Instructions to use karths/binary_classification_train_service with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use karths/binary_classification_train_service with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="karths/binary_classification_train_service")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("karths/binary_classification_train_service") model = AutoModelForSequenceClassification.from_pretrained("karths/binary_classification_train_service") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c2958d136e6a88dbbabfeb5b6af4c521e488c4403cfae182293b0632088e8a3f
- Size of remote file:
- 92.5 MB
- SHA256:
- 712dc65aae40ec65db0fe89f640a7ddfebf83d0fceaa50bdf5259a645314c2de
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