A classification result correction method, system, equipment and medium

Abstract

The invention discloses a method for correcting classification results, comprising the steps of: constructing a data set and marking each data in the data set with a classification label of a corresponding category; inputting each data in the data set into a trained model to obtain the corresponding The probability of the classification label and use the classification label probability corresponding to each data to calculate the correction matrix; expand the classification label of each category into multiple sublabels; adjust the output of the trained model to the probability of multiple sublabels corresponding to each category; Input the data to be classified into the trained model to obtain the probability of multiple sub-labels corresponding to each category; use the probability of multiple sub-labels corresponding to each category and the correction matrix to determine the final category of the data to be classified. The invention also discloses a system, computer equipment and readable storage medium. The scheme proposed by the present invention eliminates the bias caused by different frequencies of occurrence of labels by expanding the labels.

Description

technical field [0001] The invention relates to the field of classification, in particular to a classification result correction method, system, equipment and storage medium. Background technique [0002] The core capabilities of the massive model are zero-sample learning and small-sample learning. That is, when faced with different application tasks, there is no need to retrain the model. However, the huge amount of models will bring bias from the corpus during pre-training, resulting in low accuracy or unstable performance of downstream tasks. The current solution is to compensate the biased label words through text-free input, calibrate them to an unbiased state, and reduce the difference between different prompt choices. However, due to the difference in the frequency of tags appearing in the pre-training corpus, the model will have a preference for the prediction results, that is, the model output accuracy is low. Therefore, the existing correction methods can only c...

AI Technical Summary

Problems solved by technology

However, due to the difference in the frequency of labels in the pre-training corpus, the model will have a preference for the prediction results, that is, the model output accuracy is low

Therefore, the existing correction methods can only correct the bias of the model to the label, and cannot correct the bias caused by the input sample.

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