Code Trans T5 Small Code Documentation Generation Java

🚀 CodeTrans model for code documentation generation in Java

This is a pre - trained model for the Java programming language, leveraging the T5 small model architecture. It was initially released in this repository. The model is trained on tokenized Java code functions and performs optimally with such tokenized functions.

🚀 Quick Start

✨ Features

• Based on the t5 - small model with its own SentencePiece vocabulary model.
• Trained using single - task training on the CodeSearchNet Corpus Java dataset.
• Can generate descriptions for Java functions or be fine - tuned for other Java code tasks.
• Can handle unparsed and untokenized Java code, but performs better with tokenized code.

📦 Installation

There is no explicit installation step provided in the original document.

💻 Usage Examples

Basic Usage

Here is how to use this model to generate Java function documentation using Transformers SummarizationPipeline:

from transformers import AutoTokenizer, AutoModelWithLMHead, SummarizationPipeline

pipeline = SummarizationPipeline(
    model=AutoModelWithLMHead.from_pretrained("SEBIS/code_trans_t5_small_code_documentation_generation_java"),
    tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_small_code_documentation_generation_java", skip_special_tokens=True),
    device=0
)

tokenized_code = "public static < T , U > Function < T , U > castFunction  ( Class < U > target ) { return new CastToClass < T , U > ( target ) ; }"
pipeline([tokenized_code])

Run this example in colab notebook.

📚 Documentation

Model description

This CodeTrans model is based on the t5 - small model. It has its own SentencePiece vocabulary model. It used single - task training on CodeSearchNet Corpus Java dataset.

Intended uses & limitations

The model could be used to generate the description for the Java function or be fine - tuned on other Java code tasks. It can be used on unparsed and untokenized Java code. However, if the Java code is tokenized, the performance should be better.

🔧 Technical Details

The model uses single - task training on the CodeSearchNet Corpus Java dataset. It has its own SentencePiece vocabulary model which is crucial for handling the Java code input.

📄 License

There is no license information provided in the original document.

📦 Training data

The supervised training tasks datasets can be downloaded on Link

📊 Evaluation results

For the code documentation tasks, different models achieve the following results on different programming languages (in BLEU score):

Test results :

Created by Ahmed Elnaggar | [LinkedIn](https://www.linkedin.com/in/prof - ahmed - elnaggar/) and Wei Ding | [LinkedIn](https://www.linkedin.com/in/wei - ding - 92561270/)