Instructions to use karths/binary_classification_train_design with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use karths/binary_classification_train_design with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="karths/binary_classification_train_design")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("karths/binary_classification_train_design") model = AutoModelForSequenceClassification.from_pretrained("karths/binary_classification_train_design", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4c6c62f2afa814ccf45b7f9e413e723d5a8cac1b2661bfa2258a29dee6da8ce9
- Size of remote file:
- 21.8 MB
- SHA256:
- 1d489c29450e32a500a6c603e4dbfb2ef194230a1d208ce860f77862ba085daa
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