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--- |
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tags: |
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- summarization |
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widget: |
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- text: "public static < T , U > Function < T , U > castFunction ( Class < U > target ) { return new CastToClass < T , U > ( target ) ; }" |
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--- |
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# CodeTrans model for code documentation generation java |
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Pretrained model on programming language java using the t5 base model architecture. It was first released in |
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[this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized java code functions: it works best with tokenized java 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 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 java function or be fine-tuned on other java code tasks. It can be used on unparsed and untokenized java code. However, if the java 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 java 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_base_code_documentation_generation_java_multitask"), |
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tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_base_code_documentation_generation_java_multitask", skip_special_tokens=True), |
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device=0 |
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) |
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tokenized_code = "public static < T , U > Function < T , U > castFunction ( Class < U > target ) { return new CastToClass < T , U > ( target ) ; }" |
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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/function%20documentation%20generation/java/base_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 480,000 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 code documentation 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 | Java | Go | Php | Ruby | JavaScript | |
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| -------------------- | :------------: | :------------: | :------------: | :------------: | :------------: | :------------: | |
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| CodeTrans-ST-Small | 17.31 | 16.65 | 16.89 | 23.05 | 9.19 | 13.7 | |
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| CodeTrans-ST-Base | 16.86 | 17.17 | 17.16 | 22.98 | 8.23 | 13.17 | |
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| CodeTrans-TF-Small | 19.93 | 19.48 | 18.88 | 25.35 | 13.15 | 17.23 | |
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| CodeTrans-TF-Base | 20.26 | 20.19 | 19.50 | 25.84 | 14.07 | 18.25 | |
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| CodeTrans-TF-Large | 20.35 | 20.06 | **19.54** | 26.18 | 14.94 | **18.98** | |
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| CodeTrans-MT-Small | 19.64 | 19.00 | 19.15 | 24.68 | 14.91 | 15.26 | |
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| CodeTrans-MT-Base | **20.39** | 21.22 | 19.43 | **26.23** | **15.26** | 16.11 | |
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| CodeTrans-MT-Large | 20.18 | **21.87** | 19.38 | 26.08 | 15.00 | 16.23 | |
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| CodeTrans-MT-TF-Small | 19.77 | 20.04 | 19.36 | 25.55 | 13.70 | 17.24 | |
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| CodeTrans-MT-TF-Base | 19.77 | 21.12 | 18.86 | 25.79 | 14.24 | 18.62 | |
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| CodeTrans-MT-TF-Large | 18.94 | 21.42 | 18.77 | 26.20 | 14.19 | 18.83 | |
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| 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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