---
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: 100.0
    - 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: 89.0
    - 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
---
<h2 align="left">XYZ-embedding-zh-v2</h2>

## Usage (Sentence Transformers)

First install the Sentence Transformers library:

```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
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]
```