| --- |
| tags: |
| - summarization |
| widget: |
| - text: '''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 ) ''' |
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| --- |
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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](https://github.com/agemagician/CodeTrans). This model is trained on tokenized python code functions: it works best with tokenized python functions. |
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| ## Model description |
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| 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. |
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| ## Intended uses & limitations |
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| 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 |
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| 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](https://github.com/agemagician/CodeTrans/blob/main/prediction/single%20task/source%20code%20summarization/python/base_model.ipynb). |
| ## Training data |
|
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| 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) |
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| ## Evaluation results |
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| For the source code summarization tasks, different models achieves the following results on different programming languages (in BLEU score): |
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| Test results : |
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| | 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 | |
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| > 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/) |
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