# Huggingface Discovery Parser Spider (`getdataforme/huggingface-discovery-parser-spider`) Actor The Huggingface Discovery Parser Spider efficiently scrapes and parses data from the Hugging Face platform, extracting valuable AI model metadata like author details, descriptions, categories, and more.... - **URL**: https://apify.com/getdataforme/huggingface-discovery-parser-spider.md - **Developed by:** [GetDataForMe](https://apify.com/getdataforme) (community) - **Categories:** AI, Automation, E-commerce - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $9.00 / 1,000 results 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 --- ## Huggingface Discovery Parser Spider ### Introduction The **Huggingface Discovery Parser Spider** is a powerful tool designed to efficiently scrape and parse data from the Hugging Face platform. It allows users to extract valuable information about AI models, including metadata such as author details, model descriptions, categories, and more. This Actor simplifies the process of gathering insights into trending and relevant AI technologies. ### Features - **Comprehensive Data Extraction**: Retrieves detailed information on AI models from Hugging Face. - **Customizable Search**: Allows users to specify search keys for targeted data scraping. - **Flexible Item Limiting**: Users can set a maximum number of items to scrape per run, including unlimited options. - **High-Quality Output**: Provides structured JSON output with essential metadata fields. - **User-Friendly Configuration**: Simple setup with clear input parameters and easy-to-follow instructions. ### Input Parameters | Parameter | Type | Required | Description | Example | |--------------|--------|----------|-----------------------------------------------------------------------------|------------------| | SearchKey | string | No | The search key for the spider. | `"gpt"` | | item_limit | integer| No | Maximum items to scrape per actor run. Set to 0 for no limit. | `10` | ### Example Usage #### Input JSON ```json { "SearchKey": "gpt", "item_limit": 10 } ```` #### Output JSON ```json [ { "id": "lj1995/GPT-SoVITS-ProPlus", "author": "lj1995", "author_name": "lj1995", "author_fullname": "liu", "author_avatar_url": "https://huggingface.co/avatars/b9065b1db98ec504e348128f25ae93d4.svg", "title": "GPT SoVITS V2 Pro Plus", "description": "Generate speech from text using a reference voice", "category": "Speech Synthesis", "tags": ["gradio", "region:us"], "likes": 237, "created_at": "2024-08-12T09:49:49.000Z", "last_modified": "2026-05-17T11:29:27.000Z", "pinned": false, "private": false, "featured": false, "visibility": "public", "trending_score": 1, "semantic_relevancy_score": 0.9177493006766734, "sdk": "gradio", "repo_type": "space", "runtime_stage": "RUNNING", "runtime_hardware_current": "zero-a10g", "runtime_hardware_requested": "zero-a10g", "runtime_replicas_current": 1, "runtime_replicas_requested": 1, "runtime_domain": "lj1995-gpt-sovits-proplus.hf.space", "runtime_domain_stage": "READY", "actor_id": "8Y27cMbGfoZEN0tmM", "run_id": "5wu1wNUskjFyjUQXG" }, { "id": "XXXXRT/GPT-SoVITS", "author": "XXXXRT", "author_name": "XXXXRT", "author_fullname": "XXXXRT", "author_avatar_url": "https://huggingface.co/avatars/71db5f5569c1fd60646fd2131118911b.svg", "title": "GPT-SoVITS-DEMO", "description": "Generate speech from text using reference audio", "category": "Speech Synthesis", "tags": ["gradio", "region:us"], "likes": 27, "created_at": "2025-06-27T09:15:48.000Z", "last_modified": "2025-09-18T21:38:23.000Z", "pinned": false, "private": false, "featured": false, "visibility": "public", "trending_score": 1, "semantic_relevancy_score": 0.86931203757813, "sdk": "gradio", "repo_type": "space", "runtime_stage": "RUNNING", "runtime_hardware_current": "zero-a10g", "runtime_hardware_requested": "zero-a10g", "runtime_replicas_current": 1, "runtime_replicas_requested": 1, "runtime_domain": "xxxxrt-gpt-sovits.hf.space", "runtime_domain_stage": "READY", "actor_id": "8Y27cMbGfoZEN0tmM", "run_id": "5wu1wNUskjFyjUQXG" }, { "id": "mkmenta/try-gpt-1-and-gpt-2", "author": "mkmenta", "author_name": "mkmenta", "author_fullname": "Mikel Menta Garde", "author_avatar_url": "https://huggingface.co/avatars/92823717fb0691b7ecb3ebe42ff86d75.svg", "title": "Try GPT-1 and GPT-2", "description": "Generate creative text using GPT-1 or GPT-2 models", "category": "Text Generation", "tags": ["gradio", "region:us"], "likes": 26, "created_at": "2023-07-02T14:42:48.000Z", "last_modified": "2024-11-05T09:19:48.000Z", "pinned": false, "private": false, "featured": false, "visibility": "public", "trending_score": 2, "semantic_relevancy_score": 