datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
rag-mini-wikipediaIn this huggingface discussion you can share what you used the dataset for.
Derives from https://www.kaggle.com/datasets/rtatman/questionanswer-dataset?resource=download we generated our own subset using generate.py.
bird_mini_dev
BIRD-SQL Mini-Dev
Update 2025-07-04
We are grateful for the valuable feedback from the community over the past year regarding BIRD Mini-Dev. Based on your suggestions, we have made significant updates to the BIRD Mini-Dev dataset.
For New Users
If you are new to BIRD Mini-Dev, you can download the complete databases and datasets using the following link:
Download BIRD Mini-Dev Complete Package
For Existing Users
If you have already downloaded the… See the full description on the dataset page: https://huggingface.co/datasets/birdsql/bird_mini_dev.rag-mini-bioasqSee here for an updated version without nans in text-corpus.
In this huggingface discussion you can share what you used the dataset for.
Derives from http://participants-area.bioasq.org/Tasks/11b/trainingDataset/ we generated our own subset using generate.py.
YuLan-Mini-Datasets-Phasae-27The tokenized datasets for YuLan-Mini phase 27, where each line has been packed to 28K tokens.
Usage
dataset = []
dataset_path = "/path/to/YuLan-Mini-Datasets-Phasae-27"
seed = 42
for data_name in sorted(os.listdir(dataset_path)):
d = load_dataset(
os.path.join(dataset_path, data_name),
split="train",
num_proc=8,
)
dataset.append(d)
print(f"Num subsets: {len(dataset)}")
dataset = concatenate_datasets(dataset).shuffle(seed=seed)… See the full description on the dataset page: https://huggingface.co/datasets/yulan-team/YuLan-Mini-Datasets-Phasae-27.YuLan-Mini-Datasets-Phasae-26The tokenized datasets for YuLan-Mini phase 26, where each line has been packed to 28K tokens.
Usage
dataset = []
dataset_path = "/path/to/YuLan-Mini-Datasets-Phasae-26"
seed = 42
for data_name in sorted(os.listdir(dataset_path)):
d = load_dataset(
os.path.join(dataset_path, data_name),
split="train",
num_proc=8,
)
dataset.append(d)
print(f"Num subsets: {len(dataset)}")
dataset = concatenate_datasets(dataset).shuffle(seed=seed)… See the full description on the dataset page: https://huggingface.co/datasets/yulan-team/YuLan-Mini-Datasets-Phasae-26.rag-mini-bioasqThis dataset is a subset of a training dataset by the BioASQ Challenge, which is available here.
It is derived from rag-datasets/rag-mini-bioasq.
Modifications include:
filling in missing passages (some of them contained "nan" instead of actual text),
changing relevant_passage_ids' type from string to sequence of ints,
deduplicating the passages (removed 40 duplicates) and fixing the relevant_passage_ids in QAP triplets to point to the corrected, deduplicated passages' ids,
splitting QAP… See the full description on the dataset page: https://huggingface.co/datasets/enelpol/rag-mini-bioasq.STRIDE-QA-Dataset-Mini
STRIDE-QA-Dataset-Mini
STRIDE-QA is a large-scale visual question answering (VQA) dataset for physically grounded spatiotemporal reasoning in autonomous driving. Constructed from 100 hours of multi-sensor driving data in Tokyo, it offers 16 M QA pairs over 270 K frames with dense annotations including 3D bounding boxes, segmentation masks, and multi-object tracks.
⚠️ Note: STRIDE-QA-Dataset-Mini is provided as a preliminary version and does not fully match the format of the… See the full description on the dataset page: https://huggingface.co/datasets/turing-motors/STRIDE-QA-Dataset-Mini.Dr.SCI
Dr. SCI Dataset (Reproduced)
[📜 Original Paper] •
[🤗 Reproduced Dataset] •
[💻 Reproduced Github]
Disclaimer: This is an unofficial reproduction of the Dr. SCI dataset introduced in"Improving Data and Reward Design for Scientific Reasoning in Large Language Models" [arXiv].A detailed implementation of the curation process is available in my GitHub Repo.This work is not affiliated with or endorsed by the original authors. Please refer to the original paper for… See the full description on the dataset page: https://huggingface.co/datasets/MiniByte-666/Dr.SCI.minimax-m3-deepsearchqa-skill-eval
MiniMax M3 DeepSearchQA Skill Eval
Evaluates minimax/minimax-m3 on google/deepsearchqa using a Pi agent, You.com MCP tools, and a research skill optimized for this harness, model, and tool surface.
