nerblackbox: A High-level Library for Named Entity Recognition in Python

Felix Stollenwerk


Abstract
We present **nerblackbox**, a python library to facilitate the use of state-of-the-art transformer-based models for named entity recognition. It provides simple-to-use yet powerful methods to access data and models from a wide range of sources, for fully automated model training and evaluation as well as versatile model inference. While many technical challenges are solved and hidden from the user by default, **nerblackbox** also offers fine-grained control and a rich set of customizable features. It is thus targeted both at application-oriented developers as well as machine learning experts and researchers.
Anthology ID:
2023.nlposs-1.20
Volume:
Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023)
Month:
December
Year:
2023
Address:
Singapore
Editors:
Liling Tan, Dmitrijs Milajevs, Geeticka Chauhan, Jeremy Gwinnup, Elijah Rippeth
Venues:
NLPOSS | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
174–178
Language:
URL:
https://aclanthology.org/2023.nlposs-1.20
DOI:
10.18653/v1/2023.nlposs-1.20
Bibkey:
Cite (ACL):
Felix Stollenwerk. 2023. nerblackbox: A High-level Library for Named Entity Recognition in Python. In Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023), pages 174–178, Singapore. Association for Computational Linguistics.
Cite (Informal):
nerblackbox: A High-level Library for Named Entity Recognition in Python (Stollenwerk, NLPOSS-WS 2023)
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PDF:
https://aclanthology.org/2023.nlposs-1.20.pdf
Video:
 https://aclanthology.org/2023.nlposs-1.20.mp4