This repository provides a sequence labeling model for Named Entity Recognition using character-level and word-level embeddings processed through bidirectional LSTM layers, with a linear-chain CRF for decoding. The model concatenates character-based word representations with pre-trained GloVe vectors to capture both morphological and semantic features, achieving F1 scores between 90 and 91 on standard benchmarks.