
## 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.

## 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={11},
  number={},
  pages={60267-60278},
  doi={10.1109/ACCESS.2023.3285536}
}
```
{"language": ["hu"], "license": "apache-2.0", "metrics": ["accuracy"], "widget": [{"text": "A veget\u00e1ci\u00f3s id\u0151ben az orsz\u00e1gban rendszeresen jelentkez\u0151 j\u00e9ges\u0151k ellen is van m\u00f3d v\u00e9dekezni lok\u00e1lisan, ki-ki a saj\u00e1t nagy \u00e9rt\u00e9k\u0171 \u00fcltetv\u00e9ny\u00e9n.", "example_title": "Positive"}, {"text": "Magyarorsz\u00e1g t\u00f6bb \u00e9vtizede k\u00fczd demogr\u00e1fiai v\u00e1ls\u00e1ggal, \u00e9s egyre t\u00f6bb gyermekre v\u00e1gy\u00f3 p\u00e1r medd\u0151s\u00e9gi probl\u00e9m\u00e1kkal n\u00e9z szembe.", "exmaple_title": "Negative"}, {"text": "Tisztelt fideszes, KDNP-s K\u00e9pvi\u00adsel\u0151t\u00e1rsaim!", "example_title": "Neutral"}]}
transformers pytorch tensorboard safetensors bert text-classification hu license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #safetensors #bert #text-classification #hu #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

Model description
-----------------


Cased fine-tuned BERT model for Hungarian, trained on (manually annotated) parliamentary pre-agenda speeches scraped from 'URL'.


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.


Eval results
------------



Usage
-----


### BibTeX entry and citation info


If you use the model, please cite the following paper:


Bibtex:



### BibTeX entry and citation info


If you use the model, please cite the following paper:


Bibtex:
TAGS
#transformers #pytorch #tensorboard #safetensors #bert #text-classification #hu #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### BibTeX entry and citation info


If you use the model, please cite the following paper:


Bibtex:
TAGS
#transformers #pytorch #tensorboard #safetensors #bert #text-classification #hu #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
### BibTeX entry and citation info


If you use the model, please cite the following paper:


Bibtex: