What is a zero-shot BERT pipeline for grant classification?

Zero-shot BERT classifies text into categories without training data — just feed descriptions and labels, get semantic matches with confidence. Perfect for quick, accurate tagging of unstructured grant blurbs.

How do I build a Rust scraper for government data?

Use scraper and csv crates, target paginated HTML endpoints, compile for speed. Check github.com/Sher213/GrantsInvestments for a full ETL example on Canada's grants.

Where can I find the GitHub repo for Grants to Investments?

It's at github.com/Sher213/GrantsInvestments — open-source ETL with Rust extraction and BERT classification, ready to fork for your data project.

🤖 AI & Machine Learning

Rust Scrapes Canada's Grant Goldmine — With BERT for Brains

Python scrapers? So 2023. This Rust-ETL monster rips through Canada's grant portal, tags funding with BERT smarts, and tees up analytics no one's touched before.

theAIcatchup Apr 10, 2026 4 min read

Flow diagram of Rust scraper extracting Canadian grants, BERT classifying by sector, outputting to CSV dashboard

⚡ Key Takeaways

Rust delivers real speedups for large-scale scraping over Python, ideal for ETL hot paths. 𝕏
Zero-shot BERT enables fast, label-free classification, turning raw grants into sector insights. 𝕏
Modular pipelines — extract/transform/load separated — speed iteration and scale effortlessly. 𝕏

Published by

theAIcatchup

Community-driven. Code-first.

#Canadian grants data #ETL pipeline #Rust ETL #Rust scraping #data pipeline #data scraping #government data scraping #government grants data #zero-shot BERT

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Originally reported by Dev.to

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