datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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.LongBench-v2
LongBench v2: Towards Deeper Understanding and Reasoning on Realistic Long-context Multitasks
🌐 Project Page: https://longbench2.github.io
💻 Github Repo: https://github.com/THUDM/LongBench
📚 Arxiv Paper: https://arxiv.org/abs/2412.15204
LongBench v2 is designed to assess the ability of LLMs to handle long-context problems requiring deep understanding and reasoning across real-world multitasks. LongBench v2 has the following features: (1) Length: Context length ranging from 8k to… See the full description on the dataset page: https://huggingface.co/datasets/zai-org/LongBench-v2.requests
Open LLM Leaderboard Requests
This repository contains the request files of models that have been submitted to the Open LLM Leaderboard.
You can take a look at the current status of your model by finding its request file in this dataset. If your model failed, feel free to open an issue on the Open LLM Leaderboard! (We don't follow issues in this repository as often)
Evaluation Methodology
The evaluation process involves running your models against several benchmarks from… See the full description on the dataset page: https://huggingface.co/datasets/open-llm-leaderboard-old/requests.tiny-supervised-datasetxlam-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.chm-corr-prj-giangToolACE
ToolACE
ToolACE is an automatic agentic pipeline designed to generate Accurate, Complex, and divErse tool-learning data.
ToolACE leverages a novel self-evolution synthesis process to curate a comprehensive API pool of 26,507 diverse APIs.
Dialogs are further generated through the interplay among multiple agents, guided by a formalized thinking process.
To ensure data accuracy, we implement a dual-layer verification system combining rule-based and model-based checks.
More details… See the full description on the dataset page: https://huggingface.co/datasets/lockon/ToolACE.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.
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.ParseBench
ParseBench
Quick links: [🌐 Website] [📜 Paper] [💻 Code]
ParseBench is a benchmark for evaluating document parsing systems on real-world enterprise documents, with the following characteristics:
Multi-dimensional evaluation. The benchmark is stratified into five capability dimensions — tables, charts, content faithfulness, semantic formatting, and visual grounding — each with task-specific metrics designed to capture what agentic workflows depend on.
Real-world enterprise… See the full description on the dataset page: https://huggingface.co/datasets/llamaindex/ParseBench.MathInstruct
🦣 MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning
MathInstruct is a meticulously curated instruction tuning dataset that is lightweight yet generalizable. MathInstruct is compiled from 13 math rationale datasets, six of which are newly curated by this work. It uniquely focuses on the hybrid use of chain-of-thought (CoT) and program-of-thought (PoT) rationales, and ensures extensive coverage of diverse mathematical fields.
Project Page:… See the full description on the dataset page: https://huggingface.co/datasets/TIGER-Lab/MathInstruct.VideoChat3-LV116k
VideoChat3-LV116K
VideoChat3-LV116K is the long-video instruction data used by VideoChat3. It is designed to complement short academic video data with supervision over longer temporal contexts, where evidence can be sparse, delayed, and distributed across multiple video segments.
The dataset is constructed through a long-video synthesis pipeline. Candidate long videos are filtered for visual quality, semantic content, and temporal coherence. Videos are then split into manageable… See the full description on the dataset page: https://huggingface.co/datasets/MCG-NJU/VideoChat3-LV116k.FIM-Midtraining-400K
FIM-Midtraining-400K
📄 Paper · 💻 GitHub · 🤗 Collection
The mid-training corpus of "Function-Aware Fill-in-the-Middle as Mid-Training for Coding Agent Foundation Models": 400K function-aware FIM samples (~2.6B tokens under the Qwen2.5-Coder tokenizer) drawn from 75,568 Python files across 968 permissively-licensed GitHub repositories, fully decontaminated against SWE-Bench.
A coding agent's inner loop — act → observe → continue — is structurally isomorphic to a function call… See the full description on the dataset page: https://huggingface.co/datasets/TIGER-Lab/FIM-Midtraining-400K.ExtractBench
ExtractBench
Quick links: [🌐 Website] [📜 Paper] [💻 Code]
Given a document and a schema, a system returns structured data with evidence. The input is a full document, born-digital or scanned, and a schema written by the user. The output is a schema-valid JSON object, with the source page and a bounding box for each value as evidence. It must return correct, exhaustive values (including repeated records), correctly use null for absent information, and ground each extracted… See the full description on the dataset page: https://huggingface.co/datasets/llamaindex/ExtractBench.pile-val-backupThis is a backup for the pile val dataset downloaded from here: https://the-eye.eu/public/AI/pile/val.jsonl.zst
Please respect the original license of the dataset.
open-australian-legal-corpus
Open Australian Legal Corpus ⚖️
The Open Australian Legal Corpus by Isaacus, a foundational legal AI research company, is the first and only multijurisdictional open corpus of Australian legislative and judicial documents.
Comprised of 229,122 texts totalling over 60 million lines and 1.4 billion tokens, the Corpus includes every in force statute and regulation in the Commonwealth, New South Wales, Queensland, Western Australia, South Australia, Tasmania and Norfolk Island, in… See the full description on the dataset page: https://huggingface.co/datasets/isaacus/open-australian-legal-corpus.leaderboard-requestsM3_VOS
[CVPR 2025] M3-VOS: Multi-Phase, Multi-Transition, and Multi-Scenery Video Object Segmentation
If you like our project, please give us a star ⭐ on GitHub for the latest update.
