Instructions to use UMCU/PII_RobBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UMCU/PII_RobBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="UMCU/PII_RobBERT", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("UMCU/PII_RobBERT", trust_remote_code=True) model = AutoModelForTokenClassification.from_pretrained("UMCU/PII_RobBERT", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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@@ -18,7 +18,7 @@ Finetuning was done using [MedNER](https://github.com/UPOD-datascience/MedNER.nl
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We limited the training to Dutch.
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The model was trained in a
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(that is **O**utside the span, **B**eginning of the span, **I**nside the span)
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We replaced the standard 768-weight linear layer by 3x768 dense layers with 10\% dropout and ReLu activations.
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We limited the training to Dutch.
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The model was trained in a multiclass, using a cross-entropy loss, which followed the standard IOB-schema
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(that is **O**utside the span, **B**eginning of the span, **I**nside the span)
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We replaced the standard 768-weight linear layer by 3x768 dense layers with 10\% dropout and ReLu activations.
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