Upload 6 files

README.md

@@ -1,3 +1,1134 @@
 ---
+tags:
+- mteb
+- sentence-similarity
+- sentence-transformers
+- Sentence Transformers
+model-index:
+- name: gte-large-zh
+  results:
+  - task:
+      type: STS
+    dataset:
+      type: C-MTEB/AFQMC
+      name: MTEB AFQMC
+      config: default
+      split: validation
+      revision: None
+    metrics:
+    - type: cos_sim_pearson
+      value: 48.94131905219026
+    - type: cos_sim_spearman
+      value: 54.58261199731436
+    - type: euclidean_pearson
+      value: 52.73929210805982
+    - type: euclidean_spearman
+      value: 54.582632097533676
+    - type: manhattan_pearson
+      value: 52.73123295724949
+    - type: manhattan_spearman
+      value: 54.572941830465794
+  - task:
+      type: STS
+    dataset:
+      type: C-MTEB/ATEC
+      name: MTEB ATEC
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: cos_sim_pearson
+      value: 47.292931669579005
+    - type: cos_sim_spearman
+      value: 54.601019783506466
+    - type: euclidean_pearson
+      value: 54.61393532658173
+    - type: euclidean_spearman
+      value: 54.60101865708542
+    - type: manhattan_pearson
+      value: 54.59369555606305
+    - type: manhattan_spearman
+      value: 54.601098593646036
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_reviews_multi
+      name: MTEB AmazonReviewsClassification (zh)
+      config: zh
+      split: test
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+    metrics:
+    - type: accuracy
+      value: 47.233999999999995
+    - type: f1
+      value: 45.68998446563349
+  - task:
+      type: STS
+    dataset:
+      type: C-MTEB/BQ
+      name: MTEB BQ
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: cos_sim_pearson
+      value: 62.55033151404683
+    - type: cos_sim_spearman
+      value: 64.40573802644984
+    - type: euclidean_pearson
+      value: 62.93453281081951
+    - type: euclidean_spearman
+      value: 64.40574149035828
+    - type: manhattan_pearson
+      value: 62.839969210895816
+    - type: manhattan_spearman
+      value: 64.30837945045283
+  - task:
+      type: Clustering
+    dataset:
+      type: C-MTEB/CLSClusteringP2P
+      name: MTEB CLSClusteringP2P
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: v_measure
+      value: 42.098169316685045
+  - task:
+      type: Clustering
+    dataset:
+      type: C-MTEB/CLSClusteringS2S
+      name: MTEB CLSClusteringS2S
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: v_measure
+      value: 38.90716707051822
+  - task:
+      type: Reranking
+    dataset:
+      type: C-MTEB/CMedQAv1-reranking
+      name: MTEB CMedQAv1
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map
+      value: 86.09191911031553
+    - type: mrr
+      value: 88.6747619047619
+  - task:
+      type: Reranking
+    dataset:
+      type: C-MTEB/CMedQAv2-reranking
+      name: MTEB CMedQAv2
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map
+      value: 86.45781885502122
+    - type: mrr
+      value: 89.01591269841269
+  - task:
+      type: Retrieval
+    dataset:
+      type: C-MTEB/CmedqaRetrieval
+      name: MTEB CmedqaRetrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 24.215
+    - type: map_at_10
+      value: 36.498000000000005
+    - type: map_at_100
+      value: 38.409
+    - type: map_at_1000
+      value: 38.524
+    - type: map_at_3
+      value: 32.428000000000004
+    - type: map_at_5
+      value: 34.664
+    - type: mrr_at_1
+      value: 36.834
+    - type: mrr_at_10
+      value: 45.196
+    - type: mrr_at_100
+      value: 46.214
+    - type: mrr_at_1000
+      value: 46.259
+    - type: mrr_at_3
+      value: 42.631
+    - type: mrr_at_5
+      value: 44.044
+    - type: ndcg_at_1
+      value: 36.834
+    - type: ndcg_at_10
+      value: 43.146
+    - type: ndcg_at_100
+      value: 50.632999999999996
+    - type: ndcg_at_1000
+      value: 52.608999999999995
+    - type: ndcg_at_3
+      value: 37.851
+    - type: ndcg_at_5
