Code Trans T5 Base Api Generation Transfer Learning Finetune

🚀 CodeTrans model for api recommendation generation

A pre-trained model for API recommendation generation using the T5 base model architecture.

🚀 Quick Start

This is a pre-trained model for API recommendation generation, which uses the T5 base model architecture. It was first released in this repository.

✨ Features

• Based on the t5-base model with its own SentencePiece vocabulary model.
• Utilized transfer - learning pre - training on 7 unsupervised datasets in the software development domain.
• Fine - tuned on the API recommendation generation task for Java APIs.

📦 Installation

No specific installation steps are provided in the original README.

💻 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_transfer_learning_finetune"),
    tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_base_api_generation_transfer_learning_finetune", 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 transfer - learning pre - training on 7 unsupervised datasets in the software development domain. It is then fine - tuned on the API recommendation generation task for the Java APIs.

Intended Uses & Limitations

The model could be used to generate API usage for the Java programming tasks.

🔧 Technical Details

Training Data

The supervised training tasks datasets can be downloaded on Link

Training Procedure

Transfer - learning Pretraining

The model was trained on a single TPU Pod V3 - 8 for 240,000 steps in total, using sequence length 512 (batch size 4096). It has a total of approximately 220M parameters and was trained using the encoder - decoder architecture. The optimizer used is AdaFactor with inverse square root learning rate schedule for pre - training.

Fine - tuning

This model was then fine - tuned on a single TPU Pod V3 - 8 for 1,400,000 steps in total, using sequence length 512 (batch size 256), using only the dataset only containing API recommendation generation data.

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