datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
monet
Dataset Card for MONET
MONET (Massive, Open, Non-redundant and Enriched Text-to-image dataset) is a large-scale, curated image-text dataset designed for training text-to-image (T2I) systems. It contains 103.8 million high-quality image-text pairs distilled from 2.9 billion raw pairs across nine heterogeneous open sources (6 real and 3 synthetic) through successive stages of safety filtering, domain-based filtering, exact and near-duplicate removal, and re-captioning with… See the full description on the dataset page: https://huggingface.co/datasets/jasperai/monet.pile-uncopyrighted
Pile Uncopyrighted
In response to authors demanding that LLMs stop using their works, here's a copy of The Pile with all copyrighted content removed.Please consider using this dataset to train your future LLMs, to respect authors and abide by copyright law.Creating an uncopyrighted version of a larger dataset (ie RedPajama) is planned, with no ETA.
MethodologyCleaning was performed by removing everything from the Books3, BookCorpus2, OpenSubtitles, YTSubtitles, and OWT2… See the full description on the dataset page: https://huggingface.co/datasets/monology/pile-uncopyrighted.montok
MonTok: A Suite of Monolingual Tokenizers
This is a set of monolingual tokenizers for 98 languages. For each language, there are Unigram, BPE, and SuperBPE tokenizers, ranging in vocabulary size from around 6k to over 200k.
Training Details
Training Data
All tokenizers are trained on samples of the data used to the train the Goldfish language models.
The tokenizers were either trained on scaled or unscaled data. This refers to whether the models are trained on… See the full description on the dataset page: https://huggingface.co/datasets/catherinearnett/montok.hermes-system-monitor-v2VLN_Dataset_2This repository contains encrypted visual features for an ongoing academic research project. Decryption keys are managed internally for reproducibility.
Charge-040_0040-Sparse-Monotesting_dataThis is testing data for use with MONAI unit tests.
CiQi-VQA
CiQi-Agent
Github | Model | Dataset | Paper
CiQi-Agent: Aligning Vision, Tools and Aesthetics in Multimodal Agent for Cultural Reasoning on Chinese Porcelains
Accepted to ECCV 2026
🎯 Overview
CiQi-Agent has been accepted to ECCV 2026.
We present CiQi-Agent, a domain-specific multimodal agent for antique Chinese porcelain connoisseurship. The project is designed to combine fine-grained visual perception, tool-augmented reasoning, and cultural-heritage knowledge… See the full description on the dataset page: https://huggingface.co/datasets/SII-Monument-Valley/CiQi-VQA.Japanese-Political-Money-OCR-with-Qwenwikipedia-monthly
🚀 Wikipedia Monthly
Last updated: March 14, 2026, 21:06 UTC
This repository provides monthly, multilingual dumps of Wikipedia, processed and prepared for easy use in NLP projects.
📊 Current Statistics
Metric
Current Export (March 2026)
All Exports (Total)
Languages
343
361
Articles
62.8M
62.8M
Usage
Load any language with a single line of code using 🤗 datasets.
latest always refers to the most recent dump, while dated configs refer to… See the full description on the dataset page: https://huggingface.co/datasets/omarkamali/wikipedia-monthly.mala-monolingual-filter
MaLA Corpus: Massive Language Adaptation Corpus
This is a cleaned version with some necessary data cleaning.
Dataset Summary
The MaLA Corpus (Massive Language Adaptation) is a comprehensive, multilingual dataset designed to support the continual pre-training of large language models. It covers 939 languages and consists of over 74 billion tokens, making it one of the largest datasets of its kind. With a focus on improving the representation of low-resource… See the full description on the dataset page: https://huggingface.co/datasets/MaLA-LM/mala-monolingual-filter.monorepo
Persona Cartography — artifact monorepo
Artifact store for the paper Persona Cartography: Charting Language Model
Personality Traits in Weight
Space (arXiv:2607.07916). Code:
persona-cartography/persona-cartography.
This is not a load_dataset-able dataset — it is a single shared repo
holding every artifact the paper's pipeline produces: trained LoRA adapters,
their training data, evaluation results, and the figures' source data. The
paper's figure scripts hydrate from the paths… See the full description on the dataset page: https://huggingface.co/datasets/persona-cartography/monorepo.monopoly-assetspi-mono
Coding agent session traces for badlogicgames/pi-mono
This dataset contains redacted coding agent session traces collected while working on https://github.com/badlogic/pi-mono.git. The traces were exported with pi-share-hf from a local pi workspace and filtered to keep only sessions that passed deterministic redaction and LLM review.
