# Hugging Face Datasets Scraper (`fetch_cat/hugging-face-datasets-scraper`) Actor

Scrape public Hugging Face dataset metadata, tags, access flags, downloads, likes, and recency signals.

- **URL**: https://apify.com/fetch\_cat/hugging-face-datasets-scraper.md
- **Developed by:** [Hanna Nosova](https://apify.com/fetch_cat) (community)
- **Categories:** AI, Developer tools, Automation
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $0.30 / 1,000 dataset metadata records

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#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 Datasets Scraper

Export public Hugging Face dataset catalog metadata to structured datasets. Use it to find training/RAG datasets, compare licenses and tags, monitor updates, or enrich a list of known dataset IDs.

### At a glance

- **Primary job**: Search Hugging Face datasets and save clean metadata rows.
- **Input**: Search keywords and/or exact dataset IDs, plus result limits and filters.
- **Output**: One row per dataset with IDs, URL, author, downloads, likes, tags, license/task/format fields, access flags, timestamps, and diagnostics.
- **Best for**: AI teams, data scientists, compliance reviewers, research analysts, RAG builders, and fine-tuning dataset discovery.

### Who is it for?

This Actor is for AI engineers, data scientists, ML platform teams, compliance reviewers, RAG builders, and research analysts who need structured Hugging Face dataset metadata without manually opening dataset pages.

### Common workflows

- **Dataset discovery**: Search topics such as `finance`, `medical imaging`, or `instruction tuning` and sort by downloads, likes, recency, or trending score.
- **License review prep**: Export raw Hugging Face tags plus parsed license, task, language, size, format, and library fields for spreadsheet review.
- **Known-ID enrichment**: Provide exact dataset IDs such as `squad` or `HuggingFaceH4/ultrachat_200k` and receive current public metadata.
- **Monitoring**: Schedule repeat runs with `sort: "lastModified"` and compare output datasets over time.

### Ready-to-run examples

Use these verified public Examples to launch common runs without building input JSON from scratch:

- [Hugging Face NLP datasets](https://apify.com/fetch_cat/hugging-face-datasets-scraper/examples/hugging-face-nlp-datasets) — find popular NLP dataset metadata for discovery and license review.
- [Specific Hugging Face datasets](https://apify.com/fetch_cat/hugging-face-datasets-scraper/examples/specific-hugging-face-datasets) — enrich exact public dataset IDs such as `squad`.
- [Trending vision datasets](https://apify.com/fetch_cat/hugging-face-datasets-scraper/examples/trending-vision-datasets) — monitor trending computer-vision dataset metadata.

### Input recipes

**Popular finance datasets**

```json
{
  "searchQueries": ["finance"],
  "maxItems": 20,
  "sort": "downloads",
  "dedupe": true
}
```

**Exact dataset lookup**

```json
{
  "datasetIds": ["squad", "HuggingFaceH4/ultrachat_200k"],
  "maxItems": 5,
  "dedupe": true
}
```

**Recently modified medical imaging datasets**

```json
{
  "searchQueries": ["medical imaging"],
  "maxItems": 20,
  "sort": "lastModified",
  "dedupe": true
}
```

### What data can you extract?

| Field | Description |
| --- | --- |
| `datasetId` | Stable Hugging Face dataset ID, for example `squad` or `org/name`. |
| `url` | Dataset page URL on Hugging Face. |
| `author`, `name` | Parsed owner and repository name when available. |
| `description` | Public API description, truncated when very long. |
| `downloads`, `likes`, `trendingScore` | Popularity and trending metrics exposed by Hugging Face. |
| `createdAt`, `lastModified`, `sha` | Repository recency and revision metadata. |
| `gated`, `private`, `disabled`, `accessStatus` | Access flags; the Actor does not bypass gated/private access. |
| `tags` | Raw Hugging Face tags preserved for auditability. |
| `license`, `languages`, `tasks`, `sizeCategory`, `formats`, `libraries`, `modalities`, `region` | Parsed convenience fields from raw tags. |
| `cardData` | Optional public README dataset card text, truncated and off by default. |
| `sourceQuery`, `fetchedAt`, `schemaVersion` | Run provenance fields. |

