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MATE: Efficient Table Transformer Model by siddharthgor3333 is a document available to read on EtoBox.

What is MATE: Efficient Table Transformer Model about?

The document introduces MATE, a sparse-attention Transformer architecture designed to efficiently model large tables in documents, overcoming the limitations of traditional Transformers that are restricted to 512 tokens. MATE allows attention heads to focus on either rows or columns, enabling it to handle over 8000 tokens while improving accuracy on table reasoning tasks, including a significant performance boost on the H YBRID QA dataset. The architecture is open-source and demonstrates state-of-the-art re

Author

siddharthgor3333

Language

EN