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string: "# encodechka\n## encodechka-eval\n\n\u042D\u0442\u043E\u0442 \u0440\u0435\u043F\u043E\u0437\u0438\u0442\u043E\u0440\u0438\u0439
- \u0440\u0430\u0437\u0432\u0438\u0442\u0438\u0435 \u043F\u043E\u0434\u0445\u043E\u0434\u0430
\u043A \u043E\u0446\u0435\u043D\u043A\u0435 \u043C\u043E\u0434\u0435\u043B\u0435\u0439
\u0438\u0437 \u043F\u043E\u0441\u0442\u0430\n[\u041C\u0430\u043B\u0435\u043D\u044C\u043A\u0438\u0439
\u0438 \u0431\u044B\u0441\u0442\u0440\u044B\u0439 BERT \u0434\u043B\u044F
\u0440\u0443\u0441\u0441\u043A\u043E\u0433\u043E \u044F\u0437\u044B\u043A\u0430](https://habr.com/ru/post/562064),
\n\u044D\u0432\u043E\u043B\u044E\u0446\u0438\u043E\u043D\u0438\u0440\u043E\u0432\u0430\u0432\u0448\u0435\u0433\u043E
\u0432 [\u0420\u0435\u0439\u0442\u0438\u043D\u0433 \u0440\u0443\u0441\u0441\u043A\u043E\u044F\u0437\u044B\u0447\u043D\u044B\u0445
\u044D\u043D\u043A\u043E\u0434\u0435\u0440\u043E\u0432 \u043F\u0440\u0435\u0434\u043B\u043E\u0436\u0435\u043D\u0438\u0439](https://habr.com/ru/post/669674/).\n\u0418\u0434\u0435\u044F
\u0432 \u0442\u043E\u043C, \u0447\u0442\u043E\u0431\u044B \u043F\u043E\u043D\u044F\u0442\u044C,
\u043A\u0430\u043A \u0445\u043E\u0440\u043E\u0448\u043E \u0440\u0430\u0437\u043D\u044B\u0435
\u043C\u043E\u0434\u0435\u043B\u0438 \u043F\u0440\u0435\u0432\u0440\u0430\u0449\u0430\u044E\u0442
\u043A\u043E\u0440\u043E\u0442\u043A\u0438\u0435 \u0442\u0435\u043A\u0441\u0442\u044B\n\u0432
\u043E\u0441\u043C\u044B\u0441\u043B\u0435\u043D\u043D\u044B\u0435 \u0432\u0435\u043A\u0442\u043E\u0440\u044B.\n\n\u041F\u043E\u0445\u043E\u0436\u0438\u0435
\u043F\u0440\u043E\u0435\u043A\u0442\u044B:\n* [RussianSuperGLUE](https://russiansuperglue.com/):
\u0444\u043E\u043A\u0443\u0441 \u043D\u0430 \u0434\u043E\u043E\u0431\u0443\u0447\u0430\u0435\u043C\u044B\u0445
\u043C\u043E\u0434\u0435\u043B\u044F\u0445\n* [MOROCCO](https://github.com/RussianNLP/MOROCCO/):
RussianSuperGLUE + \u043E\u0446\u0435\u043D\u043A\u0430 \u043F\u0440\u043E\u0438\u0437\u0432\u043E\u0434\u0438\u0442\u0435\u043B\u044C\u043D\u043E\u0441\u0442\u0438,
\u0442\u0440\u0443\u0434\u043D\u043E\u0432\u043E\u0441\u043F\u0440\u043E\u0438\u0437\u0432\u043E\u0434\u0438\u043C\n*
