datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
datasets-tests-compressionFlashRAG_datasets
⚡FlashRAG: A Python Toolkit for Efficient RAG Research
FlashRAG is a Python toolkit for the reproduction and development of Retrieval Augmented Generation (RAG) research. Our toolkit includes 36 pre-processed benchmark RAG datasets and 16 state-of-the-art RAG algorithms.
With FlashRAG and provided resources, you can effortlessly reproduce existing SOTA works in the RAG domain or implement your custom RAG processes and components.
For more information, please view our GitHub repo… See the full description on the dataset page: https://huggingface.co/datasets/RUC-NLPIR/FlashRAG_datasets.sci-datasets
Mainly science focused but other datasets exist too!
Einstein models are based on this repo.
Handwritten-Latex-Datasets
Dataset
This data set includes common handwritten formulas in junior high schools and high schools, and is labeled in Latex format. Can be used to train models that recognize common numbers, fractions, and sets.
Dataset source
Collected in various junior high schools and high schools, handwritten by students.
Usage
The label is stored at json folder and scanned hand-writted pictures are stored at pic folder.
Scan the qr code of the picture to get the index and… See the full description on the dataset page: https://huggingface.co/datasets/WindyVerse/Handwritten-Latex-Datasets.normalized-datasets-for-koreanLLM
Normalized Datasets for Korean LLM (Stage 0.5)
This is a comprehensive pretraining dataset for Korean LLMs containing ~944M normalized documents from 42 source datasets across English, Korean, Code, and Science domains.
Quick Stats
Metric
Value
Total Documents
~944,545,628
Source Datasets
42
Format
JSONL (one JSON object per line)
Languages
English, Korean
Domains
English, Korean, Code, Science
Pipeline Stage
Stage 0.5 (after download, before… See the full description on the dataset page: https://huggingface.co/datasets/mkd-chanwoo/normalized-datasets-for-koreanLLM.doc-formats-jsonl-1
[doc] formats - jsonl - 1
This dataset contains one jsonl file at the root.
taco-datasetsThis repo consists of the datasets used for the TaCo paper. There are four datasets:
Multilingual Alpaca-52K GPT-4 dataset
Multilingual Dolly-15K GPT-4 dataset
TaCo dataset
Multilingual Vicuna Benchmark dataset
We translated the first three datasets using Google Cloud Translation.
The TaCo dataset is created by using the TaCo approach as described in our paper, combining the Alpaca-52K and Dolly-15K datasets.
If you would like to create the TaCo dataset for a specific language, you can… See the full description on the dataset page: https://huggingface.co/datasets/saillab/taco-datasets.MUTAG
Dataset Card for MUTAG
Dataset Summary
The MUTAG dataset is 'a collection of nitroaromatic compounds and the goal is to predict their mutagenicity on Salmonella typhimurium'.
Supported Tasks and Leaderboards
MUTAG should be used for molecular property prediction (aiming to predict whether molecules have a mutagenic effect on a given bacterium or not), a binary classification task. The score used is accuracy, using a 10-fold cross-validation.
External… See the full description on the dataset page: https://huggingface.co/datasets/graphs-datasets/MUTAG.fractus-datasets
Fractus Datasets — the neuroscience-grounded training corpus
A proprietary, neuroscience-derived training corpus for the Fractus Continuous Thought Engine — ~3–4B tokens mapping real brain mechanisms to software/AI architecture, plus cognitive skills, code, esoteric tradition, and lexical knowledge.
Curator: Philippe-Antoine Robert · rpa.tu@proton.me · 2026
What this dataset collection IS
Fractus is a non-transformer Continuous Cognitive Agent whose architecture… See the full description on the dataset page: https://huggingface.co/datasets/thefinalboss/fractus-datasets.verified-defi-datasets
Verified Solana Sealevel & Anchor Program Optimization Fine-Tuning Corpus
Dataset Description
High-density, verified AI fine-tuning dataset in ALPACA format.
Domain: Solana Sealevel & Anchor Program Optimization
Verified Records: 3
Estimated Tokens: 339
Quality QA Score: 99.0%
Monetization Status: Direct Zero-Gas Web3 & HuggingFace Distribution
Entropy-Valley-Datasets
Entropy-Valley Datasets
📄 Paper (arXiv:2608.22274) | 💻 GitHub | 🤗 Models
This repository contains every data file read by Entropy-Valley (EV), the training-free target-length selector for masked diffusion machine translation introduced in "Length-Adaptive Decoding for Masked Diffusion Machine Translation" (EMNLP 2026 Main Conference).
