AI Training Data Collection
Collect and structure large-scale web datasets for fine-tuning and training AI models.
Quick Answer
Use CrawlForge batch_scrape (5 credits) to fetch hundreds of URLs in parallel, then extract_content (2 credits) to return clean, boilerplate-free text or markdown ready for a training pipeline. You collect structured content instead of noisy raw HTML, which improves dataset quality and lowers preprocessing cost -- about 7 credits per document.
The Problem
Training and fine-tuning AI models requires large, clean datasets from diverse web sources. Collecting this data manually is impractical, and raw HTML is too noisy for model training.
The Solution
CrawlForge batch_scrape processes hundreds of URLs in parallel for scale, while extract_content returns clean, structured text ready for training pipelines. Build datasets from any web source.
Code Example
// Collect training data from documentation sites
const batch = await mcp.batch_scrape({
urls: [
"https://docs.example.com/guide/intro",
"https://docs.example.com/guide/setup",
"https://docs.example.com/guide/advanced",
// ... hundreds more URLs
],
format: "markdown",
});
// Extract clean content for each page
const dataset = await Promise.all(
batch.results.map(page =>
mcp.extract_content({
url: page.url,
format: "text",
remove_navigation: true,
})
)
);
console.log(`Collected ${dataset.length} documents`);Tools Used
Estimated cost: ~7 credits per document
Frequently Asked Questions
How do I collect clean training data from the web at scale?
Use CrawlForge batch_scrape to fetch hundreds of URLs in parallel, then extract_content to return clean, boilerplate-free text ready for a training pipeline. You get structured content instead of noisy raw HTML.
Why not just use raw HTML for model training?
Raw HTML is full of navigation, ads, and markup that adds noise and wastes tokens. extract_content uses a readability pass to return only the main content as clean text or markdown, which improves dataset quality and lowers preprocessing cost.
Can I build a large dataset from many sources?
Yes. batch_scrape at 5 credits per batch parallelizes fetching across hundreds of URLs, and extract_content at 2 credits cleans each one. Combine with map_site to enumerate a source first, then batch the resulting URLs.
Does CrawlForge respect robots.txt when collecting data?
CrawlForge honors robots directives and you control which sources you crawl. You are responsible for the rights to data you collect, so target sites you are permitted to use for training and keep your crawl scope deliberate.
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