DNABERT applies bidirectional transformer architecture to genomic data, treating DNA k-mers as tokens analogous to words. The model provides pre-trained DNA embeddings that can be fine-tuned for downstream genomic tasks such as prediction, classification, and sequence understanding. It extends HuggingFace Transformers framework for DNA-specific preprocessing and training pipelines.