This paper introduces a variety of Long Short-Term Memory (LSTM) based models for sequence tagging tasks, including LSTM networks, bidirectional LSTM (BI-LSTM) networks, LSTM with a Conditional Random Field (CRF) layer (LSTM-CRF), and bidirectional LSTM with a CRF layer (BI-LSTM-CRF). The authors claim that the BI-LSTM-CRF model is the first to be applied to NLP benchmark sequence tagging datasets, and it effectively utilizes both past and future input features through the bidirectional LSTM component and sentence-level tag information through the CRF layer. The model achieves state-of-the-art or close-to-state-of-the-art accuracy on POS, chunking, and NER datasets. Additionally, the BI-LSTM-CRF model is shown to be robust and less dependent on word embeddings compared to previous models. The paper also discusses the training procedure, experimental results, and comparisons with existing models, highlighting the superior performance of the proposed models.This paper introduces a variety of Long Short-Term Memory (LSTM) based models for sequence tagging tasks, including LSTM networks, bidirectional LSTM (BI-LSTM) networks, LSTM with a Conditional Random Field (CRF) layer (LSTM-CRF), and bidirectional LSTM with a CRF layer (BI-LSTM-CRF). The authors claim that the BI-LSTM-CRF model is the first to be applied to NLP benchmark sequence tagging datasets, and it effectively utilizes both past and future input features through the bidirectional LSTM component and sentence-level tag information through the CRF layer. The model achieves state-of-the-art or close-to-state-of-the-art accuracy on POS, chunking, and NER datasets. Additionally, the BI-LSTM-CRF model is shown to be robust and less dependent on word embeddings compared to previous models. The paper also discusses the training procedure, experimental results, and comparisons with existing models, highlighting the superior performance of the proposed models.