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type: cosine_pearson value: 38.597910358452815 - type: cosine_spearman value: 42.766194189894094 - type: euclidean_pearson value: 39.991306255692045 - type: euclidean_spearman value: 42.766194189894094 - type: main_score value: 42.766194189894094 - type: manhattan_pearson value: 39.74918349185897 - type: manhattan_spearman value: 42.574140880355976 task: type: STS - dataset: config: es-it name: MTEB STS22 (es-it) revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3 split: test type: mteb/sts22-crosslingual-sts metrics: - type: cosine_pearson value: 31.245905627830638 - type: cosine_spearman value: 32.83240215980029 - type: euclidean_pearson value: 33.06481984956772 - type: euclidean_spearman value: 32.83240215980029 - type: main_score value: 32.83240215980029 - type: manhattan_pearson value: 32.75706899386791 - type: manhattan_spearman value: 32.334081823391806 task: type: STS - dataset: config: es name: MTEB STS22 (es) revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3 split: test type: mteb/sts22-crosslingual-sts metrics: - type: cosine_pearson value: 16.966347433363 - type: cosine_spearman value: 45.3129129914676 - type: euclidean_pearson value: 28.50940505249936 - type: euclidean_spearman value: 45.3129129914676 - type: main_score value: 45.3129129914676 - type: manhattan_pearson value: 28.314847203862147 - type: manhattan_spearman value: 45.72042962859271 task: type: STS - dataset: config: zh-en name: MTEB STS22 (zh-en) revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3 split: test type: mteb/sts22-crosslingual-sts metrics: - type: cosine_pearson value: 34.66358594216254 - type: cosine_spearman value: 31.24659955360722 - type: euclidean_pearson value: 34.878197534840744 - type: euclidean_spearman value: 31.24659955360722 - type: main_score value: 31.24659955360722 - type: manhattan_pearson value: 34.70743093532992 - type: manhattan_spearman value: 30.441251812127955 task: type: STS - dataset: config: en name: MTEB STS22 (en) revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3 split: test type: mteb/sts22-crosslingual-sts metrics: - type: cosine_pearson value: 41.376318618780324 - type: cosine_spearman value: 47.061970299820764 - type: euclidean_pearson value: 44.89590651276241 - type: euclidean_spearman value: 47.061970299820764 - type: main_score value: 47.061970299820764 - type: manhattan_pearson value: 44.780089700405576 - type: manhattan_spearman value: 46.742447019531525 task: type: STS - dataset: config: default name: MTEB SensitiveTopicsClassification (default) revision: 416b34a802308eac30e4192afc0ff99bb8dcc7f2 split: test type: ai-forever/sensitive-topics-classification metrics: - type: accuracy value: 24.443359375 - type: f1 value: 21.903258801323084 - type: lrap value: 36.34758843315896 - type: main_score value: 24.443359375 task: type: MultilabelClassification - dataset: config: default name: MTEB StackExchangeClustering (default) revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 split: test type: mteb/stackexchange-clustering metrics: - type: main_score value: 33.50613168016603 - type: v_measure value: 33.50613168016603 - type: v_measure_std value: 3.91782276122889 task: type: Clustering - dataset: config: default name: MTEB StackExchangeClusteringP2P (default) revision: 815ca46b2622cec33ccafc3735d572c266efdb44 split: test type: mteb/stackexchange-clustering-p2p metrics: - type: main_score value: 27.98150942889309 - type: v_measure value: 27.98150942889309 - type: v_measure_std value: 2.0056109104136226 task: type: Clustering - dataset: config: default name: MTEB TERRa (default) revision: 7b58f24536063837d644aab9a023c62199b2a612 split: dev type: ai-forever/terra-pairclassification metrics: - type: cosine_accuracy value: 59.60912052117264 - type: cosine_accuracy_threshold value: 81.55556917190552 - type: cosine_ap value: 56.08760299515377 - type: cosine_f1 value: 67.33167082294264 - type: cosine_f1_threshold value: 78.14505100250244 - type: cosine_precision value: 54.43548387096774 - type: cosine_recall value: 88.23529411764706 - type: dot_accuracy value: 59.60912052117264 - type: dot_accuracy_threshold value: 81.55556917190552 - type: dot_ap value: 56.08760299515377 - type: dot_f1 value: 67.33167082294264 - type: dot_f1_threshold value: 78.14503908157349 - type: dot_precision value: 54.43548387096774 - type: dot_recall value: 88.23529411764706 - type: euclidean_accuracy value: 59.60912052117264 - type: euclidean_accuracy_threshold value: 60.736143589019775 - type: euclidean_ap value: 56.08760299515377 - type: euclidean_f1 value: 