gliner pytorch biomedical clinical life sciences token-classification fr dataset:rntc/nuner-pubmed-e3c-french-umls license:mit region:us
---
datasets:
- rntc/nuner-pubmed-e3c-french-umls
language:
- fr
library_name: gliner
license: mit
pipeline_tag: token-classification
tags:
- biomedical
- clinical
- life sciences
---

<a href=https://camembert-bio-model.fr/>
  <img width="300px" src="https://camembert-bio-model.fr/authors/camembert-bio/camembert-bio-ner-logo.png">
</a>

# 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