--- license: mit language: - fr pipeline_tag: token-classification tags: - biomedical - clinical - life sciences datasets: - rntc/nuner-pubmed-e3c-french-umls # widget: # - text: >- # Les médicaments typiques sont largement utilisés dans le traitement # de première intention des patients schizophrènes. library_name: gliner --- # CamemBERT-bio-gliner-v0.1 : Zero-shot French Biomedical Named Entity Recognition CamemBERT-bio-gliner is a Named Entity Recognition (NER) model capable of identifying any french biomedical entity type using a BERT-like encoder. It provides a practical alternative to traditional NER models, which are limited to predefined entities, and Large Language Models (LLMs) that, despite their flexibility, are costly and large for resource-constrained scenarios. [CamemBERT-bio](https://huggingface.co/almanach/camembert-bio-base) is used as a backbone. This model is based on the fantastic work of [Urchade Zaratiana](https://huggingface.co/urchade) on the [GLiNER](https://github.com/urchade/GLiNER) architecture. ## Important This is the v0.1 of the CamemBERT-bio-gliner model. There might be a few quirks or unexpected predictions. So, if you notice anything off or have suggestions for improvements, we'd really appreciate hearing from you! ## Installation To use this model, you must install the GLiNER Python library: ``` !pip install gliner ``` ## Usage Once you've downloaded the GLiNER library, you can import the GLiNER class. You can then load this model using `GLiNER.from_pretrained` and predict entities with `predict_entities`. ```python from gliner import GLiNER model = GLiNER.from_pretrained("almanach/camembert-bio-gliner-v0.1") text = """ Mme A.P. âgée de 52 ans, non tabagique, ayant un diabète de type 2 a été hospitalisée pour une pneumopathie infectieuse. Cette patiente présentait depuis 2 ans des infections respiratoires traités en ambulatoire. L’examen physique a trouvé une fièvre à 38ºc et un foyer de râles crépitants de la base pulmonaire droite. """ labels = ["Âge", "Patient", "Maladie", "Symptômes"] entities = model.predict_entities(text, labels, threshold=0.5, flat_ner=True) for entity in entities: print(entity["text"], "=>", entity["label"]) ``` ```bash Mme A.P. => Patient 52 ans => Âge pneumopathie infectieuse => Maladie infections respiratoires => Maladie fièvre => Symptômes râles crépitants => Symptômes ``` ## Links * Model: https://huggingface.co/almanach/camembert-bio-gliner-v0.1 * Backbone model: https://huggingface.co/almanach/camembert-bio-base * GLiNER library: https://github.com/urchade/GLiNER * Developed by: [Rian Touchent](https://rian-t.github.io), [Eric Villemonte de La Clergerie](http://pauillac.inria.fr/~clerger/) * Logo by: [Alix Chagué](https://alix-tz.github.io/), [Rian Touchent](https://rian-t.github.io) * License: MIT