63990f21cc50af73d29ecfa3

fka/awesome-chatgpt-prompts

fka

{"license": "cc0-1.0", "tags": ["ChatGPT"], "task_categories": ["question-answering"], "size_categories": ["100K<n<1M"]}

68ba7694e23014788dcc8ab5afe613824f45a05c

🧠 Awesome ChatGPT Prompts [CSV dataset] This is a Dataset Repository of Awesome ChatGPT Prompts View All Prompts on GitHub License CC-0

66cbf7ef92e9f5b19fcd65aa

cfahlgren1/react-code-instructions

cfahlgren1

{"license": "mit", "pretty_name": "React Code Instructions"}

809623581765243ac82ba0bd09553f36c9f6ac9c

React Code Instructions Popular Queries Number of instructions by Model Unnested Messages Instructions Added Per Day Dataset of Claude Artifact esque React Apps generated by Llama 3.1 70B, Llama 3.1 405B, and Deepseek Chat V3. Examples Virtual Fitness Trainer Website LinkedIn Clone iPhone Calculator Chipotle Waitlist Apple Store

67750882633d421965733171

DAMO-NLP-SG/multimodal_textbook

DAMO-NLP-SG

{"license": "apache-2.0", "task_categories": ["text-generation", "summarization"], "language": ["en"], "tags": ["Pretraining", "Interleaved", "Reasoning"], "size_categories": ["1M<n<10M"]}

b83d307b2682d6b12420f5b93f4360880ea89df4

Multimodal-Textbook-6.5M Overview This dataset is for "2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining", containing 6.5M images interleaving with 0.8B text from instructional videos. It contains pre-training corpus using interleaved image-text format. Specifically, our multimodal-textbook includes 6.5M keyframes extracted from instructional videos, interleaving with 0.8B ASR texts. All the images and text are extracted from… See the full description on the dataset page: https://huggingface.co./datasets/DAMO-NLP-SG/multimodal_textbook.

6758176e04e2f15d7bfacd54

PowerInfer/QWQ-LONGCOT-500K

PowerInfer

{"license": "apache-2.0", "language": ["en"]}

10a787d967281599e9be6761717147817c018424

This repository contains approximately 500,000 instances of responses generated using QwQ-32B-Preview language model. The dataset combines prompts from multiple high-quality sources to create diverse and comprehensive training data. The dataset is available under the Apache 2.0 license. Over 75% of the responses exceed 8,000 tokens in length. The majority of prompts were carefully created using persona-based methods to create challenging instructions. Bias, Risks, and Limitations… See the full description on the dataset page: https://huggingface.co./datasets/PowerInfer/QWQ-LONGCOT-500K.

66a6da71f0dc7c8df2e0f979

OpenLeecher/lmsys_chat_1m_clean

OpenLeecher

{"language": ["en"], "size_categories": ["100K<n<1M"], "pretty_name": "Cleaned LMSYS dataset", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "conversations", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "category", "dtype": "string"}, {"name": "grounded", "dtype": "bool"}, {"name": "deepseek_response", "struct": [{"name": "moralization", "dtype": "int64"}, {"name": "reward", "dtype": "float64"}, {"name": "value", "dtype": "string"}]}, {"name": "phi-3-mini_response", "struct": [{"name": "moralization", "dtype": "int64"}, {"name": "reward", "dtype": "float64"}, {"name": "value", "dtype": "string"}]}, {"name": "flaw", "dtype": "string"}, {"name": "agreement", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 1673196622, "num_examples": 273402}], "download_size": 906472159, "dataset_size": 1673196622}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

e9f2f6838a2dbba87c216bb6bc406e8d7ce0f389

Cleaning and Categorizing A few weeks ago, I had the itch to do some data crunching, so I began this project - to clean and classify lmsys-chat-1m. The process was somewhat long and tedious, but here is the quick overview: 1. Removing Pure Duplicate Instructions The first step was to eliminate pure duplicate instructions. This involved: Removing whitespace and punctuation. Ensuring that if two instructions matched after that, only one was retained. This step… See the full description on the dataset page: https://huggingface.co./datasets/OpenLeecher/lmsys_chat_1m_clean.

