Instructions to use karths/binary_classification_train_perf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use karths/binary_classification_train_perf with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="karths/binary_classification_train_perf")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("karths/binary_classification_train_perf") model = AutoModelForSequenceClassification.from_pretrained("karths/binary_classification_train_perf") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0803157fbd9cb08fb4e11f2019ea169a757656d0b06087b987a2d05f6a308b3b
- Size of remote file:
- 328 MB
- SHA256:
- 1f01e46867283f004e1a8cfb5e210970bf9a931d0d442e42e4c9f63a7f938bed
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