datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
commonsense_qa
Dataset Card for "commonsense_qa"
Dataset Summary
CommonsenseQA is a new multiple-choice question answering dataset that requires different types of commonsense knowledge
to predict the correct answers . It contains 12,102 questions with one correct answer and four distractor answers.
The dataset is provided in two major training/validation/testing set splits: "Random split" which is the main evaluation
split, and "Question token split", see paper for details.… See the full description on the dataset page: https://huggingface.co/datasets/tau/commonsense_qa.MMLU-Pro
MMLU-Pro Dataset
MMLU-Pro dataset is a more robust and challenging massive multi-task understanding dataset tailored to more rigorously benchmark large language models' capabilities. This dataset contains 12K complex questions across various disciplines.
|Github | 🏆Leaderboard | 📖Paper |
🚀 What's New
[2026.03.11] Added more cutting-edge frontier models to the leaderboard, including the Claude-4.6 series, Seed2.0 series, Qwen3.5 series, and Gemini-3.1-Pro… See the full description on the dataset page: https://huggingface.co/datasets/TIGER-Lab/MMLU-Pro.truthful_qa
Dataset Card for truthful_qa
Dataset Summary
TruthfulQA is a benchmark to measure whether a language model is truthful in generating answers to questions. The benchmark comprises 817 questions that span 38 categories, including health, law, finance and politics. Questions are crafted so that some humans would answer falsely due to a false belief or misconception. To perform well, models must avoid generating false answers learned from imitating human texts.… See the full description on the dataset page: https://huggingface.co/datasets/truthfulqa/truthful_qa.trivia_qa
Dataset Card for "trivia_qa"
Dataset Summary
TriviaqQA is a reading comprehension dataset containing over 650K
question-answer-evidence triples. TriviaqQA includes 95K question-answer
pairs authored by trivia enthusiasts and independently gathered evidence
documents, six per question on average, that provide high quality distant
supervision for answering the questions.
Supported Tasks and Leaderboards
More Information Needed
Languages… See the full description on the dataset page: https://huggingface.co/datasets/mandarjoshi/trivia_qa.Nemotron-Terminal-Corpus
Terminal-Corpus: Large-Scale SFT Dataset for Terminal Agents
Terminal-Corpus is a large-scale Supervised Fine-Tuning (SFT) dataset designed to scale the terminal interaction capabilities of Large Language Models (LLMs). Developed by NVIDIA, this dataset was built using the Terminal-Task-Gen pipeline, which combines dataset adaptation with synthetic task generation across diverse domains.
🚀 Key Results & Performance
The high-quality trajectories in Terminal-Corpus enable… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-Terminal-Corpus.TOFU
TOFU: Task of Fictitious Unlearning 🍢
The TOFU dataset serves as a benchmark for evaluating unlearning performance of large language models on realistic tasks. The dataset comprises question-answer pairs based on autobiographies of 200 different authors that do not exist and are completely fictitiously generated by the GPT-4 model. The goal of the task is to unlearn a fine-tuned model on various fractions of the forget set.
Quick Links
Website: The landing page for TOFU… See the full description on the dataset page: https://huggingface.co/datasets/locuslab/TOFU.mmlu_no_trainThis dataset contains a copy of the cais/mmlu HF dataset but without the auxiliary_train split that takes a long time to generate again each time when loading multiple subsets of the dataset.
Please visit https://huggingface.co/datasets/cais/mmlu for more information on the MMLU dataset.
mmluMMLU (hendrycks_test on huggingface) without auxiliary train. It is much lighter (7MB vs 162MB) and faster than the original implementation, in which auxiliary train is loaded (+ duplicated!) by default for all the configs in the original version, making it quite heavy.
We use this version in tasksource.
Reference to original dataset:
Measuring Massive Multitask Language Understanding - https://github.com/hendrycks/test
@article{hendryckstest2021,
title={Measuring Massive Multitask Language… See the full description on the dataset page: https://huggingface.co/datasets/tasksource/mmlu.browsecomp-plus
BrowseComp-Plus
BrowseComp-Plus is a new benchmark for Deep-Research system, isolating the effect of the retriever and the LLM agent to enable fair, transparent comparisons of Deep-Research agents. The benchmark sources challenging, reasoning-intensive queries from OpenAI's BrowseComp. However, instead of searching the live web, BrowseComp-Plus evaluates against a fixed, curated corpus of ~100K web documents from the web. The corpus includes both human-verified evidence documents… See the full description on the dataset page: https://huggingface.co/datasets/Tevatron/browsecomp-plus.tiny-supervised-datasetreddit_dataset_157
Bittensor Subnet 13 Reddit Dataset
Dataset Summary
This dataset is part of the Bittensor Subnet 13 decentralized network, containing preprocessed Reddit data. The data is continuously updated by network miners, providing a real-time stream of Reddit content for various analytical and machine learning tasks.
For more information about the dataset, please visit the official repository.
