Upload 12 files

1_Pooling/config.json

@@ -0,0 +1,7 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false
+}

README.md

@@ -0,0 +1,1254 @@
+---
+tags:
+  - sentence-transformers
+  - feature-extraction
+  - sentence-similarity
+  - mteb
+  - transformers
+  - transformers.js
+inference: false
+license: apache-2.0
+language:
+- en
+- zh
+model-index:
+- name: jina-embeddings-v2-base-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: 48.51403119231363
+    - type: cos_sim_spearman
+      value: 50.5928547846445
+    - type: euclidean_pearson
+      value: 48.750436310559074
+    - type: euclidean_spearman
+      value: 50.50950238691385
+    - type: manhattan_pearson
+      value: 48.7866189440328
+    - type: manhattan_spearman
+      value: 50.58692402017165
+  - task:
+      type: STS
+    dataset:
+      type: C-MTEB/ATEC
+      name: MTEB ATEC
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: cos_sim_pearson
+      value: 50.25985700105725
+    - type: cos_sim_spearman
+      value: 51.28815934593989
+    - type: euclidean_pearson
+      value: 52.70329248799904
+    - type: euclidean_spearman
+      value: 50.94101139559258
+    - type: manhattan_pearson
+      value: 52.6647237400892
+    - type: manhattan_spearman
+      value: 50.922441325406176
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_reviews_multi
+      name: MTEB AmazonReviewsClassification (zh)
+      config: zh
+      split: test
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+    metrics:
+    - type: accuracy
+      value: 34.944
+    - type: f1
+      value: 34.06478860660109
+  - task:
+      type: STS
+    dataset:
+      type: C-MTEB/BQ
+      name: MTEB BQ
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: cos_sim_pearson
+      value: 65.15667035488342
+    - type: cos_sim_spearman
+      value: 66.07110142081
+    - type: euclidean_pearson
+      value: 60.447598102249714
+    - type: euclidean_spearman
+      value: 61.826575796578766
+    - type: manhattan_pearson
+      value: 60.39364279354984
+    - type: manhattan_spearman
+      value: 61.78743491223281
+  - task:
+      type: Clustering
+    dataset:
+      type: C-MTEB/CLSClusteringP2P
+      name: MTEB CLSClusteringP2P
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: v_measure
+      value: 39.96714175391701
+  - task:
+      type: Clustering
+    dataset:
+      type: C-MTEB/CLSClusteringS2S
+      name: MTEB CLSClusteringS2S
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: v_measure
+      value: 38.39863566717934
+  - task:
+      type: Reranking
+    dataset:
+      type: C-MTEB/CMedQAv1-reranking
+      name: MTEB CMedQAv1
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map
+      value: 83.63680381780644
+    - type: mrr
+      value: 86.16476190476192
+  - task:
+      type: Reranking
+    dataset:
+      type: C-MTEB/CMedQAv2-reranking
+      name: MTEB CMedQAv2
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map
+      value: 83.74350667859487
+    - type: mrr
+      value: 86.10388888888889
+  - task:
+      type: Retrieval
+    dataset:
+      type: C-MTEB/CmedqaRetrieval
+      name: MTEB CmedqaRetrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 22.072
+    - type: map_at_10
+      value: 32.942
+    - type: map_at_100
+      value: 34.768
+    - type: map_at_1000
+      value: 34.902
+    - type: map_at_3
+      value: 29.357
+    - type: map_at_5
+      value: 31.236000000000004
+    - type: mrr_at_1
+      value: 34.259
+    - type: mrr_at_10
+      value: 41.957
+    - type: mrr_at_100
+      value: 42.982
+    - type: mrr_at_1000
+      value: 43.042
+    - type: mrr_at_3
+      value: 39.722
+    - type: mrr_at_5
+      value: 40.898
+    - type: ndcg_at_1
+      value: 34.259
+    - type: ndcg_at_10
+      value: 39.153
+    - type: ndcg_at_100
+      value: 46.493
+    - type: ndcg_at_1000
+      value: 49.01
+    - type: ndcg_at_3
+      value: 34.636
+    - type: ndcg_at_5
+      value: 36.278
+    - type: precision_at_1
+      value: 34.259
+    - type: precision_at_10
+      value: 8.815000000000001
+    - type: precision_at_100
+      value: 1.474
+    - type: precision_at_1000
+      value: 0.179
+    - type: precision_at_3
+      value: 19.73
+    - type: precision_at_5
+      value: 14.174000000000001
+    - type: recall_at_1
+      value: 22.072
