Method of retrieving data, method of training deep learning model, electronic device and storage medium

A method of training a deep learning model, wherein the deep learning model comprises a query processing sub-model and a text processing sub-model, and the method comprises:

• determining a positive example text of a sample query and a negative example text of the sample query;
• processing the sample query by using the query processing sub-model to obtain a semantic feature of the sample query;
• processing the positive example text and the negative example text by using the text processing sub-model to obtain a feature of the positive example text and a feature of the negative example text, respectively;
• determining a similarity between the sample query and the positive example text and a similarity between the sample query and the negative example text according to the semantic feature, the feature of the positive example text and the feature of the negative example text;
• determining a loss of the deep learning model according to the similarity between the sample query and the positive example text and the similarity between the sample query and the negative example text;
• and
• adjusting a parameter of the deep learning model according to the loss.

An electronic device, comprising:

• at least one processor;
• and
• a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions, when executed by the at least one processor, are configured to cause the at least one processor to at least:
• determine M candidate texts from a text library based on a semantic information in a query to be processed, wherein M is an integer greater than or equal to 1;
• determine N candidate texts from the text library based on a keyword information in the query to be processed, wherein N is an integer greater than or equal to 1;
• and
• determine at least one target text based on the M candidate texts and the N candidate texts.