datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
StreamingBench
StreamingBench: Assessing the Gap for MLLMs to Achieve Streaming Video Understanding
🏠 Project Page |
📄 arXiv Paper |
📦 Dataset |
🏅Leaderboard
StreamingBench evaluates Multimodal Large Language Models (MLLMs) in real-time, streaming video understanding tasks. 🌟
[NEW! 2025.05.15] 🔥: Seed1.5-VL achieved ALL model SOTA with a score of 82.80 on the Proactive Output.
[NEW! 2025.03.17] ⭐: ViSpeeker achieved Open-Source SOTA with a score of 61.60 on the… See the full description on the dataset page: https://huggingface.co/datasets/mjuicem/StreamingBench.STARK_10k
STARK: Spatial-Temporal reAsoning benchmaRK
STARK is a comprehensive benchmark designed to systematically evaluate large language models (LLMs) and large reasoning models (LRMs) on spatial-temporal reasoning tasks, particularly for applications in cyber-physical systems (CPS) such as robotics, autonomous vehicles, and smart city infrastructure.
Dataset Summary
Hierarchical Benchmark: Tasks are structured across three levels of reasoning complexity:
State Estimation:… See the full description on the dataset page: https://huggingface.co/datasets/prquan/STARK_10k.stark
STaRK
Website | Github | Paper
STaRK is a large-scale semi-structure retrieval benchmark on Textual and Relational Knowledge Bases
Downstream Task
Retrieval systems driven by LLMs are tasked with extracting relevant answers from a knowledge base in response to user queries. Each knowledge base is semi-structured, featuring large-scale relational data among entities and comprehensive textual information for each entity. We have constructed three knowledge bases: Amazon SKB… See the full description on the dataset page: https://huggingface.co/datasets/snap-stanford/stark.STARK_1k
Benchmarking Spatiotemporal Reasoning in Large Language Models: Capabilities and Challenges
Dataset for our paper: Benchmarking Spatiotemporal Reasoning in Large Language Models: Capabilities and Challenges
Contact Information
If you have any questions or feedback, feel free to reach out:
Name: Pengrui Quan
Email: prquan@ucla.edu
License
Copyright (c) 2025, UCLA Networked and Embedded Systems Laboratory (NESL)
All rights reserved.
Redistribution and use in… See the full description on the dataset page: https://huggingface.co/datasets/prquan/STARK_1k.Asclepius-Synthetic-Clinical-Notes
Asclepius: Synthetic Clincal Notes & Instruction Dataset
Dataset Summary
This dataset is official dataset for Asclepius (arxiv)
This dataset is composed with Clinical Note - Question - Answer format to build a clinical LLMs.
We first synthesized synthetic notes from PMC-Patients case reports with GPT-3.5
Then, we generate instruction-answer pairs for 157k synthetic discharge summaries
Supported Tasks
This dataset covers below 8 tasks
Named Entity… See the full description on the dataset page: https://huggingface.co/datasets/starmpcc/Asclepius-Synthetic-Clinical-Notes.legalbench-staging
Dataset Card for Dataset Name
Homepage: https://hazyresearch.stanford.edu/legalbench/
Repository: https://github.com/HazyResearch/legalbench/
Paper: https://arxiv.org/abs/2308.11462
Dataset Description
Dataset Summary
The LegalBench project is an ongoing open science effort to collaboratively curate tasks for evaluating legal reasoning in English large language models (LLMs). The benchmark currently consists of 162 tasks gathered from 40… See the full description on the dataset page: https://huggingface.co/datasets/nguha/legalbench-staging.SimpleQA-VerifiedSimpleQA Verified is a 1,000-prompt benchmark for reliably evaluating Large Language Models (LLMs) on short-form factuality and parametric knowledge. The authors from Google DeepMind and Google Research address various limitations of SimpleQA, originally designed by Wei et al. (2024) at OpenAI, including noisy and incorrect labels, topical biases, and question redundancy.
SimpleQA Verified was created to provide the research community with a more precise instrument to track genuine progress in… See the full description on the dataset page: https://huggingface.co/datasets/stalkermustang/SimpleQA-Verified.stack-exchange-dataset
Overview
This dataset consists of three TSV files, namely: cs.tsv, ds.tsv, and p.tsv.
Each file includes the data for the questions asked on a Stack Exchange (SE) question-answering community, from the creation of the community until May 2021.
cs.tsv --> Computer Science SE
ds.csv --> Data Science SE
p.csv --> Political Science SE
File Structure
Each file has the following columns:
id: the question id
title: the title of the question
body: the body or text of the… See the full description on the dataset page: https://huggingface.co/datasets/habedi/stack-exchange-dataset.StreamingBench-Slice
StreamingBench: Assessing the Gap for MLLMs to Achieve Streaming Video Understanding
🏠 Project Page |
📄 arXiv Paper |
📦 Dataset |
🏅Leaderboard
StreamingBench evaluates Multimodal Large Language Models (MLLMs) in real-time, streaming video understanding tasks. 🌟
[NEW! 2025.05.15] 🔥: Seed1.5-VL achieved ALL model SOTA with a score of 82.80 on the Proactive Output.
