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  2. Word embedding - Wikipedia

    en.wikipedia.org/wiki/Word_embedding

    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]

  3. Sentence embedding - Wikipedia

    en.wikipedia.org/wiki/Sentence_embedding

    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.

  4. WordPress - Wikipedia

    en.wikipedia.org/wiki/WordPress

    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.

  5. Elementor - Wikipedia

    en.wikipedia.org/wiki/Elementor

    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] .

  6. BERT (language model) - Wikipedia

    en.wikipedia.org/wiki/BERT_(language_model)

    High-level schematic diagram of BERT. It takes in a text, tokenizes it into a sequence of tokens, add in optional special tokens, and apply a Transformer encoder. The hidden states of the last layer can then be used as contextual word embeddings. BERT is an "encoder-only" transformer architecture. At a high level, BERT consists of 4 modules:

  7. fastText - Wikipedia

    en.wikipedia.org/wiki/FastText

    fastText is a library for learning of word embeddings and text classification created by Facebook's AI Research (FAIR) lab. [3] [4] ...

  8. Help:Advanced text formatting - Wikipedia

    en.wikipedia.org/wiki/Help:Advanced_text_formatting

    Typography is the art and technique of setting written subject matter in type using a combination of typeface styles, point sizes, line lengths, line leading, character spacing, and word spacing to produce typeset artwork in physical or digital form. The same block of text set with line-height 1.5 is easier to read: Typography is the art and technique of setting written subject matter in type ...

  9. Embedding - Wikipedia

    en.wikipedia.org/wiki/Embedding

    Other typical requirements are: any extremal monomorphism is an embedding and embeddings are stable under pullbacks. Ideally the class of all embedded subobjects of a given object, up to isomorphism, should also be small, and thus an ordered set. In this case, the category is said to be well powered with respect to the class of embeddings.