datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
MetaMathQAView the project page:
https://meta-math.github.io/
see our paper at https://arxiv.org/abs/2309.12284
Note
All MetaMathQA data are augmented from the training sets of GSM8K and MATH.
None of the augmented data is from the testing set.
You can check the original_question in meta-math/MetaMathQA, each item is from the GSM8K or MATH train set.
Model Details
MetaMath-Mistral-7B is fully fine-tuned on the MetaMathQA datasets and based on the powerful Mistral-7B model. It is… See the full description on the dataset page: https://huggingface.co/datasets/meta-math/MetaMathQA.SlimPajama-Meta-rater
Annotated SlimPajama Dataset
Dataset Description
This dataset contains the first fully annotated SlimPajama dataset with comprehensive quality metrics for data-centric large language model research. The dataset includes approximately 580 billion tokens from the training set of the original SlimPajama dataset, annotated across 25 different quality dimensions.
Note: This dataset contains only the training set portion of the original SlimPajama dataset, which is why the… See the full description on the dataset page: https://huggingface.co/datasets/opendatalab/SlimPajama-Meta-rater.meta-active-readingmetaicl-dataThis is the downloaded and processed data from Meta's MetaICL.
We follow their "How to Download and Preprocess" instructions to obtain their modified versions of CrossFit and UnifiedQA.
Citation information
@inproceedings{ min2022metaicl,
title={ Meta{ICL}: Learning to Learn In Context },
author={ Min, Sewon and Lewis, Mike and Zettlemoyer, Luke and Hajishirzi, Hannaneh },
booktitle={ NAACL-HLT },
year={ 2022 }
}
@inproceedings{ ye2021crossfit,
title={ {C}ross{F}it:… See the full description on the dataset page: https://huggingface.co/datasets/allenai/metaicl-data.MetaMathQA-40Karxiv.org/abs/2309.12284
View the project page:
https://meta-math.github.io/
transformers-metadata
Transformers metadata
Japanese_NicoNico_Douga_Movie_Meta_Data_2016c4-en-html-with-metadatametaiclThis is the downloaded and processed data from Meta's MetaICL.
We follow their "How to Download and Preprocess" instructions to obtain their modified versions of CrossFit and UnifiedQA.
Citation information
@inproceedings{ min2022metaicl,
title={ Meta{ICL}: Learning to Learn In Context },
author={ Min, Sewon and Lewis, Mike and Zettlemoyer, Luke and Hajishirzi, Hannaneh },
booktitle={ NAACL-HLT },
year={ 2022 }
}
@inproceedings{ ye2021crossfit,
title={ {C}ross{F}it:… See the full description on the dataset page: https://huggingface.co/datasets/friendshipkim/metaicl.diffusers-metadataGSM8K_zh
Dataset
GSM8K_zh is a dataset for mathematical reasoning in Chinese, question-answer pairs are translated from GSM8K (https://github.com/openai/grade-school-math/tree/master) by GPT-3.5-Turbo with few-shot prompting.
The dataset consists of 7473 training samples and 1319 testing samples. The former is for supervised fine-tuning, while the latter is for evaluation.
for training samples, question_zh and answer_zh are question and answer keys, respectively;
for testing samples, only… See the full description on the dataset page: https://huggingface.co/datasets/meta-math/GSM8K_zh.meta-llama-Llama-3.2-1B-toksuite-detokenizedTraining data of the model detokenized in the exact order seen by the model.
The training data is partitioned into 8 chunks (chunk-0 through chunk-7), based on the GPU rank that generated the data. Each chunk contains detokenized text files in JSON Lines format (.jsonl).
arxiv-metadata-2020-2026
arXiv Metadata, enriched (2020–2026)
Per-paper metadata for 1,517,185 arXiv papers spanning 2020-01 → 2026-09,
enriched with abstracts, citation counts, and Semantic Scholar identifiers, and
organized as a two-level hierarchy: field of study → year.
