Instructions to use Ensheng/graphcodebert-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ensheng/graphcodebert-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Ensheng/graphcodebert-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Ensheng/graphcodebert-v1") model = AutoModelForMaskedLM.from_pretrained("Ensheng/graphcodebert-v1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e851af90a45b4bddcaaf046c22aa91c8b0212fbb82c9a229f4be98e76bcfbcf1
- Size of remote file:
- 499 MB
- SHA256:
- 131b35b10ed6b5b59c2abeb62d92f3b66a131e53b6a3e550e16a07f72c9c89a3
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