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:
- 7fd330d74b24a0058b29b27fd14c191375ef0074558d4c493525167577e28600
- Size of remote file:
- 871 MB
- SHA256:
- 2a0d5fd22aefaa226895d974f21332c65b50c447cd75e3bb6a51ad72f6c6e32d
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