Welcome to the world of CodeBERTa, the fancy programming language identification algorithm that can classify code samples into their respective programming languages with astonishing accuracy! In this blog, we will guide you through how to leverage this powerful tool effectively. 🚀

What is CodeBERTa?

CodeBERTa is a pretrained model fine-tuned for programming language identification. It successfully classifies samples of code into languages such as Python, Java, JavaScript, and more. It achieves an impressive evaluation accuracy of 0.999, demonstrating its powerful performance particularly due to the structured nature of programming languages.

Quick Start

Let’s get started with using the raw model and the pipelines:

Using the Raw Model

CODEBERTA_LANGUAGE_ID = "huggingface/CodeBERTa-language-id"
tokenizer = RobertaTokenizer.from_pretrained(CODEBERTA_LANGUAGE_ID)
model = RobertaForSequenceClassification.from_pretrained(CODEBERTA_LANGUAGE_ID)
input_ids = tokenizer.encode(CODE_TO_IDENTIFY)
logits = model(input_ids)
language_idx = logits.argmax()  # index for the resulting label

Using Pipelines

from transformers import TextClassificationPipeline
pipeline = TextClassificationPipeline(
    model=RobertaForSequenceClassification.from_pretrained(CODEBERTA_LANGUAGE_ID),
    tokenizer=RobertaTokenizer.from_pretrained(CODEBERTA_LANGUAGE_ID)
)
pipeline(CODE_TO_IDENTIFY)

Understanding the Code

Think of using CodeBERTa like a highly skilled language translator. Just as a translator uses specific rules and characteristics of languages to determine meaning, CodeBERTa employs the rigid syntax and unique tokens of programming languages to identify them accurately. Imagine you send a short letter (your code) to our translator (CodeBERTa), and based on its understanding of languages, it quickly tells you which language the letter is written in (the programming language).

Example Classifications

Here are some examples of how CodeBERTa identifies different code samples:

pipeline(def f(x): return x**2) # label: python, score: 0.9999965
pipeline(const foo = bar) # label: javascript, score: 0.9977546
pipeline(foo = bar) # label: javascript, score: 0.7176245
pipeline(foo = ubar) # label: python, score: 0.7638422
pipeline(echo $FOO) # label: php, score: 0.9995257
pipeline(outcome := rand.Intn(6) + 1) # label: go, score: 0.9936151

Fine-tuning the Model

Adjusting the CodeBERTa model to improve its performance is much like tuning a musical instrument. Just as musicians tweak their instruments to produce harmonious sounds, we fine-tune our CodeBERTa model for optimal classification. Below is a detailed code snippet to guide you through the fine-tuning process:

import gzip
import json
import logging
import os
...
# Setup tokenizer
tokenizer = ByteLevelBPETokenizer(.pretrainedvocab.json, .pretrainedmerges.txt,)
tokenizer.enable_truncation(max_length=512)
...
# Define training loop
for _ in train_iterator:
    ...
    evaluate()
model.save_pretrained(.modelsCodeBERT-language-id)

Troubleshooting Tips

If you encounter challenges while using CodeBERTa, consider the following tips:

• Ensure that you have properly installed the required libraries and that you’re using compatible versions.
• Check if your input code sample is correctly formatted and adheres to the syntax of the language you expect it to be.
• Review the output logits and perform further analysis if the classification does not meet expectations.

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

Conclusion

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.