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| tags: | |
| - summarization | |
| widget: | |
| - text: "new file mode 100644 index 000000000 . . 892fda21b Binary files / dev / null and b / src / plugins / gateway / lib / joscar . jar differ" | |
| --- | |
| # CodeTrans model for git commit message generation | |
| Pretrained model on git commit using the t5 base model architecture. It was first released in | |
| [this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized git commit: it works best with tokenized git commit. | |
| ## Model description | |
| 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. It is then fine-tuned on the git commit message generation task for the java commit changes. | |
| ## Intended uses & limitations | |
| The model could be used to generate the git commit message for the git commit changes or be fine-tuned on other relevant tasks. It can be used on unparsed and untokenized commit changes. However, if the change is tokenized, the performance should be better. | |
| ### How to use | |
| Here is how to use this model to generate git commit message using Transformers SummarizationPipeline: | |
| ```python | |
| from transformers import AutoTokenizer, AutoModelWithLMHead, SummarizationPipeline | |
| pipeline = SummarizationPipeline( | |
| model=AutoModelWithLMHead.from_pretrained("SEBIS/code_trans_t5_base_commit_generation_multitask_finetune"), | |
| tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_base_commit_generation_multitask_finetune", skip_special_tokens=True), | |
| device=0 | |
| ) | |
| tokenized_code = "new file mode 100644 index 000000000 . . 892fda21b Binary files / dev / null and b / src / plugins / gateway / lib / joscar . jar differ" | |
| pipeline([tokenized_code]) | |
| ``` | |
| Run this example in [colab notebook](https://github.com/agemagician/CodeTrans/blob/main/prediction/multitask/fine-tuning/commit%20generation/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) | |
| ## Training procedure | |
| ### Multi-task Pretraining | |
| The model was trained on a single TPU Pod V3-8 for 500,000 steps in total, using sequence length 512 (batch size 4096). | |
| It has a total of approximately 220M parameters and was trained using the encoder-decoder architecture. | |
| The optimizer used is AdaFactor with inverse square root learning rate schedule for pre-training. | |
| ### Fine-tuning | |
| This model was then fine-tuned on a single TPU Pod V2-8 for 16,000 steps in total, using sequence length 512 (batch size 256), using only the dataset only containing commit changes. | |
| ## Evaluation results | |
| For the git commit message generation task, different models achieves the following results on different programming languages (in BLEU score): | |
| Test results : | |
| | Language / Model | Java | | |
| | -------------------- | :------------: | | |
| | CodeTrans-ST-Small | 39.61 | | |
| | CodeTrans-ST-Base | 38.67 | | |
| | CodeTrans-TF-Small | 44.22 | | |
| | CodeTrans-TF-Base | 44.17 | | |
| | CodeTrans-TF-Large | **44.41** | | |
| | CodeTrans-MT-Small | 36.17 | | |
| | CodeTrans-MT-Base | 39.25 | | |
| | CodeTrans-MT-Large | 41.18 | | |
| | CodeTrans-MT-TF-Small | 43.96 | | |
| | CodeTrans-MT-TF-Base | 44.19 | | |
| | CodeTrans-MT-TF-Large | 44.34 | | |
| | State of the art | 32.81 | | |
| > 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/) | |