Code Trans T5 Base Api Generation

🚀 CodeTrans model for api recommendation generation

A pre-trained model for API recommendation generation using the T5 base model architecture, initially released in this repository.

🚀 Quick Start

The CodeTrans model is designed for API recommendation generation. It's based on the t5-base architecture and can be used to generate API usage for Java programming tasks.

✨ Features

• Based on the t5-base model architecture.
• Utilizes its own SentencePiece vocabulary model.
• Trained on the Api Recommendation Generation dataset using single-task training.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

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

from transformers import AutoTokenizer, AutoModelWithLMHead, SummarizationPipeline

pipeline = SummarizationPipeline(
    model=AutoModelWithLMHead.from_pretrained("SEBIS/code_trans_t5_base_api_generation"),
    tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_base_api_generation", skip_special_tokens=True),
    device=0
)

tokenized_code = "parse the uses licence node of this package , if any , and returns the license definition if theres"
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 Api Recommendation Generation dataset.

Intended uses & limitations

The model could be used to generate api usage for the java programming tasks.

📄 Training data

The supervised training tasks datasets can be downloaded on Link

📊 Evaluation results

For the code documentation tasks, different models achieves the following results on different programming languages (in BLEU score):

Test results :

Created by Ahmed Elnaggar | LinkedIn and Wei Ding | LinkedIn