Instructions to use samrawal/bert-base-uncased_clinical-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use samrawal/bert-base-uncased_clinical-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="samrawal/bert-base-uncased_clinical-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("samrawal/bert-base-uncased_clinical-ner") model = AutoModelForTokenClassification.from_pretrained("samrawal/bert-base-uncased_clinical-ner") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 56e333f7875287109c094f6e799e36a7cddb93607e31a8bd1d97e48aa87bf7d1
- Size of remote file:
- 436 MB
- SHA256:
- 25bbafc662ce892fdc6fa157c2a936f24bba9e4f9a4459e6ef76c6b656c8c005
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