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Tags / Machine learning /

Machine learning dataset

A dataset, or a library that loads datasets, for training or testing machine learning models.

Repositories

20

Net stars gained, last 7 days

+6,136

Top-level Tag

Machine learning

Net stars gained each day, last 90 days

2026-10-21: +1,181 net stars. Most from ctresb/leis, +526.2026-10-25: +1,648 net stars. Most from designfuchen/word-atlas, +964.2026-10-26: +1,586 net stars. Most from designfuchen/word-atlas, +998.2026-10-27: +1,319 net stars. Most from designfuchen/word-atlas, +821.2026-10-28: +1,311 net stars. Most from designfuchen/word-atlas, +581.

Peak days

  1. 1

    Oct 21, 2026 +1,181 net stars (median day +69)

  2. 2

    Oct 25, 2026 +1,648 net stars (median day +69)

  3. 3

    Oct 26, 2026 +1,586 net stars (median day +69)

  4. 4

    Oct 27, 2026 +1,319 net stars (median day +69)

  5. 5

    Oct 28, 2026 +1,311 net stars (median day +69)

A peak day has at least 2 times the net stars of the median day of this chart, and at least 100 more. For each peak day, the list shows the repositories that gained the most net stars that day. It also shows their GitHub releases of that day or the day before (UTC), and their creation date when it was 14 days or less before. These events occurred near the same day. They do not show why the stars came.

Largest

Most stars in total now.

  1. chinese-poetry/chinese-poetry54K

    readme: 此数据库通过 JSON 格式分发，可以让你很方便的开始你的项目。

  2. tatsu-lab/stanford_alpaca30K

    readme: `alpaca_data.json` contains 52K instruction-following data we used for fine-tuning the Alpaca model.

  3. huggingface/lerobot29K

    readme: Add the extras you need, for example `pip install 'lerobot[dataset]'` to load datasets

  4. huggingface/datasets22K

    readme: get any of these datasets ready to use in a dataloader for training/evaluating a ML model

  5. pytorch/vision18K

    readme: This is a utility library that downloads and prepares public datasets.

  6. tensorflow/tensor2tensorarchived · 17K

    readme: For image classification, we have a number of standard data-sets:

  7. alex000kim/nsfw_data_scraper13K

    description: Collection of scripts to aggregate image data for the purposes of training an NSFW Image Classifier

  8. xiaoyang111222/cn-math-taxonomy6.4K

    description: 1644 个知识点、2403 条先修边、6723 道题，对齐课标，附自适应前置诊断

  9. designfuchen/word-atlas4.1K

    description: Open-source app + CC BY 4.0 dataset.

  10. ctresb/leis2.4K

    readme: A Constituição Federal e normas federais, estaduais, distritais e municipais, convertidas de fontes oficiais para Markdown

Newest Admissions

Entered the index by the rising rule, newest first.

  1. alchaincyf/huashu-bird-sticker618

    description: Stickers for all 11,064 living bird species, with names in 43 languages and structured data

  2. wquguru/shisei400

    description: Free CC0 mannequin pose reference library for AI image tools (Nano Banana, ChatGPT, ComfyUI, Midjourney) — 250+ poses × 5 angles in line art

  3. akitaonrails/worldreports106

    readme: Every chart is regenerated from CSVs committed in this repository.

  4. designfuchen/word-atlas4.1K

    description: Open-source app + CC BY 4.0 dataset.

  5. PKU-VaLuE-Lab/OmniReasoning228

    readme: The released datasets are **OmniReasoning-SFT-112K** (112,463 samples with synthesized thinking)

  6. metrica-sports/more-than-a-hack-2027195

    readme: A football data challenge on real professional match data, open to analysts, data scientists and engineers.

  7. xiaoyang111222/cn-math-taxonomy6.4K

    description: 1644 个知识点、2403 条先修边、6723 道题，对齐课标，附自适应前置诊断

  8. dirt-bike-graphics/dirt-bike-graphics.github.io105

    description: Which dirt bikes share the same plastics and can run the same graphics kit. 175 model years, 76 models, 11 brands.

  9. ctresb/leis2.4K

    readme: A Constituição Federal e normas federais, estaduais, distritais e municipais, convertidas de fontes oficiais para Markdown

  10. BaruqueRodrigues/llm-legislativo497

    readme: Publicação aberta de **5 Datasets Oficiais** cobrindo desde os TARs brutos até as matrizes curadas em formato Apache Parquet.

All 20 in the Repository Index

How a repository gets this Tag

An agent read the description, the GitHub topics, and the start of the README of each repository, and chose Tags only from the checked-in Tag list. For each Tag it quoted the text, and a code check kept the Tag only when the quote is in that text. The quote under each repository is that evidence. A Tag does not rank repositories and does not say that one can replace another. A wrong Tag? Use "Report a problem with this page" on the page of the repository. Withheld Repositories are left out of all numbers on this page.