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<!-- Provide a quick summary of what the model is/does. -->
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The [SwissBERT](https://huggingface.co/ZurichNLP/swissbert) model finetuned via [SimCSE](http://dx.doi.org/10.18653/v1/2021.emnlp-main.552) (Gao et al., EMNLP 2021) for sentence embeddings, using ~1 million Swiss news articles published in 2022 from [Swissdox@LiRI](https://t.uzh.ch/1hI). Following the [Sentence Transformers](https://huggingface.co/sentence-transformers) approach (Reimers and Gurevych,
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2019), the average of the last hidden states (pooler_type=avg) is used as sentence representation.
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The fine-tuning script can be accessed [here](Link).
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The [SwissBERT](https://huggingface.co/ZurichNLP/swissbert) model was finetuned via [SimCSE](http://dx.doi.org/10.18653/v1/2021.emnlp-main.552) (Gao et al., EMNLP 2021) for sentence embeddings, using ~1 million Swiss news articles published in 2022 from [Swissdox@LiRI](https://t.uzh.ch/1hI). Following the [Sentence Transformers](https://huggingface.co/sentence-transformers) approach (Reimers and Gurevych,
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2019), the average of the last hidden states (pooler_type=avg) is used as sentence representation.
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The fine-tuning script can be accessed [here](Link).
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