Model description
Fine-tuned xlm-RoBERTa 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 xlm-RoBERTa model. It has been tested recognizing "accessible" and "original" sentences, where:
- "accessible" - "Label_1": sentence, that can be considered as comprehensible (regarding to Plain Language directives)
- "original" - "Label_0": sentence, that needs to rephrased in order to follow Plain Language Guidelines.
Training
Fine-tuned version of the xlm-RoBERTa model (FacebookAI/xlm-roberta-base
), trained on information materials provided by NAV linguistic experts.
Eval results
Class | Precision | Recall | F-Score |
---|---|---|---|
Original / Label_0 | 0.76 | 0.71 | 0.73 |
Accessible / Label_1 | 0.72 | 0.78 | 0.75 |
accuracy | 0.74 | ||
macro avg | 0.74 | 0.74 | 0.74 |
weighted avg | 0.74 | 0.74 | 0.74 |
Usage
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("uvegesistvan/Hun_RoBERTa_Plain")
model = AutoModelForSequenceClassification.from_pretrained("uvegesistvan/Hun_RoBERTa_Plain")
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Evaluation results
- accuracyself-reported0.740
- f1self-reported0.740