MiniMax M3 Medium Reasoning with the You.com research skill reached 74.85% adjusted F1 on DeepSearchQA, above the paper's GPT-5 High Reasoning F1 result. Public artifacts are available for inspection and reproduction.
Links
GitHub:… See the full description on the dataset page: https://huggingface.co/datasets/youdotcom/minimax-m3-deepsearchqa-skill-eval.minimal_video_pairs
Minimal Video Pairs
A shortcut-aware benchmark for spatio-temporal and intuitive physics video understanding (VideoQA) using minimally different video pairs.
Github
For legal reasons, we are unable to upload the videos directly to Huggingface. However, we provide scripts in this repository for downloading the videos in our github repository. Our benchmark is built on top of videos source from 9 domains:
Subset
Data sources
Human object interactions
PerceptionTest… See the full description on the dataset page: https://huggingface.co/datasets/facebook/minimal_video_pairs.Medical-Reasoning-SFT-Trinity-Mini
Medical-Reasoning-SFT-Trinity-Mini
A large-scale medical reasoning dataset generated using arcee-ai/Trinity-Mini, containing over 810,000 samples with detailed chain-of-thought reasoning for medical and healthcare questions.
Dataset Overview
Metric
Value
Model
arcee-ai/Trinity-Mini
Total Samples
~810,374
Estimated Tokens
~1.52 Billion
Content Tokens
~542 Million
Reasoning Tokens
~977 Million
Language
English
Schema
Each… See the full description on the dataset page: https://huggingface.co/datasets/OpenMed/Medical-Reasoning-SFT-Trinity-Mini.latent-mining
Latent Mining
Latent Mining is a benchmark-construction method for scientific-agent tasks where useful public evidence diverges from a withheld verifier-backed outcome. The resulting tasks test whether agents can make calibrated scientific triage decisions under incomplete information.
This dataset contains the first public biology subset: 165 cross-locus regulatory-edit triage tasks. Each task asks an agent to choose among candidate noncoding edits for a specified assay and… See the full description on the dataset page: https://huggingface.co/datasets/Jarrodbarnes/latent-mining.Poseidon-Reasoning-Mini-300K
Poseidon-Reasoning-Mini-300K
Poseidon-Reasoning-Mini-300K is a compact, high-quality reasoning dataset designed for advanced tasks in mathematics, coding, and science. This smaller-scale collection maintains the depth and quality of its larger counterparts, with a focus on multi-step and general reasoning—making it ideal for model pretraining, fine-tuning, benchmarking, and STEM educational applications.
Quick Start with Hugging Face Datasets🤗
pip install -U… See the full description on the dataset page: https://huggingface.co/datasets/prithivMLmods/Poseidon-Reasoning-Mini-300K.rag-mini-bioasq-with-metadataThis dataset is an extension of the rag-mini-bioasq dataset.
Its difference resides in the text-corpus part of the aforementioned set where the metadata was added for each passage.
Metadata contains six separate categories, each in a dedicated column:
Year of the publication (publish_year)
Type of the publication (publish_type)
Country of the publication - often correlated with the homeland of the authors (country)
Number of pages (no_pages)
Authors (authors)
Keywords (keywords)
EMMA-mini
Dataset Description
We introduce EMMA (Enhanced MultiModal reAsoning), a benchmark targeting organic multimodal reasoning across mathematics, physics, chemistry, and coding.
EMMA tasks demand advanced cross-modal reasoning that cannot be solved by thinking separately in each modality, offering an enhanced test suite for MLLMs' reasoning capabilities.
EMMA is composed of 2,788 problems, of which 1,796 are newly constructed, across four domains. Within each subject, we further… See the full description on the dataset page: https://huggingface.co/datasets/luckychao/EMMA-mini.EMMA-mini
Dataset Description
EMMA (Enhanced MultiModal reAsoning) is a benchmark targeting organic multimodal reasoning across mathematics, physics, chemistry, and coding.
