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SEBIS/
code_trans_t5_base_source_code_summarization_python

Name: code_trans_t5_base_source_code_summarization_python
Rating: 2.5 (21 reviews)

text-generation-inference

endpoints_compatible

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CodeTrans model for source code summarization python

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

Intended uses & limitations

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

How to use

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

python

from transformers import AutoTokenizer, AutoModelWithLMHead, SummarizationPipeline

pipeline = SummarizationPipeline(
    model=AutoModelWithLMHead.from_pretrained("SEBIS/code_trans_t5_base_source_code_summarization_python"),
    tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_base_source_code_summarization_python", skip_special_tokens=True),
    device=0
)

tokenized_code = '''with open ( CODE_STRING , CODE_STRING ) as in_file : buf = in_file . readlines ( )  with open ( CODE_STRING , CODE_STRING ) as out_file : for line in buf :          if line ==   " ; Include this text   " :              line = line +   " Include below  "          out_file . write ( line ) '''
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