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:
- d1510ca2290152d3bcdc855cb15d5367304fc5c264399cc0106d1f7c3c5dba7c
- Size of remote file:
- 331 MB
- SHA256:
- f394a6c4efddd904e53e194feccb39f5a4a096b31a8c7ff3fb55fb911a4dc579
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