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 base model comes with 12 layers and a hidden size of 768. It has only 86M backbone parameters with a vocabulary containing 128K tokens which introduces 98M 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 base 50 86 83.7/80.5 87.6/ XLNet base 32 92 /80.2 86.8/ ELECTR…
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