+      value: 40.005
+    - type: precision_at_1
+      value: 36.834
+    - type: precision_at_10
+      value: 9.647
+    - type: precision_at_100
+      value: 1.574
+    - type: precision_at_1000
+      value: 0.183
+    - type: precision_at_3
+      value: 21.48
+    - type: precision_at_5
+      value: 15.649
+    - type: recall_at_1
+      value: 24.215
+    - type: recall_at_10
+      value: 54.079
+    - type: recall_at_100
+      value: 84.943
+    - type: recall_at_1000
+      value: 98.098
+    - type: recall_at_3
+      value: 38.117000000000004
+    - type: recall_at_5
+      value: 44.775999999999996
+  - task:
+      type: PairClassification
+    dataset:
+      type: C-MTEB/CMNLI
+      name: MTEB Cmnli
+      config: default
+      split: validation
+      revision: None
+    metrics:
+    - type: cos_sim_accuracy
+      value: 82.51352976548407
+    - type: cos_sim_ap
+      value: 89.49905141462749
+    - type: cos_sim_f1
+      value: 83.89334489486234
+    - type: cos_sim_precision
+      value: 78.19761567993534
+    - type: cos_sim_recall
+      value: 90.48398410100538
+    - type: dot_accuracy
+      value: 82.51352976548407
+    - type: dot_ap
+      value: 89.49108293121158
+    - type: dot_f1
+      value: 83.89334489486234
+    - type: dot_precision
+      value: 78.19761567993534
+    - type: dot_recall
+      value: 90.48398410100538
+    - type: euclidean_accuracy
+      value: 82.51352976548407
+    - type: euclidean_ap
+      value: 89.49904709975154
+    - type: euclidean_f1
+      value: 83.89334489486234
+    - type: euclidean_precision
+      value: 78.19761567993534
+    - type: euclidean_recall
+      value: 90.48398410100538
+    - type: manhattan_accuracy
+      value: 82.48947684906794
+    - type: manhattan_ap
+      value: 89.49231995962901
+    - type: manhattan_f1
+      value: 83.84681215233205
+    - type: manhattan_precision
+      value: 77.28258726089528
+    - type: manhattan_recall
+      value: 91.62964694879588
+    - type: max_accuracy
+      value: 82.51352976548407
+    - type: max_ap
+      value: 89.49905141462749
+    - type: max_f1
+      value: 83.89334489486234
+  - task:
+      type: Retrieval
+    dataset:
+      type: C-MTEB/CovidRetrieval
+      name: MTEB CovidRetrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 78.583
+    - type: map_at_10
+      value: 85.613
+    - type: map_at_100
+      value: 85.777
+    - type: map_at_1000
+      value: 85.77900000000001
+    - type: map_at_3
+      value: 84.58
+    - type: map_at_5
+      value: 85.22800000000001
+    - type: mrr_at_1
+      value: 78.925
+    - type: mrr_at_10
+      value: 85.667
+    - type: mrr_at_100
+      value: 85.822
+    - type: mrr_at_1000
+      value: 85.824
+    - type: mrr_at_3
+      value: 84.651
+    - type: mrr_at_5
+      value: 85.299
+    - type: ndcg_at_1
+      value: 78.925
+    - type: ndcg_at_10
+      value: 88.405
+    - type: ndcg_at_100
+      value: 89.02799999999999
+    - type: ndcg_at_1000
+      value: 89.093
+    - type: ndcg_at_3
+      value: 86.393
+    - type: ndcg_at_5
+      value: 87.5
+    - type: precision_at_1
+      value: 78.925
+    - type: precision_at_10
+      value: 9.789
+    - type: precision_at_100
+      value: 1.005
+    - type: precision_at_1000
+      value: 0.101
+    - type: precision_at_3
+      value: 30.769000000000002
+    - type: precision_at_5
+      value: 19.031000000000002
+    - type: recall_at_1
+      value: 78.583
+    - type: recall_at_10
+      value: 96.891
+    - type: recall_at_100
+      value: 99.473
+    - type: recall_at_1000
+      value: 100.0
+    - type: recall_at_3
+      value: 91.438
+    - type: recall_at_5
+      value: 94.152
+  - task:
+      type: Retrieval
+    dataset:
+      type: C-MTEB/DuRetrieval
+      name: MTEB DuRetrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 25.604
+    - type: map_at_10
+      value: 77.171
+    - type: map_at_100
+      value: 80.033
