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", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 90a766fed64a74082773e97b62ecff4eea9249902d4f016d7a82fcf4f8b0890f
- Size of remote file:
- 436 MB
- SHA256:
- 90bad4af900151870a8de3e791ab1bd511b9638b3e0fa5110294f4eb626db49a
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