CodeTrans model for source code summarization sql

Pretrained model on programming language sql using the t5 base model architecture. It was first released in this repository. This model is trained on tokenized sql code functions: it works best with tokenized sql functions.

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 source code summarization sql dataset.

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.

How to use

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_base_source_code_summarization_sql"),
    tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_base_source_code_summarization_sql", skip_special_tokens=True),
    device=0
)

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

Run this example in colab notebook.

Training data

The supervised training tasks datasets can be downloaded on Link

Evaluation results

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

Test results :

Language / Model	Python	SQL	C#
CodeTrans-ST-Small	8.45	17.55	19.74
CodeTrans-ST-Base	9.12	15.00	18.65
CodeTrans-TF-Small	10.06	17.71	20.40
CodeTrans-TF-Base	10.94	17.66	21.12
CodeTrans-TF-Large	12.41	18.40	21.43
CodeTrans-MT-Small	13.11	19.15	22.39
CodeTrans-MT-Base	13.37	19.24	23.20
CodeTrans-MT-Large	13.24	19.40	23.57
CodeTrans-MT-TF-Small	12.10	18.25	22.03
CodeTrans-MT-TF-Base	10.64	16.91	21.40
CodeTrans-MT-TF-Large	12.14	19.98	21.10
CODE-NN	--	18.40	20.50

Created by Ahmed Elnaggar | LinkedIn and Wei Ding | LinkedIn