tuan.ljn
commited on
Commit
•
bbf3698
1
Parent(s):
fea4158
Add: update score of evaluation
Browse files- .ipynb_checkpoints/README-checkpoint.md +1547 -0
- README.md +1466 -13
.ipynb_checkpoints/README-checkpoint.md
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|
| 1 |
+
---
|
| 2 |
+
tags:
|
| 3 |
+
- mteb
|
| 4 |
+
model-index:
|
| 5 |
+
- name: fea4158624861f232b237be2e106dc7d8295e0da
|
| 6 |
+
results:
|
| 7 |
+
- task:
|
| 8 |
+
type: Clustering
|
| 9 |
+
dataset:
|
| 10 |
+
type: lyon-nlp/alloprof
|
| 11 |
+
name: MTEB AlloProfClusteringP2P
|
| 12 |
+
config: default
|
| 13 |
+
split: test
|
| 14 |
+
revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
|
| 15 |
+
metrics:
|
| 16 |
+
- type: v_measure
|
| 17 |
+
value: 56.77190187231352
|
| 18 |
+
- type: v_measures
|
| 19 |
+
value: [0.5591529760439443, 0.5974955147482336, 0.5823433887463434, 0.5582030533486634, 0.5608784889863708]
|
| 20 |
+
- task:
|
| 21 |
+
type: Clustering
|
| 22 |
+
dataset:
|
| 23 |
+
type: lyon-nlp/alloprof
|
| 24 |
+
name: MTEB AlloProfClusteringS2S
|
| 25 |
+
config: default
|
| 26 |
+
split: test
|
| 27 |
+
revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
|
| 28 |
+
metrics:
|
| 29 |
+
- type: v_measure
|
| 30 |
+
value: 43.8035296814567
|
| 31 |
+
- type: v_measures
|
| 32 |
+
value: [0.48563274496248593, 0.39879051227524587, 0.4846924607458884, 0.48918774336682036, 0.42626900366019754]
|
| 33 |
+
- task:
|
| 34 |
+
type: Reranking
|
| 35 |
+
dataset:
|
| 36 |
+
type: lyon-nlp/mteb-fr-reranking-alloprof-s2p
|
| 37 |
+
name: MTEB AlloprofReranking
|
| 38 |
+
config: default
|
| 39 |
+
split: test
|
| 40 |
+
revision: 65393d0d7a08a10b4e348135e824f385d420b0fd
|
| 41 |
+
metrics:
|
| 42 |
+
- type: map
|
| 43 |
+
value: 71.51753654257872
|
| 44 |
+
- type: mrr
|
| 45 |
+
value: 72.59564245963209
|
| 46 |
+
- type: nAUC_map_diff1
|
| 47 |
+
value: 54.75803232873865
|
| 48 |
+
- type: nAUC_map_max
|
| 49 |
+
value: 22.763024994564173
|
| 50 |
+
- type: nAUC_mrr_diff1
|
| 51 |
+
value: 54.34234068011684
|
| 52 |
+
- type: nAUC_mrr_max
|
| 53 |
+
value: 23.607169979872587
|
| 54 |
+
- task:
|
| 55 |
+
type: Retrieval
|
| 56 |
+
dataset:
|
| 57 |
+
type: lyon-nlp/alloprof
|
| 58 |
+
name: MTEB AlloprofRetrieval
|
| 59 |
+
config: default
|
| 60 |
+
split: test
|
| 61 |
+
revision: fcf295ea64c750f41fadbaa37b9b861558e1bfbd
|
| 62 |
+
metrics:
|
| 63 |
+
- type: map_at_1
|
| 64 |
+
value: 26.598
|
| 65 |
+
- type: map_at_10
|
| 66 |
+
value: 37.808
|
| 67 |
+
- type: map_at_100
|
| 68 |
+
value: 38.726
|
| 69 |
+
- type: map_at_1000
|
| 70 |
+
value: 38.778
|
| 71 |
+
- type: map_at_20
|
| 72 |
+
value: 38.34
|
| 73 |
+
- type: map_at_3
|
| 74 |
+
value: 34.599999999999994
|
| 75 |
+
- type: map_at_5
|
| 76 |
+
value: 36.385
|
| 77 |
+
- type: mrr_at_1
|
| 78 |
+
value: 26.59758203799655
|
| 79 |
+
- type: mrr_at_10
|
| 80 |
+
value: 37.80750541437081
|
| 81 |
+
- type: mrr_at_100
|
| 82 |
+
value: 38.72559086585011
|
| 83 |
+
- type: mrr_at_1000
|
| 84 |
+
value: 38.77782362787768
|
| 85 |
+
- type: mrr_at_20
|
| 86 |
+
value: 38.33973921272315
|
| 87 |
+
- type: mrr_at_3
|
| 88 |
+
value: 34.59988485895229
|
| 89 |
+
- type: mrr_at_5
|
| 90 |
+
value: 36.385290731145794
|
| 91 |
+
- type: nauc_map_at_1000_diff1
|
| 92 |
+
value: 35.14662281558396
|
| 93 |
+
- type: nauc_map_at_1000_max
|
| 94 |
+
value: 33.29444147034975
|
| 95 |
+
- type: nauc_map_at_100_diff1
|
| 96 |
+
value: 35.14901176407076
|
| 97 |
+
- type: nauc_map_at_100_max
|
| 98 |
+
value: 33.32860103491456
|
| 99 |
+
- type: nauc_map_at_10_diff1
|
| 100 |
+
value: 34.96490139273049
|
| 101 |
+
- type: nauc_map_at_10_max
|
| 102 |
+
value: 33.252929811567526
|
| 103 |
+
- type: nauc_map_at_1_diff1
|
| 104 |
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value: 15.766
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dataset:
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type: lyon-nlp/clustering-hal-s2s
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name: MTEB HALClusteringS2S
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config: default
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split: test
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value: 24.858750161104126
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type: Clustering
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dataset:
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type: reciTAL/mlsum
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name: MTEB MLSUMClusteringP2P
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config: default
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split: test
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type: Clustering
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dataset:
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type: reciTAL/mlsum
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| 537 |
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name: MTEB MLSUMClusteringS2S
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config: default
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split: test
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value: 38.90242301200363
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type: Classification
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dataset:
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| 549 |
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type: mteb/mtop_domain
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| 550 |
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name: MTEB MTOPDomainClassification
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config: default
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split: test
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value: 86.88380833072345
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value: 86.96787213648228
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value: 86.83432508604707
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type: Classification
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dataset:
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| 564 |
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type: mteb/mtop_intent
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| 565 |
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name: MTEB MTOPIntentClassification
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config: default
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value: 63.780144065142494
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value: 46.070400220183394
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value: 66.2871618019472
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type: Classification
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dataset:
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type: mteb/masakhanews
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name: MTEB MasakhaNEWSClassification
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value: 73.64928909952606
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value: 73.78534895009892
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type: Clustering
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dataset:
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type: masakhane/masakhanews
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name: MTEB MasakhaNEWSClusteringP2P
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value: 53.94667838037061
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type: Clustering
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dataset:
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type: masakhane/masakhanews
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name: MTEB MasakhaNEWSClusteringS2S
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type: Classification
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dataset:
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| 620 |
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type: mteb/amazon_massive_intent
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| 621 |
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name: MTEB MassiveIntentClassification
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value: 66.42905178211164
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type: Classification
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dataset:
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| 635 |
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type: mteb/amazon_massive_scenario
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name: MTEB MassiveScenarioClassification
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value: 70.47189927452128
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type: Retrieval
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dataset:
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type: jinaai/mintakaqa
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name: MTEB MintakaRetrieval
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config: default
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split: test
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value: 19.41
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+
- type: nauc_recall_at_1000_diff1
|
| 797 |
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value: 2.219609497359936
|
| 798 |
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- type: nauc_recall_at_1000_max
|
| 799 |
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value: 40.257890004202366
|
| 800 |
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- type: nauc_recall_at_100_diff1
|
| 801 |
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value: 8.63682048375225
|
| 802 |
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- type: nauc_recall_at_100_max
|
| 803 |
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value: 32.320525162903365
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| 804 |
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- type: nauc_recall_at_10_diff1
|
| 805 |
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value: 12.71705832443547
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| 806 |
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- type: nauc_recall_at_10_max
|
| 807 |
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value: 35.26525151297903
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| 808 |
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- type: nauc_recall_at_1_diff1
|
| 809 |
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value: 26.23217963475422
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| 810 |
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- type: nauc_recall_at_1_max
|
| 811 |
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value: 25.259209130612533
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| 812 |
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- type: nauc_recall_at_20_diff1
|
| 813 |
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value: 11.220279583941677
|
| 814 |
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- type: nauc_recall_at_20_max
|
| 815 |
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value: 35.203925473157646
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| 816 |
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- type: nauc_recall_at_3_diff1
|
| 817 |
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value: 17.86340381937489
|
| 818 |
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- type: nauc_recall_at_3_max
