readme: ---
  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 large 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-large` model. It has its own SentencePiece vocabulary model. It used transfer-learning pre-training on 7 unsupervised datasets in the software development domain. 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_large_commit_generation_transfer_learning_finetune"),
      tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_large_commit_generation_transfer_learning_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/transfer%20learning%20fine-tuning/commit%20generation/large_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
  
  ### Transfer-learning Pretraining
  
  The model was trained on a single TPU Pod V3-8 for 240,000 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 4,000 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/)
  
  