--- language: - sk pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity license: cc datasets: - glue metrics: - spearmanr widget: source_sentence: "Izrael uskutočnil letecké údery v blízkosti Damasku." sentences: - "Izrael uskutočnil vzdušný útok na Sýriu." - "Pes leží na gauči a má hlavu na bielom vankúši." --- # Sentence similarity model based on SlovakBERT This is a sentence similarity model based on [SlovakBERT](https://huggingface.co/gerulata/slovakbert). The model was fine-tuned using [STSbenchmark](ixa2.si.ehu.eus/stswiki/index.php/STSbenchmark) [Cer et al 2017] translated to Slovak using [M2M100](https://huggingface.co/facebook/m2m100_1.2B). The model can be used as an universal sentence encoder for Slovak sentences. ## Results The model was evaluated in [our paper](https://arxiv.org/abs/2109.15254) [Pikuliak et al 2021, Section 4.3]. It achieves \\(0.791\\) Spearman correlation on STSbenchmark test set. ## Usage Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('kinit/slovakbert-sts-stsb') embeddings = model.encode(sentences) print(embeddings) ``` ## Cite ``` @article{DBLP:journals/corr/abs-2109-15254, author = {Mat{\'{u}}s Pikuliak and Stefan Grivalsky and Martin Konopka and Miroslav Blst{\'{a}}k and Martin Tamajka and Viktor Bachrat{\'{y}} and Mari{\'{a}}n Simko and Pavol Bal{\'{a}}zik and Michal Trnka and Filip Uhl{\'{a}}rik}, title = {SlovakBERT: Slovak Masked Language Model}, journal = {CoRR}, volume = {abs/2109.15254}, year = {2021}, url = {https://arxiv.org/abs/2109.15254}, eprinttype = {arXiv}, eprint = {2109.15254}, } ```