Add new SentenceTransformer model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 512,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

@@ -0,0 +1,2781 @@
+---
+tags:
+  - sentence-transformers
+  - feature-extraction
+  - sentence-similarity
+  - mteb
+datasets:
+  - jinaai/negation-dataset
+language: en
+inference: false
+license: apache-2.0
+model-index:
+- name: jina-embedding-s-en-v2
+  results:
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_counterfactual
+      name: MTEB AmazonCounterfactualClassification (en)
+      config: en
+      split: test
+      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
+    metrics:
+    - type: accuracy
+      value: 71.35820895522387
+    - type: ap
+      value: 33.99931933598115
+    - type: f1
+      value: 65.3853685535555
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_polarity
+      name: MTEB AmazonPolarityClassification
+      config: default
+      split: test
+      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
+    metrics:
+    - type: accuracy
+      value: 82.90140000000001
+    - type: ap
+      value: 78.01434597815617
+    - type: f1
+      value: 82.83357802722676
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_reviews_multi
+      name: MTEB AmazonReviewsClassification (en)
+      config: en
+      split: test
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+    metrics:
+    - type: accuracy
+      value: 40.88999999999999
+    - type: f1
+      value: 39.209432767163456
+  - task:
+      type: Retrieval
+    dataset:
+      type: arguana
+      name: MTEB ArguAna
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 23.257
+    - type: map_at_10
+      value: 37.946000000000005
+    - type: map_at_100
+      value: 39.17
+    - type: map_at_1000
+      value: 39.181
+    - type: map_at_3
+      value: 32.99
+    - type: map_at_5
+      value: 35.467999999999996
+    - type: mrr_at_1
+      value: 23.541999999999998
+    - type: mrr_at_10
+      value: 38.057
+    - type: mrr_at_100
+      value: 39.289
+    - type: mrr_at_1000
+      value: 39.299
+    - type: mrr_at_3
+      value: 33.096
+    - type: mrr_at_5
+      value: 35.628
+    - type: ndcg_at_1
+      value: 23.257
+    - type: ndcg_at_10
+      value: 46.729
+    - type: ndcg_at_100
+      value: 51.900999999999996
+    - type: ndcg_at_1000
+      value: 52.16
+    - type: ndcg_at_3
+      value: 36.323
+    - type: ndcg_at_5
+      value: 40.766999999999996
+    - type: precision_at_1
+      value: 23.257
+    - type: precision_at_10
+      value: 7.510999999999999
+    - type: precision_at_100
+      value: 0.976
+    - type: precision_at_1000
+      value: 0.1
+    - type: precision_at_3
+      value: 15.339
+    - type: precision_at_5
+      value: 11.350999999999999
+    - type: recall_at_1
+      value: 23.257
+    - type: recall_at_10
+      value: 75.107
+    - type: recall_at_100
+      value: 97.58200000000001
+    - type: recall_at_1000
+      value: 99.57300000000001
+    - type: recall_at_3
+      value: 46.017
+    - type: recall_at_5
+      value: 56.757000000000005
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-p2p
+      name: MTEB ArxivClusteringP2P
+      config: default
+      split: test
+      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
+    metrics:
+    - type: v_measure
+      value: 44.02420878391967
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-s2s
+      name: MTEB ArxivClusteringS2S
+      config: default
+      split: test
+      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
+    metrics:
+    - type: v_measure
+      value: 35.16136856000258
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/askubuntudupquestions-reranking
+      name: MTEB AskUbuntuDupQuestions
+      config: default
+      split: test
+      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
+    metrics:
+    - type: map
+      value: 59.61809790513646
+    - type: mrr
+      value: 73.07215406938397
+  - task:
+      type: STS
+    dataset:
