# ModelScope Model Catalog Scraper (`jungle_synthesizer/modelscope-model-catalog-scraper`) Actor

Scrape the ModelScope (modelscope.cn) AI model catalog — China's Alibaba-backed model hub. Export model IDs, tasks, frameworks, download stats, stars, licenses, and READMEs.

- **URL**: https://apify.com/jungle\_synthesizer/modelscope-model-catalog-scraper.md
- **Developed by:** [BowTiedRaccoon](https://apify.com/jungle_synthesizer) (community)
- **Categories:** AI, Developer tools
- **Stats:** 1 total users, 0 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

Pay per event

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

## ModelScope Model Catalog Scraper

Scrape the [ModelScope (modelscope.cn)](https://www.modelscope.cn/models) AI model catalog — China's Alibaba-backed model registry hosting ~200k models. Export model IDs, tasks, frameworks, download statistics, star counts, licenses, READMEs, and full metadata for all models in the catalog.

### What it does

Sweeps the ModelScope JSON API task-by-task (text-generation, image-generation, multimodal, and 26 other task categories), deduplicates across task overlaps, and optionally enriches each model record with the full README from the per-model detail endpoint.

**Output fields per model:**

- `model_id` — full identifier (`namespace/name`)
- `namespace`, `name` — publisher slug and model name
- `chinese_name` — display name in Chinese if present
- `task` — primary task tag used for discovery
- `tasks_all` — all task tags, pipe-separated
- `frameworks` — ML frameworks (pytorch, tensorflow, mindspore, etc.), pipe-separated
- `languages` — supported languages (en, zh, multilingual, etc.), pipe-separated
- `license` — SPDX identifier (apache-2.0, mit, etc.)
- `downloads_30d` — downloads in the last 30 days
- `stars` — star count
- `last_updated`, `created_at` — ISO-8601 timestamps
- `readme_text` — README content, truncated to 8 KB (requires `includeDetails: true`)
- `model_size_params` — parameter count label when tagged (7B, 72B, MoE-22B-A2B)
- `quantization_variants` — available quantization types from tensor metadata, pipe-separated
- `base_model` — base model ID if this is a fine-tune
- `publisher_org`, `publisher_url` — organization name and profile URL
- `has_demo`, `has_inference_api` — boolean flags

### Input

| Field | Type | Default | Description |
|-------|------|---------|-------------|
| `tasks` | array | *(all tasks)* | Limit to specific task slugs (e.g. `text-generation`, `image-generation`). Leave empty to sweep all 29 canonical tasks. |
| `maxItems` | integer | 100 | Maximum number of models to return. Set to `0` for unlimited (full catalog run). |
| `includeDetails` | boolean | true | Fetch the per-model detail endpoint for full README text and quantization variant metadata. Disabling this speeds up runs but leaves `readme_text` and `quantization_variants` empty. |

### Example use cases

- **West+East parity datasets** — pair with the HuggingFace Model Scraper to build a combined index of both Western and Chinese open-weights releases (Qwen, DeepSeek, Yi, GLM, InternLM, ERNIE, MiniMax, etc.).
- **Model landscape research** — filter by task, framework, or license to survey which Chinese labs are publishing in specific domains.
- **Download trend tracking** — schedule regular runs and track `downloads_30d` growth for specific namespaces or model families.
- **README content analysis** — extract model cards from `readme_text` for NLP-based capability assessment or feature extraction.

### Notes

- Full catalog sweeps (all tasks, `includeDetails: true`) are long-running. Use `maxItems` to cap output for targeted queries.
- Array output fields (`tasks_all`, `frameworks`, `languages`, `quantization_variants`) use `|` as separator for flat dataset compatibility. Split on `|` in downstream processing.

# Actor input Schema

## `sp_intended_usage` (type: `string`):

What will this data feed? E.g. lead lists, KYB checks, price tracking.

## `sp_improvement_suggestions` (type: `string`):

Provide any feedback or suggestions for improvements.

## `sp_contact` (type: `string`):

We'll personally help with your use case. No spam.

## `tasks` (type: `array`):

Limit to specific task slugs (e.g. text-generation, image-generation). Leave empty to scrape all tasks.

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

Maximum number of models to scrape. Set to 0 for unlimited.

## `includeDetails` (type: `boolean`):

Fetch per-model detail endpoint for README text, quantization variants, and deeper metadata. Slower but richer output.

## Actor input object example

```json
{
  "sp_intended_usage": "Describe your intended use...",
  "sp_improvement_suggestions": "Share your suggestions here...",
  "sp_contact": "Share your email here...",
  "tasks": [
    "text-generation"
  ],
  "maxItems": 10,
  "includeDetails": true
}
```

# Actor output Schema

## `results` (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 = {
    "sp_intended_usage": "Describe your intended use...",
    "sp_improvement_suggestions": "Share your suggestions here...",
    "sp_contact": "Share your email here...",
    "tasks": [
        "text-generation"
    ],
    "maxItems": 10,
    "includeDetails": true
};

// Run the Actor and wait for it to finish
const run = await client.actor("jungle_synthesizer/modelscope-model-catalog-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 = {
    "sp_intended_usage": "Describe your intended use...",
    "sp_improvement_suggestions": "Share your suggestions here...",
    "sp_contact": "Share your email here...",
    "tasks": ["text-generation"],
    "maxItems": 10,
    "includeDetails": True,
}

# Run the Actor and wait for it to finish
run = client.actor("jungle_synthesizer/modelscope-model-catalog-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 '{
  "sp_intended_usage": "Describe your intended use...",
  "sp_improvement_suggestions": "Share your suggestions here...",
  "sp_contact": "Share your email here...",
  "tasks": [
    "text-generation"
  ],
  "maxItems": 10,
  "includeDetails": true
}' |
apify call jungle_synthesizer/modelscope-model-catalog-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,jungle_synthesizer/modelscope-model-catalog-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/Jot0IoKtmP3dYqYqG/builds/aFmUFZ9UBUfiC1J4P/openapi.json