distilrubert-small-cased-conversational

Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT). It can be considered as small copy of Conversational DistilRuBERT-base.

Our DistilRuBERT-small 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 averaged six consecutive hidden states from teacher's encoder and one hidden state of the student)
• MSE loss (between averaged six consecutive attention maps from teacher's encoder and one attention map of the student)

The model was trained for about 80 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-small-cased-conversational)	409	0.1656	0.015	0.9692	71.3553

To evaluate model quality, we fine-tuned DistilRuBERT-small on classification, NER and question answering tasks. Scores and archives with fine-tuned models can be found in DeepPavlov docs. Also, results could be found in the paper Tables 1&2 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 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