+      type: mteb/biosses-sts
+      name: MTEB BIOSSES
+      config: default
+      split: test
+      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
+    metrics:
+    - type: cos_sim_pearson
+      value: 82.0167350090749
+    - type: cos_sim_spearman
+      value: 80.51569002630401
+    - type: euclidean_pearson
+      value: 81.46820525099726
+    - type: euclidean_spearman
+      value: 80.51569002630401
+    - type: manhattan_pearson
+      value: 81.35596555056757
+    - type: manhattan_spearman
+      value: 80.12592210903303
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/banking77
+      name: MTEB Banking77Classification
+      config: default
+      split: test
+      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
+    metrics:
+    - type: accuracy
+      value: 78.25
+    - type: f1
+      value: 77.34950913540605
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-p2p
+      name: MTEB BiorxivClusteringP2P
+      config: default
+      split: test
+      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
+    metrics:
+    - type: v_measure
+      value: 35.57238596005698
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-s2s
+      name: MTEB BiorxivClusteringS2S
+      config: default
+      split: test
+      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
+    metrics:
+    - type: v_measure
+      value: 29.066444306196683
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackAndroidRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 31.891000000000002
+    - type: map_at_10
+      value: 42.772
+    - type: map_at_100
+      value: 44.108999999999995
+    - type: map_at_1000
+      value: 44.236
+    - type: map_at_3
+      value: 39.289
+    - type: map_at_5
+      value: 41.113
+    - type: mrr_at_1
+      value: 39.342
+    - type: mrr_at_10
+      value: 48.852000000000004
+    - type: mrr_at_100
+      value: 49.534
+    - type: mrr_at_1000
+      value: 49.582
+    - type: mrr_at_3
+      value: 46.089999999999996
+    - type: mrr_at_5
+      value: 47.685
+    - type: ndcg_at_1
+      value: 39.342
+    - type: ndcg_at_10
+      value: 48.988
+    - type: ndcg_at_100
+      value: 53.854
+    - type: ndcg_at_1000
+      value: 55.955
+    - type: ndcg_at_3
+      value: 43.877
+    - type: ndcg_at_5
+      value: 46.027
+    - type: precision_at_1
+      value: 39.342
+    - type: precision_at_10
+      value: 9.285
+    - type: precision_at_100
+      value: 1.488
+    - type: precision_at_1000
+      value: 0.194
+    - type: precision_at_3
+      value: 20.696
+    - type: precision_at_5
+      value: 14.878
+    - type: recall_at_1
+      value: 31.891000000000002
+    - type: recall_at_10
+      value: 60.608
+    - type: recall_at_100
+      value: 81.025
+    - type: recall_at_1000
+      value: 94.883
+    - type: recall_at_3
+      value: 45.694
+    - type: recall_at_5
+      value: 51.684
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackEnglishRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 28.778
+    - type: map_at_10
+      value: 37.632
+    - type: map_at_100
+      value: 38.800000000000004
+    - type: map_at_1000
+      value: 38.934999999999995
+    - type: map_at_3
+      value: 35.293
+    - type: map_at_5
+      value: 36.547000000000004
+    - type: mrr_at_1
+      value: 35.35
+    - type: mrr_at_10
+      value: 42.936
+    - type: mrr_at_100
+      value: 43.69
+    - type: mrr_at_1000
+      value: 43.739
+    - type: mrr_at_3
+      value: 41.062
+    - type: mrr_at_5
+      value: 42.097
+    - type: ndcg_at_1
+      value: 35.35
+    - type: ndcg_at_10
+      value: 42.528
+    - type: ndcg_at_100
+      value: 46.983000000000004
+    - type: ndcg_at_1000
+      value: 49.187999999999995
+    - type: ndcg_at_3
+      value: 39.271
+    - type: ndcg_at_5
+      value: 40.654
+    - type: precision_at_1
+      value: 35.35
+    - type: precision_at_10
+      value: 7.828
+    - type: precision_at_100
+      value: 1.3010000000000002
