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Jonatan Borkowski

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jborkowski

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upvoted a paper about 6 hours ago

To Code, or Not To Code? Exploring Impact of Code in Pre-training

liked a model 1 day ago

Qwen/Qwen3-Coder-480B-A35B-Instruct

reacted to Norod78's post with 👍 1 day ago

Multilingual Tokenization Showdown Analyzing 12 LLM Tokenizers Across 204 Languages. First, I've created a dataset with Wikipedia's "Cat" article text in 272 languages: https://huggingface.co/datasets/Norod78/WikiCat-Multilingual For each language entry with at least 100 words, I tokenized the text using 12 tokenizers and calculated the "Characters per token" ratio and "Word per token" ratio. The higher this ratio is, the more information each token represents on average for that language (and perhaps allowing the llm to potentially learn more per-parameter if trained on a dataset of that language). You can see a slideshow summary of the results here: https://norod.github.io/wikicat-tokenizer-eval/tokenizer-slideshow.html I hope I interpreted the results correctly, I've made the code available on GitHub so you can re-create the raw results jsonl with this repo: https://github.com/Norod/wikicat-tokenizer-eval Post on X: https://x.com/Norod78/status/1984366900550266999

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