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2728
+ dataset:
2729
+ type: mteb/summeval
2730
+ name: MTEB SummEval
2731
+ config: default
2732
+ split: test
2733
+ revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
2734
+ metrics:
2735
+ - type: cos_sim_pearson
2736
+ value: 29.97416289382191
2737
+ - type: cos_sim_spearman
2738
+ value: 29.871890597161432
2739
+ - type: dot_pearson
2740
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2741
+ - type: dot_spearman
2742
+ value: 28.872458999448686
2743
+ - task:
2744
+ type: Retrieval
2745
+ dataset:
2746
+ type: trec-covid
2747
+ name: MTEB TRECCOVID
2748
+ config: default
2749
+ split: test
2750
+ revision: None
2751
+ metrics:
2752
+ - type: map_at_1
2753
+ value: 0.22599999999999998
2754
+ - type: map_at_10
2755
+ value: 1.646
2756
+ - type: map_at_100
2757
+ value: 9.491
2758
+ - type: map_at_1000
2759
+ value: 23.75
2760
+ - type: map_at_3
2761
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2762
+ - type: map_at_5
2763
+ value: 0.9129999999999999
2764
+ - type: mrr_at_1
2765
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+ - type: mrr_at_10
2767
+ value: 89.889
2768
+ - type: mrr_at_100
2769
+ value: 89.889
2770
+ - type: mrr_at_1000
2771
+ value: 89.889
2772
+ - type: mrr_at_3
2773
+ value: 89.667
2774
+ - type: mrr_at_5
2775
+ value: 89.667
2776
+ - type: ndcg_at_1
2777
+ value: 75.0
2778
+ - type: ndcg_at_10
2779
+ value: 67.368
2780
+ - type: ndcg_at_100
2781
+ value: 52.834
2782
+ - type: ndcg_at_1000
2783
+ value: 49.144
2784
+ - type: ndcg_at_3
2785
+ value: 72.866
2786
+ - type: ndcg_at_5
2787
+ value: 70.16
2788
+ - type: precision_at_1
2789
+ value: 84.0
2790
+ - type: precision_at_10
2791
+ value: 71.8
2792
+ - type: precision_at_100
2793
+ value: 54.04
2794
+ - type: precision_at_1000
2795
+ value: 21.709999999999997
2796
+ - type: precision_at_3
2797
+ value: 77.333
2798
+ - type: precision_at_5
2799
+ value: 74.0
2800
+ - type: recall_at_1
2801
+ value: 0.22599999999999998
2802
+ - type: recall_at_10
2803
+ value: 1.9029999999999998
2804
+ - type: recall_at_100
2805
+ value: 13.012
2806
+ - type: recall_at_1000
2807
+ value: 46.105000000000004
2808
+ - type: recall_at_3
2809
+ value: 0.63
2810
+ - type: recall_at_5
2811
+ value: 1.0030000000000001
2812
+ - task:
2813
+ type: Retrieval
2814
+ dataset:
2815
+ type: webis-touche2020
2816
+ name: MTEB Touche2020
2817
+ config: default
2818
+ split: test
2819
+ revision: None
2820
+ metrics:
2821
+ - type: map_at_1
2822
+ value: 1.5
2823
+ - type: map_at_10
2824
+ value: 8.193999999999999
2825
+ - type: map_at_100
2826
+ value: 14.01
2827
+ - type: map_at_1000
2828
+ value: 15.570999999999998
2829
+ - type: map_at_3
2830
+ value: 4.361000000000001
2831
+ - type: map_at_5
2832
+ value: 5.9270000000000005
2833
+ - type: mrr_at_1
2834
+ value: 16.326999999999998
2835
+ - type: mrr_at_10
2836
+ value: 33.326
2837
+ - type: mrr_at_100
2838
+ value: 34.592
2839
+ - type: mrr_at_1000
2840
+ value: 34.592
2841
+ - type: mrr_at_3
2842
+ value: 29.252
2843
+ - type: mrr_at_5
2844
+ value: 30.680000000000003
