datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
hermes-function-calling-v1
Hermes Function-Calling V1
This dataset is the compilation of structured output and function calling data used in the Hermes 2 Pro series of models.
This repository contains a structured output dataset with function-calling conversations, json-mode, agentic json-mode and structured extraction samples, designed to train LLM models in performing function calls and returning structured output based on natural language instructions. The dataset features various conversational scenarios… See the full description on the dataset page: https://huggingface.co/datasets/NousResearch/hermes-function-calling-v1.xlam-function-calling-60k
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/lockon/xlam-function-calling-60k.xlam-function-calling-60k
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… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k.COIG-CQIA
COIG-CQIA：Quality is All you need for Chinese Instruction Fine-tuning
Dataset Details
Dataset Description
欢迎来到COIG-CQIA，COIG-CQIA全称为Chinese Open Instruction Generalist - Quality is All You Need， 是一个开源的高质量指令微调数据集，旨在为中文NLP社区提供高质量且符合人类交互行为的指令微调数据。COIG-CQIA以中文互联网获取到的问答及文章作为原始数据，经过深度清洗、重构及人工审核构建而成。本项目受LIMA: Less Is More for Alignment等研究启发，使用少量高质量的数据即可让大语言模型学习到人类交互行为，因此在数据构建中我们十分注重数据的来源、质量与多样性，数据集详情请见数据介绍以及我们接下来的论文。
Welcome to the… See the full description on the dataset page: https://huggingface.co/datasets/m-a-p/COIG-CQIA.CodeFeedback-Filtered-Instruction OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement
[🏠Homepage]
|
[🛠️Code]
OpenCodeInterpreter
OpenCodeInterpreter is a family of open-source code generation systems designed to bridge the gap between large language models and advanced proprietary systems like the GPT-4 Code Interpreter. It significantly advances code generation capabilities by integrating execution and iterative refinement functionalities.
For further information and… See the full description on the dataset page: https://huggingface.co/datasets/m-a-p/CodeFeedback-Filtered-Instruction.python-codes-25k
License
MIT
This is a Cleaned Python Dataset Covering 25,000 Instructional Tasks
Overview
The dataset has 4 key features (fields): instruction, input, output, and text.It's a rich source for Python codes, tasks, and extends into behavioral aspects.
Dataset Statistics
Total Entries: 24,813
Unique Instructions: 24,580
Unique Inputs: 3,666
Unique Outputs: 24,581
Unique Texts: 24,813
Average Tokens per example: 508
Features… See the full description on the dataset page: https://huggingface.co/datasets/flytech/python-codes-25k.CRAGDatasets are taken from Facebook's CRAG: Comprehensive RAG Benchmark, see their arXiv paper for details about the dataset construction.
CRAG Sampler
We have added a simple Python tool for performing stratified sampling on CRAG data.
Installation
Local Development Install (Recommended)
git clone https://huggingface.co/Quivr/CRAG.git
cd CRAG
pip install -r requirements.txt # Install dependencies
pip install -e . # Install package in development mode… See the full description on the dataset page: https://huggingface.co/datasets/Quivr/CRAG.car-bench-dataset
CAR-Bench Dataset
CAR-Bench is a benchmark for evaluating AI voice assistants in a realistic automotive (car) environment.
It tests an agent's ability to correctly use vehicle control tools, handle disambiguation, and avoid hallucinations.
Dataset Structure
The dataset is organized into task configs and mock data configs:
Tasks
Each task defines a user persona, an instruction, the initial vehicle/environment context, and the ground-truth sequence of tool-call… See the full description on the dataset page: https://huggingface.co/datasets/johanneskirmayr/car-bench-dataset.sql-create-context
Overview
This dataset builds from WikiSQL and Spider.
There are 78,577 examples of natural language queries, SQL CREATE TABLE statements, and SQL Query answering the question using the CREATE statement as context. This dataset was built with text-to-sql LLMs in mind, intending to prevent hallucination of column and table names often seen when trained on text-to-sql datasets. The CREATE TABLE statement can often be copy and pasted from different DBMS and provides table names, column… See the full description on the dataset page: https://huggingface.co/datasets/b-mc2/sql-create-context.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.cyber-security
Cybersecurity AI Knowledge Base — PhD-Level Dataset
Overview
This is the most comprehensive cybersecurity knowledge base ever assembled for AI training. It covers all domains of cybersecurity at PhD-level depth — from offensive red teaming and bug bounty exploitation to defensive SOC operations, digital forensics, and cutting-edge AI/LLM security.
Size: 16 GB | Files: 507 | Domains: 30+ | Sources: 15+ platforms
Purpose
Train the world's most… See the full description on the dataset page: https://huggingface.co/datasets/Vyber07/cyber-security.Code-Feedback OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement
[🏠Homepage]
|
[🛠️Code]
Introduction
OpenCodeInterpreter is a family of open-source code generation systems designed to bridge the gap between large language models and advanced proprietary systems like the GPT-4 Code Interpreter. It significantly advances code generation capabilities by integrating execution and iterative refinement functionalities.
