

# POS tagger based on SlovakBERT

This is a POS tagger based on [SlovakBERT](https://huggingface.co/gerulata/slovakbert). The model uses [Universal POS tagset (UPOS)](https://universaldependencies.org/u/pos/). The model was fine-tuned using Slovak part of [Universal Dependencies dataset](https://universaldependencies.org/) [Zeman 2017] containing 10k manually annotated Slovak sentences.

## Results

The model was evaluated in [our paper](https://arxiv.org/abs/2109.15254) [Pikuliak et al 2021, Section 4.2]. It achieves \\(97.84\%\\) accuracy.

## 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": ["pos"], "datasets": ["universal_dependencies"], "metrics": ["accuracy"], "widget": [{"text": "Kde t\u00e1 \u013eudsk\u00e1 du\u0161a drieme?"}]}
transformers pytorch roberta token-classification pos sk dataset:universal_dependencies arxiv:2109.15254 license:cc autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #token-classification #pos #sk #dataset-universal_dependencies #arxiv-2109.15254 #license-cc #autotrain_compatible #endpoints_compatible #region-us 



# POS tagger based on SlovakBERT

This is a POS tagger based on SlovakBERT. The model uses Universal POS tagset (UPOS). The model was fine-tuned using Slovak part of Universal Dependencies dataset [Zeman 2017] containing 10k manually annotated Slovak sentences.

## Results

The model was evaluated in our paper [Pikuliak et al 2021, Section 4.2]. It achieves \\(97.84\%\\) accuracy.

## Cite


# POS tagger based on SlovakBERT

This is a POS tagger based on SlovakBERT. The model uses Universal POS tagset (UPOS). The model was fine-tuned using Slovak part of Universal Dependencies dataset [Zeman 2017] containing 10k manually annotated Slovak sentences. ## Results

The model was evaluated in our paper [Pikuliak et al 2021, Section 4.2]. It achieves \\(97.84\%\\) accuracy. ## Cite
TAGS
#transformers #pytorch #roberta #token-classification #pos #sk #dataset-universal_dependencies #arxiv-2109.15254 #license-cc #autotrain_compatible #endpoints_compatible #region-us 
 # POS tagger based on SlovakBERT

This is a POS tagger based on SlovakBERT. The model uses Universal POS tagset (UPOS). The model was fine-tuned using Slovak part of Universal Dependencies dataset [Zeman 2017] containing 10k manually annotated Slovak sentences. ## Results

The model was evaluated in our paper [Pikuliak et al 2021, Section 4.2]. It achieves \\(97.84\%\\) accuracy. ## Cite
passage: TAGS
#transformers #pytorch #roberta #token-classification #pos #sk #dataset-universal_dependencies #arxiv-2109.15254 #license-cc #autotrain_compatible #endpoints_compatible #region-us 
# POS tagger based on SlovakBERT

This is a POS tagger based on SlovakBERT. The model uses Universal POS tagset (UPOS). The model was fine-tuned using Slovak part of Universal Dependencies dataset [Zeman 2017] containing 10k manually annotated Slovak sentences.## Results

The model was evaluated in our paper [Pikuliak et al 2021, Section 4.2]. It achieves \\(97.84\%\\) accuracy.## Cite