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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. Word2vec - Wikipedia

    en.wikipedia.org/wiki/Word2vec

    The use of different model parameters and different corpus sizes can greatly affect the quality of a word2vec model. Accuracy can be improved in a number of ways, including the choice of model architecture (CBOW or Skip-Gram), increasing the training data set, increasing the number of vector dimensions, and increasing the window size of words ...

  4. 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.

  5. 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. ...

  6. Latent space - Wikipedia

    en.wikipedia.org/wiki/Latent_space

    These models learn the embeddings by leveraging statistical techniques and machine learning algorithms. Here are some commonly used embedding models: Word2Vec: [4] Word2Vec is a popular embedding model used in natural language processing (NLP). It learns word embeddings by training a neural network on a large corpus of text.

  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. ELMo - Wikipedia

    en.wikipedia.org/wiki/ELMo

    ELMo (embeddings from language model) is a word embedding method for representing a sequence of words as a corresponding sequence of vectors. [1] It was created by researchers at the Allen Institute for Artificial Intelligence , [ 2 ] and University of Washington and first released in February, 2018.

  9. Salesforce Marketing Cloud - Wikipedia

    en.wikipedia.org/wiki/Salesforce_Marketing_Cloud

    Salesforce Marketing Cloud is a provider of digital marketing automation and analytics software and services. It was founded in 2000 under the name ExactTarget . The company filed for an IPO in 2007, but withdrew its filing two years later and raised $145 million in funding.