+    - type: recall_at_10
+      value: 48.484
+    - type: recall_at_100
+      value: 79.035
+    - type: recall_at_1000
+      value: 96.15
+    - type: recall_at_3
+      value: 34.607
+    - type: recall_at_5
+      value: 40.064
+  - task:
+      type: PairClassification
+    dataset:
+      type: C-MTEB/CMNLI
+      name: MTEB Cmnli
+      config: default
+      split: validation
+      revision: None
+    metrics:
+    - type: cos_sim_accuracy
+      value: 76.7047504509922
+    - type: cos_sim_ap
+      value: 85.26649874800871
+    - type: cos_sim_f1
+      value: 78.13528724646915
+    - type: cos_sim_precision
+      value: 71.57587548638132
+    - type: cos_sim_recall
+      value: 86.01823708206688
+    - type: dot_accuracy
+      value: 70.13830426939266
+    - type: dot_ap
+      value: 77.01510412382171
+    - type: dot_f1
+      value: 73.56710042713817
+    - type: dot_precision
+      value: 63.955094991364426
+    - type: dot_recall
+      value: 86.57937806873977
+    - type: euclidean_accuracy
+      value: 75.53818400481059
+    - type: euclidean_ap
+      value: 84.34668448241264
+    - type: euclidean_f1
+      value: 77.51741608613047
+    - type: euclidean_precision
+      value: 70.65614777756399
+    - type: euclidean_recall
+      value: 85.85457096095394
+    - type: manhattan_accuracy
+      value: 75.49007817197835
+    - type: manhattan_ap
+      value: 84.40297506704299
+    - type: manhattan_f1
+      value: 77.63185324160932
+    - type: manhattan_precision
+      value: 70.03949595636637
+    - type: manhattan_recall
+      value: 87.07037643207856
+    - type: max_accuracy
+      value: 76.7047504509922
+    - type: max_ap
+      value: 85.26649874800871
+    - type: max_f1
+      value: 78.13528724646915
+  - task:
+      type: Retrieval
+    dataset:
+      type: C-MTEB/CovidRetrieval
+      name: MTEB CovidRetrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 69.178
+    - type: map_at_10
+      value: 77.523
+    - type: map_at_100
+      value: 77.793
+    - type: map_at_1000
+      value: 77.79899999999999
+    - type: map_at_3
+      value: 75.878
+    - type: map_at_5
+      value: 76.849
+    - type: mrr_at_1
+      value: 69.44200000000001
+    - type: mrr_at_10
+      value: 77.55
+    - type: mrr_at_100
+      value: 77.819
+    - type: mrr_at_1000
+      value: 77.826
+    - type: mrr_at_3
+      value: 75.957
+    - type: mrr_at_5
+      value: 76.916
+    - type: ndcg_at_1
+      value: 69.44200000000001
+    - type: ndcg_at_10
+      value: 81.217
+    - type: ndcg_at_100
+      value: 82.45
+    - type: ndcg_at_1000
+      value: 82.636
+    - type: ndcg_at_3
+      value: 77.931
+    - type: ndcg_at_5
+      value: 79.655
+    - type: precision_at_1
+      value: 69.44200000000001
+    - type: precision_at_10
+      value: 9.357
+    - type: precision_at_100
+      value: 0.993
+    - type: precision_at_1000
+      value: 0.101
+    - type: precision_at_3
+      value: 28.1
+    - type: precision_at_5
+      value: 17.724
+    - type: recall_at_1
+      value: 69.178
+    - type: recall_at_10
+      value: 92.624
+    - type: recall_at_100
+      value: 98.209
+    - type: recall_at_1000
+      value: 99.684
+    - type: recall_at_3
+      value: 83.772
+    - type: recall_at_5
+      value: 87.882
+  - task:
+      type: Retrieval
+    dataset:
+      type: C-MTEB/DuRetrieval
+      name: MTEB DuRetrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 25.163999999999998
+    - type: map_at_10
+      value: 76.386
+    - type: map_at_100
+      value: 79.339
+    - type: map_at_1000
+      value: 79.39500000000001
+    - type: map_at_3
+      value: 52.959
+    - type: map_at_5
+      value: 66.59
+    - type: mrr_at_1
+      value: 87.9
+    - type: mrr_at_10
+      value: 91.682
+    - type: mrr_at_100
+      value: 91.747
+    - type: mrr_at_1000
+      value: 91.751
+    - type: mrr_at_3
+      value: 91.267
+    - type: mrr_at_5
+      value: 91.527
+    - type: ndcg_at_1
+      value: 87.9
+    - type: ndcg_at_10
+      value: 84.569
+    - type: ndcg_at_100
+      value: 87.83800000000001
+    - type: ndcg_at_1000
+      value: 88.322
+    - type: ndcg_at_3
+      value: 83.473
+    - type: ndcg_at_5
+      value: 82.178
+    - type: precision_at_1
+      value: 87.9
+    - type: precision_at_10
