@inproceedings{pressel-etal-2018-baseline,
title = "{B}aseline: A Library for Rapid Modeling, Experimentation and Development of Deep Learning Algorithms targeting {NLP}",
author = "Pressel, Daniel and
Ray Choudhury, Sagnik and
Lester, Brian and
Zhao, Yanjie and
Barta, Matt",
editor = "Park, Eunjeong L. and
Hagiwara, Masato and
Milajevs, Dmitrijs and
Tan, Liling",
booktitle = "Proceedings of Workshop for {NLP} Open Source Software ({NLP}-{OSS})",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-2506",
doi = "10.18653/v1/W18-2506",
pages = "34--40",
abstract = "We introduce Baseline: a library for reproducible deep learning research and fast model development for NLP. The library provides easily extensible abstractions and implementations for data loading, model development, training and export of deep learning architectures. It also provides implementations for simple, high-performance, deep learning models for various NLP tasks, against which newly developed models can be compared. Deep learning experiments are hard to reproduce, Baseline provides functionalities to track them. The goal is to allow a researcher to focus on model development, delegating the repetitive tasks to the library.",
}
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%0 Conference Proceedings
%T Baseline: A Library for Rapid Modeling, Experimentation and Development of Deep Learning Algorithms targeting NLP
%A Pressel, Daniel
%A Ray Choudhury, Sagnik
%A Lester, Brian
%A Zhao, Yanjie
%A Barta, Matt
%Y Park, Eunjeong L.
%Y Hagiwara, Masato
%Y Milajevs, Dmitrijs
%Y Tan, Liling
%S Proceedings of Workshop for NLP Open Source Software (NLP-OSS)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F pressel-etal-2018-baseline
%X We introduce Baseline: a library for reproducible deep learning research and fast model development for NLP. The library provides easily extensible abstractions and implementations for data loading, model development, training and export of deep learning architectures. It also provides implementations for simple, high-performance, deep learning models for various NLP tasks, against which newly developed models can be compared. Deep learning experiments are hard to reproduce, Baseline provides functionalities to track them. The goal is to allow a researcher to focus on model development, delegating the repetitive tasks to the library.
%R 10.18653/v1/W18-2506
%U https://aclanthology.org/W18-2506
%U https://doi.org/10.18653/v1/W18-2506
%P 34-40
Markdown (Informal)
[Baseline: A Library for Rapid Modeling, Experimentation and Development of Deep Learning Algorithms targeting NLP](https://aclanthology.org/W18-2506) (Pressel et al., NLPOSS 2018)
ACL