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language: |
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- ru |
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# distilrubert-base-cased-conversational |
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Conversational DistilRuBERT \(Russian, cased, 6‑layer, 768‑hidden, 12‑heads, 135.4M parameters\) was trained on OpenSubtitles\[1\], [Dirty](https://d3.ru/), [Pikabu](https://pikabu.ru/), and a Social Media segment of Taiga corpus\[2\] (as [Conversational RuBERT](https://huggingface.co/DeepPavlov/rubert-base-cased-conversational)). |
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Our DistilRuBERT was highly inspired by \[3\], \[4\]. Namely, we used |
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* KL loss (between teacher and student output logits) |
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* MLM loss (between tokens labels and student output logits) |
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* Cosine embedding loss between mean of two consecutive hidden states of the teacher and one hidden state of the student |
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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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\[3\]: Sanh, V., Debut, L., Chaumond, J., & Wolf, T. \(2019\). DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108. |
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\[4\]: <https://github.com/huggingface/transformers/tree/master/examples/research_projects/distillation> |
