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:
- 313febc2d4db797f8257a6f3c82c4823a79448c8f65e751392bb46132e426769
- Size of remote file:
- 735 MB
- SHA256:
- a9deeeae5ebf10e289bce7e2a978235a48c2e6f13272f7d48a227015f2f3af69
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