This paper introduces a novel end-to-end sequence labeling architecture that combines bidirectional LSTM, CNN, and CRF to automatically learn both word- and character-level representations. The model requires no feature engineering or data preprocessing, making it applicable to a wide range of sequence labeling tasks. It achieves state-of-the-art performance on two tasks: POS tagging on the Penn Treebank WSJ corpus (97.55% accuracy) and named entity recognition (NER) on the CoNLL 2003 corpus (91.21% F1 score). The model first uses CNNs to encode character-level information into character-level representations. These are then combined with word embeddings and fed into a bidirectional LSTM to model context. A CRF layer is used for joint decoding of label sequences. The model outperforms previous state-of-the-art systems, demonstrating the effectiveness of jointly decoding label sequences for improved performance. The model is trained using mini-batch stochastic gradient descent with momentum and gradient clipping. It uses pre-trained word embeddings (e.g., GloVe) and fine-tunes them during training. Character embeddings are initialized with uniform samples, and weight matrices and bias vectors are initialized randomly. Dropout is applied to regularize the model and reduce overfitting. The model is evaluated on two tasks: POS tagging and NER. For POS tagging, it outperforms previous systems, including Senna and CharWNN. For NER, it achieves a state-of-the-art F1 score of 91.21%, slightly improving upon the previous best result of 91.20%. The model performs best with GloVe embeddings, which outperform other embeddings like Senna and Word2Vec on both tasks. The model is also tested on out-of-vocabulary words and shows significant improvements on words not present in the training or embedding vocabularies. The model's performance is further enhanced by using a CRF layer for joint decoding, which is more effective than using separate decoders for each label. The model is end-to-end, requiring no task-specific resources or data preprocessing. It can be applied to various sequence labeling tasks across different languages and domains. The model's success demonstrates the effectiveness of combining CNNs, LSTMs, and CRFs for sequence labeling tasks, and highlights the importance of using pre-trained embeddings and joint decoding for improved performance.This paper introduces a novel end-to-end sequence labeling architecture that combines bidirectional LSTM, CNN, and CRF to automatically learn both word- and character-level representations. The model requires no feature engineering or data preprocessing, making it applicable to a wide range of sequence labeling tasks. It achieves state-of-the-art performance on two tasks: POS tagging on the Penn Treebank WSJ corpus (97.55% accuracy) and named entity recognition (NER) on the CoNLL 2003 corpus (91.21% F1 score). The model first uses CNNs to encode character-level information into character-level representations. These are then combined with word embeddings and fed into a bidirectional LSTM to model context. A CRF layer is used for joint decoding of label sequences. The model outperforms previous state-of-the-art systems, demonstrating the effectiveness of jointly decoding label sequences for improved performance. The model is trained using mini-batch stochastic gradient descent with momentum and gradient clipping. It uses pre-trained word embeddings (e.g., GloVe) and fine-tunes them during training. Character embeddings are initialized with uniform samples, and weight matrices and bias vectors are initialized randomly. Dropout is applied to regularize the model and reduce overfitting. The model is evaluated on two tasks: POS tagging and NER. For POS tagging, it outperforms previous systems, including Senna and CharWNN. For NER, it achieves a state-of-the-art F1 score of 91.21%, slightly improving upon the previous best result of 91.20%. The model performs best with GloVe embeddings, which outperform other embeddings like Senna and Word2Vec on both tasks. The model is also tested on out-of-vocabulary words and shows significant improvements on words not present in the training or embedding vocabularies. The model's performance is further enhanced by using a CRF layer for joint decoding, which is more effective than using separate decoders for each label. The model is end-to-end, requiring no task-specific resources or data preprocessing. It can be applied to various sequence labeling tasks across different languages and domains. The model's success demonstrates the effectiveness of combining CNNs, LSTMs, and CRFs for sequence labeling tasks, and highlights the importance of using pre-trained embeddings and joint decoding for improved performance.