SEBIS / code_trans_t5_small_source_code_summarization_csharp

CodeTrans model for source code summarization csharp

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

Intended uses & limitations

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

How to use

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

from transformers import AutoTokenizer, AutoModelWithLMHead, SummarizationPipeline

pipeline = SummarizationPipeline(
    model=AutoModelWithLMHead.from_pretrained("SEBIS/code_trans_t5_small_source_code_summarization_csharp"),
    tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_small_source_code_summarization_csharp", skip_special_tokens=True),
    device=0
)

tokenized_code = "public static DateTime ParseUnixDateTime ( double unixTime ) { var dt = new DateTime ( CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , System . DateTimeKind . Utc ) ; dt = dt . AddSeconds ( unixTimeStamp ) . ToLocalTime ( ) ; return dt ; }"
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 :

Created by Ahmed Elnaggar | LinkedIn and Wei Ding | LinkedIn