# Hugging Face Models & Datasets List to CSV (Model Scraper) (`badboxx_newlife/hugging-face-model-scraper`) Actor

- **URL**: https://apify.com/badboxx_newlife/hugging-face-model-scraper.md
- **Developed by:** [Francesco Correnti](https://apify.com/badboxx_newlife) (community)
- **Categories:** Developer tools, AI
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

$4.00 / 1,000 rows

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?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
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.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## 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.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## Hugging Face Models & Datasets List to CSV (Hugging Face Model Scraper)

Hugging Face model scraper: export a **hugging face models list csv** (or datasets) with one flat row per repo. Uses the public Hugging Face Hub API, no API key. Filter by task and license, sort by downloads, likes, last modified or trending. Rows include card-completeness flags (README, license, dataset link, model-index evals). **$0.004 per row.**

### Input

```json
{ "kind": "model", "task": "text-classification", "license": "mit", "sort": "downloads", "maxItems": 100 }
```

| Field | Default | Meaning |
|---|---|---|
| `kind` | model | `model` or `dataset` |
| `task` | empty | Models: `pipeline_tag`. Datasets: `task_categories`. Empty = no filter |
| `license` | empty | License id, e.g. `mit`. Empty = no filter |
| `sort` | downloads | `downloads`, `likes`, `lastModified`, `trending` (descending) |
| `maxItems` | 50 | 1 to 10000 |

### Output

One row per item, keys in this order: `id, author, downloads, likes, license, tags, pipeline_tag, lastModified, has_readme, has_license, has_dataset_link, has_model_index_eval`. `tags` is joined with `;` so the CSV stays flat. Missing numbers are 0, missing text is "", never null. Export as CSV from the dataset tab.

Sample row:

| Field | Example |
|---|---|
| `id` | BAAI/bge-small-en-v1.5 |
| `author` | BAAI |
| `downloads` | 59413346 |
| `likes` | 643 |
| `license` | mit |
| `tags` | sentence-transformers;pytorch;onnx;safetensors;bert;feature-... |
| `pipeline_tag` | feature-extraction |
| `lastModified` | 2024-02-22T03:36:23.000Z |
| `has_readme` | true |
| `has_license` | true |
| `has_dataset_link` | false |
| `has_model_index_eval` | true |

### Pricing

Pay per event: **`row` = $0.004** per row pushed. No other events. The run stops cleanly when your max-charge limit is reached.

Limits: public Hugging Face API only, no private or gated repos, API rate limits apply (429/5xx retried up to 5 times with backoff, 30 s timeout), `maxItems` capped at 10000.

# Actor input Schema

## `kind` (type: `string`):

List models or datasets.

## `task` (type: `string`):

Optional task filter, e.g. text-classification. Models: pipeline_tag. Datasets: task_categories. Empty = no filter.

## `license` (type: `string`):

Optional license id, e.g. mit or apache-2.0. Empty = no filter.

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

Sort order, descending.

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

Rows to output (1-10000). One row is charged each.

## Actor input object example

```json
{
  "kind": "model",
  "sort": "downloads",
  "maxItems": 50
}
```

# Actor output Schema

## `rows` (type: `string`):

No description

## `csv` (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 = {};

// Run the Actor and wait for it to finish
const run = await client.actor("badboxx_newlife/hugging-face-model-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 = {}

# Run the Actor and wait for it to finish
run = client.actor("badboxx_newlife/hugging-face-model-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 '{}' |
apify call badboxx_newlife/hugging-face-model-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,badboxx_newlife/hugging-face-model-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/6hcJdc0lprNCbI8Qy/builds/jc5lhisTBZI2fw7EQ/openapi.json