Model training method and device, text classification method, electronic equipment and storage medium
A model training and text technology, applied in the field of image processing, can solve the problems of low accuracy and low classification efficiency, and achieve the effect of improving the recognition accuracy.
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Embodiment 1
[0037] The solutions provided in the embodiments of the present application can be applied to any system with text processing capabilities, such as a server system including a chip with text processing functions and related components, and the like. figure 1 A schematic diagram of a scenario of the model training scheme provided in the embodiment of the present application, figure 1 The shown scenario is only one of the scenarios to which the technical solution of the present application can be applied.
[0038] With the development of media technology, news also appears more and more in people's life and work, becoming an important part of it. Especially for enterprises, the rapid increase in the number of news and the increase in the speed of dissemination make news management one of the important tasks of enterprise risk control. For example, enterprise managers need to classify news that appears and disseminates on various media according to risk categories according to v...
Embodiment 2
[0055] figure 2 A flowchart of an embodiment of the model training method provided in this application, the execution body of the method may be various terminal or server devices with text classification capabilities, or may be devices or chips integrated on these devices. like figure 2 As shown, the model training method includes the following steps:
[0056] S201 , according to the labeled text with the label, obtain a relational graph with the labeled text as a central node.
[0057] In step S201, one or more text data classified by labels that have been labelled may be obtained, so that the batch of labelled texts is used as a training set. For example, in figure 1In the scenario shown in , in step S201, a labeled text N_labeled may be obtained from a labeling data source such as the Internet, and the labeled text may have a classification label determined for it through labeling processing. Therefore, this annotated text can serve as a central node in the relational...
Embodiment 3
[0073] image 3 It is a flowchart of another embodiment of the model training method provided in this application. The execution body of the method can be various terminal or server devices with text classification capabilities, or can be devices or chips integrated on these devices. like image 3 As shown, the model training method provided in the embodiment of the present application may include the following steps:
[0074] S301 , according to the marked text with the label, obtain a relational graph with the marked text as a central node.
[0075] In step S301, one or more text data classified by labels that have been labelled may be obtained, so that the batch of labelled texts is used as a training set. For example, in figure 1 In the scenario shown in , in step S301, a labeled text N_labeled may be obtained from a labeling data source such as the Internet, and the labeled text may have a classification label determined for it through labeling processing. Therefore, ...
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