# 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:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN 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/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. 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. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use 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 - The API requires no authentication. No proxy is needed — direct access from Apify infrastructure works without restriction. - 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`): Please describe how you plan to use the data extracted by this crawler. ## `sp_improvement_suggestions` (type: `string`): Provide any feedback or suggestions for improvements. ## `sp_contact` (type: `string`): Provide your email address so we can get in touch with you. ## `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 '' with your token const client = new ApifyClient({ 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 '' with your token. client = ApifyClient("") # 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("💾 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 '{ "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": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jungle_synthesizer/modelscope-model-catalog-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "ModelScope Model Catalog Scraper", "description": "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.", "version": "0.1", "x-build-id": "zgCsOSipe2Nga7dfL" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jungle_synthesizer~modelscope-model-catalog-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jungle_synthesizer-modelscope-model-catalog-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/jungle_synthesizer~modelscope-model-catalog-scraper/runs": { "post": { "operationId": "runs-sync-jungle_synthesizer-modelscope-model-catalog-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/jungle_synthesizer~modelscope-model-catalog-scraper/run-sync": { "post": { "operationId": "run-sync-jungle_synthesizer-modelscope-model-catalog-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "sp_intended_usage": { "title": "What is the intended usage of this data?", "minLength": 1, "type": "string", "description": "Please describe how you plan to use the data extracted by this crawler." }, "sp_improvement_suggestions": { "title": "How can we improve this crawler for you?", "minLength": 1, "type": "string", "description": "Provide any feedback or suggestions for improvements." }, "sp_contact": { "title": "Contact Email", "minLength": 1, "type": "string", "description": "Provide your email address so we can get in touch with you." }, "tasks": { "title": "Tasks", "type": "array", "description": "Limit to specific task slugs (e.g. text-generation, image-generation). Leave empty to scrape all tasks.", "items": { "type": "string" } }, "maxItems": { "title": "Max Items", "type": "integer", "description": "Maximum number of models to scrape. Set to 0 for unlimited.", "default": 100 }, "includeDetails": { "title": "Include Details", "type": "boolean", "description": "Fetch per-model detail endpoint for README text, quantization variants, and deeper metadata. Slower but richer output.", "default": true } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```