pytorch distilbert ru arxiv:2205.02340 transformers
---
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>