[RuSentEval](https://github.com/RussianNLP/RuSentEval): \u0431\u043E\u043B\u0435\u0435
\u0430\u043A\u0430\u0434\u0435\u043C\u0438\u0447\u0435\u0441\u043A\u0438\u0435/\u043B\u0438\u043D\u0433\u0432\u0438\u0441\u0442\u0438\u0447\u0435\u0441\u043A\u0438\u0435
\u0437\u0430\u0434\u0430\u0447\u0438\n* \u0421\u0442\u0430\u0442\u044C\u044F
\u043E\u0442 \u0412\u044B\u0448\u043A\u0438 [Popov et al, 2019](https://arxiv.org/abs/1910.13291):
\u043F\u0435\u0440\u0432\u0430\u044F \u043D\u0430\u0443\u0447\u043D\u0430\u044F
\u0441\u0442\u0430\u0442\u044C\u044F \u043D\u0430 \u044D\u0442\u0443 \u0442\u0435\u043C\u0443,
\u043D\u043E \u043C\u0430\u043B\u043E\u0432\u0430\u0442\u043E \u043C\u043E\u0434\u0435\u043B\u0435\u0439
\u0438 \u0437\u0430\u0434\u0430\u0447\n* [SentEvalRu](https://github.com/comptechml/SentEvalRu)
\u0438 [deepPavlovEval](https://github.com/deepmipt/deepPavlovEval): \u0434\u0432\u0430
\u0445\u043E\u0440\u043E\u0448\u0438\u0445, \u043D\u043E \u0434\u0430\u0432\u043D\u043E
\u043D\u0435 \u043E\u0431\u043D\u043E\u0432\u043B\u044F\u0432\u0448\u0438\u0445\u0441\u044F
\u0431\u0435\u043D\u0447\u043C\u0430\u0440\u043A\u0430. \n\n\u041F\u0440\u0438\u043C\u0435\u0440
\u0437\u0430\u043F\u0443\u0441\u043A\u0430 \u043C\u0435\u0442\u0440\u0438\u043A
\u2013 \u0432 \u0431\u043B\u043E\u043A\u043D\u043E\u0442\u0435 [evaluation
example](https://github.com/avidale/encodechka/blob/master/evaluation%20example.ipynb).
\n\n\u0411\u043B\u043E\u043A\u043D\u043E\u0442 \u0434\u043B\u044F \u0432\u043E\u0441\u043F\u0440\u043E\u0438\u0437\u0432\u0435\u0434\u0435\u043D\u0438\u044F
\u043B\u0438\u0434\u0435\u0440\u0431\u043E\u0440\u0434\u0430: [v2021](https://colab.research.google.com/drive/1fu2i7A-Yr-85Ex_NvIyeCIO7lN2R7P-k?usp=sharing),
\n[v2023](https://colab.research.google.com/drive/1t956aJsp5qPnst3379vI8NNRqiqJUFMn?usp=sharing).\n\n###
\u041B\u0438\u0434\u0435\u0440\u0431\u043E\u0440\u0434\n\n\u0420\u0430\u043D\u0436\u0438\u0440\u043E\u0432\u0430\u043D\u0438\u0435
\u043C\u043E\u0434\u0435\u043B\u0435\u0439 \u0432 \u043F\u043E \u0441\u0440\u0435\u0434\u043D\u0435\u043C\u0443
\u043A\u0430\u0447\u0435\u0441\u0442\u0432\u0443 \u0438 \u043F\u0440\u043E\u0438\u0437\u0432\u043E\u0434\u0438\u0442\u0435\u043B\u044C\u043D\u043E\u0441\u0442\u0438.
