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
File size: 359 Bytes
2ef4ea4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | # Core torch>=2.4 transformers>=5.0 datasets>=3.0 accelerate>=1.0 tokenizers>=0.20 # GraphCodeBERT data-flow extraction (tree-sitter is required by the original # GraphCodeBERT preprocessing pipeline -- it is not an optional extra). tree-sitter>=0.21 tree-sitter-python>=0.21 # Data / metrics numpy>=1.24 pyarrow>=14 scikit-learn>=1.3 huggingface-hub>=0.26 |