+      value: 40.605000000000004
+    - type: precision_at_100
+      value: 4.752
+    - type: precision_at_1000
+      value: 0.488
+    - type: precision_at_3
+      value: 74.9
+    - type: precision_at_5
+      value: 62.96000000000001
+    - type: recall_at_1
+      value: 25.163999999999998
+    - type: recall_at_10
+      value: 85.97399999999999
+    - type: recall_at_100
+      value: 96.63000000000001
+    - type: recall_at_1000
+      value: 99.016
+    - type: recall_at_3
+      value: 55.611999999999995
+    - type: recall_at_5
+      value: 71.936
+  - task:
+      type: Retrieval
+    dataset:
+      type: C-MTEB/EcomRetrieval
+      name: MTEB EcomRetrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 48.6
+    - type: map_at_10
+      value: 58.831
+    - type: map_at_100
+      value: 59.427
+    - type: map_at_1000
+      value: 59.44199999999999
+    - type: map_at_3
+      value: 56.383
+    - type: map_at_5
+      value: 57.753
+    - type: mrr_at_1
+      value: 48.6
+    - type: mrr_at_10
+      value: 58.831
+    - type: mrr_at_100
+      value: 59.427
+    - type: mrr_at_1000
+      value: 59.44199999999999
+    - type: mrr_at_3
+      value: 56.383
+    - type: mrr_at_5
+      value: 57.753
+    - type: ndcg_at_1
+      value: 48.6
+    - type: ndcg_at_10
+      value: 63.951
+    - type: ndcg_at_100
+      value: 66.72200000000001
+    - type: ndcg_at_1000
+      value: 67.13900000000001
+    - type: ndcg_at_3
+      value: 58.882
+    - type: ndcg_at_5
+      value: 61.373
+    - type: precision_at_1
+      value: 48.6
+    - type: precision_at_10
+      value: 8.01
+    - type: precision_at_100
+      value: 0.928
+    - type: precision_at_1000
+      value: 0.096
+    - type: precision_at_3
+      value: 22.033
+    - type: precision_at_5
+      value: 14.44
+    - type: recall_at_1
+      value: 48.6
+    - type: recall_at_10
+      value: 80.10000000000001
+    - type: recall_at_100
+      value: 92.80000000000001
+    - type: recall_at_1000
+      value: 96.1
+    - type: recall_at_3
+      value: 66.10000000000001
+    - type: recall_at_5
+      value: 72.2
+  - task:
+      type: Classification
+    dataset:
+      type: C-MTEB/IFlyTek-classification
+      name: MTEB IFlyTek
+      config: default
+      split: validation
+      revision: None
+    metrics:
+    - type: accuracy
+      value: 47.36437091188918
+    - type: f1
+      value: 36.60946954228577
+  - task:
+      type: Classification
+    dataset:
+      type: C-MTEB/JDReview-classification
+      name: MTEB JDReview
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: accuracy
+      value: 79.5684803001876
+    - type: ap
+      value: 42.671935929201524
+    - type: f1
+      value: 73.31912729103752
+  - task:
+      type: STS
+    dataset:
+      type: C-MTEB/LCQMC
+      name: MTEB LCQMC
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: cos_sim_pearson
+      value: 68.62670112113864
+    - type: cos_sim_spearman
+      value: 75.74009123170768
+    - type: euclidean_pearson
+      value: 73.93002595958237
+    - type: euclidean_spearman
+      value: 75.35222935003587
+    - type: manhattan_pearson
+      value: 73.89870445158144
+    - type: manhattan_spearman
+      value: 75.31714936339398
+  - task:
+      type: Reranking
+    dataset:
+      type: C-MTEB/Mmarco-reranking
+      name: MTEB MMarcoReranking
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map
+      value: 31.5372713650176
+    - type: mrr
+      value: 30.163095238095238
+  - task:
+      type: Retrieval
+    dataset:
+      type: C-MTEB/MMarcoRetrieval
+      name: MTEB MMarcoRetrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 65.054
+    - type: map_at_10
+      value: 74.156
+    - type: map_at_100
+      value: 74.523
+    - type: map_at_1000
+      value: 74.535
+    - type: map_at_3
+      value: 72.269
+    - type: map_at_5
+      value: 73.41
+    - type: mrr_at_1
+      value: 67.24900000000001
+    - type: mrr_at_10
+      value: 74.78399999999999
+    - type: mrr_at_100
+      value: 75.107
+    - type: mrr_at_1000
+      value: 75.117
+    - type: mrr_at_3
+      value: 73.13499999999999
+    - type: mrr_at_5
+      value: 74.13499999999999
+    - type: ndcg_at_1
+      value: 67.24900000000001
+    - type: ndcg_at_10
+      value: 77.96300000000001
