DeBERTa: A Powerful Language Model with Disentangled Attention

DeBERTa (Decoding-enhanced BERT with disentangled attention) is a state-of-the-art transformer-based neural network model that has been designed to improve upon the performance of BERT (Bidirectional Encoder Representations from Transformers), which is a popular pre-trained language model. DeBERTa has been developed by Microsoft Research in collaboration with the University of Science and Technology of China. It incorporates several advancements such as disentangled attention, which allows the model to selectively attend to specific parts of the input, and decoding-enhancement, which improves the decoding process during inference. DeBERTa has achieved state-of-the-art results on several benchmark natural language processing tasks, including question answering, sentiment analysis, and named entity recognition.