67449661149efb6edaa63b98

HuggingFaceTB/finemath

HuggingFaceTB

{"license": "odc-by", "dataset_info": [{"config_name": "finemath-3plus", "features": [{"name": "url", "dtype": "string"}, {"name": "fetch_time", "dtype": "int64"}, {"name": "content_mime_type", "dtype": "string"}, {"name": "warc_filename", "dtype": "string"}, {"name": "warc_record_offset", "dtype": "int32"}, {"name": "warc_record_length", "dtype": "int32"}, {"name": "text", "dtype": "string"}, {"name": "token_count", "dtype": "int32"}, {"name": "char_count", "dtype": "int32"}, {"name": "metadata", "dtype": "string"}, {"name": "score", "dtype": "float64"}, {"name": "int_score", "dtype": "int64"}, {"name": "crawl", "dtype": "string"}, {"name": "snapshot_type", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "language_score", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 137764105388.93857, "num_examples": 21405610}], "download_size": 65039196945, "dataset_size": 137764105388.93857}, {"config_name": "finemath-4plus", "features": [{"name": "url", "dtype": "string"}, {"name": "fetch_time", "dtype": "int64"}, {"name": "content_mime_type", "dtype": "string"}, {"name": "warc_filename", "dtype": "string"}, {"name": "warc_record_offset", "dtype": "int32"}, {"name": "warc_record_length", "dtype": "int32"}, {"name": "text", "dtype": "string"}, {"name": "token_count", "dtype": "int32"}, {"name": "char_count", "dtype": "int32"}, {"name": "metadata", "dtype": "string"}, {"name": "score", "dtype": "float64"}, {"name": "int_score", "dtype": "int64"}, {"name": "crawl", "dtype": "string"}, {"name": "snapshot_type", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "language_score", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 39101488149.09091, "num_examples": 6699493}], "download_size": 18365184633, "dataset_size": 39101488149.09091}, {"config_name": "infiwebmath-3plus", "features": [{"name": "url", "dtype": "string"}, {"name": "metadata", "dtype": "string"}, {"name": "score", "dtype": "float64"}, {"name": "int_score", "dtype": "int64"}, {"name": "token_count", "dtype": "int64"}, {"name": "char_count", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 96485696853.10182, "num_examples": 13882669}], "download_size": 46808660851, "dataset_size": 96485696853.10182}, {"config_name": "infiwebmath-4plus", "features": [{"name": "url", "dtype": "string"}, {"name": "metadata", "dtype": "string"}, {"name": "score", "dtype": "float64"}, {"name": "int_score", "dtype": "int64"}, {"name": "token_count", "dtype": "int64"}, {"name": "char_count", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 40002719500.1551, "num_examples": 6296212}], "download_size": 19234328998, "dataset_size": 40002719500.1551}], "configs": [{"config_name": "finemath-3plus", "data_files": [{"split": "train", "path": "finemath-3plus/train-*"}]}, {"config_name": "finemath-4plus", "data_files": [{"split": "train", "path": "finemath-4plus/train-*"}]}, {"config_name": "infiwebmath-3plus", "data_files": [{"split": "train", "path": "infiwebmath-3plus/train-*"}]}, {"config_name": "infiwebmath-4plus", "data_files": [{"split": "train", "path": "infiwebmath-4plus/train-*"}]}]}

8f233cf84cff0b817b3ffb26d5be7370990dd557

📐 FineMath What is it? 📐 FineMath consists of 34B tokens (FineMath-3+) and 54B tokens (FineMath-3+ with InfiMM-WebMath-3+) of mathematical educational content filtered from CommonCrawl. To curate this dataset, we trained a mathematical content classifier using annotations generated by LLama-3.1-70B-Instruct. We used the classifier to retain only the most educational mathematics content, focusing on clear explanations and step-by-step problem solving rather than… See the full description on the dataset page: https://huggingface.co./datasets/HuggingFaceTB/finemath.

6763e94724dee5a47c7c77f7

agibot-world/AgiBotWorld-Alpha

agibot-world

{"pretty_name": "AgiBot World", "size_categories": ["n>1T"], "task_categories": ["other"], "language": ["en"], "tags": ["real-world", "dual-arm", "Robotics manipulation"], "extra_gated_prompt": "### AgiBot World COMMUNITY LICENSE AGREEMENT\nAgiBot World Alpha Release Date: December 30, 2024 All the data and code within this repo are under [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/).", "extra_gated_fields": {"First Name": "text", "Last Name": "text", "Email": "text", "Country": "country", "Affiliation": "text", "Phone": "text", "Job title": {"type": "select", "options": ["Student", "Research Graduate", "AI researcher", "AI developer/engineer", "Reporter", "Other"]}, "Research interest": "text", "geo": "ip_location", "By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the AgiBot Privacy Policy": "checkbox"}, "extra_gated_description": "The information you provide will be collected, stored, processed and shared in accordance with the AgiBot Privacy Policy.", "extra_gated_button_content": "Submit"}

53f3739cc041164023f988d7c7b98f6af3f0d2c0

Key Features 🔑 1 million+ trajectories from 100 robots. 100+ real-world scenarios across 5 target domains. Cutting-edge hardware: visual tactile sensors / 6-DoF dexterous hand / mobile dual-arm robots Tasks involving: Contact-rich manipulation Long-horizon planning Multi-robot collaboration Your browser does not support the video tag. Your browser does not support the video tag.… See the full description on the dataset page: https://huggingface.co./datasets/agibot-world/AgiBotWorld-Alpha.

676f70846bf205795346d2be

FreedomIntelligence/medical-o1-reasoning-SFT

FreedomIntelligence

{"license": "apache-2.0", "task_categories": ["question-answering", "text-generation"], "language": ["en"], "tags": ["medical", "biology"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "medical_o1_sft.json"}]}]}

06ac0b8d4960fa84ef55198ea8086266f1e3da81

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, which searches for solutions to verifiable medical problems and validates them through a medical verifier. For details, see our paper and GitHub repository. Citation If you find our data useful, please consider citing our work! @misc{chen2024huatuogpto1medicalcomplexreasoning, title={HuatuoGPT-o1… See the full description on the dataset page: https://huggingface.co./datasets/FreedomIntelligence/medical-o1-reasoning-SFT.

