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 single-task training on Git Commit Message Generation dataset.
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:
from transformers import AutoTokenizer, AutoModelWithLMHead, SummarizationPipeline pipeline = SummarizationPipeline( model=AutoModelWithLMHead.from_pretrained("SEBIS/code_trans_t5_small_commit_generation"), tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_small_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.
The supervised training tasks datasets can be downloaded on Link
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|
|State of the art||32.81|
Select AutoNLP in the “Train” menu to fine-tune this model automatically.
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