File size: 2,002 Bytes
a1dacca 36ff83e a1dacca f9a388c 467bb81 f9a388c 467bb81 f9a388c 467bb81 f9a388c 467bb81 f9a388c 467bb81 f9a388c 467bb81 f9a388c 467bb81 a1dacca |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 |
---
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_Python/pre_trained_model_epochs_2__length_512](https://console.cloud.google.com/storage/browser/cubert/20210711_Python/pre_trained_model_epochs_2__length_512). It was trained 2021-07-11 for 2 epochs with a 512 token context window on the Python 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},
}
``` |