  With increasing applications in areas such as biomedical information
extraction pipelines and social media analytics, Named Entity Recognition (NER)
has become an indispensable tool for knowledge extraction. However, with the
gradual shift in language structure and vocabulary, NERs are plagued with
distribution shifts, making them redundant or not as profitable without
re-training. Re-training NERs based on Large Language Models (LLMs) from
scratch over newly acquired data poses economic disadvantages. In contrast,
re-training only with newly acquired data will result in Catastrophic
Forgetting of previously acquired knowledge. Therefore, we propose NERDA-Con, a
pipeline for training NERs with LLM bases by incorporating the concept of
Elastic Weight Consolidation (EWC) into the NER fine-tuning NERDA pipeline. As
we believe our work has implications to be utilized in the pipeline of
continual learning and NER, we open-source our code as well as provide the
fine-tuning library of the same name NERDA-Con at
https://github.com/SupritiVijay/NERDA-Con and
https://pypi.org/project/NERDA-Con/.
