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--- |
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language: |
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- hu |
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tags: |
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- text-classification |
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license: mit |
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metrics: |
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- accuracy |
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widget: |
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- text: Jó reggelt! majd küldöm az élményhozókat :). |
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--- |
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# Hungarian Sentence-level Sentiment Analysis Model with XLM-RoBERTa |
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For further models, scripts and details, see [our repository](https://github.com/nytud/sentiment-analysis) or [our demo site](https://juniper.nytud.hu/demo/nlp). |
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- Pretrained model used: XLM-RoBERTa base |
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- Finetuned on Hungarian Twitter Sentiment (HTS) Corpus |
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- Labels: 0 (negative), 1 (positive) |
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## Limitations |
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- max_seq_length = 128 |
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## Results |
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| Model | HTS2 | HTS5 | |
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| ------------- | ------------- | ------------- | |
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| huBERT | 85.56 | 68.99 | |
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| XLM-RoBERTa| **85.56** | 66.50 | |
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## Citation |
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If you use this model, please cite the following paper: |
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``` |
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@article {laki-yang-sentiment, |
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author = {Laki, László János and Yang, Zijian Győző}, |
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title = {Sentiment Analysis with Neural Models for Hungarian}, |
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journal = {Acta Polytechnica Hungarica}, |
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year = {2023}, |
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publisher = {Obuda University}, |
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volume = {20}, |
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number = {5}, |
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doi = {10.12700/APH.20.5.2023.5.8}, |
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pages= {109--128}, |
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url = {https://acta.uni-obuda.hu/Laki_Yang_134.pdf} |
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} |
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``` |
