metadata
model-index:
- name: XYZ-embedding-zh-v2
results:
- dataset:
config: default
name: MTEB AFQMC
revision: None
split: validation
type: C-MTEB/AFQMC
metrics:
- type: cos_sim_pearson
value: 55.51799059309076
- type: cos_sim_spearman
value: 58.407433584137806
- type: manhattan_pearson
value: 57.17473672145622
- type: manhattan_spearman
value: 58.389018054159955
- type: euclidean_pearson
value: 57.19483956761451
- type: euclidean_spearman
value: 58.407433584137806
- type: main_score
value: 58.407433584137806
task:
type: STS
- dataset:
config: default
name: MTEB ATEC
revision: None
split: test
type: C-MTEB/ATEC
metrics:
- type: cos_sim_pearson
value: 57.31078155367183
- type: cos_sim_spearman
value: 57.59782762324478
- type: manhattan_pearson
value: 62.525487007985035
- type: manhattan_spearman
value: 57.591139966303615
- type: euclidean_pearson
value: 62.53702437760052
- type: euclidean_spearman
value: 57.597828749091384
- type: main_score
value: 57.59782762324478
task:
type: STS
- dataset:
config: zh
name: MTEB AmazonReviewsClassification (zh)
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
split: test
type: mteb/amazon_reviews_multi
metrics:
- type: accuracy
value: 49.374
- type: accuracy_stderr
value: 1.436636349254743
- type: f1
value: 47.115240601017774
- type: f1_stderr
value: 1.5642799356594534
- type: main_score
value: 49.374
task:
type: Classification
- dataset:
config: default
name: MTEB BQ
revision: None
split: test
type: C-MTEB/BQ
metrics:
- type: cos_sim_pearson
value: 71.49514309404829
- type: cos_sim_spearman
value: 72.66161713021279
- type: manhattan_pearson
value: 71.03443640254005
- type: manhattan_spearman
value: 72.63439621980275
- type: euclidean_pearson
value: 71.06830370642658
- type: euclidean_spearman
value: 72.66161713043078
- type: main_score
value: 72.66161713021279
task:
type: STS
- dataset:
config: default
name: MTEB CLSClusteringP2P
revision: None
split: test
type: C-MTEB/CLSClusteringP2P
metrics:
- type: v_measure
value: 57.237692641281
- type: v_measure_std
value: 1.2777768354339174
- type: main_score
value: 57.237692641281
task:
type: Clustering
- dataset:
config: default
name: MTEB CLSClusteringS2S
revision: None
split: test
type: C-MTEB/CLSClusteringS2S
metrics:
- type: v_measure
value: 48.41686666939331
- type: v_measure_std
value: 1.7663118461900793
- type: main_score
value: 48.41686666939331
task:
type: Clustering
- dataset:
config: default
name: MTEB CMedQAv1
revision: None
split: test
type: C-MTEB/CMedQAv1-reranking
metrics:
- type: map
value: 89.9766367822762
- type: mrr
value: 91.88896825396824
- type: main_score
value: 89.9766367822762
task:
type: Reranking
- dataset:
config: default
name: MTEB CMedQAv2
revision: None
split: test
type: C-MTEB/CMedQAv2-reranking
metrics:
- type: map
value: 89.04628340075982
- type: mrr
value: 91.21702380952381
- type: main_score
value: 89.04628340075982
task:
type: Reranking
- dataset:
config: default
name: MTEB CmedqaRetrieval
revision: None
split: dev
type: C-MTEB/CmedqaRetrieval
metrics:
- type: map_at_1
value: 27.796
- type: map_at_10
value: 41.498000000000005
- type: map_at_100
value: 43.332
- type: map_at_1000
value: 43.429
- type: map_at_3
value: 37.172
- type: map_at_5
value: 39.617000000000004
- type: mrr_at_1
value: 42.111
- type: mrr_at_10
