English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This technical report presents the application of a recurrent memory toextend the context length of BERT, one of the most effective Transformer-basedmodels in natural language processing. By leveraging the Recurrent MemoryTransformer architecture, we have successfully increased the model's effectivecontext length to an unprecedented two million tokens, while maintaining highmemory retrieval accuracy. Our method allows for the storage and processing ofboth local and global information and enables information flow between segmentsof the input sequence through the use of recurrence. Our experimentsdemonstrate the effectiveness of our approach, which holds significantpotential to enhance long-term dependency handling in natural languageunderstanding and generation tasks as well as enable large-scale contextprocessing for memory-intensive applications.

Scaling Transformer to 1M tokens and beyond with RMT