677c1f196b1653e3955dbce7

Rapidata/text-2-image-Rich-Human-Feedback

Rapidata

{"license": "apache-2.0", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "word_scores", "dtype": "string"}, {"name": "alignment_score_norm", "dtype": "float32"}, {"name": "coherence_score_norm", "dtype": "float32"}, {"name": "style_score_norm", "dtype": "float32"}, {"name": "alignment_heatmap", "sequence": {"sequence": "float16"}}, {"name": "coherence_heatmap", "sequence": {"sequence": "float16"}}, {"name": "alignment_score", "dtype": "float32"}, {"name": "coherence_score", "dtype": "float32"}, {"name": "style_score", "dtype": "float32"}], "splits": [{"name": "train", "num_bytes": 25257389633.104, "num_examples": 13024}], "download_size": 17856619960, "dataset_size": 25257389633.104}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "task_categories": ["text-to-image", "text-classification", "image-classification", "image-to-text", "image-segmentation"], "language": ["en"], "tags": ["t2i", "preferences", "human", "flux", "midjourney", "imagen", "dalle", "heatmap", "coherence", "alignment", "style", "plausiblity"], "pretty_name": "Rich Human Feedback for Text to Image Models", "size_categories": ["1M<n<10M"]}

e77afd00e481d9d2ca41a5b5c4f89cb704de45c6

Building upon Google's research Rich Human Feedback for Text-to-Image Generation we have collected over 1.5 million responses from 152'684 individual humans using Rapidata via the Python API. Collection took roughly 5 days. If you get value from this dataset and would like to see more in the future, please consider liking it. Overview We asked humans to evaluate AI-generated images in style, coherence and prompt alignment. For images that contained flaws, participants were… See the full description on the dataset page: https://huggingface.co./datasets/Rapidata/text-2-image-Rich-Human-Feedback.

6695831f2d25bd04e969b0a2

AI-MO/NuminaMath-CoT

AI-MO

{"dataset_info": {"features": [{"name": "source", "dtype": "string"}, {"name": "problem", "dtype": "string"}, {"name": "solution", "dtype": "string"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 2495457595.0398345, "num_examples": 859494}, {"name": "test", "num_bytes": 290340.31593470514, "num_examples": 100}], "download_size": 1234351634, "dataset_size": 2495747935.355769}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "license": "apache-2.0", "task_categories": ["text-generation"], "language": ["en"], "tags": ["aimo", "math"], "pretty_name": "NuminaMath CoT"}

9d8d210c9f6a36c8f3cd84045668c9b7800ef517

Dataset Card for NuminaMath CoT Dataset Summary Approximately 860k math problems, where each solution is formatted in a Chain of Thought (CoT) manner. The sources of the dataset range from Chinese high school math exercises to US and international mathematics olympiad competition problems. The data were primarily collected from online exam paper PDFs and mathematics discussion forums. The processing steps include (a) OCR from the original PDFs, (b) segmentation… See the full description on the dataset page: https://huggingface.co./datasets/AI-MO/NuminaMath-CoT.

66a1d16a27fd84b81d732482

TEAMREBOOTT-AI/SciCap-MLBCAP

TEAMREBOOTT-AI

{"license": "cc-by-nc-sa-4.0", "task_categories": ["text-generation", "image-to-text"], "language": ["en"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "id", "dtype": "int64"}, {"name": "figure_type", "dtype": "string"}, {"name": "ocr", "dtype": "string"}, {"name": "paragraph", "dtype": "string"}, {"name": "mention", "dtype": "string"}, {"name": "figure_description", "dtype": "string"}, {"name": "mlbcap_long", "dtype": "string"}, {"name": "mlbcap_short", "dtype": "string"}, {"name": "categories", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2444177418.129, "num_examples": 47639}], "download_size": 2487129056, "dataset_size": 2444177418.129}, "size_categories": ["10K<n<100K"]}

44f062ec4e5ec42898326cbea2f80f147a1ba861

MLBCAP: Multi-LLM Collaborative Caption Generation in Scientific Documents 📄 PaperMLBCAP has been accepted for presentation at AI4Research @ AAAI 2025. 🎉 📌 Introduction Scientific figure captioning is a challenging task that demands contextually accurate descriptions of visual content. Existing approaches often oversimplify the task by treating it as either an image-to-text conversion or text summarization problem, leading to suboptimal results. Furthermore… See the full description on the dataset page: https://huggingface.co./datasets/TEAMREBOOTT-AI/SciCap-MLBCAP.

