Instructions to use sjiang1/codecse with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sjiang1/codecse with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="sjiang1/codecse")# Load model directly from transformers import GraphCodeBERTForCL model = GraphCodeBERTForCL.from_pretrained("sjiang1/codecse", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 98c960750203eeda83255cccfbfe3b1baa0b9b7f793c25303aea45c68efe609e
- Size of remote file:
- 4.53 kB
- SHA256:
- c7c230bdd4aa90260265bb4b3e6f2fac7d63ad581fcaa8ffd24b18de508168b6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.