nlp最新模型的deberta

nlp最新模型的deberta nlp最新模型的deberta
Recent progress in pre-trained neural language models has significantly improved
the performance of many natural language processing (NLP) tasks. In this paper we propose a new model architecture DeBERTa (Decoding-enhanced BERT
with disentangled attention) that improves the BERT and RoBERTa models using
two novel techniques. The first is the disentangled attention mechanism, where
each word is represented using two vectors that encode its content and position,
respectively, and the attention weights among words are computed using disentangled matrices on their contents and relative positions, respectively. Second,
an enhanced mask decoder is used to incorporate absolute positions in the de

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