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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4d41f7bb99980a2e8213578079f7ea7f905e696e42443ff2047338fae24a439a
- Size of remote file:
- 46.9 MB
- SHA256:
- 7ab09a3218b8df2c541e23c71dad9aaad23ddbcc83b31acd4f2d3a3e3f3d8b99
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