
## Model description

Cased fine-tuned BERT model for Hungarian, trained on a dataset provided by National Tax and Customs Administration - Hungary (NAV): Public Accessibilty Programme.

## Intended uses & limitations

The model can be used as any other (cased) BERT model. It has been tested recognizing "accessible" and "original" sentences, where:
* "accessible" - "Label_0": sentence, that can be considered as comprehensible (regarding to Plain Language directives)
* "original" - "Label_1": sentence, that needs to rephrased in order to follow Plain Language Guidelines.

## Training

Fine-tuned version of the original huBERT model (`SZTAKI-HLT/hubert-base-cc`), trained on information materials provided by NAV linguistic experts.

## Eval results

| Class | Precision | Recall | F-Score |
|-----|------------|------------|------|
| **Accessible / Label_0** | **0.71** | **0.79** | **0.75**|
| **Original / Label_1** | **0.76** | **0.67** | **0.71**|
| **accuracy** | | | **0.73**|
| **macro avg** | **0.74** | **0.73** | **0.73**|
| **weighted avg** | **0.74** | **0.73** | **0.73**|

## Usage

```py
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("uvegesistvan/huBERTPlain")
model = AutoModelForSequenceClassification.from_pretrained("uvegesistvan/huBERTPlain")
```

### BibTeX entry and citation info

If you use the model, please cite the following dissertation (to be submitted for workshop discussion):

Bibtex:
```bibtex
@PhDThesis{ Uveges:2023,
  author = {{"U}veges, Istv{\'a}n},
  title  = {K{\"o}z{\'e}rthet{\"o} kommunik{\'a}ci{\'o} a jogi dom{\'e}n sz{\"o}vegeiben - term{\'e}szetesnyelv-feldolgoz{\'a}s {\'e}s jog},
  year   = {2023},
  school = {Szegedi Tudom\'anyegyetem}
}
```
{"language": ["hu"], "license": "cc-by-nc-4.0", "metrics": ["accuracy"], "widget": [{"text": "Az egys\u00e9ges gyakorlati alkalmaz\u00e1s \u00e9rdek\u00e9ben, illetve abb\u00f3l a c\u00e9lb\u00f3l, hogy a f\u00fcggetlen kis\u00fczemi termel\u0151i st\u00e1tuszt valamennyi tag\u00e1llamban k\u00f6nnyebben elismerj\u00e9k a Bizotts\u00e1g 2022. janu\u00e1r 1-j\u00e9vel kezd\u0151d\u0151en uni\u00f3s v\u00e9grehajt\u00e1si rendeletben hat\u00e1rozta meg: egys\u00e9ges \u0171rlap rendszeres\u00edt\u00e9s\u00e9vel a tan\u00fas\u00edtv\u00e1ny form\u00e1j\u00e1t, tartalm\u00e1t \u00e9s a ki\u00e1ll\u00edt\u00e1s\u00e1ra vonatkoz\u00f3 r\u00e9szlet szab\u00e1lyokat; a tan\u00fas\u00edtv\u00e1ny meghat\u00e1rozott adatainak a 2008/118/EK ir\u00e1nyelv IV. fejezete szerinti sz\u00e1ll\u00edt\u00e1s\u00e1hoz sz\u00fcks\u00e9ges adminisztrat\u00edv okm\u00e1nyban, azaz az Adminisztrat\u00edv k\u00eds\u00e9r\u0151okm\u00e1nyon (NAV_VP_IE815 jel\u0171 nyomtatv\u00e1ny) t\u00f6rt\u00e9n\u0151 szerepeltet\u00e9s\u00e9t; a tan\u00fas\u00edtv\u00e1ny meghat\u00e1rozott adatainak 2008/118/EK\u00a0ir\u00e1nyelv V. fejezete szerinti sz\u00e1ll\u00edt\u00e1s\u00e1hoz sz\u00fcks\u00e9ges adminisztrat\u00edv okm\u00e1nyban, azaz az Egyszer\u0171s\u00edtett K\u00eds\u00e9r\u0151 Okm\u00e1nyon (NAV_VP_HU815e jel\u0171 nyomtatv\u00e1ny) t\u00f6rt\u00e9n\u0151 szerepeltet\u00e9s\u00e9t.", "example_title": "Incomprehensible"}, {"text": "Az AEO-enged\u00e9ly birtokosainak list\u00e1j\u00e1n \u2013 keres\u00e9sre \u2013 megjelen\u0151 inform\u00e1ci\u00f3k: az enged\u00e9lyes neve, az enged\u00e9lyt kibocs\u00e1t\u00f3 orsz\u00e1g, az enged\u00e9ly t\u00edpusa.", "exmaple_title": "Comprehensible"}]}
transformers pytorch tensorboard safetensors bert text-classification hu doi:10.57967/hf/0810 license:cc-by-nc-4.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #safetensors #bert #text-classification #hu #doi-10.57967/hf/0810 #license-cc-by-nc-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

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


Cased fine-tuned BERT model for Hungarian, trained on a dataset provided by National Tax and Customs Administration - Hungary (NAV): Public Accessibilty Programme.


Intended uses & limitations
---------------------------


The model can be used as any other (cased) BERT model. It has been tested recognizing "accessible" and "original" sentences, where:


* "accessible" - "Label\_0": sentence, that can be considered as comprehensible (regarding to Plain Language directives)
* "original" - "Label\_1": sentence, that needs to rephrased in order to follow Plain Language Guidelines.


Training
--------


Fine-tuned version of the original huBERT model ('SZTAKI-HLT/hubert-base-cc'), trained on information materials provided by NAV linguistic experts.


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



Usage
-----


### BibTeX entry and citation info


If you use the model, please cite the following dissertation (to be submitted for workshop discussion):


Bibtex:



### BibTeX entry and citation info


If you use the model, please cite the following dissertation (to be submitted for workshop discussion):


Bibtex:
TAGS
#transformers #pytorch #tensorboard #safetensors #bert #text-classification #hu #doi-10.57967/hf/0810 #license-cc-by-nc-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### BibTeX entry and citation info


If you use the model, please cite the following dissertation (to be submitted for workshop discussion):


Bibtex:
TAGS
#transformers #pytorch #tensorboard #safetensors #bert #text-classification #hu #doi-10.57967/hf/0810 #license-cc-by-nc-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
### BibTeX entry and citation info


If you use the model, please cite the following dissertation (to be submitted for workshop discussion):


Bibtex: