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  2. 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] [5] [6] The model allows one to ...

  3. 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 ]

  4. Word2vec - Wikipedia

    en.wikipedia.org/wiki/Word2vec

    The word2vec algorithm estimates these representations by modeling text in a large corpus. Once trained, such a model can detect synonymous words or suggest additional words for a partial sentence. Word2vec was developed by Tomáš Mikolov and colleagues at Google and published in 2013.

  5. 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.

  6. Bidirectional text - Wikipedia

    en.wikipedia.org/wiki/Bidirectional_text

    The text within the scope of the embedding formatting characters is not independent of the surrounding text. Also, characters within an embedding can affect the ordering of characters outside. Unicode 6.3 recognized that directional embeddings usually have too strong an effect on their surroundings and are thus unnecessarily difficult to use.

  7. T5 (language model) - Wikipedia

    en.wikipedia.org/wiki/T5_(language_model)

    T5 (Text-to-Text Transfer Transformer) is a series of large language models developed by Google AI introduced in 2019. [ 1 ] [ 2 ] Like the original Transformer model, [ 3 ] T5 models are encoder-decoder Transformers , where the encoder processes the input text, and the decoder generates the output text.

  8. Certificate of current cost or pricing data - Wikipedia

    en.wikipedia.org/wiki/Certificate_of_current...

    Certified cost or pricing data may not be obtained for acquisitions at or below the simplified acquisition threshold. [3] Other exceptions are stated in FAR 15.403-1(b) or may be adopted under a waiver requested by the contracting officer in exceptional circumstances. If certified cost or pricing data has been requested by the Government and ...

  9. Object Linking and Embedding - Wikipedia

    en.wikipedia.org/wiki/Object_Linking_and_Embedding

    The main benefit of OLE is to add different kinds of data to a document from different applications, like a text editor and an image editor. This creates a Compound File Binary Format document and a master file to which the document makes reference. Changes to data in the master file immediately affect the document that references it.