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 training_args.bin from karths/binary_classification_train_defect: direct link, hf CLI and curl.
- Browser
- Download file 4.66 kB
-
https://huggingface.co/karths/binary_classification_train_defect/resolve/main/training_args.bin
- Command line
-
hf download hf://karths/binary_classification_train_defect/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/karths/binary_classification_train_defect/resolve/main/training_args.bin
4.66 kB
- Xet hash:
- f454d53d7cd201aeefc451cbf7734f296574571c3a350cce8fa1f4f188184167
- Size of remote file:
- 4.66 kB
- SHA256:
- 75fde7a443c84bb6edaed6e2339a5af44809b1340bff8c460bc6e9299ca0cfff
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.