readme: ---
  language:
  - ru
  ---
  # distilrubert-tiny-cased-conversational-5k
  Conversational DistilRuBERT-tiny-5k \(Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 3.6M parameters, 5k vocab\) 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-tiny-5k is highly inspired by \[3\], \[4\] and architecture is very close to \[5\]. Namely, we use 
  * MLM loss (between token labels and student output distribution)
  * KL loss (between averaged student and teacher hidden states)
  
  The key feature is:
  * reduced vocabulary size (5K vs 30K in *tiny* vs. 100K in *base* and *small*)
  
  Here is comparison between teacher model (`Conversational RuBERT`) and other distilled models.
  
  | Model name  | \# params, M  | \# vocab, K  | Mem., MB |
  |---|---|---|---|
  | `rubert-base-cased-conversational` | 177.9 | 120 | 679 |
  | `distilrubert-base-cased-conversational` | 135.5 | 120 | 517 |
  | `distilrubert-small-cased-conversational` | 107.1 | 120 | 409 |
  | `cointegrated/rubert-tiny` | 11.8 | 30 | 46 |
  | `cointegrated/rubert-tiny2` | 29.3 | 84 | 112 |
  | `distilrubert-tiny-cased-conversational-v1` | 10.4 | 31 |  41 |
  | `distilrubert-tiny-cased-conversational-5k` | **3.6** | 5 |  **14** |
  
  
  DistilRuBERT-tiny was trained for about 100 hrs. on 7 nVIDIA Tesla P100-SXM2.0 16Gb.
  
  We used `PyTorchBenchmark` from `transformers` to evaluate model's performance and compare it with other pre-trained language models for Russian. All tests were performed on NVIDIA GeForce GTX 1080 Ti and Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz
  
  | Model name  | Batch size  |  Seq len | Time, s  ||   Mem, MB  ||
  |---|---|---|------||------||
  |   |   |   |  CPU |  GPU |  CPU |  GPU |
  | `rubert-base-cased-conversational` | 16  | 512  | 5.283  | 0.1866  | 1550  |  1938 |
  | `distilrubert-base-cased-conversational` |  16 |  512 | 2.335  | 0.0553  |  2177 |  2794 |
  | `distilrubert-small-cased-conversational` |  16 |  512 | 0.802  |  **0.0015** | 1541  |  1810 |
  | `cointegrated/rubert-tiny` |  16 |  512 |  0.942 | 0.0022  |  1308 |  2088 |
  | `cointegrated/rubert-tiny2` |  16 |  512 |  1.786 | 0.0023  |  3054 |  3848 |
  | `distilrubert-tiny-cased-conversational-v1` | 16 | 512  |  **0.374** | **0.002**  | **714**  | **1158** |
  | `distilrubert-tiny-cased-conversational-5k` | 16 | 512  |  **0.354** | **0.0018**  | **664**  | **1126** |
  
  
  To evaluate model quality, we fine-tuned DistilRuBERT-tiny-5k on classification (RuSentiment, ParaPhraser), NER and question answering data sets for Russian. The results could be found in the [paper](https://arxiv.org/abs/2205.02340) Table 4 as well as performance benchmarks and training details.
  
  # 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>
  
  \[5\]: <https://habr.com/ru/post/562064/>, <https://huggingface.co/cointegrated/rubert-tiny>