|
| 819 |
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value: 34.42645261456199
|
| 820 |
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- type: nauc_recall_at_5_diff1
|
| 821 |
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value: 17.11444481655362
|
| 822 |
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- type: nauc_recall_at_5_max
|
| 823 |
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value: 36.59616112935631
|
| 824 |
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- type: ndcg_at_1
|
| 825 |
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value: 19.41
|
| 826 |
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- type: ndcg_at_10
|
| 827 |
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value: 32.956
|
| 828 |
+
- type: ndcg_at_100
|
| 829 |
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value: 38.504
|
| 830 |
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- type: ndcg_at_1000
|
| 831 |
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value: 41.217
|
| 832 |
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- type: ndcg_at_20
|
| 833 |
+
value: 35.35
|
| 834 |
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- type: ndcg_at_3
|
| 835 |
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value: 27.898
|
| 836 |
+
- type: ndcg_at_5
|
| 837 |
+
value: 30.249
|
| 838 |
+
- type: precision_at_1
|
| 839 |
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value: 19.41
|
| 840 |
+
- type: precision_at_10
|
| 841 |
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value: 4.795
|
| 842 |
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- type: precision_at_100
|
| 843 |
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value: 0.744
|
| 844 |
+
- type: precision_at_1000
|
| 845 |
+
value: 0.096
|
| 846 |
+
- type: precision_at_20
|
| 847 |
+
value: 2.869
|
| 848 |
+
- type: precision_at_3
|
| 849 |
+
value: 11.289
|
| 850 |
+
- type: precision_at_5
|
| 851 |
+
value: 7.912
|
| 852 |
+
- type: recall_at_1
|
| 853 |
+
value: 19.41
|
| 854 |
+
- type: recall_at_10
|
| 855 |
+
value: 47.952
|
| 856 |
+
- type: recall_at_100
|
| 857 |
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value: 74.36500000000001
|
| 858 |
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- type: recall_at_1000
|
| 859 |
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value: 96.478
|
| 860 |
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- type: recall_at_20
|
| 861 |
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value: 57.371
|
| 862 |
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- type: recall_at_3
|
| 863 |
+
value: 33.866
|
| 864 |
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- type: recall_at_5
|
| 865 |
+
value: 39.558
|
| 866 |
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- task:
|
| 867 |
+
type: PairClassification
|
| 868 |
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dataset:
|
| 869 |
+
type: GEM/opusparcus
|
| 870 |
+
name: MTEB OpusparcusPC
|
| 871 |
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config: default
|
| 872 |
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split: test
|
| 873 |
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revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
|
| 874 |
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metrics:
|
| 875 |
+
- task:
|
| 876 |
+
type: PairClassification
|
| 877 |
+
dataset:
|
| 878 |
+
type: google-research-datasets/paws-x
|
| 879 |
+
name: MTEB PawsX
|
| 880 |
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config: default
|
| 881 |
+
split: test
|
| 882 |
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revision: 8a04d940a42cd40658986fdd8e3da561533a3646
|
| 883 |
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metrics:
|
| 884 |
+
- type: cos_sim_accuracy
|
| 885 |
+
value: 64.25
|
| 886 |
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- type: cos_sim_ap
|
| 887 |
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value: 64.3383111759356
|
| 888 |
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- type: cos_sim_f1
|
| 889 |
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value: 63.03703703703704
|
| 890 |
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- type: cos_sim_precision
|
| 891 |
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value: 47.35670562047857
|
| 892 |
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- type: cos_sim_recall
|
| 893 |
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value: 94.24141749723145
|
| 894 |
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- type: dot_accuracy
|
| 895 |
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value: 64.25
|
| 896 |
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- type: dot_ap
|
| 897 |
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value: 64.33647413039195
|
| 898 |
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- type: dot_f1
|
| 899 |
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value: 63.03703703703704
|
| 900 |
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- type: dot_precision
|
| 901 |
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value: 47.35670562047857
|
| 902 |
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- type: dot_recall
|
| 903 |
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value: 94.24141749723145
|
| 904 |
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- type: euclidean_accuracy
|
| 905 |
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value: 64.25
|
| 906 |
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- type: euclidean_ap
|
| 907 |
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value: 64.33837256418407
|
| 908 |
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- type: euclidean_f1
|
| 909 |
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value: 63.03703703703704
|
| 910 |
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- type: euclidean_precision
|
| 911 |
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value: 47.35670562047857
|
| 912 |
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- type: euclidean_recall
|
| 913 |
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value: 94.24141749723145
|
| 914 |
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- type: manhattan_accuracy
|
| 915 |
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value: 64.3
|
| 916 |
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- type: manhattan_ap
|
| 917 |
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value: 64.32795078191543
|
| 918 |
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- type: manhattan_f1
|
| 919 |
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value: 62.935507783543365
|
| 920 |
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- type: manhattan_precision
|
| 921 |
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value: 47.298050139275766
|
| 922 |
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- type: manhattan_recall
|
| 923 |
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value: 94.01993355481729
|
| 924 |
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- type: max_accuracy
|
| 925 |
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value: 64.3
|
| 926 |
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- type: max_ap
|
| 927 |
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value: 64.33837256418407
|
| 928 |
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- type: max_f1
|
| 929 |
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value: 63.03703703703704
|
| 930 |
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- task:
|
| 931 |
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type: STS
|
| 932 |
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dataset:
|
| 933 |
+
type: Lajavaness/SICK-fr
|
| 934 |
+
name: MTEB SICKFr
|
| 935 |
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config: default
|
| 936 |
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split: test
|
| 937 |
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revision: e077ab4cf4774a1e36d86d593b150422fafd8e8a
|
| 938 |
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metrics:
|
| 939 |
+
- type: cos_sim_pearson
|
| 940 |
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value: 84.72950355896789
|
| 941 |
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- type: cos_sim_spearman
|
| 942 |
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value: 79.4608290812696
|
| 943 |
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- type: euclidean_pearson
|
| 944 |
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value: 81.32539142627735
|
| 945 |
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- type: euclidean_spearman
|
| 946 |
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value: 79.46019403205177
|
| 947 |
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- type: manhattan_pearson
|
| 948 |
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value: 81.35998006674681
|
| 949 |
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- type: manhattan_spearman
|
| 950 |
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value: 79.41438354042496
|
| 951 |
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- task:
|
| 952 |
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type: STS
|
| 953 |
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dataset:
|
| 954 |
+
type: mteb/sts22-crosslingual-sts
|
| 955 |
+
name: MTEB STS22
|
| 956 |
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config: default
|
| 957 |
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split: test
|
| 958 |
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revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
|
| 959 |
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metrics:
|
| 960 |
+
- type: cos_sim_pearson
|
| 961 |
+
value: 81.94721498063055
|
| 962 |
+
- type: cos_sim_spearman
|
| 963 |
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value: 83.27561639335909
|
| 964 |
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- type: euclidean_pearson
|
| 965 |
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value: 80.74250472409508
|
| 966 |
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- type: euclidean_spearman
|
| 967 |
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value: 83.27561639335909
|
| 968 |
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- type: manhattan_pearson
|
| 969 |
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value: 80.55336080634422
|
| 970 |
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- type: manhattan_spearman
|
| 971 |
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value: 83.46556509775091
|
| 972 |
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- task:
|
| 973 |
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type: STS
|
| 974 |
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dataset:
|
| 975 |
+
type: mteb/stsb_multi_mt
|
| 976 |
+
name: MTEB STSBenchmarkMultilingualSTS
|
| 977 |
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config: default
|
| 978 |
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split: test
|
| 979 |
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revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
|
| 980 |
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metrics:
|
| 981 |
+
- type: cos_sim_pearson
|
| 982 |
+
value: 86.42208373352562
|
| 983 |
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- type: cos_sim_spearman
|
| 984 |
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value: 86.99991276887566
