add models

README.md

@@ -1,3 +1,1322 @@
 ---
-license: mit
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- mteb
+model-index:
+- name: stella-base-zh-v2
+  results:
+  - task:
+      type: STS
+    dataset:
+      type: C-MTEB/AFQMC
+      name: MTEB AFQMC
+      config: default
+      split: validation
+      revision: None
+    metrics:
+    - type: cos_sim_pearson
+      value: 44.62083443545288
+    - type: cos_sim_spearman
+      value: 46.72814628391134
+    - type: euclidean_pearson
+      value: 45.11522093816821
+    - type: euclidean_spearman
+      value: 46.72818648900957
+    - type: manhattan_pearson
+      value: 44.98820754682395
+    - type: manhattan_spearman
+      value: 46.63576705524296
+  - task:
+      type: STS
+    dataset:
+      type: C-MTEB/ATEC
+      name: MTEB ATEC
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: cos_sim_pearson
+      value: 49.543902370260234
+    - type: cos_sim_spearman
+      value: 51.22161152883018
+    - type: euclidean_pearson
+      value: 53.49586541060596
+    - type: euclidean_spearman
+      value: 51.22161490583934
+    - type: manhattan_pearson
+      value: 53.51023339947787
+    - type: manhattan_spearman
+      value: 51.22426632538443
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_reviews_multi
+      name: MTEB AmazonReviewsClassification (zh)
+      config: zh
+      split: test
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+    metrics:
+    - type: accuracy
+      value: 39.644
+    - type: f1
+      value: 37.67897186741224
+  - task:
+      type: STS
+    dataset:
+      type: C-MTEB/BQ
+      name: MTEB BQ
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: cos_sim_pearson
+      value: 61.96416237112325
+    - type: cos_sim_spearman
+      value: 64.80484064041543
+    - type: euclidean_pearson
+      value: 63.281983537100594
+    - type: euclidean_spearman
+      value: 64.80483024694405
+    - type: manhattan_pearson
+      value: 63.266046412399426
+    - type: manhattan_spearman
+      value: 64.79643672829964
+  - task:
+      type: Clustering
+    dataset:
+      type: C-MTEB/CLSClusteringP2P
+      name: MTEB CLSClusteringP2P
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: v_measure
+      value: 40.25857488823951
+  - task:
+      type: Clustering
+    dataset:
+      type: C-MTEB/CLSClusteringS2S
+      name: MTEB CLSClusteringS2S
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: v_measure
+      value: 37.17501553349549
+  - task:
+      type: Reranking
+    dataset:
+      type: C-MTEB/CMedQAv1-reranking
+      name: MTEB CMedQAv1
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map
+      value: 84.69751849160603
+    - type: mrr
+      value: 87.16257936507937
+  - task:
+      type: Reranking
+    dataset:
+      type: C-MTEB/CMedQAv2-reranking
+      name: MTEB CMedQAv2
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map
+      value: 85.31468551417655
+    - type: mrr
+      value: 87.74658730158731
+  - task:
+      type: Retrieval
+    dataset:
+      type: C-MTEB/CmedqaRetrieval
+      name: MTEB CmedqaRetrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 24.181
+    - type: map_at_10
+      value: 35.615
+    - type: map_at_100
+      value: 37.444
+    - type: map_at_1000
+      value: 37.573
+    - type: map_at_3
+      value: 31.679000000000002
+    - type: map_at_5
+      value: 33.854
+    - type: mrr_at_1
+      value: 37.108999999999995
+    - type: mrr_at_10
+      value: 44.653
+    - type: mrr_at_100
+      value: 45.647
+    - type: mrr_at_1000
+      value: 45.701
+    - type: mrr_at_3
+      value: 42.256
+    - type: mrr_at_5
+      value: 43.497
+    - type: ndcg_at_1
+      value: 37.108999999999995
+    - type: ndcg_at_10
+      value: 42.028999999999996
+    - type: ndcg_at_100
+      value: 49.292
+    - type: ndcg_at_1000
+      value: 51.64
+    - type: ndcg_at_3
+      value: 37.017
+    - type: ndcg_at_5
+      value: 38.997
+    - type: precision_at_1
+      value: 37.108999999999995
+    - type: precision_at_10
+      value: 9.386999999999999
+    - type: precision_at_100
+      value: 1.536
+    - type: precision_at_1000
+      value: 0.183
+    - type: precision_at_3
+      value: 20.93
+    - type: precision_at_5
+      value: 15.268999999999998
+    - type: recall_at_1
+      value: 24.181
+    - type: recall_at_10
+      value: 51.961999999999996
+    - type: recall_at_100
+      value: 82.122
+    - type: recall_at_1000
+      value: 98.059
+    - type: recall_at_3
+      value: 36.730000000000004
+    - type: recall_at_5
+      value: 42.884