673a1149a7a311f5bed5c624

HuggingFaceTB/smoltalk

HuggingFaceTB

{"language": ["en"], "tags": ["synthetic"], "pretty_name": "SmolTalk", "size_categories": ["1M<n<10M"], "configs": [{"config_name": "all", "data_files": [{"split": "train", "path": "data/all/train-*"}, {"split": "test", "path": "data/all/test-*"}]}, {"config_name": "smol-magpie-ultra", "data_files": [{"split": "train", "path": "data/smol-magpie-ultra/train-*"}, {"split": "test", "path": "data/smol-magpie-ultra/test-*"}]}, {"config_name": "smol-constraints", "data_files": [{"split": "train", "path": "data/smol-constraints/train-*"}, {"split": "test", "path": "data/smol-constraints/test-*"}]}, {"config_name": "smol-rewrite", "data_files": [{"split": "train", "path": "data/smol-rewrite/train-*"}, {"split": "test", "path": "data/smol-rewrite/test-*"}]}, {"config_name": "smol-summarize", "data_files": [{"split": "train", "path": "data/smol-summarize/train-*"}, {"split": "test", "path": "data/smol-summarize/test-*"}]}, {"config_name": "apigen-80k", "data_files": [{"split": "train", "path": "data/apigen-80k/train-*"}, {"split": "test", "path": "data/apigen-80k/test-*"}]}, {"config_name": "everyday-conversations", "data_files": [{"split": "train", "path": "data/everyday-conversations/train-*"}, {"split": "test", "path": "data/everyday-conversations/test-*"}]}, {"config_name": "explore-instruct-rewriting", "data_files": [{"split": "train", "path": "data/explore-instruct-rewriting/train-*"}, {"split": "test", "path": "data/explore-instruct-rewriting/test-*"}]}, {"config_name": "longalign", "data_files": [{"split": "train", "path": "data/longalign/train-*"}, {"split": "test", "path": "data/longalign/test-*"}]}, {"config_name": "metamathqa-50k", "data_files": [{"split": "train", "path": "data/metamathqa-50k/train-*"}, {"split": "test", "path": "data/metamathqa-50k/test-*"}]}, {"config_name": "numina-cot-100k", "data_files": [{"split": "train", "path": "data/numina-cot-100k/train-*"}, {"split": "test", "path": "data/numina-cot-100k/test-*"}]}, {"config_name": "openhermes-100k", "data_files": [{"split": "train", "path": "data/openhermes-100k/train-*"}, {"split": "test", "path": "data/openhermes-100k/test-*"}]}, {"config_name": "self-oss-instruct", "data_files": [{"split": "train", "path": "data/self-oss-instruct/train-*"}, {"split": "test", "path": "data/self-oss-instruct/test-*"}]}, {"config_name": "systemchats-30k", "data_files": [{"split": "train", "path": "data/systemchats-30k/train-*"}, {"split": "test", "path": "data/systemchats-30k/test-*"}]}]}

5a40ecb185e55dd30edf3c24b77e67f6ea0d659b

SmolTalk Dataset description This is a synthetic dataset designed for supervised finetuning (SFT) of LLMs. It was used to build SmolLM2-Instruct family of models and contains 1M samples. During the development of SmolLM2, we observed that models finetuned on public SFT datasets underperformed compared to other models with proprietary instruction datasets. To address this gap, we created new synthetic datasets that improve instruction following while covering… See the full description on the dataset page: https://huggingface.co./datasets/HuggingFaceTB/smoltalk.

673e9e53cdad8a9744b0bf1b

O1-OPEN/OpenO1-SFT

O1-OPEN

{"license": "apache-2.0", "task_categories": ["question-answering"], "language": ["en", "zh"], "size_categories": ["10K<n<100K"]}

63112de109aa755e9cdfad63a13f08a92dd7df36

SFT Data for CoT Activation 🎉🎉🎉This repository contains the dataset used for fine-tuning a language model using SFT for Chain-of-Thought Activation. 🌈🌈🌈The dataset is designed to enhance the model's ability to generate coherent and logical reasoning sequences. ☄☄☄By using this dataset, the model can learn to produce detailed and structured reasoning steps, enhancing its performance on complex reasoning tasks. Statistics 1️⃣Total Records: 77,685… See the full description on the dataset page: https://huggingface.co./datasets/O1-OPEN/OpenO1-SFT.

67734d5c7ec2413faa8d3c85

PowerInfer/LONGCOT-Refine-500K

PowerInfer

{"language": ["en"], "license": "apache-2.0"}

88bf8410db01197006e572a46c88311720a23577

This repository contains approximately 500,000 instances of responses generated using Qwen2.5-72B-Instruct. The dataset combines prompts from multiple high-quality sources to create diverse and comprehensive training data. The dataset is available under the Apache 2.0 license. Bias, Risks, and Limitations This dataset is mainly in English. The dataset inherits the biases, errors, and omissions known to exist in data used for seed sources and models used for data generation.… See the full description on the dataset page: https://huggingface.co./datasets/PowerInfer/LONGCOT-Refine-500K.

