Instructions to use karths/binary_classification_train_architecture with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use karths/binary_classification_train_architecture with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="karths/binary_classification_train_architecture")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("karths/binary_classification_train_architecture") model = AutoModelForSequenceClassification.from_pretrained("karths/binary_classification_train_architecture", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3f56e777d6e7f7584d0272798475b4e02ecec0ce42e01bb755857e737983a975
- Size of remote file:
- 4.66 kB
- SHA256:
- 5eaf06d5345ffe465dd8018ae971057d4233655e8d37542173575dacd209a4c0
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