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---
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model-index:
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- name: ru-en-RoSBERTa
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results:
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- dataset:
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config: default
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name: MTEB CEDRClassification (default)
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revision: c0ba03d058e3e1b2f3fd20518875a4563dd12db4
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| 9 |
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split: test
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type: ai-forever/cedr-classification
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metrics:
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- type: accuracy
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value: 44.68650371944739
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| 14 |
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- type: f1
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| 15 |
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value: 40.7601061886426
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| 16 |
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- type: lrap
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| 17 |
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value: 70.69633368756747
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| 18 |
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- type: main_score
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| 19 |
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value: 44.68650371944739
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| 20 |
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task:
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| 21 |
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type: MultilabelClassification
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| 22 |
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- dataset:
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| 23 |
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config: default
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| 24 |
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name: MTEB GeoreviewClassification (default)
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revision: 3765c0d1de6b7d264bc459433c45e5a75513839c
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| 26 |
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split: test
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| 27 |
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type: ai-forever/georeview-classification
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metrics:
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- type: accuracy
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| 30 |
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value: 49.697265625
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| 31 |
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- type: f1
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| 32 |
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value: 47.793186725286866
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| 33 |
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- type: f1_weighted
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| 34 |
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value: 47.79131720298068
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| 35 |
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- type: main_score
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| 36 |
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value: 49.697265625
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| 37 |
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task:
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| 38 |
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type: Classification
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| 39 |
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- dataset:
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| 40 |
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config: default
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| 41 |
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name: MTEB GeoreviewClusteringP2P (default)
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| 42 |
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revision: 97a313c8fc85b47f13f33e7e9a95c1ad888c7fec
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| 43 |
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split: test
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| 44 |
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type: ai-forever/georeview-clustering-p2p
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| 45 |
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metrics:
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| 46 |
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- type: main_score
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| 47 |
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value: 65.42249614873316
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| 48 |
-
- type: v_measure
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| 49 |
-
value: 65.42249614873316
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| 50 |
-
- type: v_measure_std
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| 51 |
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value: 0.8524815312312278
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| 52 |
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task:
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| 53 |
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type: Clustering
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| 54 |
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- dataset:
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| 55 |
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config: default
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| 56 |
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name: MTEB HeadlineClassification (default)
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| 57 |
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revision: 2fe05ee6b5832cda29f2ef7aaad7b7fe6a3609eb
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| 58 |
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split: test
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| 59 |
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type: ai-forever/headline-classification
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| 60 |
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metrics:
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| 61 |
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- type: accuracy
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| 62 |
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value: 78.0029296875
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| 63 |
-
- type: f1
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| 64 |
-
value: 77.95151940601424
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| 65 |
-
- type: f1_weighted
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| 66 |
-
value: 77.95054643947716
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| 67 |
-
- type: main_score
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| 68 |
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value: 78.0029296875
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| 69 |
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task:
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| 70 |
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type: Classification