6649d353babc0b33565e1a4a

HumanLLMs/Human-Like-DPO-Dataset

HumanLLMs

{"language": ["en"], "license": "other", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data.json"}]}]}

77522f471820b963b4f81e7492a3e37febde5f18

676593a303cc6dbb6e857610

Rapidata/text-2-video-human-preferences

Rapidata

{"license": "apache-2.0", "task_categories": ["text-to-video", "video-classification"], "tags": ["human", "preferences", "coherence", "plausibilty", "style", "alignment"], "language": ["en"], "pretty_name": "Human Preferences for Text to Video Models", "size_categories": ["1K<n<10K"], "dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "video1", "dtype": "string"}, {"name": "video2", "dtype": "string"}, {"name": "weighted_results1_Alignment", "dtype": "float64"}, {"name": "weighted_results2_Alignment", "dtype": "float64"}, {"name": "detailedResults_Alignment", "list": [{"name": "userDetails", "struct": [{"name": "country", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "userScore", "dtype": "float64"}]}, {"name": "votedFor", "dtype": "string"}]}, {"name": "weighted_results1_Coherence", "dtype": "float64"}, {"name": "weighted_results2_Coherence", "dtype": "float64"}, {"name": "detailedResults_Coherence", "list": [{"name": "userDetails", "struct": [{"name": "country", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "userScore", "dtype": "float64"}]}, {"name": "votedFor", "dtype": "string"}]}, {"name": "weighted_results1_Preference", "dtype": "float64"}, {"name": "weighted_results2_Preference", "dtype": "float64"}, {"name": "detailedResults_Preference", "list": [{"name": "userDetails", "struct": [{"name": "country", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "userScore", "dtype": "float64"}]}, {"name": "votedFor", "dtype": "string"}]}, {"name": "file_name1", "dtype": "string"}, {"name": "file_name2", "dtype": "string"}, {"name": "model1", "dtype": "string"}, {"name": "model2", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 478042, "num_examples": 316}], "download_size": 121718, "dataset_size": 478042}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

ec394c772df65ab3377c9c481e76459c23028aff

Rapidata Video Generation Preference Dataset This dataset was collected in ~12 hours using the Rapidata Python API, accessible to anyone and ideal for large scale data annotation. The data collected in this dataset informs our text-2-video model benchmark. We just started so currently only two models are represented in this set: Sora Hunyouan Pika 2.0 is currently being evaluated and will be added next. Explore our latest model rankings on our website. If you get value… See the full description on the dataset page: https://huggingface.co./datasets/Rapidata/text-2-video-human-preferences.

67744720363e2be467b7c2b5

qingy2024/FineQwQ-142k

qingy2024

{"language": ["en"], "dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "source", "dtype": "string"}], "splits": [{"name": "10k", "num_bytes": 87273156.45129532, "num_examples": 10000}, {"name": "25k", "num_bytes": 218182891.12823832, "num_examples": 25000}, {"name": "50k", "num_bytes": 436365782.25647664, "num_examples": 50000}, {"name": "100k", "num_bytes": 872731564.5129533, "num_examples": 100000}, {"name": "142k", "num_bytes": 1239278821.6083937, "num_examples": 142000}], "download_size": 1265768860, "dataset_size": 2853832215.9573574}, "configs": [{"config_name": "default", "data_files": [{"split": "10k", "path": "data/10k-*"}, {"split": "25k", "path": "data/25k-*"}, {"split": "50k", "path": "data/50k-*"}, {"split": "100k", "path": "data/100k-*"}, {"split": "142k", "path": "data/142k-*"}]}]}

f7443bb54d207f590a5d13924c80c9eacfd66fe1

Shakker-Labs/FLUX.1-dev-LoRA-Logo-Design Original Sources: qingy2024/QwQ-LongCoT-Verified-130K (amphora/QwQ-LongCoT-130K), amphora/QwQ-LongCoT-130K-2, PowerInfer/QWQ-LONGCOT-500K. Source Information Rows % powerinfer/qwq-500k Only coding problems kept to avoid overlap 50,899 35.84% qwq-longcot-verified Verified math problems 64,096 45.14% amphora-magpie Diverse general purpose reasoning 27,015 19.02%

677396c13cd7faf7e8f9dc8c

PRIME-RL/Eurus-2-RL-Data

PRIME-RL

{"license": "mit"}

5cbc5bc54c9c8417afd3539fb267422c33b525e6

Eurus-2-RL-Data Links 📜 Blog 🤗 PRIME Collection Introduction Eurus-2-RL-Data is a high-quality RL training dataset of mathematics and coding problems with outcome verifiers (LaTeX answers for math and test cases for coding). For math, we source from NuminaMath-CoT. The problems span from Chinese high school mathematics to International Mathematical Olympiad competition questions. For coding, we source from APPS, CodeContests, TACO, and… See the full description on the dataset page: https://huggingface.co./datasets/PRIME-RL/Eurus-2-RL-Data.

6775e1c326815bf20d874413

fal/cosmos-openvid-1m

fal

{"size_categories": ["100K<n<1M"], "viewer": true, "license": "apache-2.0"}

10b41fc29006eff62ff64b8795b8ae8ef7ff9cde

Cosmos-Tokenized OpenVid-1M Cosmos-Tokenized OpenVid-1M How to use Shards are stored in parquet format. It has 4 columns: serialized_latent, caption, fps, video. serialized_latent is the latent vector of the video, serialized using torch.save(). Please use the following function to deserialize it:def deserialize_tensor( serialized_tensor: bytes, device: Optional[str] = None ) -> torch.Tensor: return torch.load( io.BytesIO(serialized_tensor)… See the full description on the dataset page: https://huggingface.co./datasets/fal/cosmos-openvid-1m.

