How to Scrape Hugging Face: The Complete Technical Guide
Master Hugging Face scraping to extract AI models, datasets, and metadata. Learn how to bypass Cloudflare and automate data collection for AI market research.
Anti-Bot Protection Detected
- Cloudflare
- Enterprise-grade WAF and bot management. Uses JavaScript challenges, CAPTCHAs, and behavioral analysis. Requires browser automation with stealth settings.
- Rate Limiting
- Limits requests per IP/session over time. Can be bypassed with rotating proxies, request delays, and distributed scraping.
- IP Blocking
- Blocks known datacenter IPs and flagged addresses. Requires residential or mobile proxies to circumvent effectively.
- Bot Detection
About Hugging Face
Learn what Hugging Face offers and what valuable data can be extracted from it.
Hugging Face is the leading platform and community for machine learning and artificial intelligence, often described as the GitHub for AI. It provides a central hub where researchers and developers share, discover, and collaborate on models, datasets, and demo applications known as Spaces. It hosts contributions from major tech entities like Google, Meta, and Microsoft, alongside a massive community of independent developers. The platform contains a vast array of structured data, including model performance metrics, dataset configurations, user activity logs, and library compatibility information.
Scraping Hugging Face is highly valuable for organizations looking to perform competitive intelligence, track the adoption of specific AI frameworks, or aggregate metadata for academic research. By extracting data from the platform, users can monitor trending models, identify top contributors, and stay updated on the rapidly evolving landscape of generative AI. The platform organizes content by tasks such as Natural Language Processing (NLP), Computer Vision, and Audio, making it a critical repository for the state of the art in machine learning.
Why Scrape Hugging Face?
Discover the business value and use cases for extracting data from Hugging Face.
Conduct market research on the most popular AI models and frameworks.
Perform competitive analysis by tracking model releases from specific organizations.
Aggregate metadata for academic studies on the evolution of open-source AI.
Monitor new datasets for specific industries like healthcare or finance.
Build a directory of AI experts and high-performing research teams.
Identify emerging trends in machine learning model architectures.
Scraping Challenges
Technical challenges you may encounter when scraping Hugging Face.
The website relies heavily on JavaScript rendering for loading search results and model lists.
Cloudflare protection can block automated requests that do not mimic real browser behavior.
Hugging Face implements strict rate limiting, especially when accessing the Hub API.
The page structure for Model Cards and Readmes is dynamic and varies significantly.
Frequent changes to the UI can break CSS-based scrapers without warning.
Scrape Hugging Face with AI
No coding required. Extract data in minutes with AI-powered automation.
How It Works
Describe What You Need
Tell the AI what data you want to extract from Hugging Face. Just type it in plain language — no coding or selectors needed.
AI Extracts the Data
Our artificial intelligence navigates Hugging Face, handles dynamic content, and extracts exactly what you asked for.
Get Your Data
Receive clean, structured data ready to export as CSV, JSON, or send directly to your apps and workflows.
Why Use AI for Scraping
AI makes it easy to scrape Hugging Face without writing any code. Our AI-powered platform uses artificial intelligence to understand what data you want — just describe it in plain language and the AI extracts it automatically.
How to scrape with AI:
- Describe What You Need: Tell the AI what data you want to extract from Hugging Face. Just type it in plain language — no coding or selectors needed.
- AI Extracts the Data: Our artificial intelligence navigates Hugging Face, handles dynamic content, and extracts exactly what you asked for.
- Get Your Data: Receive clean, structured data ready to export as CSV, JSON, or send directly to your apps and workflows.
Why use AI for scraping:
- No-code interface allows building scrapers for models and datasets without technical expertise.
- Handles dynamic content and JavaScript rendering automatically without extra configuration.
- Cloud-based execution ensures scraping tasks run reliably without taxing local resources.
- Built-in features to handle pagination and complex element selection effectively.
- Easily export extracted metadata directly to Google Sheets, CSV, or via API.
No-Code Web Scrapers for Hugging Face
Point-and-click alternatives to AI-powered scraping
Several no-code tools like Browse.ai, Octoparse, Axiom, and ParseHub can help you scrape Hugging Face. These tools use visual interfaces to select elements, but they come with trade-offs compared to AI-powered solutions.
