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:
- f86ce4bdbb595c8208c121109ff6b4bb858ec4dcd2e64210137613015143fd80
- Size of remote file:
- 328 MB
- SHA256:
- 01ee2fe2c9bf2796b9cb010ba17e83cbc0967d8b797898781ba11430ac79b7eb
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