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| 71 |
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- dataset:
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| 72 |
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config: default
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| 73 |
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name: MTEB InappropriatenessClassification (default)
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| 74 |
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revision: 601651fdc45ef243751676e62dd7a19f491c0285
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| 75 |
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split: test
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| 76 |
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type: ai-forever/inappropriateness-classification
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| 77 |
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metrics:
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| 78 |
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- type: accuracy
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| 79 |
-
value: 61.32324218750001
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| 80 |
-
- type: ap
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| 81 |
-
value: 57.11029460364367
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| 82 |
-
- type: ap_weighted
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| 83 |
-
value: 57.11029460364367
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| 84 |
-
- type: f1
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| 85 |
-
value: 60.971337406307214
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| 86 |
-
- type: f1_weighted
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| 87 |
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value: 60.971337406307214
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| 88 |
-
- type: main_score
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| 89 |
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value: 61.32324218750001
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| 90 |
-
task:
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| 91 |
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type: Classification
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| 92 |
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- dataset:
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config: default
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| 94 |
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name: MTEB KinopoiskClassification (default)
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revision: 5911f26666ac11af46cb9c6849d0dc80a378af24
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| 96 |
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split: test
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| 97 |
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type: ai-forever/kinopoisk-sentiment-classification
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| 98 |
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metrics:
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- type: accuracy
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| 100 |
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value: 63.27333333333334
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| 101 |
-
- type: f1
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| 102 |
-
value: 61.007042785228116
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| 103 |
-
- type: f1_weighted
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| 104 |
-
value: 61.007042785228116
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| 105 |
-
- type: main_score
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| 106 |
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value: 63.27333333333334
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| 107 |
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task:
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| 108 |
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type: Classification
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| 109 |
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- dataset:
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| 110 |
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config: ru
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| 111 |
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name: MTEB MIRACLReranking (ru)
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revision: 6d1962c527217f8927fca80f890f14f36b2802af
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| 113 |
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split: dev
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| 114 |
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type: miracl/mmteb-miracl-reranking
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| 115 |
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metrics:
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| 116 |
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- type: MAP@1(MIRACL)
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| 117 |
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value: 30.691000000000003
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| 118 |
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- type: MAP@10(MIRACL)
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| 119 |
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value: 49.178
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| 120 |
-
- type: MAP@100(MIRACL)
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| 121 |
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value: 51.225
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| 122 |
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- type: MAP@1000(MIRACL)
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| 123 |
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value: 51.225
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| 124 |
-
- type: MAP@20(MIRACL)
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| 125 |
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value: 50.613
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| 126 |
-
- type: MAP@3(MIRACL)
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| 127 |
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value: 42.457
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| 128 |
-
- type: MAP@5(MIRACL)
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| 129 |
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value: 46.172000000000004
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| 130 |
-
- type: NDCG@1(MIRACL)
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| 131 |
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value: 51.002
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| 132 |
-
- type: NDCG@10(MIRACL)
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| 133 |
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value: 56.912
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| 134 |
-
- type: NDCG@100(MIRACL)
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| 135 |
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value: 61.197
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| 136 |
-
- type: NDCG@1000(MIRACL)
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| 137 |
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value: 61.197
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| 138 |
-
- type: NDCG@20(MIRACL)
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| 139 |
-
value: 59.453
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| 140 |
-
- type: NDCG@3(MIRACL)
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| 141 |
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value: 51.083
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| 142 |