For further information and related… See the full description on the dataset page: https://huggingface.co/datasets/m-a-p/Code-Feedback.CareQA
CareQA
Dataset Summary
CareQA is a healthcare QA dataset with two versions:
Closed-Ended Version: A multichoice question answering (MCQA) dataset containing 5,621 QA pairs across six categories. Available in English and Spanish.
Open-Ended Version: A free-response dataset derived from the closed version, containing 2,769 QA pairs (English only).
The dataset originates from… See the full description on the dataset page: https://huggingface.co/datasets/HPAI-BSC/CareQA.CMB
CMB: A Comprehensive Medical Benchmark in Chinese
🌐 Github • 🌐 Website • 🤗 HuggingFace
🌈 Update
[2024.02.21] The answers to the CMB-Exam test has been updated and some errors caused by omissions in version management have been fixed.
[2024.01.08] In order to facilitate testing, we disclose the answers to the CMB-Exam test
[2023.09.22] CMB is included in OpenCompass.
[2023.08.21] Paper released.
[2023.08.01] 🎉🎉🎉 CMB is published！🎉🎉🎉
🌐… See the full description on the dataset page: https://huggingface.co/datasets/FreedomIntelligence/CMB.JMedBench
Maintainers
Junfeng Jiang@Aizawa Lab: jiangjf (at) is.s.u-tokyo.ac.jp
Jiahao Huang@Aizawa Lab: jiahao-huang (at) g.ecc.u-tokyo.ac.jp
If you find any error in this benchmark or want to contribute to this benchmark, please feel free to contact us.
Introduction
This is a dataset collection of JMedBench, which is a benchmark for evaluating Japanese biomedical large language models (LLMs).
Details can be found in this paper. We also provide an evaluation framework, med-eval… See the full description on the dataset page: https://huggingface.co/datasets/Coldog2333/JMedBench.LongBench-Pro
LongBench Pro: A More Realistic and Comprehensive Bilingual Long-Context Evaluation Benchmark
LongBench-Pro, containing 1,500 samples, is entirely built on authentic, natural long documents and includes 11 primary tasks and 25 secondary tasks, covering all long-context capabilities assessed by existing benchmarks. It employs diverse evaluation metrics, enabling a more fine-grained measurement of model abilities, and provides a balanced set of bilingual samples in both… See the full description on the dataset page: https://huggingface.co/datasets/caskcsg/LongBench-Pro.PrimeBench
PrimeBench
Practical Real-world Industry and Multi-domain Evaluation benchmark.
PrimeBench is a benchmark for evaluating evaluators. Each of its 400 examples is a pair of
responses to the same prompt, deliberately edited so that one is better than the other along a
named criterion. A reward model or LLM judge passes an example if it scores the chosen response
above the rejected one.
Built and maintained by Composo.
Why it exists
Most preference datasets score… See the full description on the dataset page: https://huggingface.co/datasets/ComposoAI/PrimeBench.mimo-claude-code-traces-1k
MIMO Claude Code Traces
MIMO Claude Code Traces is a collection of coding-agent trajectories in a Claude Code-style environment. Each record contains a user coding task, the full multi-turn message trace, available tool schemas, assistant reasoning fields, tool calls, tool outputs, and metadata such as model name, category, duration, cost, token usage, and whether the trace used tools.
The traces were generated with mimo-v2.5-pro, MiMo's most capable model at the time of… See the full description on the dataset page: https://huggingface.co/datasets/choucsan/mimo-claude-code-traces-1k.counselbench-100
CounselBench-100
CounselBench-100 v3.2.5 is a synthetic legal-work benchmark with 100
authored matters across ten practice workflows. Every task has a natural employee
request, a 97-asset evidence room, twelve portfolio decisions, 5–9 supported
actions, 3–7 evidence holds, and a distinct deep multi-provider MCP trajectory.
The answer is not preclassified in the evidence. Each portfolio item requires an
immutable identity join, an operative-authority and revision lookup, a… See the full description on the dataset page: https://huggingface.co/datasets/SamuelChien821/counselbench-100.Chinese-SimpleQA
Overview
🌐 Website • 🤗 Hugging Face • ⏬ Data • 📃 Paper • 📊 Leaderboard
Chinese SimpleQA is the first comprehensive Chinese benchmark to evaluate the factuality ability of language models to answer short questions, and Chinese SimpleQA mainly has five properties (i.e., Chinese, Diverse, High-quality, Static, Easy-to-evaluate). Specifically, our benchmark covers 6 major topics with 99 diverse subtopics.
Please visit our website or check our paper for more details.… See the full description on the dataset page: https://huggingface.co/datasets/OpenStellarTeam/Chinese-SimpleQA.copycolors_mcqaThis dataset consists of formatted n-way multiple choice questions, where n is in [2,10]. The task itself is simply to copy the prototypical color from the context and produce the corresponding color's answer choice letter.