+    - type: ndcg_at_100
+      value: 79.584
+    - type: ndcg_at_1000
+      value: 79.884
+    - type: ndcg_at_3
+      value: 74.342
+    - type: ndcg_at_5
+      value: 76.278
+    - type: precision_at_1
+      value: 67.24900000000001
+    - type: precision_at_10
+      value: 9.466
+    - type: precision_at_100
+      value: 1.027
+    - type: precision_at_1000
+      value: 0.105
+    - type: precision_at_3
+      value: 27.955999999999996
+    - type: precision_at_5
+      value: 17.817
+    - type: recall_at_1
+      value: 65.054
+    - type: recall_at_10
+      value: 89.113
+    - type: recall_at_100
+      value: 96.369
+    - type: recall_at_1000
+      value: 98.714
+    - type: recall_at_3
+      value: 79.45400000000001
+    - type: recall_at_5
+      value: 84.06
+  - 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: 68.1977135171486
+    - type: f1
+      value: 67.23114308718404
+  - 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: 71.92669804976462
+    - type: f1
+      value: 72.90628475628779
+  - 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: 54.539
+    - type: map_at_100
+      value: 55.135
+    - type: map_at_1000
+      value: 55.19199999999999
+    - type: map_at_3
+      value: 53.383
+    - type: map_at_5
+      value: 54.142999999999994
+    - type: mrr_at_1
+      value: 49.2
+    - type: mrr_at_10
+      value: 54.539
+    - type: mrr_at_100
+      value: 55.135999999999996
+    - type: mrr_at_1000
+      value: 55.19199999999999
+    - type: mrr_at_3
+      value: 53.383
+    - type: mrr_at_5
+      value: 54.142999999999994
+    - type: ndcg_at_1
+      value: 49.2
+    - type: ndcg_at_10
+      value: 57.123000000000005
+    - type: ndcg_at_100
+      value: 60.21300000000001
+    - type: ndcg_at_1000
+      value: 61.915
+    - type: ndcg_at_3
+      value: 54.772
+    - type: ndcg_at_5
+      value: 56.157999999999994
+    - type: precision_at_1
+      value: 49.2
+    - type: precision_at_10
+      value: 6.52
+    - type: precision_at_100
+      value: 0.8009999999999999
+    - type: precision_at_1000
+      value: 0.094
+    - type: precision_at_3
+      value: 19.6
+    - type: precision_at_5
+      value: 12.44
+    - type: recall_at_1
+      value: 49.2
+    - type: recall_at_10
+      value: 65.2
+    - type: recall_at_100
+      value: 80.10000000000001
+    - type: recall_at_1000
+      value: 93.89999999999999
+    - type: recall_at_3
+      value: 58.8
+    - type: recall_at_5
+      value: 62.2
+  - task:
+      type: Classification
+    dataset:
+      type: C-MTEB/MultilingualSentiment-classification
+      name: MTEB MultilingualSentiment
+      config: default
+      split: validation
+      revision: None
+    metrics:
+    - type: accuracy
+      value: 63.29333333333334
+    - type: f1
+      value: 63.03293854259612
+  - task:
+      type: PairClassification
+    dataset:
+      type: C-MTEB/OCNLI
+      name: MTEB Ocnli
+      config: default
+      split: validation
+      revision: None
+    metrics:
+    - type: cos_sim_accuracy
+      value: 75.69030860855442
+    - type: cos_sim_ap
+      value: 80.6157833772759
+    - type: cos_sim_f1
+      value: 77.87524366471735
+    - type: cos_sim_precision
+      value: 72.3076923076923
+    - type: cos_sim_recall
+      value: 84.37170010559663
+    - type: dot_accuracy
+      value: 67.78559826746074
+    - type: dot_ap
+      value: 72.00871467527499
+    - type: dot_f1
+      value: 72.58722247394654
+    - type: dot_precision
+      value: 63.57142857142857
+    - type: dot_recall
+      value: 84.58289334741288
+    - type: euclidean_accuracy
+      value: 75.20303194369248
+    - type: euclidean_ap
+      value: 80.98587256415605
+    - type: euclidean_f1
+      value: 77.26396917148362
+    - type: euclidean_precision
+      value: 71.03631532329496
+    - type: euclidean_recall
+      value: 84.68848996832101
+    - type: manhattan_accuracy
+      value: 75.20303194369248
+    - type: manhattan_ap
+      value: 80.93460699513219
+    - type: manhattan_f1
+      value: 77.124773960217
+    - type: manhattan_precision
+      value: 67.43083003952569
+    - type: manhattan_recall
+      value: 90.07391763463569
+    - type: max_accuracy
+      value: 75.69030860855442
+    - type: max_ap
+      value: 80.98587256415605
