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t-bank-ai
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response-toxicity-classifier-base

Text Classification
Transformers
PyTorch
Russian
bert
russian
pretraining
conversational
Inference Endpoints
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nikitast commited on
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@@ -30,4 +30,4 @@ Model achieves the following results:
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  | DATASET_TWITTER val.csv | 0.883 | 0.913 | 0.896 | 0.368 | 0.330 | 0.348 | 0.515 | 0.468 | 0.490 | 0.659 | 0.535 | 0.591 |
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  | DATASET_GENA val.csv | 0.953 | 0.927 | 0.940 | 0.260 | 0.343 | 0.295 | 0.666 | 0.806 | 0.729 | 0.523 | 0.423 | 0.46 |
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- The work was done during internship at Tinkoff.
 
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  | DATASET_TWITTER val.csv | 0.883 | 0.913 | 0.896 | 0.368 | 0.330 | 0.348 | 0.515 | 0.468 | 0.490 | 0.659 | 0.535 | 0.591 |
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  | DATASET_GENA val.csv | 0.953 | 0.927 | 0.940 | 0.260 | 0.343 | 0.295 | 0.666 | 0.806 | 0.729 | 0.523 | 0.423 | 0.46 |
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+ The work was done during internship at Tinkoff by [Nikita Stepanov](https://huggingface.co/nikitast), mentored by [Alexander Markov](https://huggingface.co/amarkv).