Hungarian Aspect-based Sentiment Analysis with finetuned huBERT model

For further models, scripts and details, see our repository or our demo site.

  • Pretrained model used: huBERT
  • Finetuned on OpinHuBank (OHB) Corpus
  • Labels: 0 (negative), 1 (neutral), 2 (positive)
  • Separator: [SEP]

Limitations

  • max_seq_length = 256

Results

Model OHB
huBERT 82.30
XLM-R 80.59

Usage with pipeline

from transformers import pipeline

classification = pipeline(task="sentiment-analysis", model="NYTK/sentiment-ohb3-hubert-hungarian")
input_text = "Kovácsné Nagy Erzsébet [SEP] A Kovácsné Nagy Erzsébet nagyon jól érzi magát a Nokiánál, azonban a Németországból érkezett Kovács Péter nehezen boldogul a beilleszkedéssel."

print(classification(input_text)[0])

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}
}
@inproceedings {yang-asent,
    title = {Neurális entitásorientált szentimentelemző alkalmazás magyar nyelvre},
    booktitle = {XIX. Magyar Számítógépes Nyelvészeti Konferencia (MSZNY 2023)},
    year = {2023},
    publisher = {Szegedi Tudományegyetem, Informatikai Intézet},
    address = {Szeged, Hungary},
    author = {Yang, Zijian Győző and Laki, László János},
    pages = {107--117}
}
Downloads last month
138
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.