It consist of XLM-R Base fine-tuned on n2c2 (English). It is the model that obtains the best results on our French evaluation test set MedNERF in a zero-shot cross-lingual transfer setting.

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"
}