Instructions to use karths/binary_classification_train_design with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use karths/binary_classification_train_design with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="karths/binary_classification_train_design")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("karths/binary_classification_train_design") model = AutoModelForSequenceClassification.from_pretrained("karths/binary_classification_train_design") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b5f52786378556a7b05b6f8bb83b418cb28e24ab8ba5805cede6e1bc05d75648
- Size of remote file:
- 328 MB
- SHA256:
- c39f80c5310c061d91cd3bf5425806c17e87832f5fdcb0357d553a769a0af833
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