+  - task:
+      type: PairClassification
+    dataset:
+      type: C-MTEB/CMNLI
+      name: MTEB Cmnli
+      config: default
+      split: validation
+      revision: None
+    metrics:
+    - type: cos_sim_accuracy
+      value: 76.23571858087793
+    - type: cos_sim_ap
+      value: 84.75290046905519
+    - type: cos_sim_f1
+      value: 77.70114942528735
+    - type: cos_sim_precision
+      value: 73.05475504322767
+    - type: cos_sim_recall
+      value: 82.97872340425532
+    - type: dot_accuracy
+      value: 76.23571858087793
+    - type: dot_ap
+      value: 84.75113928508674
+    - type: dot_f1
+      value: 77.70114942528735
+    - type: dot_precision
+      value: 73.05475504322767
+    - type: dot_recall
+      value: 82.97872340425532
+    - type: euclidean_accuracy
+      value: 76.23571858087793
+    - type: euclidean_ap
+      value: 84.75289931658567
+    - type: euclidean_f1
+      value: 77.70114942528735
+    - type: euclidean_precision
+      value: 73.05475504322767
+    - type: euclidean_recall
+      value: 82.97872340425532
+    - type: manhattan_accuracy
+      value: 76.17558628983764
+    - type: manhattan_ap
+      value: 84.75764676597448
+    - type: manhattan_f1
+      value: 77.73437499999999
+    - type: manhattan_precision
+      value: 72.52480259161773
+    - type: manhattan_recall
+      value: 83.75029226093056
+    - type: max_accuracy
+      value: 76.23571858087793
+    - type: max_ap
+      value: 84.75764676597448
+    - type: max_f1
+      value: 77.73437499999999
+  - task:
+      type: Retrieval
+    dataset:
+      type: C-MTEB/CovidRetrieval
+      name: MTEB CovidRetrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 67.43900000000001
+    - type: map_at_10
+      value: 76.00099999999999
+    - type: map_at_100
+      value: 76.297
+    - type: map_at_1000
+      value: 76.29899999999999
+    - type: map_at_3
+      value: 74.412
+    - type: map_at_5
+      value: 75.177
+    - type: mrr_at_1
+      value: 67.65
+    - type: mrr_at_10
+      value: 76.007
+    - type: mrr_at_100
+      value: 76.322
+    - type: mrr_at_1000
+      value: 76.324
+    - type: mrr_at_3
+      value: 74.464
+    - type: mrr_at_5
+      value: 75.265
+    - type: ndcg_at_1
+      value: 67.65
+    - type: ndcg_at_10
+      value: 79.85600000000001
+    - type: ndcg_at_100
+      value: 81.34400000000001
+    - type: ndcg_at_1000
+      value: 81.44200000000001
+    - type: ndcg_at_3
+      value: 76.576
+    - type: ndcg_at_5
+      value: 77.956
+    - type: precision_at_1
+      value: 67.65
+    - type: precision_at_10
+      value: 9.283
+    - type: precision_at_100
+      value: 0.9990000000000001
+    - type: precision_at_1000
+      value: 0.101
+    - type: precision_at_3
+      value: 27.749000000000002
+    - type: precision_at_5
+      value: 17.345
+    - type: recall_at_1
+      value: 67.43900000000001
+    - type: recall_at_10
+      value: 91.781
+    - type: recall_at_100
+      value: 98.84100000000001
+    - type: recall_at_1000
+      value: 99.684
+    - type: recall_at_3
+      value: 82.719
+    - type: recall_at_5
+      value: 86.038
+  - task:
+      type: Retrieval
+    dataset:
+      type: C-MTEB/DuRetrieval
+      name: MTEB DuRetrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 25.354
+    - type: map_at_10
+      value: 79.499
+    - type: map_at_100
+      value: 82.416
+    - type: map_at_1000
+      value: 82.451
+    - type: map_at_3
+      value: 54.664
+    - type: map_at_5
+      value: 69.378
+    - type: mrr_at_1
+      value: 89.25
+    - type: mrr_at_10
+      value: 92.666
+    - type: mrr_at_100
+      value: 92.738
+    - type: mrr_at_1000
+      value: 92.74
+    - type: mrr_at_3
+      value: 92.342
+    - type: mrr_at_5
+      value: 92.562
+    - type: ndcg_at_1
+      value: 89.25
+    - type: ndcg_at_10
+      value: 86.97
+    - type: ndcg_at_100
+      value: 89.736
+    - type: ndcg_at_1000
+      value: 90.069
+    - type: ndcg_at_3
+      value: 85.476
+    - type: ndcg_at_5
+      value: 84.679
+    - type: precision_at_1
+      value: 89.25
+    - type: precision_at_10
+      value: 41.9
+    - type: precision_at_100
+      value: 4.811
+    - type: precision_at_1000
+      value: 0.48900000000000005
+    - type: precision_at_3
+      value: 76.86699999999999
+    - type: precision_at_5
+      value: 65.25
+    - type: recall_at_1
+      value: 25.354
+    - type: recall_at_10
+      value: 88.64999999999999
+    - type: recall_at_100
+      value: 97.56
+    - type: recall_at_1000
+      value: 99.37
