Feature Extraction
Transformers
PyTorch
TensorFlow
Dutch
roberta
Biomedical entity linking
sapBERT
bioNLP
embeddings
representation learning
text-embeddings-inference
Instructions to use fonshartendorp/dutch_biomedical_entity_linking with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fonshartendorp/dutch_biomedical_entity_linking with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="fonshartendorp/dutch_biomedical_entity_linking")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("fonshartendorp/dutch_biomedical_entity_linking") model = AutoModel.from_pretrained("fonshartendorp/dutch_biomedical_entity_linking") - Notebooks
- Google Colab
- Kaggle
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- evaluation results on [Mantra GSC](https://doi.org/10.1093/jamia/ocv037) corpus can be found in the [report](https://github.com/fonshartendorp/dutch_biomedical_entity_linking/blob/main/report/report.pdf)
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All code can be found
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### Usage
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- evaluation results on [Mantra GSC](https://doi.org/10.1093/jamia/ocv037) corpus can be found in the [report](https://github.com/fonshartendorp/dutch_biomedical_entity_linking/blob/main/report/report.pdf)
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All code for generating the training data, training the model and evaluating it, can be found in the [github](https://github.com/fonshartendorp/dutch_biomedical_entity_linking) repository.
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### Usage
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