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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 646724311b8bcf73cfd9103cf75d50a8fb5ea157ae65c6b1401f6c569ef98018
- Size of remote file:
- 930 kB
- SHA256:
- 7f331acf700f8f29c6ba42939c6ba842694b5a5e02a5f3e9592e971c7d08d934
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