Instructions to use cp500/Medical_condition_annotator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cp500/Medical_condition_annotator with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="cp500/Medical_condition_annotator")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("cp500/Medical_condition_annotator") model = AutoModelForTokenClassification.from_pretrained("cp500/Medical_condition_annotator") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2693d00373a3e16470d8830f17fda194dbdd341337dafcc858fb084f566e6dd6
- Size of remote file:
- 4.09 kB
- SHA256:
- dfa78eef85bfcc742b714c0e9e17d0e83eea98ea8c0be154c9a3e005e17692fa
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