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---
license: apache-2.0
language:
- hu
metrics:
- accuracy
model-index:
- name: huBERTPlain
results:
- task:
type: text-classification
metrics:
- type: f1
value: 0.91
widget:
- text: "A vegetációs időben az országban rendszeresen jelentkező jégesők ellen is van mód védekezni lokálisan, ki-ki a saját nagy értékű ültetvényén."
example_title: "Positive"
- text: "Magyarország több évtizede küzd demográfiai válsággal, és egyre több gyermekre vágyó pár meddőségi problémákkal néz szembe."
exmaple_title: "Negative"
- text: "Tisztelt fideszes, KDNP-s Képviselőtársaim!"
example_title: "Neutral"
---
## 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 positive, negative and neutral sentences in (parliamentary) pre-agenda speeches, where:
* 'Label_0': Neutral
* 'Label_1': Positive
* 'Label_2': Negative
## 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 |
|-----|------------|------------|------|
|Neutral|0.83|0.71|0.76|
|Positive|0.87|0.91|0.9|
|Negative|0.94|0.91|0.93|
|Macro AVG|0.88|0.85|0.86|
|Weighted WVG|0.91|0.91|0.91|
## Usage
```py
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("poltextlab/HunEmBERT3")
model = AutoModelForSequenceClassification.from_pretrained("poltextlab/HunEmBERT3")
```
### 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={},
number={},
pages={1-1},
doi={10.1109/ACCESS.2023.3285536}
}
``` |