Instructions to use rjac/biobert-ICD10-L3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rjac/biobert-ICD10-L3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rjac/biobert-ICD10-L3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rjac/biobert-ICD10-L3") model = AutoModelForSequenceClassification.from_pretrained("rjac/biobert-ICD10-L3") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d45dcea688fef8e30aaa49d1ad10cb856e6ef9fc259fdedd8147c2f44ab2eb92
- Size of remote file:
- 434 MB
- SHA256:
- c5645a4d135b5428d11c8e7bfc694e5e442aee208bddc4bfc75e23fc312d0e76
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