README.md

---
license: mit
language:
- hu
tags:
- text-classification
metrics:
- accuracy
widget:
- text: >-
    Kovácsné Nagy Erzsébet </s> A Kovácsné Nagy Erzsébet nagyon jól érzi magát a
    Nokiánál, azonban a Németországból érkezett Kovács Péter nehezen boldogul a
    beilleszkedéssel.
  example_title: positive
- text: >-
    Kovács Péter </s> A Kovácsné Nagy Erzsébet nagyon jól érzi magát a Nokiánál,
    azonban a Németországból érkezett Kovács Péter nehezen boldogul a
    beilleszkedéssel.
  example_title: negative
- text: >-
    Kovácsné Nagy Erzsébet </s> A Kovácsné Nagy Erzsébet azt mondta, hogy a
    Németországból érkezett Kovács Péter nehezen boldogul a beilleszkedéssel.
  example_title: neutral
---

# Hungarian Aspect-based Sentiment Analysis with finetuned XLM-RoBERTa model

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).

  - Pretrained model used: XLM-RoBERTa
  - Finetuned on OpinHuBank (OHB) Corpus
  - Labels: 0 (negative), 2 (neutral), 3 (positive)
  - Separator: \</s\>
  	
## Limitations

- max_seq_length = 256

## Results

| Model | OHB | 
| ------------- | ------------- | 
| huBERT | 82.30  |
| XLM-R | 80.59  |


## Usage with pipeline

```python
from transformers import pipeline

classification = pipeline(task="sentiment-analysis", model="NYTK/sentiment-ohb3-xlm-roberta-hungarian")
input_text = "Kovácsné Nagy Erzsébet </s> 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}
}

```