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

6782cb3d244c0e06b1362fed

NovaSky-AI/Sky-T1_data_17k

NovaSky-AI

{"size_categories": ["10K<n<100K"], "license": "apache-2.0"}

3e260822dae5d833d9b040e34265d5f9a2b8a6a5

Sky-T1_data_17k.json: The 17k training data used to train Sky-T1-32B-Preview. The final data contains 5k coding data from APPs and TACO, and 10k math data from AIME, MATH, and Olympiads subsets of the NuminaMATH dataset. In addition, we maintain 1k science and puzzle data from STILL-2.

6649d353babc0b33565e1a4a

HumanLLMs/Human-Like-DPO-Dataset

HumanLLMs

{"language": ["en"], "license": "llama3", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data.json"}]}]}

dd82ab6a284a15765964149e6a6603ff8ed7d672

Enhancing Human-Like Responses in Large Language Models 🤗 Models | 📊 Dataset | 📄 Paper Human-Like-DPO-Dataset This dataset was created as part of research aimed at improving conversational fluency and engagement in large language models. It is suitable for formats like Direct Preference Optimization (DPO) to guide models toward generating more human-like responses. The dataset includes 10,884 samples across 256 topics, including: Technology Daily Life Science… See the full description on the dataset page: https://huggingface.co/datasets/HumanLLMs/Human-Like-DPO-Dataset.

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.

66cbf7ef92e9f5b19fcd65aa

cfahlgren1/react-code-instructions

cfahlgren1

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

2b19c334ba37efe38142d5e0c2404fadcca0cbe3

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

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"}]}

4c9573e7de1e8660b88158db2efa7c7204bbd269

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.

6695831f2d25bd04e969b0a2

AI-MO/NuminaMath-CoT

AI-MO

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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.

67449661149efb6edaa63b98

HuggingFaceTB/finemath

HuggingFaceTB

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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.

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.

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.

66212f29fb07c3e05ad0432e

HuggingFaceFW/fineweb

HuggingFaceFW

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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.

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.

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.

677c6dded25ebab44ca8267b

BIOMEDICA/biomedica_webdataset

BIOMEDICA

{"tags": ["medical", "biology", "chemistry"], "size_categories": ["n>1T"], "extra_gated_prompt": "I understand that this dataset contains articles grouped under three licensing categories: Commercial Use Allowed (CC0, CC BY, CC BY-SA, CC BY-ND licenses), Non-Commercial Use Only (CC BY-NC, CC BY-NC-SA, CC BY-NC-ND licenses), and Other (no machine-readable Creative Commons license, no license, or a custom license). I acknowledge that each individual data point in the dataset specifies its corresponding license type, and I agree that it is my responsibility to verify compliance with the licensing terms before using any specific data point. I further agree to comply with the specific licensing terms of each group when using the dataset in accordance to what is established by the PubMed Central: PMC Open Acces Subset", "extra_gated_fields": {"I confirm that I have read and agree to the data usage agreement outlined above by checking this box": "checkbox", "I want to use this dataset for": "text"}}

f5c128c71123deb732786e895e3b464911b1707e

Dataset Card for Dataset Name Arxiv: Arxiv     |     Website: Biomedica     |     Training instructions: OpenCLIP     |     Tutorial: Google Colab BIOMEDICA Dataset is a large-scale, deep-learning-ready biomedical dataset containing over 24M imagecaption pairs and 30M image-references from 6M unique open-source articles. Each data point is highly annotated with over 27 unique metadata fields, including article level information (e.g., license… See the full description on the dataset page: https://huggingface.co/datasets/BIOMEDICA/biomedica_webdataset.

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-*"}]}]}

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… See the full description on the dataset page: https://huggingface.co/datasets/openai/gsm8k.

gsm8k

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"]}

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.

6760cf1c46ba6c841069988a

O1-OPEN/OpenO1-SFT-Ultra

O1-OPEN

2762ca378dbb954419b053fa347835d14a0379a8

openo1-sft-ultra-35m-data Instruction We have released the openo1-sft-ultra-35m-data, which contains 35 million data points. It is based on existing open-source datasets and synthesized using the openo1-qwen-sft model. We first collected open-source datasets and then annotated the data based on difficulty, quality, and question types using the qwen-2.5-72b-instruct model. To ensure the difficulty and quality of the data, we only retained data where both the… See the full description on the dataset page: https://huggingface.co/datasets/O1-OPEN/OpenO1-SFT-Ultra.

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.

