Instructions to use JunHwi/kold_binary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JunHwi/kold_binary with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JunHwi/kold_binary")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JunHwi/kold_binary") model = AutoModelForSequenceClassification.from_pretrained("JunHwi/kold_binary") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9597bd9d3522b753e924ad0aacca3c14c0ab71fa591e1c766f6d75f0e6aad497
- Size of remote file:
- 452 MB
- SHA256:
- 4993115763238b01f880067865c3350c8f445e603dad886c9f93ab66e3d8aae4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.