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:
- 0c8b47053b2f0a9a488d43000d4f08736d5604f8d695228d5a66e22e93f9806c
- Size of remote file:
- 328 MB
- SHA256:
- 333fbbc95fb85fecbe62828369c3fb2ac3ffcccc010f5ace64025b042d018f73
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