\n\u041F\u043E\u0434\u0441\u0432\u0435\u0447\u0435\u043D\u044B \u041F\u0430\u0440\u0435\u0442\u043E-\u043E\u043F\u0442\u0438\u043C\u0430\u043B\u044C\u043D\u044B\u0435
\u043C\u043E\u0434\u0435\u043B\u0438 \u043F\u043E \u043A\u0430\u0436\u0434\u043E\u043C\u0443
\u0438\u0437 \u043A\u0440\u0438\u0442\u0435\u0440\u0438\u0435\u0432. \n\n|
model | CPU |
GPU | size | Mean S | Mean S+W | dim |\n|:------------------------------------------------------------|:----------|:---------|:--------------|---------:|:-----------|------:|\n|
BAAI/bge-m3 | 523.4 |
22.5 | **2166.0** | 0.787 | 0.696 | 1024 |\n| intfloat/multilingual-e5-large-instruct
\ | 501.5 | 25.71 | **2136.0** | 0.784 | 0.684
\ | 1024 |\n| intfloat/multilingual-e5-large |
**506.8** | **30.8** | **2135.9389** | 0.78 | 0.686 | 1024 |\n|
sentence-transformers/paraphrase-multilingual-mpnet-base-v2 | **20.5** |
**19.9** | **1081.8485** | 0.762 | | 768 |\n| intfloat/multilingual-e5-base
\ | 130.61 | 14.39 | **1061.0** | 0.761
| 0.669 | 768 |\n| intfloat/multilingual-e5-small |
40.86 | 12.09 | **449.0** | 0.742 | 0.645 | 384 |\n|
symanto/sn-xlm-roberta-base-snli-mnli-anli-xnli | **20.2** |
**16.5** | **1081.8474** | 0.739 | | 768 |\n| cointegrated/LaBSE-en-ru
\ | 133.4 | **15.3** | **489.6621**
\ | 0.739 | 0.668 | 768 |\n| sentence-transformers/LaBSE |
135.1 | **13.3** | 1796.5078 | 0.739 | 0.667 | 768 |\n|
MUSE-3 | 200.1 |
30.7 | **303.0** | 0.736 | | 512 |\n| text-embedding-ada-002
\ | ? | | ? |
\ 0.734 | | 1536 |\n| sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
| **18.2** | 14.9 | 479.2547 | 0.734 | | 384 |\n|
sentence-transformers/distiluse-base-multilingual-cased-v1 | **11.8** |
**8.0** | 517.7452 | 0.722 | | 512 |\n| SONAR |
? | ? | 3060.0 | 0.721 | | 1024 |\n|
facebook/nllb-200-distilled-600M | 252.3 |
15.9 | 1577.4828 | 0.709 | 0.64 | 1024 |\n| sentence-transformers/distiluse-base-multilingual-cased-v2
\ | **11.2** | 9.2 | 517.7453 | 0.708 | | 512 |\n|
cointegrated/rubert-tiny2 | **6.2** |
**4.6** | **111.3823** | 0.704 | 0.638 | 312 |\n| ai-forever/sbert_large_mt_nlu_ru
\ | 504.5 | 29.7 | 1628.6539 | 0.703
| 0.626 | 1024 |\n| laser |
192.5 | 13.5 | 200.0 | 0.699 | | 1024 |\n|
laser2 | 163.4 |
8.6 | 175.0 | 0.694 | | 1024 |\n| ai-forever/sbert_large_nlu_ru
\ | 497.7 | 29.9 | 1628.6539 | 0.688
| 0.626 | 1024 |\n| clips/mfaq |
18.1 | 18.2 | 1081.8576 | 0.687 | | 768 |\n|