|
| 985 |
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- type: euclidean_pearson
|
| 986 |
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value: 85.50325028600815
|
| 987 |
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- type: euclidean_spearman
|
| 988 |
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value: 87.00166758198344
|
| 989 |
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- type: manhattan_pearson
|
| 990 |
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value: 85.51048739822163
|
| 991 |
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- type: manhattan_spearman
|
| 992 |
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value: 86.98373812309134
|
| 993 |
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- task:
|
| 994 |
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type: Summarization
|
| 995 |
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dataset:
|
| 996 |
+
type: lyon-nlp/summarization-summeval-fr-p2p
|
| 997 |
+
name: MTEB SummEvalFr
|
| 998 |
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config: default
|
| 999 |
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split: test
|
| 1000 |
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revision: b385812de6a9577b6f4d0f88c6a6e35395a94054
|
| 1001 |
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metrics:
|
| 1002 |
+
- type: cos_sim_pearson
|
| 1003 |
+
value: 31.911797754639164
|
| 1004 |
+
- type: cos_sim_spearman
|
| 1005 |
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value: 32.17186521965941
|
| 1006 |
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- type: dot_pearson
|
| 1007 |
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value: 31.911796813216963
|
| 1008 |
+
- type: dot_spearman
|
| 1009 |
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value: 32.17186521965941
|
| 1010 |
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- task:
|
| 1011 |
+
type: Reranking
|
| 1012 |
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dataset:
|
| 1013 |
+
type: lyon-nlp/mteb-fr-reranking-syntec-s2p
|
| 1014 |
+
name: MTEB SyntecReranking
|
| 1015 |
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config: default
|
| 1016 |
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split: test
|
| 1017 |
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revision: daf0863838cd9e3ba50544cdce3ac2b338a1b0ad
|
| 1018 |
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metrics:
|
| 1019 |
+
- type: map
|
| 1020 |
+
value: 86.04285714285714
|
| 1021 |
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- type: mrr
|
| 1022 |
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value: 86.04285714285714
|
| 1023 |
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- type: nAUC_map_diff1
|
| 1024 |
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value: 67.26948440486838
|
| 1025 |
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- type: nAUC_map_max
|
| 1026 |
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value: 1.8106095852919237
|
| 1027 |
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- type: nAUC_mrr_diff1
|
| 1028 |
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value: 67.26948440486838
|
| 1029 |
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- type: nAUC_mrr_max
|
| 1030 |
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value: 1.8106095852919237
|
| 1031 |
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- task:
|
| 1032 |
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type: Retrieval
|
| 1033 |
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dataset:
|
| 1034 |
+
type: lyon-nlp/mteb-fr-retrieval-syntec-s2p
|
| 1035 |
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name: MTEB SyntecRetrieval
|
| 1036 |
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config: default
|
| 1037 |
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split: test
|
| 1038 |
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revision: 19661ccdca4dfc2d15122d776b61685f48c68ca9
|
| 1039 |
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metrics:
|
| 1040 |
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- type: map_at_1
|
| 1041 |
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value: 72.0
|
| 1042 |
+
- type: map_at_10
|
| 1043 |
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value: 81.294
|
| 1044 |
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- type: map_at_100
|
| 1045 |
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value: 81.428
|
| 1046 |
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- type: map_at_1000
|
| 1047 |
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value: 81.428
|
| 1048 |
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- type: map_at_20
|
| 1049 |
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value: 81.38499999999999
|
| 1050 |
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- type: map_at_3
|
| 1051 |
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value: 79.833
|
| 1052 |
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- type: map_at_5
|
| 1053 |
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value: 80.88300000000001
|
| 1054 |
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- type: mrr_at_1
|
| 1055 |
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value: 72.0
|
| 1056 |
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- type: mrr_at_10
|
| 1057 |
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value: 81.2940476190476
|
| 1058 |
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- type: mrr_at_100
|
| 1059 |
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value: 81.42843497082626
|
| 1060 |
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- type: mrr_at_1000
|
| 1061 |
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value: 81.42843497082626
|
| 1062 |
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- type: mrr_at_20
|
| 1063 |
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value: 81.3849567099567
|
| 1064 |
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- type: mrr_at_3
|
| 1065 |
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value: 79.83333333333334
|
| 1066 |
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- type: mrr_at_5
|
| 1067 |
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value: 80.88333333333333
|
| 1068 |
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|
| 1069 |
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value: 49.54228699135176
|
| 1070 |
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|
| 1071 |
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value: 0.06067639126124744
|
| 1072 |
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|
| 1073 |
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value: 49.54228699135176
|
| 1074 |
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- type: nauc_map_at_100_max
|
| 1075 |
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value: 0.06067639126124744
|
| 1076 |
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- type: nauc_map_at_10_diff1
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| 1077 |
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value: 49.53852081026105
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| 1078 |
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- type: nauc_map_at_10_max
|
| 1079 |
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value: 0.3053366025276881
|
| 1080 |
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|
| 1081 |
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value: 46.31028693528697
|
| 1082 |
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- type: nauc_map_at_1_max
|
| 1083 |
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value: -1.3144841269841376
|
| 1084 |
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|
| 1085 |
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value: 49.506524393346865
|
| 1086 |
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- type: nauc_map_at_20_max
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| 1087 |
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value: -0.08620516816116486
|
| 1088 |
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| 1089 |
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value: 51.19253694434259
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| 1090 |
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|
| 1091 |
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value: 0.668963341320456
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value: 49.37737335974092
|
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|
| 1095 |
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value: -0.33590080679527184
|
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- type: nauc_mrr_at_1000_diff1
|
| 1097 |
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value: 49.54228699135176
|
| 1098 |
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|
| 1099 |
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value: 0.06067639126124744
|
| 1100 |
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- type: nauc_mrr_at_100_diff1
|
| 1101 |
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value: 49.54228699135176
|
| 1102 |
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|
| 1103 |
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value: 0.06067639126124744
|
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- type: nauc_mrr_at_10_diff1
|
| 1105 |
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value: 49.53852081026105
|
| 1106 |
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- type: nauc_mrr_at_10_max
|
| 1107 |
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value: 0.3053366025276881
|
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|
| 1109 |
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value: 46.31028693528697
|
| 1110 |
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|
| 1111 |
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value: -1.3144841269841376
|
| 1112 |
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- type: nauc_mrr_at_20_diff1
|
| 1113 |
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value: 49.506524393346865
|
| 1114 |
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- type: nauc_mrr_at_20_max
|
| 1115 |
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value: -0.08620516816116486
|
| 1116 |
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- type: nauc_mrr_at_3_diff1
|
| 1117 |
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value: 51.19253694434259
|
| 1118 |
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- type: nauc_mrr_at_3_max
|
| 1119 |
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value: 0.668963341320456
|
| 1120 |
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- type: nauc_mrr_at_5_diff1
|
| 1121 |
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value: 49.37737335974092
|
| 1122 |
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- type: nauc_mrr_at_5_max
|
| 1123 |
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value: -0.33590080679527184
|
| 1124 |
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- type: nauc_ndcg_at_1000_diff1
|
| 1125 |
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value: 49.6848382380357
|
| 1126 |
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- type: nauc_ndcg_at_1000_max
|
| 1127 |
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value: 0.04870501937096382
|
| 1128 |
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|
| 1129 |
