Method and device for training model, equipment, medium and program product

A training model and model technology, applied in the field of deep learning, can solve problems such as complex models, and achieve the effect of improving generalization ability and robustness

Active Publication Date: 2022-02-25
BEIJING BAIDU NETCOM SCI & TECH CO LTD
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, as the model becomes more and more complex, the problems of mode

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  • Method and device for training model, equipment, medium and program product
  • Method and device for training model, equipment, medium and program product
  • Method and device for training model, equipment, medium and program product

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[0024] The exemplary embodiments of the present disclosure will be described below, including various details of the embodiments of the present disclosure to facilitate understanding, and they should be considered simply exemplary. Accordingly, it will be appreciated by those skilled in the art that various changes and modifications can be made without departing from the scope and spirit of the disclosure. Also, for the sake of clarity and concise, the following description is omitted in the following description.

[0025] As mentioned above, the problem of model training has been equipped with poor robustness begins. The disturbance-based traditional model training method mainly includes the following: 1) Word replacement, randomly replace a part of the words in parallel corners to any word in the word table; 2) Word discard, randomly use all true word vector rather than The true word vector is carried out; 3) Virtual confrontation training, through regular item, make the model t...

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Abstract

The invention provides a method and device for training a model, equipment, a medium and a program product, and relates to the field of deep learning. According to the specific implementation scheme, the method comprises the steps of generating first disturbance data, wherein the first disturbance data is used for disturbing a first word feature representation set associated with a training text of a model; generating first mask data and first complementary mask data, wherein the first mask data are used for masking a first part of data in the first disturbance data, and the first complementary mask data are used for masking data except the first part of data in the first disturbance data; generating first masking disturbance data based on the first mask data and the first disturbance data; generating second masking disturbance data based on the first complementary mask data and the first disturbance data; and generating a second word feature representation set for training the model based on the first masking perturbation data, the second masking perturbation data and the first word feature representation set. Therefore, the generalization ability and robustness of the model can be improved.

Description

technical field [0001] The present disclosure relates to the field of deep learning, and in particular to a method, device, electronic device, storage medium and computer program product for training a model for natural language processing. Background technique [0002] Models for natural language processing, such as text classification models, translation models, etc., are commonly used services on the Internet. In recent years, especially after the use of neural networks, models for natural language processing have made significant progress in the above tasks. However, as the model becomes more and more complex, the problems of model training overfitting and poor robustness have gradually become prominent. Contents of the invention [0003] The present disclosure provides a method, apparatus, electronic device, storage medium and computer program product for training a model for natural language processing. [0004] According to a first aspect of the present disclosure...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06F40/284G06F16/35G06F40/40G06N3/08
CPCG06F40/284G06F16/35G06F40/40G06N3/08G06F18/214
Inventor 高鹏至何中军吴华王海峰
Owner BEIJING BAIDU NETCOM SCI & TECH CO LTD
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