Remove ipynb checkpoints

Edit README

Upd. README with performance test

README typo fixed

.ipynb_checkpoints/README-checkpoint.md

@@ -1,16 +0,0 @@
----
-language:
-- ru
----
-# distilrubert-base-cased-conversational
-Conversational DistilRuBERT \(Russian, cased, 6‑layer, 768‑hidden, 12‑heads, 135.4M parameters\) 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)).
-
-Our DistilRuBERT 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 two consecutive hidden states of the teacher and one hidden state of the student
-
-\[1\]: 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>

README.md

@@ -10,7 +10,20 @@ Our DistilRuBERT was highly inspired by \[3\], \[4\]. Namely, we used
 * MLM loss (between tokens labels and student output logits)
 * Cosine embedding loss between mean of two consecutive hidden states of the teacher and one hidden state of the student
 
+The model was trained for about 100 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-base-cased-conversational)| 517        | 0.3285           | 0.0212            | 0.5803                       | 52.2495                      |
+
 \[1\]: 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>