huggingface-api

name	huggingface-api
description	Search and discover ML models, datasets, and Spaces on Hugging Face
metadata	{"openclaw":{"emoji":"🤗","category":"domains","subcategory":"ai-ml","keywords":["Hugging Face","ML models","datasets","model hub","transformers","NLP","computer vision"],"source":"https://huggingface.co/docs/hub/api"}}

Hugging Face Hub API

Overview

The Hugging Face Hub is the largest open-source ML ecosystem, hosting over 1 million models, 200,000+ datasets, and 400,000+ Spaces (demo apps). The Hub API at https://huggingface.co/api provides programmatic access to search, discover, and retrieve metadata for all public resources without authentication.

For academic researchers, the Hub API enables systematic model selection for benchmarking, dataset discovery for experiments, tracking community adoption metrics (downloads, likes), and building reproducible ML pipelines that reference specific model revisions by SHA.

Authentication

Read endpoints require no authentication. All search and metadata queries work without a token.

For write operations (uploading models, creating repos), set a User Access Token:

export HF_TOKEN="hf_..."
# Pass via header:
curl -H "Authorization: Bearer $HF_TOKEN" https://huggingface.co/api/...

Generate tokens at: https://huggingface.co/settings/tokens

Core Endpoints

Search Models

GET https://huggingface.co/api/models?search={query}&limit={n}&sort={field}&direction={-1|1}

Parameters: search (query string), limit (max results), sort (field: downloads, likes, lastModified, trending), direction (-1 descending, 1 ascending), filter (pipeline tag like text-classification), author (org/user filter), library (e.g. transformers, pytorch)

Example -- top 2 models for "bert" by downloads:

curl -s "https://huggingface.co/api/models?search=bert&limit=2&sort=downloads&direction=-1"

[
  {
    "id": "google-bert/bert-base-uncased",
    "likes": 2587,
    "downloads": 71053483,
    "pipeline_tag": "fill-mask",
    "library_name": "transformers",
    "tags": ["transformers","pytorch","tf","jax","bert","fill-mask","en",
             "dataset:bookcorpus","dataset:wikipedia","arxiv:1810.04805",
             "license:apache-2.0"]
  },
  {
    "id": "google-bert/bert-base-multilingual-uncased",
    "likes": 153
     
     
     
  

Get Model Details

GET https://huggingface.co/api/models/{owner}/{model_name}

Returns full metadata including config.architectures, cardData (license, datasets, language), siblings (file listing), sha (exact revision), and lastModified.

curl -s "https://huggingface.co/api/models/google-bert/bert-base-uncased"

Key fields in response:

{
  "id": "google-bert/bert-base-uncased",
  "sha": "86b5e0934494bd15c9632b12f734a8a67f723594",
  "lastModified": "2024-02-19T11:06:12.000Z",
  "downloads": 71053483,
  "config": { "architectures": ["BertForMaskedLM"], "model_type": "bert" },
  "cardData": { "language": "en", "license": "apache-2.0",
                "datasets": ["bookcorpus","wikipedia"] }
}

Search Datasets

GET https://huggingface.co/api/datasets?search={query}&limit={n}

Parameters: search, limit, sort, direction, author, filter (task tag like question-answering)

curl -s "https://huggingface.co/api/datasets?search=squad&limit=2"

[
  {
    "id": "rajpurkar/squad_v2",
    "likes": 242,
    "downloads": 36017,
    "description": "Stanford Question Answering Dataset (SQuAD)...",
    "tags": ["task_categories:question-answering","language:en",
             "license:cc-by-sa-4.0","size_categories:100K<n<1M",
             "arxiv:1806.03822"]
  }
]

Get Dataset Details

GET https://huggingface.co/api/datasets/{owner}/{dataset_name}

curl -s "https://huggingface.co/api/datasets/rajpurkar/squad_v2"

Returns cardData with structured metadata (task categories, languages, license, size), description, paperswithcode_id for cross-referencing, and tags with arXiv paper IDs.

Search Spaces

GET https://huggingface.co/api/spaces?search={query}&limit={n}

curl -s "https://huggingface.co/api/spaces?search=chatbot&limit=2"

[
  {
    "id": "21Hg/chatbot",
    "likes": 5,
    "sdk": "docker",
    "tags": ["docker","streamlit","region:us"]
  },
  {
    "id": "lmarena-ai/chatbot-arena",
    "likes": 234,
    "sdk": "static"
  }
]

Advanced Filters

Combine filters via query params to narrow results:

# PyTorch text-generation models with 1000+ likes
curl -s "https://huggingface.co/api/models?filter=text-generation&library=pytorch&sort=likes&direction=-1&limit=5"

# Datasets for NER tasks in Chinese
curl -s "https://huggingface.co/api/datasets?filter=token-classification&language=zh&limit=10"

# Gradio Spaces sorted by trending
curl -s "https://huggingface.co/api/spaces?filter=gradio&sort=trending&direction=-1&limit=5"

Rate Limits

• Unauthenticated: generous but undocumented; suitable for interactive use and small scripts
• Authenticated: higher limits with Bearer token
• Best practice: add limit parameter to avoid fetching thousands of results; cache responses locally for batch analysis
• No strict per-minute quota is published; if you receive HTTP 429, back off exponentially

Academic Use Cases

1. Model selection for benchmarks: Search by pipeline tag (text-classification, token-classification, summarization) and sort by downloads to find community-validated baselines
2. Dataset discovery: Filter by task_categories, language, and size_categories tags to find training data matching your experimental requirements
3. Reproducibility: Pin model versions using the sha field from model details -- load exact revisions with revision="86b5e093..." in transformers
4. Citation tracking: Extract arxiv: tags from model/dataset metadata to trace foundational papers
5. Ecosystem analysis: Aggregate download/like counts across model families to study adoption trends in ML research

Code Examples

Python with requests

import requests

# Search for top text-classification models
resp = requests.get("https://huggingface.co/api/models", params={
    "filter": "text-classification",
    "sort": "downloads",
    "direction": -1,
    "limit": 10
})
models = resp.json()
for m in models:
    print(f"{m['id']:50s}  downloads={m.get('downloads',0):>12,}")

# Get specific model metadata
detail = requests.get("https://huggingface.co/api/models/google-bert/bert-base-uncased").json()
print(f"SHA: {detail['sha']}")
print(f"License: {detail['cardData'].get('license')}")

Python with huggingface_hub library

from huggingface_hub import HfApi

api = HfApi()

# Search models (returns ModelInfo objects)
models = api.list_models(search="bert", sort="downloads", direction=-1, limit=5)
for m in models:
    print(f"{m.id}  downloads={m.downloads}")

# Get full model info
info = api.model_info("google-bert/bert-base-uncased")
print(f"Pipeline: {info.pipeline_tag}, SHA: {info.sha}")

# Search datasets
datasets = api.list_datasets(search="squad", sort="downloads", direction=-1, limit=5)
for d in datasets:
    print(f"{d.id}  downloads={d.downloads}")

# List Spaces
spaces = api.list_spaces(search="chatbot", limit=5)
for s in spaces:
    print(f"{s.id}  sdk={s.sdk}")

References

• Hub API documentation: https://huggingface.co/docs/hub/api
• huggingface_hub Python library: https://huggingface.co/docs/huggingface_hub/
• Model Hub: https://huggingface.co/models
• Dataset Hub: https://huggingface.co/datasets
• Spaces: https://huggingface.co/spaces
• OpenAPI spec: https://huggingface.co/docs/hub/api#openapi