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--- |
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language: |
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- sk |
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tags: |
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- twitter |
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- sentiment-analysis |
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license: cc |
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metrics: |
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- f1 |
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widget: |
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- text: "Najkrajšia vianočná reklama: Toto milé video vám vykúzli čarovnú atmosféru: Vianoce sa nezadržateľne blížia." |
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- text: "A opäť sa objavili nebezpečné výrobky. Pozrite sa, či ich nemáte doma" |
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--- |
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# Sentiment Analysis model based on SlovakBERT |
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This is a sentiment analysis classifier based on [SlovakBERT](https://huggingface.co/gerulata/slovakbert). The model can distinguish three level of sentiment: |
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- `-1` - Negative sentiment |
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- `0` - Neutral sentiment |
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- `1` - Positive setiment |
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The model was fine-tuned using Slovak part of [Multilingual Twitter Sentiment Analysis Dataset](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0155036) [Mozetič et al 2016] containing 50k manually annotated Slovak tweets. As such, it is fine-tuned for tweets and it is not advised to use the model for general-purpose sentiment analysis. |
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## Results |
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The model was evaluated in [our paper](https://arxiv.org/abs/2109.15254) [Pikuliak et al 2021, Section 4.4]. It achieves \\(0.67\\) F1-score on the original dataset and \\(0.58\\) F1-score on general reviews dataset. |
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## Cite |
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``` |
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@inproceedings{pikuliak-etal-2022-slovakbert, |
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title = "{S}lovak{BERT}: {S}lovak Masked Language Model", |
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author = "Pikuliak, Mat{\'u}{\v{s}} and |
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Grivalsk{\'y}, {\v{S}}tefan and |
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Kon{\^o}pka, Martin and |
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Bl{\v{s}}t{\'a}k, Miroslav and |
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Tamajka, Martin and |
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Bachrat{\'y}, Viktor and |
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Simko, Marian and |
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Bal{\'a}{\v{z}}ik, Pavol and |
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Trnka, Michal and |
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Uhl{\'a}rik, Filip", |
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booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022", |
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month = dec, |
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year = "2022", |
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address = "Abu Dhabi, United Arab Emirates", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2022.findings-emnlp.530", |
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pages = "7156--7168", |
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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.", |
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} |
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``` |
