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

We generated 25000 conversations labeled with Big Five Personality traitsusing prompt programming at GPT-3. Then we train Big Five classification modelswith these data and evaluate them with 2500 data from generated dialogues andreal conversational datasets labeled in Big Five by human annotators. Theresults indicated that this approach is promising for creating effectivetraining data. We then compare the performance by different training approachesand models. Our results suggest that using Adapter-Transformers and transferlearning from pre-trained RoBERTa sentiment analysis model will perform bestwith the generated data. Our best model obtained an accuracy of 0.71 ingenerated data and 0.65 in real datasets. Finally, we discuss this approach'spotential limitations and confidence metric.

Generate labeled training data using Prompt Programming and GPT-3. An example of Big Five Personality Classification