# Hugging Face Models & Datasets Scraper (API) (`ahmdshrif/huggingface-models-datasets-scraper`) Actor

Exports Hugging Face model and dataset metadata—downloads, likes, tags, license, pipeline task, and update dates—via the official public API.

- **URL**: https://apify.com/ahmdshrif/huggingface-models-datasets-scraper.md
- **Developed by:** [Ahmed](https://apify.com/ahmdshrif) (community)
- **Categories:** Developer tools, AI, Lead generation
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: 5.00 out of 5 stars

## Pricing

$0.30 / 1,000 model or dataset exporteds

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## Hugging Face Models & Datasets Scraper

Exports Hugging Face model and dataset metadata — downloads, likes, tags, license, pipeline task, library, and update dates — by calling the official public huggingface.co API. Built for AI/ML engineers tracking model popularity or licensing, researchers building datasets of the Hugging Face ecosystem, and teams doing competitive or trend analysis on model releases.

### Why this scraper

- Covers both models and datasets from a single actor run against the same official API — no need to run separate tools for each.
- Priced at $0.0003 per item, which is below the cheapest per-item alternative on the store.
- Pulls from the official unauthenticated JSON API, so output fields match what Hugging Face itself publishes (downloads, likes, trending score, license, tags) with no scraping of rendered HTML.

### Output fields

| Field | Type | Description |
|---|---|---|
| id | string | Full model or dataset ID, e.g. author/name |
| itemType | string | Either 'model' or 'dataset' |
| author | string | Organization or user that owns the item |
| pipelineTag | string | Pipeline task, e.g. text-generation (models only) |
| libraryName | string | Library used, e.g. transformers |
| downloads | integer | Total download count |
| likes | integer | Number of likes |
| trendingScore | integer | Hugging Face trending score |
| license | string | License identifier parsed from tags, e.g. mit |
| tags | string | Comma-separated list of all tags |
| gated | boolean | Whether access requires approval |
| private | boolean | Whether the item is private |
| sha | string | Latest commit SHA |
| createdAt | string | Creation timestamp, ISO 8601 |
| lastModified | string | Last modification timestamp, ISO 8601 |
| siblingsCount | integer | Number of files in the repo |

### Input

```json
{
  "startUrls": [
    { "url": "https://huggingface.co/api/models?limit=50&full=true" }
  ],
  "maxItems": 50
}
```

To scrape datasets instead, point `startUrls` at a datasets endpoint, e.g. `https://huggingface.co/api/datasets?limit=50`.

### Output

```json
{
  "id": "Qwen/Qwen3.8-Flash-Next",
  "itemType": "model",
  "author": "Qwen",
  "pipelineTag": "image-text-to-text",
  "libraryName": "transformers",
  "downloads": 4810,
  "likes": 4176,
  "trendingScore": 4045,
  "license": "other",
  "tags": "transformers,safetensors,qwen4_exp,image-text-to-text,conversational,license:other,eval-results,endpoints_compatible,region:us",
  "gated": false,
  "private": false,
  "sha": "de4b8e4d43b917e7706784d8bb445c9af86a3540",
  "createdAt": "2026-08-24T08:24:59.000Z",
  "lastModified": "2026-08-27T05:03:36.000Z",
  "siblingsCount": 144
}
```

### Pricing

$0.0003 per model or dataset exported. A run pulling 50 items costs $0.015; a run of 1,000 items costs $0.30.

### Use cases

- Tracking weekly download and like counts for a shortlist of models to decide which to fine-tune or deploy.
- Building a license-compliance audit of datasets used internally by filtering exported records on the `license` field.
- Feeding a research database of pipeline tasks and library usage trends across newly released models.

# Actor input Schema

## `startUrls` (type: `array`):

Pages to export.

## `maxItems` (type: `integer`):

Stop after this many records.

## Actor input object example

```json
{
  "startUrls": [
    {
      "url": "https://huggingface.co/api/models?limit=50&full=true"
    },
    {
      "url": "https://huggingface.co/api/models/bert-base-uncased"
    },
    {
      "url": "https://huggingface.co/api/datasets?limit=50"
    }
  ],
  "maxItems": 50
}
```

# Actor output Schema

## `results` (type: `string`):

One structured record per input URL.

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "startUrls": [
        {
            "url": "https://huggingface.co/api/models?limit=50&full=true"
        },
        {
            "url": "https://huggingface.co/api/models/bert-base-uncased"
        },
        {
            "url": "https://huggingface.co/api/datasets?limit=50"
        }
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("ahmdshrif/huggingface-models-datasets-scraper").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = { "startUrls": [
        { "url": "https://huggingface.co/api/models?limit=50&full=true" },
        { "url": "https://huggingface.co/api/models/bert-base-uncased" },
        { "url": "https://huggingface.co/api/datasets?limit=50" },
    ] }

# Run the Actor and wait for it to finish
run = client.actor("ahmdshrif/huggingface-models-datasets-scraper").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "startUrls": [
    {
      "url": "https://huggingface.co/api/models?limit=50&full=true"
    },
    {
      "url": "https://huggingface.co/api/models/bert-base-uncased"
    },
    {
      "url": "https://huggingface.co/api/datasets?limit=50"
    }
  ]
}' |
apify call ahmdshrif/huggingface-models-datasets-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,ahmdshrif/huggingface-models-datasets-scraper"
        }
    }
}

```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/lyH8rzGbpPYAYNPVJ/builds/QmuIelMmDGVHc2ezA/openapi.json