--- language: - ru --- # distilrubert-tiny-cased-conversational Conversational DistilRuBERT-tiny \(Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 11.8M parameters\) 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-small-cased-conversational). Our DistilRuBERT-tiny 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) * MSE loss (between averaged student and teacher hidden states) The key features are: * unlike most of distilled language models, we **didn't** use KL loss during pre-training * reduced vocabulary size (30K in *tiny* vs. 100K in *base* and *small* ) * two separate inputs for student: tokens obtained using student tokenizer (for MLM) and teacher tokens greedily splitted by student tokens (for MSE) 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 | | **`distilrubert-tiny-cased-conversational`** | **10.4** | 31 | **41** | 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 Intel(R) Xeon(R) CPU E5-2698 v4 @ 2.20GHz and nVIDIA Tesla P100-SXM2.0 16Gb. | Model | # params | Size, Mb | Batch size | Seq len | Inference | |---|---|---|---|---|---| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 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-small-cased-conversational)| 409 | 0.1656 | 0.015 | 0.9692 | 71.3553 | To evaluate model quality, we fine-tuned DistilRuBERT-small 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-small-cased-conversational). \[1\]: 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. \[4\]: \[5\]: ,