677e5956e84a20259e43d869

Rapidata/Translation-gpt4o_mini-v-gpt4o-v-deepl

Rapidata

{"dataset_info": {"features": [{"name": "original_text", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "total_responses", "dtype": "int64"}, {"name": "weighted_votes_1", "dtype": "float64"}, {"name": "weighted_votes_2", "dtype": "float64"}, {"name": "translation_model_1", "dtype": "string"}, {"name": "translation_model_2", "dtype": "string"}, {"name": "model1", "dtype": "string"}, {"name": "model2", "dtype": "string"}, {"name": "detailed_results", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 10792019, "num_examples": 746}], "download_size": 1059070, "dataset_size": 10792019}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

c243b1f54587f0ec225fdc6ff910d48e44668aa3

If you get value from this dataset and would like to see more in the future, please consider liking it. Overview This dataset compares the translation capabilities of GPT-4o and GPT-4o-mini against DeepL across different languages. The comparison involved 100 distinct texts in 4 languages, with each translation being rated by 100 native speakers. Texts that were translated identically across platforms were excluded from the analysis. Results The comparative… See the full description on the dataset page: https://huggingface.co./datasets/Rapidata/Translation-gpt4o_mini-v-gpt4o-v-deepl.

676f70968756741d47c691df

FreedomIntelligence/medical-o1-verifiable-problem

FreedomIntelligence

{"license": "apache-2.0", "task_categories": ["question-answering", "text-generation"], "language": ["en"], "tags": ["medical", "biology"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "medical_o1_verifiable_problem.json"}]}]}

46d5175eb74fdef3516d51d52e8c40db04bbdf35

Introduction This dataset features open-ended medical problems designed to improve LLMs' medical reasoning. Each entry includes a open-ended question and a ground-truth answer based on challenging medical exams. The verifiable answers enable checking LLM outputs, refining their reasoning processes. For details, see our paper and GitHub repository. Citation If you find our data useful, please consider citing our work!… See the full description on the dataset page: https://huggingface.co./datasets/FreedomIntelligence/medical-o1-verifiable-problem.

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."}

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… See the full description on the dataset page: https://huggingface.co./datasets/mozilla-foundation/common_voice_17_0.

@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 }

common-voice

66c84764a47b2d6c582bbb02

amphion/Emilia-Dataset

amphion

{"license": "cc-by-nc-4.0", "task_categories": ["text-to-speech", "automatic-speech-recognition"], "language": ["zh", "en", "ja", "fr", "de", "ko"], "pretty_name": "Emilia", "size_categories": ["10M<n<100M"], "extra_gated_prompt": "Terms of Access: The researcher has requested permission to use the Emilia dataset and the Emilia-Pipe preprocessing pipeline. In exchange for such permission, the researcher hereby agrees to the following terms and conditions:\n1. The researcher shall use the dataset ONLY for non-commercial research and educational purposes.\n2. The authors make no representations or warranties regarding the dataset, \n including but not limited to warranties of non-infringement or fitness for a particular purpose.\n\n3. The researcher accepts full responsibility for their use of the dataset and shall defend and indemnify the authors of Emilia, \n including their employees, trustees, officers, and agents, against any and all claims arising from the researcher's use of the dataset, \n including but not limited to the researcher's use of any copies of copyrighted content that they may create from the dataset.\n\n4. The researcher may provide research associates and colleagues with access to the dataset,\n provided that they first agree to be bound by these terms and conditions.\n \n5. The authors reserve the right to terminate the researcher's access to the dataset at any time.\n6. If the researcher is employed by a for-profit, commercial entity, the researcher's employer shall also be bound by these terms and conditions, and the researcher hereby represents that they are fully authorized to enter into this agreement on behalf of such employer.", "extra_gated_fields": {"Name": "text", "Email": "text", "Affiliation": "text", "Position": "text", "Your Supervisor/manager/director": "text", "I agree to the Terms of Access": "checkbox"}}

bcaad00d13e7c101485990a46e88f5884ffed3fc

Emilia: An Extensive, Multilingual, and Diverse Speech Dataset for Large-Scale Speech Generation This is the official repository 👑 for the Emilia dataset and the source code for the Emilia-Pipe speech data preprocessing pipeline. News 🔥 2024/08/28: Welcome to join Amphion's Discord channel to stay connected and engage with our community! 2024/08/27: The Emilia dataset is now publicly available! Discover the most extensive and diverse speech generation… See the full description on the dataset page: https://huggingface.co./datasets/amphion/Emilia-Dataset.