-
- type: NDCG@5(MIRACL)
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| 143 |
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value: 53.358000000000004
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| 144 |
-
- type: P@1(MIRACL)
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| 145 |
-
value: 51.002
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| 146 |
-
- type: P@10(MIRACL)
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| 147 |
-
value: 14.852000000000002
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| 148 |
-
- type: P@100(MIRACL)
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| 149 |
-
value: 1.9529999999999998
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| 150 |
-
- type: P@1000(MIRACL)
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| 151 |
-
value: 0.19499999999999998
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| 152 |
-
- type: P@20(MIRACL)
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| 153 |
-
value: 8.657
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| 154 |
-
- type: P@3(MIRACL)
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| 155 |
-
value: 31.435000000000002
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| 156 |
-
- type: P@5(MIRACL)
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| 157 |
-
value: 23.608999999999998
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| 158 |
-
- type: Recall@1(MIRACL)
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| 159 |
-
value: 30.691000000000003
|
| 160 |
-
- type: Recall@10(MIRACL)
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| 161 |
-
value: 67.006
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| 162 |
-
- type: Recall@100(MIRACL)
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| 163 |
-
value: 79.952
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| 164 |
-
- type: Recall@1000(MIRACL)
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| 165 |
-
value: 79.952
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| 166 |
-
- type: Recall@20(MIRACL)
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| 167 |
-
value: 73.811
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| 168 |
-
- type: Recall@3(MIRACL)
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| 169 |
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value: 49.142
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| 170 |
-
- type: Recall@5(MIRACL)
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| 171 |
-
value: 57.553
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| 172 |
-
- type: main_score
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| 173 |
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value: 56.912
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| 174 |
-
- type: nAUC_MAP@1000_diff1(MIRACL)
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| 175 |
-
value: 10.786403475779332
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| 176 |
-
- type: nAUC_MAP@1000_max(MIRACL)
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| 177 |
-
value: 29.477246196287275
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| 178 |
-
- type: nAUC_MAP@1000_std(MIRACL)
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| 179 |
-
value: 15.938834129839046
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| 180 |
-
- type: nAUC_MAP@100_diff1(MIRACL)
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| 181 |
-
value: 10.786403475779332
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| 182 |
-
- type: nAUC_MAP@100_max(MIRACL)
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| 183 |
-
value: 29.477246196287275
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| 184 |
-
- type: nAUC_MAP@100_std(MIRACL)
|
| 185 |
-
value: 15.938834129839046
|
| 186 |
-
- type: nAUC_MAP@10_diff1(MIRACL)
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| 187 |
-
value: 12.255091348037595
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| 188 |
-
- type: nAUC_MAP@10_max(MIRACL)
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| 189 |
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value: 26.72625370045134
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| 190 |
-
- type: nAUC_MAP@10_std(MIRACL)
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| 191 |
-
value: 14.180071586837812
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| 192 |
-
- type: nAUC_MAP@1_diff1(MIRACL)
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| 193 |
-
value: 28.616487922173768
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| 194 |
-
- type: nAUC_MAP@1_max(MIRACL)
|
| 195 |
-
value: 12.986192530664518
|
| 196 |
-
- type: nAUC_MAP@1_std(MIRACL)
|
| 197 |
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value: 4.086145762604503
|
| 198 |
-
- type: nAUC_MAP@20_diff1(MIRACL)
|
| 199 |
-
value: 11.360341572700476
|
| 200 |
-
- type: nAUC_MAP@20_max(MIRACL)
|
| 201 |
-
value: 28.612330384153832
|
| 202 |
-
- type: nAUC_MAP@20_std(MIRACL)
|
| 203 |
-
value: 15.787480742877937
|
| 204 |
-
- type: nAUC_MAP@3_diff1(MIRACL)
|
| 205 |
-
value: 18.033783954867623
|
| 206 |
-
- type: nAUC_MAP@3_max(MIRACL)
|
| 207 |
-
value: 20.97092332905034
|
| 208 |
-
- type: nAUC_MAP@3_std(MIRACL)
|
| 209 |
-
value: 9.106058710108279
|
| 210 |
-
- type: nAUC_MAP@5_diff1(MIRACL)
|
| 211 |
-
value: 14.784231238848433
|
| 212 |
-
- type: nAUC_MAP@5_max(MIRACL)
|
| 213 |
-
value: 23.841145797143
|
| 214 |
-
- type: nAUC_MAP@5_std(MIRACL)
|
| 215 |
-
value: 11.25686258970321
|
| 216 |
-
- type: nAUC_NDCG@1000_diff1(MIRACL)
|
| 217 |
-
value: 1.4728095471561125
|
| 218 |
-
- type: nAUC_NDCG@1000_max(MIRACL)
|
| 219 |
-
value: 39.84262968697792
|
| 220 |
-
- type: nAUC_NDCG@1000_std(MIRACL)
|
| 221 |
-
value: 22.4186410243652
|
| 222 |
-
- type: nAUC_NDCG@100_diff1(MIRACL)
|
| 223 |
-
value: 1.4728095471561125
|
| 224 |
-
- type: nAUC_NDCG@100_max(MIRACL)
|
| 225 |
-
value: 39.84262968697792
|
| 226 |
-
- type: nAUC_NDCG@100_std(MIRACL)
|
| 227 |
-
value: 22.4186410243652
|
| 228 |
-
- type: nAUC_NDCG@10_diff1(MIRACL)
|
| 229 |
-
value: 5.242996478950954
|
| 230 |
-
- type: nAUC_NDCG@10_max(MIRACL)
|
| 231 |
-
value: 33.86925934510759
|
| 232 |
-
- type: nAUC_NDCG@10_std(MIRACL)
|
| 233 |
-
value: 19.457386638149625
|
| 234 |
-
- type: nAUC_NDCG@1_diff1(MIRACL)
|
| 235 |
-
value: 16.925455715967676
|
| 236 |
-
- type: nAUC_NDCG@1_max(MIRACL)
|
| 237 |
-
value: 36.72266755084653
|
| 238 |
-
- type: nAUC_NDCG@1_std(MIRACL)
|
| 239 |
-
value: 18.357456476212622
|
| 240 |
-
- type: nAUC_NDCG@20_diff1(MIRACL)
|
| 241 |
-
value: 3.361697278095995
|
| 242 |
-
- type: nAUC_NDCG@20_max(MIRACL)
|
| 243 |
-
value: 37.38923489423496
|
| 244 |
-
- type: nAUC_NDCG@20_std(MIRACL)
|
| 245 |
-
value: 22.29168372402657
|
| 246 |
-
- type: nAUC_NDCG@3_diff1(MIRACL)
|
| 247 |
-
value: 10.936904314592084
|
| 248 |
-
- type: nAUC_NDCG@3_max(MIRACL)
|
| 249 |
-
value: 30.547718047674284
|
| 250 |
-
- type: nAUC_NDCG@3_std(MIRACL)
|
| 251 |
-
value: 15.142352896765665
|
| 252 |
-
- type: nAUC_NDCG@5_diff1(MIRACL)
|
| 253 |
-
value: 8.618074920961075
|
| 254 |
-
- type: nAUC_NDCG@5_max(MIRACL)
|
| 255 |
-
value: 30.808600807482367
|
| 256 |
-
- type: nAUC_NDCG@5_std(MIRACL)
|
| 257 |
-
value: 15.793512242130051
|
| 258 |
-
- type: nAUC_P@1000_diff1(MIRACL)
|
| 259 |
-
value: -24.81839490148569
|
| 260 |
-
- type: nAUC_P@1000_max(MIRACL)
|
| 261 |
-
value: 34.16200383739091
|
| 262 |
-
- type: nAUC_P@1000_std(MIRACL)
|
| 263 |
-
value: 20.95890369662007
|
| 264 |
-
- type: nAUC_P@100_diff1(MIRACL)
|
| 265 |
-
value: -24.818394901485657
|
| 266 |
-
- type: nAUC_P@100_max(MIRACL)
|
| 267 |
-
value: 34.16200383739092
|
| 268 |
-
- type: nAUC_P@100_std(MIRACL)
|
| 269 |
-
value: 20.958903696620112
|
| 270 |
-
- type: nAUC_P@10_diff1(MIRACL)
|
| 271 |
-
value: -22.646461560750986
|
| 272 |
-
- type: nAUC_P@10_max(MIRACL)
|
| 273 |
-
value: 34.57373514819872
|
| 274 |
-
- type: nAUC_P@10_std(MIRACL)
|
| 275 |
-
value: 24.27599718176041
|
| 276 |
-
- type: nAUC_P@1_diff1(MIRACL)
|
| 277 |
-
value: 16.925455715967676
|
| 278 |
