

# 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 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_large_commit_generation_multitask_finetune"),
    tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_large_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/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

### 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 3,000 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/)


{"tags": ["summarization"], "widget": [{"text": "new file mode 100644 index 000000000 . . 892fda21b Binary files / dev / null and b / src / plugins / gateway / lib / joscar . jar differ"}]}
transformers pytorch jax t5 feature-extraction summarization endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us 

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. 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 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:


Run this example in colab notebook.


Training data
-------------


The supervised training tasks datasets can be downloaded on Link


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 3,000 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 :




> 
> Created by Ahmed Elnaggar | LinkedIn and Wei Ding | LinkedIn
> 
> 
> 



### How to use


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


Run this example in colab notebook.


Training data
-------------


The supervised training tasks datasets can be downloaded on Link


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 3,000 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 :




> 
> Created by Ahmed Elnaggar | LinkedIn and Wei Ding | LinkedIn
> 
> 
>
TAGS
#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us 
 ### How to use


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


Run this example in colab notebook.


Training data
-------------


The supervised training tasks datasets can be downloaded on Link


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 3,000 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 :




> 
> Created by Ahmed Elnaggar | LinkedIn and Wei Ding | LinkedIn
> 
> 
>
passage: TAGS
#transformers #pytorch #jax #t5 #feature-extraction #summarization #endpoints_compatible #text-generation-inference #region-us 
### How to use


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


Run this example in colab notebook.


Training data
-------------


The supervised training tasks datasets can be downloaded on Link


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 3,000 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 :




> 
> Created by Ahmed Elnaggar | LinkedIn and Wei Ding | LinkedIn
> 
> 
>