Typical Workflow with No-Code Tools
Common Challenges
Learning curve
Understanding selectors and extraction logic takes time
Selectors break
Website changes can break your entire workflow
Dynamic content issues
JavaScript-heavy sites often require complex workarounds
CAPTCHA limitations
Most tools require manual intervention for CAPTCHAs
IP blocking
Aggressive scraping can get your IP banned
No-Code Web Scrapers for Hugging Face
Several no-code tools like Browse.ai, Octoparse, Axiom, and ParseHub can help you scrape Hugging Face. These tools use visual interfaces to select elements, but they come with trade-offs compared to AI-powered solutions.
Typical Workflow with No-Code Tools
- Install browser extension or sign up for the platform
- Navigate to the target website and open the tool
- Point-and-click to select data elements you want to extract
- Configure CSS selectors for each data field
- Set up pagination rules to scrape multiple pages
- Handle CAPTCHAs (often requires manual solving)
- Configure scheduling for automated runs
- Export data to CSV, JSON, or connect via API
Common Challenges
- Learning curve: Understanding selectors and extraction logic takes time
- Selectors break: Website changes can break your entire workflow
- Dynamic content issues: JavaScript-heavy sites often require complex workarounds
- CAPTCHA limitations: Most tools require manual intervention for CAPTCHAs
- IP blocking: Aggressive scraping can get your IP banned
Code Examples
import requests
from bs4 import BeautifulSoup
url = 'https://huggingface.co/models?sort=downloads'
headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'}
try:
response = requests.get(url, headers=headers)
response.raise_for_status()
soup = BeautifulSoup(response.text, 'html.parser')
# Extracting model articles
models = soup.find_all('article')
for model in models:
name = model.find('h4').text.strip()
print(f'Model Name: {name}')
except Exception as e:
print(f'Error occurred: {e}')When to Use
Best for static HTML pages where content is loaded server-side. The fastest and simplest approach when JavaScript rendering isn't required.
Advantages
- ●Fastest execution (no browser overhead)
- ●Lowest resource consumption
- ●Easy to parallelize with asyncio
- ●Great for APIs and static pages
Limitations
- ●Cannot execute JavaScript
- ●Fails on SPAs and dynamic content
- ●May struggle with complex anti-bot systems
How to Scrape Hugging Face with Code
Python + Requests
import requests
from bs4 import BeautifulSoup
url = 'https://huggingface.co/models?sort=downloads'
headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'}
try:
response = requests.get(url, headers=headers)
response.raise_for_status()
soup = BeautifulSoup(response.text, 'html.parser')
# Extracting model articles
models = soup.find_all('article')
for model in models:
name = model.find('h4').text.strip()
print(f'Model Name: {name}')
except Exception as e:
print(f'Error occurred: {e}')Python + Playwright
from playwright.sync_api import sync_playwright
def scrape_hf():
with sync_playwright() as p:
browser = p.chromium.launch(headless=True)
page = browser.new_page()
page.goto('https://huggingface.co/models')
# Wait for model list to render
page.wait_for_selector('article')
models = page.query_selector_all('article h4')
for m in models:
print(m.inner_text())
browser.close()
scrape_hf()Python + Scrapy
import scrapy
class HuggingFaceSpider(scrapy.Spider):
name = 'hf_spider'
start_urls = ['https://huggingface.co/models']
def parse(self, response):
for model in response.css('article'):
yield {
'title': model.css('h4::text').get(),
'author': model.css('span.text-gray-400::text').get()
}
# Handle pagination
next_page = response.css('a[aria-label="Next"]::attr(href)').get()
if next_page:
yield response.follow(next_page, self.parse)Node.js + Puppeteer
const puppeteer = require('puppeteer');
(async () => {
const browser = await puppeteer.launch();
const page = await browser.newPage();
await page.goto('https://huggingface.co/models');
// Wait for the dynamic content to load
await page.waitForSelector('article');
const data = await page.evaluate(() => {
return Array.from(document.querySelectorAll('article h4')).map(h => h.innerText);
});
console.log(data);
await browser.close();
})();What You Can Do With Hugging Face Data
Explore practical applications and insights from Hugging Face data.
AI Market Trend Identification
Companies benefit by identifying which AI tasks are gaining the most traction globally.
How to implement:
- 1Scrape download counts for all models within specific task categories monthly.
- 2Aggregate the data to see percentage growth by category.
- 3Identify breakout models that show sudden spikes in popularity.
Use Automatio to extract data from Hugging Face and build these applications without writing code.
What You Can Do With Hugging Face Data
- AI Market Trend Identification
Companies benefit by identifying which AI tasks are gaining the most traction globally.
