datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
explicit-edit-benchmark
Explicit Edit Benchmark
226 deterministic exact-edit tasks, run by different agents, harnesses, models and configurations. Every observation records what the harness did and whether the resulting files matched byte for byte.
Source code and benchmark runner: GitHub — Explicit Edit Benchmark
Open the interactive Explorer to compare agents, harnesses, models, versions, reasoning modes, correctness, recovery, time, cost and tokens.
Leaderboard by model route
Score v2… See the full description on the dataset page: https://huggingface.co/datasets/alexshpunt/explicit-edit-benchmark.stackv2_edu_filtered
Stack V2 Edu
Description
We filter the Stack V2 to only include code from openly licensed repositories, based on the license detection performed by the creators of Stack V2. When multiple licenses are detected in a single repository, we ensure that all of the licenses are on the Blue Oak Council certified license list. Per-document license information is available in the license entry of the metadata field of each example. Code for collecting, processing, and preparing… See the full description on the dataset page: https://huggingface.co/datasets/common-pile/stackv2_edu_filtered.irish_fineweb_eduData translation project of https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu, sample-10BT subset. Data are translated from English to Irish using NLLB-3.3B.
hplt2_edu_scores
HPLT2-Edu-scores
Dataset summary
HPLT2-JQL-Education is a model-annotated language subset of HPLT2, spanning 35 languages.
Our model-annotations allow for a filtering that achieves higher-quality training outcomes without excessively aggressive data reduction.
The original FW2 heuristic filtering method serves as our baseline, providing reference points for both the volume of retained tokens and downstream model performance.
For example, in the Spanish language case… See the full description on the dataset page: https://huggingface.co/datasets/JQL-AI/hplt2_edu_scores.fw2_edu_scores
Fineweb2-Edu-scores
Dataset summary
FineWeb2-JQL-Education is a model-annotated language subset of FineWeb2, spanning 36 languages.
Our model-annotations allow for a filtering that achieves higher-quality training outcomes without excessively aggressive data reduction.
The original FW2 heuristic filtering method serves as our baseline, providing reference points for both the volume of retained tokens and downstream model performance.
For example, in the Spanish language… See the full description on the dataset page: https://huggingface.co/datasets/JQL-AI/fw2_edu_scores.Inter-Edit-Train
Inter-Edit-Train
Inter-Edit-Train is the official large-scale training set released for the CVPR 2026 paper Inter-Edit: First Benchmark for Interactive Instruction-Based Image Editing.
This dataset is designed for the Interactive Instruction-based Image Editing (I^3E) task, where a model performs localized image edits from a concise textual instruction together with imprecise spatial guidance.
Highlights
1,099,964 image editing pairs
610,186 unique source images
Four… See the full description on the dataset page: https://huggingface.co/datasets/a1557811266/Inter-Edit-Train.fineweb-edu-zh-chengyu-cpt
Fineweb-Edu Chinese — Chengyu-Tagged Continued-Pretraining Corpus
A 3.74M-document Chinese corpus (~7.8B tokens) for continued pretraining on
cultural knowledge in figurative language, built from the highest-quality
tier of opencsg/Fineweb-Edu-Chinese-V2.1.
Each document is educational Chinese text containing at least one culturally
vetted chengyu, with an appended 【成语注释】 knowledge block listing every
matched idiom's figurative meaning(s) and classical source citation.
This is a… See the full description on the dataset page: https://huggingface.co/datasets/jiviteshjn/fineweb-edu-zh-chengyu-cpt.STER
STER: Zero-shot 3D Geometric Entity Resolution Benchmark
Multi-city, cross-LoD 3D building matching benchmark for the NS-D2S paper (AAAI 2026).
Strictly follows the 3dSAGER (SIGMOD 2026) methodology and data format.
Dataset Overview
Dataset
City
Country
Buildings
LOD Source
Urban Typology
amsterdam
Amsterdam
NL
123,259
3DBAG LOD1.2/1.3/2.2
Historic canal city
rotterdam
Rotterdam
NL
152,694
3DBAG LOD1.2/1.3/2.2
Post-war modern
hague
Den Haag
NL
181… See the full description on the dataset page: https://huggingface.co/datasets/eduzrh/STER.JQL-LLM-Edu-Annotations
📚 JQL Educational Quality Annotations from LLMs
This dataset provides 17,186,606 documents with high-quality LLM annotations for evaluating the educational value of web documents, and serves as a benchmark for training and evaluating multilingual LLM annotators as described in the JQL paper.
