nerblackbox: A High-level Library for Named Entity Recognition in Python
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 15 references that are in the map. First posted on arXiv 7 Dec 2023.
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References · 6
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 4,993
- 2021Datasets: A Community Library for Natural Language Processingcited by 526
- 2020On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselinescited by 254
- 2022T-NER: An All-Round Python Library for Transformer-based Named Entity Recognitioncited by 37
- 2022Evaluate & Evaluation on the Hub: Better Best Practices for Data and Model Measurementscited by 21
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