+    - type: recall_at_3
+      value: 57.325
+    - type: recall_at_5
+      value: 74.614
+  - task:
+      type: Retrieval
+    dataset:
+      type: C-MTEB/EcomRetrieval
+      name: MTEB EcomRetrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 48.3
+    - type: map_at_10
+      value: 57.765
+    - type: map_at_100
+      value: 58.418000000000006
+    - type: map_at_1000
+      value: 58.43899999999999
+    - type: map_at_3
+      value: 54.883
+    - type: map_at_5
+      value: 56.672999999999995
+    - type: mrr_at_1
+      value: 48.3
+    - type: mrr_at_10
+      value: 57.765
+    - type: mrr_at_100
+      value: 58.418000000000006
+    - type: mrr_at_1000
+      value: 58.43899999999999
+    - type: mrr_at_3
+      value: 54.883
+    - type: mrr_at_5
+      value: 56.672999999999995
+    - type: ndcg_at_1
+      value: 48.3
+    - type: ndcg_at_10
+      value: 62.846000000000004
+    - type: ndcg_at_100
+      value: 65.845
+    - type: ndcg_at_1000
+      value: 66.369
+    - type: ndcg_at_3
+      value: 56.996
+    - type: ndcg_at_5
+      value: 60.214999999999996
+    - type: precision_at_1
+      value: 48.3
+    - type: precision_at_10
+      value: 7.9
+    - type: precision_at_100
+      value: 0.9259999999999999
+    - type: precision_at_1000
+      value: 0.097
+    - type: precision_at_3
+      value: 21.032999999999998
+    - type: precision_at_5
+      value: 14.180000000000001
+    - type: recall_at_1
+      value: 48.3
+    - type: recall_at_10
+      value: 79.0
+    - type: recall_at_100
+      value: 92.60000000000001
+    - type: recall_at_1000
+      value: 96.7
+    - type: recall_at_3
+      value: 63.1
+    - type: recall_at_5
+      value: 70.89999999999999
+  - task:
+      type: Classification
+    dataset:
+      type: C-MTEB/IFlyTek-classification
+      name: MTEB IFlyTek
+      config: default
+      split: validation
+      revision: None
+    metrics:
+    - type: accuracy
+      value: 47.895344363216616
+    - type: f1
+      value: 34.95151253165417
+  - task:
+      type: Classification
+    dataset:
+      type: C-MTEB/JDReview-classification
+      name: MTEB JDReview
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: accuracy
+      value: 84.78424015009381
+    - type: ap
+      value: 52.436279969597685
+    - type: f1
+      value: 79.49258679392281
+  - task:
+      type: STS
+    dataset:
+      type: C-MTEB/LCQMC
+      name: MTEB LCQMC
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: cos_sim_pearson
+      value: 70.2307617475436
+    - type: cos_sim_spearman
+      value: 76.88912653700545
+    - type: euclidean_pearson
+      value: 75.47976675486538
+    - type: euclidean_spearman
+      value: 76.88912210059333
+    - type: manhattan_pearson
+      value: 75.45834919257487
+    - type: manhattan_spearman
+      value: 76.8669208121889
+  - task:
+      type: Reranking
+    dataset:
+      type: C-MTEB/Mmarco-reranking
+      name: MTEB MMarcoReranking
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map
+      value: 28.047948482579244
+    - type: mrr
+      value: 26.63809523809524
+  - task:
+      type: Retrieval
+    dataset:
+      type: C-MTEB/MMarcoRetrieval
+      name: MTEB MMarcoRetrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 65.837
+    - type: map_at_10
+      value: 74.72
+    - type: map_at_100
+      value: 75.068
+    - type: map_at_1000
+      value: 75.079
+    - type: map_at_3
+      value: 72.832
+    - type: map_at_5
+      value: 74.07000000000001
+    - type: mrr_at_1
+      value: 68.009
+    - type: mrr_at_10
+      value: 75.29400000000001
+    - type: mrr_at_100
+      value: 75.607
+    - type: mrr_at_1000
+      value: 75.617
+    - type: mrr_at_3
+      value: 73.677
+    - type: mrr_at_5
+      value: 74.74199999999999
+    - type: ndcg_at_1
+      value: 68.009
+    - type: ndcg_at_10
+      value: 78.36
+    - type: ndcg_at_100
+      value: 79.911
+    - type: ndcg_at_1000
+      value: 80.226
+    - type: ndcg_at_3
+      value: 74.825
+    - type: ndcg_at_5
+      value: 76.9
+    - type: precision_at_1
+      value: 68.009
+    - type: precision_at_10
+      value: 9.463000000000001
+    - type: precision_at_100
+      value: 1.023
+    - type: precision_at_1000
+      value: 0.105
+    - type: precision_at_3
+      value: 28.075
+    - type: precision_at_5
+      value: 17.951
+    - type: recall_at_1
+      value: 65.837
+    - type: recall_at_10
+      value: 89.00099999999999
+    - type: recall_at_100
+      value: 95.968
+    - type: recall_at_1000
+      value: 98.461
+    - type: recall_at_3
