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BookCorpus was chosen as a training dataset partly because the long passages of continuous text helped the model learn to handle long-range information. [6] It contained over 7,000 unpublished fiction books from various genres.
Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] It was originally used as a form of semi-supervised learning, as the model is trained first on an unlabelled dataset (pretraining step) by learning to generate datapoints in the dataset, and then it is trained to classify a labelled dataset.
Artificial intelligence is a recurrent theme in science fiction, whether utopian, emphasising the potential benefits, or dystopian, emphasising the dangers.. The notion of machines with human-like intelligence dates back at least to Samuel Butler's 1872 novel Erewhon.
New series take viewers into the future, into space and into the heart of very human matters.
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 ...
American science fiction author and editor Lester del Rey wrote, "Even the devoted aficionado or fan—has a hard time trying to explain what science fiction is," and the lack of a "full satisfactory definition" is because "there are no easily delineated limits to science fiction." [3] Another definition comes from The Literature Book by DK and ...
The late 19th century witnessed a new generation of writers, such as J.-H. Rosny aîné, utilizing science and pseudoscience for purely fictional purposes. [15] This marked a significant departure from their predecessors, who employed the conjectural element as a pretext, following in the footsteps of Savinian Cyrano de Bergerac's utopian, Jonathan Swift's satires, and Camille Flammarion's ...
A language model is a probabilistic model of a natural language. [1] In 1980, the first significant statistical language model was proposed, and during the decade IBM performed ‘Shannon-style’ experiments, in which potential sources for language modeling improvement were identified by observing and analyzing the performance of human subjects in predicting or correcting text.