| | --- |
| | arxiv: 2102.02017 |
| | language: |
| | - code |
| | --- |
| | |
| | # Studying the Usage of Text-To-Text Transfer Transformer to Support Code-Related Tasks |
| | ## Using Transfer Learning for Code-Related Tasks |
| |
|
| | This is an *unofficial* reupload of `t5-learning-no-pretraining-ag-task` based off the [author's repo](https://github.com/antonio-mastropaolo/TransferLearning4Code), in the `SafeTensors` format using `transformers` `4.40.1`. I manually converted the checkpoints using the `tf_2_pytorch_T5.py` script and converted the tokenizers with my own script. The goal of this reupload is to prevent older models that are still relevant baselines from becoming stale as a result of changes in HuggingFace. Additionally, I may include minor corrections, such as model max length configuration. |
| |
|
| | ## Citation |
| |
|
| | ```bibtex |
| | @article{Mastropaolo2021StudyingTU, |
| | title={Studying the Usage of Text-To-Text Transfer Transformer to Support Code-Related Tasks}, |
| | author={Antonio Mastropaolo and Simone Scalabrino and Nathan Cooper and David Nader-Palacio and Denys Poshyvanyk and Rocco Oliveto and Gabriele Bavota}, |
| | journal={2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE)}, |
| | year={2021}, |
| | pages={336-347} |
| | } |
| | ``` |
