# HuggingFace Model Scraper - AI/ML Model Data (`jungle_synthesizer/huggingface-model-scraper`) Actor Scrape AI/ML model metadata from the HuggingFace Hub. Extract model names, task types, download counts, likes, libraries, authors, tags, licenses, model sizes, and model card excerpts. Filter by task type, library, author, and search query. - **URL**: https://apify.com/jungle\_synthesizer/huggingface-model-scraper.md - **Developed by:** [BowTiedRaccoon](https://apify.com/jungle_synthesizer) (community) - **Categories:** AI, Developer tools, Business - **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 ## HuggingFace Model Scraper - AI/ML Model Data Extract comprehensive AI/ML model metadata from the HuggingFace Hub. The HuggingFace Hub hosts over 1 million public models and is the central repository for the AI/ML community. This actor queries the public HuggingFace API to retrieve model names, task types, download counts, popularity metrics, licenses, libraries, and model card excerpts. ### What You Can Do - **Browse top models** sorted by total downloads, likes, trending score, or recently modified - **Filter by task type** (text-generation, image-classification, sentence-similarity, and 25+ other pipeline tags) - **Filter by ML library** (transformers, diffusers, sentence-transformers, GGUF, ONNX, and more) - **Filter by author/organization** (meta-llama, google, microsoft, BAAI, Qwen, etc.) - **Search by keyword** across model names and descriptions - **Extract model card excerpts** — first 500 characters of each model's README - **Get spaces usage** — count of HuggingFace Spaces using each model - **Retrieve dataset provenance** — datasets referenced in model card metadata ### Use Cases - **AI market intelligence** — track which models are gaining downloads and likes - **VC and investment research** — monitor model ecosystem trends by organization - **Enterprise model evaluation** — shortlist foundation models by task type, license, and popularity - **Competitive analysis** — compare model adoption across ML libraries and providers - **Dataset discovery** — find which training datasets are most commonly used ### Input Parameters | Parameter | Description | Default | |-----------|-------------|---------| | `searchQuery` | Search across model names and descriptions | — | | `pipelineTag` | Filter by task type (text-generation, image-classification, etc.) | All tasks | | `library` | Filter by ML framework (transformers, diffusers, gguf, etc.) | All libraries | | `author` | Filter by author or organization username | All authors | | `sortBy` | Sort by `downloads`, `likes`, `lastModified`, or `trending` | `downloads` | | `maxItems` | Maximum number of records to return (0 = unlimited) | 10 | | `proxyConfiguration` | Optional proxy settings | Disabled | ### Output Fields Each record contains: | Field | Type | Description | |-------|------|-------------| | `model_id` | string | Full model identifier (e.g., `meta-llama/Llama-3.3-70B-Instruct`) | | `model_name` | string | Short model name without the author prefix | | `pipeline_tag` | string | Primary task type (text-generation, sentence-similarity, etc.) | | `downloads_total` | integer | Total all-time download count | | `downloads_30d` | integer | Download count in the last 30 days (when available) | | `likes` | integer | Number of likes on HuggingFace | | `library` | string | Primary ML library (transformers, diffusers, etc.) | | `author` | string | Model author or organization username | | `tags` | array | Tags including language, dataset references, and framework tags | | `license` | string | License identifier (apache-2.0, mit, llama3.3, etc.) | | `model_size_params` | string | Parameter count if encoded in tags (7B, 13B, 70B, etc.) | | `last_modified` | string | ISO 8601 timestamp of last update | | `readme_excerpt` | string | First 500 characters of the model card README | | `spaces_count` | integer | Number of HuggingFace Spaces using this model | | `datasets_used` | array | Datasets referenced in model card metadata | ### Example Output ```json { "model_id": "sentence-transformers/all-MiniLM-L6-v2", "model_name": "all-MiniLM-L6-v2", "pipeline_tag": "sentence-similarity", "downloads_total": 262278076, "downloads_30d": null, "likes": 4833, "library": "sentence-transformers", "author": "sentence-transformers", "tags": ["sentence-transformers", "pytorch", "onnx", "safetensors", "bert", "en"], "license": "apache-2.0", "model_size_params": null, "last_modified": "2025-03-06T13:37:44.000Z", "readme_excerpt": "## all-MiniLM-L6-v2\nThis is a sentence-transformers model...", "spaces_count": 100, "datasets_used": ["s2orc", "ms_marco", "gooaq", "natural_questions"] } ```` ### Technical Notes - **No authentication required** — uses the public HuggingFace Hub API - **No proxy required** — the API is publicly accessible without IP restrictions - **Rate limits** — generous unauthenticated limits; a courtesy 100ms delay is applied between detail fetches - **Pagination** — handles cursor-based pagination automatically, allowing retrieval of any number of models - **Two-pass enrichment** — basic metadata is retrieved from the list endpoint; detailed fields (readme\_excerpt, spaces\_count, datasets\_used) are fetched from the model detail endpoint ### Data Source [HuggingFace Hub API](https://huggingface.co/docs/hub/api) — `https://huggingface.co/api/models` # 