+    - type: max_f1
+      value: 77.87524366471735
+  - task:
+      type: Classification
+    dataset:
+      type: C-MTEB/OnlineShopping-classification
+      name: MTEB OnlineShopping
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: accuracy
+      value: 87.00000000000001
+    - type: ap
+      value: 83.24372135949511
+    - type: f1
+      value: 86.95554191530607
+  - task:
+      type: STS
+    dataset:
+      type: C-MTEB/PAWSX
+      name: MTEB PAWSX
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: cos_sim_pearson
+      value: 37.57616811591219
+    - type: cos_sim_spearman
+      value: 41.490259084930045
+    - type: euclidean_pearson
+      value: 38.9155043692188
+    - type: euclidean_spearman
+      value: 39.16056534305623
+    - type: manhattan_pearson
+      value: 38.76569892264335
+    - type: manhattan_spearman
+      value: 38.99891685590743
+  - task:
+      type: STS
+    dataset:
+      type: C-MTEB/QBQTC
+      name: MTEB QBQTC
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: cos_sim_pearson
+      value: 35.44858610359665
+    - type: cos_sim_spearman
+      value: 38.11128146262466
+    - type: euclidean_pearson
+      value: 31.928644189822457
+    - type: euclidean_spearman
+      value: 34.384936631696554
+    - type: manhattan_pearson
+      value: 31.90586687414376
+    - type: manhattan_spearman
+      value: 34.35770153777186
+  - 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: 66.54931957553592
+    - type: cos_sim_spearman
+      value: 69.25068863016632
+    - type: euclidean_pearson
+      value: 50.26525596106869
+    - type: euclidean_spearman
+      value: 63.83352741910006
+    - type: manhattan_pearson
+      value: 49.98798282198196
+    - type: manhattan_spearman
+      value: 63.87649521907841
+  - task:
+      type: STS
+    dataset:
+      type: C-MTEB/STSB
+      name: MTEB STSB
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: cos_sim_pearson
+      value: 82.52782476625825
+    - type: cos_sim_spearman
+      value: 82.55618986168398
+    - type: euclidean_pearson
+      value: 78.48190631687673
+    - type: euclidean_spearman
+      value: 78.39479731354655
+    - type: manhattan_pearson
+      value: 78.51176592165885
+    - type: manhattan_spearman
+      value: 78.42363787303265
+  - task:
+      type: Reranking
+    dataset:
+      type: C-MTEB/T2Reranking
+      name: MTEB T2Reranking
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map
+      value: 67.36693873615643
+    - type: mrr
+      value: 77.83847701797939
+  - task:
+      type: Retrieval
+    dataset:
+      type: C-MTEB/T2Retrieval
+      name: MTEB T2Retrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 25.795
+    - type: map_at_10
+      value: 72.258
+    - type: map_at_100
+      value: 76.049
+    - type: map_at_1000
+      value: 76.134
+    - type: map_at_3
+      value: 50.697
+    - type: map_at_5
+      value: 62.324999999999996
+    - type: mrr_at_1
+      value: 86.634
+    - type: mrr_at_10
+      value: 89.792
+    - type: mrr_at_100
+      value: 89.91900000000001
+    - type: mrr_at_1000
+      value: 89.923
+    - type: mrr_at_3
+      value: 89.224
+    - type: mrr_at_5
+      value: 89.608
+    - type: ndcg_at_1
+      value: 86.634
+    - type: ndcg_at_10
+      value: 80.589
+    - type: ndcg_at_100
+      value: 84.812
+    - type: ndcg_at_1000
+      value: 85.662
+    - type: ndcg_at_3
+      value: 82.169
+    - type: ndcg_at_5
+      value: 80.619
+    - type: precision_at_1
+      value: 86.634
+    - type: precision_at_10
+      value: 40.389
+    - type: precision_at_100
+      value: 4.93
+    - type: precision_at_1000
+      value: 0.513
+    - type: precision_at_3
+      value: 72.104
+    - type: precision_at_5
+      value: 60.425
+    - type: recall_at_1
+      value: 25.795
+    - type: recall_at_10
+      value: 79.565
+    - type: recall_at_100
+      value: 93.24799999999999
+    - type: recall_at_1000
+      value: 97.595
+    - type: recall_at_3
+      value: 52.583999999999996
+    - type: recall_at_5
+      value: 66.175
+  - task:
+      type: Classification
+    dataset:
+      type: C-MTEB/TNews-classification
+      name: MTEB TNews
+      config: default
+      split: validation
+      revision: None
+    metrics:
+    - type: accuracy
+      value: 47.648999999999994
