Text Classification
Transformers
Safetensors
code
roberta
clone-detection
graphcodebert
code-similarity
Eval Results (legacy)
text-embeddings-inference
Instructions to use thealper2/graphcodebert-code-clone-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use thealper2/graphcodebert-code-clone-detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="thealper2/graphcodebert-code-clone-detection")# Load model directly from transformers import AutoTokenizer, GraphCodeBERTForCloneDetection tokenizer = AutoTokenizer.from_pretrained("thealper2/graphcodebert-code-clone-detection") model = GraphCodeBERTForCloneDetection.from_pretrained("thealper2/graphcodebert-code-clone-detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8b02e72bf65717c711f7851dfce785bc6dc822296d10a4943dc42c34558c4591
- Size of remote file:
- 501 MB
- SHA256:
- 1bb8855bff29d33fc1d8d32f58798090a0e1ce3844d01de4c4c7774367caf59e
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