From d13f327dc43058892baf0202b2ff8e1a8adff8ac Mon Sep 17 00:00:00 2001 From: Wei Ding Date: Mon, 15 Feb 2021 11:37:56 +0000 Subject: [PATCH] Update README.md --- README.md | 62 +++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 62 insertions(+) diff --git a/README.md b/README.md index b363582..edf9169 100644 --- a/README.md +++ b/README.md @@ -5,3 +5,65 @@ widget: - text: "def e ( message , exit_code = None ) : print_log ( message , YELLOW , BOLD ) if exit_code is not None : sys . exit ( exit_code )" --- + + +# CodeTrans model for code documentation generation python +Pretrained model on programming language python using the t5 base model architecture. It was first released in +[this repository](https://github.com/agemagician/CodeTrans). 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 CodeSearchNet Corpus 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_code_documentation_generation_python"), + tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_base_code_documentation_generation_python", skip_special_tokens=True), + device=0 +) + +tokenized_code = "def e ( message , exit_code = None ) : print_log ( message , YELLOW , BOLD ) if exit_code is not None : sys . exit ( exit_code )" +pipeline([tokenized_code]) +``` +Run this example in [colab notebook](https://github.com/agemagician/CodeTrans/blob/main/prediction/single%20task/function%20documentation%20generation/python/base_model.ipynb). +## Training data + +The supervised training tasks datasets can be downloaded on [Link](https://www.dropbox.com/sh/488bq2of10r4wvw/AACs5CGIQuwtsD7j_Ls_JAORa/finetuning_dataset?dl=0&subfolder_nav_tracking=1) + + +## 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](https://twitter.com/Elnaggar_AI) | [LinkedIn](https://www.linkedin.com/in/prof-ahmed-elnaggar/) and Wei Ding | [LinkedIn](https://www.linkedin.com/in/wei-ding-92561270/) + +