Transformers Support Long-Sequence Processing

Sparse attention models like Longformer and BigBird extend Transformers to thousands of tokens.

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🤯 Did You Know (click to read)

Sparse attention allows processing of sequences over 4,000 tokens, compared to standard Transformers limited to around 512 tokens.

By replacing full attention with sparse patterns, Transformers can process long sequences efficiently, reducing memory complexity from quadratic to linear. This allows document-level tasks like summarization, legal text analysis, and research paper processing without truncation.

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💥 Impact (click to read)

Long-sequence Transformers enable scalable document understanding and information extraction.

Researchers and students can summarize, search, and analyze long texts efficiently using Transformer-based models.

Source

Beltagy et al., 2020 - Longformer

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