Update README.md

README.md

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+language: nl
 license: mit
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+
+# 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.
+
+## 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.  
+
+## 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.
+
+## 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 Electroniz 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