2845
+ - type: ndcg_at_1
2846
+ value: 15.306000000000001
2847
+ - type: ndcg_at_10
2848
+ value: 19.819
2849
+ - type: ndcg_at_100
2850
+ value: 33.428000000000004
2851
+ - type: ndcg_at_1000
2852
+ value: 45.024
2853
+ - type: ndcg_at_3
2854
+ value: 19.667
2855
+ - type: ndcg_at_5
2856
+ value: 19.625
2857
+ - type: precision_at_1
2858
+ value: 16.326999999999998
2859
+ - type: precision_at_10
2860
+ value: 18.367
2861
+ - type: precision_at_100
2862
+ value: 7.367
2863
+ - type: precision_at_1000
2864
+ value: 1.496
2865
+ - type: precision_at_3
2866
+ value: 23.128999999999998
2867
+ - type: precision_at_5
2868
+ value: 21.633
2869
+ - type: recall_at_1
2870
+ value: 1.5
2871
+ - type: recall_at_10
2872
+ value: 14.362
2873
+ - type: recall_at_100
2874
+ value: 45.842
2875
+ - type: recall_at_1000
2876
+ value: 80.42
2877
+ - type: recall_at_3
2878
+ value: 5.99
2879
+ - type: recall_at_5
2880
+ value: 8.701
2881
+ - task:
2882
+ type: Classification
2883
+ dataset:
2884
+ type: mteb/toxic_conversations_50k
2885
+ name: MTEB ToxicConversationsClassification
2886
+ config: default
2887
+ split: test
2888
+ revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
2889
+ metrics:
2890
+ - type: accuracy
2891
+ value: 70.04740000000001
2892
+ - type: ap
2893
+ value: 13.58661943759992
2894
+ - type: f1
2895
+ value: 53.727487131754195
2896
+ - task:
2897
+ type: Classification
2898
+ dataset:
2899
+ type: mteb/tweet_sentiment_extraction
2900
+ name: MTEB TweetSentimentExtractionClassification
2901
+ config: default
2902
+ split: test
2903
+ revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
2904
+ metrics:
2905
+ - type: accuracy
2906
+ value: 61.06395019807584
2907
+ - type: f1
2908
+ value: 61.36753664680866
2909
+ - task:
2910
+ type: Clustering
2911
+ dataset:
2912
+ type: mteb/twentynewsgroups-clustering
2913
+ name: MTEB TwentyNewsgroupsClustering
2914
+ config: default
2915
+ split: test
2916
+ revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
2917
+ metrics:
2918
+ - type: v_measure
2919
+ value: 40.19881263066229
2920
+ - task:
2921
+ type: PairClassification
2922
+ dataset:
2923
+ type: mteb/twittersemeval2015-pairclassification
2924
+ name: MTEB TwitterSemEval2015
2925
+ config: default
2926
+ split: test
2927
+ revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
2928
+ metrics:
2929
+ - type: cos_sim_accuracy
2930
+ value: 85.19401561661799
2931
+ - type: cos_sim_ap
2932
+ value: 71.62462506173092
2933
+ - type: cos_sim_f1
2934
+ value: 66.0641327225455
2935
+ - type: cos_sim_precision
2936
+ value: 62.234662934453
2937
+ - type: cos_sim_recall
2938
+ value: 70.3957783641161
2939
+ - type: dot_accuracy
2940
+ value: 84.69333015437802
2941
+ - type: dot_ap
2942
+ value: 69.83805526490895
2943
+ - type: dot_f1
2944
+ value: 64.85446235265817
2945
+ - type: dot_precision
2946
+ value: 59.59328028293546
2947
+ - type: dot_recall
2948
+ value: 71.13456464379946
2949
+ - type: euclidean_accuracy
2950
+ value: 85.38475293556655
2951
+ - type: euclidean_ap
2952
+ value: 72.05594596250286
2953
+ - type: euclidean_f1
2954
