| ----- |
| license: apache-2.0 |
| language: tr |
| tags: |
| - text-classification |
| - turk-dunyasi |
| - cultural-ai |
| - sozce |
| datasets: |
| - sozce_turk_dunyasi_train |
| model-index: |
| - name: sozce-turk-dunyasi |
| results: [] |
| --- |
| - |
| tags: |
| - summarization |
| widget: |
| - text: "new file mode 100644 index 000000000 . . 892fda21b Binary files / dev / null and b / src / plugins / gateway / lib / joscar . jar differ" |
| |
| ------ |
| l |
| --- |
| |
| |
| |
| # 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 single-task training on Git Commit Message Generation dataset. |
| |
| ## 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"), |
| tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_base_commit_generation", 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/single%20task/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) |
| |
| |
| ## 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/) |
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