Fine-tuning method, device and application for classification model of knowledge representation decoupling

A fine-tuning method for classification model of knowledge representation decoupling, comprising the following steps:

• step 1:
• building a knowledge base for retrieval, where multiple instance phrases are stored in the form of key value pair, the key stores an embedding vector of the instance phrase, and the value stores a true value of label of the instance phrase;
• step 2, constructing a classification model that comprises a pre-trained language model and a prediction and classification module;
• step 3:
• using the pre-trained language model to extract a first embedding vector of the masked words in the input instance text, and using this first embedding vector as a first query vector, for each label class, querying multiple instance phrases closest to the first query vector from the knowledge base as a first neighboring instance phrase, using an aggregation result obtained by aggregating all the first neighboring instance phrases with the first query vector as an input data for the pre-trained language model;
• step 4:
• using the pre-trained language model to extract a second embedding vector of the masked words in the input data, and using the prediction and classification module to classify and predict the second embedding vector to obtain a classification and prediction probability, based on the classification and prediction probability and the true value of label of the masked words, calculating a classification loss;
• step 5, constructing a weight factor based on the true value of label of the masking word, and adjusting the classification loss based on the weight factor to make the classification loss more focused on misclassified instances;
• step 6, optimizing parameters of the classification model by using the adjusted classification loss to obtain a classification model after parameters optimization.