Instructions to use karths/binary_classification_train_automation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use karths/binary_classification_train_automation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="karths/binary_classification_train_automation")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("karths/binary_classification_train_automation") model = AutoModelForSequenceClassification.from_pretrained("karths/binary_classification_train_automation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f05810184072f313e92a8c095c1f760d254a8640a04783df751ad9f7a2f78a60
- Size of remote file:
- 331 MB
- SHA256:
- afe04e59469d082ceb9031f7d3f31229257d70dbcce694c4cc42519bdf3941cf
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