K-BERT extends BERT by injecting knowledge from knowledge graphs (CNDbpedia, HowNet, Medical) into the embedding process, enabling richer contextual representations. The implementation is built on the UER-py framework and supports downstream tasks like text classification and named entity recognition. Users download a pre-trained Google BERT model and knowledge base files to run classification and NER pipelines.