Model training method and device, short message auditing method and device, equipment and storage medium

Abstract

The invention discloses a model training method, a short message auditing method and device, equipment and a storage medium, and relates to the field of artificial intelligence. According to the specific implementation scheme, model training is as follows: performing sample reduction on a first unlabeled sample to obtain a second unlabeled sample; inputting the second unlabeled sample into a machine learning model for prediction to obtain a probability corresponding to a prediction result of the second unlabeled sample; selecting a third unlabeled sample from the second unlabeled samples according to the probability; and training the machine learning model by using the labeled third unlabeled sample. According to the embodiment of the application, redundant samples are removed through sample reduction, so that the selected samples have certain representativeness. And an active learning technology is used, and a machine learning model is used to further select a sample with the most annotation value for the current model and a large amount of information, so that the annotation cost is reduced.

Description

technical field [0001] The present application relates to the field of computer technology, in particular to the field of artificial intelligence. Background technique [0002] Model training requires a large amount of manually labeled data. And with the continuous development of the business, it is necessary to continuously supplement the latest annotation data, so that the model can be iteratively optimized with the business. However, in related technologies, the newly added labeled samples may not provide new information, which does not contribute significantly to the performance improvement of the model. Taking the SMS review business as an example, a large number of SMS logs are generated every day. If samples to be labeled are randomly selected from them, after a certain labeling cost is spent, it may not be significantly helpful to improve the performance of the model. Contents of the invention [0003] The present application provides a model training method, a s...

AI Technical Summary

Problems solved by technology

However, in related technologies, the newly added labeled samples may not provide new information, which does not significantly help the performance of the model.

Taking the SMS review business as an example, a large number of SMS logs are generated every day. If samples to be labeled are randomly selected from them, after a certain labeling cost is spent, it may not significantly help the performance of the model.

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