DeBERTaV3: Improving DeBERTa using ELECTRA Style Pre Training with Gradient Disentangled Embedding Sharing DeBERTa improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. With those two improvements, DeBERTa out perform RoBERTa on a majority of NLU tasks with 80GB training data. In DeBERTa V3, we further improved the efficiency of DeBERTa using ELECTRA Style pre training with Gradient Disentangled Embedding Sharing. Compared to DeBERTa, our V3 version significantly improves the model performance on downstream tasks. You can find more technique details about the new model from our paper. Please check the official repository for more implementation details and updates. The DeBERTa V3 large model comes with 24 layers and a hidden size of 1024. It has 304M backbone parameters with a vocabulary containing 128K tokens which introduces 131M parameters in the Embedding layer. This model was trained using the 160GB data as DeBERTa V2. Fine tuning on NLU tasks We present the dev results on SQuAD 2.0 and MNLI tasks. Model Vocabulary(K) Backbone Params(M) SQuAD 2.0(F1/EM) MNLI m/mm(ACC) RoBERTa large 50 304 89.4/86.5 90.2 XLNet large 32 90.6/87.9 90.8 DeBER…
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