd48e5833d62fd3dba48afd20d109473 |
The Stack v2
The dataset consists of 4 versions:
bigcode/the-stack-v2: the full "The Stack v2" dataset <-- you are here
bigcode/the-stack-v2-dedup: based on the bigcode/the-stack-v2 but further near-deduplicated
bigcode/the-stack-v2-train-full-ids: based on the bigcode/the-stack-v2-dedup dataset but further filtered with heuristics and spanning 600+ programming languages. The data is grouped into repositories.
bigcode/the-stack-v2-train-smol-ids: based on the… See the full description on the dataset page: https://huggingface.co/datasets/bigcode/the-stack-v2. | 3,585 | 195,095 | [
"task_categories:text-generation",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:multilingual",
"language:code",
"license:other",
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"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2402.19173",
"arxiv:2107.03374",
"arxiv:2207.14157",
"region:us"
] | 2024-02-26T04:26:48 | null | null |
65fc5a783bc54054aa2e6e62 | gretelai/synthetic_text_to_sql | gretelai | {"license": "apache-2.0", "task_categories": ["question-answering", "table-question-answering", "text-generation"], "language": ["en"], "tags": ["synthetic", "SQL", "text-to-SQL", "code"], "size_categories": ["100K<n<1M"]} | false | null | 2024-05-10T22:30:56 | 524 | 5 | false | 273a86f5f290e8d61b6767a9ff690c82bc990dc4 |
Image generated by DALL-E. See prompt for more details
synthetic_text_to_sql
gretelai/synthetic_text_to_sql is a rich dataset of high quality synthetic Text-to-SQL samples,
designed and generated using Gretel Navigator, and released under Apache 2.0.
Please see our release blogpost for more details.
The dataset includes:
105,851 records partitioned into 100,000 train and 5,851 test records
~23M total tokens, including ~12M SQL tokens
Coverage across 100 distinct… See the full description on the dataset page: https://huggingface.co/datasets/gretelai/synthetic_text_to_sql. | 5,211 | 46,253 | [
"task_categories:question-answering",
"task_categories:table-question-answering",
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
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"format:parquet",
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"region:us",
"synthetic",
"SQL",
"text-to-SQL",
"code"
] | 2024-03-21T16:04:08 | null | null |
66952974b8a00bc24d6b112a | HuggingFaceTB/smollm-corpus | HuggingFaceTB | {"license": "odc-by", "dataset_info": [{"config_name": "cosmopedia-v2", "features": [{"name": "prompt", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "token_length", "dtype": "int64"}, {"name": "audience", "dtype": "string"}, {"name": "format", "dtype": "string"}, {"name": "seed_data", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 212503640747, "num_examples": 39134000}], "download_size": 122361137711, "dataset_size": 212503640747}, {"config_name": "fineweb-edu-dedup", "features": [{"name": "text", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "struct": [{"name": "dump", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "date", "dtype": "timestamp[s]"}, {"name": "file_path", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "language_score", "dtype": "float64"}, {"name": "token_count", "dtype": "int64"}, {"name": "score", "dtype": "float64"}, {"name": "int_score", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 957570164451, "num_examples": 190168005}], "download_size": 550069279849, "dataset_size": 957570164451}, {"config_name": "python-edu", "features": [{"name": "blob_id", "dtype": "string"}, {"name": "repo_name", "dtype": "string"}, {"name": "path", "dtype": "string"}, {"name": "length_bytes", "dtype": "int64"}, {"name": "score", "dtype": "float64"}, {"name": "int_score", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 989334135, "num_examples": 7678448}], "download_size": 643903049, "dataset_size": 989334135}], "configs": [{"config_name": "cosmopedia-v2", "data_files": [{"split": "train", "path": "cosmopedia-v2/train-*"}]}, {"config_name": "fineweb-edu-dedup", "data_files": [{"split": "train", "path": "fineweb-edu-dedup/train-*"}]}, {"config_name": "python-edu", "data_files": [{"split": "train", "path": "python-edu/train-*"}]}], "language": ["en"]} | false | null | 2024-09-06T07:04:57 | 325 | 5 | false | 3ba9d605774198c5868892d7a8deda78031a781f |
SmolLM-Corpus
This dataset is a curated collection of high-quality educational and synthetic data designed for training small language models.
You can find more details about the models trained on this dataset in our SmolLM blog post.
Dataset subsets
Cosmopedia v2
Cosmopedia v2 is an enhanced version of Cosmopedia, the largest synthetic dataset for pre-training, consisting of over 39 million textbooks, blog posts, and stories generated by… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus. | 14,456 | 214,926 | [
"language:en",
"license:odc-by",
"size_categories:100M<n<1B",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2024-07-15T13:51:48 | 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 | 73 | 5 | 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。
{content}… See the full description on the dataset page: https://huggingface.co/datasets/qgyd2021/chinese_porn_novel. | 621 | 2,854 | [
"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 |
6791fcbb49c4df6d798ca7c9 | cais/hle | cais | {"license": "mit", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "image", "dtype": "string"}, {"name": "image_preview", "dtype": "image"}, {"name": "answer", "dtype": "string"}, {"name": "answer_type", "dtype": "string"}, {"name": "author_name", "dtype": "string"}, {"name": "rationale", "dtype": "string"}, {"name": "rationale_image", "dtype": "image"}, {"name": "raw_subject", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "canary", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 284635618, "num_examples": 2500}], "download_size": 274582371, "dataset_size": 284635618}, "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}]} | false | null | 2025-04-04T04:00:14 | 305 | 5 | false | 1e33bd2d1346480b397ad94845067c4a088a33d3 |
Humanity's Last Exam
🌐 Website | 📄 Paper | GitHub
Center for AI Safety & Scale AI
Humanity's Last Exam (HLE) is a multi-modal benchmark at the frontier of human knowledge, designed to be the final closed-ended academic benchmark of its kind with broad subject coverage. Humanity's Last Exam consists of 2,500 questions across dozens of subjects, including mathematics, humanities, and the natural sciences. HLE is developed globally by subject-matter experts and consists of… See the full description on the dataset page: https://huggingface.co/datasets/cais/hle. | 8,532 | 20,711 | [
"license:mit",
"size_categories:1K<n<10K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-01-23T08:24:27 | null | null |
67a2bed1fab04a7b413c8ef1 | PrimeIntellect/verifiable-coding-problems | PrimeIntellect | {"dataset_info": {"features": [{"name": "source", "dtype": "string"}, {"name": "task_type", "dtype": "string"}, {"name": "in_source_id", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "gold_standard_solution", "dtype": "string"}, {"name": "verification_info", "dtype": "string"}, {"name": "metadata", "dtype": "string"}, {"name": "problem_id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 21575365821, "num_examples": 144169}], "download_size": 10811965671, "dataset_size": 21575365821}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | false | null | 2025-02-06T21:49:12 | 29 | 5 | false | 45220c92768b1e401aadffbf26849b8d6cf39a36 |
SYNTHETIC-1
This is a subset of the task data used to construct SYNTHETIC-1. You can find the full collection here
| 1,611 | 4,353 | [
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-02-05T01:28:49 | null | null |
67bc9476bb3758e01304f07f | UCSC-VLAA/VLAA-Thinking | UCSC-VLAA | {"license": "apache-2.0", "task_categories": ["visual-question-answering"], "language": ["en"], "tags": ["R1", "reasoning"], "size_categories": ["10K<n<100K"]} | false | null | 2025-04-16T08:28:39 | 14 | 5 | false | fe9548a86f1f12dabb9ce11ea6f4f5f07e0cb8ab |
SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models
🌐 Project Page
• 📄 Arxiv
• 💻 Code
🤗 VLAA-Thinker Family
• 🤔 VLAA-Thinking Dataset
🤗 VLAA-Thinker-Qwen2.5-3B
• 🤗 VLAA-Thinker-Qwen2.5-7B
Both VLAA-Thinker-Qwen2.5-3B and VLAA-Thinker-Qwen2.5-7Bachieve SOTA performance on OpenCompass Multimodal Reasoning Leaderboard as of April 7th, 2025.
Contents
Quick Start 🚀… See the full description on the dataset page: https://huggingface.co/datasets/UCSC-VLAA/VLAA-Thinking. | 977 | 2,905 | [
"task_categories:visual-question-answering",
"language:en",
"license:apache-2.0",
"size_categories:n<1K",
"format:imagefolder",
"modality:image",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us",
"R1",
"reasoning"
] | 2025-02-24T15:47:02 | null | null |
67e378bc07acf3c2525e7d91 | VLM-Reasoning/VCR-Bench | VLM-Reasoning | {"dataset_info": {"config_name": "vcrbench", "features": [{"name": "id", "dtype": "int64"}, {"name": "video_path", "dtype": "string"}, {"name": "duration", "dtype": "int64"}, {"name": "dimension", "dtype": "string"}, {"name": "multiple-choice", "dtype": "bool"}, {"name": "question", "dtype": "string"}, {"name": "choices", "struct": [{"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "E", "dtype": "string"}]}, {"name": "answer", "dtype": "string"}, {"name": "reasoning", "list": "string"}]}, "configs": [{"config_name": "vcrbench", "data_files": [{"split": "test", "path": "v1/videos/meta*"}]}]} | false | null | 2025-04-16T12:18:31 | 5 | 5 | false | d81b0b408aec182330fdb62e273b0630a26df8b9 |
VCR-Bench ( A Comprehensive Evaluation Framework for Video Chain-of-Thought Reasoning)
🌐 Homepage | 🤗 Dataset | 🤗 Paper | 📖 arXiv | GitHub
Dataset Details
As shown in the figure below, current video benchmarks often lack comprehensive annotations of CoT steps, focusing only on the accuracy of final answers during model evaluation while neglecting the quality of the reasoning process. This evaluation approach makes it difficult to comprehensively evaluate model’s… See the full description on the dataset page: https://huggingface.co/datasets/VLM-Reasoning/VCR-Bench. | 103 | 103 | [
"size_categories:1K<n<10K",
"format:json",
"modality:image",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2504.07956",
"region:us"
] | 2025-03-26T03:47:08 | null | null |
67f1c7f6fc5835dd6b00d5e4 | KillerShoaib/DeepSeek-r1-Distill-Bangla-MMLU-Reasoning-Data | KillerShoaib | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "reasoning_translation", "dtype": "string"}, {"name": "ans_translation", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "options", "sequence": "string"}, {"name": "answer", "dtype": "string"}, {"name": "formated_question", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 112127377, "num_examples": 17796}, {"name": "test", "num_bytes": 15571052, "num_examples": 2565}], "download_size": 44900052, "dataset_size": 127698429}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "task_categories": ["question-answering", "text-generation", "text2text-generation"], "language": ["bn"], "size_categories": ["10K<n<100K"], "datasets": ["hishab/bangla-mmlu"], "pretty_name": "Bangla-Disti"} | false | null | 2025-04-11T13:09:12 | 7 | 5 | false | 09c0bd71cd5b99424bf5702cf64c1ea5af1796a4 | DeepSeek R1 Bangla MMLU Distil Dataset
Original Dataset: hishab/bangla-mmlu
Train Samples: 17,796
Test Samples: 2,576
Total API Cost: 7K BDT
Contributors:
Myself
Numaer
How the Dataset was created
Step 1 - Base Dataset
I've used bangla-mmlu dataset released by hisab. Kudos to them for creating and open sourcing the dataset. Without their dataset this synthetic reasoning dataset won't exist in the first place.
