Instructions to use karths/binary_classification_train_documentation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use karths/binary_classification_train_documentation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="karths/binary_classification_train_documentation")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("karths/binary_classification_train_documentation") model = AutoModelForSequenceClassification.from_pretrained("karths/binary_classification_train_documentation") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f9cf99641e4c9884f305168c152476da9166fc51dc7112e16f4cf72336fb2cc2
- Size of remote file:
- 328 MB
- SHA256:
- e1b58662fe2ed9acfb01ffced17a3680cc0708c8a5107b653a9ce8c497179458
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