| | --- |
| | language: nl |
| | license: mit |
| | --- |
| | |
| | # 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. Note that we also trained a biLSTM which can be incorporated in [MedCAT](https://github.com/CogStack/MedCAT). |
| |
|
| | ## 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. |
| |
|
| |
|
| | ## 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. |
| |
|
| | ## 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: 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 |
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