In the world of software development, identifying duplicate code or “code clones” can be a crucial yet challenging task. This blog will guide you through using a powerful CodeBERT model specifically fine-tuned for detecting Python clone codes. Whether you’re a seasoned developer or a novice, this article offers a straightforward approach to enhance your code quality!

Getting Started

To begin, you should be aware that our model has been fine-tuned on a dataset shared by PoolC and is hosted on the Hugging Face Hub. This makes the setup easy, allowing you to focus on the implementation of detection rather than the underlying framework.

Step-by-Step Guide to Use the Model

Step 1: Install Required Libraries

Ensure that you have the required libraries installed. If you haven’t already, you can do so using pip:

pip install transformers torch

Step 2: Initialize the Pipeline

With the correct dependencies in place, you can initialize the model with just two lines of code. Think of it as setting up a coffee machine – once you plug it in, you’re ready to brew!

from transformers import pipeline
pipe = pipeline(model="Lazyhope/python-clone-detection", trust_remote_code=True)

Step 3: Prepare Your Code for Detection

Next, you’ll need to prepare the code pairs that you want to analyze for clones. Here’s how to set it up:

code1 = def token_to_inputs(feature):
    inputs = {}
    for k, v in feature.items():
        inputs[k] = torch.tensor(v).unsqueeze(0)
    return inputs

code2 = def f(feature):
    return {k: torch.tensor(v).unsqueeze(0) for k, v in feature.items() }

Step 4: Analyze Clone Detection

The final step is to invoke the pipeline on your code pairs.

is_clone = pipe((code1, code2))
is_clone

The output will give you a confidence score indicating whether the two pieces of code are clones or not. Think of it as a lie detector test for your code – the higher the score, the more likely it is that the code is a clone!

Troubleshooting Ideas

If you encounter issues while using the model, here are a few troubleshooting tips:

• Installation Problems: Ensure all required libraries are installed properly, and you have the latest version of Python.
• Pipeline Not Responding: Check your internet connection as the model may need to download components from the remote repository.
• Unexpected Output: Verify the format of the input code pairs to ensure they conform to the expected structure.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

And there you have it! Detecting Python clone codes can be as easy as following a recipe. With this guide, you’ll not only improve your code quality but also save time in the long run. At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

Credits

Special thanks to the original team behind the model and the fine-tuning dataset:

• PoolC
• sangHa0411
• snoop2head