Instructions to use jkefeli/CancerStage_Classifier_M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jkefeli/CancerStage_Classifier_M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jkefeli/CancerStage_Classifier_M")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jkefeli/CancerStage_Classifier_M") model = AutoModelForSequenceClassification.from_pretrained("jkefeli/CancerStage_Classifier_M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c759e9cb233be41bde40933123b6240ce41490df3dad8d2a6246b1944aaea199
- Size of remote file:
- 512 MB
- SHA256:
- 8cee61f451f72bef066439500cae68cefdc455dd6a9bdf16bf3687e090d8a1dd
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