upd. README with performance test

README.md

@@ -3,7 +3,7 @@ language:
 - ru
 ---
 # distilrubert-tiny-cased-conversational
-Conversational DistilRuBERT-tiny \(Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters\) 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 tiny copy of our [Conversational DistilRuBERT-base](https://huggingface.co/DeepPavlov/distilrubert-base-cased-conversational)
+Conversational DistilRuBERT-tiny \(Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters\) 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 tiny 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 
 * KL loss (between teacher and student output logits)
@@ -11,6 +11,17 @@ Our DistilRuBERT-tiny was highly inspired by \[3\], \[4\]. Namely, we used
 * Cosine embedding loss (between mean of six consecutive hidden states from teacher's encoder and one hidden state of the student)
 * MSE loss (between six consecutive attention maps from teacher's encoder and one attention map of the student)
 
+The model was trained for about 30 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-tiny-cased-conversational)| 409        | 0.1656           | 0.015             | 0.9692                       | 71.3553                      |
+
+
 \[1\]: 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.