CodeTrans model for code documentation 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 CodeSearchNet Corpus java 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_documentation_generation_java"),
    tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_base_code_documentation_generation_java", skip_special_tokens=True),
    device=0
)

tokenized_code = "public static < T , U > Function < T , U > castFunction  ( Class < U > target ) { return new CastToClass < T , U > ( target ) ; }"
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	Python	Java	Go	Php	Ruby	JavaScript
CodeTrans-ST-Small	17.31	16.65	16.89	23.05	9.19	13.7
CodeTrans-ST-Base	16.86	17.17	17.16	22.98	8.23	13.17
CodeTrans-TF-Small	19.93	19.48	18.88	25.35	13.15	17.23
CodeTrans-TF-Base	20.26	20.19	19.50	25.84	14.07	18.25
CodeTrans-TF-Large	20.35	20.06	19.54	26.18	14.94	18.98
CodeTrans-MT-Small	19.64	19.00	19.15	24.68	14.91	15.26
CodeTrans-MT-Base	20.39	21.22	19.43	26.23	15.26	16.11
CodeTrans-MT-Large	20.18	21.87	19.38	26.08	15.00	16.23
CodeTrans-MT-TF-Small	19.77	20.04	19.36	25.55	13.70	17.24
CodeTrans-MT-TF-Base	19.77	21.12	18.86	25.79	14.24	18.62
CodeTrans-MT-TF-Large	18.94	21.42	18.77	26.20	14.19	18.83
State of the art	19.06	17.65	18.07	25.16	12.16	14.90

Created by Ahmed Elnaggar | LinkedIn and Wei Ding | LinkedIn