diff --git a/model_cards/DeepPavlov/bert-base-bg-cs-pl-ru-cased/README.md b/model_cards/DeepPavlov/bert-base-bg-cs-pl-ru-cased/README.md
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+---
+language:
+- bulgarian
+- czech
+- polish
+- russian
+---
+
+# bert-base-bg-cs-pl-ru-cased
+
+SlavicBERT\[1\] \(Slavic \(bg, cs, pl, ru\), cased, 12-layer, 768-hidden, 12-heads, 180M parameters\) was trained
+on Russian News and four Wikipedias: Bulgarian, Czech, Polish, and Russian.
+Subtoken vocabulary was built using this data. Multilingual BERT was used as an initialization for SlavicBERT.
+
+
+\[1\]: Arkhipov M., Trofimova M., Kuratov Y., Sorokin A. \(2019\).
+[Tuning Multilingual Transformers for Language-Specific Named Entity Recognition](https://www.aclweb.org/anthology/W19-3712/).
+ACL anthology W19-3712.
diff --git a/model_cards/DeepPavlov/bert-base-cased-conversational/README.md b/model_cards/DeepPavlov/bert-base-cased-conversational/README.md
new file mode 100644
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+---
+language:
+- english
+---
+
+# bert-base-cased-conversational
+
+Conversational BERT \(English, cased, 12-layer, 768-hidden, 12-heads, 110M parameters\) was trained
+on the English part of Twitter, Reddit, DailyDialogues\[1\], OpenSubtitles\[2\], Debates\[3\], Blogs\[4\],
+Facebook News Comments. We used this training data to build the vocabulary of English subtokens and took
+English cased version of BERT-base as an initialization for English Conversational BERT.
+
+
+\[1\]: Yanran Li, Hui Su, Xiaoyu Shen, Wenjie Li, Ziqiang Cao, and Shuzi Niu. DailyDialog: A Manually Labelled
+Multi-turn Dialogue Dataset. IJCNLP 2017.
+
+\[2\]: 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 \(LREC 2016\)
+
+\[3\]: Justine Zhang, Ravi Kumar, Sujith Ravi, Cristian Danescu-Niculescu-Mizil. Proceedings of NAACL, 2016.
+
+\[4\]: J. Schler, M. Koppel, S. Argamon and J. Pennebaker \(2006\). Effects of Age and Gender on Blogging
+in Proceedings of 2006 AAAI Spring Symposium on Computational Approaches for Analyzing Weblogs.
diff --git a/model_cards/DeepPavlov/bert-base-multilingual-cased-sentence/README.md b/model_cards/DeepPavlov/bert-base-multilingual-cased-sentence/README.md
new file mode 100644
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+++ b/model_cards/DeepPavlov/bert-base-multilingual-cased-sentence/README.md
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+---
+language:
+- multilingual
+---
+
+# bert-base-multilingual-cased-sentence
+
+Sentence Multilingual BERT \(101 languages, cased, 12-layer, 768-hidden, 12-heads, 180M parameters\)
+is a representation-based sentence encoder for 101 languages of Multilingual BERT.
+It is initialized with Multilingual BERT and then fine-tuned on english MultiNLI\[1\] and on dev set
+of multilingual XNLI\[2\].
+Sentence representations are mean pooled token embeddings in the same manner as in Sentence-BERT\[3\].
+
+
+\[1\]: Williams A., Nangia N. & Bowman S. \(2017\) A Broad-Coverage Challenge Corpus for Sentence Understanding
+through Inference. arXiv preprint [arXiv:1704.05426](https://arxiv.org/abs/1704.05426)
+
+\[2\]: Williams A., Bowman S. \(2018\) XNLI: Evaluating Cross-lingual Sentence Representations.
+arXiv preprint [arXiv:1809.05053](https://arxiv.org/abs/1809.05053)
+
+\[3\]: N. Reimers, I. Gurevych \(2019\) Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks.
+arXiv preprint [arXiv:1908.10084](https://arxiv.org/abs/1908.10084)
diff --git a/model_cards/DeepPavlov/rubert-base-cased-conversational/README.md b/model_cards/DeepPavlov/rubert-base-cased-conversational/README.md
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+++ b/model_cards/DeepPavlov/rubert-base-cased-conversational/README.md
@@ -0,0 +1,18 @@
+---
+language:
+- russian
+---
+
+# rubert-base-cased-conversational
+
+Conversational RuBERT \(Russian, cased, 12-layer, 768-hidden, 12-heads, 180M parameters\) was trained
+on OpenSubtitles\[1\], [Dirty](https://d3.ru/), [Pikabu](https://pikabu.ru/),
+and a Social Media segment of Taiga corpus\[2\]. We assembled a new vocabulary for Conversational RuBERT model
+on this data and initialized the model with [RuBERT](../rubert-base-cased).
+
+
+\[1\]: 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.
diff --git a/model_cards/DeepPavlov/rubert-base-cased-sentence/README.md b/model_cards/DeepPavlov/rubert-base-cased-sentence/README.md
new file mode 100644
index 00000000..9bac3846
--- /dev/null
+++ b/model_cards/DeepPavlov/rubert-base-cased-sentence/README.md
@@ -0,0 +1,21 @@
+---
+language:
+- russian
+---
+
+# rubert-base-cased-sentence
+
+Sentence RuBERT \(Russian, cased, 12-layer, 768-hidden, 12-heads, 180M parameters\)
+is a representation-based sentence encoder for Russian. It is initialized with RuBERT and fine-tuned on SNLI\[1\]
+google-translated to russian and on russian part of XNLI dev set\[2\]. Sentence representations are mean pooled
+token embeddings in the same manner as in Sentence-BERT\[3\].
+
+
+\[1\]: S. R. Bowman, G. Angeli, C. Potts, and C. D. Manning. \(2015\) A large annotated corpus for learning
+natural language inference. arXiv preprint [arXiv:1508.05326](https://arxiv.org/abs/1508.05326)
+
+\[2\]: Williams A., Bowman S. \(2018\) XNLI: Evaluating Cross-lingual Sentence Representations.
+arXiv preprint [arXiv:1809.05053](https://arxiv.org/abs/1809.05053)
+
+\[3\]: N. Reimers, I. Gurevych \(2019\) Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks.
+arXiv preprint [arXiv:1908.10084](https://arxiv.org/abs/1908.10084)
diff --git a/model_cards/DeepPavlov/rubert-base-cased/README.md b/model_cards/DeepPavlov/rubert-base-cased/README.md
new file mode 100644
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--- /dev/null
+++ b/model_cards/DeepPavlov/rubert-base-cased/README.md
@@ -0,0 +1,14 @@
+---
+language:
+- russian
+---
+
+# rubert-base-cased
+
+RuBERT \(Russian, cased, 12-layer, 768-hidden, 12-heads, 180M parameters\) was trained on the Russian part of Wikipedia
+and news data. We used this training data to build a vocabulary of Russian subtokens and took a multilingual version
+of BERT-base as an initialization for RuBERT\[1\].
+
+
+\[1\]: Kuratov, Y., Arkhipov, M. \(2019\). Adaptation of Deep Bidirectional Multilingual Transformers for Russian Language.
+arXiv preprint [arXiv:1905.07213](https://arxiv.org/abs/1905.07213).