EMMA tasks demand advanced cross-modal reasoning that cannot be solved by thinking separately in each modality, offering an enhanced test suite for MLLMs' reasoning capabilities.
EMMA is composed of 2,788 problems, of which 1,796 are newly constructed, across four domains. Within each subject, we further provide… See the full description on the dataset page: https://huggingface.co/datasets/lmms-lab-encoder/EMMA-mini.OpenMathReasoning-mini-ko
OpenMathReasoning-mini Korean (한국어 번역)
이 데이터셋은 unsloth/OpenMathReasoning-mini 데이터셋을 한국어로 번역한 버전입니다.
원본 데이터셋은 nvidia/OpenMathReasoning 에서 파생되었습니다.
데이터셋 정보
항목
내용
원본 데이터셋
unsloth/OpenMathReasoning-mini
원본 출처
nvidia/OpenMathReasoning
라이선스
CC-BY-4.0
행 수
19,252
언어
한국어 (Korean), 영어 (English)
번역 대상 컬럼
expected_answer, problem, generated_solution
라이선스 및 귀속
이 데이터셋은 원본 데이터셋인 nvidia/OpenMathReasoning의 CC-BY-4.0 라이선스를 따릅니다.
원저작자: NVIDIA… See the full description on the dataset page: https://huggingface.co/datasets/neuralfoundry-coder/OpenMathReasoning-mini-ko.reasoning-sft-minimax-microsoft-orca-agentinstruct-1M-v1
MiniMax-M2.5 Reasoning SFT (Orca AgentInstruct 1M v1)
Reasoning SFT dataset generated by MiniMaxAI/MiniMax-M2.5 on prompts from the Stratified K-Means Diverse Instruction-Following 100K-1M dataset (Orca AgentInstruct subset).
Format
Each row has three columns:
input — list of dicts [{"role": "...", "content": "..."}, ...] (conversation turns)
response — model-generated response with <think> reasoning block
source — task category (creative_content, text_modification, rc… See the full description on the dataset page: https://huggingface.co/datasets/AmanPriyanshu/reasoning-sft-minimax-microsoft-orca-agentinstruct-1M-v1.code-minier
Code minier, non-instruct (2025-09-18)
The objective of this project is to provide researchers, professionals and law students with simplified, up-to-date access to all French legal texts, enriched with a wealth of data to facilitate their integration into Community and European projects.
Normally, the data is refreshed daily on all legal codes, and aims to simplify the production of training sets and labeling pipelines for the development of free, open-source language models based… See the full description on the dataset page: https://huggingface.co/datasets/louisbrulenaudet/code-minier.Medical-Reasoning-SFT-MiniMax-M2.1
Medical-Reasoning-SFT-MiniMax-M2.1
A large-scale medical reasoning dataset generated using MiniMaxAI/MiniMax-M2.1, containing over 204,000 samples with detailed chain-of-thought reasoning for medical and healthcare questions.
Dataset Overview
Metric
Value
Model
MiniMaxAI/MiniMax-M2.1
Total Samples
204,773
Samples with Reasoning
204,773 (100%)
Estimated Tokens
~621 Million
Content Tokens
~344 Million
Reasoning Tokens
~277 Million
Language
English… See the full description on the dataset page: https://huggingface.co/datasets/OpenMed/Medical-Reasoning-SFT-MiniMax-M2.1.MovieTection_Mini
Dataset Description 🎬
The MovieTection_Mini dataset is a benchmark designed for detecting pretraining data in Large Vision-Language Models (VLMs). It serves as a resource for analyzing model exposure to Copyrighted Visual Content ©️.
This dataset is a compact subset of the full MovieTection dataset, containing only 4 movies instead of 100. It is designed for users who want to experiment with the benchmark without the need to download the entire dataset, making it a more… See the full description on the dataset page: https://huggingface.co/datasets/DIS-CO/MovieTection_Mini.rag-mini-wikipediaIn this huggingface discussion you can share what you used the dataset for.
Derives from https://www.kaggle.com/datasets/rtatman/questionanswer-dataset?resource=download we generated our own subset using generate.py.
