Data labeling method and device and data processing equipment

Abstract

The invention provides a data labeling method and device and data processing equipment, and the method comprises the steps: carrying out the at least one iteration processing of a classification model, so as to enable the accuracy of the classification model to meet a preset condition; and processing at least one part of the plurality of pieces of to-be-labeled data by utilizing the obtained classification model to obtain an automatic labeling result, wherein each iterative processing comprises the following steps of: respectively inputting other to-be-labeled data, except a target data set, in a plurality of pieces of to-be-labeled data into the classification model to obtain a classification result; selecting at least part of the to-be-labeled data of which the confidence coefficient ofthe classification result is within a preset range from the other to-be-labeled data, and adding the selected to-be-labeled data into a target data set; and training a classification module accordingto the manual labeling result of the to-be-annotated data in the target data set. Therefore, automatic labeling of batch data can be realized under the condition of improving the data labeling quality.

Description

technical field [0001] The present application relates to the technical field of data processing, and in particular, to a data labeling method, device, and data processing equipment. Background technique [0002] With the development of computer technology, the application of machine learning algorithms has become more and more extensive, and supervised learning algorithms are one of the commonly used algorithms. Supervised learning algorithms usually need to use a large number of labeled data to train the pre-established recognition model. The quantity and accuracy of the labeled data directly affect the accuracy of the trained recognition model. [0003] At present, the labeled data is mainly obtained by manually adding labels, which is inefficient and error-prone, resulting in low accuracy of the final trained model. Contents of the invention [0004] In view of this, the purpose of the embodiments of the present application is to provide a data labeling method, device...

AI Technical Summary

Problems solved by technology

[0003] At present, labeling data is mainly obtained by manually adding labels, which is inefficient and error-prone, resulting in low accuracy of the final trained model

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