Continuous Pre-training of TAPAS Model with E-commerce Structured Data

Currently, large models perform extremely well in natural language processing (NLP). Pre-trained large-scale language models (such as GPT-4, BERT, T5, etc.) have achieved significant performance improvements in a variety of NLP tasks. However, applying these models to specific domains still faces significant challenges, such as lack of domain-specific knowledge and insufficient adaptation to domain-specific structured data. In this article, we take the e-commerce field as an example, clean and convert various collected e-commerce structured data into specific formats, and conduct continuous pre-training of the TAPAS model. During the pre-training process, parameters are gradually adjusted through backpropagation to minimize the loss function to improve the accuracy of the model. By comparing various indicators with other models. Experimental results show that the TAPAS model after continuous pre-training shows better performance in processing structured data tasks in the e-commerce field, significantly improving its accuracy in tasks such as question answering and information extraction in this field.