This model is trained for toxicity classification task. The dataset used for training is the merge of the English parts of the three datasets by
Jigsaw
(
Jigsaw 2018
,
Jigsaw 2019
,
Jigsaw 2020
), containing around 2 million examples. We split it into two parts and fine-tune a RoBERTa model (
RoBERTa: A Robustly Optimized BERT Pretraining Approach
) on it. The classifiers perform closely on the test set of the first Jigsaw competition, reaching the
AUC-ROC
of 0.98 and
F1-score
of 0.76.
How to use
from transformers import RobertaTokenizer, RobertaForSequenceClassification
# load tokenizer and model weights
tokenizer = RobertaTokenizer.from_pretrained('SkolkovoInstitute/roberta_toxicity_classifier')
model = RobertaForSequenceClassification.from_pretrained('SkolkovoInstitute/roberta_toxicity_classifier')
# prepare the input
batch = tokenizer.encode('you are amazing', return_tensors='pt')
# inference
model(batch)
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