67.33167082294264 - type: euclidean_f1_threshold value: 66.11342430114746 - type: euclidean_precision value: 54.43548387096774 - type: euclidean_recall value: 88.23529411764706 - type: main_score value: 56.265447472512676 - type: manhattan_accuracy value: 60.91205211726385 - type: manhattan_accuracy_threshold value: 877.9421806335449 - type: manhattan_ap value: 56.265447472512676 - type: manhattan_f1 value: 67.16791979949875 - type: manhattan_f1_threshold value: 930.9440612792969 - type: manhattan_precision value: 54.47154471544715 - type: manhattan_recall value: 87.58169934640523 - type: max_ap value: 56.265447472512676 - type: max_f1 value: 67.33167082294264 - type: max_precision value: 54.47154471544715 - type: max_recall value: 88.23529411764706 - type: similarity_accuracy value: 59.60912052117264 - type: similarity_accuracy_threshold value: 81.55557513237 - type: similarity_ap value: 56.08760299515377 - type: similarity_f1 value: 67.33167082294264 - type: similarity_f1_threshold value: 78.1450629234314 - type: similarity_precision value: 54.43548387096774 - type: similarity_recall value: 88.23529411764706 task: type: PairClassification - dataset: config: default name: MTEB TelemarketingSalesRuleLegalBenchClassification (default) revision: 12ca3b695563788fead87a982ad1a068284413f4 split: test type: nguha/legalbench metrics: - type: accuracy value: 51.06382978723404 - type: ap value: 64.12529550827422 - type: ap_weighted value: 64.12529550827422 - type: f1 value: 48.74348032242769 - type: f1_weighted value: 46.65516580410197 - type: main_score value: 51.06382978723404 task: type: Classification - dataset: config: default name: MTEB TextualismToolDictionariesLegalBenchClassification (default) revision: 12ca3b695563788fead87a982ad1a068284413f4 split: test type: nguha/legalbench metrics: - type: accuracy value: 69.1588785046729 - type: ap value: 13.91484942886812 - type: ap_weighted value: 13.91484942886812 - type: f1 value: 53.57001972386588 - type: f1_weighted value: 75.94757507050821 - type: main_score value: 69.1588785046729 task: type: Classification - dataset: config: default name: MTEB TextualismToolPlainLegalBenchClassification (default) revision: 12ca3b695563788fead87a982ad1a068284413f4 split: test type: nguha/legalbench metrics: - type: accuracy value: 52.121212121212125 - type: ap value: 44.68029172320217 - type: ap_weighted value: 44.68029172320217 - type: f1 value: 50.48433048433048 - type: f1_weighted value: 48.79288612621945 - type: main_score value: 52.121212121212125 task: type: Classification - dataset: config: default name: MTEB ToxicChatClassification (default) revision: 3e0319203c7162b9c9f8015b594441f979c199bc split: test type: lmsys/toxic-chat metrics: - type: accuracy value: 73.56529209621992 - type: ap value: 21.641229801673067 - type: ap_weighted value: 21.641229801673067 - type: f1 value: 60.19489676894062 - type: f1_weighted value: 77.21280694246968 - type: main_score value: 73.56529209621992 task: type: Classification - dataset: config: default name: MTEB ToxicConversationsClassification (default) revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de split: test type: mteb/toxic_conversations_50k metrics: - type: accuracy value: 57.7734375 - type: ap value: 9.305482173252097 - type: ap_weighted value: 9.305482173252097 - type: f1 value: 44.43839832998249 - type: f1_weighted value: 67.10615100631958 - type: main_score value: 57.7734375 task: type: Classification - dataset: config: default name: MTEB TweetSentimentExtractionClassification (default) revision: d604517c81ca91fe16a244d1248fc021f9ecee7a split: test type: mteb/tweet_sentiment_extraction metrics: - type: accuracy value: 55.29994340690435 - type: f1 value: 55.3098112653406 - type: f1_weighted value: 54.4846442708958 - type: main_score value: 55.29994340690435 task: type: Classification - dataset: config: default name: MTEB TweetTopicSingleClassification (default) revision: 87b7a0d1c402dbb481db649569c556d9aa27ac05 split: test_2021 type: cardiffnlp/tweet_topic_single metrics: - type: accuracy value: 52.522150029533364 - type: f1 value: 40.24714634897976 - type: f1_weighted value: 57.39523757985323 - type: main_score value: 52.522150029533364 task: type: Classification - dataset: config: default name: MTEB TwentyNewsgroupsClustering (default) revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 split: test type: mteb/twentynewsgroups-clustering metrics: - type: main_score value: 19.90344454285597 - type: v_measure value: 19.90344454285597 - type: v_measure_std value: 1.8260774855268984 task: type: Clustering - dataset: config: default name: MTEB