Update README.md

README.md

@@ -23,7 +23,7 @@ model-index:
     metrics:
       - type: f1
         value: 0.804
-        name: micro-F1 score         # Optional. Example: Test WER
+        name: micro-F1 score
       - type: precision
         value: 0.817
         name: precision
@@ -33,4 +33,43 @@ model-index:
       - 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"
+}
+```