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:
- 4cf3a3a3e42bc8cbd36f1494aae85f65cc55433fe4901c465fb8358b3f05344e
- Size of remote file:
- 10.8 MB
- SHA256:
- 132b9bf2bfbc87c122e18c1f166fb90f5589245cd00d07ddf0bd55b64c501e07
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