RoBERTa

RoBERTa (Robustly optimized BERT approach) is a natural language processing model developed by Facebook AI, which builds upon the BERT (Bidirectional Encoder Representations from Transformers) architecture. Unlike BERT, RoBERTa modifies the pre-training methodology by utilizing a larger dataset and removing the next sentence prediction objective, allowing for more efficient learning of language representations. It employs dynamic masking during training, which enhances the model's ability to generalize from context. RoBERTa excels in various language understanding tasks, including sentiment analysis, question-answering, and text classification, demonstrating state-of-the-art performance on several benchmarks. Its architecture is centered around the transformer model, which processes input text through attention mechanisms to capture relationships between words in a sentence, enabling a deeper understanding of context and semantics.