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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 ... Facebook makes available pretrained models for 294 languages. ...

  3. Word2vec - Wikipedia

    en.wikipedia.org/wiki/Word2vec

    They developed a set of 8,869 semantic relations and 10,675 syntactic relations which they use as a benchmark to test the accuracy of a model. When assessing the quality of a vector model, a user may draw on this accuracy test which is implemented in word2vec, [ 28 ] or develop their own test set which is meaningful to the corpora which make up ...

  4. 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 ...

  5. Wechsler Test of Adult Reading - Wikipedia

    en.wikipedia.org/wiki/Wechsler_Test_of_Adult_Reading

    The Wechsler Test of Adult Reading (WTAR) is a neuropsychological assessment tool used to provide a measure of premorbid intelligence, the degree of Intellectual function prior to the onset of illness or disease. [1]

  6. 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]

  7. 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.

  8. 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.

  9. National Adult Reading Test - Wikipedia

    en.wikipedia.org/wiki/National_Adult_Reading_Test

    The test comprises 50 written words in British English which all have irregular spellings (e.g. "aisle"), so as to test the participant's vocabulary rather than their ability to apply regular pronunciation rules. The manual includes equations for converting NART scores to predicted IQ scores on the Wechsler Adult Intelligence Scale.