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
Download tokenizer.json from thealper2/graphcodebert-code-clone-detection: direct link, hf CLI and curl.
- Browser
- Download file 3.56 MB
-
https://huggingface.co/thealper2/graphcodebert-code-clone-detection/resolve/main/tokenizer.json
- Command line
-
hf download hf://thealper2/graphcodebert-code-clone-detection/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/thealper2/graphcodebert-code-clone-detection/resolve/main/tokenizer.json
3.56 MB
File too large to display, you can check the raw version instead.