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Generative AI systems such as MusicLM [56] and MusicGen [57] can also be trained on the audio waveforms of recorded music along with text annotations, in order to generate new musical samples based on text descriptions such as a calming violin melody backed by a distorted guitar riff.
Text annotations can serve a variety of functions for both private and public reading and communication practices. In their article "From the Margins to the Center: The Future of Annotation," scholars Joanna Wolfe and Christine Neuwirth identify four primary functions that text annotations commonly serve in the modern era, including: (1)"facilitat[ing] reading and later writing tasks," which ...
Prompt engineering is the process of structuring an instruction that can be interpreted and understood by a generative artificial intelligence (AI) model. [1] [2] A prompt is natural language text describing the task that an AI should perform. [3]
The basic data structures of Web Annotation (Fig. 1) are target (the element being annotated, e.g., a web document or a part of it), body (the content of the annotation, e.g., a string value), and; annotation (the element that serves to relate body and target of an annotation) Fig. 1. Basic view on the Web Annotation data model
The latest version of ACE is able to generate natural language sentences. [4] LOGON infrastructure is a collection of software and DELPH-IN grammars to provide transfer-based machine translation. The LOGON approach to machine translation has proven to provide quality oriented hybrid (rule-based and stochastic) translations. [5]
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 ...
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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.
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