| | --- |
| | tags: |
| | - summarization |
| | widget: |
| | - text: "public static function update ( $ table ) { if ( ! is_array ( $ table ) ) { $ table = json_decode ( $ table , true ) ; } if ( ! SchemaManager :: tableExists ( $ table [ 'oldName' ] ) ) { throw SchemaException :: tableDoesNotExist ( $ table [ 'oldName' ] ) ; } $ updater = new self ( $ table ) ; $ updater -> updateTable ( ) ; }" |
| |
|
| | --- |
| | |
| |
|
| | # CodeTrans model for code documentation generation php |
| | Pretrained model on programming language php using the t5 base model architecture. It was first released in |
| | [this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized php code functions: it works best with tokenized php 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 php dataset. |
| |
|
| | ## Intended uses & limitations |
| |
|
| | The model could be used to generate the description for the php function or be fine-tuned on other php code tasks. It can be used on unparsed and untokenized php code. However, if the php code is tokenized, the performance should be better. |
| |
|
| | ### How to use |
| |
|
| | Here is how to use this model to generate php 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_php"), |
| | tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_base_code_documentation_generation_php", skip_special_tokens=True), |
| | device=0 |
| | ) |
| | |
| | tokenized_code = "public static function update ( $ table ) { if ( ! is_array ( $ table ) ) { $ table = json_decode ( $ table , true ) ; } if ( ! SchemaManager :: tableExists ( $ table [ 'oldName' ] ) ) { throw SchemaException :: tableDoesNotExist ( $ table [ 'oldName' ] ) ; } $ updater = new self ( $ table ) ; $ updater -> updateTable ( ) ; }" |
| | pipeline([tokenized_code]) |
| | ``` |
| | Run this example in [colab notebook](https://github.com/agemagician/CodeTrans/blob/main/prediction/single%20task/function%20documentation%20generation/php/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 | |
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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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