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:
- 10d9a028edf86e566d2663d3f303bfb3b52495a93ead2aa6ad05a8afb44fd4b7
- Size of remote file:
- 47.9 MB
- SHA256:
- 049a972f9efdbea10e21ab37ab596677a68d5fc1e21e6f4a6dba47fc4ca5a87d
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