Method for training text retrieval model, method for retrieving text, and related apparatuses

An apparatus for training a text retrieval model, the apparatus comprising:

• at least one processor;
• and
• a memory, in communication with the at least one processor,
• wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor, to enable the at least one processor to perform operations comprising:
• compressing each pair of a query term sample and a candidate text sample into one character string sample;
• processing the character string sample using a feature extraction sub-model in a text retrieval model to obtain hidden-layer features of tokens in the character string sample;
• calculating feature weights of the hidden-layer features of the tokens using a weight determination sub-model in the text retrieval model;
• calculating, based on the feature weights and the hidden-layer features of the corresponding tokens and using a similarity calculation sub-model in the text retrieval model, to obtain a correlation score between the query term sample and the candidate text sample;
• and
• training, based on the correlation score, the text retrieval model by means of contrastive learning.

A non-transitory computer readable storage medium, storing a computer instruction, wherein the computer instruction is used to cause a computer to perform operations comprising:

• compressing each pair of a query term sample and a candidate text sample into one character string sample;
• processing the character string sample using a feature extraction sub-model in a text retrieval model to obtain hidden-layer features of tokens in the character string sample;
• calculating feature weights of the hidden-layer features of the tokens using a weight determination sub-model in the text retrieval model;
• calculating, based on the feature weights and the hidden-layer features of the corresponding tokens and using a similarity calculation sub-model in the text retrieval model, to obtain a correlation score between the query term sample and the candidate text sample;
• and
• training, based on the correlation score, the text retrieval model by means of contrastive learning.