+    - type: map_at_1000
+      value: 80.099
+    - type: map_at_3
+      value: 54.364000000000004
+    - type: map_at_5
+      value: 68.024
+    - type: mrr_at_1
+      value: 89.85
+    - type: mrr_at_10
+      value: 93.009
+    - type: mrr_at_100
+      value: 93.065
+    - type: mrr_at_1000
+      value: 93.068
+    - type: mrr_at_3
+      value: 92.72500000000001
+    - type: mrr_at_5
+      value: 92.915
+    - type: ndcg_at_1
+      value: 89.85
+    - type: ndcg_at_10
+      value: 85.038
+    - type: ndcg_at_100
+      value: 88.247
+    - type: ndcg_at_1000
+      value: 88.837
+    - type: ndcg_at_3
+      value: 85.20299999999999
+    - type: ndcg_at_5
+      value: 83.47
+    - type: precision_at_1
+      value: 89.85
+    - type: precision_at_10
+      value: 40.275
+    - type: precision_at_100
+      value: 4.709
+    - type: precision_at_1000
+      value: 0.486
+    - type: precision_at_3
+      value: 76.36699999999999
+    - type: precision_at_5
+      value: 63.75999999999999
+    - type: recall_at_1
+      value: 25.604
+    - type: recall_at_10
+      value: 85.423
+    - type: recall_at_100
+      value: 95.695
+    - type: recall_at_1000
+      value: 98.669
+    - type: recall_at_3
+      value: 56.737
+    - type: recall_at_5
+      value: 72.646
+  - task:
+      type: Retrieval
+    dataset:
+      type: C-MTEB/EcomRetrieval
+      name: MTEB EcomRetrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 51.800000000000004
+    - type: map_at_10
+      value: 62.17
+    - type: map_at_100
+      value: 62.649
+    - type: map_at_1000
+      value: 62.663000000000004
+    - type: map_at_3
+      value: 59.699999999999996
+    - type: map_at_5
+      value: 61.23499999999999
+    - type: mrr_at_1
+      value: 51.800000000000004
+    - type: mrr_at_10
+      value: 62.17
+    - type: mrr_at_100
+      value: 62.649
+    - type: mrr_at_1000
+      value: 62.663000000000004
+    - type: mrr_at_3
+      value: 59.699999999999996
+    - type: mrr_at_5
+      value: 61.23499999999999
+    - type: ndcg_at_1
+      value: 51.800000000000004
+    - type: ndcg_at_10
+      value: 67.246
+    - type: ndcg_at_100
+      value: 69.58
+    - type: ndcg_at_1000
+      value: 69.925
+    - type: ndcg_at_3
+      value: 62.197
+    - type: ndcg_at_5
+      value: 64.981
+    - type: precision_at_1
+      value: 51.800000000000004
+    - type: precision_at_10
+      value: 8.32
+    - type: precision_at_100
+      value: 0.941
+    - type: precision_at_1000
+      value: 0.097
+    - type: precision_at_3
+      value: 23.133
+    - type: precision_at_5
+      value: 15.24
+    - type: recall_at_1
+      value: 51.800000000000004
+    - type: recall_at_10
+      value: 83.2
+    - type: recall_at_100
+      value: 94.1
+    - type: recall_at_1000
+      value: 96.8
+    - type: recall_at_3
+      value: 69.39999999999999
+    - type: recall_at_5
+      value: 76.2
+  - task:
+      type: Classification
+    dataset:
+      type: C-MTEB/IFlyTek-classification
+      name: MTEB IFlyTek
+      config: default
+      split: validation
+      revision: None
+    metrics:
+    - type: accuracy
+      value: 49.60369372835706
+    - type: f1
+      value: 38.24016248875209
+  - task:
+      type: Classification
+    dataset:
+      type: C-MTEB/JDReview-classification
+      name: MTEB JDReview
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: accuracy
+      value: 86.71669793621012
+    - type: ap
+      value: 55.75807094995178
+    - type: f1
+      value: 81.59033162805417
+  - task:
+      type: STS
+    dataset:
+      type: C-MTEB/LCQMC
+      name: MTEB LCQMC
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: cos_sim_pearson
+      value: 69.50947272908907
+    - type: cos_sim_spearman
+      value: 74.40054474949213
+    - type: euclidean_pearson
+      value: 73.53007373987617
+    - type: euclidean_spearman
+      value: 74.40054474732082
+    - type: manhattan_pearson
+      value: 73.51396571849736