-
- type: nAUC_P@1_max(MIRACL)
|
| 279 |
-
value: 36.72266755084653
|
| 280 |
-
- type: nAUC_P@1_std(MIRACL)
|
| 281 |
-
value: 18.357456476212622
|
| 282 |
-
- type: nAUC_P@20_diff1(MIRACL)
|
| 283 |
-
value: -23.33449798384014
|
| 284 |
-
- type: nAUC_P@20_max(MIRACL)
|
| 285 |
-
value: 34.92822081787735
|
| 286 |
-
- type: nAUC_P@20_std(MIRACL)
|
| 287 |
-
value: 25.048280657629267
|
| 288 |
-
- type: nAUC_P@3_diff1(MIRACL)
|
| 289 |
-
value: -11.60659490286
|
| 290 |
-
- type: nAUC_P@3_max(MIRACL)
|
| 291 |
-
value: 38.187883056013035
|
| 292 |
-
- type: nAUC_P@3_std(MIRACL)
|
| 293 |
-
value: 21.234776997940628
|
| 294 |
-
- type: nAUC_P@5_diff1(MIRACL)
|
| 295 |
-
value: -18.86697977242918
|
| 296 |
-
- type: nAUC_P@5_max(MIRACL)
|
| 297 |
-
value: 35.6110661197626
|
| 298 |
-
- type: nAUC_P@5_std(MIRACL)
|
| 299 |
-
value: 22.11165620702996
|
| 300 |
-
- type: nAUC_Recall@1000_diff1(MIRACL)
|
| 301 |
-
value: -31.456413113303867
|
| 302 |
-
- type: nAUC_Recall@1000_max(MIRACL)
|
| 303 |
-
value: 63.785265733309636
|
| 304 |
-
- type: nAUC_Recall@1000_std(MIRACL)
|
| 305 |
-
value: 36.587933217871914
|
| 306 |
-
- type: nAUC_Recall@100_diff1(MIRACL)
|
| 307 |
-
value: -31.456413113303867
|
| 308 |
-
- type: nAUC_Recall@100_max(MIRACL)
|
| 309 |
-
value: 63.785265733309636
|
| 310 |
-
- type: nAUC_Recall@100_std(MIRACL)
|
| 311 |
-
value: 36.587933217871914
|
| 312 |
-
- type: nAUC_Recall@10_diff1(MIRACL)
|
| 313 |
-
value: -9.518740341549913
|
| 314 |
-
- type: nAUC_Recall@10_max(MIRACL)
|
| 315 |
-
value: 35.00853357699468
|
| 316 |
-
- type: nAUC_Recall@10_std(MIRACL)
|
| 317 |
-
value: 22.79313936486099
|
| 318 |
-
- type: nAUC_Recall@1_diff1(MIRACL)
|
| 319 |
-
value: 28.616487922173768
|
| 320 |
-
- type: nAUC_Recall@1_max(MIRACL)
|
| 321 |
-
value: 12.986192530664518
|
| 322 |
-
- type: nAUC_Recall@1_std(MIRACL)
|
| 323 |
-
value: 4.086145762604503
|
| 324 |
-
- type: nAUC_Recall@20_diff1(MIRACL)
|
| 325 |
-
value: -17.771143411342166
|
| 326 |
-
- type: nAUC_Recall@20_max(MIRACL)
|
| 327 |
-
value: 47.59780316487735
|
| 328 |
-
- type: nAUC_Recall@20_std(MIRACL)
|
| 329 |
-
value: 33.25494707686132
|
| 330 |
-
- type: nAUC_Recall@3_diff1(MIRACL)
|
| 331 |
-
value: 10.171226133119783
|
| 332 |
-
- type: nAUC_Recall@3_max(MIRACL)
|
| 333 |
-
value: 21.097634288680847
|
| 334 |
-
- type: nAUC_Recall@3_std(MIRACL)
|
| 335 |
-
value: 10.087211861733298
|
| 336 |
-
- type: nAUC_Recall@5_diff1(MIRACL)
|
| 337 |
-
value: 1.6868374913242932
|
| 338 |
-
- type: nAUC_Recall@5_max(MIRACL)
|
| 339 |
-
value: 25.874440474993165
|
| 340 |
-
- type: nAUC_Recall@5_std(MIRACL)
|
| 341 |
-
value: 13.46380924822079
|
| 342 |
-
task:
|
| 343 |
-
type: Reranking
|
| 344 |
-
- dataset:
|
| 345 |
-
config: ru
|
| 346 |
-
name: MTEB MIRACLRetrieval (ru)
|
| 347 |
-
revision: main
|
| 348 |
-
split: dev
|
| 349 |
-
type: miracl/mmteb-miracl
|
| 350 |
-
metrics:
|
| 351 |
-
- type: main_score
|
| 352 |
-
value: 53.909
|
| 353 |
-
- type: map_at_1
|
| 354 |
-
value: 24.308
|
| 355 |
-
- type: map_at_10
|
| 356 |
-
value: 43.258
|
| 357 |
-
- type: map_at_100
|
| 358 |
-
value: 46.053
|
| 359 |
-
- type: map_at_1000
|
| 360 |
-
value: 46.176
|
| 361 |
-
- type: map_at_20
|
| 362 |
-
value: 44.962
|
| 363 |
-
- type: map_at_3
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| 364 |
-
value: 36.129
|
| 365 |
-
- type: map_at_5
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| 366 |
-
value: 40.077
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| 367 |
-
- type: mrr_at_1
|
| 368 |
-
value: 49.92012779552716
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-
- type: mrr_at_10
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value: 62.639554490592865
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| 371 |
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value: 63.09260401526302
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| 373 |
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value: 63.10428906436666
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| 375 |
-
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-
value: 62.94919151853632
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| 377 |
-
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value: 60.15708200212997
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-
value: 61.83439829605969
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| 381 |
-
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-
value: 24.249990208199268
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-
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value: 25.29688440384686
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value: 2.4312163206740536
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-
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value: 24.2554939267347
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-
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| 390 |
-
value: 25.25054164924535
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-
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-
value: 2.4121726280069757
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value: 24.411765629418987
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value: 23.13035697774593
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value: -0.1673711528601927
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| 400 |
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value: 30.55123128484441
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| 401 |
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value: 13.83849108263988
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value: -7.087181528435525
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| 405 |
-
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| 406 |
-
value: 24.125033292556417
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| 407 |
-
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value: 24.563171125814296
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value: 1.266006461448722
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value: 25.71581305774253
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value: 18.708623514300097
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value: -4.772722288463871
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value: 25.352787694389097
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| 419 |
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value: 20.974296353287084
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value: -3.4007260047029835
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| 423 |
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- type: nauc_mrr_at_1000_diff1
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value: 29.492072727604622
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-
- type: nauc_mrr_at_1000_max
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value: 34.60333674990558
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value: 11.223537361751173
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value: 29.47919553914885
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value: 34.618795300361995
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value: 11.243824787491663
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value: 29.481060608078298
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value: 34.752363175415745
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value: 10.98618160728943
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value: 31.81056902767142
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value: 30.351978574096773
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value: 9.735911194663025
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value: 29.390754002995035
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value: 34.75816984434079
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value: 11.325226515477347
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value: 29.948364490803186