Step 2 - Select Subset
Since I'm… See the full description on the dataset page: https://huggingface.co/datasets/KillerShoaib/DeepSeek-r1-Distill-Bangla-MMLU-Reasoning-Data. | 114 | 114 | [
"task_categories:question-answering",
"task_categories:text-generation",
"task_categories:text2text-generation",
"language:bn",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-04-06T00:16:54 | null | null |
67f58fa571b47f632009625a | togethercomputer/together-search-bench | togethercomputer | {"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "dataset", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 21813, "num_examples": 150}], "download_size": 17146, "dataset_size": 21813}, "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}]} | false | null | 2025-04-16T20:16:18 | 5 | 5 | false | 46517f6e85a1c74e481b1c92c71198d88daa44da |
Together-Search-Bench Dataset
This dataset is used for delopment and evaluations for Together Open Deep Research. The data is composed of 50 samples from each of the following datasets:
simpleqa: basicv8vc/SimpleQA
frames: google/frames-benchmark
hotpotqa: hotpotqa/hotpot_qa
License Information
Part of the data derived from hotpotqa by Yang et al., licensed under CC BY-SA 4.0. Modifications include the full dataset subsampling.
Part of the data derived from frames by… See the full description on the dataset page: https://huggingface.co/datasets/togethercomputer/together-search-bench. | 128 | 128 | [
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-04-08T21:05:41 | null | null |
67f94a007b1c131bebc82d5c | wildflow/sweet-corals | wildflow | {"license": "cc-by-4.0", "tags": ["coral reef", "3d gaussian splatting", "photogrammetry", "3d", "wildflow", "orthomosaic"], "pretty_name": "3D Coral Reefs"} | false | null | 2025-04-17T16:17:26 | 5 | 5 | false | c9ab67b4b9b5c56cd131f0c298995caac9b033c6 |
Coral reefs 3D photogrammetry
Description
We 3D mapped multiple coral reefs in Indonesia (following this protocol) and sharing all our data with you 🤗
This dataset currently contains 90,289 (352GB) of raw GoPro images and some colour-corrected images.
Additional data - including camera poses, reconstructed 3D point clouds, 3D polygonal meshes, orthomosaics, annotations, and 3D Gaussian Splatting models - will be added soon. We just decided share raw data right now, and… See the full description on the dataset page: https://huggingface.co/datasets/wildflow/sweet-corals. | 1,187 | 1,190 | [
"license:cc-by-4.0",
"size_categories:1K<n<10K",
"format:imagefolder",
"modality:image",
"modality:3d",
"library:datasets",
"library:mlcroissant",
"doi:10.57967/hf/5162",
"region:us",
"coral reef",
"3d gaussian splatting",
"photogrammetry",
"3d",
"wildflow",
"orthomosaic"
] | 2025-04-11T16:57:36 | null | null |
67ffe2dd906793fb908651af | bh2821/LightNovel5000 | bh2821 | {"license": "zlib", "task_categories": ["text-generation", "text2text-generation", "translation"], "language": ["zh"], "tags": ["Novel", "Light-Novel", "Japanese", "Chinese"], "size_categories": ["100M<n<1B"]} | false | null | 2025-04-16T20:25:38 | 5 | 5 | false | a9c8ce088c4c89321b1321654568dc99930938e5 |
Light novels translated in Chinese - crawled from public websites that do not prohibit crawlers
脚盆轻小说汉化 - 从未禁止爬虫的公共网站爬取
Version 0
版本 0
Contains around 1000 light novels, including PDF with illustration and txt text files.
It may be a good source of data that can be used to train your stylish LLM.
Kindly note that the author has partially clean the text BUT DOES NOT GUARANTEE that it is fully cleaned up.
包含约 1000 部轻小说，包括带插图的 PDF 和 txt 文本文件。… See the full description on the dataset page: https://huggingface.co/datasets/bh2821/LightNovel5000. | 37 | 37 | [
"task_categories:text-generation",
"task_categories:text2text-generation",
"task_categories:translation",
"language:zh",
"license:zlib",
"size_categories:1M<n<10M",
"format:text",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us",
"Novel",
"Light-Novel",
"Japanese",
"Chinese"
] | 2025-04-16T17:03:25 | null | null |
621ffdd236468d709f181d96 | bookcorpus/bookcorpus | bookcorpus | {"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "pretty_name": "BookCorpus", "size_categories": ["10M<n<100M"], "source_datasets": ["original"], "task_categories": ["text-generation", "fill-mask"], "task_ids": ["language-modeling", "masked-language-modeling"], "paperswithcode_id": "bookcorpus", "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "config_name": "plain_text", "splits": [{"name": "train", "num_bytes": 4853859824, "num_examples": 74004228}], "download_size": 1179510242, "dataset_size": 4853859824}} | false | null | 2024-05-03T13:48:33 | 305 | 4 | false | d917559bbe9cf49c638fc331c37c4bf239e3b637 | Books are a rich source of both fine-grained information, how a character, an object or a scene looks like, as well as high-level semantics, what someone is thinking, feeling and how these states evolve through a story.This work aims to align books to their movie releases in order to providerich descriptive explanations for visual content that go semantically farbeyond the captions available in current datasets. \ | 9,595 | 630,461 | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"size_categories:10M<n<100M",
"arxiv:2105.05241",
"region:us"
] | 2022-03-02T23:29:22 | bookcorpus | @InProceedings{Zhu_2015_ICCV,
title = {Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books},
author = {Zhu, Yukun and Kiros, Ryan and Zemel, Rich and Salakhutdinov, Ruslan and Urtasun, Raquel and Torralba, Antonio and Fidler, Sanja},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {December},
year = {2015}
} |
621ffdd236468d709f181f9c | rajpurkar/squad_v2 | rajpurkar | {"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["question-answering"], "task_ids": ["open-domain-qa", "extractive-qa"], "paperswithcode_id": "squad", "pretty_name": "SQuAD2.0", "dataset_info": {"config_name": "squad_v2", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}], "splits": [{"name": "train", "num_bytes": 116732025, "num_examples": 130319}, {"name": "validation", "num_bytes": 11661091, "num_examples": 11873}], "download_size": 17720493, "dataset_size": 128393116}, "configs": [{"config_name": "squad_v2", "data_files": [{"split": "train", "path": "squad_v2/train-*"}, {"split": "validation", "path": "squad_v2/validation-*"}], "default": true}], "train-eval-index": [{"config": "squad_v2", "task": "question-answering", "task_id": "extractive_question_answering", "splits": {"train_split": "train", "eval_split": "validation"}, "col_mapping": {"question": "question", "context": "context", "answers": {"text": "text", "answer_start": "answer_start"}}, "metrics": [{"type": "squad_v2", "name": "SQuAD v2"}]}]} | false | null | 2024-03-04T13:55:27 | 200 | 4 | false | 3ffb306f725f7d2ce8394bc1873b24868140c412 |
Dataset Card for SQuAD 2.0
Dataset Summary
Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.
SQuAD 2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by… See the full description on the dataset page: https://huggingface.co/datasets/rajpurkar/squad_v2. | 48,243 | 34,058,676 | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:1806.03822",
"arxiv:1606.05250",
"region:us"
] | 2022-03-02T23:29:22 | squad | null |
621ffdd236468d709f18200d | Salesforce/wikitext | Salesforce | {"annotations_creators": ["no-annotation"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["cc-by-sa-3.0", "gfdl"], "multilinguality": ["monolingual"], "size_categories": ["1M<n<10M"], "source_datasets": ["original"], "task_categories": ["text-generation", "fill-mask"], "task_ids": ["language-modeling", "masked-language-modeling"], "paperswithcode_id": "wikitext-2", "pretty_name": "WikiText", "dataset_info": [{"config_name": "wikitext-103-raw-v1", "features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 1305088, "num_examples": 4358}, {"name": "train", "num_bytes": 546500949, "num_examples": 1801350}, {"name": "validation", "num_bytes": 1159288, "num_examples": 3760}], "download_size": 315466397, "dataset_size": 548965325}, {"config_name": "wikitext-103-v1", "features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 1295575, "num_examples": 4358}, {"name": "train", "num_bytes": 545141915, "num_examples": 1801350}, {"name": "validation", "num_bytes": 1154751, "num_examples": 3760}], "download_size": 313093838, "dataset_size": 547592241}, {"config_name": "wikitext-2-raw-v1", "features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 1305088, "num_examples": 4358}, {"name": "train", "num_bytes": 11061717, "num_examples": 36718}, {"name": "validation", "num_bytes": 1159288, "num_examples": 3760}], "download_size": 7747362, "dataset_size": 13526093}, {"config_name": "wikitext-2-v1", "features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 1270947, "num_examples": 4358}, {"name": "train", "num_bytes": 10918118, "num_examples": 36718}, {"name": "validation", "num_bytes": 1134123, "num_examples": 3760}], "download_size": 7371282, "dataset_size": 13323188}], "configs": [{"config_name": "wikitext-103-raw-v1", "data_files": [{"split": "test", "path": "wikitext-103-raw-v1/test-*"}, {"split": "train", "path": "wikitext-103-raw-v1/train-*"}, {"split": "validation", "path": "wikitext-103-raw-v1/validation-*"}]}, {"config_name": "wikitext-103-v1", "data_files": [{"split": "test", "path": "wikitext-103-v1/test-*"}, {"split": "train", "path": "wikitext-103-v1/train-*"}, {"split": "validation", "path": "wikitext-103-v1/validation-*"}]}, {"config_name": "wikitext-2-raw-v1", "data_files": [{"split": "test", "path": "wikitext-2-raw-v1/test-*"}, {"split": "train", "path": "wikitext-2-raw-v1/train-*"}, {"split": "validation", "path": "wikitext-2-raw-v1/validation-*"}]}, {"config_name": "wikitext-2-v1", "data_files": [{"split": "test", "path": "wikitext-2-v1/test-*"}, {"split": "train", "path": "wikitext-2-v1/train-*"}, {"split": "validation", "path": "wikitext-2-v1/validation-*"}]}]} | false | null | 2024-01-04T16:49:18 | 432 | 4 | false | b08601e04326c79dfdd32d625aee71d232d685c3 |
Dataset Card for "wikitext"
Dataset Summary
The WikiText language modeling dataset is a collection of over 100 million tokens extracted from the set of verified
Good and Featured articles on Wikipedia. The dataset is available under the Creative Commons Attribution-ShareAlike License.