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value: 49.6848382380357
|
| 1130 |
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|
| 1131 |
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value: 0.04870501937096382
|
| 1132 |
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- type: nauc_ndcg_at_10_diff1
|
| 1133 |
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value: 49.57645777272915
|
| 1134 |
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- type: nauc_ndcg_at_10_max
|
| 1135 |
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value: 0.6430420679440534
|
| 1136 |
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- type: nauc_ndcg_at_1_diff1
|
| 1137 |
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value: 46.31028693528697
|
| 1138 |
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- type: nauc_ndcg_at_1_max
|
| 1139 |
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value: -1.3144841269841376
|
| 1140 |
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- type: nauc_ndcg_at_20_diff1
|
| 1141 |
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value: 49.45017977018584
|
| 1142 |
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- type: nauc_ndcg_at_20_max
|
| 1143 |
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value: -0.9049646537819854
|
| 1144 |
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- type: nauc_ndcg_at_3_diff1
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| 1145 |
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value: 52.898658060430904
|
| 1146 |
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| 1147 |
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value: 1.3070987858400047
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value: 49.054456158711595
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value: -1.177736876794348
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value: nan
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| 1155 |
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value: nan
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| 1156 |
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value: nan
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value: nan
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| 1160 |
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value: 45.611577964519334
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value: 7.936507936508234
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value: 46.31028693528697
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value: -1.3144841269841376
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value: 35.80765639589114
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value: -56.34920634920767
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value: 61.56395891690006
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value: 4.509803921568394
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value: 43.15592903828254
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value: -11.783380018673482
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| 1180 |
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value: nan
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| 1182 |
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|
| 1183 |
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value: nan
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| 1185 |
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value: nan
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| 1186 |
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| 1187 |
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value: nan
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| 1188 |
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| 1189 |
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value: 45.61157796451899
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value: 7.9365079365084235
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value: 46.31028693528697
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value: -1.3144841269841376
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value: 35.80765639589109
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| 1198 |
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| 1199 |
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value: -56.34920634920657
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| 1201 |
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value: 61.5639589169002
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value: 4.509803921568655
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value: 43.15592903828185
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value: -11.783380018674132
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- type: ndcg_at_1
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value: 72.0
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| 1210 |
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- type: ndcg_at_10
|
| 1211 |
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value: 85.39999999999999
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| 1212 |
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- type: ndcg_at_100
|
| 1213 |
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value: 85.897
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| 1214 |
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- type: ndcg_at_1000
|
| 1215 |
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value: 85.897
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| 1216 |
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- type: ndcg_at_20
|
| 1217 |
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value: 85.679
|
| 1218 |
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- type: ndcg_at_3
|
| 1219 |
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value: 82.44
|
| 1220 |
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- type: ndcg_at_5
|
| 1221 |
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value: 84.418
|
| 1222 |
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- type: precision_at_1
|
| 1223 |
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value: 72.0
|
| 1224 |
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- type: precision_at_10
|
| 1225 |
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value: 9.8
|
| 1226 |
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- type: precision_at_100
|
| 1227 |
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value: 1.0
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| 1228 |
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- type: precision_at_1000
|
| 1229 |
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value: 0.1
|
| 1230 |
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- type: precision_at_20
|
| 1231 |
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value: 4.95
|
| 1232 |
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- type: precision_at_3
|
| 1233 |
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value: 30.0
|
| 1234 |
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- type: precision_at_5
|
| 1235 |
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value: 19.0
|
| 1236 |
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- type: recall_at_1
|
| 1237 |
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value: 72.0
|
| 1238 |
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- type: recall_at_10
|
| 1239 |
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value: 98.0
|
| 1240 |
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- type: recall_at_100
|
| 1241 |
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value: 100.0
|
| 1242 |
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- type: recall_at_1000
|
| 1243 |
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value: 100.0
|
| 1244 |
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- type: recall_at_20
|
| 1245 |
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value: 99.0
|
| 1246 |
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- type: recall_at_3
|
| 1247 |
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value: 90.0
|
| 1248 |
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- type: recall_at_5
|
| 1249 |
+
value: 95.0
|
| 1250 |
+
- task:
|
| 1251 |
+
type: Retrieval
|
| 1252 |
+
dataset:
|
| 1253 |
+
type: jinaai/xpqa
|
| 1254 |
+
name: MTEB XPQARetrieval
|
| 1255 |
+
config: default
|
| 1256 |
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split: test
|
| 1257 |
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revision: c99d599f0a6ab9b85b065da6f9d94f9cf731679f
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| 1258 |
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metrics:
|
| 1259 |
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- type: map_at_1
|
| 1260 |
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value: 39.007999999999996
|
| 1261 |
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- type: map_at_10
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| 1262 |
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value: 60.319
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| 1263 |
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- type: map_at_100
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| 1264 |
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value: 61.644
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| 1265 |
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- type: map_at_1000
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| 1266 |
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value: 61.712
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| 1268 |
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value: 61.053000000000004
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| 1269 |
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- type: map_at_3
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| 1270 |
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value: 53.942
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| 1271 |
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- type: map_at_5
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| 1272 |
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value: 58.132
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| 1273 |
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- type: mrr_at_1
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| 1274 |
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value: 60.747663551401864
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- type: mrr_at_10
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value: 68.10636404094345
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value: 68.57873500135119
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value: 68.60183171580495
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value: 68.36478690417064
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value: 66.08811748998662
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value: 67.12950600801062
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value: 46.633293504114434
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value: 49.64101214126472
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value: 46.591074735810764
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value: 49.60435205919251
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value: 46.43486044009182