The "prototypical colors" dataset instances themselves come from Memory Colors (Norland et al. 2021) and corypaik/coda (instances whose object_group is 0, indicating participants agreed on a prototypical color of that object).
ck12-tqa-instruction
CK-12 TQA: Textbook Question Answering (Instruction Format)
Dataset Description
Dataset Summary
This is a reformatted version of the TQA (Textbook Question Answering) dataset, converted into an instruction-following format suitable for training and evaluating large language models on science question answering and multimodal reasoning tasks.
The TQA dataset consists of 1,076 lessons from Life Science, Earth Science, and Physical Science textbooks sourced from… See the full description on the dataset page: https://huggingface.co/datasets/notefill/ck12-tqa-instruction.R1-Code-Interpreter-Data
R1-Code-Interpreter: Training LLMs to Reason with Code via Supervised and Reinforcement Learning
Our code is based on Llama-factory/VeRL/Search-R1 for the SFT and RL training and SymBench/BIG-Bench-Hard/reasoning-gym for datasets/benchmarks of reasoning/planning tasks.
📝 Introduction
R1-Code-Interpreter is the first framework to train LLMs for step-by-step code reasoning using multi-turn supervised fine-tuning and reinforcement learning. By curating 144 diverse… See the full description on the dataset page: https://huggingface.co/datasets/yongchao98/R1-Code-Interpreter-Data.CogSense-Bench
CogSense-Bench
Project Page | Paper | GitHub
CogSense-Bench is a comprehensive visual question answering (VQA) benchmark designed to evaluate the cognitive capabilities of Multimodal Large Language Models (MLLMs). It was introduced in the paper "Toward Cognitive Supersensing in Multimodal Large Language Model".
The benchmark assesses MLLMs across five cognitive dimensions:
Fluid intelligence
Crystallized intelligence
Visuospatial cognition
Mental simulation
Visual routines… See the full description on the dataset page: https://huggingface.co/datasets/PediaMedAI/CogSense-Bench.casimedicos-exp
Antidote CasiMedicos Dataset - Possible Answers Explanations in Resident Medical Exams
We present a new multilingual parallel medical dataset of commented medical exams which includes not only explanatory arguments
for the correct answer but also arguments to explain why the remaining possible answers are incorrect.
This dataset can be used for various NLP tasks including: Medical Question Answering, Explanatory Argument Extraction or Explanation Generation.
The… See the full description on the dataset page: https://huggingface.co/datasets/HiTZ/casimedicos-exp.WebWalkerQA📑 The paper of WebWalkerQA is available at arXiv.
📊 The dataset resource is a collection of 680 questions and answers from the WebWebWalker dataset.
🙋 The dataset is in the form of a JSON file.
The keys in the JSON include:
Question, Answer, Root_Url, and Info. The Info field contains
more detailed information, including Hop, Domain, Language,
Difficulty_Level, Source Website, and Golden_Path.
{
"Question": "When is the paper submission deadline for the ACL 2025 Industry Track, and what… See the full description on the dataset page: https://huggingface.co/datasets/callanwu/WebWalkerQA.ShareGPT4V
News
[2024/5/8] We released ShareGPT4Video, a large-scale video-caption dataset, with 40K captions annotated by GPT4V and 4.8M captions annotated by our ShareCaptioner-Video. The total videos last with 300 hours and 3000 hours separately!
ShareGPT4V 1.2M Dataset Card
Dataset details
Dataset type:
ShareGPT4V Captions 1.2M is a set of GPT4-Vision-powered multi-modal captions data.
It is constructed to enhance modality alignment and fine-grained visual concept… See the full description on the dataset page: https://huggingface.co/datasets/Lin-Chen/ShareGPT4V.vsr_random
VSR: Visual Spatial Reasoning
This is the random set of VSR: Visual Spatial Reasoning (TACL 2023) [paper].
Usage
from datasets import load_dataset
data_files = {"train": "train.jsonl", "dev": "dev.jsonl", "test": "test.jsonl"}
dataset = load_dataset("cambridgeltl/vsr_random", data_files=data_files)
Note that the image files still need to be downloaded separately. See data/ for details.
Go to our github repo for more introductions.
Citation
If you find VSR… See the full description on the dataset page: https://huggingface.co/datasets/cambridgeltl/vsr_random.race-cRace-C : additional data for race (high school/middle school) but for college level
https://github.com/mrcdata/race-c
@InProceedings{pmlr-v101-liang19a,
title={A New Multi-choice Reading Comprehension Dataset for Curriculum Learning},
author={Liang, Yichan and Li, Jianheng and Yin, Jian},
booktitle={Proceedings of The Eleventh Asian Conference on Machine Learning},
pages={742--757},
year={2019}
}
commonsense_qa_2.0https://github.com/allenai/csqa2
@article{talmor2022commonsenseqa,
title={CommonsenseQA 2.0: Exposing the limits of AI through gamification},
author={Talmor, Alon and Yoran, Ori and Bras, Ronan Le and Bhagavatula, Chandra and Goldberg, Yoav and Choi, Yejin and Berant, Jonathan},
journal={arXiv preprint arXiv:2201.05320},
year={2022}
}