+      value: 79.69800000000001
+    - type: recall_at_5
+      value: 84.623
+  - 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: 68.08675184936112
+    - type: f1
+      value: 65.51466585063827
+  - 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: 73.22461331540013
+    - type: f1
+      value: 72.675432030145
+  - task:
+      type: Retrieval
+    dataset:
+      type: C-MTEB/MedicalRetrieval
+      name: MTEB MedicalRetrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 49.2
+    - type: map_at_10
+      value: 55.394
+    - type: map_at_100
+      value: 55.883
+    - type: map_at_1000
+      value: 55.93900000000001
+    - type: map_at_3
+      value: 53.733
+    - type: map_at_5
+      value: 54.778000000000006
+    - type: mrr_at_1
+      value: 49.3
+    - type: mrr_at_10
+      value: 55.444
+    - type: mrr_at_100
+      value: 55.933
+    - type: mrr_at_1000
+      value: 55.989
+    - type: mrr_at_3
+      value: 53.783
+    - type: mrr_at_5
+      value: 54.827999999999996
+    - type: ndcg_at_1
+      value: 49.2
+    - type: ndcg_at_10
+      value: 58.501999999999995
+    - type: ndcg_at_100
+      value: 61.181
+    - type: ndcg_at_1000
+      value: 62.848000000000006
+    - type: ndcg_at_3
+      value: 55.143
+    - type: ndcg_at_5
+      value: 57.032000000000004
+    - type: precision_at_1
+      value: 49.2
+    - type: precision_at_10
+      value: 6.83
+    - type: precision_at_100
+      value: 0.815
+    - type: precision_at_1000
+      value: 0.095
+    - type: precision_at_3
+      value: 19.733
+    - type: precision_at_5
+      value: 12.76
+    - type: recall_at_1
+      value: 49.2
+    - type: recall_at_10
+      value: 68.30000000000001
+    - type: recall_at_100
+      value: 81.5
+    - type: recall_at_1000
+      value: 95.0
+    - type: recall_at_3
+      value: 59.199999999999996
+    - type: recall_at_5
+      value: 63.800000000000004
+  - task:
+      type: Classification
+    dataset:
+      type: C-MTEB/MultilingualSentiment-classification
+      name: MTEB MultilingualSentiment
+      config: default
+      split: validation
+      revision: None
+    metrics:
+    - type: accuracy
+      value: 71.66666666666666
+    - type: f1
+      value: 70.92944632461379
+  - task:
+      type: PairClassification
+    dataset:
+      type: C-MTEB/OCNLI
+      name: MTEB Ocnli
+      config: default
+      split: validation
+      revision: None
+    metrics:
+    - type: cos_sim_accuracy
+      value: 70.00541418516514
+    - type: cos_sim_ap
+      value: 75.16499510773514
+    - type: cos_sim_f1
+      value: 73.09435517099301
+    - type: cos_sim_precision
+      value: 59.932432432432435
+    - type: cos_sim_recall
+      value: 93.66420274551214
+    - type: dot_accuracy
+      value: 70.00541418516514
+    - type: dot_ap
+      value: 75.16499510773514
+    - type: dot_f1
+      value: 73.09435517099301
+    - type: dot_precision
+      value: 59.932432432432435
+    - type: dot_recall
+      value: 93.66420274551214
+    - type: euclidean_accuracy
+      value: 70.00541418516514
+    - type: euclidean_ap
+      value: 75.16499510773514
+    - type: euclidean_f1
+      value: 73.09435517099301
+    - type: euclidean_precision
+      value: 59.932432432432435
+    - type: euclidean_recall
+      value: 93.66420274551214
+    - type: manhattan_accuracy
+      value: 70.11369788846778
+    - type: manhattan_ap
+      value: 75.1259071890593
+    - type: manhattan_f1
+      value: 72.91399229781771
+    - type: manhattan_precision
+      value: 61.294964028776974
+    - type: manhattan_recall
+      value: 89.96832101372756
+    - type: max_accuracy
+      value: 70.11369788846778
+    - type: max_ap
+      value: 75.16499510773514
+    - type: max_f1
+      value: 73.09435517099301
+  - task:
+      type: Classification
+    dataset:
+      type: C-MTEB/OnlineShopping-classification
+      name: MTEB OnlineShopping
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: accuracy
+      value: 91.38000000000002
+    - type: ap
+      value: 89.12250244489272
+    - type: f1
+      value: 91.36604511107015
+  - task:
+      type: STS
+    dataset:
+      type: C-MTEB/PAWSX
+      name: MTEB PAWSX
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: cos_sim_pearson
+      value: 24.231255568030463
+    - type: cos_sim_spearman
+      value: 29.6964906904186
+    - type: euclidean_pearson
+      value: 30.166130502867016
+    - type: euclidean_spearman
+      value: 29.69614167804371
+    - type: manhattan_pearson
+      value: 30.166606116745935
+    - type: manhattan_spearman