Medical-Reasoning-SFT-Trinity-Mini
Medical-Reasoning-SFT-Trinity-Mini
A large-scale medical reasoning dataset generated using arcee-ai/Trinity-Mini, containing over 810,000 samples with detailed chain-of-thought reasoning for medical and healthcare questions.
Dataset Overview
Metric
Value
Model
arcee-ai/Trinity-Mini
Total Samples
~810,374
Estimated Tokens
~1.52 Billion
Content Tokens
~542 Million
Reasoning Tokens
~977 Million
Language
English
Schema
Each sample… See the full description on the dataset page: https://huggingface.co/datasets/Ashray27/Medical-Reasoning-SFT-Trinity-Mini.ralphdev-ko-instruct-mini
RALPHDEV 한국어 짧은 답변 실습 데이터
이 데이터는 한국어 문단과 질문을 대화 형식으로 바꾼 소규모 SFT 실습 자료다. 모델이 주어진 문단에서 답을 찾아 짧게 쓰도록 학습하는 과정을 재현하는 데 쓴다. 공식 KLUE 벤치마크 점수를 보고하거나 일반적인 한국어 질의응답 성능을 대표하는 데이터가 아니다.
구성
config
split
행 수
question_type 1
question_type 2
mini-128
train
128
80
48
main-1024
train
1,024
640
384
두 config 모두 train split만 공개한다. dev와 final은 모델 선택과 최종 평가의 누출을 막기 위해 포함하지 않았다. SPRi 자료, 원문 PDF, 인증 정보도 포함하지 않았다.
각 train.jsonl 행은 원천 GUID를 유지한 id와 messages만 담는다. messages는… See the full description on the dataset page: https://huggingface.co/datasets/YoungjaeDev/ralphdev-ko-instruct-mini.rag-mini-wikipediaIn this huggingface discussion you can share what you used the dataset for.
Derives from https://www.kaggle.com/datasets/rtatman/questionanswer-dataset?resource=download we generated our own subset using generate.py.
smol-smoltalk-mini-Interaction-SFT
Dataset Card for ReactiveAI/Smol-Smoltalk-Mini Interaction SFT
Derived from HuggingFaceTB/smol-smoltalk (used 25% of train & test splits). Made for Interaction Supervised
Fine-Tuning of Reactive Transformer Proof-of-Concept models, especially RxT-Alpha-Mini (more info soon).
Full version available in ReactiveAI/smol-smoltalk-Interaction-SFT
Dataset Details
Dataset Description
Reactive Transformers are processing only the single interactions in… See the full description on the dataset page: https://huggingface.co/datasets/ReactiveAI/smol-smoltalk-mini-Interaction-SFT.TinyStories-mini-Interaction-SFT
Dataset Card for ReactiveAI/TinyStories-mini-Interaction-SFT
Interaction Supervised Fine-Tuning Dataset based on roneneldan/TinyStories dataset, made for Reactive
Transformer second training stage Proof-of-Concept.
Full version available in ReactiveAI/TinyStories-Interaction-SFT
Dataset Details
Dataset Description
Curated by: Reactive AI
Language(s) (NLP): English
License: apache-2.0
Uses
This dataset is made for Supervised… See the full description on the dataset page: https://huggingface.co/datasets/ReactiveAI/TinyStories-mini-Interaction-SFT.GPT-OSS-20B-Distilled-Reasoning-Mini
Dataset Card for Dataset Name
GPT-OSS-20B Distilled Reasoning Dataset Mini
(Multi-stage Evaluative Refinement Method for Reasoning Generation)
Dataset Details and Description
This is a high-quality instruction fine-tuning dataset constructed through knowledge distillation, featuring detailed Chain-of-Thought (CoT) reasoning processes. The dataset is designed to enhance the capabilities of smaller language models in complex reasoning, logical analysis, and instruction… See the full description on the dataset page: https://huggingface.co/datasets/Jackrong/GPT-OSS-20B-Distilled-Reasoning-Mini.Magpie-Pro-10K-GPT4o-minifiltered-rag-mini-bioasq-80-20This dataset is a fork of rag-datasets/rag-mini-bioasq.
The use case for this dataset is to test and evaluate an LLM or a RAG application's ability to filter out irrelevant information to answer a given question.
This forks filters away all questions with one or more "NaN" passages, and adds an average of 80% irrelevant passages to each question.