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François Remy

FremyCompany

http://fremycompany.com

AI & ML interests

NLP; Clinical NLP; Medical NLP; EHR; Web development;

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new activity 2 days ago

FremyCompany/BioLORD-2023:finetune the model

reacted to julien-c's post with 🤗 19 days ago

After some heated discussion 🔥, we clarify our intent re. storage limits on the Hub TL;DR: - public storage is free, and (unless blatant abuse) unlimited. We do ask that you consider upgrading to PRO and/or Enterprise Hub if possible - private storage is paid above a significant free tier (1TB if you have a paid account, 100GB otherwise) docs: https://huggingface.co/docs/hub/storage-limits We optimize our infrastructure continuously to scale our storage for the coming years of growth in Machine learning, to the benefit of the community 🔥 cc: @reach-vb @pierric @victor and the HF team

liked a dataset 22 days ago

HuggingFaceFW/fineweb-2

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Today, April 26, is the Day of the Tatar Language! 🌟
To celebrate, we release our new language model, Tweety Tatar 🐣

https://huggingface.co/Tweeties/tweety-tatar-base-7b-2024-v1

The model was converted from Mistral Instruct v0.2 using a novel technique called trans-tokenization. As a result, the model uses a brand-new tokenizer, fully tailored for the Tatar language.

We also release a model which can be finetuned for translation of English or Russian into Tatar, and achieves a performance similar to commercial offerings:

https://huggingface.co/Tweeties/tweety-tatar-hydra-base-7b-2024-v1

More details in our upcoming paper 👀
François REMY, Pieter Delobelle, Alfiya Khabibullina

Татар теле көне белән!

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🔥 What's that biomedical model that got 170,763 downloads last month on HuggingFace?! Well, the paper is finally published! #BioLORD

📰 Read our article in the Journal of the American Medical Informatics Association:
https://academic.oup.com/jamia/advance-article/doi/10.1093/jamia/ocae029/7614965

📝TLDR: BioLORD-2023 is a series of semantic language models for the biomedical domain, capable of representing clinical concepts and sentences in a semantic space aligned with human preferences. Our new multilingual version supports 50+ languages and is further finetuned on 7 European languages. These models were trained contrastively and through distillations, using a corpus unifying in the same latent space the concept names of biomedical concepts and their descriptions. For concepts which didn't have a description written by humans in UMLS, we use information contained in the SnomedCT knowledge graph and the capabilities of ChatGPT to generate synthetic data and improve our results.

🤗 Access our models from the HuggingFace hub, including the new 2023-C and 2023-S variants:
FremyCompany/BioLORD-2023
FremyCompany/BioLORD-2023-M
FremyCompany/BioLORD-2023-S
FremyCompany/BioLORD-2023-C