+    - type: manhattan_spearman
+      value: 74.38395696630835
+  - task:
+      type: Reranking
+    dataset:
+      type: C-MTEB/Mmarco-reranking
+      name: MTEB MMarcoReranking
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map
+      value: 31.188333827724108
+    - type: mrr
+      value: 29.84801587301587
+  - task:
+      type: Retrieval
+    dataset:
+      type: C-MTEB/MMarcoRetrieval
+      name: MTEB MMarcoRetrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 64.685
+    - type: map_at_10
+      value: 73.803
+    - type: map_at_100
+      value: 74.153
+    - type: map_at_1000
+      value: 74.167
+    - type: map_at_3
+      value: 71.98
+    - type: map_at_5
+      value: 73.21600000000001
+    - type: mrr_at_1
+      value: 66.891
+    - type: mrr_at_10
+      value: 74.48700000000001
+    - type: mrr_at_100
+      value: 74.788
+    - type: mrr_at_1000
+      value: 74.801
+    - type: mrr_at_3
+      value: 72.918
+    - type: mrr_at_5
+      value: 73.965
+    - type: ndcg_at_1
+      value: 66.891
+    - type: ndcg_at_10
+      value: 77.534
+    - type: ndcg_at_100
+      value: 79.106
+    - type: ndcg_at_1000
+      value: 79.494
+    - type: ndcg_at_3
+      value: 74.13499999999999
+    - type: ndcg_at_5
+      value: 76.20700000000001
+    - type: precision_at_1
+      value: 66.891
+    - type: precision_at_10
+      value: 9.375
+    - type: precision_at_100
+      value: 1.0170000000000001
+    - type: precision_at_1000
+      value: 0.105
+    - type: precision_at_3
+      value: 27.932000000000002
+    - type: precision_at_5
+      value: 17.86
+    - type: recall_at_1
+      value: 64.685
+    - type: recall_at_10
+      value: 88.298
+    - type: recall_at_100
+      value: 95.426
+    - type: recall_at_1000
+      value: 98.48700000000001
+    - type: recall_at_3
+      value: 79.44200000000001
+    - type: recall_at_5
+      value: 84.358
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_intent
+      name: MTEB MassiveIntentClassification (zh-CN)
+      config: zh-CN
+      split: test
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+    metrics:
+    - type: accuracy
+      value: 73.30531271015468
+    - type: f1
+      value: 70.88091430578575
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (zh-CN)
+      config: zh-CN
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 75.7128446536651
+    - type: f1
+      value: 75.06125593532262
+  - task:
+      type: Retrieval
+    dataset:
+      type: C-MTEB/MedicalRetrieval
+      name: MTEB MedicalRetrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 52.7
+    - type: map_at_10
+      value: 59.532
+    - type: map_at_100
+      value: 60.085
+    - type: map_at_1000
+      value: 60.126000000000005
+    - type: map_at_3
+      value: 57.767
+    - type: map_at_5
+      value: 58.952000000000005
+    - type: mrr_at_1
+      value: 52.900000000000006
+    - type: mrr_at_10
+      value: 59.648999999999994
+    - type: mrr_at_100
+      value: 60.20100000000001
+    - type: mrr_at_1000
+      value: 60.242
+    - type: mrr_at_3
+      value: 57.882999999999996
+    - type: mrr_at_5
+      value: 59.068
+    - type: ndcg_at_1
+      value: 52.7
+    - type: ndcg_at_10
+      value: 62.883
+    - type: ndcg_at_100
+      value: 65.714
+    - type: ndcg_at_1000
+      value: 66.932
+    - type: ndcg_at_3
+      value: 59.34700000000001
+    - type: ndcg_at_5
+      value: 61.486
+    - type: precision_at_1
+      value: 52.7
+    - type: precision_at_10
+      value: 7.340000000000001
+    - type: precision_at_100
+      value: 0.8699999999999999
+    - type: precision_at_1000
+      value: 0.097
+    - type: precision_at_3
+      value: 21.3
+    - type: precision_at_5
+      value: 13.819999999999999
+    - type: recall_at_1
+      value: 52.7
+    - type: recall_at_10
+      value: 73.4
+    - type: recall_at_100
+      value: 87.0
+    - type: recall_at_1000
+      value: 96.8
+    - type: recall_at_3
