Instructions to use karths/binary_classification_train_service with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use karths/binary_classification_train_service with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="karths/binary_classification_train_service")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("karths/binary_classification_train_service") model = AutoModelForSequenceClassification.from_pretrained("karths/binary_classification_train_service", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1d8761bfd9f25cfa27420da26bef3bc16563859bd7890e04541a1d5bf98f0ff4
- Size of remote file:
- 40.4 MB
- SHA256:
- 1144a215dec2d5f9b075f5fdf90aaf5ce65acd9ac074797fa0f53242ef4ae3da
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