Instructions to use BrundageLab/SpotRemover-bioclinicalbert-baseline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BrundageLab/SpotRemover-bioclinicalbert-baseline with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="BrundageLab/SpotRemover-bioclinicalbert-baseline")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("BrundageLab/SpotRemover-bioclinicalbert-baseline") model = AutoModelForTokenClassification.from_pretrained("BrundageLab/SpotRemover-bioclinicalbert-baseline") - Notebooks
- Google Colab
- Kaggle
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Upload best checkpoint: Best performing ClinicalBert model (90% synth) for veterinary de-ID.
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