
# 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 use the following referrals;
(model) https://doi.org/10.5281/zenodo.6980076 and (paper) https://doi.org/10.1186/s12859-022-05130-x


## 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

{"language": "nl", "license": "mit"}
transformers pytorch safetensors roberta token-classification nl arxiv:2209.00470 license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #roberta #token-classification #nl #arxiv-2209.00470 #license-mit #autotrain_compatible #endpoints_compatible #region-us 


# URL 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 URL The publication associated with the negation detection task can be found at URL The code for finetuning the model can be found at URL

## 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.

## Minimal example



It is perhaps good to note that we assume the Inside-Outside-Beginning) format.


## Data
The pre-trained model was trained the Dutch section of OSCAR (about 39GB), and is described here: URL

## 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: URL

## Usage

If you use the model in your work please use the following referrals;
(model) URL and (paper) URL


## 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

# URL 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 URL The publication associated with the negation detection task can be found at URL The code for finetuning the model can be found at URL ## 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. ## Minimal example



It is perhaps good to note that we assume the Inside-Outside-Beginning) format. ## Data
The pre-trained model was trained the Dutch section of OSCAR (about 39GB), and is described here: URL ## 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: URL ## Usage

If you use the model in your work please use the following referrals;
(model) URL and (paper) URL ## 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
TAGS
#transformers #pytorch #safetensors #roberta #token-classification #nl #arxiv-2209.00470 #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 # URL 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 URL The publication associated with the negation detection task can be found at URL The code for finetuning the model can be found at URL ## 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. ## Minimal example



It is perhaps good to note that we assume the Inside-Outside-Beginning) format. ## Data
The pre-trained model was trained the Dutch section of OSCAR (about 39GB), and is described here: URL ## 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: URL ## Usage

If you use the model in your work please use the following referrals;
(model) URL and (paper) URL ## 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
passage: TAGS
#transformers #pytorch #safetensors #roberta #token-classification #nl #arxiv-2209.00470 #license-mit #autotrain_compatible #endpoints_compatible #region-us 
# URL 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 URL The publication associated with the negation detection task can be found at URL The code for finetuning the model can be found at URL## 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.## Minimal example



It is perhaps good to note that we assume the Inside-Outside-Beginning) format.## Data
The pre-trained model was trained the Dutch section of OSCAR (about 39GB), and is described here: URL## 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: URL## Usage

If you use the model in your work please use the following referrals;
(model) URL and (paper) URL