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:
- 48a69ed0b5ed133f1acbd9e47063cd64d55b0ba17294f391f8055bf8fb023a70
- Size of remote file:
- 331 MB
- SHA256:
- 8bc44e4d2a3e8f2117a5754ee081634630fa549bf4fce5a28c424636978fe064
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