Instructions to use karths/binary_classification_train_code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use karths/binary_classification_train_code with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="karths/binary_classification_train_code")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("karths/binary_classification_train_code") model = AutoModelForSequenceClassification.from_pretrained("karths/binary_classification_train_code") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6d6654de2642c25915b19d5da335bf3b51b87bb3806db68ba96399a7901cf298
- Size of remote file:
- 929 kB
- SHA256:
- 3300bfba7f48ffd8bfaaac52f13b8026fb6ee1347e0a8cb4edfe9f2507ef2f49
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