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uvegesistvan/huBERTPlain

sourceHugging Facecc-by-nc-4.0updated 2y agoView on Hugging Face

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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:

bibtex

@PhDThesis{ Uveges:2024,
  author = {{"U}veges, Istv{\'a}n},
  title  = {K{\"o}z{\'e}rthet{\"o} és automatiz{\'a}ci{\'o} - k{\'i}s{\'e}rletek a jog, term{\'e}szetesnyelv-feldolgoz{\'a}s {\'e}s informatika hat{\'a}r{\'a}n.},
  year   = {2024},
  school = {Szegedi Tudom{\'a}nyegyetem}
}