💡 Description
Venue: CVPR2025
Repository: 🛠️Tool, 🏠Page
Paper: arxiv.org/html/2412.13803v2
Point of Contact: Jiaxin Li , Zixuan Chen
📁 Structure
This dataset contains annotated videos and images for object segmentation tasks with phase transition information. The directory… See the full description on the dataset page: https://huggingface.co/datasets/Lijiaxin0111/M3_VOS.ASearcher-Local-Knowledgetm-system_promptdrh-System-Prompt-processedswallow-math-v2
SwallowMath-v2
Resources
📑 arXiv: Read our paper for detailed methodology at arXiv:2505.02881.
🤗 Sister Dataset: Discover SwallowCode2, our companion dataset for code generation.
🧮 What is it?
SwallowMath-v2 is a large-scale mathematical dataset containing 32 billion tokens, developed as the successor to SwallowMath-v1.
Building on the success of v1, this release aims to construct a larger-scale and more permissively licensed corpus to support open and… See the full description on the dataset page: https://huggingface.co/datasets/tokyotech-llm/swallow-math-v2.OIG
This is the Open Instruction Generalist Dataset
This is our attempt to create a large instruction dataset of medium quality along with a smaller high quality instruciton dataset (OIG-small-chip2).
The data is in the form of jsonl objects, with at least a 'text' field. Some datasets may also include a 'metadata' field. The 'text' field contains a string of the form of one or more of:
<human>: instruction\n<bot>: response
<human>: instruction\n<bot>: response .. <human>:… See the full description on the dataset page: https://huggingface.co/datasets/laion/OIG.Long-Horizon-Terminal-Bench
Long-Horizon Terminal-Bench (LHTB)
LHTB is a 46-task benchmark for measuring how well LLM agents sustain useful
work in a containerized terminal over hundreds of steps. Unlike short-horizon
coding benchmarks where an agent writes one artifact and stops, LHTB drops the agent
into a stateful environment and grades it with hidden, rebuild-from-artifact
verifiers — self-reported progress does not count.
📝 Blog: https://zli12321.github.io/LHTB/
🏆 Leaderboard:… See the full description on the dataset page: https://huggingface.co/datasets/IntelligenceLab/Long-Horizon-Terminal-Bench.LIMODataset for LIMO: Less is More for Reasoning
Usage
from datasets import load_dataset
dataset = load_dataset("GAIR/LIMO", split="train")
Citation
If you find our dataset useful, please cite:
@misc{ye2025limoreasoning,
title={LIMO: Less is More for Reasoning},
author={Yixin Ye and Zhen Huang and Yang Xiao and Ethan Chern and Shijie Xia and Pengfei Liu},
year={2025},
eprint={2502.03387},
archivePrefix={arXiv},
primaryClass={cs.CL}… See the full description on the dataset page: https://huggingface.co/datasets/GAIR/LIMO.turkish-llm-dataset
Turkish Pretraining Corpus
Dataset Description
This dataset is a Turkish pretraining corpus created by combining BellaTurca (excluding ForumSohbetleri), Cosmos-Turkish-Corpus-v1.0, and FineWeb-2 Turkish Categorized, followed by cleaning, normalization, and deduplication. It is intended for the development, training, and evaluation of Turkish language models.
This dataset was prepared as part of a capstone project conducted by a group of students from Sabancı… See the full description on the dataset page: https://huggingface.co/datasets/tascib/turkish-llm-dataset.RoboPulsePlusPlus
RoboPulse++
RoboPulse++ is an interval-level benchmark introduced in PRM-as-a-Judge 1.5: A Toolkit for Robot Process Assessment for evaluating progress judge models throughout complete robot manipulation trajectories.
This Hugging Face release contains 700 episodes with natural-language task instructions, temporally ordered observations, and human-annotated progress intervals.
Overview
RoboPulse++ extends progress judging from isolated state pairs to temporal… See the full description on the dataset page: https://huggingface.co/datasets/lyy0715/RoboPulsePlusPlus.RegexEval
Dataset Card for RegexEval
Re(gEx|DoS)Eval is a framework that includes a dataset of 762 regex descriptions (prompts) from real users, refined prompts with examples, and a robust set of tests.
Dataset Details
Dataset Sources
Repository: https://github.com/s2e-lab/RegexEval
Paper: https://s2e-lab.github.io/preprints/icse_nier24-preprint.pdf
Dataset Structure
dataset.jsonl: dataset file in jsonl format. Every line contains a JSON object with… See the full description on the dataset page: https://huggingface.co/datasets/s2e-lab/RegexEval.swallow-code-v2
SwallowCode-v2
Resources
📑 arXiv: Read our paper for detailed methodology and results at arXiv:2505.02881.
🤗 Sister Dataset: Discover SwallowMath-v2, our companion dataset for mathematical reasoning.
💻 What is it?
SwallowCode-v1 was a high-quality Python code dataset generated through an LLM-based rewriting pipeline.
However, it had two significant limitations:
(1) it was distributed under the Llama 3.3 Community License, and
(2) its size was limited to… See the full description on the dataset page: https://huggingface.co/datasets/tokyotech-llm/swallow-code-v2.hateful_memes_expanded