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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b0c4d519ef07e58497595208b3eed07358b42d8026a4c47cbc06eed1667b1a24
- Size of remote file:
- 14.2 kB
- SHA256:
- 7b776737e1e6d31f9e734e4f1e3f4d7f487d6e54fa570b12ee6db23810f2bfc6
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