### Input configuration

| Setting | JSON key | Use it for | Example |
| --- | --- | --- | --- |
| Search queries | `searchQueries` | Find datasets by public catalog keyword. | `["finance"]` |
| Dataset IDs | `datasetIds` | Fetch exact dataset metadata. | `["squad"]` |
| Maximum records | `maxItems` | Cap output rows and cost. | `50` |
| Sort order | `sort` | Rank search results. | `"downloads"` |
| Minimum downloads | `minDownloads` | Skip low-download records. | `1000` |
| Minimum likes | `minLikes` | Skip low-like records. | `10` |
| Include dataset card | `includeCardData` | Add bounded README text for saved records. | `false` |
| De-duplicate | `dedupe` | Skip repeated dataset IDs across inputs. | `true` |

### Example input

```json
{
  "searchQueries": ["finance"],
  "datasetIds": ["squad"],
  "maxItems": 25,
  "sort": "downloads",
  "dedupe": true
}
```

### Example output

```json
{
  "datasetId": "squad",
  "url": "https://huggingface.co/datasets/squad",
  "author": null,
  "name": "squad",
  "description": "Stanford Question Answering Dataset...",
  "descriptionTruncated": false,
  "downloads": 12345,
  "likes": 100,
  "trendingScore": 0,
  "createdAt": "2022-03-02T00:00:00.000Z",
  "lastModified": "2024-01-01T00:00:00.000Z",
  "sha": "...",
  "gated": false,
  "private": false,
  "disabled": false,
  "accessStatus": "public",
  "tags": ["task_categories:question-answering", "language:en"],
  "license": null,
  "languages": ["en"],
  "tasks": ["question-answering"],
  "formats": ["parquet"],
  "sourceQuery": "dataset:squad",
  "fetchedAt": "2026-07-17T00:00:00.000Z",
  "schemaVersion": "1.0.0"
}
```

### Pricing

| Event | Price | Charged when |
| --- | --- | --- |
| Run start | `$0.005` per run | Once when the Actor starts. |
| Dataset metadata record | Tiered from `$0.000575` down to `$0.00014` per saved dataset row | Each dataset metadata item is saved. |

Store discount tiers for dataset metadata records: Free `$0.000575`, Bronze `$0.0005`, Silver `$0.00039`, Gold `$0.0003`, Platinum `$0.0002`, Diamond `$0.00014`. Competitor PPE benchmarks are documented in `docs/competitors/pricing.md`; the Bronze item price is below the median equivalent dataset metadata price.

### Tips for best results

- **Start small**: Use `maxItems: 10-50` while tuning queries.
- **Use focused terms**: Domain phrases such as `legal documents` or `medical imaging` are usually better than one-word broad searches.
- **Leave card data off by default**: Enable `includeCardData` only when you need README text because it adds one request per saved item.
- **Respect access flags**: Gated datasets are marked; this Actor does not fetch gated contents.

### API usage

Node.js:

```js
import { ApifyClient } from "apify-client";

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const run = await client.actor("fetch_cat/hugging-face-datasets-scraper").call({
  searchQueries: ["finance"],
  maxItems: 20,
  sort: "downloads"
});
console.log(run.defaultDatasetId);
```

Python:

```python
from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")
run = client.actor("fetch_cat/hugging-face-datasets-scraper").call(run_input={
    "searchQueries": ["finance"],
    "maxItems": 20,
    "sort": "downloads",
})
print(run["defaultDatasetId"])
```

cURL:

```bash
curl -X POST "https://api.apify.com/v2/acts/fetch_cat~hugging-face-datasets-scraper/runs?token=YOUR_APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"searchQueries":["finance"],"maxItems":20,"sort":"downloads"}'
```

### MCP and AI agents

Use this Actor from MCP-compatible tools through the official Apify MCP Server.

```bash
claude mcp add --transport http apify "https://mcp.apify.com?tools=fetch_cat/hugging-face-datasets-scraper"
```

```json
{
  "mcpServers": {
    "apify": {
      "url": "https://mcp.apify.com?tools=fetch_cat/hugging-face-datasets-scraper"
    }
  }
}
```

Example prompt: "Find 20 popular finance datasets on Hugging Face and summarize their licenses, tasks, and access flags."