cointegrated/rut5-base-paraphraser | 137.0 |
15.6 | 412.0015 | 0.685 | 0.634 | 768 |\n| DeepPavlov/rubert-base-cased-sentence
\ | 128.4 | 13.2 | 678.5215 | 0.678 |
0.612 | 768 |\n| DeepPavlov/distilrubert-base-cased-conversational
\ | 64.2 | 10.4 | 514.002 | 0.676 | 0.624 |
\ 768 |\n| DeepPavlov/distilrubert-tiny-cased-conversational |
21.2 | **3.3** | 405.8292 | 0.67 | 0.616 | 768 |\n|
cointegrated/rut5-base-multitask | 136.9 |
12.7 | 412.0015 | 0.668 | 0.623 | 768 |\n| ai-forever/ruRoberta-large
\ | 512.3 | 25.5 | 1355.7162 |
\ 0.666 | 0.609 | 1024 |\n| DeepPavlov/rubert-base-cased-conversational
\ | 127.5 | 16.3 | 678.5215 | 0.653 | 0.606
\ | 768 |\n| deepvk/deberta-v1-base |
128.6 | 19.0 | 473.2402 | 0.653 | 0.591 | 768 |\n|
cointegrated/rubert-tiny | 7.5 |
5.9 | **44.97** | 0.645 | 0.575 | 312 |\n| ai-forever/FRED-T5-large
\ | 479.4 | 23.3 | 1372.9988 |
\ 0.639 | 0.551 | 1024 |\n| inkoziev/sbert_synonymy |
6.9 | 4.2 | 111.3823 | 0.637 | 0.566 | 312 |\n|
numind/NuNER-multilingual-v0.1 | 186.9 |
10 | 678.0 | 0.633 | 0.572 | 768 |\n| cointegrated/rubert-tiny-toxicity
\ | 10 | 5.5 | 47.2 | 0.621
| 0.553 | 312 |\n| ft_geowac_full |
**0.3** | | 1910.0 | 0.617 | 0.55 | 300 |\n|
bert-base-multilingual-cased | 141.4 |
13.7 | 678.5215 | 0.614 | 0.565 | 768 |\n| ai-forever/ruT5-large
\ | 489.6 | 20.2 | 1277.7571
\ | 0.61 | 0.578 | 1024 |\n| cointegrated/rut5-small |
37.6 | 8.6 | 111.3162 | 0.602 | 0.564 | 512 |\n|
ft_geowac_21mb | 1.2 |
\ | **21.0** | 0.597 | 0.531 | 300 |\n| inkoziev/sbert_pq
\ | 7.4 | 4.2 | 111.3823
\ | 0.596 | 0.526 | 312 |\n| ai-forever/ruT5-base |
126.3 | 12.8 | 418.2325 | 0.571 | 0.544 | 768 |\n|
hashing_1000_char | 0.5 |
\ | **1.0** | 0.557 | 0.464 | 1000 |\n| cointegrated/rut5-base
\ | 127.8 | 15.5 | 412.0014 |
\ 0.554 | 0.53 | 768 |\n| hashing_300_char |
0.8 | | 1.0 | 0.529 | 0.433 | 300 |\n|
hashing_1000 | **0.2** |
\ | 1.0 | 0.513 | 0.416 | 1000 |\n| hashing_300
\ | 0.3 | |
1.0 | 0.491 | 0.397 | 300 |\n\n\u0420\u0430\u043D\u0436\u0438\u0440\u043E\u0432\u0430\u043D\u0438\u0435
\u043C\u043E\u0434\u0435\u043B\u0435\u0439 \u043F\u043E \u0437\u0430\u0434\u0430\u0447\u0430\u043C.\n\u041F\u043E\u0434\u0441\u0432\u0435\u0447\u0435\u043D\u044B
\u043D\u0430\u0438\u043B\u0443\u0447\u0448\u0438\u0435 \u043C\u043E\u0434\u0435\u043B\u0438
\u043F\u043E \u043A\u0430\u0436\u0434\u043E\u0439 \u0438\u0437 \u0437\u0430\u0434\u0430\u0447.
