| | --- |
| | tags: |
| | - summarization |
| | widget: |
| | - text: "select time ( col0 ) from tab0" |
| |
|
| | --- |
| | |
| |
|
| |
|
| | # CodeTrans model for source code summarization sql |
| | Pretrained model on programming language sql using the t5 small model architecture. It was first released in |
| | [this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized sql code functions: it works best with tokenized sql 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 sql dataset. |
| |
|
| | ## Intended uses & limitations |
| |
|
| | The model could be used to generate the description for the sql function or be fine-tuned on other sql code tasks. It can be used on unparsed and untokenized sql code. However, if the sql code is tokenized, the performance should be better. |
| |
|
| | ### How to use |
| |
|
| | Here is how to use this model to generate sql function documentation using Transformers SummarizationPipeline: |
| |
|
| | ```python |
| | from transformers import AutoTokenizer, AutoModelWithLMHead, SummarizationPipeline |
| | |
| | pipeline = SummarizationPipeline( |
| | model=AutoModelWithLMHead.from_pretrained("SEBIS/code_trans_t5_small_source_code_summarization_sql"), |
| | tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_small_source_code_summarization_sql", skip_special_tokens=True), |
| | device=0 |
| | ) |
| | |
| | tokenized_code = "select time ( col0 ) from tab0" |
| | pipeline([tokenized_code]) |
| | ``` |
| | Run this example in [colab notebook](https://github.com/agemagician/CodeTrans/blob/main/prediction/single%20task/source%20code%20summarization/sql/small_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 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 | |
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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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