dardem/xlm-roberta-large-uk-toxicity

Binary toxicity classifier for Ukrainian

This is the fine-tuned on the downstream task "xlm-roberta-large" instance.

The evaluation metrics for binary toxicity classification are:

Precision: 0.9468 Recall: 0.9465 F1: 0.9465

The training and evaluation data will be clarified later.

How to use

from transformers import AutoTokenizer, AutoModelForSequenceClassification

# load tokenizer and model weights
tokenizer = AutoTokenizer.from_pretrained('dardem/xlm-roberta-large-uk-toxicity')
model = AutoModelForSequenceClassification.from_pretrained('dardem/xlm-roberta-large-uk-toxicity')

# prepare the input
batch = tokenizer.encode('Ти неймовірна!', return_tensors='pt')

# inference
model(batch)

Citation

@article{dementieva2024toxicity,
  title={Toxicity Classification in Ukrainian},
  author={Dementieva, Daryna and Khylenko, Valeriia and Babakov, Nikolay and Groh, Georg},
  journal={arXiv preprint arXiv:2404.17841},
  year={2024}
}