Instructions to use karths/binary_classification_train_architecture with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use karths/binary_classification_train_architecture with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="karths/binary_classification_train_architecture")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("karths/binary_classification_train_architecture") model = AutoModelForSequenceClassification.from_pretrained("karths/binary_classification_train_architecture", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bf78e552cab5b03e2e5b0ec0a7dfd717692c8f4d1794a5e20bb26e7dd0ee6176
- Size of remote file:
- 657 MB
- SHA256:
- 92c81ea5bb425b97c225a8b0f86a3c40904e7207260e5e7e146556eefb2e9f1f
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