metadata

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
        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

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"
}