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

Traditional comparative learning sentence embedding directly uses the encoderto extract sentence features, and then passes in the comparative loss functionfor learning. However, this method pays too much attention to the sentence bodyand ignores the influence of some words in the sentence on the sentencesemantics. To this end, we propose CMLM-CSE, an unsupervised contrastivelearning framework based on conditional MLM. On the basis of traditionalcontrastive learning, an additional auxiliary network is added to integratesentence embedding to perform MLM tasks, forcing sentence embedding to learnmore masked word information. Finally, when Bertbase was used as thepretraining language model, we exceeded SimCSE by 0.55 percentage points onaverage in textual similarity tasks, and when Robertabase was used as thepretraining language model, we exceeded SimCSE by 0.3 percentage points onaverage in textual similarity tasks.

CMLM-CSE: Based on Conditional MLM Contrastive Learning for Sentence Embeddings