xyz模型

README.md

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+---
+model-index:
+- name: XYZ-embedding
+  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]
+```

config.json

@@ -0,0 +1,32 @@
+{
+  "_name_or_path": "",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "pad_token_id": 0,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 21128
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.41.0",
+    "pytorch": "2.2.2+cu118"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Dense",
+    "type": "sentence_transformers.models.Dense"
+  }
+]

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8090436280027987a24ffb67f66976b4069d4812c580f271ef7fe4720a037bcf
+size 1302216550

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "max_length": 512,
+  "model_max_length": 1000000000000000019884624838656,
+  "never_split": null,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}