value: 50.726000000000006
- type: mrr_at_100
value: 51.632
- type: mrr_at_1000
value: 51.67
- type: mrr_at_3
value: 48.429
- type: mrr_at_5
value: 49.662
- type: ndcg_at_1
value: 42.111
- type: ndcg_at_10
value: 48.294
- type: ndcg_at_100
value: 55.135999999999996
- type: ndcg_at_1000
value: 56.818000000000005
- type: ndcg_at_3
value: 43.185
- type: ndcg_at_5
value: 45.266
- type: precision_at_1
value: 42.111
- type: precision_at_10
value: 10.635
- type: precision_at_100
value: 1.619
- type: precision_at_1000
value: 0.183
- type: precision_at_3
value: 24.539
- type: precision_at_5
value: 17.644000000000002
- type: recall_at_1
value: 27.796
- type: recall_at_10
value: 59.034
- type: recall_at_100
value: 86.991
- type: recall_at_1000
value: 98.304
- type: recall_at_3
value: 43.356
- type: recall_at_5
value: 49.998
- type: main_score
value: 48.294
task:
type: Retrieval
- dataset:
config: default
name: MTEB Cmnli
revision: None
split: validation
type: C-MTEB/CMNLI
metrics:
- type: cos_sim_accuracy
value: 82.8983764281419
- type: cos_sim_accuracy_threshold
value: 56.05731010437012
- type: cos_sim_ap
value: 90.23156362696572
- type: cos_sim_f1
value: 83.83207278307574
- type: cos_sim_f1_threshold
value: 52.05453634262085
- type: cos_sim_precision
value: 78.91044160132068
- type: cos_sim_recall
value: 89.40846387654898
- type: dot_accuracy
value: 82.8983764281419
- type: dot_accuracy_threshold
value: 56.05730414390564
- type: dot_ap
value: 90.20952356258861
- type: dot_f1
value: 83.83207278307574
- type: dot_f1_threshold
value: 52.054524421691895
- type: dot_precision
value: 78.91044160132068
- type: dot_recall
value: 89.40846387654898
- type: euclidean_accuracy
value: 82.8983764281419
- type: euclidean_accuracy_threshold
value: 93.74719858169556
- type: euclidean_ap
value: 90.23156283510565
- type: euclidean_f1
value: 83.83207278307574
- type: euclidean_f1_threshold
value: 97.92392253875732
- type: euclidean_precision
value: 78.91044160132068
- type: euclidean_recall
value: 89.40846387654898
- type: manhattan_accuracy
value: 82.85027059530968
- type: manhattan_accuracy_threshold
value: 3164.584159851074
- type: manhattan_ap
value: 90.23178004516869
- type: manhattan_f1
value: 83.82157123834887
- type: manhattan_f1_threshold
value: 3273.5992431640625
- type: manhattan_precision
value: 79.76768743400211
- type: manhattan_recall
value: 88.30956277764788
- type: max_accuracy
value: 82.8983764281419
- type: max_ap
value: 90.23178004516869
- type: max_f1
value: 83.83207278307574
task:
type: PairClassification
- dataset:
config: default
name: MTEB CovidRetrieval
revision: None
split: dev
type: C-MTEB/CovidRetrieval
metrics:
- type: map_at_1
value: 80.479
- type: map_at_10
value: 87.984
- type: map_at_100
value: 88.036
- type: map_at_1000
value: 88.03699999999999
- type: map_at_3
value: 87.083
- type: map_at_5
value: 87.694
- type: mrr_at_1
value: 80.927
- type: mrr_at_10
value: 88.046
- type: mrr_at_100
value: 88.099
- type: mrr_at_1000
value: 88.1
- type: mrr_at_3
value: 87.215
- type: mrr_at_5
value: 87.768
- type: ndcg_at_1
value: 80.927
- type: ndcg_at_10
value: 90.756
- type: ndcg_at_100
value: 90.96
- type: ndcg_at_1000
value: 90.975
- type: ndcg_at_3
value: 89.032
- type: ndcg_at_5
value: 90.106
- type: precision_at_1
value: 80.927
- type: precision_at_10