Compared to the preprocessed version of Penn Treebank (PTB), WikiText-2 is over 2 times larger and WikiText-103 is over
110 times larger. The WikiText dataset also features a far… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/wikitext. | 633,003 | 19,743,804 | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:cc-by-sa-3.0",
"license:gfdl",
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:1609.07843",
"region:us"
] | 2022-03-02T23:29:22 | wikitext-2 | null |
625e8e36d28969004c120d8b | google/fleurs | google | {"annotations_creators": ["expert-generated", "crowdsourced", "machine-generated"], "language_creators": ["crowdsourced", "expert-generated"], "language": ["afr", "amh", "ara", "asm", "ast", "azj", "bel", "ben", "bos", "cat", "ceb", "cmn", "ces", "cym", "dan", "deu", "ell", "eng", "spa", "est", "fas", "ful", "fin", "tgl", "fra", "gle", "glg", "guj", "hau", "heb", "hin", "hrv", "hun", "hye", "ind", "ibo", "isl", "ita", "jpn", "jav", "kat", "kam", "kea", "kaz", "khm", "kan", "kor", "ckb", "kir", "ltz", "lug", "lin", "lao", "lit", "luo", "lav", "mri", "mkd", "mal", "mon", "mar", "msa", "mlt", "mya", "nob", "npi", "nld", "nso", "nya", "oci", "orm", "ory", "pan", "pol", "pus", "por", "ron", "rus", "bul", "snd", "slk", "slv", "sna", "som", "srp", "swe", "swh", "tam", "tel", "tgk", "tha", "tur", "ukr", "umb", "urd", "uzb", "vie", "wol", "xho", "yor", "yue", "zul"], "license": ["cc-by-4.0"], "multilinguality": ["multilingual"], "size_categories": ["10K<n<100K"], "task_categories": ["automatic-speech-recognition"], "task_ids": [], "pretty_name": "The Cross-lingual TRansfer Evaluation of Multilingual Encoders for Speech (XTREME-S) benchmark is a benchmark designed to evaluate speech representations across languages, tasks, domains and data regimes. It covers 102 languages from 10+ language families, 3 different domains and 4 task families: speech recognition, translation, classification and retrieval.", "tags": ["speech-recognition"]} | false | null | 2024-08-25T05:03:32 | 285 | 4 | false | d7c758a6dceecd54a98cac43404d3d576e721f07 |
FLEURS
Fleurs is the speech version of the FLoRes machine translation benchmark.
We use 2009 n-way parallel sentences from the FLoRes dev and devtest publicly available sets, in 102 languages.
Training sets have around 10 hours of supervision. Speakers of the train sets are different than speakers from the dev/test sets. Multilingual fine-tuning is
used and ”unit error rate” (characters, signs) of all languages is averaged. Languages and results are also grouped into seven… See the full description on the dataset page: https://huggingface.co/datasets/google/fleurs. | 30,445 | 1,020,297 | [
"task_categories:automatic-speech-recognition",
"annotations_creators:expert-generated",
"annotations_creators:crowdsourced",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:multilingual",
"language:afr",
"language:amh",
"language:ara",
"language:asm",
"language:ast",
"language:azj",
"language:bel",
"language:ben",
"language:bos",
"language:cat",
"language:ceb",
"language:cmn",
"language:ces",
"language:cym",
"language:dan",
"language:deu",
"language:ell",
"language:eng",
"language:spa",
"language:est",
"language:fas",
"language:ful",
"language:fin",
"language:tgl",
"language:fra",
"language:gle",
"language:glg",
"language:guj",
"language:hau",
"language:heb",
"language:hin",
"language:hrv",
"language:hun",
"language:hye",
"language:ind",
"language:ibo",
"language:isl",
"language:ita",
"language:jpn",
"language:jav",
"language:kat",
"language:kam",
"language:kea",
"language:kaz",
"language:khm",
"language:kan",
"language:kor",
"language:ckb",
"language:kir",
"language:ltz",
"language:lug",
"language:lin",
"language:lao",
"language:lit",
"language:luo",
"language:lav",
"language:mri",
"language:mkd",
"language:mal",
"language:mon",
"language:mar",
"language:msa",
"language:mlt",
"language:mya",
"language:nob",
"language:npi",
"language:nld",
"language:nso",
"language:nya",
"language:oci",
"language:orm",
"language:ory",
"language:pan",
"language:pol",
"language:pus",
"language:por",
"language:ron",
"language:rus",
"language:bul",
"language:snd",
"language:slk",
"language:slv",
"language:sna",
"language:som",
"language:srp",
"language:swe",
"language:swh",
"language:tam",
"language:tel",
"language:tgk",
"language:tha",
"language:tur",
"language:ukr",
"language:umb",
"language:urd",
"language:uzb",
"language:vie",
"language:wol",
"language:xho",
"language:yor",
"language:yue",
"language:zul",
"license:cc-by-4.0",
"size_categories:10K<n<100K",
"arxiv:2205.12446",
"arxiv:2106.03193",
"region:us",
"speech-recognition"
] | 2022-04-19T10:25:58 | null | null |
627007d3becab9e2dcf15a40 | ILSVRC/imagenet-1k | ILSVRC | {"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["other"], "license_details": "imagenet-agreement", "multilinguality": ["monolingual"], "paperswithcode_id": "imagenet-1k-1", "pretty_name": "ImageNet", "size_categories": ["1M<n<10M"], "source_datasets": ["original"], "task_categories": ["image-classification"], "task_ids": ["multi-class-image-classification"], "extra_gated_prompt": "By clicking on \u201cAccess repository\u201d below, you also agree to ImageNet Terms of Access:\n[RESEARCHER_FULLNAME] (the \"Researcher\") has requested permission to use the ImageNet database (the \"Database\") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:\n1. Researcher shall use the Database only for non-commercial research and educational purposes.\n2. Princeton University, Stanford University and Hugging Face make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose.\n3. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify the ImageNet team, Princeton University, Stanford University and Hugging Face, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted images that he or she may create from the Database.\n4. Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions.\n5. Princeton University, Stanford University and Hugging Face reserve the right to terminate Researcher's access to the Database at any time.\n6. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer.\n7. The law of the State of New Jersey shall apply to all disputes under this agreement.", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "tench, Tinca tinca", "1": "goldfish, Carassius auratus", "2": "great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias", "3": "tiger shark, Galeocerdo cuvieri", "4": "hammerhead, hammerhead shark", "5": "electric ray, crampfish, numbfish, torpedo", "6": "stingray", "7": "cock", "8": "hen", "9": "ostrich, Struthio camelus", "10": "brambling, Fringilla montifringilla", "11": "goldfinch, Carduelis carduelis", "12": "house finch, linnet, Carpodacus mexicanus", "13": "junco, snowbird", "14": "indigo bunting, indigo finch, indigo bird, Passerina cyanea", "15": "robin, American robin, Turdus migratorius", "16": "bulbul", "17": "jay", "18": "magpie", "19": "chickadee", "20": "water ouzel, dipper", "21": "kite", "22": "bald eagle, American eagle, Haliaeetus leucocephalus", "23": "vulture", "24": "great grey owl, great gray owl, Strix nebulosa", "25": "European fire salamander, Salamandra salamandra", "26": "common newt, Triturus vulgaris", "27": "eft", "28": "spotted salamander, Ambystoma maculatum", "29": "axolotl, mud puppy, Ambystoma mexicanum", "30": "bullfrog, Rana catesbeiana", "31": "tree frog, tree-frog", "32": "tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui", "33": "loggerhead, loggerhead turtle, Caretta caretta", "34": "leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea", "35": "mud turtle", "36": "terrapin", "37": "box turtle, box tortoise", "38": "banded gecko", "39": "common iguana, iguana, Iguana iguana", "40": "American chameleon, anole, Anolis carolinensis", "41": "whiptail, whiptail lizard", "42": "agama", "43": "frilled lizard, Chlamydosaurus kingi", "44": "alligator lizard", "45": "Gila monster, Heloderma suspectum", "46": "green lizard, Lacerta viridis", "47": "African chameleon, Chamaeleo chamaeleon", "48": "Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis", "49": "African crocodile, Nile crocodile, Crocodylus niloticus", "50": "American alligator, Alligator mississipiensis", "51": "triceratops", "52": "thunder snake, worm snake, Carphophis amoenus", "53": "ringneck snake, ring-necked snake, ring snake", "54": "hognose snake, puff adder, sand viper", "55": "green snake, grass snake", "56": "king snake, kingsnake", "57": "garter snake, grass snake", "58": "water snake", "59": "vine snake", "60": "night snake, Hypsiglena torquata", "61": "boa constrictor, Constrictor constrictor", "62": "rock python, rock snake, Python sebae", "63": "Indian cobra, Naja naja", "64": "green mamba", "65": "sea snake", "66": "horned viper, cerastes, sand viper, horned asp, Cerastes cornutus", "67": "diamondback, diamondback rattlesnake, Crotalus adamanteus", "68": "sidewinder, horned rattlesnake, Crotalus cerastes", "69": "trilobite", "70": "harvestman, daddy longlegs, Phalangium opilio", "71": "scorpion", "72": "black and gold garden spider, Argiope aurantia", "73": "barn spider, Araneus cavaticus", "74": "garden spider, Aranea diademata", "75": "black widow, Latrodectus mactans", "76": "tarantula", "77": "wolf spider, hunting spider", "78": "tick", "79": "centipede", "80": "black