Hugging Face Scraper — Models, Datasets & Spaces with Stats

Get models, datasets and Spaces from the Hugging Face Hub with their numbers: downloads in the last 30 days and all-time, likes, trending score, task (pipeline tag), library, license, tags, languages, base models, papers, gated flag, created and last-updated dates — and the first paragraph of each repo's card. Paste your huggingface.co search links, type search terms, authors, a task or a library — or list single repos.

Why this one

• 🔗 Your search, as you set it up. Set every filter you like on huggingface.co/models, /datasets or /spaces — task, library, language, license, parameter size, sort — and paste the link. Or type search terms and authors here and choose models, datasets or Spaces, a task, a library, tags and the sort order.
• ⚡ Steady, within Hugging Face's limits, and no page limit. A live test run read four searches of 250 (diffusers text-to-image models, MIT-licensed text-classification datasets, "flux" Spaces, Qwen's models) and two single repos — 1,002 repos, each with its card summary, in 114 seconds, without meeting the Hub's rate limit once. Without card summaries a run read 5,000 BERT models, most downloaded first, in under 3 minutes.
• 📝 What each repo is, in one paragraph. cardSummary is the first real paragraph of the repo's card, without the badges, link bars, notes and code around it. In the live run, 87% of the models and 88% of the datasets had one; most Spaces have no card text, and their short description stands in (12%).
• 🧭 Forgiving input. An author typed "qwen" found Qwen's models (the Hub itself matches authors case-sensitively). A profile link (huggingface.co/TheBloke) gives all that account's models; a repo link works with any sub-page (…/tree/main, …/discussions).
• 🔒 No personal data beyond the public repo id. The owner's handle is part of every repo id and is kept; nothing else about a person is read. In card summaries, the names of persons after "created by", "maintained by", "Authors:" and the like are replaced with [name removed] (the repo's own owner and organizations stay), @handles with [handle removed], and e-mail addresses and phone numbers with [e-mail removed] / [phone removed].
• 💸 You pay only for repos delivered. A link that is not a Hugging Face link, a repo that does not exist or an unknown author comes back as a free record with the reason.

What you get

For every repo:

Field	What it is
id, type, author, name, url	the repo (model, dataset or space) and its owner's handle
pipelineTag, library	the model's task (text-generation, text-to-image…) and library (transformers, diffusers, gguf…)
downloads, downloadsAllTime	downloads in the last 30 days and in total (models and datasets)
likes, trendingScore	as the Hub counts them at the time of the run
createdAt, lastModified	when the repo was created and last updated
license, gated, gatedApproval, private, disabled	the license, and whether access has to be requested (auto or manual approval)
languages, baseModels, datasets, papers	from the card's header and tags: languages, the models it was built on, training datasets, arXiv ids
taskCategories, sizeCategory, modalities, formats	for datasets
title, sdk, runtimeStage, hardware, shortDescription	for Spaces: gradio / docker / static…, RUNNING or SLEEPING, the hardware it runs on
tags	every tag the Hub shows
cardSummary	the first paragraph of the card (see above)
input, position, ignoredFilters, scrapedAt, status, error	which input it came from, its place in the results, and why an input gave nothing

Example

A real record from a live run, 26 September 2026 (tags shortened):

{
  "input": "meta-llama/Llama-3.1-8B-Instruct",
  "id": "meta-llama/Llama-3.1-8B-Instruct",
  "type": "model",
  "author": "meta-llama",
  "name": "Llama-3.1-8B-Instruct",
  "url": "https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct",
  "pipelineTag": "text-generation",
  "library": "transformers",
  "downloads": 6184751,
  "downloadsAllTime": 186289232,
  "likes": 7888,
  "trendingScore": 85,
  "createdAt": "2024-07-18T08:56:00.000Z",
  "lastModified": "2024-09-25T17:00:57.000Z",
  "license": "llama3.1",
  "gated": true,
  "gatedApproval": "manual",
  "private": false,
  "languages": ["en", "de", "fr", "it", "pt", "hi", "es", "th"],
  "baseModels": ["meta-llama/Meta-Llama-3.1-8B"],
  "papers": ["arxiv:2204.05149"],
  "tags": ["transformers", "safetensors", "llama", "text-generation", "facebook", "meta", "…"],
  "cardSummary": "The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction tuned generative models in 8B, 70B and 405B sizes (text in/text out). The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks.",
  "status": "OK",
  "scrapedAt": "2026-09-26T02:17:34.191Z"
}

How to use it

1. Paste Search links from huggingface.co/models, /datasets or /spaces — or type Search terms and/or Authors / organizations, choose Models, datasets or Spaces, and optionally a Task, a Library, Tags and the Sort order. A task, library or tag alone is a search too (for example all text-to-speech models by downloads).
2. For single repos, list them under Repos — links or ids, one record each.
3. Set Max results per search, and untick Add the card summary if you only need the numbers (faster).
4. Press Start, and download the results from the Output tab as JSON, CSV or Excel.

To follow what is new or rising, save a search sorted by Trending or Newest as a task and schedule it daily.

You can also call it from the Apify API, from Make, Zapier or n8n, or from an AI agent through the Apify MCP server.

Pricing

You are charged per repo delivered — see the price on this page. Card summaries cost nothing extra, only time. Links that are not Hugging Face links, repos that do not exist and unknown authors are free.

FAQ

How many results per search? As many as the Hub has — it pages by cursor, 1,000 at a time, with no page limit; a live run read 5,000 BERT models in one search. The Hub does not say in advance how many there are.

Which filters of a search link are used? Search, author, task, library, language, license, other tags, dataset filters (task category, modality, size, format), parameter size, inference provider and app, and the sort. A few website-only filters (such as base_model_relation or hardware) have no counterpart in the Hub's API; they are left out and listed in ignoredFilters on each record of that search. A live test link for German conversational models up to 3B parameters, sorted by likes, came back with every result tagged conversational and de, in likes order, and none of the sampled models above 3B parameters.

Why do Spaces have no downloads? The Hub does not count downloads for Spaces; sorting Spaces by downloads sorts them by likes instead.

Do I need a Hugging Face token? No. Without one, the Hub allows one IP 500 API calls and 3,000 file reads per 5 minutes (its own rate-limit headers say so). The Actor reads those headers on every answer and spreads its requests over what is left of the window, so a normal run stays under the limit: a list call brings up to 1,000 repos, and each card summary is one file read — so over a long run, card summaries come at most about 3,000 per 5 minutes (untick Add the card summary for the numbers alone, which need only one call per 1,000 repos). If the Hub still asks it to slow down, the Actor waits as long as the Hub says and asks again; it never switches IPs to get around the limit (the proxy is only for network failures and server errors). With a token, the Hub applies your account's own, higher limits and the Actor paces by those; the token is sent only to huggingface.co, and also gives access to your private repos.

Why is cardSummary empty for some repos? Their card has no paragraph of text — only a header, a table, a list or links — or there is no card at all.

Is this affiliated with Hugging Face? No. This is an independent tool that reads public data from the Hugging Face Hub.

Like it? A short review on the Store page helps other people find this Actor. Something missing or broken? Tell us on the Issues tab — we read every one.