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value: 49.222376322201065
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| 1300 |
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value: 54.05448738315762
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value: 24.89423418246206
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value: 46.5131815367993
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value: 49.59934686413147
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value: 48.9688624037045
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value: 42.49957358403678
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value: 46.99183039261338
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value: 47.53639129265315
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value: 55.19161872624107
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- type: nauc_mrr_at_1000_max
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value: 57.00358990989949
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| 1320 |
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value: 55.18922613003231
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| 1322 |
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value: 56.99910514727928
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value: 55.030904237371224
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value: 56.99207425207498
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value: 57.34582863547949
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| 1330 |
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value: 56.98349812853321
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| 1332 |
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value: 55.0844531407365
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value: 57.00264208604279
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value: 55.64735827895618
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value: 57.29703659670222
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| 1340 |
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value: 55.38932070005733
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value: 57.25407452051235
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value: 48.35929794131023
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value: 52.522778834890325
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value: 47.56861995950162
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| 1350 |
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value: 51.88529740719871
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value: 46.87985909159253
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value: 51.38385177949589
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value: 57.34582863547949
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value: 56.98349812853321
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| 1360 |
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value: 47.02262106267654
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value: 51.98897867452814
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value: 49.03406876232345
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value: 51.51024175391598
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value: 47.91197516404686
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value: 49.81634328349405
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value: -17.71701381388244
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value: 21.388531798131364
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value: -15.539336210367946
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value: 24.698569356368356
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| 1380 |
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value: -4.847564718245167
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- type: nauc_precision_at_10_max
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value: 36.89232193362945
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value: 57.34582863547949
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value: 56.98349812853321
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value: -8.665616069723095
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value: 33.10610080847679
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value: 11.05940284130611
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value: 46.70869419036014
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| 1396 |
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value: 1.5834393191629197
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- type: nauc_precision_at_5_max
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| 1398 |
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value: 41.862527913783865
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| 1400 |
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value: -10.569638499832076
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- type: nauc_recall_at_1000_max
|
| 1402 |
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value: 8.749844537815326
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| 1404 |
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value: 27.487071443233262
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value: 35.4122293490153
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| 1408 |
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value: 35.91432645476102
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- type: nauc_recall_at_10_max
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| 1410 |
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value: 44.19204765665408
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| 1412 |
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value: 54.05448738315762
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- type: nauc_recall_at_1_max
|
| 1414 |
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value: 24.89423418246206
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- type: nauc_recall_at_20_diff1
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value: 34.4589376419412
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- type: nauc_recall_at_20_max
|
| 1418 |
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value: 44.798804712797136
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| 1420 |
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value: 45.60515874084114
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| 1421 |
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- type: nauc_recall_at_3_max
|
| 1422 |
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value: 39.109212749550764
|
| 1423 |
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- type: nauc_recall_at_5_diff1
|
| 1424 |
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value: 41.35313462606428
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- type: nauc_recall_at_5_max
|
| 1426 |
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value: 43.39841866434438
|
| 1427 |
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- type: ndcg_at_1
|
| 1428 |
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value: 60.748000000000005
|
| 1429 |
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- type: ndcg_at_10
|
| 1430 |
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value: 66.232
|
| 1431 |
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- type: ndcg_at_100
|
| 1432 |
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value: 70.745
|
| 1433 |
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- type: ndcg_at_1000
|
| 1434 |
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value: 71.87
|
| 1435 |
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- type: ndcg_at_20
|
| 1436 |
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value: 67.977
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| 1437 |
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- type: ndcg_at_3
|
| 1438 |
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value: 61.06399999999999
|
| 1439 |
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- type: ndcg_at_5
|
| 1440 |
+
value: 62.744
|
| 1441 |
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- type: precision_at_1
|
| 1442 |
+
value: 60.748000000000005
|
| 1443 |
+
- type: precision_at_10
|
| 1444 |
+
value: 15.434000000000001
|
| 1445 |
+
- type: precision_at_100
|
| 1446 |
+
value: 1.924
|
| 1447 |
+
- type: precision_at_1000
|
| 1448 |
+
value: 0.207
|
| 1449 |
+
- type: precision_at_20
|
| 1450 |
+
value: 8.378
|
| 1451 |
+
- type: precision_at_3
|
| 1452 |
+
value: 37.161
|
| 1453 |
+
- type: precision_at_5
|
| 1454 |
+
value: 26.916
|
| 1455 |
+
- type: recall_at_1
|
| 1456 |
+
value: 39.007999999999996
|
| 1457 |
+
- type: recall_at_10
|
| 1458 |
+
value: 74.929
|
| 1459 |
+
- type: recall_at_100
|
| 1460 |
+
value: 92.508
|
| 1461 |
+
- type: recall_at_1000
|
| 1462 |
+
value: 99.8
|
| 1463 |
+
- type: recall_at_20
|
| 1464 |
+
value: 80.343
|
| 1465 |
+
- type: recall_at_3
|
| 1466 |
+
value: 58.367000000000004
|
| 1467 |
+
- type: recall_at_5
|
| 1468 |
+
value: 66.446
|
| 1469 |
+
---
|
| 1470 |
+
|
| 1471 |
+
# [bilingual-embedding-large](https://huggingface.co/Lajavaness/bilingual-embedding-large)
|
| 1472 |
+
|
| 1473 |
+
bilingual-embedding is the Embedding Model for bilingual language: french and english. This model is a specialized sentence-embedding trained specifically for the bilingual language, leveraging the robust capabilities of [XLM-RoBERTa](https://huggingface.co/FacebookAI/xlm-roberta-large), a pre-trained language model based on the [XLM-RoBERTa](https://huggingface.co/FacebookAI/xlm-roberta-large) architecture. The model utilizes xlm-roberta to encode english-french sentences into a 1024-dimensional vector space, facilitating a wide range of applications from semantic search to text clustering. The embeddings capture the nuanced meanings of english-french sentences, reflecting both the lexical and contextual layers of the language.
|
| 1474 |
+
|
| 1475 |
+
|
| 1476 |
+
## Full Model Architecture
|
| 1477 |
+
```
|
| 1478 |
+
SentenceTransformer(
|
| 1479 |
+
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BilingualModel
|
| 1480 |
+
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
|
| 1481 |
+
(2): Normalize()
|
| 1482 |
+
)
|
| 1483 |
+
```
|
| 1484 |
+
|
| 1485 |
+
## Training and Fine-tuning process
|
| 1486 |
+
#### Stage 1: NLI Training
|
| 1487 |
+
- Dataset: [(SNLI+XNLI) for english+french]
|
| 1488 |
+
- Method: Training using Multi-Negative Ranking Loss. This stage focused on improving the model's ability to discern and rank nuanced differences in sentence semantics.
|
| 1489 |
+
### Stage 3: Continued Fine-tuning for Semantic Textual Similarity on STS Benchmark
|
| 1490 |
+
- Dataset: [STSB-fr and en]
|
| 1491 |
+
- Method: Fine-tuning specifically for the semantic textual similarity benchmark using Siamese BERT-Networks configured with the 'sentence-transformers' library.
|
| 1492 |
+
### Stage 4: Advanced Augmentation Fine-tuning
|
| 1493 |
+
- Dataset: STSB-vn with generate [silver sample from gold sample](https://www.sbert.net/examples/training/data_augmentation/README.html)
|
| 1494 |
+
- Method: Employed an advanced strategy using [Augmented SBERT](https://arxiv.org/abs/2010.08240) with Pair Sampling Strategies, integrating both Cross-Encoder and Bi-Encoder models. This stage further refined the embeddings by enriching the training data dynamically, enhancing the model's robustness and accuracy.
