Hugging Face Scraper

Search and export the Hugging Face Hub at scale. Pull models, datasets and Spaces by keyword, task, library, language or author — with downloads, likes, tags, task, files and dates — into clean JSON, CSV or Excel. No API key needed for public repos.

Perfect for model discovery, competitive/landscape research, tracking trending models, building ML dashboards, dataset sourcing and market monitoring.

What you can do

• 🔎 Search models, datasets or Spaces by keyword (llama, whisper, stable diffusion)
• 🏷️ Filter by tag — task (text-generation, text-to-image, automatic-speech-recognition), library (transformers, pytorch), language (en, fr) or license
• 👤 Filter by author/org — meta-llama, google, openai, mistralai…
• 📊 Sort by downloads, likes, trending, recently updated or recently created
• 📥 Bulk export — thousands of rows per run via automatic pagination
• 🔓 No key required for public data (add an optional read token for gated repos you can access)

Example input

{
  "contentType": "models",
  "searches": ["llama"],
  "filters": ["text-generation", "pytorch"],
  "author": "",
  "sort": "downloads",
  "maxResultsPerSearch": 500
}

Leave searches empty to browse the top items by your chosen sort (e.g. the 1,000 most-downloaded text-to-image models).

Output

Each row is one repo.

{
  "type": "model",
  "id": "meta-llama/Llama-3.2-1B-Instruct",
  "author": "meta-llama",
  "name": "Llama-3.2-1B-Instruct",
  "downloads": 10449365,
  "likes": 1548,
  "pipelineTag": "text-generation",
  "library": "transformers",
  "tags": ["transformers", "safetensors", "llama", "conversational", "en", "…"],
  "filesCount": 14,
  "gated": true,
  "createdAt": "2024-09-18T…",
  "lastModified": "2024-10-24T…",
  "url": "https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct"
}

Datasets add description; Spaces add sdk, emoji and runtimeStage. The url always links straight to the repo page.

Output schema

Input fields

Use cases

• Model discovery & benchmarking — find the top models for a task, ranked by real downloads/likes
• Trend monitoring — track what's trending on the Hub over time
• Dataset sourcing — surface the best datasets for a domain (medical, finance, code)
• Competitive research — audit an org's full catalog of models/datasets/Spaces
• ML dashboards & alerts — feed clean Hub metadata into your own tooling

FAQ

Do I need a Hugging Face API key? No. All public models, datasets and Spaces are scraped without a key. Add a read token only if you want gated/private repos you already have access to.

How many results can I get? Up to 50,000 per search term. Results are paginated automatically — leave searches empty and set a high limit to pull the entire top-of-catalog for a task.

Which tasks/filters are supported? Any Hub tag — tasks like text-generation, text-to-image, image-classification, automatic-speech-recognition; libraries like transformers, diffusers, pytorch; languages; and licenses. Combine several to narrow results.

How does billing work? Pay per item scraped plus a small per-request charge — you only pay for what you pull.

Can I connect this Actor to other apps? The Hugging Face Scraper can be connected with almost any cloud service or web app thanks to integrations on the Apify platform. It works with Make, Zapier, Slack, Airbyte, GitHub, Google Drive and many more, plus the Apify API, JavaScript/Python clients and MCP. Or use webhooks to trigger an action whenever a run finishes.

Notes

Original clean-room implementation using Hugging Face's public Hub API. You are responsible for how you use the scraped data and for complying with the Hub's terms and each repo's license. This is an independent tool and is not affiliated with or endorsed by Hugging Face.

You pay only for what the run returns. Set a maximum cost per run and it stops cleanly at that limit.