+      value: 29.62681453661945
+  - task:
+      type: STS
+    dataset:
+      type: C-MTEB/QBQTC
+      name: MTEB QBQTC
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: cos_sim_pearson
+      value: 34.88835755574809
+    - type: cos_sim_spearman
+      value: 37.3797926051053
+    - type: euclidean_pearson
+      value: 35.46629492698549
+    - type: euclidean_spearman
+      value: 37.37987510604593
+    - type: manhattan_pearson
+      value: 35.4953353526957
+    - type: manhattan_spearman
+      value: 37.41397231689605
+  - 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: 67.79575721136626
+    - type: cos_sim_spearman
+      value: 69.02068400784196
+    - type: euclidean_pearson
+      value: 68.30675023447176
+    - type: euclidean_spearman
+      value: 69.02068400784196
+    - type: manhattan_pearson
+      value: 69.91284259797827
+    - type: manhattan_spearman
+      value: 70.31717787763641
+  - task:
+      type: STS
+    dataset:
+      type: C-MTEB/STSB
+      name: MTEB STSB
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: cos_sim_pearson
+      value: 79.05026785034129
+    - type: cos_sim_spearman
+      value: 79.62719014756249
+    - type: euclidean_pearson
+      value: 79.13305301290063
+    - type: euclidean_spearman
+      value: 79.62710682651051
+    - type: manhattan_pearson
+      value: 79.07012559140433
+    - type: manhattan_spearman
+      value: 79.58333069893605
+  - task:
+      type: Reranking
+    dataset:
+      type: C-MTEB/T2Reranking
+      name: MTEB T2Reranking
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map
+      value: 66.34533369244325
+    - type: mrr
+      value: 75.93632792769557
+  - task:
+      type: Retrieval
+    dataset:
+      type: C-MTEB/T2Retrieval
+      name: MTEB T2Retrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 26.995
+    - type: map_at_10
+      value: 76.083
+    - type: map_at_100
+      value: 79.727
+    - type: map_at_1000
+      value: 79.798
+    - type: map_at_3
+      value: 53.455
+    - type: map_at_5
+      value: 65.747
+    - type: mrr_at_1
+      value: 89.536
+    - type: mrr_at_10
+      value: 91.972
+    - type: mrr_at_100
+      value: 92.07
+    - type: mrr_at_1000
+      value: 92.07499999999999
+    - type: mrr_at_3
+      value: 91.52900000000001
+    - type: mrr_at_5
+      value: 91.806
+    - type: ndcg_at_1
+      value: 89.536
+    - type: ndcg_at_10
+      value: 83.756
+    - type: ndcg_at_100
+      value: 87.468
+    - type: ndcg_at_1000
+      value: 88.16199999999999
+    - type: ndcg_at_3
+      value: 85.349
+    - type: ndcg_at_5
+      value: 83.855
+    - type: precision_at_1
+      value: 89.536
+    - type: precision_at_10
+      value: 41.713
+    - type: precision_at_100
+      value: 4.994
+    - type: precision_at_1000
+      value: 0.515
+    - type: precision_at_3
+      value: 74.81400000000001
+    - type: precision_at_5
+      value: 62.678
+    - type: recall_at_1
+      value: 26.995
+    - type: recall_at_10
+      value: 82.586
+    - type: recall_at_100
+      value: 94.726
+    - type: recall_at_1000
+      value: 98.276
+    - type: recall_at_3
+      value: 55.106
+    - type: recall_at_5
+      value: 69.096
+  - task:
+      type: Classification
+    dataset:
+      type: C-MTEB/TNews-classification
+      name: MTEB TNews
+      config: default
+      split: validation
+      revision: None
+    metrics:
+    - type: accuracy
+      value: 51.25200000000001
+    - type: f1
+      value: 49.43760438233612
+  - task:
+      type: Clustering
+    dataset:
+      type: C-MTEB/ThuNewsClusteringP2P
+      name: MTEB ThuNewsClusteringP2P
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: v_measure
+      value: 62.18575394560257
+  - task:
+      type: Clustering
+    dataset:
+      type: C-MTEB/ThuNewsClusteringS2S
+      name: MTEB ThuNewsClusteringS2S
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: v_measure
+      value: 57.97489103903411
+  - task:
+      type: Retrieval
+    dataset:
+      type: C-MTEB/VideoRetrieval
+      name: MTEB VideoRetrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 52.2
+    - type: map_at_10
+      value: 63.23800000000001
+    - type: map_at_100
+      value: 63.788
+    - type: map_at_1000
+      value: 63.800999999999995
+    - type: map_at_3
+      value: 61.016999999999996
+    - type: map_at_5
+      value: 62.392
+    - type: mrr_at_1
+      value: 52.2
+    - type: mrr_at_10
+      value: 63.23800000000001
+    - type: mrr_at_100
+      value: 63.788
+    - type: mrr_at_1000
