MATE

MATE

MATE is a type of Transformer architecture that has been specifically designed to help people model web tables. Its design is centered around sparse attention, which enables each head to attend to either the rows or the columns of a table in an efficient way. Additionally, MATE makes use of attention heads that can reorder the tokens found either at the rows or columns of the table, and then apply a windowed attention mechanism. Understanding Sparse Attention in MATE The sparse attention mech

TAPAS

TAPAS

What are TAPAS and How Do They Work? TAPAS is a type of weakly supervised question answering model designed to reason over tables without generating logical forms. The name "TAPAS" stands for "Table-based Parser" and was coined by its creators at Google Research. It allows users to make complex queries over large tables in a way that more closely mimics how humans approach the problem. TAPAS is implemented by extending the architecture of BERT (Bidirectional Encoder Representations from Transf

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