+      value: 63.9
+    - type: recall_at_5
+      value: 69.1
+  - task:
+      type: Classification
+    dataset:
+      type: C-MTEB/MultilingualSentiment-classification
+      name: MTEB MultilingualSentiment
+      config: default
+      split: validation
+      revision: None
+    metrics:
+    - type: accuracy
+      value: 76.47666666666667
+    - type: f1
+      value: 76.4808576632057
+  - task:
+      type: PairClassification
+    dataset:
+      type: C-MTEB/OCNLI
+      name: MTEB Ocnli
+      config: default
+      split: validation
+      revision: None
+    metrics:
+    - type: cos_sim_accuracy
+      value: 77.58527341635084
+    - type: cos_sim_ap
+      value: 79.32131557636497
+    - type: cos_sim_f1
+      value: 80.51948051948052
+    - type: cos_sim_precision
+      value: 71.7948717948718
+    - type: cos_sim_recall
+      value: 91.65786694825766
+    - type: dot_accuracy
+      value: 77.58527341635084
+    - type: dot_ap
+      value: 79.32131557636497
+    - type: dot_f1
+      value: 80.51948051948052
+    - type: dot_precision
+      value: 71.7948717948718
+    - type: dot_recall
+      value: 91.65786694825766
+    - type: euclidean_accuracy
+      value: 77.58527341635084
+    - type: euclidean_ap
+      value: 79.32131557636497
+    - type: euclidean_f1
+      value: 80.51948051948052
+    - type: euclidean_precision
+      value: 71.7948717948718
+    - type: euclidean_recall
+      value: 91.65786694825766
+    - type: manhattan_accuracy
+      value: 77.15213860314023
+    - type: manhattan_ap
+      value: 79.26178519246496
+    - type: manhattan_f1
+      value: 80.22028453418999
+    - type: manhattan_precision
+      value: 70.94155844155844
+    - type: manhattan_recall
+      value: 92.29144667370645
+    - type: max_accuracy
+      value: 77.58527341635084
+    - type: max_ap
+      value: 79.32131557636497
+    - type: max_f1
+      value: 80.51948051948052
+  - task:
+      type: Classification
+    dataset:
+      type: C-MTEB/OnlineShopping-classification
+      name: MTEB OnlineShopping
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: accuracy
+      value: 92.68
+    - type: ap
+      value: 90.78652757815115
+    - type: f1
+      value: 92.67153098230253
+  - task:
+      type: STS
+    dataset:
+      type: C-MTEB/PAWSX
+      name: MTEB PAWSX
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: cos_sim_pearson
+      value: 35.301730226895955
+    - type: cos_sim_spearman
+      value: 38.54612530948101
+    - type: euclidean_pearson
+      value: 39.02831131230217
+    - type: euclidean_spearman
+      value: 38.54612530948101
+    - type: manhattan_pearson
+      value: 39.04765584936325
+    - type: manhattan_spearman
+      value: 38.54455759013173
+  - task:
+      type: STS
+    dataset:
+      type: C-MTEB/QBQTC
+      name: MTEB QBQTC
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: cos_sim_pearson
+      value: 32.27907454729754
+    - type: cos_sim_spearman
+      value: 33.35945567162729
+    - type: euclidean_pearson
+      value: 31.997628193815725
+    - type: euclidean_spearman
+      value: 33.3592386340529
+    - type: manhattan_pearson
+      value: 31.97117833750544
+    - type: manhattan_spearman
+      value: 33.30857326127779
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts22-crosslingual-sts
+      name: MTEB STS22 (zh)
+      config: zh
+      split: test
+      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
+    metrics:
+    - type: cos_sim_pearson
+      value: 62.53712784446981
+    - type: cos_sim_spearman
+      value: 62.975074386224286
+    - type: euclidean_pearson
+      value: 61.791207731290854
+    - type: euclidean_spearman
+      value: 62.975073716988064
+    - type: manhattan_pearson
+      value: 62.63850653150875
+    - type: manhattan_spearman
+      value: 63.56640346497343
+  - task:
+      type: STS
+    dataset:
+      type: C-MTEB/STSB
+      name: MTEB STSB