UCCVCommonLawLegalBenchClassification (default) revision: 12ca3b695563788fead87a982ad1a068284413f4 split: test type: nguha/legalbench metrics: - type: accuracy value: 52.127659574468076 - type: ap value: 42.829212190914326 - type: ap_weighted value: 42.829212190914326 - type: f1 value: 50.50895050895051 - type: f1_weighted value: 51.84200503349441 - type: main_score value: 52.127659574468076 task: type: Classification - dataset: config: default name: MTEB UnfairTOSLegalBenchClassification (default) revision: 12ca3b695563788fead87a982ad1a068284413f4 split: test type: nguha/legalbench metrics: - type: accuracy value: 19.3359375 - type: f1 value: 11.24236763925133 - type: f1_weighted value: 27.137659267661597 - type: main_score value: 19.3359375 task: type: Classification - dataset: config: default name: MTEB VieMedEVBitextMining (default) revision: d03c69413bc53d1cea5a5375b3a953c4fee35ecd split: test type: nhuvo/MedEV metrics: - type: accuracy value: 8.69140625 - type: f1 value: 7.772120924359041 - type: main_score value: 7.772120924359041 - type: precision value: 7.525730353438241 - type: recall value: 8.69140625 task: type: BitextMining - dataset: config: default name: MTEB WikiCitiesClustering (default) revision: ddc9ee9242fa65332597f70e967ecc38b9d734fa split: test type: jinaai/cities_wiki_clustering metrics: - type: main_score value: 56.66855146861069 - type: v_measure value: 56.66855146861069 - type: v_measure_std value: 0.0 task: type: Clustering - dataset: config: default name: MTEB YahooAnswersTopicsClassification (default) revision: 78fccffa043240c80e17a6b1da724f5a1057e8e5 split: test type: community-datasets/yahoo_answers_topics metrics: - type: accuracy value: 41.787109375 - type: f1 value: 40.33967050694529 - type: f1_weighted value: 40.3509380795682 - type: main_score value: 41.787109375 task: type: Classification - dataset: config: default name: MTEB YelpReviewFullClassification (default) revision: c1f9ee939b7d05667af864ee1cb066393154bf85 split: test type: Yelp/yelp_review_full metrics: - type: accuracy value: 43.5888671875 - type: f1 value: 42.36578282497966 - type: f1_weighted value: 42.363220099893724 - type: main_score value: 43.5888671875 task: type: Classification --- Быстрая модель BERT для расчетов эмбедингов предложений на русском языке. Модель основана на [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2) - имеет аналогичные размеры контекста (2048), ембединга (312) и быстродействие. ## Использование ```Python from sentence_transformers import SentenceTransformer, util model = SentenceTransformer('sergeyzh/rubert-tiny-turbo') sentences = ["привет мир", "hello world", "здравствуй вселенная"] embeddings = model.encode(sentences) print(util.dot_score(embeddings, embeddings)) ``` ## Метрики Оценки модели на бенчмарке [encodechka](https://github.com/avidale/encodechka): | model | CPU | GPU | size | Mean S | Mean S+W | dim | |:-----------------------------------|----------:|---------:|---------:|----------:|-----------:|-------:| | [sergeyzh/LaBSE-ru-turbo](https://huggingface.co/sergeyzh/LaBSE-ru-turbo) | 120.40 | 8.05 | 490 | 0.789 | 0.702 | 768 | | BAAI/bge-m3 | 523.40 | 22.50 | 2166 | 0.787 | 0.696 | 1024 | | intfloat/multilingual-e5-large | 506.80 | 30.80 | 2136 | 0.780 | 0.686 | 1024 | | intfloat/multilingual-e5-base | 130.61 | 14.39 | 1061 | 0.761 | 0.669 | 768 | | **sergeyzh/rubert-tiny-turbo** | 5.51 | 3.25 | 111 | 0.749 | 0.667 | 312 | | intfloat/multilingual-e5-small | 40.86 | 12.09 | 449 | 0.742 | 0.645 | 384 | | cointegrated/rubert-tiny2 | 5.51 | 3.25 | 111 | 0.704 | 0.638 | 312 | | model | STS | PI | NLI | SA | TI | IA | IC | ICX | NE1 | NE2 | |:-----------------------------------|:---------|:---------|:---------|:---------|:---------|:---------|:---------|:---------|:---------|:---------| | [sergeyzh/LaBSE-ru-turbo](https://huggingface.co/sergeyzh/LaBSE-ru-turbo) | 0.864 | 0.748 | 0.490 | 0.814 | 0.974 | 0.806 | 0.815 | 0.801 | 0.305 | 0.404 | | BAAI/bge-m3 | 0.864 | 0.749 | 0.510 | 0.819 | 0.973 | 0.792 | 0.809 | 0.783 | 0.240 | 0.422 | | intfloat/multilingual-e5-large | 0.862 | 0.727 | 0.473 | 0.810 | 0.979 | 0.798 | 0.819 | 0.773 | 0.224 | 0.374 | | intfloat/multilingual-e5-base | 0.835 | 0.704 | 0.459 | 0.796 | 0.964 | 0.783 | 0.802 | 0.738 | 0.235 | 0.376 | | **sergeyzh/rubert-tiny-turbo** | 0.828 | 0.722 | 0.476 | 0.787 | 0.955 | 0.757 | 0.780 | 0.685 | 0.305 | 0.373 | | intfloat/multilingual-e5-small | 0.822 | 0.714 | 0.457 | 0.758 | 0.957 | 0.761 | 0.779 | 0.691 | 0.234 | 0.275 | | cointegrated/rubert-tiny2 | 0.750 | 0.651 | 0.417 | 0.737 | 0.937 | 0.746 | 0.757 | 0.638 | 0.360 | 0.386 |