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

Existing works, including ELMO and BERT, have revealed the importance ofpre-training for NLP tasks. While there does not exist a single pre-trainingmodel that works best in all cases, it is of necessity to develop a frameworkthat is able to deploy various pre-training models efficiently. For thispurpose, we propose an assemble-on-demand pre-training toolkit, namelyUniversal Encoder Representations (UER). UER is loosely coupled, andencapsulated with rich modules. By assembling modules on demand, users caneither reproduce a state-of-the-art pre-training model or develop apre-training model that remains unexplored. With UER, we have built a modelzoo, which contains pre-trained models based on different corpora, encoders,and targets (objectives). With proper pre-trained models, we could achieve newstate-of-the-art results on a range of downstream datasets.

UER: An Open-Source Toolkit for Pre-training Models