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---
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"
}
``` |