metadata
license: apache-2.0
language:
- hu
metrics:
- accuracy
model-index:
- name: huBERTPlain
results:
- task:
type: text-classification
metrics:
- type: f1
value: 0.77
Model description
Cased fine-tuned BERT model for Hungarian, trained on (manuallay anniated) parliamentary pre-agenda speeches scraped from parlament.hu.
Intended uses & limitations
The model can be used as any other (cased) BERT model. It has been tested recognizing emotions at the sentence level in (parliamentary) pre-agenda speeches, where:
- 'Label_0': Neutral
- 'Label_1': Fear
- 'Label_2': Sadness
- 'Label_3': Anger
- 'Label_4': Disgust
- 'Label_5': Success
- 'Label_6': Joy
- 'Label_7': Trust
Training
Fine-tuned version of the original huBERT model (SZTAKI-HLT/hubert-base-cc), trained on HunEmPoli corpus.
Eval results
| Class | Precision | Recall | F-Score |
|---|---|---|---|
| Fear | 0.625 | 0.625 | 0.625 |
| Sadness | 0.8535 | 0.6291 | 0.7243 |
| Anger | 0.7857 | 0.3437 | 0.4782 |
| Disgust | 0.7154 | 0.8790 | 0.7888 |
| Success | 0.8579 | 0.8683 | 0.8631 |
| Joy | 0.549 | 0.6363 | 0.5894 |
| Trust | 0.4705 | 0.5581 | 0.5106 |
| Macro AVG | 0.7134 | 0.6281 | 0.6497 |
| Weighted AVG | 0.791 | 0.7791 | 0.7743 |
Usage
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("poltextlab/HunEmBERT8")
model = AutoModelForSequenceClassification.from_pretrained("poltextlab/HunEmBERT8")
BibTeX entry and citation info
If you use the model, please cite the following paper:
Bibtex:
@ARTICLE{10149341,
author={{"U}veges, Istv{\'a}n and Ring, Orsolya},
journal={IEEE Access},
title={HunEmBERT: a fine-tuned BERT-model for classifying sentiment and emotion in political communication},
year={2023},
volume={11},
number={},
pages={60267-60278},
doi={10.1109/ACCESS.2023.3285536}
}