Instructions to use karths/binary_classification_train_port with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use karths/binary_classification_train_port with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="karths/binary_classification_train_port")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("karths/binary_classification_train_port") model = AutoModelForSequenceClassification.from_pretrained("karths/binary_classification_train_port") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b835835df2f01db5c083e7301b867ca0cc740c65d2d732e8a215e66f19ee1dfe
- Size of remote file:
- 929 kB
- SHA256:
- 3c82f188757836f6cd960d0cae2d3dd5161e8ee8b2192658e2eea4ea667060a8
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