\n\n| model | STS |
PI | NLI | SA | TI | IA | IC | ICX |
NE1 | NE2 |\n|:------------------------------------------------------------|:---------|:---------|:---------|:---------|:---------|:---------|:---------|:---------|:---------|:---------|\n|
BAAI/bge-m3 | **0.86** | **0.75**
| 0.51 | **0.82** | 0.97 | 0.79 | 0.81 | **0.78** | 0.24 |
0.42 |\n| intfloat/multilingual-e5-large-instruct |
0.86 | 0.74 | 0.47 | 0.81 | 0.98 | 0.8 | **0.82**
| 0.77 | 0.21 | 0.35 |\n| intfloat/multilingual-e5-large |
0.86 | 0.73 | 0.47 | 0.81 | 0.98 | 0.8 | 0.82 |
0.77 | 0.24 | 0.37 |\n| sentence-transformers/paraphrase-multilingual-mpnet-base-v2
| 0.85 | 0.66 | 0.54 | 0.79 | 0.95 | 0.78 | 0.79 |
0.74 | | |\n| intfloat/multilingual-e5-base |
0.83 | 0.7 | 0.46 | 0.8 | 0.96 | 0.78 | 0.8 |
0.74 | 0.23 | 0.38 |\n| intfloat/multilingual-e5-small |
0.82 | 0.71 | 0.46 | 0.76 | 0.96 | 0.76 | 0.78 |
0.69 | 0.23 | 0.27 |\n| symanto/sn-xlm-roberta-base-snli-mnli-anli-xnli
\ | 0.76 | 0.6 | **0.86** | 0.76 | 0.91 | 0.72
\ | 0.71 | 0.6 | | |\n| cointegrated/LaBSE-en-ru
\ | 0.79 | 0.66 | 0.43 | 0.76
\ | 0.95 | 0.77 | 0.79 | 0.77 | 0.35 | 0.42 |\n|
sentence-transformers/LaBSE | 0.79 | 0.66
\ | 0.43 | 0.76 | 0.95 | 0.77 | 0.79 | 0.76 | 0.35
\ | 0.41 |\n| MUSE-3 |
0.81 | 0.61 | 0.42 | 0.77 | 0.96 | 0.79 | 0.77 |
0.75 | | |\n| text-embedding-ada-002 |
0.78 | 0.66 | 0.44 | 0.77 | 0.96 | 0.77 | 0.75 |
0.73 | | |\n| sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
| 0.84 | 0.62 | 0.5 | 0.76 | 0.92 | 0.74 | 0.77 |
0.72 | | |\n| sentence-transformers/distiluse-base-multilingual-cased-v1
\ | 0.8 | 0.6 | 0.43 | 0.75 | 0.94 | 0.76 | 0.76
\ | 0.74 | | |\n| SONAR |
0.71 | 0.58 | 0.41 | 0.77 | 0.98 | 0.79 | 0.78 |
0.74 | | |\n| facebook/nllb-200-distilled-600M |
0.71 | 0.54 | 0.41 | 0.76 | 0.95 | 0.76 | 0.8 |
0.75 | 0.31 | 0.42 |\n| sentence-transformers/distiluse-base-multilingual-cased-v2
\ | 0.79 | 0.55 | 0.42 | 0.75 | 0.91 | 0.75 | 0.76
\ | 0.73 | | |\n| cointegrated/rubert-tiny2 |
0.75 | 0.65 | 0.42 | 0.74 | 0.94 | 0.75 | 0.76 |
0.64 | 0.36 | 0.39 |\n| ai-forever/sbert_large_mt_nlu_ru |
0.78 | 0.65 | 0.4 | 0.8 | 0.98 | 0.8 | 0.76 |
0.45 | 0.3 | 0.34 |\n| laser |
0.75 | 0.6 | 0.41 | 0.73 | 0.96 | 0.72 | 0.72 |
0.7 | | |\n| laser2 |
0.74 | 0.6 | 0.41 | 0.73 | 0.95 | 0.72 | 0.72 |