+    - type: precision_at_1000
+      value: 0.17700000000000002
+    - type: precision_at_3
+      value: 18.96
+    - type: precision_at_5
+      value: 13.120999999999999
+    - type: recall_at_1
+      value: 28.778
+    - type: recall_at_10
+      value: 50.775000000000006
+    - type: recall_at_100
+      value: 69.66799999999999
+    - type: recall_at_1000
+      value: 83.638
+    - type: recall_at_3
+      value: 40.757
+    - type: recall_at_5
+      value: 44.86
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGamingRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 37.584
+    - type: map_at_10
+      value: 49.69
+    - type: map_at_100
+      value: 50.639
+    - type: map_at_1000
+      value: 50.702999999999996
+    - type: map_at_3
+      value: 46.61
+    - type: map_at_5
+      value: 48.486000000000004
+    - type: mrr_at_1
+      value: 43.009
+    - type: mrr_at_10
+      value: 52.949999999999996
+    - type: mrr_at_100
+      value: 53.618
+    - type: mrr_at_1000
+      value: 53.65299999999999
+    - type: mrr_at_3
+      value: 50.605999999999995
+    - type: mrr_at_5
+      value: 52.095
+    - type: ndcg_at_1
+      value: 43.009
+    - type: ndcg_at_10
+      value: 55.278000000000006
+    - type: ndcg_at_100
+      value: 59.134
+    - type: ndcg_at_1000
+      value: 60.528999999999996
+    - type: ndcg_at_3
+      value: 50.184
+    - type: ndcg_at_5
+      value: 52.919000000000004
+    - type: precision_at_1
+      value: 43.009
+    - type: precision_at_10
+      value: 8.821
+    - type: precision_at_100
+      value: 1.161
+    - type: precision_at_1000
+      value: 0.133
+    - type: precision_at_3
+      value: 22.424
+    - type: precision_at_5
+      value: 15.436
+    - type: recall_at_1
+      value: 37.584
+    - type: recall_at_10
+      value: 68.514
+    - type: recall_at_100
+      value: 85.099
+    - type: recall_at_1000
+      value: 95.123
+    - type: recall_at_3
+      value: 55.007
+    - type: recall_at_5
+      value: 61.714999999999996
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGisRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 24.7
+    - type: map_at_10
+      value: 32.804
+    - type: map_at_100
+      value: 33.738
+    - type: map_at_1000
+      value: 33.825
+    - type: map_at_3
+      value: 30.639
+    - type: map_at_5
+      value: 31.781
+    - type: mrr_at_1
+      value: 26.328000000000003
+    - type: mrr_at_10
+      value: 34.679
+    - type: mrr_at_100
+      value: 35.510000000000005
+    - type: mrr_at_1000
+      value: 35.577999999999996
+    - type: mrr_at_3
+      value: 32.58
+    - type: mrr_at_5
+      value: 33.687
+    - type: ndcg_at_1
+      value: 26.328000000000003
+    - type: ndcg_at_10
+      value: 37.313
+    - type: ndcg_at_100
+      value: 42.004000000000005
+    - type: ndcg_at_1000
+      value: 44.232
+    - type: ndcg_at_3
+      value: 33.076
+    - type: ndcg_at_5
+      value: 34.966
+    - type: precision_at_1
+      value: 26.328000000000003
+    - type: precision_at_10
+      value: 5.627
+    - type: precision_at_100
+      value: 0.8410000000000001
+    - type: precision_at_1000
+      value: 0.106
+    - type: precision_at_3
+      value: 14.011000000000001
+    - type: precision_at_5
+      value: 9.582
+    - type: recall_at_1
+      value: 24.7
+    - type: recall_at_10
+      value: 49.324
+    - type: recall_at_100
+      value: 71.018
+    - type: recall_at_1000
+      value: 87.905
+    - type: recall_at_3
+      value: 37.7
+    - type: recall_at_5
+      value: 42.281
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackMathematicaRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 14.350999999999999
+    - type: map_at_10
+      value: 21.745
+    - type: map_at_100
+      value: 22.731
+    - type: map_at_1000
+      value: 22.852
+    - type: map_at_3
+      value: 19.245
+    - type: map_at_5
+      value: 20.788
+    - type: mrr_at_1
+      value: 18.159
+    - type: mrr_at_10
