metadata
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 (manually annotated) 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
The fine-tuned version of the original huBERT model (SZTAKI-HLT/hubert-base-cc), trained on HunEmPoli corpus.
| Category | Count | Ratio | Sentiment | Count | Ratio |
|---|---|---|---|---|---|
| Neutral | 351 | 1.85% | Neutral | 351 | 1.85% |
| Fear | 162 | 0.85% | Negative | 11180 | 58.84% |
| Sadness | 4258 | 22.41% | |||
| Anger | 643 | 3.38% | |||
| Disgust | 6117 | 32.19% | |||
| Success | 6602 | 34.74% | Positive | 7471 | 39.32% |
| Joy | 441 | 2.32% | |||
| Trust | 428 | 2.25% | |||
| Sum | 19002 |
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
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:
@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}
}