- Scrape download counts for all models within specific task categories monthly.
- Aggregate the data to see percentage growth by category.
- Identify breakout models that show sudden spikes in popularity.
- Competitive Intelligence
Tech firms track the open-source output of competitors like Meta or Google to stay ahead.
- Set up a targeted scrape for specific organization profiles on Hugging Face.
- Monitor for new repository creations or updates to existing model cards.
- Alert product teams when a competitor releases a new model in a relevant domain.
- Lead Generation for Tech Talent
Recruiters find top-tier AI researchers by analyzing contribution quality and community impact.
- Extract lists of authors from high-performing models with over 100k downloads.
- Scrape user profiles to find linked social media or personal websites.
- Filter for individuals with a consistent history of popular open-source contributions.
- Academic Research Datasets
Researchers analyze the collaborative nature and evolution of the AI research ecosystem.
- Scrape metadata including author lists, citation counts, and organization affiliations.
- Map the relationships between different organizations and individual contributors.
- Apply network analysis to visualize the hubs of the AI research ecosystem.
Supercharge your workflow with AI Automation
Automatio combines the power of AI agents, web automation, and smart integrations to help you accomplish more in less time.
Pro Tips for Scraping Hugging Face
Expert advice for successfully extracting data from Hugging Face.
Always check for the 'config.json' file in the model repository for the most accurate technical metadata.
Use the official Hugging Face Hub Python library instead of raw scraping when possible to avoid blocks.
Rotate your IP addresses using a high-quality residential proxy service if scraping thousands of models.
Schedule your scraping tasks during off-peak hours to ensure faster response times and lower detection risk.
Clean extracted text data by removing Markdown syntax and URLs to make it more useful for analysis.
Monitor the Hugging Face blog for UI updates that might change CSS selectors for your scraper.
Testimonials
What Our Users Say
Join thousands of satisfied users who have transformed their workflow
Jonathan Kogan
Co-Founder/CEO, rpatools.io
Automatio is one of the most used for RPA Tools both internally and externally. It saves us countless hours of work and we realized this could do the same for other startups and so we choose Automatio for most of our automation needs.
Mohammed Ibrahim
CEO, qannas.pro
I have used many tools over the past 5 years, Automatio is the Jack of All trades.. !! it could be your scraping bot in the morning and then it becomes your VA by the noon and in the evening it does your automations.. its amazing!
Ben Bressington
CTO, AiChatSolutions
Automatio is fantastic and simple to use to extract data from any website. This allowed me to replace a developer and do tasks myself as they only take a few minutes to setup and forget about it. Automatio is a game changer!
Sarah Chen
Head of Growth, ScaleUp Labs
We've tried dozens of automation tools, but Automatio stands out for its flexibility and ease of use. Our team productivity increased by 40% within the first month of adoption.
David Park
Founder, DataDriven.io
The AI-powered features in Automatio are incredible. It understands context and adapts to changes in websites automatically. No more broken scrapers!
Emily Rodriguez
Marketing Director, GrowthMetrics
Automatio transformed our lead generation process. What used to take our team days now happens automatically in minutes. The ROI is incredible.
Jonathan Kogan
Co-Founder/CEO, rpatools.io
Automatio is one of the most used for RPA Tools both internally and externally. It saves us countless hours of work and we realized this could do the same for other startups and so we choose Automatio for most of our automation needs.
Mohammed Ibrahim
CEO, qannas.pro
I have used many tools over the past 5 years, Automatio is the Jack of All trades.. !! it could be your scraping bot in the morning and then it becomes your VA by the noon and in the evening it does your automations.. its amazing!
Ben Bressington
CTO, AiChatSolutions
Automatio is fantastic and simple to use to extract data from any website. This allowed me to replace a developer and do tasks myself as they only take a few minutes to setup and forget about it. Automatio is a game changer!
Sarah Chen
Head of Growth, ScaleUp Labs
We've tried dozens of automation tools, but Automatio stands out for its flexibility and ease of use. Our team productivity increased by 40% within the first month of adoption.
David Park
Founder, DataDriven.io
The AI-powered features in Automatio are incredible. It understands context and adapts to changes in websites automatically. No more broken scrapers!
Emily Rodriguez
Marketing Director, GrowthMetrics
Automatio transformed our lead generation process. What used to take our team days now happens automatically in minutes. The ROI is incredible.
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Frequently Asked Questions About Hugging Face
Find answers to common questions about Hugging Face