Unlike a bare title index, every record here carries the abstract, the
full author list, citation counts, and the Semantic Scholar corpusId,
so you can do retrieval, classification, citation analysis, and corpus building
directly… See the full description on the dataset page: https://huggingface.co/datasets/yufan/arxiv-metadata-2020-2026.c4-en-html-with-metadata-ppl-cleanFile list:
"c4-en-html_cc-main-2019-18_pq00-000.jsonl.gz",
"c4-en-html_cc-main-2019-18_pq00-001.jsonl.gz",
"c4-en-html_cc-main-2019-18_pq00-002.jsonl.gz",
"c4-en-html_cc-main-2019-18_pq00-003.jsonl.gz",
"c4-en-html_cc-main-2019-18_pq00-004.jsonl.gz",
"c4-en-html_cc-main-2019-18_pq00-005.jsonl.gz",
"c4-en-html_cc-main-2019-18_pq00-006.jsonl.gz",
"c4-en-html_cc-main-2019-18_pq00-007.jsonl.gz",
"c4-en-html_cc-main-2019-18_pq00-008.jsonl.gz",
"c4-en-html_cc-main-2019-18_pq00-009.jsonl.gz"… See the full description on the dataset page: https://huggingface.co/datasets/masoudjs/c4-en-html-with-metadata-ppl-clean.c4-en-html-with-training_metadata_allnornikel-metallurgy-vl-dataset
Nornikel Metallurgy VL Dataset (SFT / DPO / GRPO)
Датасет для дообучения мультимодальной модели Qwen3-VL по схеме
SFT → DPO → GRPO в предметной области металлургии, горного дела и
обогащения полезных ископаемых. Построен из корпуса технических документов
(PDF-книги/сборники, DOCX-отчёты, PPTX-презентации, XLSX-таблицы) и
изображений (схемы, диаграммы, таблицы).
Конфигурации (config_name)
config
train
validation
назначение
sft
111 351
12 372… See the full description on the dataset page: https://huggingface.co/datasets/brics-edtech/nornikel-metallurgy-vl-dataset.AweAgent-Meta-NL2Repo
AweAgent-Meta-NL2Repo
This dataset provides the metadata used by AweAgent to run the NL2RepoBench evaluation.
If you are looking for the underlying benchmark itself (task design, repositories, test suites), please refer to the original project: multimodal-art-projection/NL2RepoBench.
Purpose
The AweAgent repository evaluates end-to-end repo-level code generation: given a natural-language project specification, the agent must produce a working Python package that… See the full description on the dataset page: https://huggingface.co/datasets/AweAI-Team/AweAgent-Meta-NL2Repo.arXiv-metadata-oai-snapshot
About Dataset
Dataset name: arXiv academic paper metadata
Data source: https://arxiv.org/
Submission date: 1986-04-25 ~ 2025-05-13 (data updated weekly)
Number of papers: 2,710,806 (as of 2025.5.14)
Fields included: title, author, abstract, journal information, DOI, etc.
Data format: json
Data volume: 4.58G
About ArXiv
For nearly 30 years, ArXiv has served the public and research communities by providing open access to scholarly articles, from the vast branches of… See the full description on the dataset page: https://huggingface.co/datasets/jackkuo/arXiv-metadata-oai-snapshot.gpqa-metadata-blind-answerMetacognitive
FINAL Bench: Functional Metacognitive Reasoning Benchmark
"Not how much AI knows — but whether it knows what it doesn't know, and can fix it."
---
Overview
FINAL Bench (Frontier Intelligence Nexus for AGI-Level Verification) is the first comprehensive benchmark for evaluating functional metacognition in Large Language Models (LLMs).