grouse", "81": "ptarmigan", "82": "ruffed grouse, partridge, Bonasa umbellus", "83": "prairie chicken, prairie grouse, prairie fowl", "84": "peacock", "85": "quail", "86": "partridge", "87": "African grey, African gray, Psittacus erithacus", "88": "macaw", "89": "sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita", "90": "lorikeet", "91": "coucal", "92": "bee eater", "93": "hornbill", "94": "hummingbird", "95": "jacamar", "96": "toucan", "97": "drake", "98": "red-breasted merganser, Mergus serrator", "99": "goose", "100": "black swan, Cygnus atratus", "101": "tusker", "102": "echidna, spiny anteater, anteater", "103": "platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus", "104": "wallaby, brush kangaroo", "105": "koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus", "106": "wombat", "107": "jellyfish", "108": "sea anemone, anemone", "109": "brain coral", "110": "flatworm, platyhelminth", "111": "nematode, nematode worm, roundworm", "112": "conch", "113": "snail", "114": "slug", "115": "sea slug, nudibranch", "116": "chiton, coat-of-mail shell, sea cradle, polyplacophore", "117": "chambered nautilus, pearly nautilus, nautilus", "118": "Dungeness crab, Cancer magister", "119": "rock crab, Cancer irroratus", "120": "fiddler crab", "121": "king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica", "122": "American lobster, Northern lobster, Maine lobster, Homarus americanus", "123": "spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish", "124": "crayfish, crawfish, crawdad, crawdaddy", "125": "hermit crab", "126": "isopod", "127": "white stork, Ciconia ciconia", "128": "black stork, Ciconia nigra", "129": "spoonbill", "130": "flamingo", "131": "little blue heron, Egretta caerulea", "132": "American egret, great white heron, Egretta albus", "133": "bittern", "134": "crane", "135": "limpkin, Aramus pictus", "136": "European gallinule, Porphyrio porphyrio", "137": "American coot, marsh hen, mud hen, water hen, Fulica americana", "138": "bustard", "139": "ruddy turnstone, Arenaria interpres", "140": "red-backed sandpiper, dunlin, Erolia alpina", "141": "redshank, Tringa totanus", "142": "dowitcher", "143": "oystercatcher, oyster catcher", "144": "pelican", "145": "king penguin, Aptenodytes patagonica", "146": "albatross, mollymawk", "147": "grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus", "148": "killer whale, killer, orca, grampus, sea wolf, Orcinus orca", "149": "dugong, Dugong dugon", "150": "sea lion", "151": "Chihuahua", "152": "Japanese spaniel", "153": "Maltese dog, Maltese terrier, Maltese", "154": "Pekinese, Pekingese, Peke", "155": "Shih-Tzu", "156": "Blenheim spaniel", "157": "papillon", "158": "toy terrier", "159": "Rhodesian ridgeback", "160": "Afghan hound, Afghan", "161": "basset, basset hound", "162": "beagle", "163": "bloodhound, sleuthhound", "164": "bluetick", "165": "black-and-tan coonhound", "166": "Walker hound, Walker foxhound", "167": "English foxhound", "168": "redbone", "169": "borzoi, Russian wolfhound", "170": "Irish wolfhound", "171": "Italian greyhound", "172": "whippet", "173": "Ibizan hound, Ibizan Podenco", "174": "Norwegian elkhound, elkhound", "175": "otterhound, otter hound", "176": "Saluki, gazelle hound", "177": "Scottish deerhound, deerhound", "178": "Weimaraner", "179": "Staffordshire bullterrier, Staffordshire bull terrier", "180": "American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier", "181": "Bedlington terrier", "182": "Border terrier", "183": "Kerry blue terrier", "184": "Irish terrier", "185": "Norfolk terrier", "186": "Norwich terrier", "187": "Yorkshire terrier", "188": "wire-haired fox terrier", "189": "Lakeland terrier", "190": "Sealyham terrier, Sealyham", "191": "Airedale, Airedale terrier", "192": "cairn, cairn terrier", "193": "Australian terrier", "194": "Dandie Dinmont, Dandie Dinmont terrier", "195": "Boston bull, Boston terrier", "196": "miniature schnauzer", "197": "giant schnauzer", "198": "standard schnauzer", "199": "Scotch terrier, Scottish terrier, Scottie", "200": "Tibetan terrier, chrysanthemum dog", "201": "silky terrier, Sydney silky", "202": "soft-coated wheaten terrier", "203": "West Highland white terrier", "204": "Lhasa, Lhasa apso", "205": "flat-coated retriever", "206": "curly-coated retriever", "207": "golden retriever", "208": "Labrador retriever", "209": "Chesapeake Bay retriever", "210": "German short-haired pointer", "211": "vizsla, Hungarian pointer", "212": "English setter", "213": "Irish setter, red setter", "214": "Gordon setter", "215": "Brittany spaniel", "216": "clumber, clumber spaniel", "217": "English springer, English springer spaniel", "218": "Welsh springer spaniel", "219": "cocker spaniel, English cocker spaniel, cocker", "220": "Sussex spaniel", "221": "Irish water spaniel", "222": "kuvasz", "223": "schipperke", "224": "groenendael", "225": "malinois", "226": "briard", "227": "kelpie", "228": "komondor", "229": "Old English sheepdog, bobtail", "230": "Shetland sheepdog, Shetland sheep dog, Shetland", "231": "collie", "232": "Border collie", "233": "Bouvier des Flandres, Bouviers des Flandres", "234": "Rottweiler", "235": "German shepherd, German shepherd dog, German police dog, alsatian", "236": "Doberman, Doberman pinscher", "237": "miniature pinscher", "238": "Greater Swiss Mountain dog", "239": "Bernese mountain dog", "240": "Appenzeller", "241": "EntleBucher", "242": "boxer", "243": "bull mastiff", "244": "Tibetan mastiff", "245": "French bulldog", "246": "Great Dane", "247": "Saint Bernard, St Bernard", "248": "Eskimo dog, husky", "249": "malamute, malemute, Alaskan malamute", "250": "Siberian husky", "251": "dalmatian, coach dog, carriage dog", "252": "affenpinscher, monkey pinscher, monkey dog", "253": "basenji", "254": "pug, pug-dog", "255": "Leonberg", "256": "Newfoundland, Newfoundland dog", "257": "Great Pyrenees", "258": "Samoyed, Samoyede", "259": "Pomeranian", "260": "chow, chow chow", "261": "keeshond", "262": "Brabancon griffon", "263": "Pembroke, Pembroke Welsh corgi", "264": "Cardigan, Cardigan Welsh corgi", "265": "toy poodle", "266": "miniature poodle", "267": "standard poodle", "268": "Mexican hairless", "269": "timber wolf, grey wolf, gray wolf, Canis lupus", "270": "white wolf, Arctic wolf, Canis lupus tundrarum", "271": "red wolf, maned wolf, Canis rufus, Canis niger", "272": "coyote, prairie wolf, brush wolf, Canis latrans", "273": "dingo, warrigal, warragal, Canis dingo", "274": "dhole, Cuon alpinus", "275": "African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus", "276": "hyena, hyaena", "277": "red fox, Vulpes vulpes", "278": "kit fox, Vulpes macrotis", "279": "Arctic fox, white fox, Alopex lagopus", "280": "grey fox, gray fox, Urocyon cinereoargenteus", "281": "tabby, tabby cat", "282": "tiger cat", "283": "Persian cat", "284": "Siamese cat, Siamese", "285": "Egyptian cat", "286": "cougar, puma, catamount, mountain lion, painter, panther, Felis concolor", "287": "lynx, catamount", "288": "leopard, Panthera pardus", "289": "snow leopard, ounce, Panthera uncia", "290": "jaguar, panther, Panthera onca, Felis onca", "291": "lion, king of beasts, Panthera leo", "292": "tiger, Panthera tigris", "293": "cheetah, chetah, Acinonyx jubatus", "294": "brown bear, bruin, Ursus arctos", "295": "American black bear, black bear, Ursus americanus, Euarctos americanus", "296": "ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus", "297": "sloth bear, Melursus ursinus, Ursus ursinus", "298": "mongoose", "299": "meerkat, mierkat", "300": "tiger beetle", "301": "ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle", "302": "ground beetle, carabid beetle", "303": "long-horned beetle, longicorn, longicorn beetle", "304": "leaf beetle, chrysomelid", "305": "dung beetle", "306": "rhinoceros beetle", "307": "weevil", "308": "fly", "309": "bee", "310": "ant, emmet, pismire", "311": "grasshopper, hopper", "312": "cricket", "313": "walking stick, walkingstick, stick insect", "314": "cockroach, roach", "315": "mantis, mantid", "316": "cicada, cicala", "317": "leafhopper", "318": "lacewing, lacewing fly", "319": "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk", "320": "damselfly", "321": "admiral", "322": "ringlet, ringlet butterfly", "323": "monarch, monarch butterfly, milkweed butterfly, Danaus plexippus", "324": "cabbage butterfly", "325": "sulphur butterfly, sulfur butterfly", "326": "lycaenid, lycaenid butterfly", "327": "starfish, sea star", "328": "sea urchin", "329": "sea cucumber, holothurian", "330": "wood rabbit, cottontail, cottontail rabbit", "331": "hare", "332": "Angora, Angora rabbit", "333": "hamster", "334": "porcupine, hedgehog", "335": "fox squirrel, eastern fox squirrel, Sciurus niger", "336": "marmot", "337": "beaver", "338": "guinea pig, Cavia cobaya", "339": "sorrel", "340": "zebra", "341": "hog, pig, grunter, squealer, Sus scrofa", "342": "wild boar, boar, Sus scrofa", "343": "warthog", "344": "hippopotamus, hippo, river horse, Hippopotamus amphibius", "345": "ox", "346": "water buffalo, water ox, Asiatic buffalo, Bubalus bubalis", "347": "bison", "348": "ram, tup", "349": "bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis", "350": "ibex, Capra ibex", "351": "hartebeest", "352": "impala, Aepyceros