|
| 1495 |
+
|
| 1496 |
+
|
| 1497 |
+
## Usage:
|
| 1498 |
+
|
| 1499 |
+
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
|
| 1500 |
+
|
| 1501 |
+
```
|
| 1502 |
+
pip install -U sentence-transformers
|
| 1503 |
+
```
|
| 1504 |
+
|
| 1505 |
+
Then you can use the model like this:
|
| 1506 |
+
|
| 1507 |
+
```python
|
| 1508 |
+
from sentence_transformers import SentenceTransformer
|
| 1509 |
+
from pyvi.ViTokenizer import tokenize
|
| 1510 |
+
|
| 1511 |
+
sentences = ["Paris est une capitale de la France", "Paris is a capital of France"]
|
| 1512 |
+
|
| 1513 |
+
model = SentenceTransformer('Lajavaness/bilingual-embedding-large', trust_remote_code=True)
|
| 1514 |
+
print(embeddings)
|
| 1515 |
+
|
| 1516 |
+
```
|
| 1517 |
+
|
| 1518 |
+
|
| 1519 |
+
|
| 1520 |
+
|
| 1521 |
+
|
| 1522 |
+
## Evaluation
|
| 1523 |
+
|
| 1524 |
+
TODO
|
| 1525 |
+
|
| 1526 |
+
## Citation
|
| 1527 |
+
|
| 1528 |
+
@article{conneau2019unsupervised,
|
| 1529 |
+
title={Unsupervised cross-lingual representation learning at scale},
|
| 1530 |
+
author={Conneau, Alexis and Khandelwal, Kartikay and Goyal, Naman and Chaudhary, Vishrav and Wenzek, Guillaume and Guzm{\'a}n, Francisco and Grave, Edouard and Ott, Myle and Zettlemoyer, Luke and Stoyanov, Veselin},
|
| 1531 |
+
journal={arXiv preprint arXiv:1911.02116},
|
| 1532 |
+
year={2019}
|
| 1533 |
+
}
|
| 1534 |
+
|
| 1535 |
+
@article{reimers2019sentence,
|
| 1536 |
+
title={Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks},
|
| 1537 |
+
author={Nils Reimers, Iryna Gurevych},
|
| 1538 |
+
journal={https://arxiv.org/abs/1908.10084},
|
| 1539 |
+
year={2019}
|
| 1540 |
+
}
|
| 1541 |
+
|
| 1542 |
+
@article{thakur2020augmented,
|
| 1543 |
+
title={Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks},
|
| 1544 |
+
author={Thakur, Nandan and Reimers, Nils and Daxenberger, Johannes and Gurevych, Iryna},
|
| 1545 |
+
journal={arXiv e-prints},
|
| 1546 |
+
pages={arXiv--2010},
|
| 1547 |
+
year={2020}
|
README.md
CHANGED
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library_name: sentence-transformers
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pipeline_tag: sentence-similarity
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tags:
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| 16 |
---
|
| 17 |
|
| 18 |
# [bilingual-embedding-large](https://huggingface.co/Lajavaness/bilingual-embedding-large)
|
|
|
|
| 1 |
---
|
|
|
|
|
|
|
| 2 |
tags:
|
| 3 |
+
- mteb
|
| 4 |
+
model-index:
|
| 5 |
+
- name: fea4158624861f232b237be2e106dc7d8295e0da
|
| 6 |
+
results:
|
| 7 |
+
- task:
|
| 8 |
+
type: Clustering
|
| 9 |
+
dataset:
|
| 10 |
+
type: lyon-nlp/alloprof
|
| 11 |
+
name: MTEB AlloProfClusteringP2P
|
| 12 |
+
config: default
|
| 13 |
+
split: test
|
| 14 |
+
revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
|
| 15 |
+
metrics:
|
| 16 |
+
- type: v_measure
|
| 17 |
+
value: 56.77190187231352
|
| 18 |
+
- type: v_measures
|
| 19 |
+
value: [0.5591529760439443, 0.5974955147482336, 0.5823433887463434, 0.5582030533486634, 0.5608784889863708]
|
| 20 |
+
- task:
|
| 21 |
+
type: Clustering
|
| 22 |
+
dataset:
|
| 23 |
+
type: lyon-nlp/alloprof
|
| 24 |
+
name: MTEB AlloProfClusteringS2S
|
| 25 |
+
config: default
|
| 26 |
+
split: test
|
| 27 |
+
revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
|
| 28 |
+
metrics:
|
| 29 |
+
- type: v_measure
|
| 30 |
+
value: 43.8035296814567
|
| 31 |
+
- type: v_measures
|
| 32 |
+
value: [0.48563274496248593, 0.39879051227524587, 0.4846924607458884, 0.48918774336682036, 0.42626900366019754]
|
| 33 |
+
- task:
|
| 34 |
+
type: Reranking
|
| 35 |
+
dataset:
|
| 36 |
+
type: lyon-nlp/mteb-fr-reranking-alloprof-s2p
|
| 37 |
+
name: MTEB AlloprofReranking
|
| 38 |
+
config: default
|
| 39 |
+
split: test
|
| 40 |
+
revision: 65393d0d7a08a10b4e348135e824f385d420b0fd
|
| 41 |
+
metrics:
|
| 42 |
+
- type: map
|
| 43 |
+
value: 71.51753654257872
|
| 44 |
+
- type: mrr
|
| 45 |
+
value: 72.59564245963209
|
| 46 |
+
- type: nAUC_map_diff1
|
| 47 |
+
value: 54.75803232873865
|
| 48 |
+
- type: nAUC_map_max
|
| 49 |
+
value: 22.763024994564173
|
| 50 |
+
- type: nAUC_mrr_diff1
|
| 51 |
+
value: 54.34234068011684
|
| 52 |
+
- type: nAUC_mrr_max
|
| 53 |
+
value: 23.607169979872587
|
| 54 |
+
- task:
|
| 55 |
+
type: Retrieval
|
| 56 |
+
dataset:
|
| 57 |
+
type: lyon-nlp/alloprof
|
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name: MTEB AlloprofRetrieval
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split: test
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revision: fcf295ea64c750f41fadbaa37b9b861558e1bfbd
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- task:
|
| 274 |
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type: Classification
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| 275 |
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dataset:
|
| 276 |
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type: mteb/amazon_reviews_multi
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| 277 |
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name: MTEB AmazonReviewsClassification
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config: default
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split: test
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revision: 1399c76144fd37290681b995c656ef9b2e06e26d
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metrics:
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- task:
|
| 289 |
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type: Retrieval
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dataset:
|
| 291 |
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type: maastrichtlawtech/bsard
|
| 292 |
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name: MTEB BSARDRetrieval
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config: default
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split: test
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revision: 5effa1b9b5fa3b0f9e12523e6e43e5f86a6e6d59
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type: mteb/mtop_intent
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dataset:
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type: mteb/masakhanews
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type: masakhane/masakhanews
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dataset:
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name: MTEB MassiveScenarioClassification
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dataset:
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type: jinaai/mintakaqa
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| 757 |
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value: 18.76950183960116
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| 758 |
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- type: nauc_ndcg_at_20_max
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value: 31.598953492190745
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value: 20.824179655562357
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value: 31.0541305570042
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- type: nauc_ndcg_at_5_diff1
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value: 20.483978673834002
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- type: nauc_ndcg_at_5_max
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value: 2.2196094973599374
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- type: nauc_precision_at_1000_max
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value: 40.25789000420308
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value: 8.63682048375218
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| 774 |
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value: 12.717058324435426
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| 778 |
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- type: nauc_precision_at_10_max
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| 779 |
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value: 35.265251512978985
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| 780 |
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value: 26.23217963475422
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| 782 |
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- type: nauc_precision_at_1_max
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value: 25.259209130612533
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- type: nauc_precision_at_20_max
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| 787 |
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value: 35.20392547315769
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| 788 |
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- type: nauc_precision_at_3_diff1
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| 790 |
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- type: nauc_precision_at_3_max
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| 791 |
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value: 34.42645261456197
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| 792 |
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- type: nauc_precision_at_5_diff1
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| 793 |
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value: 17.114444816553625
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| 794 |
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- type: nauc_precision_at_5_max
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| 795 |
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value: 36.59616112935629
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- type: nauc_recall_at_1000_diff1
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| 797 |
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value: 2.219609497359936
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- type: nauc_recall_at_1000_max
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| 799 |
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value: 40.257890004202366
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| 800 |
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- type: nauc_recall_at_100_diff1
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| 801 |
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value: 8.63682048375225
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| 802 |
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- type: nauc_recall_at_100_max
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| 803 |
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value: 32.320525162903365
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- type: nauc_recall_at_10_diff1
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value: 12.71705832443547
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- type: nauc_recall_at_10_max
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| 807 |
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value: 35.26525151297903
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| 808 |
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- type: nauc_recall_at_1_max
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value: 25.259209130612533
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value: 11.220279583941677
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| 814 |
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- type: nauc_recall_at_20_max
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value: 35.203925473157646
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- type: nauc_recall_at_3_diff1
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value: 34.42645261456199
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| 820 |
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| 821 |
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value: 17.11444481655362
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| 822 |
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- type: nauc_recall_at_5_max
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| 823 |
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value: 36.59616112935631
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| 824 |
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- type: ndcg_at_1
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value: 19.41
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| 826 |