677e59ab4bf7f0d4735ea7da

llamaindex/vdr-multilingual-train

llamaindex

{"language": ["de", "it", "fr", "es", "en"], "multilinguality": ["multilingual"], "size_categories": ["100K<n<1M"], "pretty_name": "Multilingual Visual Document Retrieval", "dataset_info": [{"config_name": "en", "features": [{"name": "id", "dtype": "string"}, {"name": "query", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "negatives", "sequence": {"dtype": "string"}}, {"name": "language", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 19695589638, "num_examples": 94225}], "download_size": 19695589638, "dataset_size": 19695589638}, {"config_name": "es", "features": [{"name": "id", "dtype": "string"}, {"name": "query", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "negatives", "sequence": {"dtype": "string"}}, {"name": "language", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 19881676198, "num_examples": 102685}], "download_size": 19881676198, "dataset_size": 19881676198}, {"config_name": "it", "features": [{"name": "id", "dtype": "string"}, {"name": "query", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "negatives", "sequence": {"dtype": "string"}}, {"name": "language", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 20278641470, "num_examples": 98747}], "download_size": 20278641470, "dataset_size": 20278641470}, {"config_name": "de", "features": [{"name": "id", "dtype": "string"}, {"name": "query", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "negatives", "sequence": {"dtype": "string"}}, {"name": "language", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 19629975126, "num_examples": 100713}], "download_size": 19629975126, "dataset_size": 19629975126}, {"config_name": "fr", "features": [{"name": "id", "dtype": "string"}, {"name": "query", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "negatives", "sequence": {"dtype": "string"}}, {"name": "language", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 20825335207, "num_examples": 99797}], "download_size": 20825335207, "dataset_size": 20825335207}], "configs": [{"config_name": "en", "data_files": [{"split": "train", "path": "en/train-*"}]}, {"config_name": "it", "data_files": [{"split": "train", "path": "it/train-*"}]}, {"config_name": "fr", "data_files": [{"split": "train", "path": "fr/train-*"}]}, {"config_name": "es", "data_files": [{"split": "train", "path": "es/train-*"}]}, {"config_name": "de", "data_files": [{"split": "train", "path": "de/train-*"}]}], "license": "apache-2.0"}

6b92b5cae23d44509f1e05d7062befe5ec77f7c9

Multilingual Visual Document Retrieval Dataset This dataset consists of 500k multilingual query image samples, collected and generated from scratch using public internet pdfs. The queries are synthetic and generated using VLMs (gemini-1.5-pro and Qwen2-VL-72B). It was used to train the vdr-2b-multi-v1 retrieval multimodal, multilingual embedding model. How it was created This is the entire data pipeline used to create the Italian subset of this dataset. Each… See the full description on the dataset page: https://huggingface.co/datasets/llamaindex/vdr-multilingual-train.

677bb2afe4cf361eed72da2c

ngxson/MiniThinky-dataset

ngxson

{"language": ["en"], "dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 444645709, "num_examples": 88218}], "download_size": 214646754, "dataset_size": 444645709}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

df7ed56101c76cb9dae350ff2ccbc8fa0d493f33

MiniThinky dataset Merged from: https://huggingface.co/datasets/TuneIt/o1-python https://huggingface.co/datasets/O1-OPEN/OpenO1-SFT https://huggingface.co/datasets/KingNish/reasoning-base-20k Post processing: Replaced with the format below Remove any rows that does not have reasoning process (i.e remove straight responses) Deduplicated Response format <|thinking|>{thinking_process} <|answer|> {real_answer}

621ffdd236468d709f181e5e

cais/mmlu

cais

{"annotations_creators": ["no-annotation"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["question-answering"], "task_ids": ["multiple-choice-qa"], "paperswithcode_id": "mmlu", "pretty_name": "Measuring Massive Multitask Language Understanding", "language_bcp47": ["en-US"], "dataset_info": [{"config_name": "abstract_algebra", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 49618.6654322746, "num_examples": 100}, {"name": "validation", "num_bytes": 5485.515349444808, "num_examples": 11}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 17143, "dataset_size": 57303.3562203159}, {"config_name": "all", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 6967453, "num_examples": 14042}, {"name": "validation", "num_bytes": 763484, "num_examples": 1531}, {"name": "dev", "num_bytes": 125353, "num_examples": 285}, {"name": "auxiliary_train", "num_bytes": 161000625, "num_examples": 99842}], "download_size": 51503402, "dataset_size": 168856915}, {"config_name": "anatomy", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 66985.19833357072, "num_examples": 135}, {"name": "validation", "num_bytes": 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"data_files": [{"split": "test", "path": "world_religions/test-*"}, {"split": "validation", "path": "world_religions/validation-*"}, {"split": "dev", "path": "world_religions/dev-*"}]}]}

c30699e8356da336a370243923dbaf21066bb9fe

Dataset Card for MMLU Dataset Summary Measuring Massive Multitask Language Understanding by Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt (ICLR 2021). This is a massive multitask test consisting of multiple-choice questions from various branches of knowledge. The test spans subjects in the humanities, social sciences, hard sciences, and other areas that are important for some people to learn. This covers 57… See the full description on the dataset page: https://huggingface.co/datasets/cais/mmlu.

mmlu