0.782433769368772, "sdk": "gradio", "repo_type": "space", "runtime_stage": "RUNNING", "runtime_hardware_current": "cpu-basic", "runtime_hardware_requested": "cpu-basic", "runtime_replicas_current": 1, "runtime_replicas_requested": 1, "runtime_domain": "mkmenta-try-gpt-1-and-gpt-2.hf.space", "runtime_domain_stage": "READY", "actor_id": "8Y27cMbGfoZEN0tmM", "run_id": "5wu1wNUskjFyjUQXG" } ] ``` ### Use Cases - **Market Research and Analysis**: Identify trending AI models to understand market dynamics. - **Competitive Intelligence**: Monitor competitor activities by tracking their model releases. - **Price Monitoring**: Keep track of pricing changes for various AI services. - **Content Aggregation**: Compile information on new AI technologies for content creation. - **Academic Research**: Gather data for studies related to AI development and adoption. - **Business Automation**: Automate the collection of AI-related insights for strategic planning. ### Installation and Usage 1. Search for "Huggingface Discovery Parser Spider" in the Apify Store. 2. Click "Try for free" or "Run". 3. Configure input parameters as needed. 4. Click "Start" to begin extraction. 5. Monitor progress in the log. 6. Export results in your preferred format (JSON, CSV, Excel). ### Output Format The output is a JSON array where each object represents an AI model with fields such as `id`, `author`, `title`, `description`, `category`, and more. Each field provides specific metadata about the model, including creation dates, likes, visibility status, and technical details like runtime hardware. ### Support Section #### Support For custom/simplified outputs or bug reports, please contact: - Email: support@getdataforme.com - Subject line: "custom support" - Contact form: [Get Data For Me](https://getdataforme.com/contact/) We're here to help you get the most out of this Actor! ### Error Handling The Actor is designed to handle common errors gracefully, such as network issues or invalid input parameters. If an error occurs, it will be logged in the Apify console for troubleshooting. ### Rate Limiting and Best Practices To ensure optimal performance and avoid being rate-limited by Hugging Face, adhere to best practices such as setting reasonable item limits and spacing out requests if running multiple instances of the Actor. *** This documentation provides a comprehensive guide to using the Huggingface Discovery Parser Spider effectively. For any further assistance or customization needs, please reach out through our support channels. # Actor input Schema ## `SearchKey` (type: `string`): The search key for the spider. ## `item_limit` (type: `integer`): Maximum items to scrape per actor run. Set to 0 for no limit. ## Actor input object example ```json { "SearchKey": "gpt", "item_limit": 10 } ``` # Actor output Schema ## `results` (type: `string`): Scraped data items from dataset # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("getdataforme/huggingface-discovery-parser-spider").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 = {} # Run the Actor and wait for it to finish run = client.actor("getdataforme/huggingface-discovery-parser-spider").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 '{}' | apify call getdataforme/huggingface-discovery-parser-spider --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getdataforme/huggingface-discovery-parser-spider", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Huggingface Discovery Parser Spider", "description": "The Huggingface Discovery Parser Spider efficiently scrapes and parses data from the Hugging Face platform, extracting valuable AI model metadata like author details, descriptions, categories, and more....", "version": "0.0", "x-build-id": "slBzLA53YxwlKrRDC" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getdataforme~huggingface-discovery-parser-spider/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getdataforme-huggingface-discovery-parser-spider", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getdataforme~huggingface-discovery-parser-spider/runs": { "post": { "operationId": "runs-sync-getdataforme-huggingface-discovery-parser-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getdataforme~huggingface-discovery-parser-spider/run-sync": { "post": { "operationId": "run-sync-getdataforme-huggingface-discovery-parser-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "SearchKey": { "title": "Search Key", "type": "string", "description": "The search key for the spider.", "default": "gpt" }, "item_limit": { "title": "Item limit", "minimum": 0, "type": "integer", "description": "Maximum items to scrape per actor run. Set to 0 for no limit.", "default": 10 } } }, "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 } } } } } } } } } } ```