Instructions to use jkefeli/CancerStage_Classifier_N with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jkefeli/CancerStage_Classifier_N with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jkefeli/CancerStage_Classifier_N")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jkefeli/CancerStage_Classifier_N") model = AutoModelForSequenceClassification.from_pretrained("jkefeli/CancerStage_Classifier_N", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- af14f1c563fd332d1c7cc62a5a05de8c566623653a03301b338006f72803b1e5
- Size of remote file:
- 512 MB
- SHA256:
- feb6a91b917ca1c91f8690e461f5bd6a1bb348eda99c60f01c29447162d8d405
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