677ca887f1edc5b457c1eb22

lianghsun/tw-instruct-500k

lianghsun

{"license": "cc-by-nc-sa-4.0", "task_categories": ["text-generation"], "language": ["zh", "en"], "tags": ["Taiwan", "ROC", "tw", "zh-tw", "chat", "instruction"], "pretty_name": "Common Task-Oriented Dialogues in Taiwan", "size_categories": ["100K<n<1M"]}

8cce79fe4783e14eeaa1a279a6e0d29f65bb5862

Dataset Card for Dataset Name [👋歡迎加入 Discord 討論，我們正在找人一塊擴充這個對話集🎉] 台灣常見任務對話集（Common Task-Oriented Dialogues in Taiwan） 為台灣社會裡常見的任務對話，從 lianghsun/tw-instruct 截取出 50 萬筆的子集合版本。 Dataset Details Dataset Description 這個資料集為合成資料集（synthetic datasets），內容由 a. reference-based 和 b. reference-free 的子資料集組合而成。生成 reference-based 資料集時，會先以我們收集用來訓練 lianghsun/Llama-3.2-Taiwan-3B 時的繁體中文文本作為參考文本，透過 LLM 去生成指令對話集，如果參考文本有特別領域的問法，我們將會特別設計該領域或者是適合該文本的問題；生成 reference-free… See the full description on the dataset page: https://huggingface.co./datasets/lianghsun/tw-instruct-500k.

66212f29fb07c3e05ad0432e

HuggingFaceFW/fineweb

HuggingFaceFW

{"license": "odc-by", "task_categories": ["text-generation"], "language": ["en"], "pretty_name": "FineWeb", "size_categories": ["n>1T"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/*/*"}]}, {"config_name": "sample-10BT", "data_files": [{"split": "train", "path": "sample/10BT/*"}]}, {"config_name": "sample-100BT", "data_files": [{"split": "train", "path": "sample/100BT/*"}]}, {"config_name": "sample-350BT", "data_files": [{"split": "train", "path": "sample/350BT/*"}]}, {"config_name": "CC-MAIN-2024-51", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-51/*"}]}, {"config_name": "CC-MAIN-2024-46", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-46/*"}]}, {"config_name": "CC-MAIN-2024-42", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-42/*"}]}, {"config_name": "CC-MAIN-2024-38", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-38/*"}]}, {"config_name": "CC-MAIN-2024-33", "data_files": [{"split": 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e31fdfd3918d4b48e837d69d274e624a067d7091

🍷 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… See the full description on the dataset page: https://huggingface.co./datasets/HuggingFaceFW/fineweb.