0.69 | | |\n| ai-forever/sbert_large_nlu_ru |
0.68 | 0.62 | 0.39 | 0.78 | 0.98 | 0.8 | 0.78 |
0.48 | 0.36 | 0.4 |\n| clips/mfaq |
0.63 | 0.59 | 0.35 | 0.79 | 0.95 | 0.74 | 0.76 |
0.69 | | |\n| cointegrated/rut5-base-paraphraser |
0.65 | 0.53 | 0.4 | 0.78 | 0.95 | 0.75 | 0.75 |
0.67 | 0.45 | 0.41 |\n| DeepPavlov/rubert-base-cased-sentence
\ | 0.74 | 0.66 | 0.49 | 0.75 | 0.92
\ | 0.75 | 0.72 | 0.39 | 0.36 | 0.34 |\n| DeepPavlov/distilrubert-base-cased-conversational
\ | 0.7 | 0.56 | 0.39 | 0.76 | 0.98 | 0.78 |
0.76 | 0.48 | 0.4 | 0.43 |\n| DeepPavlov/distilrubert-tiny-cased-conversational
\ | 0.7 | 0.55 | 0.4 | 0.74 | 0.98 | 0.78 |
0.76 | 0.45 | 0.35 | 0.44 |\n| cointegrated/rut5-base-multitask
\ | 0.65 | 0.54 | 0.38 | 0.76 |
0.95 | 0.75 | 0.72 | 0.59 | 0.47 | 0.41 |\n| ai-forever/ruRoberta-large
\ | 0.7 | 0.6 | 0.35 | 0.78 |
0.98 | 0.8 | 0.78 | 0.32 | 0.3 | **0.46** |\n| DeepPavlov/rubert-base-cased-conversational
\ | 0.68 | 0.52 | 0.38 | 0.73 | 0.98 |
0.78 | 0.75 | 0.42 | 0.41 | 0.43 |\n| deepvk/deberta-v1-base
\ | 0.68 | 0.54 | 0.38 | 0.76
\ | 0.98 | 0.8 | 0.78 | 0.29 | 0.29 | 0.4 |\n|
cointegrated/rubert-tiny | 0.66 | 0.53
\ | 0.4 | 0.71 | 0.89 | 0.68 | 0.7 | 0.58 | 0.24
\ | 0.34 |\n| ai-forever/FRED-T5-large |
0.62 | 0.44 | 0.37 | 0.78 | 0.98 | **0.81** | 0.67 |
0.45 | 0.25 | 0.15 |\n| inkoziev/sbert_synonymy |
0.69 | 0.49 | 0.41 | 0.71 | 0.91 | 0.72 | 0.69 |
0.47 | 0.32 | 0.24 |\n| numind/NuNER-multilingual-v0.1 |
0.67 | 0.53 | 0.4 | 0.71 | 0.89 | 0.72 | 0.7 |
0.46 | 0.32 | 0.34 |\n| cointegrated/rubert-tiny-toxicity |
0.57 | 0.44 | 0.37 | 0.68 | **1.0** | 0.78 | 0.7 |
0.43 | 0.24 | 0.32 |\n| ft_geowac_full |
0.69 | 0.53 | 0.37 | 0.72 | 0.97 | 0.76 | 0.66 |
0.26 | 0.22 | 0.34 |\n| bert-base-multilingual-cased |
0.66 | 0.53 | 0.37 | 0.7 | 0.89 | 0.7 | 0.69 |
0.38 | 0.36 | 0.38 |\n| ai-forever/ruT5-large |
0.51 | 0.39 | 0.35 | 0.77 | 0.97 | 0.79 | 0.72 |
0.38 | 0.46 | 0.44 |\n| cointegrated/rut5-small |
0.61 | 0.53 | 0.34 | 0.73 | 0.92 | 0.71 | 0.7 |
0.27 | 0.44 | 0.38 |\n| ft_geowac_21mb |
0.68 | 0.52 | 0.36 | 0.72 | 0.96 | 0.74 | 0.65 |
0.15 | 0.21 | 0.32 |\n| inkoziev/sbert_pq |
0.57 | 0.41 | 0.38 | 0.7 | 0.92 | 0.69 | 0.68 |
0.43 | 0.26 | 0.24 |\n| ai-forever/ruT5-base |
0.5 | 0.28 | 0.34 | 0.73 | 0.97 | 0.76 | 0.7 |
0.29 | 0.45 | 0.41 |\n| hashing_1000_char |
