Instructions to use Namronaldo2004/results_graphcodebert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Namronaldo2004/results_graphcodebert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Namronaldo2004/results_graphcodebert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Namronaldo2004/results_graphcodebert") model = AutoModelForSequenceClassification.from_pretrained("Namronaldo2004/results_graphcodebert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3cdd14e131494d0c1bc017bfa48b7bcca317b660465012dd0a78fbf2128440bf
- Size of remote file:
- 5.27 kB
- SHA256:
- 13be888285672aba0d32841bccb2bc14f5f62cb86fa8884daa6132f1f650faac
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