metadata
license: cc-by-nc-4.0
language:
- hu
metrics:
- accuracy
- f1
model-index:
- name: Hun_RoBERTa_Plain
results:
- task:
type: text-classification
metrics:
- type: accuracy
value: 0.69
- type: f1
value: 0.69
widget:
- text: >-
A tanúsítvány meghatározott adatainak a 2008/118/EK irányelv IV. fejezete
szerinti szállításához szükséges adminisztratív okmányban...
example_title: Incomprehensible
- text: >-
Az AEO-engedély birtokosainak listáján – keresésre – megjelenő
információk: az engedélyes neve, az engedélyt kibocsátó ország...
example_title: Comprehensible
Model description
Cased fine-tuned XLM-RoBERTa-base model for Hungarian, trained on a dataset (~13k sentences) provided by National Tax and Customs Administration - Hungary (NAV): Public Accessibilty Programme.
Intended uses & limitations
The model is designed to classify sentences as either "comprehensible" or "not comprehensible" (according to Plain Language guidelines):
- Label_0 - "comprehensible" - The sentence is in Plain Language.
- Label_1 - "not comprehensible" - The sentence is not in Plain Language.
Training
Fine-tuned version of the original xlm-roberta-base model, trained on a dataset of Hungarian legal and administrative texts.
Eval results
| Class | Precision | Recall | F-Score |
|---|---|---|---|
| Comprehensible / Label_0 | 0.68 | 0.67 | 0.67 |
| Not comprehensible / Label_1 | 0.69 | 0.71 | 0.70 |
| accuracy | 0.69 | ||
| macro avg | 0.69 | 0.69 | 0.69 |
| weighted avg | 0.69 | 0.69 | 0.69 |
Usage
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("uvegesistvan/Hun_RoBERTa_Plain")
model = AutoModelForSequenceClassification.from_pretrained("uvegesistvan/Hun_RoBERTa_Plain")
Citation
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}
}