📝 Dataset Summary
Multilingual document-level quality annotations scored on a 0–5 educational value scale by three state-of-the-art LLMs:
Gemma-3-27B-it, Mistral-3.1-24B-it… See the full description on the dataset page: https://huggingface.co/datasets/JQL-AI/JQL-LLM-Edu-Annotations.hpltv2-llama33-edu-annotation
HPLT version 2.0 educational annotations
This dataset contains annotations derived from HPLT v2 cleaned samples.
There are 500,000 annotations for each language if the source contains at least 500,000 samples.
We prompt Llama-3.3-70B-Instruct to score web pages based on their educational value following FineWeb-Edu classifier.
Note 1: The dataset contains the prompt (using the first 1500 characters of the text sample), the scores, and the full Llama 3 generation. The column "idx"… See the full description on the dataset page: https://huggingface.co/datasets/LumiOpen/hpltv2-llama33-edu-annotation.SEED-Data-Edit-Part1-Openimages
SEED-Data-Edit
SEED-Data-Edit is a hybrid dataset for instruction-guided image editing with a total of 3.7 image editing pairs, which comprises three distinct types of data:
Part-1: Large-scale high-quality editing data produced by automated pipelines (3.5M editing pairs).
Part-2: Real-world scenario data collected from the internet (52K editing pairs).
Part-3: High-precision multi-turn editing data annotated by humans (95K editing pairs, 21K multi-turn rounds with a maximum of 5… See the full description on the dataset page: https://huggingface.co/datasets/AILab-CVC/SEED-Data-Edit-Part1-Openimages.fineweb-edudockersv1hplt3_edu_scores
HPLT3-Edu-scores
Dataset summary
HPLT3-JQL-Education is a model-annotated language subset of HPLT3, spanning 36 languages.
Our model-annotations allow for a filtering that achieves higher-quality training outcomes without excessively aggressive data reduction.
HPLT3-Edu-scores was created based on scores assigned by a deep learning classifier trained to identify educational samples using Snowflake's Arctic-embed-m-v2.0 embeddings.
For all training ablations, we used… See the full description on the dataset page: https://huggingface.co/datasets/Eurolingua/hplt3_edu_scores.curated_edu_scorescommon-pile-stack-edufinepdfs-edufineweb-edu-pretokenized-llama3-100b
FineWeb-Edu Pretokenized with Llama 3.1 (100B)
This repository contains the sample/100BT subset of FineWeb-Edu, pretokenized for Megatron-LM-style training with meta-llama/Meta-Llama-3.1-8B.
It is a derived, pretokenized version of the upstream data, not an official Hugging Face FineWeb release.
Dataset summary
140 indexed shards
97,270,686 non-empty documents
97,458,793,013 tokens
English web text from FineWeb-Edu sample/100BT
Source dataset revision:… See the full description on the dataset page: https://huggingface.co/datasets/ZhuofengLi/fineweb-edu-pretokenized-llama3-100b.fineweb-eduFable-5-traces
Glint Research Dataset Card
Fable 5 Pi Agent Traces
A compact, high-signal corpus of Fable 5 coding-agent traces converted into Hugging Face Agent Traces / Pi-compatible sessions for Data Studio inspection, tool-use policy learning, and reasoning/action distillation.
Primary Config
pi_agent/train
Agent Trace preview enabled
4,665 Pi trace sessions
60 source sessions
3,799 tool… See the full description on the dataset page: https://huggingface.co/datasets/edbuildingstuff/Fable-5-traces.multilingual_tokenizer_benchmark
Multilingual Tokenizer Benchmark
More details of each subset like word count, character count, original sources, etc, can be found in the dataset_meta.yaml file in the repository root.
Natural language word count functions
Download spacy models
pip install ntlk spacy pygments underthesea camel-tools
python -m spacy download ko_core_news_sm
python -m spacy download ja_core_news_sm
python -m spacy download zh_core_web_sm
import nltk
nltk.download('punkt_tab')… See the full description on the dataset page: https://huggingface.co/datasets/eduagarcia/multilingual_tokenizer_benchmark.ds-coder-instruct-v2
Dataset Card for DS Coder Instruct v2 Dataset
Changes from v1:
Added WizardLM evol data science samples
Removed R samples from v2
DS Coder is a dataset for instruction fine tuning of language models. It is a specialized dataset focusing only on
data science (eg. plotting, data wrangling, machine learnig models, deep learning, and numerical computations). The dataset contains code examples both in Python (R samples were removed in v2).
The goal of this dataset is to enable… See the full description on the dataset page: https://huggingface.co/datasets/ed001/ds-coder-instruct-v2.ark-asr-open-asr-leaderboard-results
ARK-ASR Open ASR Leaderboard Results
This dataset contains JSONL prediction manifests for AutoArk-AI/ARK-ASR-0.6B on hf-audio/open-asr-leaderboard public English short-form splits.
