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README.md
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---
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license: apache-2.0
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arxiv: 2001.00059
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pipeline_tag: fill-mask
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tags:
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- code
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- cubert
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---
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# CuBERT: Learning and Evaluating Contextual Embedding of Source Code
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## Overview
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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.
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The other versions are available here:
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[cubert-20210711-Python-512](https://huggingface.co/claudios/cubert-20210711-Python-512/)
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[cubert-20210711-Python-1024](https://huggingface.co/claudios/cubert-20210711-Python-1024/)
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[cubert-20210711-Python-2048](https://huggingface.co/claudios/cubert-20210711-Python-2048/)
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[cubert-20210711-Java-512](https://huggingface.co/claudios/cubert-20210711-Java-512/)
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[cubert-20210711-Java-1024](https://huggingface.co/claudios/cubert-20210711-Java-1024/)
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[cubert-20210711-Java-2048](https://huggingface.co/claudios/cubert-20210711-Java-2048/)
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Citation:
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```bibtex
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@inproceedings{cubert,
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author = {Aditya Kanade and
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Petros Maniatis and
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Gogul Balakrishnan and
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Kensen Shi},
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title = {Learning and evaluating contextual embedding of source code},
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booktitle = {Proceedings of the 37th International Conference on Machine Learning,
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{ICML} 2020, 12-18 July 2020},
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series = {Proceedings of Machine Learning Research},
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publisher = {{PMLR}},
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year = {2020},
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}
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```
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