Supported Tasks
The versatility of this dataset allows… See the full description on the dataset page: https://huggingface.co/datasets/tensorshield/reddit_dataset_157.ScienceQA
Dataset Card Creation Guide
Dataset Summary
Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering
Supported Tasks and Leaderboards
Multi-modal Multiple Choice
Languages
English
Dataset Structure
Data Instances
Explore more samples here.
{'image': Image,
'question': 'Which of these states is farthest north?',
'choices': ['West Virginia', 'Louisiana', 'Arizona', 'Oklahoma'],
'answer': 0… See the full description on the dataset page: https://huggingface.co/datasets/derek-thomas/ScienceQA.bigbenchBIG-Bench but it doesn't require the hellish dependencies (tensorflow, pypi-bigbench, protobuf) of the official version.
dataset = load_dataset("tasksource/bigbench",'movie_recommendation')
Code to reproduce:
https://colab.research.google.com/drive/1MKdLdF7oqrSQCeavAcsEnPdI85kD0LzU?usp=sharing
Datasets are capped to 50k examples to keep things light.
I also removed the default split when train was available also to save space, as default=train+val.
@article{srivastava2022beyond… See the full description on the dataset page: https://huggingface.co/datasets/tasksource/bigbench.Terminal-Lego-15k
[!NOTE]
This private PrimeIntellect mirror is filtered from
SWE-Lego/Terminal-Lego-15k.
It reflects ad6e280 from prime-data PR #34: 1,224
tasks are excluded and 13,825 task directories remain.
Each retained task's task.toml sets [environment].docker_image to its
prebuilt Prime platform image in the canonical org-less form
terminal-lego/<task-id>:latest (2026-07 registry homogenization; the images
were previously team-scoped as team-…/terminal-lego:<task-id>-<build-hash>).
The root… See the full description on the dataset page: https://huggingface.co/datasets/PrimeIntellect/Terminal-Lego-15k.browsecomp-plus-corpus
BrowseComp-Plus
Project Page | Paper | Code
BrowseComp-Plus is a new benchmark for Deep-Research system, isolating the effect of the retriever and the LLM agent to enable fair, transparent comparisons of Deep-Research agents. The benchmark sources challenging, reasoning-intensive queries from OpenAI's BrowseComp. However, instead of searching the live web, BrowseComp-Plus evaluates against a fixed, curated corpus of ~100K web documents from the web. The corpus includes both… See the full description on the dataset page: https://huggingface.co/datasets/Tevatron/browsecomp-plus-corpus.soc-ratchakitcha
Royal Gazette Thailand (Ratchakitcha) Dataset
ชุดข้อมูลราชกิจจานุเบกษา (แบบ Machine Readable)
โครงการ Open Law Data Thailand ร่วมกับคณะกรรมาธิการการพาณิชย์และการอุตสาหกรรม วุฒิสภา ได้รับความอนุเคราะห์ข้อมูลจาก สำนักเลขาธิการคณะรัฐมนตรี (สลค.) เพื่อเผยแพร่ข้อมูลกฎหมายไทยสู่สาธารณะในรูปแบบที่ประมวลผลได้ด้วยคอมพิวเตอร์ (Machine Readable) เพื่อส่งเสริมนวัตกรรม Legal Tech และ AI ของประเทศไทย
Dataset Description
ชุดข้อมูลนี้รวบรวมรายการประกาศในราชกิจจานุเบกษา… See the full description on the dataset page: https://huggingface.co/datasets/open-law-data-thailand/soc-ratchakitcha.glaive_toolcall_enBorrowed from: https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2
You can use it in LLaMA Factory by specifying dataset: glaive_toolcall_en.
MuSR
MuSR: Testing the Limits of Chain-of-thought with Multistep Soft Reasoning
Creating murder mysteries that require multi-step reasoning with commonsense using ChatGPT!
By: Zayne Sprague, Xi Ye, Kaj Bostrom, Swarat Chaudhuri, and Greg Durrett.
View the dataset on our custom viewer and project website!
Check out the paper. Appeared at ICLR 2024 as a spotlight presentation!
Git Repo with the source data, how to recreate the dataset (and create new ones!) here
mse-text-img-dataset
Dataset Card for MSE-text-img-dataset
We have created a custom dataset
that is extracted as a subset of the Math Stack Exchange (MSE)
dataset. This text-image dataset contains 64,860 questions with
their respective list of answers, scores, acceptance marking,
and image versions of each question and answer generated
from the stored text with embedded LaTeX math markup.
In this dataset there are 117,380 answers in total, with 1.81
answers per question on average. Each image… See the full description on the dataset page: https://huggingface.co/datasets/Zenos5/mse-text-img-dataset.tydiqa
Dataset Card for "tydiqa"
Dataset Summary
TyDi QA is a question answering dataset covering 11 typologically diverse languages with 204K question-answer pairs.
The languages of TyDi QA are diverse with regard to their typology -- the set of linguistic features that each language
expresses -- such that we expect models performing well on this set to generalize across a large number of the languages
in the world. It contains language phenomena that would not be found in… See the full description on the dataset page: https://huggingface.co/datasets/google-research-datasets/tydiqa.tmmluplus
TMMLU+ : Large scale traditional chinese massive multitask language understanding
iKala presents TMMLU+, a large-scale benchmark for evaluating LLM capabilities in Traditional Chinese, with content primarily reflecting Taiwan's linguistic, educational, and professional contexts. It covers 66 subjects, from elementary to professional domains, and is approximately six times larger than TMMLU with broader, more balanced coverage.
