Update README.md

README.md

@@ -1,3 +1,84 @@
 ---
 license: gpl-3.0
+language:
+- nl
+pipeline_tag: token-classification
+tags:
+- medical
 ---
+
+
+# MedRoBERTa.nl finetuned for experiencer
+
+## Description
+This model is a finetuned RoBERTa-based model pre-trained from scratch 
+on Dutch hospital notes sourced from Electronic Health Records. 
+All code used for the creation of MedRoBERTa.nl 
+can be found at https://github.com/cltl-students/verkijk_stella_rma_thesis_dutch_medical_language_model.
+The publication associated with the negation detection task can be found at https://arxiv.org/abs/2209.00470.
+The code for finetuning the model can be found at https://github.com/umcu/negation-detection.
+
+
+## Minimal example
+
+```python
+tokenizer = AutoTokenizer\
+             .from_pretrained("UMCU/MedRoBERTa.nl_Experiencer")
+model = AutoModelForTokenClassification\
+            .from_pretrained("UMCU/MedRoBERTa.nl_Experiencer")
+
+some_text = "De patient was niet aanspreekbaar en hij zag er grauw uit. \
+Hij heeft de inspanningstest echter goed doorstaan. \
+De broer heeft onlangs een operatie ondergaan."
+
+inputs = tokenizer(some_text, return_tensors='pt')
+output = model.forward(inputs)
+probas = torch.nn.functional.softmax(output.logits[0]).detach().numpy()
+
+#  associate with tokens
+input_tokens = tokenizer.convert_ids_to_tokens(inputs['input_ids'][0])
+target_map = {0: 'B-Patient', 1:'B-Other',2:'I-Patient',3:'I-Other'}
+results = [{'token': input_tokens[idx],
+                 'proba_patient': proba_arr[0]+proba_arr[2],
+                 'proba_other': proba_arr[1]+proba_arr[3]
+                 }  
+                 for idx,proba_arr in enumerate(probas)]
+
+```
+
+The medical entity classifiers are (being) integrated in the opensource library [clinlp](https://github.com/umcu/clinlp), feel free to contact
+us for access, either through Huggingface or through git.
+
+It is perhaps good to note that we assume the [Inside-Outside-Beginning](https://en.wikipedia.org/wiki/Inside%E2%80%93outside%E2%80%93beginning_(tagging)) format.
+
+## Intended use
+The model is finetuned for experiencer detection on Dutch clinical text.
+Since it is a domain-specific model trained on medical data, 
+it is meant to be used on medical NLP tasks for Dutch. 
+This particular model is trained on a 64-max token windows surrounding the concept-to-be negated. 
+
+## Data
+The pre-trained model was trained on nearly 10 million hospital notes from the Amsterdam University Medical Centres. 
+The training data was anonymized before starting the pre-training procedure. 
+
+The finetuning was performed on the Erasmus Dutch Clinical Corpus (EDCC), which was synthetically upsampled for the minority classses.
+The EDCC can be obtained through Jan Kors ([email protected]). 
+The EDCC is described here: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-014-0373-3
+
+## Authors
+
+MedRoBERTa.nl: Stella Verkijk, Piek Vossen,
+Finetuning: Bram van Es
+
+## Contact
+
+If you are having problems with this model please add an issue on our git: https://github.com/umcu/negation-detection/issues
+
+## Usage
+
+If you use the model in your work please use the following referral; and (paper) https://doi.org/10.1186/s12859-022-05130-x
+
+## References
+Paper: Verkijk, S. & Vossen, P. (2022) MedRoBERTa.nl: A Language Model for Dutch Electronic Health Records. Computational Linguistics in the Netherlands Journal, 11.
+
+Paper: Bram van Es, Leon C. Reteig, Sander C. Tan, Marijn Schraagen, Myrthe M. Hemker, Sebastiaan R.S. Arends, Miguel A.R. Rios, Saskia Haitjema (2022): Negation detection in Dutch clinical texts: an evaluation of rule-based and machine learning methods, Arxiv