SEBIS / code_trans_t5_small_code_documentation_generation_ruby

CodeTrans model for code documentation generation ruby

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

Model description

This CodeTrans model is based on the t5-small model. It has its own SentencePiece vocabulary model. It used single-task training on CodeSearchNet Corpus ruby dataset.

Intended uses & limitations

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

How to use

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

from transformers import AutoTokenizer, AutoModelWithLMHead, SummarizationPipeline

pipeline = SummarizationPipeline(
    model=AutoModelWithLMHead.from_pretrained("SEBIS/code_trans_t5_small_code_documentation_generation_ruby"),
    tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_small_code_documentation_generation_ruby", skip_special_tokens=True),
    device=0
)

tokenized_code = "def add ( severity , progname , & block ) return true if io . nil? || severity < level message = format_message ( severity , progname , yield ) MUTEX . synchronize { io . write ( message ) } true end"
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 :

Created by Ahmed Elnaggar | LinkedIn and Wei Ding | LinkedIn