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Updated: Jul 30, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
Published on: December 6, 2024
Comparison of pre-trained language models in terms of carbon emissions, time and accuracy in multi-label text
1Arçelik A.Ş. Karaağaç Caddesi 2-6, Sütlüce Beyoğlu 34445 Istanbul, Turkey.
Abstract:
Since Turkish is an agglutinative language and contains reduplication, idiom, and metaphor words, Turkish texts are sources of information with extremely rich meanings. For this reason, the processing and classification of Turkish texts according to their characteristics is both time-consuming and difficult. In this study, the performances of pre-trained language models for multi-text classification using Autotrain were compared in a 250 K Turkish dataset that we created. The results showed that the BERTurk (uncased, 128 k) language model on the dataset showed higher accuracy performance with a training time of 66 min compared to the other models and the CO2 emission was quite low. The ConvBERTurk mC4 (uncased) model is also the best-performing second language model. As a result of this study, we have provided a deeper understanding of the capabilities of pre-trained language models for Turkish on machine learning.
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