readme: ---
  language:
  - ru
  ---
  # distilrubert-base-cased-conversational
  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)).
  
  Our DistilRuBERT was highly inspired by \[3\], \[4\]. Namely, we used 
  * KL loss (between teacher and student output logits)
  * MLM loss (between tokens labels and student output logits)
  * Cosine embedding loss between mean of two consecutive hidden states of the teacher and one hidden state of the student
  
  The model was trained for about 100 hrs. on 8 nVIDIA Tesla P100-SXM2.0 16Gb.
  
  To evaluate improvements in the inference speed, we ran teacher and student models on random sequences with seq_len=512, batch_size = 16 (for throughput) and batch_size=1 (for latency). 
  All tests were performed on Intel(R) Xeon(R) CPU E5-2698 v4 @ 2.20GHz and nVIDIA Tesla P100-SXM2.0 16Gb.
  
  | Model                                           | Size, Mb.  | CPU latency, sec.| GPU latency, sec. | CPU throughput, samples/sec. | GPU throughput, samples/sec. |
  |-------------------------------------------------|------------|------------------|-------------------|------------------------------|------------------------------|
  | Teacher (RuBERT-base-cased-conversational)      | 679        | 0.655            | 0.031             | 0.3754                       | 36.4902                      |
  | Student (DistilRuBERT-base-cased-conversational)| 517        | 0.3285           | 0.0212            | 0.5803                       | 52.2495                      |
  
  # Citation
  If you found the model useful for your research, we are kindly ask to cite [this](https://arxiv.org/abs/2205.02340) paper:
  ```
  @misc{https://doi.org/10.48550/arxiv.2205.02340,
    doi = {10.48550/ARXIV.2205.02340},
    
    url = {https://arxiv.org/abs/2205.02340},
    
    author = {Kolesnikova, Alina and Kuratov, Yuri and Konovalov, Vasily and Burtsev, Mikhail},
    
    keywords = {Computation and Language (cs.CL), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
    
    title = {Knowledge Distillation of Russian Language Models with Reduction of Vocabulary},
    
    publisher = {arXiv},
    
    year = {2022},
    
    copyright = {arXiv.org perpetual, non-exclusive license}
  }
  ```
  
  \[1\]: 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 \(LREC 2016\)
  
  \[2\]: 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.
  
  \[3\]: 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.
  
  \[4\]: <https://github.com/huggingface/transformers/tree/master/examples/research_projects/distillation>