Code Trans T5 Base Code Documentation Generation Go

🚀 CodeTrans model for code documentation generation go

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

✨ Features

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

📦 Installation

No installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

Basic Usage

Here is how to use this model to generate Go 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_go"),
    tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_base_code_documentation_generation_go", skip_special_tokens=True),
    device=0
)

tokenized_code = "func ( pr * Progress ) needSnapshotAbort ( ) bool { return pr . State == ProgressStateSnapshot && pr . Match >= pr . PendingSnapshot   }"
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 Go dataset.

Intended uses & limitations

The model could be used to generate the description for the Go function or be fine - tuned on other Go code tasks. It can be used on unparsed and untokenized Go code. However, if the Go 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, so this section is skipped.

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