66bffb77453a7ef6c587560c

edinburgh-dawg/mmlu-redux-2.0

edinburgh-dawg

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[{"name": "test", "num_examples": 100}]}, {"config_name": "professional_accounting", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": "int64"}, {"name": "error_type", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "correct_answer", "dtype": "string"}, {"name": "potential_reason", "dtype": "string"}], "splits": [{"name": "test", "num_examples": 100}]}, {"config_name": "professional_law", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": "int64"}, {"name": "error_type", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "correct_answer", "dtype": "string"}, {"name": "potential_reason", "dtype": "string"}], "splits": [{"name": "test", "num_examples": 100}]}, {"config_name": "professional_medicine", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": "int64"}, {"name": "error_type", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "correct_answer", "dtype": "string"}, {"name": "potential_reason", "dtype": "string"}], "splits": [{"name": "test", "num_examples": 100}]}, {"config_name": "professional_psychology", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": "int64"}, {"name": "error_type", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "correct_answer", "dtype": "string"}, {"name": "potential_reason", "dtype": "string"}], "splits": [{"name": "test", "num_examples": 100}]}, {"config_name": "public_relations", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": "int64"}, {"name": "error_type", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "correct_answer", "dtype": "string"}, {"name": "potential_reason", "dtype": "string"}], "splits": [{"name": "test", "num_examples": 100}]}, {"config_name": "security_studies", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": "int64"}, {"name": "error_type", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "correct_answer", "dtype": "string"}, {"name": "potential_reason", "dtype": "string"}], "splits": [{"name": "test", "num_examples": 100}]}, {"config_name": "sociology", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": "int64"}, {"name": "error_type", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "correct_answer", "dtype": "string"}, {"name": "potential_reason", "dtype": "string"}], "splits": [{"name": "test", "num_examples": 100}]}, {"config_name": "us_foreign_policy", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": "int64"}, {"name": "error_type", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "correct_answer", "dtype": "string"}, {"name": "potential_reason", "dtype": "string"}], "splits": [{"name": "test", "num_examples": 100}]}, {"config_name": "virology", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": "int64"}, {"name": "error_type", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "correct_answer", "dtype": "string"}, {"name": "potential_reason", "dtype": "string"}], "splits": [{"name": "test", "num_examples": 100}]}, {"config_name": "world_religions", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": "int64"}, {"name": "error_type", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "correct_answer", "dtype": "string"}, {"name": "potential_reason", "dtype": "string"}], "splits": [{"name": "test", "num_examples": 100}]}], "configs": [{"config_name": "abstract_algebra", "data_files": [{"split": "test", "path": "abstract_algebra/data-*"}]}, {"config_name": "anatomy", "data_files": [{"split": "test", "path": "anatomy/data-*"}]}, {"config_name": "astronomy", "data_files": [{"split": "test", "path": "astronomy/data-*"}]}, {"config_name": "business_ethics", "data_files": [{"split": "test", "path": "business_ethics/data-*"}]}, {"config_name": "clinical_knowledge", "data_files": [{"split": "test", "path": "clinical_knowledge/data-*"}]}, {"config_name": "college_biology", "data_files": [{"split": "test", "path": "college_biology/data-*"}]}, {"config_name": "college_chemistry", "data_files": [{"split": "test", "path": "college_chemistry/data-*"}]}, {"config_name": "college_computer_science", "data_files": [{"split": "test", "path": "college_computer_science/data-*"}]}, {"config_name": "college_mathematics", "data_files": [{"split": "test", "path": "college_mathematics/data-*"}]}, {"config_name": "college_medicine", "data_files": [{"split": "test", "path": "college_medicine/data-*"}]}, {"config_name": "college_physics", "data_files": [{"split": "test", "path": "college_physics/data-*"}]}, {"config_name": "computer_security", "data_files": [{"split": "test", "path": "computer_security/data-*"}]}, {"config_name": "conceptual_physics", "data_files": [{"split": "test", "path": "conceptual_physics/data-*"}]}, {"config_name": "econometrics", "data_files": [{"split": "test", "path": "econometrics/data-*"}]}, {"config_name": "electrical_engineering", "data_files": [{"split": "test", "path": "electrical_engineering/data-*"}]}, {"config_name": "elementary_mathematics", "data_files": [{"split": "test", "path": "elementary_mathematics/data-*"}]}, {"config_name": "formal_logic", "data_files": [{"split": "test", "path": "formal_logic/data-*"}]}, {"config_name": "global_facts", "data_files": [{"split": "test", "path": 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"data_files": [{"split": "test", "path": "international_law/data-*"}]}, {"config_name": "jurisprudence", "data_files": [{"split": "test", "path": "jurisprudence/data-*"}]}, {"config_name": "logical_fallacies", "data_files": [{"split": "test", "path": "logical_fallacies/data-*"}]}, {"config_name": "machine_learning", "data_files": [{"split": "test", "path": "machine_learning/data-*"}]}, {"config_name": "management", "data_files": [{"split": "test", "path": "management/data-*"}]}, {"config_name": "marketing", "data_files": [{"split": "test", "path": "marketing/data-*"}]}, {"config_name": "medical_genetics", "data_files": [{"split": "test", "path": "medical_genetics/data-*"}]}, {"config_name": "miscellaneous", "data_files": [{"split": "test", "path": "miscellaneous/data-*"}]}, {"config_name": "moral_disputes", "data_files": [{"split": "test", "path": "moral_disputes/data-*"}]}, {"config_name": "moral_scenarios", "data_files": [{"split": "test", "path": "moral_scenarios/data-*"}]}, {"config_name": "nutrition", "data_files": [{"split": "test", "path": "nutrition/data-*"}]}, {"config_name": "philosophy", "data_files": [{"split": "test", "path": "philosophy/data-*"}]}, {"config_name": "prehistory", "data_files": [{"split": "test", "path": "prehistory/data-*"}]}, {"config_name": "professional_accounting", "data_files": [{"split": "test", "path": "professional_accounting/data-*"}]}, {"config_name": "professional_law", "data_files": [{"split": "test", "path": "professional_law/data-*"}]}, {"config_name": "professional_medicine", "data_files": [{"split": "test", "path": "professional_medicine/data-*"}]}, {"config_name": "professional_psychology", "data_files": [{"split": "test", "path": "professional_psychology/data-*"}]}, {"config_name": "public_relations", "data_files": [{"split": "test", "path": "public_relations/data-*"}]}, {"config_name": "security_studies", "data_files": [{"split": "test", "path": "security_studies/data-*"}]}, {"config_name": "sociology", "data_files": [{"split": "test", "path": "sociology/data-*"}]}, {"config_name": "us_foreign_policy", "data_files": [{"split": "test", "path": "us_foreign_policy/data-*"}]}, {"config_name": "virology", "data_files": [{"split": "test", "path": "virology/data-*"}]}, {"config_name": "world_religions", "data_files": [{"split": "test", "path": "world_religions/data-*"}]}], "license": "cc-by-4.0", "task_categories": ["question-answering"], "language": ["en"], "pretty_name": "MMLU-Redux-2.0", "size_categories": ["1K<n<10K"]}

63f54ebd32c36485c679f53b8e2f576d689b9b34

Dataset Card for MMLU-Redux-2.0 MMLU-Redux is a subset of 5,700 manually re-annotated questions across 57 MMLU subjects. Dataset Details Dataset Description Each data point in MMLU-Redux contains seven columns: question (str): The original MMLU question. choices (List[str]): The original list of four choices associated with the question from the MMLU dataset. answer (int): The MMLU ground truth label in the form of an array index between 0 and… See the full description on the dataset page: https://huggingface.co./datasets/edinburgh-dawg/mmlu-redux-2.0.

674dc01bf413e32210acb235

Rapidata/human-style-preferences-images

Rapidata

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79acd5ebcc535309c08d996ab1f88c01077a7b12

Rapidata Image Generation Preference Dataset This dataset was collected in ~4 Days using the Rapidata Python API, accessible to anyone and ideal for large scale data annotation. Explore our latest model rankings on our website. If you get value from this dataset and would like to see more in the future, please consider liking it. Overview One of the largest human preference datasets for text-to-image models, this release contains over 1,200,000 human… See the full description on the dataset page: https://huggingface.co./datasets/Rapidata/human-style-preferences-images.