Model training method and device, equipment and storage medium

A training model and training algorithm technology, applied in the field of training models, can solve the problems of consuming human resources, decreasing training efficiency, increasing artificial adjustment time, etc., and achieving the effect of efficient training files

Pending Publication Date: 2022-05-13
ONECONNECT TECH SERVICES CO LTD SHENZHEN
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] In the existing technology, the process of training the classification algorithm model needs to manually sort out the training data, control the execution order of the training tasks, allocate the resource information used for training, and deploy the trained model in the specified scene. Excessive human intervention consumes A large number of human resources, at the same time, as the data of the classification algorithm model that needs to be trained increases, the time spent on artificial adjustment also increases, which leads to a decrease in training efficiency

Method used

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  • Model training method and device, equipment and storage medium
  • Model training method and device, equipment and storage medium
  • Model training method and device, equipment and storage medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0052] Embodiments of the present invention provide a method for training models, such as figure 1 As shown, the method specifically includes the following steps:

[0053]Step S101: When a model training instruction is received, attribute information is parsed from the model training instruction, and an algorithm to be trained corresponding to the attribute information is acquired from a preset model library.

[0054] Among them, the attribute information is set in advance according to the business requirements. The attribute information includes: algorithm type, the industry of the model use object, the business scenario of the industry, the algorithm parameters used to generate the algorithm to be trained, the total number of training times, and the The preset weight value of the importance of the training task, and the interface path of the object used by the model.

[0055] Specifically, step S101 includes:

[0056] Step A1: Parsing out the algorithm type from the attrib...

Embodiment 2

[0103] Embodiments of the present invention provide a device for training models, such as figure 2 As shown, the device specifically includes the following components:

[0104] The receiving module 201 is configured to parse out attribute information from the model training instruction when a model training instruction is received, and acquire an algorithm to be trained corresponding to the attribute information from a preset model library;

[0105] The upload module 202 is configured to obtain resource information corresponding to the attribute information from a preset training configuration table, and obtain a training file for training the algorithm to be trained through a preset upload interface;

[0106] A training module 203, configured to use the resource information to train the algorithm to be trained through the training file to obtain a training result file;

[0107] An instance module 204, configured to add the training result file to a preset model loading comp...

Embodiment 3

[0132] This embodiment also provides a computer device, such as a smart phone, a tablet computer, a notebook computer, a desktop computer, a rack server, a blade server, a tower server, or a cabinet server (including an independent server, or A server cluster composed of multiple servers), etc. Such as image 3 As shown, the computer device 30 in this embodiment at least includes but is not limited to: a memory 301 and a processor 302 that can be communicated with each other through a system bus. It should be pointed out that, image 3 Only computer device 30 is shown having components 301-302, but it should be understood that implementing all of the illustrated components is not a requirement and that more or fewer components may instead be implemented.

[0133] In this embodiment, the memory 301 (that is, a readable storage medium) includes a flash memory, a hard disk, a multimedia card, a card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), s...

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PUM

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Abstract

The invention discloses a model training method and device, equipment and a storage medium, and the method comprises the steps: analyzing attribute information from a model training instruction when the model training instruction is received, and obtaining a to-be-trained algorithm corresponding to the attribute information from a preset model library; obtaining resource information corresponding to the attribute information from a preset training configuration table, and obtaining a training file for training the to-be-trained algorithm through a preset uploading interface; training the to-be-trained algorithm through the training file by utilizing the resource information so as to obtain a training result file; adding the training result file into a preset model loading component to instantiate a target model; modifying a configuration file in the model loading component so as to deploy the target model into a model use object; according to the invention, the model can be automatically and efficiently trained.

Description

technical field [0001] The present invention relates to the technical field of model building, in particular to a method, device, device and storage medium for training a model. Background technique [0002] With the development of the field of artificial intelligence, the classification algorithm model has been widely used in the field of human-computer dialogue. For different business scenarios in the field of human-computer dialogue, it is necessary to use the classification algorithm model corresponding to the business scenario in each business scenario. Therefore, it is necessary to train the classification algorithm model according to the requirements of the target business scenario to generate a target model applied to the target business scenario. [0003] In the existing technology, the process of training the classification algorithm model needs to manually sort out the training data, control the execution sequence of the training tasks, allocate the resource infor...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06F9/48G06F9/50
CPCG06F9/5005G06F9/4881G06F18/214
Inventor 张璐
Owner ONECONNECT TECH SERVICES CO LTD SHENZHEN
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