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s-nlp / russian_toxicity_classifier
Model's Last Updated: October 14 2024
text-classification
Introduction of russian_toxicity_classifier
Model Details of russian_toxicity_classifier
Bert-based classifier (finetuned from Conversational Rubert ) trained on merge of Russian Language Toxic Comments dataset collected from 2ch.hk and Toxic Russian Comments dataset collected from ok.ru.
The datasets were merged, shuffled, and split into train, dev, test splits in 80-10-10 proportion. The metrics obtained from test dataset is as follows
| precision | recall | f1-score | support | |
|---|---|---|---|---|
| 0 | 0.98 | 0.99 | 0.98 | 21384 |
| 1 | 0.94 | 0.92 | 0.93 | 4886 |
| accuracy | 0.97 | 26270 | ||
| macro avg | 0.96 | 0.96 | 0.96 | 26270 |
| weighted avg | 0.97 | 0.97 | 0.97 | 26270 |
How to use
from transformers import BertTokenizer, BertForSequenceClassification
# load tokenizer and model weights
tokenizer = BertTokenizer.from_pretrained('SkolkovoInstitute/russian_toxicity_classifier')
model = BertForSequenceClassification.from_pretrained('SkolkovoInstitute/russian_toxicity_classifier')
# prepare the input
batch = tokenizer.encode('ты супер', return_tensors='pt')
# inference
model(batch)
Licensing Information
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License .
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More Information About russian_toxicity_classifier huggingface.co Model
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Updated:November 08 2024
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