+    - type: f1
+      value: 46.28925837008413
+  - task:
+      type: Clustering
+    dataset:
+      type: C-MTEB/ThuNewsClusteringP2P
+      name: MTEB ThuNewsClusteringP2P
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: v_measure
+      value: 54.07641891287953
+  - task:
+      type: Clustering
+    dataset:
+      type: C-MTEB/ThuNewsClusteringS2S
+      name: MTEB ThuNewsClusteringS2S
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: v_measure
+      value: 53.423702062353954
+  - task:
+      type: Retrieval
+    dataset:
+      type: C-MTEB/VideoRetrieval
+      name: MTEB VideoRetrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 55.7
+    - type: map_at_10
+      value: 65.923
+    - type: map_at_100
+      value: 66.42
+    - type: map_at_1000
+      value: 66.431
+    - type: map_at_3
+      value: 63.9
+    - type: map_at_5
+      value: 65.225
+    - type: mrr_at_1
+      value: 55.60000000000001
+    - type: mrr_at_10
+      value: 65.873
+    - type: mrr_at_100
+      value: 66.36999999999999
+    - type: mrr_at_1000
+      value: 66.381
+    - type: mrr_at_3
+      value: 63.849999999999994
+    - type: mrr_at_5
+      value: 65.17500000000001
+    - type: ndcg_at_1
+      value: 55.7
+    - type: ndcg_at_10
+      value: 70.621
+    - type: ndcg_at_100
+      value: 72.944
+    - type: ndcg_at_1000
+      value: 73.25399999999999
+    - type: ndcg_at_3
+      value: 66.547
+    - type: ndcg_at_5
+      value: 68.93599999999999
+    - type: precision_at_1
+      value: 55.7
+    - type: precision_at_10
+      value: 8.52
+    - type: precision_at_100
+      value: 0.958
+    - type: precision_at_1000
+      value: 0.098
+    - type: precision_at_3
+      value: 24.733
+    - type: precision_at_5
+      value: 16
+    - type: recall_at_1
+      value: 55.7
+    - type: recall_at_10
+      value: 85.2
+    - type: recall_at_100
+      value: 95.8
+    - type: recall_at_1000
+      value: 98.3
+    - type: recall_at_3
+      value: 74.2
+    - type: recall_at_5
+      value: 80
+  - task:
+      type: Classification
+    dataset:
+      type: C-MTEB/waimai-classification
+      name: MTEB Waimai
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: accuracy
+      value: 84.54
+    - type: ap
+      value: 66.13603199670062
+    - type: f1
+      value: 82.61420654584116
+---
+<!-- TODO: add evaluation results here -->
+<br><br>
+
+<p align="center">
+<img src="https://aeiljuispo.cloudimg.io/v7/https://cdn-uploads.huggingface.co/production/uploads/603763514de52ff951d89793/AFoybzd5lpBQXEBrQHuTt.png?w=200&h=200&f=face" alt="Finetuner logo: Finetuner helps you to create experiments in order to improve embeddings on search tasks. It accompanies you to deliver the last mile of performance-tuning for neural search applications." width="150px">
+</p>
+
+
+<p align="center">
+<b>The text embedding set trained by <a href="https://jina.ai/"><b>Jina AI</b></a>.</b>
+</p>
+
+## Quick Start
+
+The easiest way to starting using `jina-embeddings-v2-base-zh` is to use Jina AI's [Embedding API](https://jina.ai/embeddings/).
+
+## Intended Usage & Model Info
+
+`jina-embeddings-v2-base-zh` is a Chinese/English bilingual text **embedding model** supporting **8192 sequence length**.
+It is based on a BERT architecture (JinaBERT) that supports the symmetric bidirectional variant of [ALiBi](https://arxiv.org/abs/2108.12409) to allow longer sequence length.
+We have designed it for high performance in mono-lingual & cross-lingual applications and trained it specifically to support mixed Chinese-English input without bias. 
+Additionally, we provide the following embedding models:
+
+`jina-embeddings-v2-base-zh` 是支持中英双语的**文本向量**模型，它支持长达**8192字符**的文本编码。
+该模型的研发基于BERT架构(JinaBERT)，JinaBERT是在BERT架构基础上的改进，首次将[ALiBi](https://arxiv.org/abs/2108.12409)应用到编码器架构中以支持更长的序列。
+不同于以往的单语言/多语言向量模型，我们设计双语模型来更好的支持单语言（中搜中）以及跨语言（中搜英）文档检索。
+除此之外，我们也提供其它向量模型:
+
+- [`jina-embeddings-v2-small-en`](https://huggingface.co/jinaai/jina-embeddings-v2-small-en): 33 million parameters.
+- [`jina-embeddings-v2-base-en`](https://huggingface.co/jinaai/jina-embeddings-v2-base-en): 137 million parameters.
+- [`jina-embeddings-v2-base-zh`](https://huggingface.co/jinaai/jina-embeddings-v2-base-zh): 161 million parameters Chinese-English Bilingual embeddings **(you are here)**.
