| | --- |
| | tags: |
| | - summarization |
| | widget: |
| | - text: "protected String renderUri ( URI uri ) { return uri . toASCIIString ( ) ; }" |
| |
|
| | --- |
| | |
| |
|
| | # CodeTrans model for code comment generation java |
| | Pretrained model on programming language java using the t5 small model architecture. It was first released in |
| | [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 |
| |
|
| | This CodeTrans model is based on the `t5-small` model. It has its own SentencePiece vocabulary model. It used single-task training on Code Comment Generation dataset. |
| |
|
| | ## Intended uses & limitations |
| |
|
| | 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. |
| |
|
| | ### How to use |
| |
|
| | Here is how to use this model to generate java function documentation using Transformers SummarizationPipeline: |
| |
|
| | ```python |
| | from transformers import AutoTokenizer, AutoModelWithLMHead, SummarizationPipeline |
| | |
| | pipeline = SummarizationPipeline( |
| | model=AutoModelWithLMHead.from_pretrained("SEBIS/code_trans_t5_small_code_comment_generation_java"), |
| | tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_small_code_comment_generation_java", skip_special_tokens=True), |
| | device=0 |
| | ) |
| | |
| | tokenized_code = "protected String renderUri ( URI uri ) { return uri . toASCIIString ( ) ; }" |
| | pipeline([tokenized_code]) |
| | ``` |
| | Run this example in [colab notebook](https://github.com/agemagician/CodeTrans/blob/main/prediction/single%20task/code%20comment%20generation/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) |
| |
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| |
|
| | ## Evaluation results |
| |
|
| | For the code documentation tasks, different models achieves the following results on different programming languages (in BLEU score): |
| |
|
| | Test results : |
| |
|
| | | Language / Model | Java | |
| | | -------------------- | :------------: | |
| | | CodeTrans-ST-Small | 37.98 | |
| | | CodeTrans-ST-Base | 38.07 | |
| | | CodeTrans-TF-Small | 38.56 | |
| | | CodeTrans-TF-Base | 39.06 | |
| | | CodeTrans-TF-Large | **39.50** | |
| | | CodeTrans-MT-Small | 20.15 | |
| | | CodeTrans-MT-Base | 27.44 | |
| | | CodeTrans-MT-Large | 34.69 | |
| | | CodeTrans-MT-TF-Small | 38.37 | |
| | | CodeTrans-MT-TF-Base | 38.90 | |
| | | CodeTrans-MT-TF-Large | 39.25 | |
| | | State of the art | 38.17 | |
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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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