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", device_map="auto")# 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:
- e6b7016b2c8c9b0a5711c8113cb7ead74f2a06cf37239b5873a9718d90dee58d
- Size of remote file:
- 499 MB
- SHA256:
- fd741c972768c133194bdd143bcb66d4c5c0860d0086bfb4dc884dd0904f04dc
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