Grosjean, J., & Vamvas, J. Fine-tuning the SwissBERT Encoder Model for Embedding Sentences and Documents.
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Abstract
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Multilingual experiments on document retrieval and text classification in a Switzerland-specific setting show that SentenceSwissBERT surpasses the accuracy of the original SwissBERT model and of a comparable baseline. Encoder models trained for the embedding of sentences or short documents have proven useful for tasks such as semantic search and topic modeling. In this paper, we present a version of the SwissBERT encoder model that we specifically fine-tuned for this purpose. SwissBERT contains language adapters for the four national languages of Switzerland -- German, French, Italian, and Romansh -- and has been pre-trained on a large number of news articles in those languages. Using contrastive learning based on a subset of these articles, we trained a fine-tuned version, which we call SentenceSwissBERT. Multilingual experiments on document retrieval and text classification in a Switzerland-specific setting show that SentenceSwissBERT surpasses the accuracy of the original SwissBERT model and of a comparable baseline. The model is openly available for research use.
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[Online; accessed 25. May 2024]
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