You can learn more about this neural approach in a previous post: https://news.ycombinator.com/item?id=43652968

Since the release of the first distilbert-based model I’ve released two more models based on a ModernBERT. All these models were pre-trained and fine-tuned primary on English texts.

But recently mmBERT(https://huggingface.co/blog/mmbert) has been released. This model pre-trained on massive dataset that contains 1833 languages. So I had an idea of fine-tuning a new multilingual Chonky model.

I’ve expanded training dataset (that previously contained bookcorpus and minipile datasets) with Project Gutenberg dataset which contains books in some widespread languages.

To make the model more robust for real-world data I’ve removed punctuation for last word for every training chunk with probability of 0.15 (no ablation was made for this technique though).

The hard part is evaluation. The real-world data are typically OCR'ed markdown, transcripts of calls, meeting notes etc. and not a clean book paragraphs. I didn’t find such labeled datasets. So I used what I had: already mentioned bookcorpus and Project Gutenberg validation, Paul Graham essays, concatenated 20_newsgroups.

I also tried to fine-tune the bigger mmBERT model (mmbert-base) but unfortunately it didn’t go well — metrics are weirdly lower in comparison with a small model.

Please give it a try. I'll appreciate a feedback.

The new multilingual model: https://huggingface.co/mirth/chonky_mmbert_small_multilingua...

All the Chonky models: https://huggingface.co/mirth

Chonky wrapper library: https://github.com/mirth/chonky