kamalkraj/BERT-NER-TF

This project is a classic example of a 'point-in-time' reference implementation. Created shortly after the release of TensorFlow 2.0 and the rise of BERT, it served as a useful boilerplate for developers transitioning to the new TF ecosystem. However, with a velocity of 0.0/hr and being over 6 years old, it is effectively legacy code. The defensibility is near zero because the functionality has been entirely commoditized by Hugging Face's `transformers` library, which offers superior abstraction, better performance, and multi-model support (RoBERTa, DeBERTa, etc.). Furthermore, the rise of Large Language Models (LLMs) has shifted the frontier of NER from fine-tuning specialized models like BERT to zero-shot or few-shot prompting, which OpenAI and Anthropic handle natively. In the open-source world, projects like spaCy (for production pipelines) and GLiNER (for zero-shot NER) have completely displaced basic BERT-only scripts. The project remains a historical artifact of how NER was implemented in the early TF2 era but lacks any modern competitive moat or maintenance.