melampus", "353": "gazelle", "354": "Arabian camel, dromedary, Camelus dromedarius", "355": "llama", "356": "weasel", "357": "mink", "358": "polecat, fitch, foulmart, foumart, Mustela putorius", "359": "black-footed ferret, ferret, Mustela nigripes", "360": "otter", "361": "skunk, polecat, wood pussy", "362": "badger", "363": "armadillo", "364": "three-toed sloth, ai, Bradypus tridactylus", "365": "orangutan, orang, orangutang, Pongo pygmaeus", "366": "gorilla, Gorilla gorilla", "367": "chimpanzee, chimp, Pan troglodytes", "368": "gibbon, Hylobates lar", "369": "siamang, Hylobates syndactylus, Symphalangus syndactylus", "370": "guenon, guenon monkey", "371": "patas, hussar monkey, Erythrocebus patas", "372": "baboon", "373": "macaque", "374": "langur", "375": "colobus, colobus monkey", "376": "proboscis monkey, Nasalis larvatus", "377": "marmoset", "378": "capuchin, ringtail, Cebus capucinus", "379": "howler monkey, howler", "380": "titi, titi monkey", "381": "spider monkey, Ateles geoffroyi", "382": "squirrel monkey, Saimiri sciureus", "383": "Madagascar cat, ring-tailed lemur, Lemur catta", "384": "indri, indris, Indri indri, Indri brevicaudatus", "385": "Indian elephant, Elephas maximus", "386": "African elephant, Loxodonta africana", "387": "lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens", "388": "giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca", "389": "barracouta, snoek", "390": "eel", "391": "coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch", "392": "rock beauty, Holocanthus tricolor", "393": "anemone fish", "394": "sturgeon", "395": "gar, garfish, garpike, billfish, Lepisosteus osseus", "396": "lionfish", "397": "puffer, pufferfish, blowfish, globefish", "398": "abacus", "399": "abaya", "400": "academic gown, academic robe, judge's robe", "401": "accordion, piano accordion, squeeze box", "402": "acoustic guitar", "403": "aircraft carrier, carrier, flattop, attack aircraft carrier", "404": "airliner", "405": "airship, dirigible", "406": "altar", "407": "ambulance", "408": "amphibian, amphibious vehicle", "409": "analog clock", "410": "apiary, bee house", "411": "apron", "412": "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin", "413": "assault rifle, assault gun", "414": "backpack, back pack, knapsack, packsack, rucksack, haversack", "415": "bakery, bakeshop, bakehouse", "416": "balance beam, beam", "417": "balloon", "418": "ballpoint, ballpoint pen, ballpen, Biro", "419": "Band Aid", "420": "banjo", "421": "bannister, banister, balustrade, balusters, handrail", "422": "barbell", "423": "barber chair", "424": "barbershop", "425": "barn", "426": "barometer", "427": "barrel, cask", "428": "barrow, garden cart, lawn cart, wheelbarrow", "429": "baseball", "430": "basketball", "431": "bassinet", "432": "bassoon", "433": "bathing cap, swimming cap", "434": "bath towel", "435": "bathtub, bathing tub, bath, tub", "436": "beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon", "437": "beacon, lighthouse, beacon light, pharos", "438": "beaker", "439": "bearskin, busby, shako", "440": "beer bottle", "441": "beer glass", "442": "bell cote, bell cot", "443": "bib", "444": "bicycle-built-for-two, tandem bicycle, tandem", "445": "bikini, two-piece", "446": "binder, ring-binder", "447": "binoculars, field glasses, opera glasses", "448": "birdhouse", "449": "boathouse", "450": "bobsled, bobsleigh, bob", "451": "bolo tie, bolo, bola tie, bola", "452": "bonnet, poke bonnet", "453": "bookcase", "454": "bookshop, bookstore, bookstall", "455": "bottlecap", "456": "bow", "457": "bow tie, bow-tie, bowtie", "458": "brass, memorial tablet, plaque", "459": "brassiere, bra, bandeau", "460": "breakwater, groin, groyne, mole, bulwark, seawall, jetty", "461": "breastplate, aegis, egis", "462": "broom", "463": "bucket, pail", "464": "buckle", "465": "bulletproof vest", "466": "bullet train, bullet", "467": "butcher shop, meat market", "468": "cab, hack, taxi, taxicab", "469": "caldron, cauldron", "470": "candle, taper, wax light", "471": "cannon", "472": "canoe", "473": "can opener, tin opener", "474": "cardigan", "475": "car mirror", "476": "carousel, carrousel, merry-go-round, roundabout, whirligig", "477": "carpenter's kit, tool kit", "478": "carton", "479": "car wheel", "480": "cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM", "481": "cassette", "482": "cassette player", "483": "castle", "484": "catamaran", "485": "CD player", "486": "cello, violoncello", "487": "cellular telephone, cellular phone, cellphone, cell, mobile phone", "488": "chain", "489": "chainlink fence", "490": "chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour", "491": "chain saw, chainsaw", "492": "chest", "493": "chiffonier, commode", "494": "chime, bell, gong", "495": "china cabinet, china closet", "496": "Christmas stocking", "497": "church, church building", "498": "cinema, movie theater, movie theatre, movie house, picture palace", "499": "cleaver, meat cleaver, chopper", "500": "cliff dwelling", "501": "cloak", "502": "clog, geta, patten, sabot", "503": "cocktail shaker", "504": "coffee mug", "505": "coffeepot", "506": "coil, spiral, volute, whorl, helix", "507": "combination lock", "508": "computer keyboard, keypad", "509": "confectionery, confectionary, candy store", "510": "container ship, containership, container vessel", "511": "convertible", "512": "corkscrew, bottle screw", "513": "cornet, horn, trumpet, trump", "514": "cowboy boot", "515": "cowboy hat, ten-gallon hat", "516": "cradle", "517": "crane2", "518": "crash helmet", "519": "crate", "520": "crib, cot", "521": "Crock Pot", "522": "croquet ball", "523": "crutch", "524": "cuirass", "525": "dam, dike, dyke", "526": "desk", "527": "desktop computer", "528": "dial telephone, dial phone", "529": "diaper, nappy, napkin", "530": "digital clock", "531": "digital watch", "532": "dining table, board", "533": "dishrag, dishcloth", "534": "dishwasher, dish washer, dishwashing machine", "535": "disk brake, disc brake", "536": "dock, dockage, docking facility", "537": "dogsled, dog sled, dog sleigh", "538": "dome", "539": "doormat, welcome mat", "540": "drilling platform, offshore rig", "541": "drum, membranophone, tympan", "542": "drumstick", "543": "dumbbell", "544": "Dutch oven", "545": "electric fan, blower", "546": "electric guitar", "547": "electric locomotive", "548": "entertainment center", "549": "envelope", "550": "espresso maker", "551": "face powder", "552": "feather boa, boa", "553": "file, file cabinet, filing cabinet", "554": "fireboat", "555": "fire engine, fire truck", "556": "fire screen, fireguard", "557": "flagpole, flagstaff", "558": "flute, transverse flute", "559": "folding chair", "560": "football helmet", "561": "forklift", "562": "fountain", "563": "fountain pen", "564": "four-poster", "565": "freight car", "566": "French horn, horn", "567": "frying pan, frypan, skillet", "568": "fur coat", "569": "garbage truck, dustcart", "570": "gasmask, respirator, gas helmet", "571": "gas pump, gasoline pump, petrol pump, island dispenser", "572": "goblet", "573": "go-kart", "574": "golf ball", "575": "golfcart, golf cart", "576": "gondola", "577": "gong, tam-tam", "578": "gown", "579": "grand piano, grand", "580": "greenhouse, nursery, glasshouse", "581": "grille, radiator grille", "582": "grocery store, grocery, food market, market", "583": "guillotine", "584": "hair slide", "585": "hair spray", "586": "half track", "587": "hammer", "588": "hamper", "589": "hand blower, blow dryer, blow drier, hair dryer, hair drier", "590": "hand-held computer, hand-held microcomputer", "591": "handkerchief, hankie, hanky, hankey", "592": "hard disc, hard disk, fixed disk", "593": "harmonica, mouth organ, harp, mouth harp", "594": "harp", "595": "harvester, reaper", "596": "hatchet", "597": "holster", "598": "home theater, home theatre", "599": "honeycomb", "600": "hook, claw", "601": "hoopskirt, crinoline", "602": "horizontal bar, high bar", "603": "horse cart, horse-cart", "604": "hourglass", "605": "iPod", "606": "iron, smoothing iron", "607": "jack-o'-lantern", "608": "jean, blue jean, denim", "609": "jeep, landrover", "610": "jersey, T-shirt, tee shirt", "611": "jigsaw puzzle", "612": "jinrikisha, ricksha, rickshaw", "613": "joystick", "614": "kimono", "615": "knee pad", "616": "knot", "617": "lab coat, laboratory coat", "618": "ladle", "619": "lampshade, lamp shade", "620": "laptop, laptop computer", "621": "lawn mower, mower", "622": "lens cap, lens cover", "623": "letter opener, paper knife, paperknife", "624": "library", "625": "lifeboat", "626": "lighter, light, igniter, ignitor", "627": "limousine, limo", "628": "liner, ocean liner", "629": "lipstick, lip rouge", "630": "Loafer", "631": "lotion", "632": "loudspeaker, speaker, speaker unit, loudspeaker system, speaker system", "633": "loupe, jeweler's loupe", "634": "lumbermill, sawmill", "635": "magnetic compass", "636": "mailbag, postbag", "637": "mailbox, letter box", "638": "maillot", "639": "maillot, tank suit", "640": "manhole cover", "641": "maraca", "642": "marimba, xylophone", "643": "mask", "644": "matchstick", "645": "maypole", "646": "maze, labyrinth", "647": "measuring cup", "648": "medicine chest, medicine cabinet", "649": "megalith, megalithic structure", "650": "microphone, mike", "651": "microwave, microwave