Code Trans T5 Base Code Documentation Generation Python

🚀 CodeTrans model for code documentation generation in Python

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

🚀 Quick Start

✨ Features

• Based on the t5 - base model with its own SentencePiece vocabulary model.
• Trained using single - task training on the CodeSearchNet Corpus Python dataset.
• Can generate descriptions for Python functions or be fine - tuned for other Python code tasks.
• Works on both unparsed/untokenized and tokenized Python code, with better performance on tokenized code.

📦 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_code_documentation_generation_python"),
    tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_base_code_documentation_generation_python", skip_special_tokens=True),
    device=0
)

tokenized_code = "def e ( message , exit_code = None ) : print_log ( message , YELLOW , BOLD ) if exit_code is not None : sys . exit ( exit_code )"
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. It used single - task training on CodeSearchNet Corpus Python dataset.

Intended uses & limitations

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

🔧 Technical Details

The supervised training tasks datasets can be downloaded on Link

📄 License

No license information is provided in the original document.

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 and Wei Ding | LinkedIn