Update README.md

README.md

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 ---
 license: mit
+language:
+- fr
+metrics:
+- seqeval
+library_name: transformers
+pipeline_tag: token-classification
+tags:
+- medical
+- biomedical
 ---
+
+
+# DrBERT-CASM2
+
+## Model description
+
+**DrBERT-CASM2** is a French Named Entity Recognition model that was fine-tuned from 
+[DrBERT](https://huggingface.co/Dr-BERT/DrBERT-4GB-CP-PubMedBERT): A PreTrained model in French for biomedical and clinical domains. 
+It has been trained  to detect the following type of entities: **problem**, **treatment** and **test** using the medkit Trainer.
+
+- **Fine-tuned using** medkit [GitHub Repo](https://github.com/TeamHeka/medkit)
+- **Developed by** @camila-ud, medkit, HeKA Research team
+- **Dataset from** @aneuraz, CASM2
+
+# Intended uses & limitations
+
+## Limitations and bias
+
+This model was trained for **development and test phases**. 
+This model is limited by its training dataset, and it should be used with caution. 
+The results are not guaranteed, and the model should be used only in data exploration stages.
+The model may be able to detect entities in the early stages of the analysis of medical documents in French.
+
+The maximum token size was reduced to **128 tokens** to minimize training time.
+
+# How to use
+
+## Install medkit
+
+First of all, please install medkit with the following command:
+
+```
+pip install 'medkit-lib[optional]'
+```
+
+Please check the [documentation](https://medkit.readthedocs.io/en/latest/user_guide/install.html) for more info and examples.
+
+## Using the model
+
+```python
+from medkit.core.text import TextDocument
+from medkit.text.ner.hf_entity_matcher import HFEntityMatcher
+
+matcher = HFEntityMatcher(model="dcariasvi/DrBERT-CASM2")
+test_doc = TextDocument("Elle souffre d'asthme mais n'a pas besoin d'Allegra")
+
+# detect entities in the raw segment
+detected_entities = matcher.run([test_doc.raw_segment]) 
+msg = "|".join(f"'{entity.label}':{entity.text}" for entity in detected_entities)
+print(f"Text: '{test_doc.text}'\n{msg}")w
+```
+```
+Text: "Elle souffre d'asthme mais n'a pas besoin d'Allegra"
+'problem':asthme|'treatment':Allegra
+```
+
+# Training data 
+
+This model was fine-tuned on **CASM2**, an internal corpus with clinical cases (in french) annotated by master students. 
+The corpus contains more than 5000 medkit documents (~ phrases) with entities to detect. 
+
+**Number of documents (~ phrases) by split**
+
+| Split      | # medkit docs |
+| ---------- | ------------- |
+| Train      | 5824          |
+| Validation | 1457          |
+| Test       | 1821          |
+
+
+**Number of examples per entity type**
+
+| Split      | treatment | test | problem |
+| ---------- | --------- | ---- | ------- |
+| Train      | 3258      | 3990 | 6808    |
+| Validation | 842       | 1007 | 1745    |
+| Test       | 994       | 1289 | 2113    |
+
+## Training procedure
+
+This model was fine-tuned using the medkit trainer on CPU, it takes about 3h.
+
+# Model perfomances
+
+Model performances computes on CASM2 test dataset (using medkit seqeval evaluator)
+
+Entity|precision|recall|f1
+-|-|-|-
+treatment|0.7492|0.7666|0.7578
+test|0.7449|0.8240|0.7824
+problem|0.6884|0.7304|0.7088
+Overall|0.7188|0.7660|0.7416
+
+## How to evaluate using medkit
+```python
+from medkit.text.metrics.ner import SeqEvalEvaluator
+
+# load the matcher and get predicted entities by document
+matcher = HFEntityMatcher(model="dcariasvi/DrBERT-CASM2")
+predicted_entities = [matchers.run([doc.raw_segment]) for doc in test_documents]
+
+# define seqeval evaluator
+evaluator  = SeqEvalEvaluator(tagging_scheme="iob2")
+evaluator.compute(test_documents,predicted_entities=predicted_entities)
+```
+
+# Citation
+
+```
+@online{medkit-lib,
+  author={HeKA Research Team},
+  title={medkit, A Python library for a learning health system},
+  url={https://pypi.org/project/medkit-lib/},
+  urldate = {2023-07-24}, 
+}
+```
+```
+HeKA Research Team, “medkit, a Python library for a learning health system.” https://pypi.org/project/medkit-lib/ (accessed Jul. 24, 2023).
+```