#NER

The project provides a PyTorch-based Korean Named Entity Recognition (NER) implementation using BERT and CRF. Designed for Python 3.x and PyTorch v1.2, it applies advanced NLP techniques to achieve high precision in entity recognition, including dates, locations, and names in a Korean context. The repository features detailed examples and logs of model performance, making it a valuable resource for developers looking to improve their NLP applications and entity recognition systems.

AdaSeq provides a versatile library for creating high-efficiency models in sequence understanding. Leveraging ModelScope, it facilitates tasks such as POS tagging, named entity recognition, and relation extraction. Highlights include a variety of sophisticated models, ease of use, and flexibility. Recent accolades for multilingual named entity recognition tasks underscore its credibility. Designed for both researchers and developers, AdaSeq constantly evolves to maintain state-of-the-art performance without promotional exaggeration.