Instructions to use karths/binary_classification_train_documentation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use karths/binary_classification_train_documentation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="karths/binary_classification_train_documentation")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("karths/binary_classification_train_documentation") model = AutoModelForSequenceClassification.from_pretrained("karths/binary_classification_train_documentation") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8f69a27e56bf134091948c369432201700261060b24c1020110c842215beb6f8
- Size of remote file:
- 4.66 kB
- SHA256:
- 2c9793c2b21f9aa39d5cdb5ef46191ea0803ddeac4cfa3e87d517ecfdb360369
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