Instructions to use karths/binary_classification_train_defect with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use karths/binary_classification_train_defect with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="karths/binary_classification_train_defect")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("karths/binary_classification_train_defect") model = AutoModelForSequenceClassification.from_pretrained("karths/binary_classification_train_defect", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download test_data_for_future_evaluation.csv from karths/binary_classification_train_defect: direct link, hf CLI and curl.
- Browser
- Download file 109 MB
-
https://huggingface.co/karths/binary_classification_train_defect/resolve/2ef093106eebeb59d145b919730a48e56f2eb267/test_data_for_future_evaluation.csv
- Command line
-
hf download hf://karths/binary_classification_train_defect@2ef093106eebeb59d145b919730a48e56f2eb267/test_data_for_future_evaluation.csv
-
curl -L -o test_data_for_future_evaluation.csv https://huggingface.co/karths/binary_classification_train_defect/resolve/2ef093106eebeb59d145b919730a48e56f2eb267/test_data_for_future_evaluation.csv
109 MB
- Xet hash:
- 9046720a0bee2851c5c58dc2dc7124b270fcee7b24943be98eefd47310c74460
- Size of remote file:
- 109 MB
- SHA256:
- bd22c53e406404b7e72a272ea0d724b06215bffc3af8be60dbde04a6dc17c9e3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.