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--- |
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tags: |
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- summarization |
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widget: |
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- text: "select time ( col0 ) from tab0" |
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--- |
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# CodeTrans model for source code summarization sql |
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Pretrained model on programming language sql using the t5 large model architecture. It was first released in |
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[this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized sql code functions: it works best with tokenized sql functions. |
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## Model description |
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This CodeTrans model is based on the `t5-large` model. It has its own SentencePiece vocabulary model. It used multi-task training on 13 supervised tasks in the software development domain and 7 unsupervised datasets. |
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## Intended uses & limitations |
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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. |
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### How to use |
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Here is how to use this model to generate sql function documentation using Transformers SummarizationPipeline: |
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```python |
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from transformers import AutoTokenizer, AutoModelWithLMHead, SummarizationPipeline |
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pipeline = SummarizationPipeline( |
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model=AutoModelWithLMHead.from_pretrained("SEBIS/code_trans_t5_large_source_code_summarization_sql_multitask"), |
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tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_large_source_code_summarization_sql_multitask", skip_special_tokens=True), |
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device=0 |
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) |
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tokenized_code = "select time ( col0 ) from tab0" |
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pipeline([tokenized_code]) |
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``` |
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Run this example in [colab notebook](https://github.com/agemagician/CodeTrans/blob/main/prediction/multitask/pre-training/source%20code%20summarization/sql/large_model.ipynb). |
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## 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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## Training procedure |
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### Multi-task Pretraining |
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The model was trained on a single TPU Pod V3-8 for 120,000 steps in total, using sequence length 512 (batch size 4096). |
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It has a total of approximately 220M parameters and was trained using the encoder-decoder architecture. |
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The optimizer used is AdaFactor with inverse square root learning rate schedule for pre-training. |
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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# | |
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| -------------------- | :------------: | :------------: | :------------: | |
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| CodeTrans-ST-Small | 8.45 | 17.55 | 19.74 | |
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| CodeTrans-ST-Base | 9.12 | 15.00 | 18.65 | |
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| CodeTrans-TF-Small | 10.06 | 17.71 | 20.40 | |
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| CodeTrans-TF-Base | 10.94 | 17.66 | 21.12 | |
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| CodeTrans-TF-Large | 12.41 | 18.40 | 21.43 | |
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| CodeTrans-MT-Small | 13.11 | 19.15 | 22.39 | |
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| CodeTrans-MT-Base | **13.37** | 19.24 | 23.20 | |
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| CodeTrans-MT-Large | 13.24 | 19.40 | **23.57** | |
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| CodeTrans-MT-TF-Small | 12.10 | 18.25 | 22.03 | |
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| CodeTrans-MT-TF-Base | 10.64 | 16.91 | 21.40 | |
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| CodeTrans-MT-TF-Large | 12.14 | **19.98** | 21.10 | |
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| 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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