README.md

---
license: apache-2.0
arxiv: 2001.00059
pipeline_tag: fill-mask
tags:
- code
- cubert
---

# CuBERT: Learning and Evaluating Contextual Embedding of Source Code

## Overview
This model is the unofficial HuggingFace version of "[CuBERT](https://github.com/google-research/google-research/tree/master/cubert)". In particular, this version comes from [gs://cubert/20210711_Java/pre_trained_model_epochs_2__length_1024](https://console.cloud.google.com/storage/browser/cubert/20210711_Java/pre_trained_model_epochs_2__length_1024). It was trained 2021-07-11 for 2 epochs with a 1024 token context window on the Java BigQuery dataset. I manually converted the Tensorflow checkpoint to PyTorch and have uploaded it here. The [tokenizer](https://github.com/google-research/google-research/blob/master/cubert/python_tokenizer.py) has not been converted yet. All credit goes to Aditya Kanade, Petros Maniatis, Gogul Balakrishnan, and Kensen Shi.

The other versions are available here:

[cubert-20210711-Python-512](https://huggingface.co/claudios/cubert-20210711-Python-512/)

[cubert-20210711-Python-1024](https://huggingface.co/claudios/cubert-20210711-Python-1024/)

[cubert-20210711-Python-2048](https://huggingface.co/claudios/cubert-20210711-Python-2048/)

[cubert-20210711-Java-512](https://huggingface.co/claudios/cubert-20210711-Java-512/)

[cubert-20210711-Java-1024](https://huggingface.co/claudios/cubert-20210711-Java-1024/)

[cubert-20210711-Java-2048](https://huggingface.co/claudios/cubert-20210711-Java-2048/)


Citation:
```bibtex
@inproceedings{cubert,
author    = {Aditya Kanade and
             Petros Maniatis and
             Gogul Balakrishnan and
             Kensen Shi},
title     = {Learning and evaluating contextual embedding of source code},
booktitle = {Proceedings of the 37th International Conference on Machine Learning,
               {ICML} 2020, 12-18 July 2020},
series    = {Proceedings of Machine Learning Research},
publisher = {{PMLR}},
year      = {2020},
}
```