value: 10.011000000000001
- type: precision_at_100
value: 1.009
- type: precision_at_1000
value: 0.101
- type: precision_at_3
value: 31.752999999999997
- type: precision_at_5
value: 19.6
- type: recall_at_1
value: 80.479
- type: recall_at_10
value: 99.05199999999999
- type: recall_at_100
value: 99.895
- type: recall_at_1000
value: 100
- type: recall_at_3
value: 94.494
- type: recall_at_5
value: 97.102
- type: main_score
value: 90.756
task:
type: Retrieval
- dataset:
config: default
name: MTEB DuRetrieval
revision: None
split: dev
type: C-MTEB/DuRetrieval
metrics:
- type: map_at_1
value: 27.853
- type: map_at_10
value: 85.13199999999999
- type: map_at_100
value: 87.688
- type: map_at_1000
value: 87.712
- type: map_at_3
value: 59.705
- type: map_at_5
value: 75.139
- type: mrr_at_1
value: 93.65
- type: mrr_at_10
value: 95.682
- type: mrr_at_100
value: 95.722
- type: mrr_at_1000
value: 95.724
- type: mrr_at_3
value: 95.467
- type: mrr_at_5
value: 95.612
- type: ndcg_at_1
value: 93.65
- type: ndcg_at_10
value: 91.155
- type: ndcg_at_100
value: 93.183
- type: ndcg_at_1000
value: 93.38499999999999
- type: ndcg_at_3
value: 90.648
- type: ndcg_at_5
value: 89.47699999999999
- type: precision_at_1
value: 93.65
- type: precision_at_10
value: 43.11
- type: precision_at_100
value: 4.854
- type: precision_at_1000
value: 0.49100000000000005
- type: precision_at_3
value: 81.11699999999999
- type: precision_at_5
value: 68.28999999999999
- type: recall_at_1
value: 27.853
- type: recall_at_10
value: 91.678
- type: recall_at_100
value: 98.553
- type: recall_at_1000
value: 99.553
- type: recall_at_3
value: 61.381
- type: recall_at_5
value: 78.605
- type: main_score
value: 91.155
task:
type: Retrieval
- dataset:
config: default
name: MTEB EcomRetrieval
revision: None
split: dev
type: C-MTEB/EcomRetrieval
metrics:
- type: map_at_1
value: 54.50000000000001
- type: map_at_10
value: 65.167
- type: map_at_100
value: 65.664
- type: map_at_1000
value: 65.67399999999999
- type: map_at_3
value: 62.633
- type: map_at_5
value: 64.208
- type: mrr_at_1
value: 54.50000000000001
- type: mrr_at_10
value: 65.167
- type: mrr_at_100
value: 65.664
- type: mrr_at_1000
value: 65.67399999999999
- type: mrr_at_3
value: 62.633
- type: mrr_at_5
value: 64.208
- type: ndcg_at_1
value: 54.50000000000001
- type: ndcg_at_10
value: 70.294
- type: ndcg_at_100
value: 72.564
- type: ndcg_at_1000
value: 72.841
- type: ndcg_at_3
value: 65.128
- type: ndcg_at_5
value: 67.96799999999999
- type: precision_at_1
value: 54.50000000000001
- type: precision_at_10
value: 8.64
- type: precision_at_100
value: 0.967
- type: precision_at_1000
value: 0.099
- type: precision_at_3
value: 24.099999999999998
- type: precision_at_5
value: 15.840000000000002
- type: recall_at_1
value: 54.50000000000001
- type: recall_at_10
value: 86.4
- type: recall_at_100
value: 96.7
- type: recall_at_1000
value: 98.9
- type: recall_at_3
value: 72.3
- type: recall_at_5
value: 79.2
- type: main_score
value: 70.294
task:
type: Retrieval
- dataset:
config: default
name: MTEB IFlyTek
revision: None
split: validation
type: C-MTEB/IFlyTek-classification
metrics:
- type: accuracy
value: 52.743362831858406
- type: accuracy_stderr
value: 0.23768288128480788
- type: f1
value: 41.1548855278405
- type: f1_stderr
value: 0.4088759842813554
- type: main_score
