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:
- 1eeaa020970784bbe3934a51762575e54e9ae257ee8fe6b5074bc4a3b0a968b1
- Size of remote file:
- 4.66 kB
- SHA256:
- 10862f3595d985d0462a37a2e0cdeb04d538eed4d7e5517914f8cae7f49e06c1
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