Instructions to use karths/binary_classification_train_automation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use karths/binary_classification_train_automation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="karths/binary_classification_train_automation")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("karths/binary_classification_train_automation") model = AutoModelForSequenceClassification.from_pretrained("karths/binary_classification_train_automation") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2254991f4c8e666e91d5aa7205c10fba78ca69964657b13ba3192d6c220b0056
- Size of remote file:
- 328 MB
- SHA256:
- b933ce116c60808439ba6dff70304b25eec39a8345712981a7ff1c0d7ea04a95
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