+      value: 63.800999999999995
+    - type: mrr_at_3
+      value: 61.016999999999996
+    - type: mrr_at_5
+      value: 62.392
+    - type: ndcg_at_1
+      value: 52.2
+    - type: ndcg_at_10
+      value: 68.273
+    - type: ndcg_at_100
+      value: 70.892
+    - type: ndcg_at_1000
+      value: 71.207
+    - type: ndcg_at_3
+      value: 63.794
+    - type: ndcg_at_5
+      value: 66.268
+    - type: precision_at_1
+      value: 52.2
+    - type: precision_at_10
+      value: 8.39
+    - type: precision_at_100
+      value: 0.96
+    - type: precision_at_1000
+      value: 0.098
+    - type: precision_at_3
+      value: 23.933
+    - type: precision_at_5
+      value: 15.559999999999999
+    - type: recall_at_1
+      value: 52.2
+    - type: recall_at_10
+      value: 83.89999999999999
+    - type: recall_at_100
+      value: 96.0
+    - type: recall_at_1000
+      value: 98.4
+    - type: recall_at_3
+      value: 71.8
+    - type: recall_at_5
+      value: 77.8
+  - task:
+      type: Classification
+    dataset:
+      type: C-MTEB/waimai-classification
+      name: MTEB Waimai
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: accuracy
+      value: 86.67999999999999
+    - type: ap
+      value: 69.96366657730151
+    - type: f1
+      value: 84.92349905611292
 ---
+
+## stella model
+
+**新闻 | News**
+
+**[2023-10-12]** 开源stella-base-zh-v2和stella-large-zh-v2, 效果更好且使用简单，**不需要任何前缀文本**。
+Release stella-base-zh-v2 and stella-large-zh-v2. The 2 models have better performance
+and **do not need any prefix text**.\
+**[2023-09-11]** 开源stella-base-zh和stella-large-zh
+
+stella是一个通用的文本编码模型，主要有以下模型：
+
+|     Model Name     | Model Size (GB) | Dimension | Sequence Length | Language | Need instruction for retrieval? |
+|:------------------:|:---------------:|:---------:|:---------------:|:--------:|:-------------------------------:|
+| stella-large-zh-v2 |      0.65       |   1024    |      1024       | Chinese  |               No                |
+| stella-base-zh-v2  |       0.2       |    768    |      1024       | Chinese  |               No                |
+|  stella-large-zh   |      0.65       |   1024    |      1024       | Chinese  |               Yes               |
+|   stella-base-zh   |       0.2       |    768    |      1024       | Chinese  |               Yes               |
+
+完整的训练思路和训练过程已记录在[博客](https://zhuanlan.zhihu.com/p/655322183)，欢迎阅读讨论。
+
+**训练数据：**
+
+1. 开源数据(wudao_base_200GB[1]、m3e[2]和simclue[3])，着重挑选了长度大于512的文本
+2. 在通用语料库上使用LLM构造一批(question, paragraph)和(sentence, paragraph)数据
+
+**训练方法：**
+
+1. 对比学习损失函数
+2. 带有难负例的对比学习损失函数(分别基于bm25和vector构造了难负例)
+3. EWC(Elastic Weights Consolidation)[4]
+4. cosent loss[5]
+5. 每一种类型的数据一个迭代器，分别计算loss进行更新
+
+stella-v2在stella模型的基础上，使用了更多的训练数据，同时知识蒸馏等方法去除了前置的instruction(
+比如piccolo的`查询:`, `结果:`, e5的`query:`和`passage:`)。
+
+**初始权重：**\
+stella-base-zh和stella-large-zh分别以piccolo-base-zh[6]和piccolo-large-zh作为基础模型，512-1024的position
+embedding使用层次分解位置编码[7]进行初始化。\
+感谢商汤科技研究院开源的[piccolo系列模型](https://huggingface.co/sensenova)。
+
+stella is a general-purpose text encoder, which mainly includes the following models:
+
+|     Model Name     | Model Size (GB) | Dimension | Sequence Length | Language | Need instruction for retrieval? |
+|:------------------:|:---------------:|:---------:|:---------------:|:--------:|:-------------------------------:|
+| stella-large-zh-v2 |      0.65       |   1024    |      1024       | Chinese  |               No                |
+| stella-base-zh-v2  |       0.2       |    768    |      1024       | Chinese  |               No                |
+|  stella-large-zh   |      0.65       |   1024    |      1024       | Chinese  |               Yes               |
+|   stella-base-zh   |       0.2       |    768    |      1024       | Chinese  |               Yes               |
+
+The training data mainly includes:
+
+1. Open-source training data (wudao_base_200GB, m3e, and simclue), with a focus on selecting texts with lengths greater
+   than 512.
+2. A batch of (question, paragraph) and (sentence, paragraph) data constructed on a general corpus using LLM.
+
+The loss functions mainly include:
+
+1. Contrastive learning loss function
+2. Contrastive learning loss function with hard negative examples (based on bm25 and vector hard negatives)
+3. EWC (Elastic Weights Consolidation)
+4. cosent loss
+
+Model weight initialization:\
+stella-base-zh and stella-large-zh use piccolo-base-zh and piccolo-large-zh as the base models, respectively, and the
+512-1024 position embedding uses the initialization strategy of hierarchical decomposed position encoding.
