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--- |
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language: |
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- ru |
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--- |
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# distilrubert-small-cased-conversational |
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Conversational DistilRuBERT-small \(Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters\) 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)). It can be considered as small copy of [Conversational DistilRuBERT-base](https://huggingface.co/DeepPavlov/distilrubert-base-cased-conversational). |
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Our DistilRuBERT-small was highly inspired by \[3\], \[4\]. Namely, we used |
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* KL loss (between teacher and student output logits) |
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* MLM loss (between tokens labels and student output logits) |
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* Cosine embedding loss (between averaged six consecutive hidden states from teacher's encoder and one hidden state of the student) |
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* MSE loss (between averaged six consecutive attention maps from teacher's encoder and one attention map of the student) |
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The model was trained for about 80 hrs. on 8 nVIDIA Tesla P100-SXM2.0 16Gb. |
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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). |
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All tests were performed on Intel(R) Xeon(R) CPU E5-2698 v4 @ 2.20GHz and nVIDIA Tesla P100-SXM2.0 16Gb. |
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| Model | Size, Mb. | CPU latency, sec.| GPU latency, sec. | CPU throughput, samples/sec. | GPU throughput, samples/sec. | |
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|-------------------------------------------------|------------|------------------|-------------------|------------------------------|------------------------------| |
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| Teacher (RuBERT-base-cased-conversational) | 679 | 0.655 | 0.031 | 0.3754 | 36.4902 | |
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| Student (DistilRuBERT-small-cased-conversational)| 409 | 0.1656 | 0.015 | 0.9692 | 71.3553 | |
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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](http://docs.deeppavlov.ai/en/master/features/overview.html#models). Also, results could be found in the [paper](https://arxiv.org/abs/2205.02340) Tables 1&2 as well as performance benchmarks and training details. |
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# Citation |
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If you found the model useful for your research, we are kindly ask to cite [this](https://arxiv.org/abs/2205.02340) paper: |
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``` |
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@misc{https://doi.org/10.48550/arxiv.2205.02340, |
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doi = {10.48550/ARXIV.2205.02340}, |
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url = {https://arxiv.org/abs/2205.02340}, |
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author = {Kolesnikova, Alina and Kuratov, Yuri and Konovalov, Vasily and Burtsev, Mikhail}, |
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keywords = {Computation and Language (cs.CL), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences}, |
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title = {Knowledge Distillation of Russian Language Models with Reduction of Vocabulary}, |
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publisher = {arXiv}, |
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year = {2022}, |
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copyright = {arXiv.org perpetual, non-exclusive license} |
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} |
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``` |
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\[1\]: 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 \(LREC 2016\) |
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\[2\]: 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. |
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\[3\]: 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. |
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\[4\]: <https://github.com/huggingface/transformers/tree/master/examples/research_projects/distillation> |
