add tiny-5k model

README.md

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+---
+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](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)).
+
+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](https://arxiv.org/abs/2205.02340) 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](https://arxiv.org/abs/2205.02340) 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>

config.json

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+{
+    "vocab_size": 5031,
+    "max_position_embeddings": 512, 
+    "n_layers": 3, 
+    "n_heads": 12,
+    "dim": 264,
+    "hidden_dim": 792, 
+    "model_type": "distilbert"
+}

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:fdcbcb6366c72c390890f20d2b0b0434cb731cbe76941ec856255eddaef77df5
+size 14581616

special_tokens_map.json

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+{"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}

tokenizer_config.json

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+{"do_lower_case": false, "do_basic_tokenize": true, "never_split": null, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null}