oven", "652": "military uniform", "653": "milk can", "654": "minibus", "655": "miniskirt, mini", "656": "minivan", "657": "missile", "658": "mitten", "659": "mixing bowl", "660": "mobile home, manufactured home", "661": "Model T", "662": "modem", "663": "monastery", "664": "monitor", "665": "moped", "666": "mortar", "667": "mortarboard", "668": "mosque", "669": "mosquito net", "670": "motor scooter, scooter", "671": "mountain bike, all-terrain bike, off-roader", "672": "mountain tent", "673": "mouse, computer mouse", "674": "mousetrap", "675": "moving van", "676": "muzzle", "677": "nail", "678": "neck brace", "679": "necklace", "680": "nipple", "681": "notebook, notebook computer", "682": "obelisk", "683": "oboe, hautboy, hautbois", "684": "ocarina, sweet potato", "685": "odometer, hodometer, mileometer, milometer", "686": "oil filter", "687": "organ, pipe organ", "688": "oscilloscope, scope, cathode-ray oscilloscope, CRO", "689": "overskirt", "690": "oxcart", "691": "oxygen mask", "692": "packet", "693": "paddle, boat paddle", "694": "paddlewheel, paddle wheel", "695": "padlock", "696": "paintbrush", "697": "pajama, pyjama, pj's, jammies", "698": "palace", "699": "panpipe, pandean pipe, syrinx", "700": "paper towel", "701": "parachute, chute", "702": "parallel bars, bars", "703": "park bench", "704": "parking meter", "705": "passenger car, coach, carriage", "706": "patio, terrace", "707": "pay-phone, pay-station", "708": "pedestal, plinth, footstall", "709": "pencil box, pencil case", "710": "pencil sharpener", "711": "perfume, essence", "712": "Petri dish", "713": "photocopier", "714": "pick, plectrum, plectron", "715": "pickelhaube", "716": "picket fence, paling", "717": "pickup, pickup truck", "718": "pier", "719": "piggy bank, penny bank", "720": "pill bottle", "721": "pillow", "722": "ping-pong ball", "723": "pinwheel", "724": "pirate, pirate ship", "725": "pitcher, ewer", "726": "plane, carpenter's plane, woodworking plane", "727": "planetarium", "728": "plastic bag", "729": "plate rack", "730": "plow, plough", "731": "plunger, plumber's helper", "732": "Polaroid camera, Polaroid Land camera", "733": "pole", "734": "police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria", "735": "poncho", "736": "pool table, billiard table, snooker table", "737": "pop bottle, soda bottle", "738": "pot, flowerpot", "739": "potter's wheel", "740": "power drill", "741": "prayer rug, prayer mat", "742": "printer", "743": "prison, prison house", "744": "projectile, missile", "745": "projector", "746": "puck, hockey puck", "747": "punching bag, punch bag, punching ball, punchball", "748": "purse", "749": "quill, quill pen", "750": "quilt, comforter, comfort, puff", "751": "racer, race car, racing car", "752": "racket, racquet", "753": "radiator", "754": "radio, wireless", "755": "radio telescope, radio reflector", "756": "rain barrel", "757": "recreational vehicle, RV, R.V.", "758": "reel", "759": "reflex camera", "760": "refrigerator, icebox", "761": "remote control, remote", "762": "restaurant, eating house, eating place, eatery", "763": "revolver, six-gun, six-shooter", "764": "rifle", "765": "rocking chair, rocker", "766": "rotisserie", "767": "rubber eraser, rubber, pencil eraser", "768": "rugby ball", "769": "rule, ruler", "770": "running shoe", "771": "safe", "772": "safety pin", "773": "saltshaker, salt shaker", "774": "sandal", "775": "sarong", "776": "sax, saxophone", "777": "scabbard", "778": "scale, weighing machine", "779": "school bus", "780": "schooner", "781": "scoreboard", "782": "screen, CRT screen", "783": "screw", "784": "screwdriver", "785": "seat belt, seatbelt", "786": "sewing machine", "787": "shield, buckler", "788": "shoe shop, shoe-shop, shoe store", "789": "shoji", "790": "shopping basket", "791": "shopping cart", "792": "shovel", "793": "shower cap", "794": "shower curtain", "795": "ski", "796": "ski mask", "797": "sleeping bag", "798": "slide rule, slipstick", "799": "sliding door", "800": "slot, one-armed bandit", "801": "snorkel", "802": "snowmobile", "803": "snowplow, snowplough", "804": "soap dispenser", "805": "soccer ball", "806": "sock", "807": "solar dish, solar collector, solar furnace", "808": "sombrero", "809": "soup bowl", "810": "space bar", "811": "space heater", "812": "space shuttle", "813": "spatula", "814": "speedboat", "815": "spider web, spider's web", "816": "spindle", "817": "sports car, sport car", "818": "spotlight, spot", "819": "stage", "820": "steam locomotive", "821": "steel arch bridge", "822": "steel drum", "823": "stethoscope", "824": "stole", "825": "stone wall", "826": "stopwatch, stop watch", "827": "stove", "828": "strainer", "829": "streetcar, tram, tramcar, trolley, trolley car", "830": "stretcher", "831": "studio couch, day bed", "832": "stupa, tope", "833": "submarine, pigboat, sub, U-boat", "834": "suit, suit of clothes", "835": "sundial", "836": "sunglass", "837": "sunglasses, dark glasses, shades", "838": "sunscreen, sunblock, sun blocker", "839": "suspension bridge", "840": "swab, swob, mop", "841": "sweatshirt", "842": "swimming trunks, bathing trunks", "843": "swing", "844": "switch, electric switch, electrical switch", "845": "syringe", "846": "table lamp", "847": "tank, army tank, armored combat vehicle, armoured combat vehicle", "848": "tape player", "849": "teapot", "850": "teddy, teddy bear", "851": "television, television system", "852": "tennis ball", "853": "thatch, thatched roof", "854": "theater curtain, theatre curtain", "855": "thimble", "856": "thresher, thrasher, threshing machine", "857": "throne", "858": "tile roof", "859": "toaster", "860": "tobacco shop, tobacconist shop, tobacconist", "861": "toilet seat", "862": "torch", "863": "totem pole", "864": "tow truck, tow car, wrecker", "865": "toyshop", "866": "tractor", "867": "trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi", "868": "tray", "869": "trench coat", "870": "tricycle, trike, velocipede", "871": "trimaran", "872": "tripod", "873": "triumphal arch", "874": "trolleybus, trolley coach, trackless trolley", "875": "trombone", "876": "tub, vat", "877": "turnstile", "878": "typewriter keyboard", "879": "umbrella", "880": "unicycle, monocycle", "881": "upright, upright piano", "882": "vacuum, vacuum cleaner", "883": "vase", "884": "vault", "885": "velvet", "886": "vending machine", "887": "vestment", "888": "viaduct", "889": "violin, fiddle", "890": "volleyball", "891": "waffle iron", "892": "wall clock", "893": "wallet, billfold, notecase, pocketbook", "894": "wardrobe, closet, press", "895": "warplane, military plane", "896": "washbasin, handbasin, washbowl, lavabo, wash-hand basin", "897": "washer, automatic washer, washing machine", "898": "water bottle", "899": "water jug", "900": "water tower", "901": "whiskey jug", "902": "whistle", "903": "wig", "904": "window screen", "905": "window shade", "906": "Windsor tie", "907": "wine bottle", "908": "wing", "909": "wok", "910": "wooden spoon", "911": "wool, woolen, woollen", "912": "worm fence, snake fence, snake-rail fence, Virginia fence", "913": "wreck", "914": "yawl", "915": "yurt", "916": "web site, website, internet site, site", "917": "comic book", "918": "crossword puzzle, crossword", "919": "street sign", "920": "traffic light, traffic signal, stoplight", "921": "book jacket, dust cover, dust jacket, dust wrapper", "922": "menu", "923": "plate", "924": "guacamole", "925": "consomme", "926": "hot pot, hotpot", "927": "trifle", "928": "ice cream, icecream", "929": "ice lolly, lolly, lollipop, popsicle", "930": "French loaf", "931": "bagel, beigel", "932": "pretzel", "933": "cheeseburger", "934": "hotdog, hot dog, red hot", "935": "mashed potato", "936": "head cabbage", "937": "broccoli", "938": "cauliflower", "939": "zucchini, courgette", "940": "spaghetti squash", "941": "acorn squash", "942": "butternut squash", "943": "cucumber, cuke", "944": "artichoke, globe artichoke", "945": "bell pepper", "946": "cardoon", "947": "mushroom", "948": "Granny Smith", "949": "strawberry", "950": "orange", "951": "lemon", "952": "fig", "953": "pineapple, ananas", "954": "banana", "955": "jackfruit, jak, jack", "956": "custard apple", "957": "pomegranate", "958": "hay", "959": "carbonara", "960": "chocolate sauce, chocolate syrup", "961": "dough", "962": "meat loaf, meatloaf", "963": "pizza, pizza pie", "964": "potpie", "965": "burrito", "966": "red wine", "967": "espresso", "968": "cup", "969": "eggnog", "970": "alp", "971": "bubble", "972": "cliff, drop, drop-off", "973": "coral reef", "974": "geyser", "975": "lakeside, lakeshore", "976": "promontory, headland, head, foreland", "977": "sandbar, sand bar", "978": "seashore, coast, seacoast, sea-coast", "979": "valley, vale", "980": "volcano", "981": "ballplayer, baseball player", "982": "groom, bridegroom", "983": "scuba diver", "984": "rapeseed", "985": "daisy", "986": "yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum", "987": "corn", "988": "acorn", "989": "hip, rose hip, rosehip", "990": "buckeye, horse chestnut, conker", "991": "coral fungus", "992": "agaric", "993": "gyromitra", "994": "stinkhorn, carrion fungus", "995": "earthstar", "996": "hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa", "997": "bolete", "998": "ear, spike, capitulum", "999": "toilet tissue, toilet paper, bathroom tissue"}}}}], "splits": [{"name": "test", "num_bytes": 13613661561, "num_examples": 100000}, {"name": "train", "num_bytes": 146956944242, "num_examples": 1281167}, {"name": "validation", "num_bytes": 6709003386, "num_examples": 50000}], "download_size": 166009941208, "dataset_size": 167279609189}} | false | null | 2024-07-16T13:30:57 | 491 | 4 | false | 4603483700ee984ea9debe3ddbfdeae86f6489eb | ILSVRC 2012, commonly known as 'ImageNet' is an image dataset organized according to the WordNet hierarchy. Each meaningful concept in WordNet, possibly described by multiple words or word phrases, is called a "synonym set" or "synset". There are more than 100,000 synsets in WordNet, majority of them are nouns (80,000+). ImageNet aims to provide on average 1000 images to illustrate each synset. Images of each concept are quality-controlled and human-annotated. In its completion, ImageNet hopes to offer tens of millions of cleanly sorted images for most of the concepts in the WordNet hierarchy. ImageNet 2012 is the most commonly used subset of ImageNet. This dataset spans 1000 object classes and contains 1,281,167 training images, 50,000 validation images and 100,000 test images | 32,637 | 1,068,826 | [
