| | --- |
| | tags: |
| | - generated_from_trainer |
| | - seq2seq |
| | model-index: |
| | - name: codebert-gpt2-commitgen |
| | results: [] |
| | language: |
| | - en |
| | metrics: |
| | - rouge |
| | --- |
| | |
| | <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| | should probably proofread and complete it, then remove this comment. --> |
| |
|
| | # codebert-gpt2-commitgen |
| |
|
| | This model is a fine-tuned version [](https://huggingface.co/) on dataset provided in the paper titled "Towards Automatic Generation of Short Summaries of Commits" by |
| | Siyuan Jiang and Collin McMillan. |
| | Heres are the links |
| |
|
| | Paper :https://arxiv.org/abs/1708.09492 |
| | Data : https://sjiang1.github.io/commitgen |
| |
|
| | ## Model description |
| |
|
| | This is a sequence2sequence model with microsoft/codebert-base as encoder and gpt2 as decoder. Givena gitdiff file, this model can generate a short commit message summarizing the change. |
| |
|
| |
|
| | ## Intended uses & limitations |
| |
|
| | The intended use is to automate github commit message. One limitation to consider is that the model can generate a summary of changes, but is only confined to type of change and might not be able to provide details about the change or output specific keywords related to change. |
| |
|
| | ## Training and evaluation data |
| |
|
| | More information needed |
| |
|
| | ## Training procedure |
| |
|
| | ### Training hyperparameters |
| |
|
| | The following hyperparameters were used during training: |
| | - learning_rate: 5e-05 |
| | - train_batch_size: 16 |
| | - eval_batch_size: 16 |
| | - seed: 42 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - lr_scheduler_warmup_steps: 2000 |
| | - num_epochs: 3 |
| | |
| | ### Training results |
| | |
| | - global_step=4521 |
| | - training_loss=3.55994465065804 |
| | - train_runtime: 3300.0492 |
| | - train_samples_per_second: 21.919 |
| | - train_steps_per_second: 1.37 |
| | - total_flos: 1.062667587499776e+16 |
| | - train_loss: 3.55994465065804 |
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.25.1 |
| | - Pytorch 1.13.0+cu116 |
| | - Tokenizers 0.13.2 |
