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In natural language processing, a word embedding is a representation of a word. The embedding is used in text analysis.Typically, the representation is a real-valued vector that encodes the meaning of the word in such a way that the words that are closer in the vector space are expected to be similar in meaning. [1]
WordPress (WP, or WordPress.org) is a web content management system.It was originally created as a tool to publish blogs but has evolved to support publishing other web content, including more traditional websites, mailing lists, Internet forums, media galleries, membership sites, learning management systems, and online stores.
In practice however, BERT's sentence embedding with the [CLS] token achieves poor performance, often worse than simply averaging non-contextual word embeddings. SBERT later achieved superior sentence embedding performance [8] by fine tuning BERT's [CLS] token embeddings through the usage of a siamese neural network architecture on the SNLI dataset.
As of February 2025, Elementor was available in 64 languages and was the most popular WordPress plugin, with over 10 million active installations worldwide. [3] It is an open-source platform licensed under GPLv3 [ 4 ] and, according to BuiltWith statistics, it powered 5.07% of the top 1 million websites globally in February 2025 [update] .
WP Rocket is the first product of WP Media, a company created in 2014 that provides software to optimize web performance. WP Media is headquartered in Lyon, France and was founded by Julio Potier, Jonathan Buttigieg and Jean-Baptiste Marchand-Arvier, and has an employee base working remotely in multiple locations throughout the world.
The word with embeddings most similar to the topic vector might be assigned as the topic's title, whereas far away word embeddings may be considered unrelated. As opposed to other topic models such as LDA , top2vec provides canonical ‘distance’ metrics between two topics, or between a topic and another embeddings (word, document, or otherwise).