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, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT).
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 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 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