+- [`jina-embeddings-v2-base-de`](https://huggingface.co/jinaai/jina-embeddings-v2-base-de): 161 million parameters German-English Bilingual embeddings.
+- [`jina-embeddings-v2-base-es`](): Spanish-English Bilingual embeddings (soon).
+- [`jina-embeddings-v2-base-code`](https://huggingface.co/jinaai/jina-embeddings-v2-base-code): 161 million parameters code embeddings.
+
+## Data & Parameters
+
+The data and training details are described in this [technical report](https://arxiv.org/abs/2402.17016).
+
+
+## Usage
+
+**<details><summary>Please apply mean pooling when integrating the model.</summary>**
+<p>
+
+### Why mean pooling?
+
+`mean poooling` takes all token embeddings from model output and averaging them at sentence/paragraph level.
+It has been proved to be the most effective way to produce high-quality sentence embeddings.
+We offer an `encode` function to deal with this.
+
+However, if you would like to do it without using the default `encode` function:
+
+```python
+import torch
+import torch.nn.functional as F
+from transformers import AutoTokenizer, AutoModel
+
+def mean_pooling(model_output, attention_mask):
+    token_embeddings = model_output[0]
+    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+
+sentences = ['How is the weather today?', '今天天气怎么样?']
+
+tokenizer = AutoTokenizer.from_pretrained('jinaai/jina-embeddings-v2-base-zh')
+model = AutoModel.from_pretrained('jinaai/jina-embeddings-v2-base-zh', trust_remote_code=True, torch_dtype=torch.bfloat16)
+
+encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
+
+with torch.no_grad():
+    model_output = model(**encoded_input)
+
+embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+embeddings = F.normalize(embeddings, p=2, dim=1)
+```
+
+</p>
+</details>
+
+You can use Jina Embedding models directly from transformers package.
+
+```python
+!pip install transformers
+import torch
+from transformers import AutoModel
+from numpy.linalg import norm
+
+cos_sim = lambda a,b: (a @ b.T) / (norm(a)*norm(b))
+model = AutoModel.from_pretrained('jinaai/jina-embeddings-v2-base-zh', trust_remote_code=True, torch_dtype=torch.bfloat16)
+embeddings = model.encode(['How is the weather today?', '今天天气怎么样?'])
+print(cos_sim(embeddings[0], embeddings[1]))
+```
+
+If you only want to handle shorter sequence, such as 2k, pass the `max_length` parameter to the `encode` function:
+
+```python
+embeddings = model.encode(
+    ['Very long ... document'],
+    max_length=2048
+)
+```
+
+If you want to use the model together with the [sentence-transformers package](https://github.com/UKPLab/sentence-transformers/), make sure that you have installed the latest release and set `trust_remote_code=True` as well:
+
+```python
+!pip install -U sentence-transformers
+from sentence_transformers import SentenceTransformer
+from numpy.linalg import norm
+
+cos_sim = lambda a,b: (a @ b.T) / (norm(a)*norm(b))
+model = SentenceTransformer('jinaai/jina-embeddings-v2-base-zh', trust_remote_code=True)
+embeddings = model.encode(['How is the weather today?', '今天天气怎么样?'])
+print(cos_sim(embeddings[0], embeddings[1]))
+```
+
+Using the its latest release (v2.3.0) sentence-transformers also supports Jina embeddings (Please make sure that you are logged into huggingface as well):
+
+```python
+!pip install -U sentence-transformers
+from sentence_transformers import SentenceTransformer
+from sentence_transformers.util import cos_sim
+
+model = SentenceTransformer(
+    "jinaai/jina-embeddings-v2-base-zh", # switch to en/zh for English or Chinese
+    trust_remote_code=True
+)
+
+# control your input sequence length up to 8192
+model.max_seq_length = 1024
+
+embeddings = model.encode([
+    'How is the weather today?',
+    '今天天气怎么样?'
+])
+print(cos_sim(embeddings[0], embeddings[1]))
+```
+
+## Alternatives to Using Transformers Package
+
+1. _Managed SaaS_: Get started with a free key on Jina AI's [Embedding API](https://jina.ai/embeddings/). 
+2. _Private and high-performance deployment_: Get started by picking from our suite of models and deploy them on [AWS Sagemaker](https://aws.amazon.com/marketplace/seller-profile?id=seller-stch2ludm6vgy).
+
+## Use Jina Embeddings for RAG
+
+According to the latest blog post from [LLamaIndex](https://blog.llamaindex.ai/boosting-rag-picking-the-best-embedding-reranker-models-42d079022e83),
+
+> In summary, to achieve the peak performance in both hit rate and MRR, the combination of OpenAI or JinaAI-Base embeddings with the CohereRerank/bge-reranker-large reranker stands out.