value: 52.743362831858406
task:
type: Classification
- dataset:
config: default
name: MTEB JDReview
revision: None
split: test
type: C-MTEB/JDReview-classification
metrics:
- type: accuracy
value: 89.08067542213884
- type: accuracy_stderr
value: 0.9559278951487445
- type: ap
value: 60.875320104586564
- type: ap_stderr
value: 2.137806661565934
- type: f1
value: 84.39314192399665
- type: f1_stderr
value: 1.132407155321657
- type: main_score
value: 89.08067542213884
task:
type: Classification
- dataset:
config: default
name: MTEB LCQMC
revision: None
split: test
type: C-MTEB/LCQMC
metrics:
- type: cos_sim_pearson
value: 73.3633875566899
- type: cos_sim_spearman
value: 79.27679599527615
- type: manhattan_pearson
value: 79.12061667088273
- type: manhattan_spearman
value: 79.26989882781706
- type: euclidean_pearson
value: 79.12871362068391
- type: euclidean_spearman
value: 79.27679377557219
- type: main_score
value: 79.27679599527615
task:
type: STS
- dataset:
config: default
name: MTEB MMarcoReranking
revision: None
split: dev
type: C-MTEB/Mmarco-reranking
metrics:
- type: map
value: 37.68251937316638
- type: mrr
value: 36.61746031746032
- type: main_score
value: 37.68251937316638
task:
type: Reranking
- dataset:
config: default
name: MTEB MMarcoRetrieval
revision: None
split: dev
type: C-MTEB/MMarcoRetrieval
metrics:
- type: map_at_1
value: 69.401
- type: map_at_10
value: 78.8
- type: map_at_100
value: 79.077
- type: map_at_1000
value: 79.081
- type: map_at_3
value: 76.97
- type: map_at_5
value: 78.185
- type: mrr_at_1
value: 71.719
- type: mrr_at_10
value: 79.327
- type: mrr_at_100
value: 79.56400000000001
- type: mrr_at_1000
value: 79.56800000000001
- type: mrr_at_3
value: 77.736
- type: mrr_at_5
value: 78.782
- type: ndcg_at_1
value: 71.719
- type: ndcg_at_10
value: 82.505
- type: ndcg_at_100
value: 83.673
- type: ndcg_at_1000
value: 83.786
- type: ndcg_at_3
value: 79.07600000000001
- type: ndcg_at_5
value: 81.122
- type: precision_at_1
value: 71.719
- type: precision_at_10
value: 9.924
- type: precision_at_100
value: 1.049
- type: precision_at_1000
value: 0.106
- type: precision_at_3
value: 29.742
- type: precision_at_5
value: 18.937
- type: recall_at_1
value: 69.401
- type: recall_at_10
value: 93.349
- type: recall_at_100
value: 98.492
- type: recall_at_1000
value: 99.384
- type: recall_at_3
value: 84.385
- type: recall_at_5
value: 89.237
- type: main_score
value: 82.505
task:
type: Retrieval
- dataset:
config: zh-CN
name: MTEB MassiveIntentClassification (zh-CN)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 77.9388029589778
- type: accuracy_stderr
value: 1.416192788478398
- type: f1
value: 74.77765701086211
- type: f1_stderr
value: 1.254859698486085
- type: main_score
value: 77.9388029589778
task:
type: Classification
- dataset:
config: zh-CN
name: MTEB MassiveScenarioClassification (zh-CN)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 83.8231338264963
- type: accuracy_stderr
value: 0.6973305760755886
- type: f1
value: 83.13105322628088
- type: f1_stderr
value: 0.600506118139685
- type: main_score
value: 83.8231338264963
task:
type: Classification
- dataset:
config: default
name: MTEB MedicalRetrieval
revision: None
split: dev
type: C-MTEB/MedicalRetrieval
metrics:
- type: map_at_1
value: 57.8
- type: map_at_10
value: 64.696
