| | --- |
| | language: nl |
| | license: mit |
| | --- |
| | |
| | # MedRoBERTa.nl finetuned for negation |
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| | ## 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. |
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| | ## 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. |
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| | ## 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. |
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| | ## Authors |
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| | RobBERT: Pieter Delobelle, Thomas Winters, Bettina Berendt, |
| | Finetuning: Bram van Es, Sebastiaan Arends. |
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| | ## Usage |
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| | 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 |
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| | ## 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 |
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| | 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 |
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