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An extension of word vectors for creating a dense vector representation of unstructured radiology reports has been proposed by Banerjee et al. [23] One of the biggest challenges with Word2vec is how to handle unknown or out-of-vocabulary (OOV) words and morphologically similar words. If the Word2vec model has not encountered a particular word ...
Similarly, an image model prompted with the text "a photo of a CEO" might disproportionately generate images of white male CEOs, [128] if trained on a racially biased data set. A number of methods for mitigating bias have been attempted, such as altering input prompts [ 129 ] and reweighting training data.
Text Python Any 2013 1.0.1 (2021) Unlicense (PD) perldoc: Larry Wall: Text Perl Any 1994 5.16.3 Artistic, GPL phpDocumentor: Joshua Eichorn Text PHP Any 2000 3.0.0 LGPL for 1.x, MIT for 2+ pydoc: Ka-Ping Yee [1] Text Python Any 2000 in Python core Python: RDoc: Dave Thomas Text C, C++, Ruby Any 2001/12/14 in Ruby core Ruby: ROBODoc: Frans ...
Flex (fast lexical analyzer generator) is a free and open-source software alternative to lex. [2] It is a computer program that generates lexical analyzers (also known as "scanners" or "lexers").
An image conditioned on the prompt an astronaut riding a horse, by Hiroshige, generated by Stable Diffusion 3.5, a large-scale text-to-image model first released in 2022. A text-to-image model is a machine learning model which takes an input natural language description and produces an image matching that description.
Abstractive summarization methods generate new text that did not exist in the original text. [12] This has been applied mainly for text. Abstractive methods build an internal semantic representation of the original content (often called a language model), and then use this representation to create a summary that is closer to what a human might express.
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