Update README.md

README.md

@@ -78,15 +78,39 @@ print(util.dot_score(embeddings, embeddings))
 
 ## Быстродействие и размеры
 
+На бенчмарке [encodechka](https://github.com/avidale/encodechka):
+
 | Модель                           | CPU       | GPU       | size      | dim       | n_ctx     | n_vocab   |
 |:---------------------------------|----------:|----------:|----------:|----------:|----------:|----------:|
 | [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large)   | 149.026   |  15.629   |   2136    |   1024    |    514    |  250002   |
 | **sergeyzh/LaBSE-ru-sts**      |**42.835** | **8.561** |  **490**  |  **768**  |  **512**  | **55083**  |
-| [sergeyzh/rubert-tiny-sts](https://huggingface.co/sergeyzh/rubert-tiny-sts)     |   3.208   |   2.866   |    111    |    312    |    2048   |   83828   |
-| [Tochka-AI/ruRoPEBert-e5-base-512](https://huggingface.co/Tochka-AI/ruRoPEBert-e5-base-512) |  43.314   |   9.338   |    530    |    768    |    512    |   69382   |
+| [sergeyzh/rubert-tiny-sts](https://huggingface.co/sergeyzh/rubert-tiny-sts)     |   3.208   |   3.379   |    111    |    312    |    2048   |   83828   |
+| [Tochka-AI/ruRoPEBert-e5-base-512](https://huggingface.co/Tochka-AI/ruRoPEBert-e5-base-512) |  43.314   |   9.338   |    532    |    768    |    512    |   69382   |
 | [cointegrated/LaBSE-en-ru](https://huggingface.co/cointegrated/LaBSE-en-ru)         |  42.867   |   8.549   |    490    |    768    |    512    |   55083   |
-| [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2)        |   3.212   |   2.850   |    111    |    312    |    2048   |   83828   |
+| [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2)        |   3.212   |   3.384   |    111    |    312    |    2048   |   83828   |
+
+
+
+При использовании батчей с `sentence_transformers`:
+
+```python
+from sentence_transformers import SentenceTransformer
+
+model_name = 'sergeyzh/LaBSE-ru-sts'
+model = SentenceTransformer(model_name, device='cpu')
+sentences = ["Тест быстродействия на CPU Ryzen 7 3800X: batch = 50"] * 50
+%timeit -n 5 -r 3 model.encode(sentences)
+
+# 882 ms ± 104 ms per loop (mean ± std. dev. of 3 runs, 5 loops each)
+# 50/0.882 = 57 snt/s
 
+model = SentenceTransformer(model_name, device='cuda')
+sentences = ["Тест быстродействия на GPU RTX 3060: batch = 1500"] * 1500
+%timeit -n 5 -r 3 model.encode(sentences)
+
+# 792 ms ± 29 ms per loop (mean ± std. dev. of 3 runs, 5 loops each)
+# 1500/0.792 = 1894 snt/s
+```
 
 ## Связанные ресурсы
 Вопросы использования модели обсуждаются в [русскоязычном чате NLP](https://t.me/natural_language_processing).