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README.md
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@@ -22,6 +22,8 @@ All tests were performed on Intel(R) Xeon(R) CPU E5-2698 v4 @ 2.20GHz and nVIDIA
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| Student (DistilRuBERT-tiny-cased-conversational)| 409 | 0.1656 | 0.015 | 0.9692 | 71.3553 |
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\[1\]: P. Lison and J. Tiedemann, 2016, OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles. In Proceedings of the 10th International Conference on Language Resources and Evaluation \(LREC 2016\)
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\[2\]: Shavrina T., Shapovalova O. \(2017\) TO THE METHODOLOGY OF CORPUS CONSTRUCTION FOR MACHINE LEARNING: «TAIGA» SYNTAX TREE CORPUS AND PARSER. in proc. of “CORPORA2017”, international conference , Saint-Petersbourg, 2017.
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| Student (DistilRuBERT-tiny-cased-conversational)| 409 | 0.1656 | 0.015 | 0.9692 | 71.3553 |
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To evaluate model quality, we fine-tuned DistilRuBERT-tiny on classification, NER and question answering tasks. Scores and archives with fine-tuned models can be found in [DeepPavlov docs](http://docs.deeppavlov.ai/en/master/features/overview.html#models).
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\[1\]: P. Lison and J. Tiedemann, 2016, OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles. In Proceedings of the 10th International Conference on Language Resources and Evaluation \(LREC 2016\)
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\[2\]: Shavrina T., Shapovalova O. \(2017\) TO THE METHODOLOGY OF CORPUS CONSTRUCTION FOR MACHINE LEARNING: «TAIGA» SYNTAX TREE CORPUS AND PARSER. in proc. of “CORPORA2017”, international conference , Saint-Petersbourg, 2017.
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