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--- |
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license: mit |
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language: |
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- fr |
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pipeline_tag: token-classification |
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tags: |
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- biomedical |
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- clinical |
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- life sciences |
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datasets: |
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- rntc/nuner-pubmed-e3c-french-umls |
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library_name: gliner |
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--- |
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<a href=https://camembert-bio-model.fr/> |
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<img width="300px" src="https://camembert-bio-model.fr/authors/camembert-bio/camembert-bio-ner-logo.png"> |
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</a> |
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# CamemBERT-bio-gliner-v0.1 : Zero-shot French Biomedical Named Entity Recognition |
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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. |
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[CamemBERT-bio](https://huggingface.co/almanach/camembert-bio-base) is used as a backbone. |
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This model is based on the fantastic work of [Urchade Zaratiana](https://huggingface.co/urchade) on the [GLiNER](https://github.com/urchade/GLiNER) architecture. |
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## Important |
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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! |
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## Installation |
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To use this model, you must install the GLiNER Python library: |
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``` |
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!pip install gliner |
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``` |
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## Usage |
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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`. |
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```python |
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from gliner import GLiNER |
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model = GLiNER.from_pretrained("almanach/camembert-bio-gliner-v0.1") |
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text = """ |
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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. |
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""" |
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labels = ["Âge", "Patient", "Maladie", "Symptômes"] |
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entities = model.predict_entities(text, labels, threshold=0.5, flat_ner=True) |
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for entity in entities: |
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print(entity["text"], "=>", entity["label"]) |
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``` |
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```bash |
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Mme A.P. => Patient |
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52 ans => Âge |
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pneumopathie infectieuse => Maladie |
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infections respiratoires => Maladie |
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fièvre => Symptômes |
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râles crépitants => Symptômes |
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``` |
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## Links |
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* Model: https://huggingface.co/almanach/camembert-bio-gliner-v0.1 |
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* Backbone model: https://huggingface.co/almanach/camembert-bio-base |
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* GLiNER library: https://github.com/urchade/GLiNER |
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* Developed by: [Rian Touchent](https://rian-t.github.io), [Eric Villemonte de La Clergerie](http://pauillac.inria.fr/~clerger/) |
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* Logo by: [Alix Chagué](https://alix-tz.github.io/), [Rian Touchent](https://rian-t.github.io) |
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* License: MIT |
