Instructions to use clda/graphcodebert-python with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use clda/graphcodebert-python with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="clda/graphcodebert-python")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("clda/graphcodebert-python") model = AutoModel.from_pretrained("clda/graphcodebert-python") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0aac2810f7f00b98f0ec431df3320665235a92a3dea04b0e36fbb4d550480e3f
- Size of remote file:
- 499 MB
- SHA256:
- 4086add41a43b8b6961c147d47ca6d55af370d5b7f7309db7f536f02ef59f800
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