---
license: cc-by-nc-sa-4.0
datasets:
- Posos/MedNERF
metrics:
- f1
tags:
- medical
widget:
- text: xeplion 50mg 2 fois par jour
- text: doliprane 500 1 comprimé effervescent le matin pendant une semaine

model-index:
- name: Posos/ClinicalNER
  results:
  - task:
      type: token-classification
      name: Clinical NER
    dataset:
      type: Posos/MedNERF
      name: MedNERF
      split: test        # Optional. Example: test
    metrics:
      - type: f1
        value: 0.804
        name: micro-F1 score
      - type: precision
        value: 0.817
        name: precision
      - type: recall
        value: 0.791
        name: recall
      - type: accuracy
        value: 0.859
        name: accuracy
---

# ClinicalNER

## Model Description

This is a multilingual clinical NER model extracting DRUG, STRENGTH, FREQUENCY, DURATION, DOSAGE and FORM entities from a medical text.

## Evaluation Metrics on [MedNERF dataset](https://huggingface.co/datasets/Posos/MedNERF)

- Loss: 0.692
- Accuracy: 0.859
- Precision: 0.817
- Recall: 0.791
- micro-F1: 0.804
- macro-F1: 0.819

## Usage

```
from transformers import AutoModelForTokenClassification, AutoTokenizer

model = AutoModelForTokenClassification.from_pretrained("Posos/ClinicalNER")
tokenizer = AutoTokenizer.from_pretrained("Posos/ClinicalNER")

inputs = tokenizer("Take 2 pills every morning", return_tensors="pt")
outputs = model(**inputs)
```

## Citation information

```
@inproceedings{mednerf,
    title = "Multilingual Clinical NER: Translation or Cross-lingual Transfer?",
    author = "Gaschi, Félix and Fontaine, Xavier and Rastin, Parisa and Toussaint, Yannick",
    booktitle = "Proceedings of the 5th Clinical Natural Language Processing Workshop",
    publisher = "Association for Computational Linguistics",
    year = "2023"
}
```