---
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-mt-task-unbalanced` 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}
}
```