README.md

---
license: cc-by-nc-4.0
language:
- hu
metrics:
- accuracy
- f1
model-index:
- name: Hun_RoBERTa_large_Plain
  results:
  - task:
      type: text-classification
    metrics:
      - type: accuracy
        value: 0.79
      - type: f1
        value: 0.79
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-large model for Hungarian, trained to classify **sentences** based on their Plain Language properties.

## 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-large` model, trained on a dataset of Hungarian legal and administrative texts.

## Eval results

| Class | Precision | Recall | F-Score |
| ----- | --------- | ------ | ------- |
| **Comprehensible / Label_0** | **0.76** | **0.86** | **0.81** |
| **Not comprehensible / Label_1** | **0.83** | **0.72** | **0.77** |
| **accuracy** | | | **0.79** |
| **macro avg** | **0.80** | **0.79** | **0.79** |
| **weighted avg** | **0.79** | **0.79** | **0.79** |

## Usage

```py
from transformers import AutoTokenizer, AutoModelForSequenceClassification

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

# Citation:
@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}
}