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 called RobBERT, this model is pre-trained on the Dutch section of OSCAR. All code used for the creation of RobBERT can be found here https://github.com/iPieter/RobBERT. 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. This particular model is trained on a 32-max token windows surrounding the concept-to-be negated.
+
+## Data
+The pre-trained model was trained the Dutch section of OSCAR (about 39GB), and is described here: http://dx.doi.org/10.18653/v1/2020.findings-emnlp.292.
+
+## Authors
+
+RobBERT: Pieter Delobelle, Thomas Winters, Bettina Berendt,
+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: Pieter Delobelle, Thomas Winters, Bettina Berendt (2020), RobBERT: a Dutch RoBERTa-based Language Model, Findings of the Association for Computational Linguistics: EMNLP 2020
+
+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