+ value: 66.53543307086615
2955
+ - type: euclidean_precision
2956
+ value: 62.332872291378514
2957
+ - type: euclidean_recall
2958
+ value: 71.34564643799473
2959
+ - type: manhattan_accuracy
2960
+ value: 85.3907134767837
2961
+ - type: manhattan_ap
2962
+ value: 72.04585410650152
2963
+ - type: manhattan_f1
2964
+ value: 66.57132642116554
2965
+ - type: manhattan_precision
2966
+ value: 60.704194740273856
2967
+ - type: manhattan_recall
2968
+ value: 73.6939313984169
2969
+ - type: max_accuracy
2970
+ value: 85.3907134767837
2971
+ - type: max_ap
2972
+ value: 72.05594596250286
2973
+ - type: max_f1
2974
+ value: 66.57132642116554
2975
+ - task:
2976
+ type: PairClassification
2977
+ dataset:
2978
+ type: mteb/twitterurlcorpus-pairclassification
2979
+ name: MTEB TwitterURLCorpus
2980
+ config: default
2981
+ split: test
2982
+ revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
2983
+ metrics:
2984
+ - type: cos_sim_accuracy
2985
+ value: 89.30414871735165
2986
+ - type: cos_sim_ap
2987
+ value: 86.4398673359918
2988
+ - type: cos_sim_f1
2989
+ value: 78.9243598692186
2990
+ - type: cos_sim_precision
2991
+ value: 75.47249350101876
2992
+ - type: cos_sim_recall
2993
+ value: 82.7071142593163
2994
+ - type: dot_accuracy
2995
+ value: 89.26145845461248
2996
+ - type: dot_ap
2997
+ value: 86.32172118414802
2998
+ - type: dot_f1
2999
+ value: 78.8277467755645
3000
+ - type: dot_precision
3001
+ value: 75.79418662497335
3002
+ - type: dot_recall
3003
+ value: 82.11425931629196
3004
+ - type: euclidean_accuracy
3005
+ value: 89.24205378973105
3006
+ - type: euclidean_ap
3007
+ value: 86.23988673522649
3008
+ - type: euclidean_f1
3009
+ value: 78.67984857951413
3010
+ - type: euclidean_precision
3011
+ value: 75.2689684269742
3012
+ - type: euclidean_recall
3013
+ value: 82.41453649522637
3014
+ - type: manhattan_accuracy
3015
+ value: 89.18189932859859
3016
+ - type: manhattan_ap
3017
+ value: 86.21003833972824
3018
+ - type: manhattan_f1
3019
+ value: 78.70972564850115
3020
+ - type: manhattan_precision
3021
+ value: 76.485544094145
3022
+ - type: manhattan_recall
3023
+ value: 81.0671388974438
3024
+ - type: max_accuracy
3025
+ value: 89.30414871735165
3026
+ - type: max_ap
3027
+ value: 86.4398673359918
3028
+ - type: max_f1
3029
+ value: 78.9243598692186
3030
+ - task:
3031
+ type: Clustering
3032
+ dataset:
3033
+ type: jinaai/cities_wiki_clustering
3034
+ name: MTEB WikiCitiesClustering
3035
+ config: default
3036
+ split: test
3037
+ revision: ddc9ee9242fa65332597f70e967ecc38b9d734fa
3038
+ metrics:
3039
+ - type: v_measure
3040
+ value: 73.254610626148
3041
+ - task:
3042
+ type: Retrieval
3043
+ dataset:
3044
+ type: jinaai/xmarket_ml
3045
+ name: MTEB XMarketES
3046
+ config: default
3047
+ split: test
3048
+ revision: 705db869e8107dfe6e34b832af90446e77d813e3
3049
+ metrics:
3050
+ - type: map_at_1
3051
+ value: 5.506
3052
+ - type: map_at_10
3053
+ value: 11.546
3054
+ - type: map_at_100
3055
+ value: 14.299999999999999
3056
+ - type: map_at_1000
3057
+ value: 15.146999999999998
3058
+ - type: map_at_3
3059
+ value: 8.748000000000001
