

# 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.",
}
```

{"language": ["sk"], "license": "cc", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "sts"], "datasets": ["glue"], "metrics": ["spearmanr"], "pipeline_tag": "sentence-similarity", "widget": [{"source_sentence": "Izrael uskuto\u010dnil leteck\u00e9 \u00fadery v bl\u00edzkosti Damasku.", "sentences": ["Izrael uskuto\u010dnil vzdu\u0161n\u00fd \u00fatok na S\u00fdriu.", "Pes le\u017e\u00ed na gau\u010di a m\u00e1 hlavu na bielom vank\u00fa\u0161i."]}]}
sentence-transformers pytorch roberta feature-extraction sentence-similarity sts sk dataset:glue arxiv:2109.15254 license:cc endpoints_compatible has_space region:us
TAGS
#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #sts #sk #dataset-glue #arxiv-2109.15254 #license-cc #endpoints_compatible #has_space #region-us 



# Sentence similarity model based on SlovakBERT

This is a sentence similarity model based on SlovakBERT. The model was fine-tuned using STSbenchmark [Cer et al 2017] translated to Slovak using M2M100. The model can be used as an universal sentence encoder for Slovak sentences.

## Results

The model was evaluated in our paper [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 installed:



Then you can use the model like this:




## Cite



# Sentence similarity model based on SlovakBERT

This is a sentence similarity model based on SlovakBERT. The model was fine-tuned using STSbenchmark [Cer et al 2017] translated to Slovak using M2M100. The model can be used as an universal sentence encoder for Slovak sentences. ## Results

The model was evaluated in our paper [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 installed:



Then you can use the model like this: ## Cite
TAGS
#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #sts #sk #dataset-glue #arxiv-2109.15254 #license-cc #endpoints_compatible #has_space #region-us 
 # Sentence similarity model based on SlovakBERT

This is a sentence similarity model based on SlovakBERT. The model was fine-tuned using STSbenchmark [Cer et al 2017] translated to Slovak using M2M100. The model can be used as an universal sentence encoder for Slovak sentences. ## Results

The model was evaluated in our paper [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 installed:



Then you can use the model like this: ## Cite
passage: TAGS
#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #sts #sk #dataset-glue #arxiv-2109.15254 #license-cc #endpoints_compatible #has_space #region-us 
# Sentence similarity model based on SlovakBERT

This is a sentence similarity model based on SlovakBERT. The model was fine-tuned using STSbenchmark [Cer et al 2017] translated to Slovak using M2M100. The model can be used as an universal sentence encoder for Slovak sentences.## Results

The model was evaluated in our paper [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 installed:



Then you can use the model like this:## Cite