Instructions to use jkefeli/CancerStage_Classifier_T with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jkefeli/CancerStage_Classifier_T with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jkefeli/CancerStage_Classifier_T")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jkefeli/CancerStage_Classifier_T") model = AutoModelForSequenceClassification.from_pretrained("jkefeli/CancerStage_Classifier_T", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cd1b179763e49180fd40875817f45af175adb9a70cc67638c7b2fb916055a5ee
- Size of remote file:
- 512 MB
- SHA256:
- 3a1f9112cf3cf1c3dbf432e8847758f89f6afc6d23844de3eecb67b615473be7
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