Baseline: A Library for Rapid Modeling, Experimentation and Development of Deep Learning Algorithms targeting NLP
Daniel Pressel, Sagnik Ray Choudhury, Brian Lester, Yanjie Zhao, Matt Barta
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.- Anthology ID:
- W18-2506
- Volume:
- Proceedings of Workshop for NLP Open Source Software (NLP-OSS)
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Editors:
- Eunjeong L. Park, Masato Hagiwara, Dmitrijs Milajevs, Liling Tan
- Venue:
- NLPOSS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 34–40
- Language:
- URL:
- https://aclanthology.org/W18-2506
- DOI:
- 10.18653/v1/W18-2506
- Cite (ACL):
- Daniel Pressel, Sagnik Ray Choudhury, Brian Lester, Yanjie Zhao, and Matt Barta. 2018. Baseline: A Library for Rapid Modeling, Experimentation and Development of Deep Learning Algorithms targeting NLP. In Proceedings of Workshop for NLP Open Source Software (NLP-OSS), pages 34–40, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Baseline: A Library for Rapid Modeling, Experimentation and Development of Deep Learning Algorithms targeting NLP (Pressel et al., NLPOSS 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/W18-2506.pdf
- Code
- dpressel/baseline
- Data
- CoNLL 2003, SST, SST-2, WNUT 2017