Instructions to use karths/binary_classification_train_code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use karths/binary_classification_train_code with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="karths/binary_classification_train_code")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("karths/binary_classification_train_code") model = AutoModelForSequenceClassification.from_pretrained("karths/binary_classification_train_code", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c572385e673cae74f93aba6a41abb0cb094ef8d46ceb8545734f163774f8b1b3
- Size of remote file:
- 1.06 kB
- SHA256:
- 20298cb29c82eeb24d41e1c40e2c637cad3f42fad4f63868541abcc66bd3c92f
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