| | --- |
| | language: |
| | - sk |
| | tags: |
| | - pos |
| | license: cc-by-4.0 |
| | datasets: |
| | - universal_dependencies |
| | metrics: |
| | - accuracy |
| | widget: |
| | - text: Kde tá ľudská duša drieme? |
| | --- |
| | |
| |
|
| | # 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.", |
| | } |
| | |
| | ``` |