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value: 33.973850208221556
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value: 9.988883050022485
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value: 29.477773016468696
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value: 34.38532892473932
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value: 10.206783034393654
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value: 24.15494700259076
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value: 32.367504385127035
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value: 10.372857487814498
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- type: nauc_ndcg_at_100_diff1
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value: 23.97247958991815
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| 473 |
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- type: nauc_ndcg_at_100_max
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value: 32.21110774026889
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value: 11.065328347817761
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- type: nauc_ndcg_at_10_diff1
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value: 24.038789867355796
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- type: nauc_ndcg_at_10_max
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| 480 |
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value: 28.14682223937745
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value: 4.518525314723316
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value: 31.81056902767142
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value: 30.351978574096773
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value: 9.735911194663025
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value: 23.157990079778138
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value: 30.521172934621703
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value: 7.660125728373433
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value: 24.44153871615053
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value: 27.08209732696818
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value: 3.8766269917792537
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value: 24.952468410841863
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value: 26.29873769608537
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value: 1.3359423751654511
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value: -9.104010991734798
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| 509 |
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| 510 |
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value: 20.36838078039637
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value: 26.889986331386297
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value: -7.181546793298205
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| 515 |
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| 516 |
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value: 24.32969645433586
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value: 31.546209514202232
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value: -1.0044021788494442
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value: 29.37074096666726
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value: 25.000959926288214
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value: 31.81056902767142
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value: 30.351978574096773
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value: 9.735911194663025
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value: -5.242529022989003
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value: 28.199268120740822
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value: 28.460986811065037
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value: 9.46419634664173
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| 539 |
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value: 32.203956451949914
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value: 30.53411024247047
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value: 17.926227114457067
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| 549 |
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| 550 |
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value: 12.347919922311121
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| 551 |
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value: 62.10824756167678
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value: 65.9625810682273
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| 555 |
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value: 11.945066948287723
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| 557 |
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value: 37.07070306829974
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| 559 |
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| 560 |
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value: 38.76495395051901
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| 561 |
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value: 14.793964290237943
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| 563 |
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| 564 |
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value: 23.170920682517334
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| 565 |
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| 566 |
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value: 5.07461971737137
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| 567 |
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value: 30.55123128484441
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value: 13.83849108263988
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| 572 |
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value: -7.087181528435525
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| 573 |
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| 574 |
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value: 10.349310874535616
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| 575 |
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| 576 |
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value: 27.72667852012557
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| 578 |
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value: 13.37946493360006
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| 579 |
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| 580 |
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value: 20.660181561801195
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| 581 |
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- type: nauc_recall_at_3_max
|
| 582 |
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value: 16.734608747226137
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| 583 |
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| 584 |
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value: -5.887299100086449
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| 585 |
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| 586 |
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value: 19.292387971699007
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| 587 |
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- type: nauc_recall_at_5_max
|
| 588 |
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value: 18.151647291256193
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| 589 |
-
- type: nauc_recall_at_5_std
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| 590 |
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value: -5.3874570564310895
|
| 591 |
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- type: ndcg_at_1
|
| 592 |
-
value: 49.919999999999995
|
| 593 |
-
- type: ndcg_at_10
|
| 594 |
