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},
}
```