Text classification model training method and device, equipment and storage medium

Abstract

The invention belongs to the technical field of artificial intelligence and provides a text classification model training method and device, equipment and a storage medium. The method comprises the steps that: a training sample set is acquired, wherein the training sample set comprises N labeled training samples and M unlabeled training samples, wherein each labeled training sample comprises textinformation and a category label of the text information, and each unlabeled training sample comprises text information, M and N are integers greater than 1; alternate iterative training is performedon an initial text classification model and an initial text enhancement model according to the training sample set and M enhanced training samples to obtain a target text classification model, whereinin the ith alternate iteration training process, the M enhanced training samples are generated by performing text enhancement processing on the M unlabeled training samples according to a text enhancement model obtained through the i-1th alternate iteration, wherein i is an integer greater than 1. According to the text classification model training method provided by the embodiment of the invention, the performance of the finally obtained text classification model is improved.

Description

technical field [0001] The present application belongs to the technical field of artificial intelligence, and in particular relates to a training method, device, equipment and storage medium of a text classification model. Background technique [0002] As an important task of natural language processing, text classification is being researched and applied more and more widely. In real-world scenarios, text classification based on the text classification model usually encounters the problem of a large amount of data and little labeling (low resources). Faced with a low-resource application scenario where labeled data is scarce, the semi-supervised training method can use a very small amount of labeled corpus and Large amounts of unlabeled data yield a high-performance text classification model. [0003] At present, the semi-supervised training method is usually carried out by Virtual Adversarial Training (VAT), which generalizes the model by introducing noise vectors (local ...

AI Technical Summary

Problems solved by technology

[0004] In view of this, the embodiment of the present application provides a text classification model training method, device, equipment and storage medium to solve the technical problem of the poor classification effect of the text classification model in the prior art

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