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")# pip install -U transformers accelerate # 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
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Download README.md from jkefeli/CancerStage_Classifier_T: direct link, hf CLI and curl.
- Browser
- Download file 299 Bytes
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https://huggingface.co/jkefeli/CancerStage_Classifier_T/resolve/refs%2Fpr%2F1/README.md
- Command line
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hf download hf://jkefeli/CancerStage_Classifier_T@refs/pr/1/README.md
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curl -L -o README.md https://huggingface.co/jkefeli/CancerStage_Classifier_T/resolve/refs%2Fpr%2F1/README.md
299 Bytes
To use the model, add the following from the transformers package:
(1) Tokenizer:
tokenizer = AutoTokenizer.from_pretrained("yikuan8/Clinical-BigBird")
(2) Model type:
#T in [1,2,3,4]
num_classes = 4
model = BigBirdForSequenceClassification.from_pretrained(directory, num_labels=num_classes)