### Limits and caveats

- **Public metadata only**: The Actor uses public Hugging Face catalog endpoints and public README files only.
- **No gated-content bypass**: Gated/private/disabled flags are exported as metadata; protected dataset contents are not downloaded.
- **Source changes**: Hugging Face fields and tags can change over time.
- **Diagnostics**: Per-source run diagnostics are stored in the `DIAGNOSTICS` key-value record.

### Legality and responsible use

Process only data you are allowed to access. Follow Hugging Face terms, dataset licenses, Apify's terms, and applicable laws.

### Related actors

- [Hugging Face Models Scraper](https://apify.com/fetch_cat/hugging-face-models-scraper)
- [Google Scholar Researcher Profile Scraper](https://apify.com/fetch_cat/google-scholar-researcher-profile-scraper)

### FAQ

#### Can it download dataset rows?

No. This Actor is for public metadata discovery and monitoring, not dataset content extraction.

#### Why are some fields empty?

Hugging Face does not expose every tag or metric for every dataset. Missing fields are left empty instead of guessed.

#### Can I export results?

Yes. Apify datasets support JSON, CSV, Excel, XML, RSS, and API access.

### Support

Open an issue from the Actor page if a run fails or output looks wrong. Include the run ID or URL, input JSON, expected output, actual output, and one reproducible public URL such as `https://huggingface.co/datasets/squad`.

### Changelog

- 2026-07-22 - Feature: Added ready-to-run Hugging Face Datasets Scraper example tasks on the Apify Store

# Actor input Schema

## `searchQueries` (type: `array`):

Keywords to search in the public Hugging Face dataset catalog. Each query is processed independently and results are de-duplicated by dataset ID.

## `datasetIds` (type: `array`):

Optional exact dataset IDs such as squad or HuggingFaceH4/ultrachat\_200k. Use this for direct metadata lookups.

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

Maximum number of dataset metadata records to save across all queries and IDs.

## `sort` (type: `string`):

Sort used for keyword searches. Exact dataset ID lookups ignore this setting.

## `minDownloads` (type: `integer`):

Only save datasets with at least this many downloads. Leave empty to disable.

## `minLikes` (type: `integer`):

Only save datasets with at least this many likes. Leave empty to disable.

## `includeCardData` (type: `boolean`):

Fetch the public README.md dataset card for each saved dataset, truncated to a safe length. This adds one extra request per saved dataset and is off by default.

## `dedupe` (type: `boolean`):

Skip duplicate dataset IDs across multiple queries and direct lookups.

## Actor input object example

```json
{
  "searchQueries": [
    "finance",
    "medical imaging"
  ],
  "datasetIds": [
    "squad",
    "HuggingFaceH4/ultrachat_200k"
  ],
  "maxItems": 20,
  "sort": "downloads",
  "includeCardData": false,
  "dedupe": true
}
```

# Actor output Schema

## `overview` (type: `string`):

No description

# 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 = {
    "searchQueries": [
        "finance",
        "medical imaging"
    ],
    "datasetIds": [
        "squad",
        "HuggingFaceH4/ultrachat_200k"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("fetch_cat/hugging-face-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 = {
    "searchQueries": [
        "finance",
        "medical imaging",
    ],
    "datasetIds": [
        "squad",
        "HuggingFaceH4/ultrachat_200k",
    ],
}

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

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

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

```

## CLI example

```bash
echo '{
  "searchQueries": [
    "finance",
    "medical imaging"
  ],
  "datasetIds": [
    "squad",
    "HuggingFaceH4/ultrachat_200k"
  ]
}' |
apify call fetch_cat/hugging-face-datasets-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "command": "npx",
            "args": [
                "mcp-remote",
                "https://mcp.apify.com/?tools=fetch_cat/hugging-face-datasets-scraper",
                "--header",
                "Authorization: Bearer <YOUR_API_TOKEN>"
            ]
        }
    }
}

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/kwHS2PKHK4gyKjVZ8/builds/mxbG50OCaCghzWGus/openapi.json