3060
+ - type: map_at_5
3061
+ value: 10.036000000000001
3062
+ - type: mrr_at_1
3063
+ value: 17.902
3064
+ - type: mrr_at_10
3065
+ value: 25.698999999999998
3066
+ - type: mrr_at_100
3067
+ value: 26.634
3068
+ - type: mrr_at_1000
3069
+ value: 26.704
3070
+ - type: mrr_at_3
3071
+ value: 23.244999999999997
3072
+ - type: mrr_at_5
3073
+ value: 24.555
3074
+ - type: ndcg_at_1
3075
+ value: 17.902
3076
+ - type: ndcg_at_10
3077
+ value: 19.714000000000002
3078
+ - type: ndcg_at_100
3079
+ value: 25.363000000000003
3080
+ - type: ndcg_at_1000
3081
+ value: 30.903999999999996
3082
+ - type: ndcg_at_3
3083
+ value: 17.884
3084
+ - type: ndcg_at_5
3085
+ value: 18.462
3086
+ - type: precision_at_1
3087
+ value: 17.902
3088
+ - type: precision_at_10
3089
+ value: 10.467
3090
+ - type: precision_at_100
3091
+ value: 3.9699999999999998
3092
+ - type: precision_at_1000
3093
+ value: 1.1320000000000001
3094
+ - type: precision_at_3
3095
+ value: 14.387
3096
+ - type: precision_at_5
3097
+ value: 12.727
3098
+ - type: recall_at_1
3099
+ value: 5.506
3100
+ - type: recall_at_10
3101
+ value: 19.997999999999998
3102
+ - type: recall_at_100
3103
+ value: 42.947
3104
+ - type: recall_at_1000
3105
+ value: 67.333
3106
+ - type: recall_at_3
3107
+ value: 11.158
3108
+ - type: recall_at_5
3109
+ value: 14.577000000000002
3110
+ - task:
3111
+ type: Retrieval
3112
+ dataset:
3113
+ type: jinaai/xpqa
3114
+ name: MTEB XPQAESRetrieval
3115
+ config: default
3116
+ split: test
3117
+ revision: None
3118
+ metrics:
3119
+ - type: map_at_1
3120
+ value: 32.53
3121
+ - type: map_at_10
3122
+ value: 58.68600000000001
3123
+ - type: map_at_100
3124
+ value: 60.45399999999999
3125
+ - type: map_at_1000
3126
+ value: 60.51499999999999
3127
+ - type: map_at_3
3128
+ value: 50.356
3129
+ - type: map_at_5
3130
+ value: 55.98
3131
+ - type: mrr_at_1
3132
+ value: 61.791
3133
+ - type: mrr_at_10
3134
+ value: 68.952
3135
+ - type: mrr_at_100
3136
+ value: 69.524
3137
+ - type: mrr_at_1000
3138
+ value: 69.538
3139
+ - type: mrr_at_3
3140
+ value: 67.087
3141
+ - type: mrr_at_5
3142
+ value: 68.052
3143
+ - type: ndcg_at_1
3144
+ value: 61.791
3145
+ - type: ndcg_at_10
3146
+ value: 65.359
3147
+ - type: ndcg_at_100
3148
+ value: 70.95700000000001
3149
+ - type: ndcg_at_1000
3150
+ value: 71.881
3151
+ - type: ndcg_at_3
3152
+ value: 59.999
3153
+ - type: ndcg_at_5
3154
+ value: 61.316
3155
+ - type: precision_at_1
3156
+ value: 61.791
3157
+ - type: precision_at_10
3158
+ value: 18.184
3159
+ - type: precision_at_100
3160
+ value: 2.317
3161
+ - type: precision_at_1000
3162
+ value: 0.245
3163
+ - type: precision_at_3
3164
+ value: 42.203
3165
+ - type: precision_at_5
3166
+ value: 31.374999999999996
3167
+ - type: recall_at_1
3168
+ value: 32.53
3169
+ - type: recall_at_10
3170
+ value: 73.098
3171
+ - type: recall_at_100
3172
+ value: 94.029
3173
+ - type: recall_at_1000
3174
+ value: 99.842
3175
+ - type: recall_at_3
3176
+ value: 54.525
3177
+ - type: recall_at_5
3178
+ value: 63.796
3179
  ---
3180
  <!-- TODO: add evaluation results here -->
3181
  <br><br>