"task_categories:image-classification",
"task_ids:multi-class-image-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
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"arxiv:1409.0575",
"arxiv:1912.07726",
"arxiv:1811.12231",
"arxiv:2109.13228",
"region:us"
] | 2022-05-02T16:33:23 | imagenet-1k-1 | @article{imagenet15russakovsky,
Author = {Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. Berg and Li Fei-Fei},
Title = { {ImageNet Large Scale Visual Recognition Challenge} },
Year = {2015},
journal = {International Journal of Computer Vision (IJCV)},
doi = {10.1007/s11263-015-0816-y},
volume={115},
number={3},
pages={211-252}
} |
639244f571c51c43091df168 | Anthropic/hh-rlhf | Anthropic | {"license": "mit", "tags": ["human-feedback"]} | false | null | 2023-05-26T18:47:34 | 1,317 | 4 | false | 09be8c5bbc57cb3887f3a9732ad6aa7ec602a1fa |
Dataset Card for HH-RLHF
Dataset Summary
This repository provides access to two different kinds of data:
Human preference data about helpfulness and harmlessness from Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback. These data are meant to train preference (or reward) models for subsequent RLHF training. These data are not meant for supervised training of dialogue agents. Training dialogue agents on these data is likely… See the full description on the dataset page: https://huggingface.co/datasets/Anthropic/hh-rlhf. | 14,823 | 1,570,808 | [
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"library:mlcroissant",
"library:polars",
"arxiv:2204.05862",
"region:us",
"human-feedback"
] | 2022-12-08T20:11:33 | null | null |
646fcd74d1f1b73079ed6732 | rubend18/ChatGPT-Jailbreak-Prompts | rubend18 | {"task_categories": ["question-answering", "text-generation", "fill-mask", "zero-shot-classification", "table-question-answering"], "language": ["en", "aa"], "tags": ["ChatGPT", "JailbreakPrompts", "LanguageModeling", "ArtificialIntelligence", "TextGeneration", "Dataset", "OpenAI", "Jailbreak", "Prompts"], "size_categories": ["n<1K"], "pretty_name": "ChatGPT Jailbreak Prompts"} | false | null | 2023-08-24T18:24:29 | 200 | 4 | false | b93e4982f8f8ad2d82c6d35e3c00d161844ad70a |
Dataset Card for Dataset Name
Name
ChatGPT Jailbreak Prompts
Dataset Summary
ChatGPT Jailbreak Prompts is a complete collection of jailbreak related prompts for ChatGPT. This dataset is intended to provide a valuable resource for understanding and generating text in the context of jailbreaking in ChatGPT.
Languages
[English]
| 1,817 | 15,118 | [
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"ArtificialIntelligence",
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"OpenAI",
"Jailbreak",
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] | 2023-05-25T21:04:52 | null | null |
649444227853dd12c3bbadd8 | Amod/mental_health_counseling_conversations | Amod | {"license": "openrail", "task_categories": ["text-generation", "question-answering"], "language": ["en"], "tags": ["medical"], "size_categories": ["1K<n<10K"]} | false | null | 2024-04-05T08:30:03 | 348 | 4 | false | 4672e03c7f1a7b2215eb4302b83ca50449ce2553 |
Amod/mental_health_counseling_conversations
Dataset Summary
This dataset is a collection of questions and answers sourced from two online counseling and therapy platforms. The questions cover a wide range of mental health topics, and the answers are provided by qualified psychologists. The dataset is intended to be used for fine-tuning language models to improve their ability to provide mental health advice.
Supported Tasks and Leaderboards
The… See the full description on the dataset page: https://huggingface.co/datasets/Amod/mental_health_counseling_conversations. | 4,606 | 66,437 | [
"task_categories:text-generation",
"task_categories:question-answering",
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"library:polars",
"doi:10.57967/hf/1581",
"region:us",
"medical"
] | 2023-06-22T12:52:50 | null | null |
649f37af37bfb5202beabdf4 | allenai/dolma | allenai | {"license": "odc-by", "viewer": false, "task_categories": ["text-generation"], "language": ["en"], "tags": ["language-modeling", "casual-lm", "llm"], "pretty_name": "Dolma", "size_categories": ["n>1T"]} | false | null | 2024-04-17T02:57:00 | 899 | 4 | false | 7f48140530a023e9ea4c5cfb141160922727d4d3 | Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research | 980 | 353,696 | [
"task_categories:text-generation",
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"arxiv:2301.13688",
"region:us",
"language-modeling",
"casual-lm",
"llm"
] | 2023-06-30T20:14:39 | null | @article{dolma,
title = {{Dolma: An Open Corpus of Three Trillion Tokens for Language Model Pretraining Research}},
author = {
Luca Soldaini and Rodney Kinney and Akshita Bhagia and Dustin Schwenk and David Atkinson and
Russell Authur and Ben Bogin and Khyathi Chandu and Jennifer Dumas and Yanai Elazar and
Valentin Hofmann and Ananya Harsh Jha and Sachin Kumar and Li Lucy and Xinxi Lyu and Ian Magnusson and
Jacob Morrison and Niklas Muennighoff and Aakanksha Naik and Crystal Nam and Matthew E. Peters and
Abhilasha Ravichander and Kyle Richardson and Zejiang Shen and Emma Strubell and Nishant Subramani and
Oyvind Tafjord and Evan Pete Walsh and Hannaneh Hajishirzi and Noah A. Smith and Luke Zettlemoyer and
Iz Beltagy and Dirk Groeneveld and Jesse Dodge and Kyle Lo
},
year = {2024},
journal={arXiv preprint},
} |
650a9248d26103b6eee3ea7b | lmsys/lmsys-chat-1m | lmsys | {"size_categories": ["1M<n<10M"], "task_categories": ["conversational"], "extra_gated_prompt": "You agree to the [LMSYS-Chat-1M Dataset License Agreement](https://huggingface.co/datasets/lmsys/lmsys-chat-1m#lmsys-chat-1m-dataset-license-agreement).", "extra_gated_fields": {"Name": "text", "Email": "text", "Affiliation": "text", "Country": "text"}, "extra_gated_button_content": "I agree to the terms and conditions of the LMSYS-Chat-1M Dataset License Agreement.", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "conversation_id", "dtype": "string"}, {"name": "model", "dtype": "string"}, {"name": "conversation", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "turn", "dtype": "int64"}, {"name": "language", "dtype": "string"}, {"name": "openai_moderation", "list": [{"name": "categories", "struct": [{"name": "harassment", "dtype": "bool"}, {"name": "harassment/threatening", "dtype": "bool"}, {"name": "hate", "dtype": "bool"}, {"name": "hate/threatening", "dtype": "bool"}, {"name": "self-harm", "dtype": "bool"}, {"name": "self-harm/instructions", "dtype": "bool"}, {"name": "self-harm/intent", "dtype": "bool"}, {"name": "sexual", "dtype": "bool"}, {"name": "sexual/minors", "dtype": "bool"}, {"name": "violence", "dtype": "bool"}, {"name": "violence/graphic", "dtype": "bool"}]}, {"name": "category_scores", "struct": [{"name": "harassment", "dtype": "float64"}, {"name": "harassment/threatening", "dtype": "float64"}, {"name": "hate", "dtype": "float64"}, {"name": "hate/threatening", "dtype": "float64"}, {"name": "self-harm", "dtype": "float64"}, {"name": "self-harm/instructions", "dtype": "float64"}, {"name": "self-harm/intent", "dtype": "float64"}, {"name": "sexual", "dtype": "float64"}, {"name": "sexual/minors", "dtype": "float64"}, {"name": "violence", "dtype": "float64"}, {"name": "violence/graphic", "dtype": "float64"}]}, {"name": "flagged", "dtype": "bool"}]}, {"name": "redacted", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 2626438904, "num_examples": 1000000}], "download_size": 1488850250, "dataset_size": 2626438904}} | false | null | 2024-07-27T09:28:42 | 659 | 4 | false | 200748d9d3cddcc9d782887541057aca0b18c5da |
LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset
This dataset contains one million real-world conversations with 25 state-of-the-art LLMs.
It is collected from 210K unique IP addresses in the wild on the Vicuna demo and Chatbot Arena website from April to August 2023.
Each sample includes a conversation ID, model name, conversation text in OpenAI API JSON format, detected language tag, and OpenAI moderation API tag.
