Code Trans T5 Large Source Code Summarization Sql Transfer Learning Finetune

🚀 CodeTrans Model for Source Code Summarization (SQL)

A pre - trained model for SQL source code summarization based on the T5 large architecture, offering accurate code description generation.

🚀 Quick Start

This CodeTrans model is a pre - trained model for SQL programming language using the T5 large model architecture. It was first released in this repository. This model is trained on tokenized SQL code functions and performs best with such input.

✨ Features

• Based on the t5 - large model with its own SentencePiece vocabulary model.
• Utilizes transfer - learning pre - training on 7 unsupervised datasets in the software development domain.
• Fine - tuned on the source code summarization task for SQL code snippets.
• Can generate descriptions for SQL functions or be fine - tuned for other SQL code tasks.

💻 Usage Examples

Basic Usage

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

from transformers import AutoTokenizer, AutoModelWithLMHead, SummarizationPipeline

pipeline = SummarizationPipeline(
    model=AutoModelWithLMHead.from_pretrained("SEBIS/code_trans_t5_large_source_code_summarization_sql_transfer_learning_finetune"),
    tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_large_source_code_summarization_sql_transfer_learning_finetune", skip_special_tokens=True),
    device=0
)

tokenized_code =  "select time ( col0 ) from tab0"
pipeline([tokenized_code])

Run this example in colab notebook.

📚 Documentation

Model description

This CodeTrans model is based on the t5 - large 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 source code summarization task for the SQL code snippets.

Intended uses & limitations

The model could be used to generate the description for the SQL function or be fine - tuned on other SQL code tasks. It can be used on unparsed and untokenized SQL code. However, if the SQL code is tokenized, the performance should be better.

📦 Installation

No specific installation steps are provided in the original README.

🔧 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 V2 - 8 for 200 steps in total, using sequence length 512 (batch size 256), using only the dataset only containing SQL code.

Evaluation results

For the source code summarization tasks, different models achieve the following results on different programming languages (in BLEU score):

Created by Ahmed Elnaggar | LinkedIn and Wei Ding | LinkedIn