---
language:
- sk
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- sts
license: cc-by-4.0
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.
base_model:
- gerulata/slovakbert
library_name: sentence-transformers
---


# 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](https://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

```
@inproceedings{pikuliak-etal-2022-slovakbert,
    title = "{S}lovak{BERT}: {S}lovak Masked Language Model",
    author = "Pikuliak, Mat{\'u}{\v{s}}  and
      Grivalsk{\'y}, {\v{S}}tefan  and
      Kon{\^o}pka, Martin  and
      Bl{\v{s}}t{\'a}k, Miroslav  and
      Tamajka, Martin  and
      Bachrat{\'y}, Viktor  and
      Simko, Marian  and
      Bal{\'a}{\v{z}}ik, Pavol  and
      Trnka, Michal  and
      Uhl{\'a}rik, Filip",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.findings-emnlp.530",
    pages = "7156--7168",
    abstract = "We introduce a new Slovak masked language model called \textit{SlovakBERT}. This is to our best knowledge the first paper discussing Slovak transformers-based language models. We evaluate our model on several NLP tasks and achieve state-of-the-art results. This evaluation is likewise the first attempt to establish a benchmark for Slovak language models. We publish the masked language model, as well as the fine-tuned models for part-of-speech tagging, sentiment analysis and semantic textual similarity.",
}
```