Instructions to use karths/binary_classification_train_infrastructure with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use karths/binary_classification_train_infrastructure with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="karths/binary_classification_train_infrastructure")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("karths/binary_classification_train_infrastructure") model = AutoModelForSequenceClassification.from_pretrained("karths/binary_classification_train_infrastructure") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4f829c1810a0eff5950a938fc76aa5cff7fd36b0cfdd9dc08cebb1f8a93131ad
- Size of remote file:
- 20.9 MB
- SHA256:
- 0d1a000058e87e016b85d730ab899fa727a4d1f962f2661400d4930c8494f2ea
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