Update README.md

README.md

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 ---
+tags:
+- finetuner
+- mteb
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- alibi
 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://github.com/jina-ai/finetuner/blob/main/docs/_static/finetuner-logo-ani.svg?raw=true" 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>, <a href="https://github.com/jina-ai/finetuner"><b>Finetuner</b></a> team.</b>
+</p>
+
+
+## Intended Usage & Model Info
+
+`jina-embeddings-v2-base-zh` is a Chinese/English bilingual text **embedding model** supporting **8192 sequence length**. Our model has 161 million parameters.
+We have designed it for high performance in cross-language applications and trained it specifically to support mixed Chinese-English input without bias. 
+
+
+You can use the embedding model either via the  Jina AI's [Embedding platform](https://jina.ai/embeddings/), AWS SageMaker or in your private deployments.
+
+## Usage Jina Embedding API
+
+The following code snippet shows the usage of the Jina Embedding API:
+```
+curl https://api.jina.ai/v1/embeddings \
+  -H "Content-Type: application/json" \
+  -H "Authorization: Bearer jina_xxxxxxx" \
+  -d '{
+    "input": ["你的输入可以是纯中文", "or purely in English", "or like mixture of 中文 and 英文"],
+    "model": "jina-embeddings-v2-base-zh"
+  }'
+
+```
+
+Get your free API key on: https://jina.ai/embeddings/
+
+## Opensource
+
+We will opensource the full model in a few days!
+
+## Contact
+
+Join our [Discord community](https://discord.jina.ai) and chat with other community members about ideas.