Instructions to use karths/binary_classification_train_process with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use karths/binary_classification_train_process with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="karths/binary_classification_train_process")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("karths/binary_classification_train_process") model = AutoModelForSequenceClassification.from_pretrained("karths/binary_classification_train_process") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dfb0ed3a8fbad9dcb32465f538557e63b3523d80ffab8c7b759afdc8248cc36d
- Size of remote file:
- 140 MB
- SHA256:
- 3be7a0e52fe9271392a3ea1b9cbb1731443d4675bac93c2b61f33bf3594e1a4c
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