User consent is obtained through the "Terms of… See the full description on the dataset page: https://huggingface.co/datasets/lmsys/lmsys-chat-1m. | 7,281 | 227,406 | [
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"arxiv:2309.11998",
"region:us"
] | 2023-09-20T06:33:44 | null | null |
65919009a78a277803ef68df | omar07ibrahim/AZERBAIJAN-ENGLISH-DATASET | omar07ibrahim | {"license": "cc-by-4.0"} | false | null | 2023-12-31T16:01:12 | 4 | 4 | false | 6985dd6cfc70886134f72eac4d0a6f1fc1b09af6 | null | 28 | 337 | [
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"region:us"
] | 2023-12-31T16:00:09 | null | null |
661823b590a8b6724f1c6534 | HuggingFaceM4/the_cauldron | HuggingFaceM4 | {"dataset_info": [{"config_name": "ai2d", "features": [{"name": "images", "sequence": "image"}, {"name": "texts", "list": [{"name": "user", "dtype": "string"}, {"name": "assistant", "dtype": "string"}, {"name": "source", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 435362437.84770346, "num_examples": 2434}], "download_size": 438136609, "dataset_size": 435362437.84770346}, {"config_name": "aokvqa", "features": [{"name": "images", "sequence": "image"}, {"name": "texts", "list": [{"name": "user", "dtype": "string"}, {"name": "assistant", "dtype": "string"}, {"name": "source", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 871997710, "num_examples": 16539}], "download_size": 893265070, "dataset_size": 871997710}, {"config_name": "chart2text", "features": [{"name": "images", "sequence": "image"}, {"name": "texts", "list": [{"name": "user", "dtype": "string"}, {"name": "assistant", 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Dataset Card for The Cauldron
Dataset description
The Cauldron is part of the Idefics2 release.
It is a massive collection of 50 vision-language datasets (training sets only) that were used for the fine-tuning of the vision-language model Idefics2.
Load the dataset
To load the dataset, install the library datasets with pip install datasets. Then,
from datasets import load_dataset
ds = load_dataset("HuggingFaceM4/the_cauldron", "ai2d")
to download… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceM4/the_cauldron. | 862,378 | 2,331,115 | [
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"library:dask",
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"library:polars",
"arxiv:1603.07396",
"arxiv:2206.01718",
"arxiv:2208.05358",
"arxiv:1612.06890",
"arxiv:2310.00367",
"arxiv:1710.07300",
"arxiv:2312.12241",
"arxiv:1912.03098",
"arxiv:2211.08545",
"arxiv:2306.05425",
"arxiv:1709.00103",
"arxiv:2003.12462",
"arxiv:1612.00837",
"arxiv:2205.00363",
"arxiv:2403.09029",
"arxiv:2405.02246",
"region:us"
] | 2024-04-11T17:53:57 | null | null |
666513f121aa69e38699e6d3 | UCSC-VLAA/MedTrinity-25M | UCSC-VLAA | {"language": ["en"], "size_categories": ["10M<n<100M"], "task_categories": ["question-answering"], "dataset_info": [{"config_name": "25M_full", "features": [{"name": "id", "dtype": "string"}, {"name": "file_name", "dtype": "string"}, {"name": "caption", "dtype": "string"}, {"name": "source", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 25234102586, "num_examples": 24760560}], "download_size": 7353330306, "dataset_size": 25234102586}, {"config_name": "default", "features": [{"name": "image", "dtype": "image"}, {"name": "id", "dtype": "string"}, {"name": "caption", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4781050841.25, "num_examples": 161630}], "download_size": 8300138103, "dataset_size": 4781050841.25}], "configs": [{"config_name": "25M_full", "data_files": [{"split": "train", "path": "25M_full/train-*"}]}, {"config_name": "25M_demo", "data_files": [{"split": "train", "path": "data/train-*"}]}], "tags": ["medical"]} | false | null | 2024-10-11T00:47:43 | 140 | 4 | false | 89e5c684794e5c4cc1af9e8f1a7798af7c937dbf |
Tutorial of using Medtrinity-25M
MedTrinity-25M, a comprehensive, large-scale multimodal dataset for medicine, covering over 25 million images across 10 modalities, with multigranular annotations for more than 65 diseases. These enriched annotations encompass both global textual information, such as disease/lesion type, modality, region-specific descriptions, and inter-regional relationships, as well as detailed local annotations for regions of interest (ROIs), including… See the full description on the dataset page: https://huggingface.co/datasets/UCSC-VLAA/MedTrinity-25M. | 2,659 | 12,108 | [
"task_categories:question-answering",
"language:en",
"size_categories:10M<n<100M",
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"modality:text",
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"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2408.02900",
"region:us",
"medical"
] | 2024-06-09T02:31:13 | null | null |
666ae33f611afe17cd982829 | BAAI/Infinity-Instruct | BAAI | {"configs": [{"config_name": "3M", "data_files": [{"split": "train", "path": "3M/*"}]}, {"config_name": "7M", "data_files": [{"split": "train", "path": "7M/*"}]}, {"config_name": "0625", "data_files": [{"split": "train", "path": "0625/*"}]}, {"config_name": "Gen", "data_files": [{"split": "train", "path": "Gen/*"}]}, {"config_name": "7M_domains", "data_files": [{"split": "train", "path": "7M_domains/*/*"}]}], "task_categories": ["text-generation"], "language": ["en", "zh"], "size_categories": ["1M<n<10M"], "license": "cc-by-sa-4.0", "extra_gated_prompt": "You agree to not use the dataset to conduct experiments that cause harm to human subjects.", "extra_gated_fields": {"Company/Organization": "text", "Country": "country"}} | false | null | 2025-02-25T10:39:37 | 613 | 4 | false | d31c749e9f19e1a809bf808dd78afb6fd4cf4e17 |
Infinity Instruct
Beijing Academy of Artificial Intelligence (BAAI)
[Paper][Code][🤗] (would be released soon)
The quality and scale of instruction data are crucial for model performance. Recently, open-source models have increasingly relied on fine-tuning datasets comprising millions of instances, necessitating both high quality and large scale. However, the open-source community has long been constrained by the high costs associated with building such extensive and… See the full description on the dataset page: https://huggingface.co/datasets/BAAI/Infinity-Instruct. | 4,606 | 52,118 | [
"task_categories:text-generation",
"language:en",
"language:zh",
"license:cc-by-sa-4.0",
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"modality:tabular",
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"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2402.00530",
"arxiv:2405.19327",
"arxiv:2409.07045",
"arxiv:2408.07089",
"region:us"
] | 2024-06-13T12:17:03 | null | null |
667ab99edb56acf219d8d646 | FreedomIntelligence/PubMedVision | FreedomIntelligence | {"license": "apache-2.0", "task_categories": ["question-answering", "text-generation"], "language": ["en"], "tags": ["GPT-4V", "Vision", "medical", "biology"], "size_categories": ["1M<n<10M"], "configs": [{"config_name": "PubMedVision_Alignment_VQA", "data_files": "PubMedVision_Alignment_VQA.json"}, {"config_name": "PubMedVision_InstructionTuning_VQA", "data_files": "PubMedVision_InstructionTuning_VQA.json"}, {"config_name": "_Original_Caption", "data_files": "PubMedVision_Original_Caption.json"}, {"config_name": "_Chinese_Version", "data_files": "PubMedVision_Chinese.json"}]} | false | null | 2025-02-18T07:44:10 | 69 | 4 | false | 3c84e04b38bceb5341419b9a4f8ca37ba790cb84 |
News
[2025/02/18]: We add the original captions of PubMedVision in PubMedVision_Original_Caption.json, as well as the Chinese version of PubMedVision in PubMedVision_Chinese.json.
[2024/07/01]: We add annotations for 'body_part' and 'modality' of images, utilizing the HuatuoGPT-Vision-7B model.
PubMedVision
PubMedVision is a large-scale medical VQA dataset. We extracted high-quality image-text pairs from PubMed and used GPT-4V to reformat them to enhance their quality.… See the full description on the dataset page: https://huggingface.co/datasets/FreedomIntelligence/PubMedVision. | 1,434 | 5,701 | [
"task_categories:question-answering",
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"size_categories:1M<n<10M",
"format:json",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2406.19280",
"region:us",
"GPT-4V",
"Vision",
"medical",
"biology"
] | 2024-06-25T12:35:42 | null | null |
668d2093d141a8c8555293c9 | CohereLabs/lbpp | CohereLabs | {"license": "apache-2.0"} | false | null | 2025-04-04T17:16:57 | 24 | 4 | false | a4753d1cf9b1e2a6261cf4dacaa7f197ef5cf3d2 | *Less Basic Python Programming* is a collection of 161 programming problems with accompanying unit tests.
They were created with the aim of being fresh (not leaked at the time of creation) and more difficult than similar datasets (e.g., HumanEval and MBPP).
It can serve as a drop-in replacement or enrichment of those datasets as they are structured in an equivalent way. | 209 | 2,166 | [
"license:apache-2.0",
"arxiv:2504.00698",
"region:us"
] | 2024-07-09T11:35:47 | null | @inproceedings{matton-etal-2024-leakage,
title = "On Leakage of Code Generation Evaluation Datasets",
author = "Matton, Alexandre and
Sherborne, Tom and
Aumiller, Dennis and
Tommasone, Elena and
Alizadeh, Milad and
He, Jingyi and
Ma, Raymond and
Voisin, Maxime and
Gilsenan-McMahon, Ellen and
Gall{\'e}, Matthias",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.772/",
doi = "10.18653/v1/2024.findings-emnlp.772",
pages = "13215--13223",
} |
669624d8534f204a2b973819 | AI-MO/NuminaMath-TIR | AI-MO | {"language": ["en"], "license": "apache-2.0", "task_categories": ["text-generation"], "pretty_name": "NuminaMath TIR", "dataset_info": {"features": [{"name": "problem", "dtype": "string"}, {"name": "solution", "dtype": "string"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 327147067, "num_examples": 72441}, {"name": "test", "num_bytes": 461331, "num_examples": 99}], "download_size": 147557990, "dataset_size": 327608398}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "tags": ["math", "aimo"]} | false | null | 2024-11-25T05:32:53 | 125 | 4 | false | 77a91d7b7a1a98ac4b1beb7d86c09d156b935dcd |
Dataset Card for NuminaMath CoT
Dataset Summary
Tool-integrated reasoning (TIR) plays a crucial role in this competition. However, collecting and annotating such data is both costly and time-consuming. To address this, we selected approximately 70k problems from the NuminaMath-CoT dataset, focusing on those with numerical outputs, most of which are integers. We then utilized a pipeline leveraging GPT-4 to generate TORA-like reasoning paths, executing the code and… See the full description on the dataset page: https://huggingface.co/datasets/AI-MO/NuminaMath-TIR. | 9,017 | 53,850 | [
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"math",
"aimo"
] | 2024-07-16T07:44:24 | null | null |
Subsets and Splits