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DeepPavlov/distilrubert-tiny-cased-conversational-5k

sourceHugging Faceupdated 4y agoView on Hugging Face

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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>