-
value: 53.909
|
| 595 |
-
- type: ndcg_at_100
|
| 596 |
-
value: 61.346999999999994
|
| 597 |
-
- type: ndcg_at_1000
|
| 598 |
-
value: 62.831
|
| 599 |
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- type: ndcg_at_20
|
| 600 |
-
value: 57.44200000000001
|
| 601 |
-
- type: ndcg_at_3
|
| 602 |
-
value: 48.034
|
| 603 |
-
- type: ndcg_at_5
|
| 604 |
-
value: 50.151
|
| 605 |
-
- type: precision_at_1
|
| 606 |
-
value: 49.919999999999995
|
| 607 |
-
- type: precision_at_10
|
| 608 |
-
value: 16.206
|
| 609 |
-
- type: precision_at_100
|
| 610 |
-
value: 2.467
|
| 611 |
-
- type: precision_at_1000
|
| 612 |
-
value: 0.27499999999999997
|
| 613 |
-
- type: precision_at_20
|
| 614 |
-
value: 9.847999999999999
|
| 615 |
-
- type: precision_at_3
|
| 616 |
-
value: 33.013999999999996
|
| 617 |
-
- type: precision_at_5
|
| 618 |
-
value: 25.495
|
| 619 |
-
- type: recall_at_1
|
| 620 |
-
value: 24.308
|
| 621 |
-
- type: recall_at_10
|
| 622 |
-
value: 64.226
|
| 623 |
-
- type: recall_at_100
|
| 624 |
-
value: 88.532
|
| 625 |
-
- type: recall_at_1000
|
| 626 |
-
value: 96.702
|
| 627 |
-
- type: recall_at_20
|
| 628 |
-
value: 73.855
|
| 629 |
-
- type: recall_at_3
|
| 630 |
-
value: 43.75
|
| 631 |
-
- type: recall_at_5
|
| 632 |
-
value: 53.293
|
| 633 |
-
task:
|
| 634 |
-
type: Retrieval
|
| 635 |
-
- dataset:
|
| 636 |
-
config: ru
|
| 637 |
-
name: MTEB MassiveIntentClassification (ru)
|
| 638 |
-
revision: 4672e20407010da34463acc759c162ca9734bca6
|
| 639 |
-
split: test
|
| 640 |
-
type: mteb/amazon_massive_intent
|
| 641 |
-
metrics:
|
| 642 |
-
- type: accuracy
|
| 643 |
-
value: 66.96704774714189
|
| 644 |
-
- type: f1
|
| 645 |
-
value: 63.75700201120695
|
| 646 |
-
- type: f1_weighted
|
| 647 |
-
value: 65.79948352494334
|
| 648 |
-
- type: main_score
|
| 649 |
-
value: 66.96704774714189
|
| 650 |
-
task:
|
| 651 |
-
type: Classification
|
| 652 |
-
- dataset:
|
| 653 |
-
config: ru
|
| 654 |
-
name: MTEB MassiveScenarioClassification (ru)
|
| 655 |
-
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
|
| 656 |
-
split: test
|
| 657 |
-
type: mteb/amazon_massive_scenario
|
| 658 |
-
metrics:
|
| 659 |
-
- type: accuracy
|
| 660 |
-
value: 71.79556153328849
|
| 661 |
-
- type: f1
|
| 662 |
-
value: 71.04798190430378
|
| 663 |
-
- type: f1_weighted
|
| 664 |
-
value: 71.11136110921589
|
| 665 |
-
- type: main_score
|
| 666 |
-
value: 71.79556153328849
|
| 667 |
-
task:
|
| 668 |
-
type: Classification
|
| 669 |
-
- dataset:
|
| 670 |
-
config: default
|
| 671 |
-
name: MTEB RUParaPhraserSTS (default)
|
| 672 |
-
revision: 43265056790b8f7c59e0139acb4be0a8dad2c8f4
|
| 673 |
-
split: test
|
| 674 |
-
type: merionum/ru_paraphraser
|
| 675 |
-
metrics:
|
| 676 |
-
- type: cosine_pearson
|
| 677 |
-
value: 69.4312341087414
|
| 678 |
-
- type: cosine_spearman
|
| 679 |
-
value: 76.16273410937974
|
| 680 |
-
- type: euclidean_pearson
|
| 681 |
-
value: 73.59970264325928
|
| 682 |
-
- type: euclidean_spearman
|
| 683 |
-
value: 76.16273410937974
|
| 684 |
-
- type: main_score
|
| 685 |
-
value: 76.16273410937974
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-
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name: MTEB RiaNewsRetrieval (default)
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| 985 |
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task:
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| 986 |
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| 987 |
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| 988 |
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config: default
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| 989 |
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name: MTEB RuBQReranking (default)
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task:
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| 1013 |
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| 1014 |
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| 1015 |
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config: default
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| 1016 |
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| 1017 |
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split: test
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value: 81.248
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| 1303 |
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task:
|
| 1304 |
-
type: Retrieval
|
| 1305 |
-
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|
| 1306 |
-
config: default
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| 1307 |
-
name: MTEB RuReviewsClassification (default)
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| 1308 |
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revision: f6d2c31f4dc6b88f468552750bfec05b4b41b05a
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split: test
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| 1310 |
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type: ai-forever/ru-reviews-classification
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| 1311 |
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metrics:
|
| 1312 |
-
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| 1313 |
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value: 67.958984375
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value: 67.958984375
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| 1320 |
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task:
|
| 1321 |
-
type: Classification
|
| 1322 |
-
- dataset:
|
| 1323 |
-
config: default
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| 1324 |
-
name: MTEB RuSTSBenchmarkSTS (default)
|
| 1325 |
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revision: 7cf24f325c6da6195df55bef3d86b5e0616f3018
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| 1326 |
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split: test
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| 1327 |
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type: ai-forever/ru-stsbenchmark-sts
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| 1328 |
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metrics:
|
| 1329 |
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|
| 1330 |
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value: 79.11336124619963
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| 1331 |
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| 1332 |
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value: 78.69157477180703
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| 1333 |
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value: 77.84066073571212
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| 1335 |
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value: 78.69157477180703
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| 1340 |
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value: 78.61384378877501
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value: 78.69157477180703
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| 1347 |
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task:
|
| 1348 |
-
type: STS
|
| 1349 |
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|
| 1350 |
-
config: default
|
| 1351 |
-
name: MTEB RuSciBenchGRNTIClassification (default)
|
| 1352 |
-
revision: 673a610d6d3dd91a547a0d57ae1b56f37ebbf6a1
|
| 1353 |
-
split: test
|
| 1354 |
-
type: ai-forever/ru-scibench-grnti-classification
|
| 1355 |
-
metrics:
|
| 1356 |
-
- type: accuracy
|
| 1357 |
-
value: 59.326171875
|
| 1358 |
-
- type: f1
|
| 1359 |
-
value: 58.01171745357119
|
| 1360 |
-
- type: f1_weighted
|
| 1361 |
-
value: 58.02106511480968
|
| 1362 |
-
- type: main_score
|
| 1363 |
-
value: 59.326171875
|
| 1364 |
-
task:
|
| 1365 |
-
type: Classification
|
| 1366 |
-
- dataset:
|
| 1367 |
-
config: default
|
| 1368 |
-
name: MTEB RuSciBenchGRNTIClusteringP2P (default)
|
| 1369 |
-
revision: 673a610d6d3dd91a547a0d57ae1b56f37ebbf6a1
|
| 1370 |
-
split: test
|
| 1371 |
-
