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Model training method and device

A technology for model training and training sets, applied in character and pattern recognition, instruments, electrical digital data processing, etc., can solve problems such as model bias, data imbalance, model deviation, etc., and achieve strong model recognition ability and good robustness Effect

Pending Publication Date: 2021-03-26
EMOTIBOT TECH LTD
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Problems solved by technology

Although the obtained trained model can alleviate the misidentification to a certain extent, due to the data imbalance problem caused by merging data, the model trained by this training method will have deviations, resulting in the final trained model. The model is seriously biased and cannot represent the situation of this model

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  • Model training method and device
  • Model training method and device

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Embodiment Construction

[0026] The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.

[0027] Like numbers and letters denote similar items in the following figures, so that once an item is defined in one figure, it does not require further definition and explanation in subsequent figures.

[0028] When a natural language processing system is actually deployed, it is very common for multiple models to be deployed in one system. For example, in the field of intelligent customer service, there are FAQ models and emotion-to-manual models. Among them, the FAQ model is used to answer business-related questions, and the emotion-to-manual model is used to deal with emotions and human-related issues. For another example, in the field of text analysis, customers need a natural language processing system to automatically extract data related to multiple businesses by analyzing email content (su...

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Abstract

The invention provides a model training method and device, and the method comprises the steps: carrying out the training through a current training set, and obtaining a current training model; predicting a non-target training set by using the current training model to obtain a current prediction result; and when the current prediction result meets an iteration stop condition, taking the current training model as an optimal training model of a target model. According to the technical scheme provided by the embodiment of the invention, the problem of interference among multiple models can be solved, and the trained model has robustness.

Description

technical field [0001] The present application relates to the technical field of natural language processing, in particular to a model training method and device. Background technique [0002] When natural language processing systems are actually deployed, they often face the problem of coexistence of multiple models, that is, multiple models are deployed in the same system at the same time. When the system is composed of multiple models, there will be problems of mutual interference between the multiple models. For example, the content originally belonging to the A model will be misidentified as the content of the B model, causing the B model to process the content of the A model, and the final processing result is wrong. [0003] In order to solve the interference problem between multiple models, the common practice is to aggregate the training data of the non-this model, and then train the model together with the training data of this model. Although the obtained traine...

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Application Information

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IPC IPC(8): G06K9/62G06F40/35
CPCG06F40/35G06F18/214
Inventor 简仁贤王海波马永宁
Owner EMOTIBOT TECH LTD
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