metadata
license: cc-by-nc-4.0
language:
- hu
metrics:
- accuracy
model-index:
- name: huBERTPlain
results:
- task:
type: text-classification
metrics:
- type: accuracy
value: 0.73
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
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:
@PhDThesis{ Uveges:2024,
author = {{"U}veges, Istv{\'a}n},
title = {A k{\"o}z{\'e}rthet{\"o}s{\'e}g lehet{\"o}s{\'e}gei a jogi dom{\'e}n sz{\"o}vegeiben},
year = {2023},
school = {Szegedi Tudom\'anyegyetem}
}