This paper proposes various LSTM-based models for sequence tagging, including LSTM, bidirectional LSTM (BI-LSTM), LSTM with a Conditional Random Field (CRF) layer (LSTM-CRF), and bidirectional LSTM with a CRF layer (BI-LSTM-CRF). The BI-LSTM-CRF model is the first to be applied to NLP benchmark sequence tagging datasets. It efficiently uses both past and future input features due to the bidirectional LSTM component and sentence-level tag information due to the CRF layer. The model achieves state-of-the-art accuracy on POS, chunking, and NER datasets and is robust with less dependence on word embeddings compared to previous models. The paper compares the performance of these models on NLP tagging datasets, showing that BI-LSTM-CRF outperforms other models in most cases. The model is trained using backpropagation through time and is robust to the removal of engineered features. It achieves high accuracy on various NLP tasks, including POS, chunking, and NER, and outperforms existing systems in these tasks. The paper concludes that the BI-LSTM-CRF model is effective for sequence tagging and has less dependence on word embeddings compared to previous models.This paper proposes various LSTM-based models for sequence tagging, including LSTM, bidirectional LSTM (BI-LSTM), LSTM with a Conditional Random Field (CRF) layer (LSTM-CRF), and bidirectional LSTM with a CRF layer (BI-LSTM-CRF). The BI-LSTM-CRF model is the first to be applied to NLP benchmark sequence tagging datasets. It efficiently uses both past and future input features due to the bidirectional LSTM component and sentence-level tag information due to the CRF layer. The model achieves state-of-the-art accuracy on POS, chunking, and NER datasets and is robust with less dependence on word embeddings compared to previous models. The paper compares the performance of these models on NLP tagging datasets, showing that BI-LSTM-CRF outperforms other models in most cases. The model is trained using backpropagation through time and is robust to the removal of engineered features. It achieves high accuracy on various NLP tasks, including POS, chunking, and NER, and outperforms existing systems in these tasks. The paper concludes that the BI-LSTM-CRF model is effective for sequence tagging and has less dependence on word embeddings compared to previous models.