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--- |
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language: |
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- ru |
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--- |
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# distilrubert-tiny-cased-conversational |
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Conversational DistilRuBERT-tiny \(Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters\) 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)). It can be considered as tiny copy of [Conversational DistilRuBERT-small](https://huggingface.co/DeepPavlov/distilrubert-tiny-cased-conversational). |
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Our DistilRuBERT-tiny is highly inspired by \[3\], \[4\] and architecture is very close to \[5\]. Namely, we use |
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* MLM loss (between token labels and student output distribution) |
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* MSE loss (between averaged student and teacher hidden states) |
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The key features are: |
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* unlike most of distilled language models, we **didn't** use KL loss during pre-training |
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* reduced vocabulary size (30K in *tiny* vs. 100K in *base* and *small* ) |
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* two separate inputs for student: tokens obtained using student tokenizer (for MLM) and teacher tokens greedily splitted by student tokens (for MSE) |
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Here is comparison between teacher model (`Conversational RuBERT`) and other distilled models. |
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| Model name | \# params, M | \# vocab, K | Mem., MB | |
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|---|---|---|---| |
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| `rubert-base-cased-conversational` | 177.9 | 120 | 679 | |
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| `distilrubert-base-cased-conversational` | 135.5 | 120 | 517 | |
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| `distilrubert-small-cased-conversational` | 107.1 | 120 | 409 | |
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| `cointegrated/rubert-tiny` | 11.8 | **30** | 46 | |
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| **distilrubert-tiny-cased-conversational** | **10.4** | 31 | **41** | |
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DistilRuBERT-tiny was trained for about 100 hrs. on 7 nVIDIA Tesla P100-SXM2.0 16Gb. |
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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 Intel(R) Xeon(R) CPU E5-2698 v4 @ 2.20GHz and nVIDIA Tesla P100-SXM2.0 16Gb. |
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| Model name | Batch size | Seq len | Time, s || Mem, MB || |
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|---|---|---|------||------|| |
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| | | | CPU | GPU | CPU | GPU | |
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| `rubert-base-cased-conversational` | 1 | 512 | 0.147 | 0.014 | 897 | 1531 | |
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| `distilrubert-base-cased-conversational` | 1 | 512 | 0.083 | 0.006 | 766 | 1423 | |
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| `distilrubert-small-cased-conversational` | 1 | 512 | 0.03 | **0.002** | 600 | 1243 | |
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| `cointegrated/rubert-tiny` | 1 | 512 | 0.041 | 0.003 | 272 | 919 | |
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| **distilrubert-tiny-cased-conversational** | 1 | 512 | **0.023** | 0.003 | **206** | **855** | |
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| `rubert-base-cased-conversational` | 16 | 512 | 2.839 | 0.182 | 1499 | 2071 | |
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| `distilrubert-base-cased-conversational` | 16 | 512 | 1.065 | 0.055 | 2541 | 2927 | |
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| `distilrubert-small-cased-conversational` | 16 | 512 | 0.373 | **0.003** | 1360 | 1943 | |
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| `cointegrated/rubert-tiny` | 16 | 512 | 0.628 | 0.004 | 1293 | 2221 | |
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| **distilrubert-tiny-cased-conversational** | 16 | 512 | **0.219** | **0.003** | **633** | **1291** | |
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To evaluate model quality, we fine-tuned DistilRuBERT-tiny on classification (RuSentiment, ParaPhraser), NER and question answering data sets for Russian and obtained scores very similar to the [Conversational DistilRuBERT-small](https://huggingface.co/DeepPavlov/distilrubert-tiny-cased-conversational). |
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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> |
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\[5\]: <https://habr.com/ru/post/562064/>, <https://huggingface.co/cointegrated/rubert-tiny> |
