Update README.md

README.md

@@ -3,7 +3,7 @@ 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).
+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-tiny-cased-conversational).
 
 
 Our DistilRuBERT-tiny is highly inspired by \[3\], \[4\] and architecture is very close to \[5\]. Namely, we use 
@@ -45,7 +45,7 @@ We used `PyTorchBenchmark` from `transformers` to evaluate model's performance a
 | **distilrubert-tiny-cased-conversational** | 16 | 512  |  **0.219** | **0.003**  | **633**  | **1291** |
 
 
-To evaluate model quality, we fine-tuned DistilRuBERT-tiny 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). 
+To evaluate model quality, we fine-tuned DistilRuBERT-tiny 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-tiny-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 \(LREC 2016\)