| | --- |
| | 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 small 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-small` 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_small_commit_generation_multitask_finetune"), |
| | tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_small_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/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) |
| |
|
| |
|
| | ## 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 8,000 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 | |
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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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