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--- |
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license: cc-by-nc-sa-4.0 |
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datasets: |
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- Posos/MedNERF |
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metrics: |
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- f1 |
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tags: |
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- medical |
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widget: |
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- text: xeplion 50mg 2 fois par jour |
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- text: doliprane 500 1 comprimé effervescent le matin pendant une semaine |
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model-index: |
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- name: Posos/ClinicalNER |
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results: |
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- task: |
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type: token-classification |
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name: Clinical NER |
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dataset: |
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type: Posos/MedNERF |
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name: MedNERF |
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split: test |
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metrics: |
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- type: f1 |
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value: 0.804 |
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name: micro-F1 score |
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- type: precision |
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value: 0.817 |
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name: precision |
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- type: recall |
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value: 0.791 |
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name: recall |
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- type: accuracy |
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value: 0.859 |
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name: accuracy |
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--- |
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# ClinicalNER |
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## Model Description |
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This is a multilingual clinical NER model extracting DRUG, STRENGTH, FREQUENCY, DURATION, DOSAGE and FORM entities from a medical text. |
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## Evaluation Metrics on [MedNERF dataset](https://huggingface.co/datasets/Posos/MedNERF) |
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- Loss: 0.692 |
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- Accuracy: 0.859 |
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- Precision: 0.817 |
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- Recall: 0.791 |
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- micro-F1: 0.804 |
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- macro-F1: 0.819 |
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## Usage |
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``` |
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from transformers import AutoModelForTokenClassification, AutoTokenizer |
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model = AutoModelForTokenClassification.from_pretrained("Posos/ClinicalNER") |
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tokenizer = AutoTokenizer.from_pretrained("Posos/ClinicalNER") |
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inputs = tokenizer("Take 2 pills every morning", return_tensors="pt") |
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outputs = model(**inputs) |
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``` |
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## Citation information |
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``` |
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@inproceedings{mednerf, |
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title = "Multilingual Clinical NER: Translation or Cross-lingual Transfer?", |
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author = "Gaschi, Félix and Fontaine, Xavier and Rastin, Parisa and Toussaint, Yannick", |
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booktitle = "Proceedings of the 5th Clinical Natural Language Processing Workshop", |
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publisher = "Association for Computational Linguistics", |
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year = "2023" |
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} |
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``` |
