Instructions to use karths/binary_classification_train_architecture with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use karths/binary_classification_train_architecture with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="karths/binary_classification_train_architecture")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("karths/binary_classification_train_architecture") model = AutoModelForSequenceClassification.from_pretrained("karths/binary_classification_train_architecture") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 55fef338dab66c27d1cc32213e12fabda092ac573bd6f0019378bd278963fdd2
- Size of remote file:
- 70.4 MB
- SHA256:
- e19993baf5dc66ee5ae047af369db08fcf807b03f191f45eb478f65143746c29
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.