Masked diffusion language models decode by filling a fixed-size canvas, so the target length must be chosen before denoising starts. EV probes… See the full description on the dataset page: https://huggingface.co/datasets/YanZhanPKU/Entropy-Valley-Datasets.SeaEval_datasets[GitHub]: https://github.com/SeaEval/SeaEval [Website]: https://seaeval.github.io/
@article{SeaEval,
title={SeaEval for Multilingual Foundation Models: From Cross-Lingual Alignment to Cultural Reasoning},
author={Wang, Bin and Liu, Zhengyuan and Huang, Xin and Jiao, Fangkai and Ding, Yang and Aw, Ai Ti and Chen, Nancy F.},
journal={NAACL},
year={2024}
}
hadith_datasets
Sunnah Dataset — Hadith JSON & CSV Collection
An open-source collection of authenticated Hadiths from the six major books of Sunnah, available in both JSON and CSV formats for research, study, and teaching purposes. This dataset is structured cleanly with English + Arabic + grading + reference links for each Hadith.
Contents
This dataset contains the following Hadith collections:
File Name
Format
Book Name
Jami' at-Tirmidhi.csv
CSV
Jami' at-Tirmidhi
Jami'… See the full description on the dataset page: https://huggingface.co/datasets/meeAtif/hadith_datasets.datasets
UdonPred datasets
Per-target protein intrinsic-disorder datasets for UdonPred: train/valid/test as jsonl ({id, y, x_0}) and FASTA, plus precomputed per-pLM embeddings under <target>/embeddings/<plm>/<split>.h5 (keyed by jsonl id).
peacock-data-public-datasetsToolGen-Datasets
How to use?
Before making use of this dataset, you may need to add the tokens to the vocabulary. For HuggingFace transformers tokenizer, the following is an example code snippet to add tokens.
from unidecode import unidecode
import transformers
with open('virtual_tokens.txt', 'r') as f:
virtual_tokens = f.readlines()
virtual_tokens = [unidecode(vt.strip()) for vt in virtual_tokens]
model_name_or_path = "meta-llama/Meta-Llama-3-8B"
# Load tokenizer and add tokens into… See the full description on the dataset page: https://huggingface.co/datasets/reasonwang/ToolGen-Datasets.votes_datasets_test2HackerSignal
HackerSignal
A large-scale, multi-source dataset linking hacker community discourse, exploit databases, vulnerability advisories, and fix commits through a shared CVE identifier space.
Overview
Statistic
Value
Documents
7,447,646 (exact-deduplicated)
Sources
64 public forum/source identifiers
Source layers
8
Temporal span
1988--2026
CVE-linked rows
360,004
Benchmark tasks
3
Quick Start
from datasets import load_dataset
# Load the… See the full description on the dataset page: https://huggingface.co/datasets/DatasetSubmission/HackerSignal.cpt_instruction_datasets
Instruction datasets
Collection of synthetic instruction datasets used during the continued pretraining of Model-small-instr-1, Model-small-instr-2 and Model-small-instr-3. You can currently find these models under: Llama-3.1-Carballo-Instr1 and Llama-3.1-Carballo-Instr3.
Dataset creation
Datasets were created using two different techniques:
Adapting already existing datasets or corpora by modifying their format to make them suitable for including instructions during… See the full description on the dataset page: https://huggingface.co/datasets/proxectonos/cpt_instruction_datasets.to-tool-call-datasets
🛠️ To-Tool-Call Datasets
A unified Qwen3-style tool-call corpus for SFT, GRPO, and agent training
To-Tool-Call Datasets is a curated mirror of public tool-call and function-calling corpora, re-serialized into one training-ready messages JSONL convention.
Quick Start ·
At a Glance ·
Format ·
Sources ·
Training Notes
[!IMPORTANT]
This repository is a format-harmonization layer, not a new claim of ownership over the… See the full description on the dataset page: https://huggingface.co/datasets/zhangdw/to-tool-call-datasets.repairllama-datasets
RepairLLaMA - Datasets
Contains the processed fine-tuning datasets for RepairLLaMA.