type: ai-forever/ru-scibench-grnti-classification
|
| 1372 |
-
metrics:
|
| 1373 |
-
- type: main_score
|
| 1374 |
-
value: 55.46570753380975
|
| 1375 |
-
- type: v_measure
|
| 1376 |
-
value: 55.46570753380975
|
| 1377 |
-
- type: v_measure_std
|
| 1378 |
-
value: 0.9813885872798612
|
| 1379 |
-
task:
|
| 1380 |
-
type: Clustering
|
| 1381 |
-
- dataset:
|
| 1382 |
-
config: default
|
| 1383 |
-
name: MTEB RuSciBenchOECDClassification (default)
|
| 1384 |
-
revision: 26c88e99dcaba32bb45d0e1bfc21902337f6d471
|
| 1385 |
-
split: test
|
| 1386 |
-
type: ai-forever/ru-scibench-oecd-classification
|
| 1387 |
-
metrics:
|
| 1388 |
-
- type: accuracy
|
| 1389 |
-
value: 46.328125
|
| 1390 |
-
- type: f1
|
| 1391 |
-
value: 44.19158709013339
|
| 1392 |
-
- type: f1_weighted
|
| 1393 |
-
value: 44.190957945676026
|
| 1394 |
-
- type: main_score
|
| 1395 |
-
value: 46.328125
|
| 1396 |
-
task:
|
| 1397 |
-
type: Classification
|
| 1398 |
-
- dataset:
|
| 1399 |
-
config: default
|
| 1400 |
-
name: MTEB RuSciBenchOECDClusteringP2P (default)
|
| 1401 |
-
revision: 26c88e99dcaba32bb45d0e1bfc21902337f6d471
|
| 1402 |
-
split: test
|
| 1403 |
-
type: ai-forever/ru-scibench-oecd-classification
|
| 1404 |
-
metrics:
|
| 1405 |
-
- type: main_score
|
| 1406 |
-
value: 47.28635342613908
|
| 1407 |
-
- type: v_measure
|
| 1408 |
-
value: 47.28635342613908
|
| 1409 |
-
- type: v_measure_std
|
| 1410 |
-
value: 0.7431017612993989
|
| 1411 |
-
task:
|
| 1412 |
-
type: Clustering
|
| 1413 |
-
- dataset:
|
| 1414 |
-
config: ru
|
| 1415 |
-
name: MTEB STS22 (ru)
|
| 1416 |
-
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
|
| 1417 |
-
split: test
|
| 1418 |
-
type: mteb/sts22-crosslingual-sts
|
| 1419 |
-
metrics:
|
| 1420 |
-
- type: cosine_pearson
|
| 1421 |
-
value: 63.10139371129796
|
| 1422 |
-
- type: cosine_spearman
|
| 1423 |
-
value: 67.06445400504978
|
| 1424 |
-
- type: euclidean_pearson
|
| 1425 |
-
value: 62.74563386470613
|
| 1426 |
-
- type: euclidean_spearman
|
| 1427 |
-
value: 67.06445400504978
|
| 1428 |
-
- type: main_score
|
| 1429 |
-
value: 67.06445400504978
|
| 1430 |
-
- type: manhattan_pearson
|
| 1431 |
-
value: 62.540465664732395
|
| 1432 |
-
- type: manhattan_spearman
|
| 1433 |
-
value: 66.65899492022648
|
| 1434 |
-
- type: pearson
|
| 1435 |
-
value: 63.10139371129796
|
| 1436 |
-
- type: spearman
|
| 1437 |
-
value: 67.06445400504978
|
| 1438 |
-
task:
|
| 1439 |
-
type: STS
|
| 1440 |
-
- dataset:
|
| 1441 |
-
config: default
|
| 1442 |
-
name: MTEB SensitiveTopicsClassification (default)
|
| 1443 |
-
revision: 416b34a802308eac30e4192afc0ff99bb8dcc7f2
|
| 1444 |
-
split: test
|
| 1445 |
-
type: ai-forever/sensitive-topics-classification
|
| 1446 |
-
metrics:
|
| 1447 |
-
- type: accuracy
|
| 1448 |
-
value: 33.0712890625
|
| 1449 |
-
- type: f1
|
| 1450 |
-
value: 38.063573562290024
|
| 1451 |
-
- type: lrap
|
| 1452 |
-
value: 49.586995442707696
|
| 1453 |
-
- type: main_score
|
| 1454 |
-
value: 33.0712890625
|
| 1455 |
-
task:
|
| 1456 |
-
type: MultilabelClassification
|
| 1457 |
-
- dataset:
|
| 1458 |
-
config: default
|
| 1459 |
-
name: MTEB TERRa (default)
|
| 1460 |
-
revision: 7b58f24536063837d644aab9a023c62199b2a612
|
| 1461 |
-
split: dev
|
| 1462 |
-
type: ai-forever/terra-pairclassification
|
| 1463 |
-
metrics:
|
| 1464 |
-
- type: cosine_accuracy
|
| 1465 |
-
value: 61.563517915309454
|
| 1466 |
-
- type: cosine_accuracy_threshold
|
| 1467 |
-
value: 75.3734290599823
|
| 1468 |
-
- type: cosine_ap
|
| 1469 |
-
value: 60.78861909325018
|
| 1470 |
-
- type: cosine_f1
|
| 1471 |
-
value: 67.25663716814158
|
| 1472 |
-
- type: cosine_f1_threshold
|
| 1473 |
-
value: 54.05237674713135
|
| 1474 |
-
- type: cosine_precision
|
| 1475 |
-
value: 50.836120401337794
|
| 1476 |
-
- type: cosine_recall
|
| 1477 |
-
value: 99.34640522875817
|
| 1478 |
-
- type: dot_accuracy
|
| 1479 |
-
value: 61.563517915309454
|
| 1480 |
-
- type: dot_accuracy_threshold
|
| 1481 |
-
value: 75.37343502044678
|
| 1482 |
-
- type: dot_ap
|
| 1483 |
-
value: 60.78861909325018
|
| 1484 |
-
- type: dot_f1
|
| 1485 |
-
value: 67.25663716814158
|
| 1486 |
-
- type: dot_f1_threshold
|
| 1487 |
-
value: 54.05237674713135
|
| 1488 |
-
- type: dot_precision
|
| 1489 |
-
value: 50.836120401337794
|
| 1490 |
-
- type: dot_recall
|
| 1491 |
-
value: 99.34640522875817
|
| 1492 |
-
- type: euclidean_accuracy
|
| 1493 |
-
value: 61.563517915309454
|
| 1494 |
-
- type: euclidean_accuracy_threshold
|
| 1495 |
-
value: 70.18057107925415
|
| 1496 |
-
- type: euclidean_ap
|
| 1497 |
-
value: 60.78861909325018
|
| 1498 |
-
- type: euclidean_f1
|
| 1499 |
-
value: 67.25663716814158
|
| 1500 |
-
- type: euclidean_f1_threshold
|
| 1501 |
-
value: 95.86195945739746
|
| 1502 |
-
- type: euclidean_precision
|
| 1503 |
-
value: 50.836120401337794
|
| 1504 |
-
- type: euclidean_recall
|
| 1505 |
-
value: 99.34640522875817
|
| 1506 |
-
- type: main_score
|
| 1507 |
-
value: 60.78861909325018
|
| 1508 |
-
- type: manhattan_accuracy
|
| 1509 |
-
value: 60.91205211726385
|
| 1510 |
-
- type: manhattan_accuracy_threshold
|
| 1511 |
-
value: 1813.1645202636719
|
| 1512 |
-
- type: manhattan_ap
|
| 1513 |
-
value: 60.478709337038936
|
| 1514 |
-
- type: manhattan_f1
|
| 1515 |
-
value: 67.10816777041943
|
| 1516 |
-
- type: manhattan_f1_threshold
|
| 1517 |
-
value: 2475.027275085449
|
| 1518 |
-
- type: manhattan_precision
|
| 1519 |
-
value: 50.66666666666667
|
| 1520 |
-
- type: manhattan_recall
|
| 1521 |
-
value: 99.34640522875817
|
| 1522 |
-
- type: max_ap
|
| 1523 |
-
value: 60.78861909325018
|
| 1524 |
-
- type: max_f1
|
| 1525 |
-
value: 67.25663716814158
|
| 1526 |
-
- type: max_precision
|
| 1527 |
-
value: 50.836120401337794
|
| 1528 |
-
- type: max_recall
|
| 1529 |
-
value: 99.34640522875817
|
| 1530 |
-
- type: similarity_accuracy
|
| 1531 |
-
value: 61.563517915309454
|
| 1532 |
-
- type: similarity_accuracy_threshold
|
| 1533 |
-
value: 75.3734290599823
|
| 1534 |
-
- type: similarity_ap
|
| 1535 |
-
value: 60.78861909325018
|
| 1536 |
-
- type: similarity_f1
|
| 1537 |
-
value: 67.25663716814158
|
| 1538 |
-
- type: similarity_f1_threshold
|
| 1539 |
-
value: 54.05237674713135
|
| 1540 |
-
- type: similarity_precision
|
| 1541 |
-
value: 50.836120401337794
|
| 1542 |
-
- type: similarity_recall
|
| 1543 |
-
value: 99.34640522875817
|
| 1544 |
-
task:
|
| 1545 |
-
type: PairClassification
|
| 1546 |
-
license: mit
|
| 1547 |
-
language:
|
| 1548 |
-
- ru
|
| 1549 |
-
- en
|
| 1550 |
-
tags:
|
| 1551 |
-
- mteb
|
| 1552 |
-
- transformers
|
| 1553 |
-
- sentence-transformers
|
| 1554 |
-
base_model: ai-forever/ruRoberta-large
|
| 1555 |
-
---
|
| 1556 |
-
|
| 1557 |
-
# Model Card for ru-en-RoSBERTa
|
| 1558 |
-
|
| 1559 |
-
The ru-en-RoSBERTa is a general text embedding model for Russian. The model is based on [ruRoBERTa](https://huggingface.co/ai-forever/ruRoberta-large) and fine-tuned with ~4M pairs of supervised, synthetic and unsupervised data in Russian and English. Tokenizer supports some English tokens from [RoBERTa](https://huggingface.co/FacebookAI/roberta-large) tokenizer.
|
| 1560 |
-
|
| 1561 |
-
For more model details please refer to our [article](https://arxiv.org/abs/2408.12503).
|
| 1562 |
-
|
| 1563 |
-
## Usage
|
| 1564 |
-
|
| 1565 |
-
The model can be used as is with prefixes. It is recommended to use CLS pooling. The choice of prefix and pooling depends on the task.
|
| 1566 |
-
|
| 1567 |
-
We use the following basic rules to choose a prefix:
|
| 1568 |
-
- `"search_query: "` and `"search_document: "` prefixes are for answer or relevant paragraph retrieval
|
| 1569 |
-
- `"classification: "` prefix is for symmetric paraphrasing related tasks (STS, NLI, Bitext Mining)
|
| 1570 |
-
- `"clustering: "` prefix is for any tasks that rely on thematic features (topic classification, title-body retrieval)
|
| 1571 |
-
|
| 1572 |
-
To better tailor the model to your needs, you can fine-tune it with relevant high-quality Russian and English datasets.
|
| 1573 |
-
|
| 1574 |
-
Below are examples of texts encoding using the Transformers and SentenceTransformers libraries.
