Update README.md

README.md

@@ -5,11 +5,11 @@ language:
 # 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-tiny was highly inspired by \[3\], \[4\]. Namely, we used 
+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 mean of six consecutive hidden states from teacher's encoder and one hidden state of the student)
-* MSE loss (between mean of six consecutive attention maps from teacher's encoder and one attention map of the student)
+* 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.
 
@@ -19,10 +19,10 @@ All tests were performed on Intel(R) Xeon(R) CPU E5-2698 v4 @ 2.20GHz and nVIDIA
 | 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-tiny-cased-conversational)| 409        | 0.1656           | 0.015             | 0.9692                       | 71.3553                      |
+| Student (DistilRuBERT-small-cased-conversational)| 409        | 0.1656           | 0.015             | 0.9692                       | 71.3553                      |
 
 
-To evaluate model quality, we fine-tuned DistilRuBERT-tiny 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).
+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).
 
 \[1\]: 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 \(LREC 2016\)