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: cos_sim_pearson
+      value: 79.52067424748047
+    - type: cos_sim_spearman
+      value: 79.68425102631514
+    - type: euclidean_pearson
+      value: 79.27553959329275
+    - type: euclidean_spearman
+      value: 79.68450427089856
+    - type: manhattan_pearson
+      value: 79.21584650471131
+    - type: manhattan_spearman
+      value: 79.6419242840243
+  - task:
+      type: Reranking
+    dataset:
+      type: C-MTEB/T2Reranking
+      name: MTEB T2Reranking
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map
+      value: 65.8563449629786
+    - type: mrr
+      value: 75.82550832339254
+  - task:
+      type: Retrieval
+    dataset:
+      type: C-MTEB/T2Retrieval
+      name: MTEB T2Retrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 27.889999999999997
+    - type: map_at_10
+      value: 72.878
+    - type: map_at_100
+      value: 76.737
+    - type: map_at_1000
+      value: 76.836
+    - type: map_at_3
+      value: 52.738
+    - type: map_at_5
+      value: 63.726000000000006
+    - type: mrr_at_1
+      value: 89.35600000000001
+    - type: mrr_at_10
+      value: 92.622
+    - type: mrr_at_100
+      value: 92.692
+    - type: mrr_at_1000
+      value: 92.694
+    - type: mrr_at_3
+      value: 92.13799999999999
+    - type: mrr_at_5
+      value: 92.452
+    - type: ndcg_at_1
+      value: 89.35600000000001
+    - type: ndcg_at_10
+      value: 81.932
+    - type: ndcg_at_100
+      value: 86.351
+    - type: ndcg_at_1000
+      value: 87.221
+    - type: ndcg_at_3
+      value: 84.29100000000001
+    - type: ndcg_at_5
+      value: 82.279
+    - type: precision_at_1
+      value: 89.35600000000001
+    - type: precision_at_10
+      value: 39.511
+    - type: precision_at_100
+      value: 4.901
+    - type: precision_at_1000
+      value: 0.513
+    - type: precision_at_3
+      value: 72.62100000000001
+    - type: precision_at_5
+      value: 59.918000000000006
+    - type: recall_at_1
+      value: 27.889999999999997
+    - type: recall_at_10
+      value: 80.636
+    - type: recall_at_100
+      value: 94.333
+    - type: recall_at_1000
+      value: 98.39099999999999
+    - type: recall_at_3
+      value: 54.797
+    - type: recall_at_5
+      value: 67.824
+  - task:
+      type: Classification
+    dataset:
+      type: C-MTEB/TNews-classification
+      name: MTEB TNews
+      config: default
+      split: validation
+      revision: None
+    metrics:
+    - type: accuracy
+      value: 51.979000000000006
+    - type: f1
+      value: 50.35658238894168
+  - task:
+      type: Clustering
+    dataset:
+      type: C-MTEB/ThuNewsClusteringP2P
+      name: MTEB ThuNewsClusteringP2P
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: v_measure
+      value: 68.36477832710159
+  - task:
+      type: Clustering
+    dataset:
+      type: C-MTEB/ThuNewsClusteringS2S
+      name: MTEB ThuNewsClusteringS2S
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: v_measure
+      value: 62.92080622759053
+  - task:
+      type: Retrieval
+    dataset:
+      type: C-MTEB/VideoRetrieval
+      name: MTEB VideoRetrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 59.3
+    - type: map_at_10
+      value: 69.299
+    - type: map_at_100
+      value: 69.669
+    - type: map_at_1000
+      value: 69.682
+    - type: map_at_3
+      value: 67.583
+    - type: map_at_5
+      value: 68.57799999999999
+    - type: mrr_at_1
+      value: 59.3
+    - type: mrr_at_10
+      value: 69.299
+    - type: mrr_at_100
+      value: 69.669
+    - type: mrr_at_1000
+      value: 69.682
+    - type: mrr_at_3
+      value: 67.583
+    - type: mrr_at_5
+      value: 68.57799999999999
+    - type: ndcg_at_1
+      value: 59.3
+    - type: ndcg_at_10
+      value: 73.699
+    - type: ndcg_at_100
+      value: 75.626
+    - type: ndcg_at_1000
+      value: 75.949
+    - type: ndcg_at_3
+      value: 70.18900000000001
+    - type: ndcg_at_5
+      value: 71.992