Data description
Each *.jsonl file is a redacted pi session. Sessions are stored as JSON Lines files where each line is a structured… See the full description on the dataset page: https://huggingface.co/datasets/badlogicgames/pi-mono.mono
Bangumi Image Base of Mono
This is the image base of bangumi Mono, we detected 48 characters, 4375 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:… See the full description on the dataset page: https://huggingface.co/datasets/BangumiBase/mono.indoor-safety-hazard-detection-and-work-zone-monitoring
Indoor Safety Hazard Detection & Work-Zone Monitoring
Generated by datapack-import.ts
This dataset mirrors public data-pack render outputs from Physicl.
Each row represents one render view. The image column contains a stable URL to the primary render image uploaded under /data; image_path stores the relative repository path and data_commit_sha pins the Hugging Face dataset commit used by those URLs. Files are uploaded as downloaded unless optional PNG recompression is enabled by… See the full description on the dataset page: https://huggingface.co/datasets/physicl/indoor-safety-hazard-detection-and-work-zone-monitoring.monash_tsfMonash Time Series Forecasting Repository which contains 30+ datasets of related time series for global forecasting research. This repository includes both real-world and competition time series datasets covering varied domains.qwen35-4b
qwen35-4b
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.38203125
Action score: 0.4375
Valid samples: 320/320
appworld-qwen35-4b-9b-s_signal_6-epoch4-iter1
appworld-qwen35-4b-9b-s_signal_6-epoch4-iter1
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.3953125
Action score: 0.446875
Valid samples: 320/320
total-300-random-jh-epoch4
total-300-random-jh-epoch4
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.3890625
Action score: 0.440625
Valid samples: 320/320
total-300-lambda00-s_signal_type6-jh-epoch4
total-300-lambda00-s_signal_type6-jh-epoch4
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.3875
Action score: 0.43125
Valid samples: 320/320
total-300-lambda02-s_signal_type6-jh-epoch4
total-300-lambda02-s_signal_type6-jh-epoch4
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.4046875
Action score: 0.4140625
Valid samples: 320/320
total-300-lambda05-s_signal_type6-jh-epoch4
total-300-lambda05-s_signal_type6-jh-epoch4
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.35703125
Action score: 0.4375
Valid samples: 320/320
total-300-lambda10-s_signal_type6-jh-epoch4
total-300-lambda10-s_signal_type6-jh-epoch4
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.36640625
Action score: 0.41875
Valid samples: 320/320
Pytorch-Code-10K
Hot Coco Training Dataset
A curated collection of 10,625 high-quality PyTorch and Transformers code examples with AI-generated captions. This dataset was specifically built for fine-tuning code-specialized language models like Qimi (Coming soon!)
Dataset Description
This dataset contains Python code snippets sourced from open-source repositories that utilize PyTorch or Hugging Face Transformers. Each sample includes:
code: The raw Python source code (typically… See the full description on the dataset page: https://huggingface.co/datasets/Monster-Code/Pytorch-Code-10K.total-300-lambda08-s_signal_type6-jh-epoch4
total-300-lambda08-s_signal_type6-jh-epoch4
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.38046875
Action score: 0.4078125
Valid samples: 320/320
total-300noapp-lambda02-s_signal_type6-jh-epoch4
total-300noapp-lambda02-s_signal_type6-jh-epoch4
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.36640625
Action score: 0.409375
Valid samples: 320/320
total-300app-lambda02-s_signal_type6-jh-epoch4
total-300app-lambda02-s_signal_type6-jh-epoch4
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.3625
Action score: 0.4015625
Valid samples: 320/320
total-131-lambda02-residual-s_signal_type6-jh-epoch4
total-131-lambda02-residual-s_signal_type6-jh-epoch4
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.3765625
Action score: 0.4171875
Valid samples: 320/320
total-300-lambda02-s_signal_type6-jh-retry-epoch4
total-300-lambda02-s_signal_type6-jh-retry-epoch4
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.36953125
Action score: 0.3984375
Valid samples: 320/320