Pretrained model on git commit using the t5 small model architecture. It was first released in this repository. This model is trained on tokenized git commit: it works best with tokenized git commit.

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.

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.

Here is how to use this model to generate git commit message using Transformers SummarizationPipeline:

Run this example in colab notebook.

The supervised training tasks datasets can be downloaded on Link

The model was trained on a single TPU Pod V3-8 for 360,000 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.

For the git commit message generation task, different models achieves the following results on different programming languages (in BLEU score):

Test results :