[NEW! 2025.03.17] ⭐: ViSpeeker achieved Open-Source SOTA with a score of 61.60 on the… See the full description on the dataset page: https://huggingface.co/datasets/jayzhu486/StreamingBench-Slice.ClimaQA
ClimaQA: An Automated Evaluation Framework for Climate Question Answering Models (ICLR 2025)
Check the paper's webpage and GitHub for more info!
The ClimaQA benchmark is designed to evaluate Large Language Models (LLMs) on climate science question-answering tasks by ensuring scientific rigor and complexity. It is built from graduate-level climate science textbooks, which provide a reliable foundation for generating questions with precise terminology and complex scientific theories.… See the full description on the dataset page: https://huggingface.co/datasets/Rose-STL-Lab/ClimaQA.statcan-dialogue-dataset-retrieval
Statcan Dialogue Dataset (Processed for Retrieval Tasks)
This is a variant of the Statcan Dialogue Dataset, which we processed specifically for multilingual retrieval (english, french). It contains everything in CSVs, rather than having metadata hosted separately.
Quickstart
from datasets import load_dataset
repo = 'McGill-NLP/statcan-dialogue-dataset-retrieval'
# load english queries, training split
queries_en = load_dataset(repo, 'queries_english', split='train') #… See the full description on the dataset page: https://huggingface.co/datasets/McGill-NLP/statcan-dialogue-dataset-retrieval.thai_buddhist_studies_exam
Thai Buddhist Studies Examination (Nak Tham)
This repository contains multiple-choice questions from the Thai Buddhist Studies
(Nak Tham) examination (2020, 2022, 2023). This dataset can be used for a benchmark for evaluating Large Language Models'
understanding of Thai Buddhist concepts and teachings.
Dataset Statistics
Year
Number of Multiple Choice Questions
2020
1,350
2022
1,400
2023
1,350
Phra Udom thought on the exam: We have reviewed the Nak… See the full description on the dataset page: https://huggingface.co/datasets/biodatlab/thai_buddhist_studies_exam.acc_rd_s1-gpqa
Dataset Card for GPQA
GPQA is a multiple-choice, Q&A dataset of very hard questions written and validated by experts in biology, physics, and chemistry. When attempting questions out of their own domain (e.g., a physicist answers a chemistry question), these experts get only 34% accuracy, despite spending >30m with full access to Google.
We request that you do not reveal examples from this dataset in plain text or images online, to reduce the risk of leakage into foundation model… See the full description on the dataset page: https://huggingface.co/datasets/stewy33/acc_rd_s1-gpqa.stackexchangeThis dataset is based entirely on HuggingFaceH4/stack-exchange-preferences, but it has been restructured.
All HTML tags have been cleaned out, and the answers column has been turned into the answer column, so instead of answers being stored in JSON format there is now a row for each answer.
Furthermore there is a separate file for every forum instead of a single file.
strix-philosophy-qa
Strix
134k question-answer pairs based on AiresPucrs' stanford-encyclopedia-philosophy dataset.
protein_stability_single_mutation
Protein Data Stability - Single Mutation
This repository contains data on the change in protein stability with a single mutation.
Attribution of Data Sources
Primary Source: Tsuboyama, K., Dauparas, J., Chen, J. et al. Mega-scale experimental analysis of protein folding stability in biology and design. Nature 620, 434–444 (2023). Link to the paper
Dataset Link: Zenodo Record
As to where the dataset comes from in this broader work, the relevant dataset (#3) is shown in… See the full description on the dataset page: https://huggingface.co/datasets/Trelis/protein_stability_single_mutation.hotpotqa-structuredThis dataset is associated with the paper Search, Inspect, Fetch: Exploiting Boolean Retrieval for Deep-Research Agents.
Official GitHub repository: https://github.com/ielab/skim-search-agent
Dense-Information-Science-Physics-Dataset
Dense Information With Multiple Fine-tuned Variations
This dataaset has multiple for each input to learn how to express the same answer in different ways
Dataset Structure
The dataset contains two columns:
Column
Description
input
A science or quantum-physics question
output
A conversational answer to the question
Example:
{
"input": "What is quantum entanglement?",
"output": "Quantum entanglement is when two quantum systems share one… See the full description on the dataset page: https://huggingface.co/datasets/StarpowerTechnology/Dense-Information-Science-Physics-Dataset.MQuAKE-ST
MQuAKE-ST KG–QA
A large knowledge-graph + question answering (KGQA) resource adapted from MQuAKE-CF and MQuAKE-Remastered and reconstructed over a fixed Wikidata-derived knowledge graph for multi-hop graph navigation.
MQuAKE-ST (where ST denotes Static) provides a shared, materialized KG together with natural-language multi-hop questions, explicit topic entities, answer entities, and path-level supervision for MultiHop KGQA.