0.7 | 0.53 | 0.4 | 0.7 | 0.84 | 0.59 | 0.63 |
0.05 | 0.05 | 0.14 |\n| cointegrated/rut5-base |
0.44 | 0.28 | 0.33 | 0.74 | 0.92 | 0.75 | 0.58 |
0.39 | **0.48** | 0.39 |\n| hashing_300_char |
0.69 | 0.51 | 0.39 | 0.67 | 0.75 | 0.57 | 0.61 |
0.04 | 0.03 | 0.08 |\n| hashing_1000 |
0.63 | 0.49 | 0.39 | 0.66 | 0.77 | 0.55 | 0.57 |
0.05 | 0.02 | 0.04 |\n| hashing_300 |
0.61 | 0.48 | 0.4 | 0.64 | 0.71 | 0.54 | 0.5 |
0.05 | 0.02 | 0.02 |\n\n#### \u0417\u0430\u0434\u0430\u0447\u0438\n-
Semantic text similarity (**STS**) \u043D\u0430 \u043E\u0441\u043D\u043E\u0432\u0435
\u043F\u0435\u0440\u0435\u0432\u0435\u0434\u0451\u043D\u043D\u043E\u0433\u043E
\u0434\u0430\u0442\u0430\u0441\u0435\u0442\u0430 [STS-B](https://huggingface.co/datasets/stsb_multi_mt);\n-
Paraphrase identification (**PI**) \u043D\u0430 \u043E\u0441\u043D\u043E\u0432\u0435
\u0434\u0430\u0442\u0430\u0441\u0435\u0442\u0430 paraphraser.ru;\n- Natural
language inference (**NLI**) \u043D\u0430 \u0434\u0430\u0442\u0430\u0441\u0435\u0442\u0435
[XNLI](https://github.com/facebookresearch/XNLI);\n- Sentiment analysis (**SA**)
\u043D\u0430 \u0434\u0430\u043D\u043D\u044B\u0445 [SentiRuEval2016](http://www.dialog-21.ru/evaluation/2016/sentiment/).\n-
Toxicity identification (**TI**) \u043D\u0430 \u0434\u0430\u0442\u0430\u0441\u0435\u0442\u0435
\u0442\u043E\u043A\u0441\u0438\u0447\u043D\u044B\u0445 \u043A\u043E\u043C\u043C\u0435\u043D\u0442\u0430\u0440\u0438\u0435\u0432
\u0438\u0437 [OKMLCup](https://cups.mail.ru/ru/contests/okmlcup2020);\n- Inappropriateness
identification (**II**) \u043D\u0430 [\u0434\u0430\u0442\u0430\u0441\u0435\u0442\u0435
\u0421\u043A\u043E\u043B\u0442\u0435\u0445\u0430](https://github.com/skoltech-nlp/inappropriate-sensitive-topics);\n-
Intent classification (**IC**) \u0438 \u0435\u0451 \u043A\u0440\u043E\u0441\u0441-\u044F\u0437\u044B\u0447\u043D\u0430\u044F
\u0432\u0435\u0440\u0441\u0438\u044F **ICX** \u043D\u0430 \u0434\u0430\u0442\u0430\u0441\u0435\u0442\u0435
[NLU-evaluation-data](https://github.com/xliuhw/NLU-Evaluation-Data), \u043A\u043E\u0442\u043E\u0440\u044B\u0439
\u044F \u0430\u0432\u0442\u043E\u043C\u0430\u0442\u0438\u0447\u0435\u0441\u043A\u0438
\u043F\u0435\u0440\u0435\u0432\u0451\u043B \u043D\u0430 \u0440\u0443\u0441\u0441\u043A\u0438\u0439.
