- 专利标题: Synthesizing hard-negative text training data
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申请号: US17127918申请日: 2020-12-18
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公开(公告)号: US11948382B2公开(公告)日: 2024-04-02
- 发明人: Ophir Azulai , Udi Barzelay
- 申请人: International Business Machines Corporation
- 申请人地址: US NY Armonk
- 专利权人: International Business Machines Corporation
- 当前专利权人: International Business Machines Corporation
- 当前专利权人地址: US NY Armonk
- 代理商 Kristofer Haggerty
- 主分类号: G06V30/413
- IPC分类号: G06V30/413 ; G06F18/21 ; G06F18/211 ; G06F18/241 ; G06F40/166 ; G06N20/00 ; G06V30/19
摘要:
A method for synthesizing negative training data associated with training models to detect text within documents and images. The method includes one or more computer processors receiving a set of dictates associated with generating one or more negative training datasets for training a set of models to classify a plurality of features found within a data source. The method further includes identifying a set of rules related to generating negative training data to detect text based on the received set of dictates. The method further includes compiling one or more arrays of elements of hard-negative training data into a negative training data dataset based on the identified set of rules and one or more dictates. The method further includes determining metadata corresponding an array of elements of hard-negative training data.
公开/授权文献
- US20220198186A1 SYNTHESIZING HARD-NEGATIVE TEXT TRAINING DATA 公开/授权日:2022-06-23
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