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  2. Bag-of-words model - Wikipedia

    en.wikipedia.org/wiki/Bag-of-words_model

    The bag-of-words model (BoW) is a model of text which uses an unordered collection (a "bag") of words. It is used in natural language processing and information retrieval (IR). It disregards word order (and thus most of syntax or grammar) but captures multiplicity .

  3. 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.

  4. 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]

  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. Word2vec - Wikipedia

    en.wikipedia.org/wiki/Word2vec

    The reasons for successful word embedding learning in the word2vec framework are poorly understood. Goldberg and Levy point out that the word2vec objective function causes words that occur in similar contexts to have similar embeddings (as measured by cosine similarity ) and note that this is in line with J. R. Firth's distributional hypothesis .

  7. BERT (language model) - Wikipedia

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

    Unlike previous models, BERT is a deeply bidirectional, unsupervised language representation, pre-trained using only a plain text corpus. Context-free models such as word2vec or GloVe generate a single word embedding representation for each word in the vocabulary, whereas BERT takes into account the context for each occurrence of a given word ...

  8. Enterprise information security architecture - Wikipedia

    en.wikipedia.org/wiki/Enterprise_information...

    Huxham Security Framework. Whilst security architecture frameworks are often custom designed in enterprise organisations, several models are commonly used and adapted to the individual requirements of the organisation Commonly used frameworks include: SABSA framework and methodology; The U.S. Department of Defense (DoD) Architecture Framework ...

  9. fastText - Wikipedia

    en.wikipedia.org/wiki/FastText

    fastText is a library for learning of word embeddings and text classification created by Facebook ... Facebook makes available pretrained models for 294 languages. ...