Wals Roberta

Recent advancements in natural language processing have led to the development of powerful language models like BERT (Bidirectional Encoder Representations from Transformers). However, the original BERT model has several limitations, including its reliance on a fixed-length context and its vulnerability to overfitting. In this paper, we present RoBERTa, a robustly optimized BERT pretraining approach that addresses these limitations. RoBERTa modifies the BERT architecture to incorporate dynamic masking, changes the optimization algorithm, and increases the batch size. Our experimental results demonstrate that RoBERTa outperforms BERT on a wide range of natural language processing tasks, including question answering, sentiment analysis, and text classification.

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However, these models still rely on the original BERT architecture and do not address the limitations of fixed-length context and overfitting. wals roberta