Project: THESEUSPaper: Theseus in the Graph… See the full description on the dataset page: https://huggingface.co/datasets/HalcyonSolutions/MQuAKE-ST.VSMRC-mrc-ABCD
VSMRC/mrc (bản tách cột A/B/C/D)
Dataset này là gì
Đây là bản định dạng lại (reformatted / derived) của dataset gốc VSMRC/mrc — phần multiple-choice reading comprehension trong bộ VSMRC (Vietnamese Text Segmentation and Multiple-Choice Reading Comprehension Dataset), do nhóm tác giả tại Đại học
Công nghệ, ĐHQGHN công bố.
Dataset gốc đã có sẵn cột choices (list Python) và correctchoice (số nguyên 0-3) — bản này chỉ map lại thành các cột A, B, C, D, answer cho khớp… See the full description on the dataset page: https://huggingface.co/datasets/p-storm/VSMRC-mrc-ABCD.stock_trading_QAbootstrapvue-standardSamples in this benchmark were generated by RELAI using the following data source(s):
Data Source Name: BootstrapVue 2.23
Documentation Data Source Link: https://bootstrap-vue.org/docs/
Data Source License: https://github.com/bootstrap-vue/bootstrap-vue/blob/dev/LICENSE
Data Source Authors: BootstrapVue Team
AI Benchmarks by Data Agents © 2025 RELAI.AI · Licensed under CC BY 4.0. Source: https://relai.ai
NCERT_Business_Studies_12thViCS-MCQ
ViCS-MCQ: Vietnamese Computer Science Multiple-Choice QA Dataset
ViCS-MCQ (Vietnamese Computer Science Multiple-Choice Questions) là bộ dữ liệu câu hỏi trắc nghiệm tiếng Việt phục vụ nghiên cứu xử lý ngôn ngữ tự nhiên (NLP), đánh giá năng lực suy luận và đọc hiểu của các mô hình ngôn ngữ lớn (LLM Benchmark), cũng như xây dựng các hệ thống hỗ trợ học tập trong lĩnh vực Khoa học Máy tính & Công nghệ Thông tin.
Bộ dữ liệu tập trung vào 2 môn học nền tảng:
Hệ điều hành (Operating… See the full description on the dataset page: https://huggingface.co/datasets/p-storm/ViCS-MCQ.tamil_stories
Summary
tamil_stories is an open source dataset of instruct-style records generated by scraping publicly available short stories on the following websites.
Siruvarmalar
Tamilsurangam
Apart from scraping and automated cleaning, the data was also tagged manually by a group of volunteers.
This dataset created as part of Aya Open Science Initiative by Cohere For AI.
This dataset can be used for any purpose, whether academic or commercial, under the terms of the Apache 2.0 License.… See the full description on the dataset page: https://huggingface.co/datasets/aitamilnadu/tamil_stories.student-mh-counseling-bilingual
Student Mental Health Counseling Dataset
This dataset contains approximately 200,000 bilingual question–answer pairs related to student mental health counseling.It is intended for research and development of natural language processing (NLP) models that can assist in supportive counseling, dialogue systems, and educational mental health applications.
Dataset Summary
Size: ~200k rows
Languages: Bilingual (English and English)
Columns:
question: A student’s query… See the full description on the dataset page: https://huggingface.co/datasets/Kush26/student-mh-counseling-bilingual.semeval-2016-absa-reviews-english-translated-stanford-alpaca
Dataset Card for Dataset Name
Derived from eastwind/semeval-2016-absa-reviews-arabic using Helsinki-NLP/opus-mt-tc-big-ar-en
opentelemetry-standardSamples in this benchmark were generated by RELAI using the following data source(s):
Data Source Name: Homebrew
Documentation Data Source Link: https://opentelemetry.io/docs/
Data Source License: https://github.com/open-telemetry/opentelemetry.io/blob/main/LICENSE
Data Source Authors: OpenTelemetry Authors
AI Benchmarks by Data Agents © 2025 RELAI.AI · Licensed under CC BY 4.0. Source: https://relai.ai
Student-Mental-Health-Counseling-EN
Student Mental Health Counseling (EN)
Dataset Overview
This dataset is the final cleaned and filtered version in a three-step pipeline
that started with the original Vietnamese counseling dataset.
Step
Dataset
Rows
1. Original
chillies/student-mental-health-counseling-vn
750,169
2. Translated
arafatanam/Student-Mental-Health-Counseling-750K
750,169
3. Filtered (this dataset)
arafatanam/Student-Mental-Health-Counseling-EN
52,254
The goal of this… See the full description on the dataset page: https://huggingface.co/datasets/arafatanam/Student-Mental-Health-Counseling-EN.Step-Instruction-Gx
Step-Instruction-GX Dataset
Overview
The Step-Instruction-GX dataset is a collection of instructional and educational content designed to assist in various learning and decision-making tasks. It includes a wide range of questions and corresponding answers, covering topics from health tips to scientific concepts.
Dataset Details
Modalities
Text: The dataset primarily contains text data in various formats.
Formats
CSV: The dataset is… See the full description on the dataset page: https://huggingface.co/datasets/prithivMLmods/Step-Instruction-Gx.