+
+<img src="https://miro.medium.com/v2/resize:fit:4800/format:webp/1*ZP2RVejCZovF3FDCg-Bx3A.png" width="780px">
+
+## Trouble Shooting
+
+**Loading of Model Code failed**
+
+If you forgot to pass the `trust_remote_code=True` flag when calling `AutoModel.from_pretrained` or initializing the model via the `SentenceTransformer` class, you will receive an error that the model weights could not be initialized.
+This is caused by tranformers falling back to creating a default BERT model, instead of a jina-embedding model:
+
+```bash
+Some weights of the model checkpoint at jinaai/jina-embeddings-v2-base-zh were not used when initializing BertModel: ['encoder.layer.2.mlp.layernorm.weight', 'encoder.layer.3.mlp.layernorm.weight', 'encoder.layer.10.mlp.wo.bias', 'encoder.layer.5.mlp.wo.bias', 'encoder.layer.2.mlp.layernorm.bias', 'encoder.layer.1.mlp.gated_layers.weight', 'encoder.layer.5.mlp.gated_layers.weight', 'encoder.layer.8.mlp.layernorm.bias', ...
+```
+
+**User is not logged into Huggingface**
+
+The model is only availabe under [gated access](https://huggingface.co/docs/hub/models-gated).
+This means you need to be logged into huggingface load load it.
+If you receive the following error, you need to provide an access token, either by using the huggingface-cli or providing the token via an environment variable as described above:
+```bash
+OSError: jinaai/jina-embeddings-v2-base-zh is not a local folder and is not a valid model identifier listed on 'https://huggingface.co/models'
+If this is a private repository, make sure to pass a token having permission to this repo with `use_auth_token` or log in with `huggingface-cli login` and pass `use_auth_token=True`.
+```
+
+## Contact
+
+Join our [Discord community](https://discord.jina.ai) and chat with other community members about ideas.
+
+## Citation
+
+If you find Jina Embeddings useful in your research, please cite the following paper:
+
+```
+@article{mohr2024multi,
+  title={Multi-Task Contrastive Learning for 8192-Token Bilingual Text Embeddings},
+  author={Mohr, Isabelle and Krimmel, Markus and Sturua, Saba and Akram, Mohammad Kalim and Koukounas, Andreas and G{\"u}nther, Michael and Mastrapas, Georgios and Ravishankar, Vinit and Mart{\'\i}nez, Joan Fontanals and Wang, Feng and others},
+  journal={arXiv preprint arXiv:2402.17016},
+  year={2024}
+}
+```

config.json

@@ -0,0 +1,38 @@
+{
+  "_name_or_path": "jinaai/jina-bert-implementation",
+  "architectures": [
+    "JinaBertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "attn_implementation": "torch",
+  "auto_map": {
+    "AutoConfig": "jinaai/jina-bert-implementation--configuration_bert.JinaBertConfig",
+    "AutoModel": "jinaai/jina-bert-implementation--modeling_bert.JinaBertModel",
+    "AutoModelForMaskedLM": "jinaai/jina-bert-implementation--modeling_bert.JinaBertForMaskedLM",
+    "AutoModelForQuestionAnswering": "jinaai/jina-bert-implementation--modeling_bert.JinaBertForQuestionAnswering",
+    "AutoModelForSequenceClassification": "jinaai/jina-bert-implementation--modeling_bert.JinaBertForSequenceClassification",
+    "AutoModelForTokenClassification": "jinaai/jina-bert-implementation--modeling_bert.JinaBertForTokenClassification"
+  },
+  "classifier_dropout": null,
+  "emb_pooler": "mean",
+  "feed_forward_type": "geglu",
+  "gradient_checkpointing": false,
+  "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": 8192,
+  "model_max_length": 8192,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "alibi",
+  "torch_dtype": "float16",
+  "transformers_version": "4.30.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 61056
+}

config_sentence_transformers.json

@@ -0,0 +1,7 @@
+{
+  "__version__": {
+    "sentence_transformers": "2.2.2",
+    "transformers": "4.30.2",
+    "pytorch": "2.0.1"
+  }
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:29b7cdda0c8fa9b18f8e0fbc4aba0c9537555fb16b139fa44be92c1e1b3253a8
+size 321648328

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_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,5 @@
+{
+  "max_seq_length": 8192,
+  "do_lower_case": false,
+  "model_args": {"trust_remote_code": true}
+}

special_tokens_map.json

@@ -0,0 +1,15 @@
+{
+  "bos_token": "<s>",
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "unk_token": "<unk>"
+}

tokenizer_config.json

@@ -0,0 +1,57 @@
+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "4": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "errors": "replace",
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "tokenizer_class": "RobertaTokenizer",
+  "trim_offsets": true,
+  "unk_token": "<unk>"
+}