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- type: ndcg_at_10
|
| 827 |
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value: 32.956
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| 828 |
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- type: ndcg_at_100
|
| 829 |
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value: 38.504
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| 830 |
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- type: ndcg_at_1000
|
| 831 |
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value: 41.217
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| 832 |
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- type: ndcg_at_20
|
| 833 |
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value: 35.35
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| 834 |
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- type: ndcg_at_3
|
| 835 |
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value: 27.898
|
| 836 |
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- type: ndcg_at_5
|
| 837 |
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value: 30.249
|
| 838 |
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- type: precision_at_1
|
| 839 |
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value: 19.41
|
| 840 |
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- type: precision_at_10
|
| 841 |
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value: 4.795
|
| 842 |
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- type: precision_at_100
|
| 843 |
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value: 0.744
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| 844 |
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- type: precision_at_1000
|
| 845 |
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value: 0.096
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| 846 |
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- type: precision_at_20
|
| 847 |
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value: 2.869
|
| 848 |
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- type: precision_at_3
|
| 849 |
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value: 11.289
|
| 850 |
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- type: precision_at_5
|
| 851 |
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value: 7.912
|
| 852 |
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- type: recall_at_1
|
| 853 |
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value: 19.41
|
| 854 |
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- type: recall_at_10
|
| 855 |
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value: 47.952
|
| 856 |
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- type: recall_at_100
|
| 857 |
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value: 74.36500000000001
|
| 858 |
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- type: recall_at_1000
|
| 859 |
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value: 96.478
|
| 860 |
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- type: recall_at_20
|
| 861 |
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value: 57.371
|
| 862 |
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- type: recall_at_3
|
| 863 |
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value: 33.866
|
| 864 |
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- type: recall_at_5
|
| 865 |
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value: 39.558
|
| 866 |
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- task:
|
| 867 |
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type: PairClassification
|
| 868 |
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dataset:
|
| 869 |
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type: GEM/opusparcus
|
| 870 |
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name: MTEB OpusparcusPC
|
| 871 |
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config: default
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| 872 |
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split: test
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| 873 |
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revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
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| 874 |
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metrics:
|
| 875 |
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- task:
|
| 876 |
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type: PairClassification
|
| 877 |
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dataset:
|
| 878 |
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type: google-research-datasets/paws-x
|
| 879 |
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name: MTEB PawsX
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| 880 |
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config: default
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| 881 |
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split: test
|
| 882 |
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revision: 8a04d940a42cd40658986fdd8e3da561533a3646
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| 883 |
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metrics:
|
| 884 |
+
- type: cos_sim_accuracy
|
| 885 |
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value: 64.25
|
| 886 |
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- type: cos_sim_ap
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| 887 |
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value: 64.3383111759356
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| 888 |
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- type: cos_sim_f1
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value: 63.03703703703704
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| 890 |
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- type: cos_sim_precision
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| 891 |
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value: 47.35670562047857
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| 892 |
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- type: cos_sim_recall
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| 893 |
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value: 94.24141749723145
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| 894 |
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- type: dot_accuracy
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| 895 |
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value: 64.25
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| 896 |
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- type: dot_ap
|
| 897 |
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value: 64.33647413039195
|
| 898 |
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- type: dot_f1
|
| 899 |
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value: 63.03703703703704
|
| 900 |
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- type: dot_precision
|
| 901 |
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value: 47.35670562047857
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| 902 |
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- type: dot_recall
|
| 903 |
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value: 94.24141749723145
|
| 904 |
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- type: euclidean_accuracy
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| 905 |
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value: 64.25
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| 906 |
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- type: euclidean_ap
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| 907 |
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value: 64.33837256418407
|
| 908 |
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- type: euclidean_f1
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| 909 |
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value: 63.03703703703704
|
| 910 |
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- type: euclidean_precision
|
| 911 |
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value: 47.35670562047857
|
| 912 |
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- type: euclidean_recall
|
| 913 |
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value: 94.24141749723145
|
| 914 |
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- type: manhattan_accuracy
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| 915 |
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value: 64.3
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| 916 |
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- type: manhattan_ap
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| 917 |
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value: 64.32795078191543
|
| 918 |
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- type: manhattan_f1
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| 919 |
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value: 62.935507783543365
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| 920 |
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- type: manhattan_precision
|
| 921 |
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value: 47.298050139275766
|
| 922 |
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- type: manhattan_recall
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| 923 |
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value: 94.01993355481729
|
| 924 |
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- type: max_accuracy
|
| 925 |
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value: 64.3
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| 926 |
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- type: max_ap
|
| 927 |
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value: 64.33837256418407
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| 928 |
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- type: max_f1
|
| 929 |
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value: 63.03703703703704
|
| 930 |
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- task:
|
| 931 |
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type: STS
|
| 932 |
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dataset:
|
| 933 |
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type: Lajavaness/SICK-fr
|
| 934 |
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name: MTEB SICKFr
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| 935 |
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config: default
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| 936 |
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split: test
|
| 937 |
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revision: e077ab4cf4774a1e36d86d593b150422fafd8e8a
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| 938 |
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metrics:
|
| 939 |
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- type: cos_sim_pearson
|
| 940 |
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value: 84.72950355896789
|
| 941 |
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- type: cos_sim_spearman
|
| 942 |
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value: 79.4608290812696
|
| 943 |
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- type: euclidean_pearson
|
| 944 |
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value: 81.32539142627735
|
| 945 |
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- type: euclidean_spearman
|
| 946 |
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value: 79.46019403205177
|
| 947 |
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- type: manhattan_pearson
|
| 948 |
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value: 81.35998006674681
|
| 949 |
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- type: manhattan_spearman
|
| 950 |
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value: 79.41438354042496
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| 951 |
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- task:
|
| 952 |
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type: STS
|
| 953 |
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dataset:
|
| 954 |
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type: mteb/sts22-crosslingual-sts
|
| 955 |