- type: map_at_100
value: 65.294
- type: map_at_1000
value: 65.328
- type: map_at_3
value: 62.949999999999996
- type: map_at_5
value: 64.095
- type: mrr_at_1
value: 58.099999999999994
- type: mrr_at_10
value: 64.85
- type: mrr_at_100
value: 65.448
- type: mrr_at_1000
value: 65.482
- type: mrr_at_3
value: 63.1
- type: mrr_at_5
value: 64.23
- type: ndcg_at_1
value: 57.8
- type: ndcg_at_10
value: 68.041
- type: ndcg_at_100
value: 71.074
- type: ndcg_at_1000
value: 71.919
- type: ndcg_at_3
value: 64.584
- type: ndcg_at_5
value: 66.625
- type: precision_at_1
value: 57.8
- type: precision_at_10
value: 7.85
- type: precision_at_100
value: 0.9289999999999999
- type: precision_at_1000
value: 0.099
- type: precision_at_3
value: 23.1
- type: precision_at_5
value: 14.84
- type: recall_at_1
value: 57.8
- type: recall_at_10
value: 78.5
- type: recall_at_100
value: 92.9
- type: recall_at_1000
value: 99.4
- type: recall_at_3
value: 69.3
- type: recall_at_5
value: 74.2
- type: main_score
value: 68.041
task:
type: Retrieval
- dataset:
config: default
name: MTEB MultilingualSentiment
revision: None
split: validation
type: C-MTEB/MultilingualSentiment-classification
metrics:
- type: accuracy
value: 78.60333333333334
- type: accuracy_stderr
value: 0.3331499495555859
- type: f1
value: 78.4814340961856
- type: f1_stderr
value: 0.45721454672060496
- type: main_score
value: 78.60333333333334
task:
type: Classification
- dataset:
config: default
name: MTEB Ocnli
revision: None
split: validation
type: C-MTEB/OCNLI
metrics:
- type: cos_sim_accuracy
value: 80.5630752571738
- type: cos_sim_accuracy_threshold
value: 53.72971296310425
- type: cos_sim_ap
value: 85.61885910463258
- type: cos_sim_f1
value: 82.40469208211144
- type: cos_sim_f1_threshold
value: 50.07883310317993
- type: cos_sim_precision
value: 76.70609645131938
- type: cos_sim_recall
value: 89.01795142555439
- type: dot_accuracy
value: 80.5630752571738
- type: dot_accuracy_threshold
value: 53.7297248840332
- type: dot_ap
value: 85.61885910463258
- type: dot_f1
value: 82.40469208211144
- type: dot_f1_threshold
value: 50.07884502410889
- type: dot_precision
value: 76.70609645131938
- type: dot_recall
value: 89.01795142555439
- type: euclidean_accuracy
value: 80.5630752571738
- type: euclidean_accuracy_threshold
value: 96.19801044464111
- type: euclidean_ap
value: 85.61885910463258
- type: euclidean_f1
value: 82.40469208211144
- type: euclidean_f1_threshold
value: 99.92111921310425
- type: euclidean_precision
value: 76.70609645131938
- type: euclidean_recall
value: 89.01795142555439
- type: manhattan_accuracy
value: 80.67135896047645
- type: manhattan_accuracy_threshold
value: 3323.1739044189453
- type: manhattan_ap
value: 85.55348220886658
- type: manhattan_f1
value: 82.26744186046511
- type: manhattan_f1_threshold
value: 3389.273452758789
- type: manhattan_precision
value: 76.00716204118174
- type: manhattan_recall
value: 89.65153115100317
- type: max_accuracy
value: 80.67135896047645
- type: max_ap
value: 85.61885910463258
- type: max_f1
value: 82.40469208211144
task:
type: PairClassification
- dataset:
config: default
name: MTEB OnlineShopping
revision: None
split: test
type: C-MTEB/OnlineShopping-classification
metrics:
- type: accuracy
value: 94.94
- type: accuracy_stderr
value: 0.49030602688525093
- type: ap