Unlike existing benchmarks (MMLU, HumanEval, GPQA) that measure only final-answer accuracy, FINAL Bench evaluates… See the full description on the dataset page: https://huggingface.co/datasets/FINAL-Bench/Metacognitive.BenchCheck-MetaBenchmark
BenchCheck meta-benchmark (v6)
840 multiple-choice video questions from 76 public video benchmarks, one item per video,
four capability groups x 210 (Perception, Temporal, Spatial / physical, Reasoning / knowledge). The set is the
difficulty-first census of the BenchCheck screened pool: items that none of the cheap attackers of the pool screening
(text-only, single frame, 32-frame 2B model, options-only, shuffled frames) could solve, ranked by the worst-case attacker
percentile… See the full description on the dataset page: https://huggingface.co/datasets/GMLRVigil/BenchCheck-MetaBenchmark.MetaMathQA_GSM8K_zh
Dataset
MetaMathQA_GSM8K_zh is a dataset for mathematical reasoning in Chinese,
question-answer pairs are translated from MetaMathQA (https://huggingface.co/datasets/meta-math/MetaMathQA) by GPT-3.5-Turbo with few-shot prompting.
The dataset consists of 231685 samples.
Citation
If you find the GSM8K_zh dataset useful for your projects/papers, please cite the following paper.
@article{yu2023metamath,
title={MetaMath: Bootstrap Your Own Mathematical Questions for Large… See the full description on the dataset page: https://huggingface.co/datasets/meta-math/MetaMathQA_GSM8K_zh.SlimPajama-Meta-rater-Readability-30B
Top 30B token SlimPajama Subset selected by the Readability rater
This repository contains the dataset described in the paper Meta-rater: A Multi-dimensional Data Selection Method for Pre-training Language Models.
Code: https://github.com/opendatalab/Meta-rater
Dataset Description
This dataset contains the top 30B tokens from the SlimPajama-627B corpus, selected using the Readability dimension of the PRRC (Professionalism, Readability, Reasoning, Cleanliness) framework.… See the full description on the dataset page: https://huggingface.co/datasets/opendatalab/SlimPajama-Meta-rater-Readability-30B.sat-bbox-metadata-sft-v1
Dataset Summary
NuTonic/sat-bbox-metadata-sft-v1 is a metadata-first, procedural VLM SFT dataset built from an existing “sat-bbox” style dataset tree (Sentinel‑2 chips + per-tile JSON metadata sidecars, optionally paired Mapbox stills).
The goal is to create high-signal, production-shaped supervision for multimodal chat models:
Captioning for satellite chips
Grounding (bounding boxes in normalized coordinates) for land-cover regions
Class-focused captions and absence checks for… See the full description on the dataset page: https://huggingface.co/datasets/NuTonic/sat-bbox-metadata-sft-v1.vqav2-full-metadatametadata_arxivMetaMathFewshot
A few-shot version of the MetaMath (https://huggingface.co/datasets/meta-math/MetaMathQA) dataset.
Each entry is formatted with 'question' and 'answer' keys. The 'question' key has a random number of query-answer pairs between 0 and 4 inclusive, before a final target query; the expected answer to this is stored in the content of 'answer'.
metamon-parsed-replays
Metamon Replay Dataset
Pokémon Showdown replay files parsed (or "reconstructed") into RL trajectories by Metamon (arXiv Appendix D)
Quick Start
The easiest way to use the replay dataset is through metamon's dataloader:
import metamon
from metamon.interface import get_observation_space, get_reward_function, get_action_space
from metamon.data importParsedReplayDataset
# see the metamon README for more on observations, actions, and rewards.
human_dset =… See the full description on the dataset page: https://huggingface.co/datasets/jakegrigsby/metamon-parsed-replays.climbmix-400b-shuffle-metadatafcv-extractions-meta-tiered-probe
fcv-extractions-meta-tiered-probe
Data-use mention extractions from the World Bank Fragility, Conflict and Violence (FCV) document corpus. Spans are extracted by the fine-tuned GLiNER model rafmacalaba/gliner_datause_tiered, scored by the tier-probe head rafmacalaba/gliner-tier-probe, and attributed (provenance + usage/impact) by rafmacalaba/lfm2.5-350M-datause-multitask-tiered (a LoRA SFT of LiquidAI/LFM2.5-350M).
Shape
nested — one row per chunk; each entity in… See the full description on the dataset page: https://huggingface.co/datasets/rafmacalaba/fcv-extractions-meta-tiered-probe.