Code Trans T5 Base Source Code Summarization Python

🚀 CodeTrans model for source code summarization in Python

A pre - trained model for summarizing Python source code using the T5 base model architecture.

This is a pre - trained model for the Python programming language, leveraging the t5 - base model architecture. It was first introduced in this repository. The model is trained on tokenized Python code functions and performs optimally with such input.

🚀 Quick Start

The CodeTrans model is designed to generate descriptions for Python functions and can be fine - tuned for other Python code - related tasks. It can handle unparsed and untokenized Python code, but tokenized code will yield better performance.

✨ Features

• Based on the t5 - base model with its own SentencePiece vocabulary model.
• Trained using single - task training on a Python source code summarization dataset.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

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

from transformers import AutoTokenizer, AutoModelWithLMHead, SummarizationPipeline

pipeline = SummarizationPipeline(
    model=AutoModelWithLMHead.from_pretrained("SEBIS/code_trans_t5_base_source_code_summarization_python"),
    tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_base_source_code_summarization_python", skip_special_tokens=True),
    device=0
)

tokenized_code = '''with open ( CODE_STRING , CODE_STRING ) as in_file : buf = in_file . readlines ( )  with open ( CODE_STRING , CODE_STRING ) as out_file : for line in buf :          if line ==   " ; Include this text   " :              line = line +   " Include below  "          out_file . write ( line ) '''
pipeline([tokenized_code])

Run this example in colab notebook.

📚 Documentation

Model description

This CodeTrans model is based on the t5 - base model. It has its own SentencePiece vocabulary model and was trained using single - task training on a Python source code summarization dataset.

Intended uses & limitations

The model can generate descriptions for Python functions or be fine - tuned for other Python code tasks. It can process unparsed and untokenized Python code, but performance improves with tokenized code.

Training data

The supervised training tasks datasets can be downloaded on Link

Evaluation results

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

Test results:

🔧 Technical Details

The model uses the t5 - base architecture and has its own SentencePiece vocabulary model. It was trained on a single - task for Python source code summarization.

📄 License

No license information is provided in the original document.

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/)