KennethEnevoldsen/dacy-large-encoder
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Kenneth C. Enevoldsen
KennethEnevoldsen
AI & ML interests
NLP, multimodal learning, Scandinavian NLP, Theory of Mind, Medical NLP, Psychiatry
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KennethEnevoldsen/dacy-large-encoder
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KennethEnevoldsen/dacy-large-encoder:Adding `safetensors` variant of this model
updated
a dataset
2 days ago
danish-foundation-models/danish-dynaword
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KennethEnevoldsen's activity
updated
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1 day ago
Adding `safetensors` variant of this model
#1 opened 2 days ago
by
SFconvertbot
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Clean up duplicates
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#151 opened 9 days ago
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pylotlight
Not actually Climate FEVER
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#1 opened 4 months ago
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sasha
Add instruct variant Euphemism of Death
#40 opened 6 days ago
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KennethEnevoldsen
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Add datasets quality metrics
#39 opened 21 days ago
by
KennethEnevoldsen
reacted to
davanstrien's
post with 🤗🔥
21 days ago
Post
3051
Introducing scandi-fine-web-cleaner
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 ( 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
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 ( 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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25 days ago
Added some 🔥
#19 opened 25 days ago
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KennethEnevoldsen
Sources for the data
#1 opened 27 days ago
by
KennethEnevoldsen
add Danish-PD
#38 opened 27 days ago
by
KennethEnevoldsen
Center dataset statistics
#37 opened 28 days ago
by
KennethEnevoldsen
Add reference to license for datasets
#36 opened 28 days ago
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KennethEnevoldsen