Instructions to use karths/binary_classification_train_process with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use karths/binary_classification_train_process with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="karths/binary_classification_train_process")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("karths/binary_classification_train_process") model = AutoModelForSequenceClassification.from_pretrained("karths/binary_classification_train_process") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6951045f85ef0eba6cf6378d27c0f7e502bf5b76f5988b06346c87e7e39e51c4
- Size of remote file:
- 657 MB
- SHA256:
- 0446c4473816de809cf21de86e9e474e45dcc87e67f3f51d603976d354385b60
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