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UPDATING TRAINING EXAMPLES FOR ARTIFICIAL INTELLIGENCE
UPDATING TRAINING EXAMPLES FOR ARTIFICIAL INTELLIGENCE
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机译:更新人工智能的培训例
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摘要
Techniques for adapting previously-annotated training examples into updated training examples for training a machine learning model are disclosed. One example includes a computer program that recognizes a find expression, a replacement expression, and a filtering constraint in which the filtering constraint distinguishes a subset of previously-annotated training examples from others of the previously-annotated training examples. An instance of the find expression is identified by the computer program within the subset of the previously-annotated training examples that were identified among the previously-annotated training examples based on the filtering constraint. The instance of the find expression identified within the subset of the previously-annotated training examples is replaced by the computer program with an instance of the replacement expression to obtain the updated subset of training examples. The updated subset of training examples is output by the computer program, which may be used for training the machine learning model.
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