+
+Training strategy:\
+One iterator for each type of data, separately calculating the loss.
+
+Based on stella models, stella-v2 use more training data and remove instruction by Knowledge Distillation.
+
+## Metric
+
+#### C-MTEB leaderboard (Chinese)
+
+|     Model Name     | Model Size (GB) | Dimension | Sequence Length | Average (35) | Classification (9) | Clustering (4) | Pair Classification (2) | Reranking (4) | Retrieval (8) | STS (8) |
+|:------------------:|:---------------:|:---------:|:---------------:|:------------:|:------------------:|:--------------:|:-----------------------:|:-------------:|:-------------:|:-------:|
+| stella-large-zh-v2 |      0.65       |   1024    |      1024       |    65.13     |       69.05        |     49.16      |          82.68          |     66.41     |     70.14     |  58.66  |
+| stella-base-zh-v2  |       0.2       |    768    |      1024       |    64.36     |       68.29        |      49.4      |          79.95          |     66.1      |     70.08     |  56.92  |
+|  stella-large-zh   |      0.65       |   1024    |      1024       |    64.54     |       67.62        |     48.65      |          78.72          |     65.98     |     71.02     |  58.3   |
+|   stella-base-zh   |       0.2       |    768    |      1024       |    64.16     |       67.77        |      48.7      |          76.09          |     66.95     |     71.07     |  56.54  |
+
+#### Reproduce our results
+
+Codes:
+
+```python
+import torch
+import numpy as np
+from typing import List
+from mteb import MTEB
+from sentence_transformers import SentenceTransformer
+
+
+class FastTextEncoder():
+    def __init__(self, model_name):
+        self.model = SentenceTransformer(model_name).cuda().half().eval()
+        self.model.max_seq_length = 512
+
+    def encode(
+            self,
+            input_texts: List[str],
+            *args,
+            **kwargs
+    ):
+        new_sens = list(set(input_texts))
+        new_sens.sort(key=lambda x: len(x), reverse=True)
+        vecs = self.model.encode(
+            new_sens, normalize_embeddings=True, convert_to_numpy=True, batch_size=256
+        ).astype(np.float32)
+        sen2arrid = {sen: idx for idx, sen in enumerate(new_sens)}
+        vecs = vecs[[sen2arrid[sen] for sen in input_texts]]
+        torch.cuda.empty_cache()
+        return vecs
+
+
+if __name__ == '__main__':
+    model_name = "infgrad/stella-base-zh-v2"
+    output_folder = "zh_mteb_results/stella-base-zh-v2"
+    task_names = [t.description["name"] for t in MTEB(task_langs=['zh', 'zh-CN']).tasks]
+    model = FastTextEncoder(model_name)
+    for task in task_names:
+        MTEB(tasks=[task], task_langs=['zh', 'zh-CN']).run(model, output_folder=output_folder)
+
+```
+
+#### Evaluation for long text
+
+经过实际观察发现，C-MTEB的评测数据长度基本都是小于512的，
+更致命的是那些长度大于512的文本，其重点都在前半部分
+这里以CMRC2018的数据为例说明这个问题：
+
+```
+question: 《无双大蛇z》是谁旗下ω-force开发的动作游戏？
+
+passage：《无双大蛇z》是光荣旗下ω-force开发的动作游戏，于2009年3月12日登陆索尼playstation3，并于2009年11月27日推......
+```
+
+passage长度为800多，大于512，但是对于这个question而言只需要前面40个字就足以检索，多的内容对于模型而言是一种噪声，反而降低了效果。\
+简言之，现有数据集的2个问题：\
+1）长度大于512的过少\
+2）即便大于512，对于检索而言也只需要前512的文本内容\
+导致**无法准确评估模型的长文本编码能力。**
+
+为了解决这个问题，搜集了相关开源数据并使用规则进行过滤，最终整理了6份长文本测试集,他们分别是：
+
+- CMRC2018，通用百科
+- CAIL，法律阅读理解
+- DRCD，繁体百科，已转简体
+- Military，军工问答
+- Squad，英文阅读理解，已转中文
+- Multifieldqa_zh，清华的大模型长文本理解能力评测数据[9]
+
+处理规则是选取答案在512长度之后的文本，短的测试数据会欠采样一下，长短文本占比约为1:2，所以模型既得理解短文本也得理解长文本。
+除了Military数据集，我们提供了其他5个测试数据的下载地址：https://drive.google.com/file/d/1WC6EWaCbVgz-vPMDFH4TwAMkLyh5WNcN/view?usp=sharing
+
+评测指标为Recall@5, 结果如下：
+
+|     Dataset     | piccolo-base-zh | piccolo-large-zh | bge-base-zh | bge-large-zh | stella-base-zh | stella-large-zh | 
+|:---------------:|:---------------:|:----------------:|:-----------:|:------------:|:--------------:|:---------------:|
+|    CMRC2018     |      94.34      |      93.82       |    91.56    |    93.12     |     96.08      |      95.56      | 
+|      CAIL       |      28.04      |      33.64       |    31.22    |    33.94     |     34.62      |      37.18      | 
