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:
- c3be9490b2aa5f6a9751873d610142e25b265fc9ee3853094b96cbac1fbce52b
- Size of remote file:
- 396 MB
- SHA256:
- 620d3442596fda772a78efc12e54fdef5aedf286051c67517d068821496f42a9
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