Browsing by Subject "FinBERT"

This thesis studies the application of language models to improve search in an online shop specialising in wholesale builder–trade product and service sales. The first aim was to determine if a Finnish language model could capture the meaning behind the search query words to improve the match between the queries and the product descriptions. Secondly, it was investigated if it was possible to train the model to recognise what products the users wanted to find with the search terms they used. Finally, it was investigated if it was possible to use the model for search ranking. Three models were trained using FinBERT as a model checkpoint and domain-specific product and clickthrough data for fine-tuning the models. The first two models were trained to classify online store products into product categories. The task was completed with a 0.98 F measure score for the model with 55 target categories and a 0.81 F measure score for the model with 762 target categories. The third model was trained to evaluate the relevance probabilities of search query-product pairs. The model generally determined more products as relevant than the current search engine solution. The F measure score for the model was 0.90, and in qualitative evaluation, the predictions made by the model made semantically sense. The restrictions for the practical use of the third model for search ranking come from the prediction inference needing to be faster to make search ranking predictions for many products.