Instructions to explore the dataset
To load the dataset, you must define which revision (i.e., which input/output representation pair) you want to load.
from datasets import load_dataset
# Load ir1xor1
dataset = load_dataset("ASSERT-KTH/repairllama-datasets", "ir1xor1")
# Load irXxorY
dataset = load_dataset("ASSERT-KTH/repairllama-datasets", "irXxorY")
Citation
If you use… See the full description on the dataset page: https://huggingface.co/datasets/ASSERT-KTH/repairllama-datasets.Instruction-tuning_DatasetsAISafetyLab_DatasetsThis is the collection of various safety related datasets for AISafetyLab.
glyph-datasets
glyph-datasets
Data artifacts for Glyph — chemical-structure transcription with two models:
OCSRGlyph (molecule image → SMILES) and MarkushGlyph (patent Markush
image → CXSMILES + R-group table). This repository holds the training indices,
training metadata, and the frozen self-contained evaluation benchmarks that the
glyph package auto-downloads.
Code: EdisonScientific/glyph (GitHub)
Weights: EdisonScientific/OCSRGlyph, EdisonScientific/MarkushGlyph
License: Apache-2.0. See… See the full description on the dataset page: https://huggingface.co/datasets/EdisonScientific/glyph-datasets.TTS_eval_datasets
TTS evaluation datasets
This repository contains three testsets for zero-shot TTS models:
dialog_testset: Chinese and English testsets for spoken dialogue generation models, introduced in paper ZipVoice-Dialog.
librispeech_pc_testset: English testset for zero-shot TTS models, introduced in paper F5-TTS.
seedtts_testset: Chinese and English testsets for zero-shot TTS models, introduced in paper Seed-TTS.
minimax_multilingual_24: 24-language testset for zero-shot TTS models… See the full description on the dataset page: https://huggingface.co/datasets/k2-fsa/TTS_eval_datasets.PROTEINS
Dataset Card for PROTEINS
Dataset Summary
The PROTEINS dataset is a medium molecular property prediction dataset.
Supported Tasks and Leaderboards
PROTEINS should be used for molecular property prediction (aiming to predict whether molecules are enzymes or not), a binary classification task. The score used is accuracy, using a 10-fold cross-validation.
External Use
PyGeometric
To load in PyGeometric, do the following:
from datasets import… See the full description on the dataset page: https://huggingface.co/datasets/graphs-datasets/PROTEINS.UniME-V2-Training-Datasets
UniME-V2: MLLM-as-a-Judge for Universal Multimodal Embedding Learning
Tiancheng Gu*,
Kaicheng Yang*,
kaichen Zhang,
Xiang An,
Ziyong Feng, Yueyi Zhang,
Weidong Cai,
Jiankang Deng,
Lidong Bing
🛠️ Implementation
git clone https://github.com/deepglint/UniME-v2.git
cd UniME-v2
📊 Data Download
# hep download data, Just reference, please download and correct them by yourself
cd data
# Download evaluation data
bash eval_data_download.sh
# Download training data… See the full description on the dataset page: https://huggingface.co/datasets/TianchengGu/UniME-V2-Training-Datasets.MMM-datasets-TestsetMultilingual Mutual Reinforcement Effect Mix Datasets
This is a Training set of OIELLM.
This Train set already formatted by OIELLM's format. The test set is in the another page in huggingface.
The MMM support 3 languages (English, Chinese and Japanese). And you must use task instruct words to define kind of task.
Mutual Reinforcement Effect.
OIELLM's input and output
MMM Dataset
The following is input and output format:
{
"input": "In 1953, filming of "On the Waterfront" starring… See the full description on the dataset page: https://huggingface.co/datasets/ganchengguang/MMM-datasets-Testset.datasets-e2eGuardian-FailCoT-OOD-datasets
Guardian FailCoT — Out-of-Distribution Real-Robot Benchmarks
This repository bundles the three real-world failure-detection benchmarks used to evaluate the Guardian vision-language model in the paper Scaling Cross-Environment Failure Reasoning Data for Vision-Language Robotic Manipulation (Pacaud et al., 2026):
UR5-Fail — our newly collected three-view real-robot benchmark.
RoboFail — single-view real-robot manipulation failure benchmark from Liu et al. (CoRL 2023).
RoboVQA —… See the full description on the dataset page: https://huggingface.co/datasets/paulpacaud/Guardian-FailCoT-OOD-datasets.