Model fusion method and system, electronic equipment and medium

A technology of model fusion and training model, applied in the field of knowledge graph, can solve problems such as reducing pertinence, and achieve the effect of improving accuracy, increasing pertinence, and the purpose and advantages are concise and easy to understand

Pending Publication Date: 2022-02-08
BEIJING MININGLAMP SOFTWARE SYST CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The embodiment of the present application provides a model fusion method, system, electronic equipment and medium, so as to at least solve the problem of ignoring task characteristics when targeting specific tasks in the process of model fusion through the present invention, resulting in reduced pertinence to specific tasks in the process of model fusion And other issues

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  • Model fusion method and system, electronic equipment and medium
  • Model fusion method and system, electronic equipment and medium
  • Model fusion method and system, electronic equipment and medium

Examples

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Embodiment 1

[0055] This embodiment provides a model fusion method. Please refer to Figure 1 to Figure 2 , figure 1 is a flowchart of a model fusion method according to an embodiment of the present application; figure 2 is a flow chart of model fusion according to the embodiment of the present application, such as Figure 1 to Figure 2 As shown, the model fusion method includes the following steps:

[0056] Step S1 of obtaining the labeling result set: performing multiple rounds of training on the model to obtain the probability vector of the training model, processing the probability vector to obtain the average probability vector, and performing entity labeling on the average probability vector to obtain the second entity labeling result set;

[0057] Entity judgment step S2: After distinguishing the new entity from the old entity according to the first entity labeling result set, select the new entity labeling result set and the old entity labeling result set corresponding to the n...

Embodiment 2

[0074] Example 2 please refer to image 3 , image 3 It is a schematic structural diagram of the model fusion system of the present invention. Such as image 3 As shown, the invented model fusion system is applicable to the above-mentioned model fusion method, and the model fusion system includes:

[0075] Annotation result set acquisition unit 51: perform multiple rounds of training on the model to obtain a probability vector of the training model, process the probability vector to obtain an average probability vector, and perform entity labeling on the average probability vector to obtain a second entity labeling result set;

[0076] Entity judging unit 52: After distinguishing the new entity and the old entity according to the first entity labeling result set, select a new entity labeling result set and an old entity labeling result set corresponding to the new entity and the old entity from the second entity labeling result set;

[0077] Credibility preset unit 53: pres...

Embodiment 3

[0080] combine Figure 4 As shown, this embodiment discloses a specific implementation manner of an electronic device. The electronic device may include a processor 81 and a memory 82 storing computer program instructions.

[0081] Specifically, the processor 81 may include a central processing unit (CPU), or an Application Specific Integrated Circuit (ASIC for short), or may be configured to implement one or more integrated circuits in the embodiments of the present application.

[0082]Among them, the memory 82 may include mass storage for data or instructions. For example without limitation, the memory 82 may include a hard disk drive (Hard Disk Drive, referred to as HDD), a floppy disk drive, a solid state drive (SolidState Drive, referred to as SSD), flash memory, optical disk, magneto-optical disk, magnetic tape or universal serial bus (Universal Serial Bus, referred to as USB) drive or a combination of two or more of the above. Storage 82 may comprise removable or no...

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Abstract

The invention discloses a model fusion method and system, electronic equipment and a medium, and the method comprises the steps: carrying out the multi-round training of a model, obtaining a probability vector of a training model, carrying out the processing of the probability vector, obtaining an average probability vector, and carrying out the entity labeling of the average probability vector to obtain a second entity labeling result set; after new entities and old entities are distinguished according to the first entity labeling result set, selecting a new entity labeling result set and an old entity labeling result set corresponding to the new entities and the old entities from the second entity labeling result set; after a first credibility threshold value and a second credibility threshold value are preset, selecting a third entity labeling result set from the second entity labeling result set according to the first credibility threshold value, and selecting a fourth entity labeling result set from the second entity labeling result set according to the second credibility threshold value; and selecting the third entity labeling result set and the fourth entity labeling result set according to the total number of the training models to obtain a final entity labeling result.

Description

technical field [0001] The present application relates to the technical field of knowledge graphs, in particular to a model fusion method, system, electronic equipment and media. Background technique [0002] In the field of machine learning, the same problem can usually be solved using multiple models with different parameters and different structures, and a method is needed to integrate different models into a robust model. It is also necessary to ensure that the integrated model outperforms the underlying sub-models. The current model fusion methods include Bagging (bootstrap aggregating), Boosting, and Stacking. Bagging is a method of predicting votes using N models for classification problems, and a method of predicting the average of N models for regression problems; Boosting is to assign equal weights to each training example at the beginning of training, and then use this algorithm to train the training set for t rounds , after each training, assign a larger weight...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F40/295G06F16/36
CPCG06F40/295G06F16/367
Inventor 刘伟硕吴信东
Owner BEIJING MININGLAMP SOFTWARE SYST CO LTD
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