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
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/2023.nlposs-1.20.pdf