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GPT-2 can generate thematically-appropriate text for a range of scenarios, even surreal ones like a CNN article about Donald Trump giving a speech praising the anime character Asuka Langley Soryu. Here, the tendency to generate nonsensical and repetitive text with increasing output length (even in the full 1.5B model) can be seen; in the second ...
IWE combines Word2vec with a semantic dictionary mapping technique to tackle the major challenges of information extraction from clinical texts, which include ambiguity of free text narrative style, lexical variations, use of ungrammatical and telegraphic phases, arbitrary ordering of words, and frequent appearance of abbreviations and acronyms ...
Their purpose is often satirical, intending to show that there is little difference between the generated text and real examples. Many work by using techniques such as Markov chains to reprocess real text examples; alternatively, they may be hand-coded. Generated texts can vary from essay length to paragraphs and tweets. (The term "quote ...
The same software used to clone voices has been used on famous musicians' voices to create songs that mimic their voices, gaining both tremendous popularity and criticism. [136] [137] [138] Similar techniques have also been used to create improved quality or full-length versions of songs that have been leaked or have yet to be released. [139]
It has a context length of 128k tokens [15] with an output token limit capped to 4,096, [16] and after a later update (gpt-4o-2024-08-06) to 16,384. [ 17 ] As of May 2024, it is the leading model in the LMSYS Elo Arena Benchmarks by the University of California, Berkeley .
GPT-J was designed to generate English text from a prompt. It was not designed for translating or generating text in other languages or for performance without first fine-tuning the model for a specific task. [2] Nonetheless, GPT-J performs reasonably well even without fine-tuning, even in translation (at least from English to French). [8]
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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 ...
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