A Deepdive into Aya Expanse: Advancing the Frontier of Multilinguality
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#13 opened 20 days ago
by
DiTy
clefourrier
authored
a
paper
5 days ago
davanstrien
posted
an
update
13 days ago
Post
1773
Why choose between strong LLM reasoning and efficient models?
Use DeepSeek to generate high-quality training data, then distil that knowledge into ModernBERT answerdotai/ModernBERT-base for fast, efficient classification.
Blog post: https://danielvanstrien.xyz/posts/2025/deepseek/distil-deepseek-modernbert.html
Use DeepSeek to generate high-quality training data, then distil that knowledge into ModernBERT answerdotai/ModernBERT-base for fast, efficient classification.
Blog post: https://danielvanstrien.xyz/posts/2025/deepseek/distil-deepseek-modernbert.html
Update tokenizer_config.json
#14 opened 14 days ago
by
alexrs
alexrs
updated
a
model
14 days ago
davanstrien
posted
an
update
14 days ago
Post
1865
Updated the ColPali Query Generator Space
davanstrien/ColPali-Query-Generator to use
Qwen/Qwen2.5-VL-7B-Instruct.
Given an input image, it generates several queries along with explanations to justify them. This approach can generate synthetic data for fine-tuning ColPali models.
Given an input image, it generates several queries along with explanations to justify them. This approach can generate synthetic data for fine-tuning ColPali models.
davanstrien
posted
an
update
15 days ago
Post
2005
🌍 Big step for multilingual AI data!
The Hugging Face community has rated educational content in languages spoken by 1.6 billion people! New additions:
• Japanese
• Italian
• Old High German
Learn more and contribute: https://huggingface.co/blog/davanstrien/fineweb2-community
These ratings can help enhance training data for major world languages.
The Hugging Face community has rated educational content in languages spoken by 1.6 billion people! New additions:
• Japanese
• Italian
• Old High German
Learn more and contribute: https://huggingface.co/blog/davanstrien/fineweb2-community
These ratings can help enhance training data for major world languages.
- 1 reply
Web search fails
1
#46 opened 25 days ago
by
kaleidoskop-hug
Adding Evaluation Results
1
#12 opened about 1 month ago
by
T145
sarahooker
authored
6
papers
22 days ago
Multilingual Arbitrage: Optimizing Data Pools to Accelerate Multilingual Progress
Paper
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2408.14960
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Published
Mix Data or Merge Models? Optimizing for Diverse Multi-Task Learning
Paper
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2410.10801
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Published
INCLUDE: Evaluating Multilingual Language Understanding with Regional Knowledge
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2411.19799
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Published
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11
Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation
Paper
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2412.03304
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Published
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17
IrokoBench: A New Benchmark for African Languages in the Age of Large Language Models
Paper
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2406.03368
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Published
Bridging the Data Provenance Gap Across Text, Speech and Video
Paper
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2412.17847
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Published
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8
sarahooker
updated
a
collection
25 days ago
MarziehFadaee
authored
a
paper
26 days ago
davanstrien
posted
an
update
29 days ago
Post
3059
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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- 1 reply