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name: MTEB STS22
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| 956 |
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config: default
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| 957 |
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split: test
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| 958 |
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revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
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| 959 |
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metrics:
|
| 960 |
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- type: cos_sim_pearson
|
| 961 |
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value: 81.94721498063055
|
| 962 |
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- type: cos_sim_spearman
|
| 963 |
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value: 83.27561639335909
|
| 964 |
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- type: euclidean_pearson
|
| 965 |
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value: 80.74250472409508
|
| 966 |
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- type: euclidean_spearman
|
| 967 |
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value: 83.27561639335909
|
| 968 |
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- type: manhattan_pearson
|
| 969 |
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value: 80.55336080634422
|
| 970 |
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- type: manhattan_spearman
|
| 971 |
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value: 83.46556509775091
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| 972 |
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- task:
|
| 973 |
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type: STS
|
| 974 |
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dataset:
|
| 975 |
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type: mteb/stsb_multi_mt
|
| 976 |
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name: MTEB STSBenchmarkMultilingualSTS
|
| 977 |
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config: default
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| 978 |
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split: test
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| 979 |
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revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
|
| 980 |
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metrics:
|
| 981 |
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- type: cos_sim_pearson
|
| 982 |
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value: 86.42208373352562
|
| 983 |
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- type: cos_sim_spearman
|
| 984 |
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value: 86.99991276887566
|
| 985 |
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- type: euclidean_pearson
|
| 986 |
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value: 85.50325028600815
|
| 987 |
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- type: euclidean_spearman
|
| 988 |
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value: 87.00166758198344
|
| 989 |
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- type: manhattan_pearson
|
| 990 |
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value: 85.51048739822163
|
| 991 |
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- type: manhattan_spearman
|
| 992 |
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value: 86.98373812309134
|
| 993 |
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- task:
|
| 994 |
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type: Summarization
|
| 995 |
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dataset:
|
| 996 |
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type: lyon-nlp/summarization-summeval-fr-p2p
|
| 997 |
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name: MTEB SummEvalFr
|
| 998 |
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config: default
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| 999 |
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split: test
|
| 1000 |
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revision: b385812de6a9577b6f4d0f88c6a6e35395a94054
|
| 1001 |
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metrics:
|
| 1002 |
+
- type: cos_sim_pearson
|
| 1003 |
+
value: 31.911797754639164
|
| 1004 |
+
- type: cos_sim_spearman
|
| 1005 |
+
value: 32.17186521965941
|
| 1006 |
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- type: dot_pearson
|
| 1007 |
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value: 31.911796813216963
|
| 1008 |
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- type: dot_spearman
|
| 1009 |
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value: 32.17186521965941
|
| 1010 |
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- task:
|
| 1011 |
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type: Reranking
|
| 1012 |
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dataset:
|
| 1013 |
+
type: lyon-nlp/mteb-fr-reranking-syntec-s2p
|
| 1014 |
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name: MTEB SyntecReranking
|
| 1015 |
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config: default
|
| 1016 |
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split: test
|
| 1017 |
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revision: daf0863838cd9e3ba50544cdce3ac2b338a1b0ad
|
| 1018 |
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metrics:
|
| 1019 |
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- type: map
|
| 1020 |
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value: 86.04285714285714
|
| 1021 |
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- type: mrr
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| 1022 |
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value: 86.04285714285714
|
| 1023 |
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- type: nAUC_map_diff1
|
| 1024 |
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value: 67.26948440486838
|
| 1025 |
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- type: nAUC_map_max
|
| 1026 |
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value: 1.8106095852919237
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| 1027 |
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|
| 1028 |
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value: 67.26948440486838
|
| 1029 |
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- type: nAUC_mrr_max
|
| 1030 |
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value: 1.8106095852919237
|
| 1031 |
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- task:
|
| 1032 |
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type: Retrieval
|
| 1033 |
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dataset:
|
| 1034 |
+
type: lyon-nlp/mteb-fr-retrieval-syntec-s2p
|
| 1035 |
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name: MTEB SyntecRetrieval
|
| 1036 |
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config: default
|
| 1037 |
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split: test
|
| 1038 |
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revision: 19661ccdca4dfc2d15122d776b61685f48c68ca9
|
| 1039 |
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metrics:
|
| 1040 |
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- type: map_at_1
|
| 1041 |
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value: 72.0
|
| 1042 |
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- type: map_at_10
|
| 1043 |
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value: 81.294
|
| 1044 |
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- type: map_at_100
|
| 1045 |
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value: 81.428
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| 1046 |
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- type: map_at_1000
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| 1047 |
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value: 81.428
|
| 1048 |
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- type: map_at_20
|
| 1049 |
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value: 81.38499999999999
|
| 1050 |
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- type: map_at_3
|
| 1051 |
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value: 79.833
|
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|
| 1053 |
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value: 80.88300000000001
|
| 1054 |
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- type: mrr_at_1
|
| 1055 |
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value: 72.0
|
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|
| 1057 |
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value: 81.2940476190476
|
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|
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value: 81.42843497082626
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|
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value: 81.42843497082626
|
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|
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value: 81.3849567099567
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value: 79.83333333333334
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value: 80.88333333333333
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value: 49.54228699135176
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value: 0.06067639126124744
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value: 0.06067639126124744
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value: 49.53852081026105
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value: 0.3053366025276881
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value: 46.31028693528697
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value: 49.506524393346865
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value: -0.08620516816116486
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value: 51.19253694434259
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value: 0.668963341320456
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value: 49.37737335974092
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value: -0.33590080679527184
|
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|
| 1099 |
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value: 0.06067639126124744
|
| 1100 |
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| 1101 |
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value: 49.54228699135176
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| 1103 |
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value: 0.06067639126124744
|
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- type: nauc_mrr_at_10_diff1
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| 1105 |
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value: 49.53852081026105
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| 1106 |
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- type: nauc_mrr_at_10_max
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| 1107 |
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value: 0.3053366025276881
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| 1439 |
+
- type: ndcg_at_5
|
| 1440 |
+
value: 62.744
|
| 1441 |
+
- type: precision_at_1
|
| 1442 |
+
value: 60.748000000000005
|
| 1443 |
+
- type: precision_at_10
|
| 1444 |
+
value: 15.434000000000001
|
| 1445 |
+
- type: precision_at_100
|
| 1446 |
+
value: 1.924
|
| 1447 |
+
- type: precision_at_1000
|
| 1448 |
+
value: 0.207
|
| 1449 |
+
- type: precision_at_20
|
| 1450 |
+
value: 8.378
|
| 1451 |
+
- type: precision_at_3
|
| 1452 |
+
value: 37.161
|
| 1453 |
+
- type: precision_at_5
|
| 1454 |
+
value: 26.916
|
| 1455 |
+
- type: recall_at_1
|
| 1456 |
+
value: 39.007999999999996
|
| 1457 |
+
- type: recall_at_10
|
| 1458 |
+
value: 74.929
|
| 1459 |
+
- type: recall_at_100
|
| 1460 |
+
value: 92.508
|
| 1461 |
+
- type: recall_at_1000
|
| 1462 |
+
value: 99.8
|
| 1463 |
+
- type: recall_at_20
|
| 1464 |
+
value: 80.343
|
| 1465 |
+
- type: recall_at_3
|
| 1466 |
+
value: 58.367000000000004
|
| 1467 |
+
- type: recall_at_5
|
| 1468 |
+
value: 66.446
|
| 1469 |
---
|
| 1470 |
|
| 1471 |
# [bilingual-embedding-large](https://huggingface.co/Lajavaness/bilingual-embedding-large)
|