add distilrubert-small-cased-conversational model

README.md

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+---
+language:
+- ru
+---
+# distilrubert-small-cased-conversational
+Conversational DistilRuBERT-small \(Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters\) 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 small copy of [Conversational DistilRuBERT-base](https://huggingface.co/DeepPavlov/distilrubert-base-cased-conversational).
+
+Our DistilRuBERT-small was highly inspired by \[3\], \[4\]. Namely, we used 
+* KL loss (between teacher and student output logits)
+* MLM loss (between tokens labels and student output logits)
+* Cosine embedding loss (between averaged six consecutive hidden states from teacher's encoder and one hidden state of the student)
+* MSE loss (between averaged six consecutive attention maps from teacher's encoder and one attention map of the student)
+
+The model was trained for about 80 hrs. on 8 nVIDIA Tesla P100-SXM2.0 16Gb.
+
+To evaluate improvements in the inference speed, we ran teacher and student models on random sequences with seq_len=512, batch_size = 16 (for throughput) and batch_size=1 (for latency). 
+All tests were performed on Intel(R) Xeon(R) CPU E5-2698 v4 @ 2.20GHz and nVIDIA Tesla P100-SXM2.0 16Gb.
+
+| 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, NER and question answering tasks. Scores and archives with fine-tuned models can be found in [DeepPavlov docs](http://docs.deeppavlov.ai/en/master/features/overview.html#models). Also, results could be found in the [paper](https://arxiv.org/abs/2205.02340) Tables 1&2 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>

config.json

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+{
+  "_name_or_path": "distilrubert-tiny-cased-conversational.pth",
+  "activation": "gelu",
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "hidden_dim": 3072,
+  "initializer_range": 0.02,
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 2,
+  "output_attentions": true,
+  "output_hidden_states": true,
+  "output_past": true,
+  "pad_token_id": 0,
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "vocab_size": 119547
+}

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a0c3ec312059f7aed902dc1b7440a0624399065ef0954ea10cf901e2306c5ff6
+size 428392490

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}