These files are intended for Open ASR Leaderboard maintainer verification.
Scoring summary from normalizer.eval_utils.score_results:
Split
WER
RTFx
ami/test
10.02
352.12
earnings22/test
9.77
331.88
gigaspeech/test
8.00
217.72
librispeech/test.clean
1.53
412.12
librispeech/test.other… See the full description on the dataset page: https://huggingface.co/datasets/Edge0/ark-asr-open-asr-leaderboard-results.ark-asr-3b-open-asr-leaderboard-results
ARK-ASR-3B Open ASR Leaderboard Results
Raw JSONL manifests for AutoArk-AI/ARK-ASR-3B on the public English
short-form hf-audio/open-asr-leaderboard splits.
These manifests were generated on a local 8x RTX 4090 machine and scored with
the shared Open ASR Leaderboard scorer:
PYTHONPATH=. python - <<'PY'
from normalizer.eval_utils import score_results
score_results(
'ark_asr/results.AutoArk-AI-ARK-ASR-3B_20260622_official',
'AutoArk-AI/ARK-ASR-3B',
)
PY
Important:… See the full description on the dataset page: https://huggingface.co/datasets/Edge0/ark-asr-3b-open-asr-leaderboard-results.massive-yt-edu-queue
Massive YouTube Educational Video Queue
Full metadata and content classification for 4,489,228 YouTube educational videos totaling 3,975,157 hours.
Description
This dataset contains metadata, content categorization, and license risk assessment for ~4.5M YouTube videos identified as potentially educational. It serves as the discovery and processing queue for the massive-yt-edu-transcriptions project, which aims to create the world's largest open educational transcript… See the full description on the dataset page: https://huggingface.co/datasets/thepowerfuldeez/massive-yt-edu-queue.MMH3_Image_Edit_WorkflowThis is just an example of using MiniMax H3 as an image editor. The actual workflow that I use requires several custom nodes, some of which are not published, so this one is simply a bare bones demonstration.
This uses the hybrid MiniMax H3 model from here: https://huggingface.co/smhfacct/Minimax-H3-fl2va-ref2va-hybrid-models/tree/main
It uses the custom VAE from here: https://huggingface.co/Mamad8/MiniMax-H3-Image-VAE/tree/main
It uses the LoRA from here:… See the full description on the dataset page: https://huggingface.co/datasets/fizzlepoof/MMH3_Image_Edit_Workflow.CTSpinoPelvic1K
CTSpinoPelvic1K
A fused spine + pelvis 3D CT segmentation dataset built by patient-level
crosswalk between three public sources:
TCIA CT COLONOGRAPHY — DICOM CT volumes (prone + supine per patient)
CTSpine1K (COLONOG subset) — VerSe-convention vertebral label masks
CTPelvic1K dataset2 — sacrum + bilateral hip label masks
Annotations are placed onto the TCIA CT volume with the highest bone
coverage (HU > 200), separately per anatomy. For ~650 patients both
annotations land on the… See the full description on the dataset page: https://huggingface.co/datasets/anonymous-neurips-ED/CTSpinoPelvic1K.github-issuesannotations_creators:
other
language_creators:
crowdsourced
languages:
en-US
licenses:
other-my-license
multilinguality:
monolingual
pretty_name: HuggingFace Github Issues
size_categories:
unknown
source_datasets:
original
task_categories:
text-classification
text-retrieval
task_ids:
multi-class-classification
multi-label-classification
document-retrieval
classic-eda-c-trajectories
nano-rl trajectories
Agent trajectories from nano-rl: an LLM is asked to specify, test and then
implement a small C program, which is compiled and executed inside a confined
sandbox and scored against tests the model wrote before it saw its own program.
When the program fails, the model is shown the build log and the failing cases
and asked to repair it, for up to 200 rounds.
Every model turn is one row, including the ones that went nowhere.
This is a partial snapshot. 279 of 1… See the full description on the dataset page: https://huggingface.co/datasets/Sudnya/classic-eda-c-trajectories.test-subset-classic-eda
nano-rl trajectories
Agent trajectories from nano-rl: an LLM is asked to specify, test and then
implement a small C program, which is compiled and executed inside a confined
sandbox and scored against tests the model wrote before it saw its own program.
When the program fails, the model is shown the build log and the failing cases
and asked to repair it, for up to 100 rounds.
Every model turn is one row, including the ones that went nowhere.
What is in it… See the full description on the dataset page: https://huggingface.co/datasets/Sudnya/test-subset-classic-eda.