+|      DRCD       |      78.25      |       77.9       |    78.34    |    80.26     |     86.14      |      84.58      | 
+|    Military     |      76.61      |      73.06       |    75.65    |    75.81     |     83.71      |      80.48      | 
+|      Squad      |      91.21      |      86.61       |    87.87    |    90.38     |     93.31      |      91.21      | 
+| Multifieldqa_zh |      81.41      |      83.92       |    83.92    |    83.42     |      79.9      |      80.4       | 
+|   **Average**   |      74.98      |      74.83       |    74.76    |    76.15     |   **78.96**    |    **78.24**    | 
+
+**注意：** 因为长文本评测数据数量稀少，所以构造时也使用了train部分，如果自行评测，请注意模型的训练数据以免数据泄露。
+
+## Usage
+
+#### stella 中文系列模型
+
+stella-base-zh 和 stella-large-zh: 本模型是在piccolo基础上训练的，因此**用法和piccolo完全一致**
+，即在检索重排任务上给query和passage加上`查询: `和`结果: `。对于短短匹配不需要做任何操作。
+
+stella-base-zh-v2 和 stella-large-zh-v2: 本模型使用简单，**任何使用场景中都不需要加前缀文本**。
+
+stella中文系列模型均使用mean pooling做为文本向量。
+
+在sentence-transformer库中的使用方法：
+
+```python
+# 对于短对短数据集，下面是通用的使用方式
+from sentence_transformers import SentenceTransformer
+
+sentences = ["数据1", "数据2"]
+model = SentenceTransformer('infgrad/stella-base-zh-v2')
+print(model.max_seq_length)
+embeddings_1 = model.encode(sentences, normalize_embeddings=True)
+embeddings_2 = model.encode(sentences, normalize_embeddings=True)
+similarity = embeddings_1 @ embeddings_2.T
+print(similarity)
+```
+
+直接使用transformers库：
+
+```python
+from transformers import AutoModel, AutoTokenizer
+from sklearn.preprocessing import normalize
+
+model = AutoModel.from_pretrained('infgrad/stella-base-zh-v2')
+tokenizer = AutoTokenizer.from_pretrained('infgrad/stella-base-zh-v2')
+sentences = ["数据1", "数据ABCDEFGH"]
+batch_data = tokenizer(
+    batch_text_or_text_pairs=sentences,
+    padding="longest",
+    return_tensors="pt",
+    max_length=1024,
+    truncation=True,
+)
+attention_mask = batch_data["attention_mask"]
+model_output = model(**batch_data)
+last_hidden = model_output.last_hidden_state.masked_fill(~attention_mask[..., None].bool(), 0.0)
+vectors = last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
+vectors = normalize(vectors, norm="l2", axis=1, )
+print(vectors.shape)  # 2,768
+```
+
+#### stella models for English
+
+developing...
+
+## Training Detail
+
+**硬件：** 单卡A100-80GB
+
+**环境：** torch1.13.*; transformers-trainer + deepspeed + gradient-checkpointing
+
+**学习率：** 1e-6
+
+**batch_size：** base模型为1024，额外增加20%的难负例；large模型为768，额外增加20%的难负例
+
+**数据量：** 第一版模型约100万，其中用LLM构造的数据约有200K. LLM模型大小为13b。v2系列模型到了2000万训练数据。
+
+## ToDoList
+
+**评测的稳定性：**
+评测过程中发现Clustering任务会和官方的结果不一致，大约有±0.0x的小差距，原因是聚类代码没有设置random_seed，差距可以忽略不计，不影响评测结论。
+
+**更高质量的长文本训练和测试数据：** 训练数据多是用13b模型构造的，肯定会存在噪声。
+测试数据基本都是从mrc数据整理来的，所以问题都是factoid类型，不符合真实分布。
+
+**OOD的性能：** 虽然近期出现了很多向量编码模型，但是对于不是那么通用的domain，这一众模型包括stella、openai和cohere,
+它们的效果均比不上BM25。
+
+## Reference
+
+1. https://www.scidb.cn/en/detail?dataSetId=c6a3fe684227415a9db8e21bac4a15ab
+2. https://github.com/wangyuxinwhy/uniem
+3. https://github.com/CLUEbenchmark/SimCLUE
+4. https://arxiv.org/abs/1612.00796
+5. https://kexue.fm/archives/8847
+6. https://huggingface.co/sensenova/piccolo-base-zh
+7. https://kexue.fm/archives/7947
+8. https://github.com/FlagOpen/FlagEmbedding
+9. https://github.com/THUDM/LongBench
+
+

config.json

@@ -0,0 +1,34 @@
+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 1024,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "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": "float16",
+  "transformers_version": "4.30.2",
+  "type_vocab_size": 2,
+  "uniem_pooling_strategy": "last_mean",
+  "use_cache": true,
+  "vocab_size": 21128
+}

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bdcd3c81b1712c88199abdd259c995ce1e088457d4472c4041b7bf60badcc18c
+size 205397037

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

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+{
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 1024,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}