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:
- fd5d4ad06f3cc067f08659bd8b81dbd4d36411c57e354f4a31ca62f89d8a694f
- Size of remote file:
- 14.2 kB
- SHA256:
- 3552afcfd6c5874fd18ffe07c96c179f62a2787d1a5bac640922240ad9117f1e
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