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/

Zendy Plus

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

Zendy Tools

Zendy Open

The text retrieval is the task of retrieving similar documents to a searchquery, and it is important to improve retrieval accuracy while maintaining acertain level of retrieval speed. Existing studies have reported accuracyimprovements using language models, but many of these do not take into accountthe reduction in search speed that comes with increased performance. In thisstudy, we propose three-stage re-ranking model using model ensembles or largerlanguage models to improve search accuracy while minimizing the search delay.We ranked the documents by BM25 and language models, and then re-ranks by amodel ensemble or a larger language model for documents with high similarity tothe query. In our experiments, we train the MiniLM language model on theMS-MARCO dataset and evaluate it in a zero-shot setting. Our proposed methodachieves higher retrieval accuracy while reducing the retrieval speed decay.

Text Retrieval with Multi-Stage Re-Ranking Models