readme: ---
  language: nl
  license: mit
  ---
  
  # MedRoBERTa.nl finetuned for negation
  
  ## 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_NegationDetection")
  model = AutoModelForTokenClassification\
              .from_pretrained("UMCU/MedRoBERTa.nl_NegationDetection")
  
  some_text = "De patient was niet aanspreekbaar en hij zag er grauw uit. \
  Hij heeft de inspanningstest echter goed doorstaan." 
  inputs = tokenizer(some_text, return_tensors='pt')
  output = model.forward(inputs)
  probas = torch.nn.functional.softmax(output.logits[0]).detach().numpy()
  
  #  koppel aan tokens
  input_tokens = tokenizer.convert_ids_to_tokens(inputs['input_ids'][0])
  target_map = {0: 'B-Negated', 1:'B-NotNegated',2:'I-Negated',3:'I-NotNegated'}
  results = [{'token': input_tokens[idx],
                   'proba_negated': proba_arr[0]+proba_arr[2],
                   'proba_not_negated': proba_arr[1]+proba_arr[3]
                   }  
                   for idx,proba_arr in enumerate(probas)]
  
  ```
  
  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 negation 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 512-max token windows surrounding the concept-to-be negated. Note that we also trained a biLSTM which can be incorporated in [MedCAT](https://github.com/CogStack/MedCAT).
  
  ## 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), and can be obtained through Jan Kors (j.kors@erasmusmc.nl). 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, Sebastiaan Arends.
  
  ## 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 refer either to 
  https://doi.org/10.5281/zenodo.6980076 or https://doi.org/10.48550/arXiv.2209.00470
  
  ## 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
  