MULTILINGUAL TEXT CLASSIFIER USING PRE-TRAINED UNIVERSAL SENTENCE ENCODER MODEL

Context. Online platforms and environments continue to generate ever-increasing content. The task of automating the moderation of user-generated content continues to be relevant. Of particular note are cases in which, for one reason or another, there is a very small amount of data to teach the classifier. To achieve results under such conditions, it is important to involve the classifier pre-trained models, which were trained on a large amount of data from a wide range. This paper deals with the use of the pre-trained multilingual Universal Sentence Encoder (USE) model as a component of the developed classifier and the affect of hyperparameters on the classification accuracy when learning on a small data amount (~ 0.05% of the dataset). Objective. The goal of this paper is the investigation of the pre-trained multilingual model and optimal hyperparameters influence for learning the text data classifier on the classification result. Method. To solve this problem, a relatively new approach to few-shot learning has recently been used – learning with a relatively small number of examples. Since text data is still the dominant way of transmitting information, the study of the possibilities of constructing a classifier of text data when learning from a small number of examples (~ 0.002–0.05% of the data set) is an actual problem. Results. It is shown that even with a small number of examples for learning (36 per class) due to the use of USE and optimal configuration in learning can achieve high accuracy of classification on English and Russian data, which is extremely important when it is impossible to collect your own large data set. The influence of the approach using USE and a set of different configurations of hyperparameters on the result of the text data classifier on the example of English and Russian data sets is evaluated. Conclusions. During the experiments, a significant degree of relevance of the correct selection of hyperparameters is shown. In particular, this paper considered the batch size, optimizer, number of learning epochs and the percentage of data from the set taken to train the classifier. In the process of experimentation, the optimal configuration of hyperparameters was selected, according to which 86.46% accuracy of classification on the Russian-language data set and 91.13% on the English-language data, respectively, can be achieved in ten seconds of training (training time can be significantly affected by technical means used).