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From Llasa to Llasagna 🍕: Finetuning LLaSA to generates Italian speech and other languages
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Introducing scandi-fine-web-cleaner https://huggingface.co/davanstrien/scandi-fine-web-cleaner, the first model trained on FineWeb-C community annotations!
FineWeb2 is a massive multilingual dataset for pre-training language models. Like any web-scale dataset, it contains low-quality content. How can we improve it?
Over the past months, an amazing community of 400+ annotators has been labelling content quality (using Argilla) across 23 languages through the FineWeb-C initiative.
Today, I'm happy to share the first classifier trained on this data.
🔍 What we've built:
- A lightweight classifier that efficiently removes low-quality content
- 90%+ precision demonstrated on Danish & Swedish
- Can process the 43M+ documents in Danish FineWeb2 with minimal compute
🌍 Why this matters: The approach can be reproduced for any of the 23 languages in FineWeb-C (https://huggingface.co/datasets/data-is-better-together/fineweb-c). We can improve training data quality at scale without massive compute resources by starting with community annotations and training small, efficient classifiers.
Want to build a classifier for your language? Check out the full blog post with code examples and implementation details: https://danielvanstrien.xyz/posts/2025/FineWeb-c/scandinavian-content-filtering-fineweb.html
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🪆 Introduction to Matryoshka Embedding Models
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