Instructions to use karths/binary_classification_train_architecture with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use karths/binary_classification_train_architecture with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="karths/binary_classification_train_architecture")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("karths/binary_classification_train_architecture") model = AutoModelForSequenceClassification.from_pretrained("karths/binary_classification_train_architecture", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download test_data_for_future_evaluation.csv from karths/binary_classification_train_architecture: direct link, hf CLI and curl.
- Browser
- Download file 13.4 MB
-
https://huggingface.co/karths/binary_classification_train_architecture/resolve/main/test_data_for_future_evaluation.csv
- Command line
-
hf download hf://karths/binary_classification_train_architecture/test_data_for_future_evaluation.csv
-
curl -L -o test_data_for_future_evaluation.csv https://huggingface.co/karths/binary_classification_train_architecture/resolve/main/test_data_for_future_evaluation.csv
13.4 MB
- Xet hash:
- f13f0d93fd7f6fc8ec20275445d50c2709a534234722b265aaaa5dab37646b0d
- Size of remote file:
- 13.4 MB
- SHA256:
- bdd8bbf6549006912fa4008b8e48069e1254f69c7524fb5cdbd26a9a8a4a4f94
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