Model training method, customer service system, data labeling system and readable storage medium

Abstract

The invention provides a model training method which comprises the following steps: taking the same annotated data set as a training data set of different classification models, and training the different classification models; Based on the trained classification model, predicting prediction samples in the unlabeled data set to obtain a prediction result; obtaining Confidence of a prediction result, and marking the prediction sample as a high-confidence prediction sample or a low-confidence prediction sample; Adding prediction samples marked as high-confidence prediction samples in each classification model to training data sets of other classification models; And based on the new training data set, performing preset rounds of iterative training, and obtaining a classification model aftermultiple times of iterative training. The invention further provides a customer service system, a data labeling system and a readable storage medium. The technical problem that the response quality ofa customer service system is poor due to the fact that the obtaining cost of the mark data needed by an existing classification model is high and the quality of the classification model is low is solved.

Description

technical field [0001] The invention relates to the field of data processing, in particular to a model training method, a customer service system, a data labeling system, and a readable storage medium. Background technique [0002] In the existing customer service system, many modules need to use different classification models. The modules that need to use the classification model include: question classification, question ambiguity judgment, sentiment analysis, etc. There are many commonly used classification models, from logistic regression to SVM (Support Vector Machine, support vector machine), XGBoost (eXtreme Gradient Boosting, extreme gradient boosting), FastText (shallow network), to LSTM in deep learning ( Long Short-Term Memory, that is, long-term short-term memory network), CNN (Convolutional Neural Network, that is, convolutional neural network), RNN ((Recurrent Neural Network, that is, cyclic neural network) are widely used in various classification tasks. ...

AI Technical Summary

Problems solved by technology

[0006] The main purpose of the present invention is to provide a model training method, a customer service system, a data labeling system, and a readable storage medium, aiming to solve the problems of high acquisition cost of label data required by existing classification models and low quality of classification models, which in turn lead to problems in customer service. Technical issues with poor system response

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