value: 93.0785841977823
- type: ap_stderr
value: 0.5447383082750599
- type: f1
value: 94.92765777406245
- type: f1_stderr
value: 0.4891510966106189
- type: main_score
value: 94.94
task:
type: Classification
- dataset:
config: default
name: MTEB PAWSX
revision: None
split: test
type: C-MTEB/PAWSX
metrics:
- type: cos_sim_pearson
value: 36.564307811370654
- type: cos_sim_spearman
value: 42.44208208349051
- type: manhattan_pearson
value: 42.099358471578306
- type: manhattan_spearman
value: 42.50283181486304
- type: euclidean_pearson
value: 42.07954956675317
- type: euclidean_spearman
value: 42.453014115018554
- type: main_score
value: 42.44208208349051
task:
type: STS
- dataset:
config: default
name: MTEB QBQTC
revision: None
split: test
type: C-MTEB/QBQTC
metrics:
- type: cos_sim_pearson
value: 39.19092968089104
- type: cos_sim_spearman
value: 41.5174661348832
- type: manhattan_pearson
value: 37.91587646684523
- type: manhattan_spearman
value: 41.536668677987194
- type: euclidean_pearson
value: 37.91079973901135
- type: euclidean_spearman
value: 41.51833855501128
- type: main_score
value: 41.5174661348832
task:
type: STS
- dataset:
config: zh
name: MTEB STS22 (zh)
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 62.029449510721605
- type: cos_sim_spearman
value: 66.31935471251364
- type: manhattan_pearson
value: 63.63179975157496
- type: manhattan_spearman
value: 66.3007950466125
- type: euclidean_pearson
value: 63.59752734041086
- type: euclidean_spearman
value: 66.31935471251364
- type: main_score
value: 66.31935471251364
task:
type: STS
- dataset:
config: default
name: MTEB STSB
revision: None
split: test
type: C-MTEB/STSB
metrics:
- type: cos_sim_pearson
value: 81.81459862563769
- type: cos_sim_spearman
value: 82.15323953301453
- type: manhattan_pearson
value: 81.61904305126016
- type: manhattan_spearman
value: 82.1361073852468
- type: euclidean_pearson
value: 81.60799063723992
- type: euclidean_spearman
value: 82.15405405083231
- type: main_score
value: 82.15323953301453
task:
type: STS
- dataset:
config: default
name: MTEB T2Reranking
revision: None
split: dev
type: C-MTEB/T2Reranking
metrics:
- type: map
value: 69.13560834260383
- type: mrr
value: 79.95749642669074
- type: main_score
value: 69.13560834260383
task:
type: Reranking
- dataset:
config: default
name: MTEB T2Retrieval
revision: None
split: dev
type: C-MTEB/T2Retrieval
metrics:
- type: map_at_1
value: 28.041
- type: map_at_10
value: 78.509
- type: map_at_100
value: 82.083
- type: map_at_1000
value: 82.143
- type: map_at_3
value: 55.345
- type: map_at_5
value: 67.899
- type: mrr_at_1
value: 90.86
- type: mrr_at_10
value: 93.31
- type: mrr_at_100
value: 93.388
- type: mrr_at_1000
value: 93.391
- type: mrr_at_3
value: 92.92200000000001
- type: mrr_at_5
value: 93.167
- type: ndcg_at_1
value: 90.86
- type: ndcg_at_10
value: 85.875
- type: ndcg_at_100
value: 89.269
- type: ndcg_at_1000
value: 89.827
- type: ndcg_at_3
value: 87.254
- type: ndcg_at_5
value: 85.855
- type: precision_at_1
value: 90.86
- type: precision_at_10
value: 42.488
- type: precision_at_100
value: 5.029
- type: precision_at_1000
value: 0.516
- type: precision_at_3
value: 76.172
- type: precision_at_5
value: 63.759
- type: recall_at_1
value: 28.041
- type: recall_at_10
value: 84.829
- type: recall_at_100
value: 95.89999999999999
