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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d68008b7a242bc17891501fa9fa463695567ed8e14e584f1aa9feadb86e2f344
- Size of remote file:
- 1.06 kB
- SHA256:
- 8af5ca846144b2ee156f8f584892f266cd09742b0b38a9e2e97aeaddb6365863
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