Instructions to use karths/binary_classification_train_code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use karths/binary_classification_train_code with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="karths/binary_classification_train_code")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("karths/binary_classification_train_code") model = AutoModelForSequenceClassification.from_pretrained("karths/binary_classification_train_code") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9a07ccb926943b77c77c4c748f27fb1a176c5f53a10d96288a23cf38789a96f4
- Size of remote file:
- 4.66 kB
- SHA256:
- 336b64d3e439ccf1f56b003354c997a02fc1f403faa604c0c892f3a88b71156d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.