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. ## `searchQuery` (type: `string`): Search for models by name, author, or description. Leave empty to browse all models. ## `pipelineTag` (type: `string`): Filter models by their primary task type. Leave empty for all tasks. ## `library` (type: `string`): Filter by ML framework/library. Leave empty for all libraries. ## `author` (type: `string`): Filter by model author or organization (e.g., 'meta-llama', 'google', 'microsoft'). ## `sortBy` (type: `string`): Sort results by the specified metric. ## `maxItems` (type: `integer`): Maximum number of model records to return. Set to 0 for unlimited. The HuggingFace Hub has 1M+ public models — use filters to narrow results. ## `proxyConfiguration` (type: `object`): Select proxies. The HuggingFace API is public and typically does not require proxies. ## 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...", "sortBy": "downloads", "maxItems": 10, "proxyConfiguration": { "useApifyProxy": false } } ``` # 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...", "searchQuery": "", "pipelineTag": "", "library": "", "author": "", "sortBy": "downloads", "maxItems": 10, "proxyConfiguration": { "useApifyProxy": false } }; // Run the Actor and wait for it to finish const run = await client.actor("jungle_synthesizer/huggingface-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 '' 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...", "searchQuery": "", "pipelineTag": "", "library": "", "author": "", "sortBy": "downloads", "maxItems": 10, "proxyConfiguration": { "useApifyProxy": False }, } # Run the Actor and wait for it to finish run = client.actor("jungle_synthesizer/huggingface-model-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...", "searchQuery": "", "pipelineTag": "", "library": "", "author": "", "sortBy": "downloads", "maxItems": 10, "proxyConfiguration": { "useApifyProxy": false } }' | apify call jungle_synthesizer/huggingface-model-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jungle_synthesizer/huggingface-model-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "HuggingFace Model Scraper - AI/ML Model Data", "description": "Scrape AI/ML model metadata from the HuggingFace Hub. Extract model names, task types, download counts, likes, libraries, authors, tags, licenses, model sizes, and model card excerpts. Filter by task type, library, author, and search query.", "version": "0.1", "x-build-id": "JDOuYwtcpLiPAzDMm" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jungle_synthesizer~huggingface-model-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jungle_synthesizer-huggingface-model-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~huggingface-model-scraper/runs": { "post": { "operationId": "runs-sync-jungle_synthesizer-huggingface-model-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~huggingface-model-scraper/run-sync": { "post": { "operationId": "run-sync-jungle_synthesizer-huggingface-model-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", "required": [ "sp_intended_usage", "sp_improvement_suggestions" ], "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." }, "searchQuery": { "title": "Search Query", "type": "string", "description": "Search for models by name, author, or description. Leave empty to browse all models." }, "pipelineTag": { "title": "Task / Pipeline Tag", "enum": [ "", "text-generation", "text2text-generation", "text-classification", "token-classification", "question-answering", "translation", "summarization", "fill-mask", "conversational", "feature-extraction", "sentence-similarity", "image-classification", "object-detection", "image-segmentation", "image-to-text", "text-to-image", "image-to-image", "automatic-speech-recognition", "text-to-speech", "audio-classification", "video-classification", "reinforcement-learning", "tabular-classification", "tabular-regression", "zero-shot-classification", "zero-shot-image-classification", "depth-estimation", "visual-question-answering" ], "type": "string", "description": "Filter models by their primary task type. Leave empty for all tasks." }, "library": { "title": "Library", "enum": [ "", "transformers", "diffusers", "sentence-transformers", "timm", "spacy", "adapter-transformers", "flair", "allennlp", "asteroid", "espnet", "fairseq", "fastai", "keras", "nemo", "paddlenlp", "peft", "stanza", "tf-lite", "jax", "onnx", "openvino", "tensorboard", "safetensors", "gguf", "mlx" ], "type": "string", "description": "Filter by ML framework/library. Leave empty for all libraries." }, "author": { "title": "Author / Organization", "type": "string", "description": "Filter by model author or organization (e.g., 'meta-llama', 'google', 'microsoft')." }, "sortBy": { "title": "Sort By", "enum": [ "downloads", "likes", "lastModified", "trending" ], "type": "string", "description": "Sort results by the specified metric.", "default": "downloads" }, "maxItems": { "title": "Max Items", "type": "integer", "description": "Maximum number of model records to return. Set to 0 for unlimited. The HuggingFace Hub has 1M+ public models — use filters to narrow results.", "default": 10 }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "Select proxies. The HuggingFace API is public and typically does not require proxies." } } }, "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 } } } } } } } } } } ```