+    - type: precision_at_1
+      value: 59.3
+    - type: precision_at_10
+      value: 8.73
+    - type: precision_at_100
+      value: 0.9650000000000001
+    - type: precision_at_1000
+      value: 0.099
+    - type: precision_at_3
+      value: 25.900000000000002
+    - type: precision_at_5
+      value: 16.42
+    - type: recall_at_1
+      value: 59.3
+    - type: recall_at_10
+      value: 87.3
+    - type: recall_at_100
+      value: 96.5
+    - type: recall_at_1000
+      value: 99.0
+    - type: recall_at_3
+      value: 77.7
+    - type: recall_at_5
+      value: 82.1
+  - task:
+      type: Classification
+    dataset:
+      type: C-MTEB/waimai-classification
+      name: MTEB Waimai
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: accuracy
+      value: 88.36999999999999
+    - type: ap
+      value: 73.29590829222836
+    - type: f1
+      value: 86.74250506247606
+language:
+- en
 license: mit
 ---
+
+# gte-large-zh
+
+General Text Embeddings (GTE) model. [Towards General Text Embeddings with Multi-stage Contrastive Learning](https://arxiv.org/abs/2308.03281)
+
+The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer three different sizes of models, including [GTE-large-zh](https://huggingface.co/thenlper/gte-large-zh), [GTE-base-zh](https://huggingface.co/thenlper/gte-base-zh), and [GTE-small-zh](https://huggingface.co/thenlper/gte-small-zh). The GTE models are trained on a large-scale corpus of relevance text pairs, covering a wide range of domains and scenarios. This enables the GTE models to be applied to various downstream tasks of text embeddings, including **information retrieval**, **semantic textual similarity**, **text reranking**, etc.
+
+## Metrics
+
+We compared the performance of the GTE models with other popular text embedding models on the MTEB benchmark. For more detailed comparison results, please refer to the [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard).
+
+## Usage
+
+Code example
+
+```python
+import torch.nn.functional as F
+from torch import Tensor
+from transformers import AutoTokenizer, AutoModel
+
+input_texts = [
+    "what is the capital of China?",
+    "how to implement quick sort in python?",
+    "Beijing",
+    "sorting algorithms"
+]
+
+tokenizer = AutoTokenizer.from_pretrained("thenlper/gte-large-zh")
+model = AutoModel.from_pretrained("thenlper/gte-large-zh")
+
+# Tokenize the input texts
+batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')
+
+outputs = model(**batch_dict)
+embeddings = outputs.last_hidden_state[:, 0]
+ 
+# (Optionally) normalize embeddings
+embeddings = F.normalize(embeddings, p=2, dim=1)
+scores = (embeddings[:1] @ embeddings[1:].T) * 100
+print(scores.tolist())
+```
+
+Use with sentence-transformers:
+```python
+from sentence_transformers import SentenceTransformer
+from sentence_transformers.util import cos_sim
+
+sentences = ['That is a happy person', 'That is a very happy person']
+
+model = SentenceTransformer('thenlper/gte-large')
+embeddings = model.encode(sentences)
+print(cos_sim(embeddings[0], embeddings[1]))
+```
+
+### Limitation
+
+This model exclusively caters to English texts, and any lengthy texts will be truncated to a maximum of 512 tokens.
+
+### Citation
+
+If you find our paper or models helpful, please consider citing them as follows:
+
+```
+@misc{li2023general,
+      title={Towards General Text Embeddings with Multi-stage Contrastive Learning},
+      author={Zehan Li and Xin Zhang and Yanzhao Zhang and Dingkun Long and Pengjun Xie and Meishan Zhang},
+      year={2023},
+      eprint={2308.03281},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```

sentence_bert_config.json

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

special_tokens_map.json

@@ -0,0 +1,7 @@
+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

@@ -0,0 +1,15 @@
+{
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 1000000000000000019884624838656,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}