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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- edd833128acc36b7fe04c19c1162afb1e3e428bdc2ab74113e9c73bf614360ad
- Size of remote file:
- 331 MB
- SHA256:
- 7059665d1da8c86de01f4d5315ee1e058fae3e670b3b060357c842f7bf95d0ca
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