Code Trans T5 Base Code Documentation Generation Javascript

🚀 CodeTrans model for code documentation generation javascript

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

✨ Features

Model description

This CodeTrans model is built upon the t5 - base model. It comes with its own SentencePiece vocabulary model and was trained using single - task training on the CodeSearchNet Corpus JavaScript dataset.

Intended uses & limitations

The model can be employed to generate descriptions for JavaScript functions or be fine - tuned for other JavaScript code - related tasks. It can handle unparsed and untokenized JavaScript code, but tokenized code generally yields better performance.

📦 Installation

Not provided in the original document, so this section is skipped.

💻 Usage Examples

Basic Usage

Here is how to use this model to generate JavaScript 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_javascript"),
    tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_base_code_documentation_generation_javascript", skip_special_tokens=True),
    device=0
)

tokenized_code = "function isStandardBrowserEnv ( ) { if ( typeof navigator !== 'undefined' && ( navigator . product === 'ReactNative' || navigator . product === 'NativeScript' || navigator . product === 'NS' ) ) { return false ; } return ( typeof window !== 'undefined' && typeof document !== 'undefined' ) ; }"
pipeline([tokenized_code])

Run this example in colab notebook.

📚 Documentation

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 :

🔧 Technical Details

Not provided in the original document, so this section is skipped.

📄 License

Not provided in the original document, so this section is skipped.

Created by Ahmed Elnaggar | LinkedIn and Wei Ding | LinkedIn