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:
- 0a990adf0ece7dc35e70f670c32c626bb3baeed03957ea6c6791969106824c8f
- Size of remote file:
- 162 MB
- SHA256:
- e2b61151db2e0bf2bea413a86cd8dd0921ac782646df8a06e6a75ed35dce3431
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