nerblackbox: A High-level Library for Named Entity Recognition in Python
This library addresses the problem of technical barriers in named entity recognition for developers and researchers, though it is incremental as it builds on existing transformer models.
The authors tackled the complexity of using state-of-the-art transformer models for named entity recognition by developing nerblackbox, a Python library that simplifies access to data and models, enabling automated training, evaluation, and inference.
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.