|
| 1575 |
-
|
| 1576 |
-
### Transformers
|
| 1577 |
-
|
| 1578 |
-
```python
|
| 1579 |
-
import torch
|
| 1580 |
-
import torch.nn.functional as F
|
| 1581 |
-
from transformers import AutoTokenizer, AutoModel
|
| 1582 |
-
|
| 1583 |
-
|
| 1584 |
-
def pool(hidden_state, mask, pooling_method="cls"):
|
| 1585 |
-
if pooling_method == "mean":
|
| 1586 |
-
s = torch.sum(hidden_state * mask.unsqueeze(-1).float(), dim=1)
|
| 1587 |
-
d = mask.sum(axis=1, keepdim=True).float()
|
| 1588 |
-
return s / d
|
| 1589 |
-
elif pooling_method == "cls":
|
| 1590 |
-
return hidden_state[:, 0]
|
| 1591 |
-
|
| 1592 |
-
inputs = [
|
| 1593 |
-
#
|
| 1594 |
-
"classification: Он нам и <unk> не нужон ваш Интернет!",
|
| 1595 |
-
"clustering: В Ярославской области разрешили работу бань, но без посетителей",
|
| 1596 |
-
"search_query: Сколько программистов нужно, чтобы вкрутить лампочку?",
|
| 1597 |
-
|
| 1598 |
-
#
|
| 1599 |
-
"classification: What a time to be alive!",
|
| 1600 |
-
"clustering: Ярославским баням разрешили работать без посетителей",
|
| 1601 |
-
"search_document: Чтобы вкрутить лампочку, требуется три программиста: один напишет программу извлечения лампочки, другой — вкручивания лампочки, а третий проведет тестирование.",
|
| 1602 |
-
]
|
| 1603 |
-
|
| 1604 |
-
tokenizer = AutoTokenizer.from_pretrained("ai-forever/ru-en-RoSBERTa")
|
| 1605 |
-
model = AutoModel.from_pretrained("ai-forever/ru-en-RoSBERTa")
|
| 1606 |
-
|
| 1607 |
-
tokenized_inputs = tokenizer(inputs, max_length=512, padding=True, truncation=True, return_tensors="pt")
|
| 1608 |
-
|
| 1609 |
-
with torch.no_grad():
|
| 1610 |
-
outputs = model(**tokenized_inputs)
|
| 1611 |
-
|
| 1612 |
-
embeddings = pool(
|
| 1613 |
-
outputs.last_hidden_state,
|
| 1614 |
-
tokenized_inputs["attention_mask"],
|
| 1615 |
-
pooling_method="cls" # or try "mean"
|
| 1616 |
-
)
|
| 1617 |
-
|
| 1618 |
-
embeddings = F.normalize(embeddings, p=2, dim=1)
|
| 1619 |
-
|
| 1620 |
-
sim_scores = embeddings[:3] @ embeddings[3:].T
|
| 1621 |
-
print(sim_scores.diag().tolist())
|
| 1622 |
-
# [0.4796873927116394, 0.9409002065658569, 0.7761015892028809]
|
| 1623 |
-
```
|
| 1624 |
-
|
| 1625 |
-
### SentenceTransformers
|
| 1626 |
-
|
| 1627 |
-
```python
|
| 1628 |
-
from sentence_transformers import SentenceTransformer
|
| 1629 |
-
|
| 1630 |
-
|
| 1631 |
-
inputs = [
|
| 1632 |
-
#
|
| 1633 |
-
"classification: Он нам и <unk> не нужон ваш Интернет!",
|
| 1634 |
-
"clustering: В Ярославской области разрешили работу бань, но без посетителей",
|
| 1635 |
-
"search_query: Сколько программистов нужно, чтобы вкрутить лампочку?",
|
| 1636 |
-
|
| 1637 |
-
#
|
| 1638 |
-
"classification: What a time to be alive!",
|
| 1639 |
-
"clustering: Ярославским баням разрешили работать без посетителей",
|
| 1640 |
-
"search_document: Чтобы вкрутить лампочку, требуется три программиста: один напишет программу извлечения лампочки, другой — вкручивания лампочки, а третий проведет тестирование.",
|
| 1641 |
-
]
|
| 1642 |
-
|
| 1643 |
-
# loads model with CLS pooling
|
| 1644 |
-
model = SentenceTransformer("ai-forever/ru-en-RoSBERTa")
|
| 1645 |
-
|
| 1646 |
-
# embeddings are normalized by default
|
| 1647 |
-
embeddings = model.encode(inputs, convert_to_tensor=True)
|
| 1648 |
-
|
| 1649 |
-
sim_scores = embeddings[:3] @ embeddings[3:].T
|
| 1650 |
-
print(sim_scores.diag().tolist())
|
| 1651 |
-
# [0.47968706488609314, 0.940900444984436, 0.7761018872261047]
|
| 1652 |
-
```
|
| 1653 |
-
|
| 1654 |
-
or using prompts (sentence-transformers>=2.4.0):
|
| 1655 |
-
|
| 1656 |
-
```python
|
| 1657 |
-
from sentence_transformers import SentenceTransformer
|
| 1658 |
-
|
| 1659 |
-
|
| 1660 |
-
# loads model with CLS pooling
|
| 1661 |
-
model = SentenceTransformer("ai-forever/ru-en-RoSBERTa")
|
| 1662 |
-
|
| 1663 |
-
classification = model.encode(["Он нам и <unk> не нужон ваш Интернет!", "What a time to be alive!"], prompt_name="classification")
|
| 1664 |
-
print(classification[0] @ classification[1].T) # 0.47968706488609314
|
| 1665 |
-
|
| 1666 |
-
clustering = model.encode(["В Ярославской области разрешили работу бань, но без посетителей", "Ярославским баням разрешили работать без посетителей"], prompt_name="clustering")
|
| 1667 |
-
print(clustering[0] @ clustering[1].T) # 0.940900444984436
|
| 1668 |
-
|
| 1669 |
-
query_embedding = model.encode("Сколько программистов нужно, чтобы вкрутить лампочку?", prompt_name="search_query")
|
| 1670 |
-
document_embedding = model.encode("Чтобы вкрутить лампочку, требуется три программиста: один напишет программу извлечения лампочки, другой — вкручивания лампочки, а третий проведет тестирование.", prompt_name="search_document")
|
| 1671 |
-
print(query_embedding @ document_embedding.T) # 0.7761018872261047
|
| 1672 |
-
```
|
| 1673 |
-
|
| 1674 |
-
## Citation
|
| 1675 |
-
|
| 1676 |
-
```
|
| 1677 |
-
@misc{snegirev2024russianfocusedembeddersexplorationrumteb,
|
| 1678 |
-
title={The Russian-focused embedders' exploration: ruMTEB benchmark and Russian embedding model design},
|
| 1679 |
-
author={Artem Snegirev and Maria Tikhonova and Anna Maksimova and Alena Fenogenova and Alexander Abramov},
|
| 1680 |
-
year={2024},
|
| 1681 |
-
eprint={2408.12503},
|
| 1682 |
-
archivePrefix={arXiv},
|
| 1683 |
-
primaryClass={cs.CL},
|
| 1684 |
-
url={https://arxiv.org/abs/2408.12503},
|
| 1685 |
-
}
|
| 1686 |
-
```
|
| 1687 |
-
|
| 1688 |
-
## Limitations
|
| 1689 |
-
|
| 1690 |
-
The model is designed to process texts in Russian, the quality in English is unknown. Maximum input text length is limited to 512 tokens.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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