Hungarian Sentence-level Sentiment Analysis Model with XLM-RoBERTa

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Pretrained model used: XLM-RoBERTa base
Finetuned on Hungarian Twitter Sentiment (HTS) Corpus
Labels: 0 (very negative), 1 (negative), 2 (neutral), 3 (positive), 4 (very positive)

Limitations

max_seq_length = 128

Results

Model	HTS2	HTS5
huBERT	85.56	68.99
XLM-RoBERTa	85.56	66.50

Citation

If you use this model, please cite the following paper:

@article {laki-yang-sentiment,
      author = {Laki, László János and Yang, Zijian Győző},
      title = {Sentiment Analysis with Neural Models for Hungarian},
      journal = {Acta Polytechnica Hungarica},
      year = {2023},
      publisher = {Obuda University},
      volume = {20},
      number = {5},
      doi = {10.12700/APH.20.5.2023.5.8},
      pages=      {109--128},
      url = {https://acta.uni-obuda.hu/Laki_Yang_134.pdf}
}