# DeepPavlov /distilrubert-base-cased-conversational

3.22 kB
 --- 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. $4$: