Code Trans T5 Small Code Documentation Generation Go

🚀 CodeTrans Model for Go Code Documentation Generation

This is a pre - trained model for the Go programming language, leveraging the T5 small 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 - small 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.

📦 Installation

No specific installation steps are provided in the original README.

💻 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_small_code_documentation_generation_go"),
    tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_small_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 - small model. It has its own SentencePiece vocabulary model and was trained using single - task training on the CodeSearchNet Corpus Go dataset.

Intended Uses & Limitations

The model can be used to generate descriptions for Go functions or be fine - tuned for other Go code tasks. It can handle unparsed and untokenized Go code, but performance is better with tokenized code.

🔧 Technical Details

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):

Created by Ahmed Elnaggar | LinkedIn and Wei Ding | LinkedIn