- type: recall_at_1000
value: 98.665
- type: recall_at_3
value: 57.009
- type: recall_at_5
value: 71.188
- type: main_score
value: 85.875
task:
type: Retrieval
- dataset:
config: default
name: MTEB TNews
revision: None
split: validation
type: C-MTEB/TNews-classification
metrics:
- type: accuracy
value: 54.309000000000005
- type: accuracy_stderr
value: 0.4694347665011627
- type: f1
value: 52.598803987889255
- type: f1_stderr
value: 0.5191189533227434
- type: main_score
value: 54.309000000000005
task:
type: Classification
- dataset:
config: default
name: MTEB ThuNewsClusteringP2P
revision: None
split: test
type: C-MTEB/ThuNewsClusteringP2P
metrics:
- type: v_measure
value: 76.64191229011249
- type: v_measure_std
value: 2.807206940615986
- type: main_score
value: 76.64191229011249
task:
type: Clustering
- dataset:
config: default
name: MTEB ThuNewsClusteringS2S
revision: None
split: test
type: C-MTEB/ThuNewsClusteringS2S
metrics:
- type: v_measure
value: 71.02529199411326
- type: v_measure_std
value: 2.0547855888165945
- type: main_score
value: 71.02529199411326
task:
type: Clustering
- dataset:
config: default
name: MTEB VideoRetrieval
revision: None
split: dev
type: C-MTEB/VideoRetrieval
metrics:
- type: map_at_1
value: 67.30000000000001
- type: map_at_10
value: 76.819
- type: map_at_100
value: 77.141
- type: map_at_1000
value: 77.142
- type: map_at_3
value: 75.233
- type: map_at_5
value: 76.163
- type: mrr_at_1
value: 67.30000000000001
- type: mrr_at_10
value: 76.819
- type: mrr_at_100
value: 77.141
- type: mrr_at_1000
value: 77.142
- type: mrr_at_3
value: 75.233
- type: mrr_at_5
value: 76.163
- type: ndcg_at_1
value: 67.30000000000001
- type: ndcg_at_10
value: 80.93599999999999
- type: ndcg_at_100
value: 82.311
- type: ndcg_at_1000
value: 82.349
- type: ndcg_at_3
value: 77.724
- type: ndcg_at_5
value: 79.406
- type: precision_at_1
value: 67.30000000000001
- type: precision_at_10
value: 9.36
- type: precision_at_100
value: 0.996
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 28.299999999999997
- type: precision_at_5
value: 17.8
- type: recall_at_1
value: 67.30000000000001
- type: recall_at_10
value: 93.60000000000001
- type: recall_at_100
value: 99.6
- type: recall_at_1000
value: 99.9
- type: recall_at_3
value: 84.89999999999999
- type: recall_at_5
value: 89
- type: main_score
value: 80.93599999999999
task:
type: Retrieval
- dataset:
config: default
name: MTEB Waimai
revision: None
split: test
type: C-MTEB/waimai-classification
metrics:
- type: accuracy
value: 89.47
- type: accuracy_stderr
value: 0.26476404589747476
- type: ap
value: 75.49555223825388
- type: ap_stderr
value: 0.596040511982105
- type: f1
value: 88.01797939221065
- type: f1_stderr
value: 0.27168216797281214
- type: main_score
value: 89.47
task:
type: Classification
tags:
- mteb
XYZ-embedding-zh-v2
Usage (Sentence Transformers)
First install the Sentence Transformers library:
pip install -U sentence-transformers
Then you can load this model and run inference.
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("fangxq/XYZ-embedding-zh-v2")
# Run inference
sentences = [
'The weather is lovely today.',
"It's so sunny outside!",
'He drove to the stadium.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1792]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]