CodeTrans model for code comment generation java

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

Intended uses & limitations

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

How to use

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_code_comment_generation_java"),
    tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_base_code_comment_generation_java", skip_special_tokens=True),
    device=0
)

tokenized_code = "protected String renderUri ( URI uri ) { return uri . toASCIIString ( ) ; }"
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 code documentation tasks, different models achieves the following results on different programming languages (in BLEU score):

Test results :

Language / Model	Java
CodeTrans-ST-Small	37.98
CodeTrans-ST-Base	38.07
CodeTrans-TF-Small	38.56
CodeTrans-TF-Base	39.06
CodeTrans-TF-Large	39.50
CodeTrans-MT-Small	20.15
CodeTrans-MT-Base	27.44
CodeTrans-MT-Large	34.69
CodeTrans-MT-TF-Small	38.37
CodeTrans-MT-TF-Base	38.90
CodeTrans-MT-TF-Large	39.25
State of the art	38.17

Created by Ahmed Elnaggar | LinkedIn and Wei Ding | LinkedIn