TMMLU+ v1.1 improves benchmark quality through… See the full description on the dataset page: https://huggingface.co/datasets/ikala/tmmluplus.MedXpertQA
Dataset Card for MedXpertQA
MedXpertQA is a highly challenging and comprehensive benchmark designed to evaluate expert-level medical knowledge and advanced reasoning capabilities. It features both text-based and multimodal question-answering tasks, with the multimodal subset leveraging structured clinical information alongside images.
Dataset Description
MedXpertQA comprises 4,460 questions spanning diverse medical specialties, tasks, body systems, and image types. It… See the full description on the dataset page: https://huggingface.co/datasets/TsinghuaC3I/MedXpertQA.TerminalWorld
TerminalWorld
Dataset Summary
TerminalWorld is a benchmark dataset for evaluating AI agents on real-world terminal and command-line tasks. It contains 1,530 terminal-based tasks reverse-engineered from publicly available terminal recordings, covering domains such as data processing, system administration, networking, security, version control, containers and orchestration, debugging and testing, environment setup, and scientific computing.
Each task includes a… See the full description on the dataset page: https://huggingface.co/datasets/EuniAI/TerminalWorld.scrollsSCROLLS: Standardized CompaRison Over Long Language Sequences.
A suite of natural language datasets that require reasoning over long texts.
https://scrolls-benchmark.com/Bitext-customer-support-llm-chatbot-training-dataset
Bitext - Customer Service Tagged Training Dataset for LLM-based Virtual Assistants
Overview
This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the Customer Support sector can be easily achieved using our two-step approach to LLM… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-customer-support-llm-chatbot-training-dataset.anle-toaan-gov-vn
Vietnamese Án lệ Corpus — anle.toaan.gov.vn
🇻🇳 Tóm tắt. Bộ dữ liệu các bản án + án lệ Việt Nam thu thập từ cổng
anle.toaan.gov.vn của Tòa án nhân dân tối cao.
Mỗi văn bản đi kèm markdown chuẩn hoá tiếng Việt và một lớp grounding mức câu
(mỗi trích dẫn mang sentence_id + char span trỏ ngược vào markdown). Bộ dữ
liệu là một phần của ViLA common-corpus và ship ba cấu hình HF theo chuẩn
chung: documents (bảng chính) · embeddings (vector 4096-D Nemotron-3-Embed-8B) ·
reduces (toạ… See the full description on the dataset page: https://huggingface.co/datasets/tmquan/anle-toaan-gov-vn.TemplateGSM
TemplateMath: Template-based Data Generation (TDG)
This is the official repository for the paper "Training and Evaluating Language Models with Template-based Data Generation", published at the ICLR 2025 DATA-FM Workshop.
Our work introduces Template-based Data Generation (TDG), a scalable paradigm to address the critical data bottleneck in training LLMs for complex reasoning tasks. We use TDG to create TemplateGSM, a massive dataset designed to unlock the next level of… See the full description on the dataset page: https://huggingface.co/datasets/math-ai/TemplateGSM.textbooks
The Textbooks Corpus in MedRAG
This HF dataset contains the chunked snippets from the Textbooks corpus used in MedRAG. It can be used for medical Retrieval-Augmented Generation (RAG).
Dataset Details
Dataset Descriptions
Textbooks is a collection of 18 widely used medical textbooks, which are important references for students taking the United States Medical Licensing Examination (USLME).
In MedRAG, the textbooks are processed as chunks with no more than 1000… See the full description on the dataset page: https://huggingface.co/datasets/MedRAG/textbooks.WebInstructSub
🦣 MAmmoTH2: Scaling Instructions from the Web
Project Page: https://tiger-ai-lab.github.io/MAmmoTH2/
Paper: https://arxiv.org/pdf/2405.03548
Code: https://github.com/TIGER-AI-Lab/MAmmoTH2
WebInstruct (Subset)
This repo contains the partial dataset used in "MAmmoTH2: Scaling Instructions from the Web". This partial data is coming mostly from the forums like stackexchange. This subset contains very high-quality data to boost LLM performance through instruction tuning.… See the full description on the dataset page: https://huggingface.co/datasets/TIGER-Lab/WebInstructSub.Trendyol-Cybersecurity-Instruction-Tuning-Dataset
Trendyol Cybersecurity Defense Instruction-Tuning Dataset (v2.0)
🚀 TL;DR
53,202 meticulously curated system/user/assistant instruction-tuning examples covering 200+ specialized cybersecurity domains. Built by the Trendyol Security Team for training state-of-the-art defensive security AI assistants. Expanded from 21K to 53K rows with comprehensive coverage of modern security challenges including cloud-native threats, AI/ML security, quantum computing risks… See the full description on the dataset page: https://huggingface.co/datasets/Trendyol/Trendyol-Cybersecurity-Instruction-Tuning-Dataset.