README.md

---
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

```py
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:
```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}
}
```