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:
- 4a7cb0d4e3b52a03bb978061696986a09e12a631e45658983bba10a00c927f57
- Size of remote file:
- 10.9 MB
- SHA256:
- 9d6807840031695fe02c74bb294ee539571c0fed47a6b7b5b7602fc088280f45
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