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  2. 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 ...

  3. Gensim - Wikipedia

    en.wikipedia.org/wiki/Gensim

    Gensim is an open-source library for unsupervised topic modeling, document indexing, retrieval by similarity, and other natural language processing functionalities, using modern statistical machine learning. Gensim is implemented in Python and Cython for performance. Gensim is designed to handle large text collections using data streaming and ...

  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. Generative artificial intelligence - Wikipedia

    en.wikipedia.org/wiki/Generative_artificial...

    Generative artificial intelligence (generative AI, GenAI, [1] or GAI) is a subset of artificial intelligence that uses generative models to produce text, images, videos, or other forms of data.

  6. Vector space model - Wikipedia

    en.wikipedia.org/wiki/Vector_space_model

    Candidate documents from the corpus can be retrieved and ranked using a variety of methods. Relevance rankings of documents in a keyword search can be calculated, using the assumptions of document similarities theory, by comparing the deviation of angles between each document vector and the original query vector where the query is represented as a vector with same dimension as the vectors that ...

  7. Latent semantic analysis - Wikipedia

    en.wikipedia.org/wiki/Latent_semantic_analysis

    The probabilistic model of LSA does not match observed data: LSA assumes that words and documents form a joint Gaussian model (ergodic hypothesis), while a Poisson distribution has been observed. Thus, a newer alternative is probabilistic latent semantic analysis, based on a multinomial model, which is reported to give better results than ...

  8. Ideogram (text-to-image model) - Wikipedia

    en.wikipedia.org/wiki/Ideogram_(text-to-image_model)

    Ideogram was founded in 2022 by Mohammad Norouzi, William Chan, Chitwan Saharia, and Jonathan Ho to develop a better text-to-image model. [3]It was first released with its 0.1 model on August 22, 2023, [4] after receiving $16.5 million in seed funding, which itself was led by Andreessen Horowitz and Index Ventures.

  9. Seq2seq - Wikipedia

    en.wikipedia.org/wiki/Seq2seq

    Shannon's diagram of a general communications system, showing the process by which a message sent becomes the message received (possibly corrupted by noise). seq2seq is an approach to machine translation (or more generally, sequence transduction) with roots in information theory, where communication is understood as an encode-transmit-decode process, and machine translation can be studied as a ...

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