\u0412 IC \u043A\u043B\u0430\u0441\u0441\u0438\u0444\u0438\u043A\u0430\u0442\u043E\u0440
\u043E\u0431\u0443\u0447\u0430\u0435\u0442\u0441\u044F \u043D\u0430 \u0440\u0443\u0441\u0441\u043A\u0438\u0445
\u0434\u0430\u043D\u043D\u044B\u0445, \u0430 \u0432 ICX \u2013 \u043D\u0430
\u0430\u043D\u0433\u043B\u0438\u0439\u0441\u043A\u0438\u0445, \u0430 \u0442\u0435\u0441\u0442\u0438\u0440\u0443\u0435\u0442\u0441\u044F
\u0432 \u043E\u0431\u043E\u0438\u0445 \u0441\u043B\u0443\u0447\u0430\u044F\u0445
\u043D\u0430 \u0440\u0443\u0441\u0441\u043A\u0438\u0445.\n- \u0420\u0430\u0441\u043F\u043E\u0437\u043D\u0430\u0432\u0430\u043D\u0438\u0435
\u0438\u043C\u0435\u043D\u043E\u0432\u0430\u043D\u043D\u044B\u0445 \u0441\u0443\u0449\u043D\u043E\u0441\u0442\u0435\u0439
\u043D\u0430 \u0434\u0430\u0442\u0430\u0441\u0435\u0442\u0430\u0445 [factRuEval-2016](https://github.com/dialogue-evaluation/factRuEval-2016)
(**NE1**) \u0438 [RuDReC](https://github.com/cimm-kzn/RuDReC) (**NE2**). \u042D\u0442\u0438
\u0434\u0432\u0435 \u0437\u0430\u0434\u0430\u0447\u0438 \u0442\u0440\u0435\u0431\u0443\u044E\u0442
\u043F\u043E\u043B\u0443\u0447\u0430\u0442\u044C \u044D\u043C\u0431\u0435\u0434\u0434\u0438\u043D\u0433\u0438
\u043E\u0442\u0434\u0435\u043B\u044C\u043D\u044B\u0445 \u0442\u043E\u043A\u0435\u043D\u043E\u0432,
\u0430 \u043D\u0435 \u0446\u0435\u043B\u044B\u0445 \u043F\u0440\u0435\u0434\u043B\u043E\u0436\u0435\u043D\u0438\u0439;
\u043F\u043E\u044D\u0442\u043E\u043C\u0443 \u0442\u0430\u043C \u0443\u0447\u0430\u0441\u0442\u0432\u0443\u044E\u0442
\u043D\u0435 \u0432\u0441\u0435 \u043C\u043E\u0434\u0435\u043B\u0438.\n\n###
Changelog\n* \u0410\u0432\u0433\u0443\u0441\u0442 2023 - \u043E\u0431\u043D\u043E\u0432\u0438\u043B
\u0440\u0435\u0439\u0442\u0438\u043D\u0433:\n * \u043F\u043E\u043F\u0440\u0430\u0432\u0438\u0432
\u043E\u0448\u0438\u0431\u043A\u0443 \u0432 \u0432\u044B\u0447\u0438\u0441\u043B\u0435\u043D\u0438\u0438
mean token embeddings\n * \u0434\u043E\u0431\u0430\u0432\u0438\u043B \u043D\u0435\u0441\u043A\u043E\u043B\u044C\u043A\u043E
\u043C\u043E\u0434\u0435\u043B\u0435\u0439, \u0432\u043A\u043B\u044E\u0447\u0430\u044F
\u043D\u043E\u0432\u043E\u0433\u043E \u043B\u0438\u0434\u0435\u0440\u0430
- `intfloat/multilingual-e5-large`\n * \u043F\u043E \u043F\u0440\u043E\u0441\u044C\u0431\u0430\u043C
\u0442\u0440\u0443\u0434\u044F\u0449\u0438\u0445\u0441\u044F, \u0434\u043E\u0431\u0430\u0432\u0438\u043B
`text-embedding-ada-002` (\u0440\u0430\u0437\u043C\u0435\u0440 \u0438 \u043F\u0440\u043E\u0438\u0437\u0432\u043E\u0434\u0438\u0442\u0435\u043B\u044C\u043D\u043E\u0441\u0442\u044C
\u0443\u043A\u0430\u0437\u0430\u043D\u044B \u043E\u0442 \u0431\u0430\u043B\u0434\u044B)\n*
\u041B\u0435\u0442\u043E 2022 - \u043E\u043F\u0443\u0431\u043B\u0438\u043A\u043E\u0432\u0430\u043B
\u043F\u0435\u0440\u0432\u044B\u0439 \u0440\u0435\u0439\u0442\u0438\u043D\u0433\n"
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