# Hugging Face Scraper (`straightforward_hydra/huggingface-scraper`) Actor AI model intelligence from the open Hugging Face Hub API: trending models, datasets and spaces by task, author and library. No API key. - **URL**: https://apify.com/straightforward\_hydra/huggingface-scraper.md - **Developed by:** [Dev D](https://apify.com/straightforward_hydra) (community) - **Categories:** AI, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $1.00 / 1,000 items 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 ## Hugging Face Scraper πŸ€— **AI model intelligence from the open Hugging Face Hub API β€” trending & most-downloaded models, datasets and spaces, by task, author and library. No API key, no login.** Track the AI landscape with clean, structured data straight from the [Hugging Face Hub](https://huggingface.co) API: **trending** and **most-downloaded** models, **datasets** and **spaces** β€” filterable by task (text-generation, text-to-image, ASR…), **author/org** (Meta, Mistral, Google…) and library. Perfect for AI-trend monitoring, competitive intelligence on model labs, dataset discovery and building model catalogs. > Uses the open Hugging Face Hub API β€” public data, no key required. --- ### What you can do with it - πŸ”₯ **Track trending models** β€” what's rising on the Hub right now, by task. - 🏒 **Watch an AI lab** β€” everything `mistralai`, `meta-llama` or `google` ships, by downloads. - πŸ“š **Discover datasets** β€” find training data by popularity, task or tag. - πŸš€ **Monitor Spaces** β€” the most-liked live demos and apps. - πŸ“Š **Build a model catalog** β€” downloads, likes, tags and update dates for a whole task area. ### Features - βœ… **No API key** β€” open Hugging Face Hub endpoints, works out of the box. - βœ… **Models, datasets & spaces** β€” one Actor, three resource types. - βœ… **Smart sorts** β€” trending, downloads, likes, newest, recently updated. - βœ… **Filters** β€” search, author/org, task/pipeline, library, tag. - βœ… **Scales** β€” cursor-paginated, 100 items per page. --- ### Input | Field | Description | |---|---| | **Resource** | models / datasets / spaces. | | **Sort by** | trending / downloads / likes / newest / recently updated. | | **Search** | Free-text name search. | | **Author / organization** | e.g. `mistralai`, `meta-llama`, `google`. | | **Task** | Models only, e.g. `text-generation`, `text-to-image`. | | **Library / Tag** | Extra model/library/tag filters. | | **Max results** | Result cap. | #### Example β€” trending text-generation models ```json { "resource": "models", "sort": "trending", "task": "text-generation", "maxResults": 100 } ```` #### Example β€” everything Mistral ships, by downloads ```json { "resource": "models", "sort": "downloads", "author": "mistralai", "maxResults": 200 } ``` ### Output One row per item: ```json { "type": "model", "id": "mistralai/Mistral-7B-Instruct-v0.3", "url": "https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3", "author": "mistralai", "downloads": 5438667, "likes": 2714, "trending_score": 12, "task": "text-generation", "library": "transformers", "tags": ["transformers", "safetensors", "mistral", "text-generation"], "last_modified": "2024-08-21T10:00:00.000Z" } ``` Datasets and spaces carry the same core fields; spaces also include `sdk` (gradio/streamlit/docker), datasets include a short `description`. ### Run it on a schedule Schedule the Actor to snapshot trending models or a lab's releases over time, and connect a **Google Sheets / Slack / webhook** integration to get alerted when new models drop or downloads spike. ### Notes & limitations - Uses the open Hugging Face Hub API β€” no key needed (an optional token raises rate limits). - Counts are point-in-time snapshots at run time. - Public repository metadata (non-personal). - Data source: Hugging Face Hub. *** #### Keywords Hugging Face, huggingface, HF Hub, AI models, machine learning models, LLM, model scraper, Hugging Face API, model downloads, trending models, AI datasets, ML datasets, Hugging Face spaces, model catalog, AI trends, transformers, text-generation, model intelligence, ML data, AI research. # Actor input Schema ## `resource` (type: `string`): What to pull from the Hub. ## `sort` (type: `string`): How to rank results. ## `search` (type: `string`): Free-text search over names, e.g. "llama", "whisper", "stable-diffusion". ## `author` (type: `string`): Limit to one org or user, e.g. meta-llama, mistralai, google, openai, stabilityai. ## `task` (type: `string`): Filter models by task, e.g. text-generation, text-to-image, automatic-speech-recognition, image-text-to-text, feature-extraction. ## `library` (type: `string`): Filter models by library, e.g. transformers, diffusers, gguf, sentence-transformers. ## `filterTag` (type: `string`): Filter by a Hub tag, e.g. a language (en), license (license:mit) or dataset task. ## `token` (type: `string`): Optional user access token (huggingface.co/settings/tokens) for higher rate limits. Not required for public data. ## `maxResults` (type: `integer`): How many items to return (paged 100 at a time). ## Actor input object example ```json { "resource": "models", "sort": "trending", "maxResults": 100 } ``` # 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("straightforward_hydra/huggingface-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`πŸ’Ύ 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); }); // πŸ“š Want to learn more πŸ“–? Go to β†’ 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("straightforward_hydra/huggingface-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("πŸ’Ύ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # πŸ“š Want to learn more πŸ“–? Go to β†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{}' | apify call straightforward_hydra/huggingface-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=straightforward_hydra/huggingface-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Hugging Face Scraper", "description": "AI model intelligence from the open Hugging Face Hub API: trending models, datasets and spaces by task, author and library. No API key.", "version": "0.1", "x-build-id": "R2L7Bzs4wKZXnzayl" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/straightforward_hydra~huggingface-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-straightforward_hydra-huggingface-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/straightforward_hydra~huggingface-scraper/runs": { "post": { "operationId": "runs-sync-straightforward_hydra-huggingface-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/straightforward_hydra~huggingface-scraper/run-sync": { "post": { "operationId": "run-sync-straightforward_hydra-huggingface-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "resource": { "title": "Resource", "enum": [ "models", "datasets", "spaces" ], "type": "string", "description": "What to pull from the Hub.", "default": "models" }, "sort": { "title": "Sort by", "enum": [ "trending", "downloads", "likes", "created", "modified" ], "type": "string", "description": "How to rank results.", "default": "trending" }, "search": { "title": "Search (optional)", "type": "string", "description": "Free-text search over names, e.g. \"llama\", \"whisper\", \"stable-diffusion\"." }, "author": { "title": "Author / organization (optional)", "type": "string", "description": "Limit to one org or user, e.g. meta-llama, mistralai, google, openai, stabilityai." }, "task": { "title": "Task / pipeline (models only)", "type": "string", "description": "Filter models by task, e.g. text-generation, text-to-image, automatic-speech-recognition, image-text-to-text, feature-extraction." }, "library": { "title": "Library (models only)", "type": "string", "description": "Filter models by library, e.g. transformers, diffusers, gguf, sentence-transformers." }, "filterTag": { "title": "Tag filter (optional)", "type": "string", "description": "Filter by a Hub tag, e.g. a language (en), license (license:mit) or dataset task." }, "token": { "title": "Hugging Face token (optional)", "type": "string", "description": "Optional user access token (huggingface.co/settings/tokens) for higher rate limits. Not required for public data." }, "maxResults": { "title": "Max results", "minimum": 1, "maximum": 50000, "type": "integer", "description": "How many items to return (paged 100 at a time).", "default": 100 } } }, "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 } } } } } } } } } } ```