+      value: 25.833000000000002
+    - type: mrr_at_100
+      value: 26.728
+    - type: mrr_at_1000
+      value: 26.802
+    - type: mrr_at_3
+      value: 23.383000000000003
+    - type: mrr_at_5
+      value: 24.887999999999998
+    - type: ndcg_at_1
+      value: 18.159
+    - type: ndcg_at_10
+      value: 26.518000000000004
+    - type: ndcg_at_100
+      value: 31.473000000000003
+    - type: ndcg_at_1000
+      value: 34.576
+    - type: ndcg_at_3
+      value: 21.907
+    - type: ndcg_at_5
+      value: 24.39
+    - type: precision_at_1
+      value: 18.159
+    - type: precision_at_10
+      value: 4.938
+    - type: precision_at_100
+      value: 0.853
+    - type: precision_at_1000
+      value: 0.125
+    - type: precision_at_3
+      value: 10.655000000000001
+    - type: precision_at_5
+      value: 7.985
+    - type: recall_at_1
+      value: 14.350999999999999
+    - type: recall_at_10
+      value: 37.284
+    - type: recall_at_100
+      value: 59.11300000000001
+    - type: recall_at_1000
+      value: 81.634
+    - type: recall_at_3
+      value: 24.753
+    - type: recall_at_5
+      value: 30.979
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackPhysicsRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 26.978
+    - type: map_at_10
+      value: 36.276
+    - type: map_at_100
+      value: 37.547000000000004
+    - type: map_at_1000
+      value: 37.678
+    - type: map_at_3
+      value: 33.674
+    - type: map_at_5
+      value: 35.119
+    - type: mrr_at_1
+      value: 32.916000000000004
+    - type: mrr_at_10
+      value: 41.798
+    - type: mrr_at_100
+      value: 42.72
+    - type: mrr_at_1000
+      value: 42.778
+    - type: mrr_at_3
+      value: 39.493
+    - type: mrr_at_5
+      value: 40.927
+    - type: ndcg_at_1
+      value: 32.916000000000004
+    - type: ndcg_at_10
+      value: 41.81
+    - type: ndcg_at_100
+      value: 47.284
+    - type: ndcg_at_1000
+      value: 49.702
+    - type: ndcg_at_3
+      value: 37.486999999999995
+    - type: ndcg_at_5
+      value: 39.597
+    - type: precision_at_1
+      value: 32.916000000000004
+    - type: precision_at_10
+      value: 7.411
+    - type: precision_at_100
+      value: 1.189
+    - type: precision_at_1000
+      value: 0.158
+    - type: precision_at_3
+      value: 17.581
+    - type: precision_at_5
+      value: 12.397
+    - type: recall_at_1
+      value: 26.978
+    - type: recall_at_10
+      value: 52.869
+    - type: recall_at_100
+      value: 75.78399999999999
+    - type: recall_at_1000
+      value: 91.545
+    - type: recall_at_3
+      value: 40.717
+    - type: recall_at_5
+      value: 46.168
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackProgrammersRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 24.641
+    - type: map_at_10
+      value: 32.916000000000004
+    - type: map_at_100
+      value: 34.165
+    - type: map_at_1000
+      value: 34.286
+    - type: map_at_3
+      value: 30.335
+    - type: map_at_5
+      value: 31.569000000000003
+    - type: mrr_at_1
+      value: 30.593999999999998
+    - type: mrr_at_10
+      value: 38.448
+    - type: mrr_at_100
+      value: 39.299
+    - type: mrr_at_1000
+      value: 39.362
+    - type: mrr_at_3
+      value: 36.244
+    - type: mrr_at_5
+      value: 37.232
+    - type: ndcg_at_1
+      value: 30.593999999999998
+    - type: ndcg_at_10
+      value: 38.2
+    - type: ndcg_at_100
+      value: 43.742
+    - type: ndcg_at_1000
+      value: 46.217000000000006
+    - type: ndcg_at_3
+      value: 33.925
+    - type: ndcg_at_5
+      value: 35.394
+    - type: precision_at_1
+      value: 30.593999999999998
+    - type: precision_at_10
+      value: 6.895
+    - type: precision_at_100
+      value: 1.1320000000000001
+    - type: precision_at_1000
+      value: 0.153
+    - type: precision_at_3
+      value: 16.096
+    - type: precision_at_5
+      value: 11.05
+    - type: recall_at_1
+      value: 24.641
+    - type: recall_at_10
+      value: 48.588
+    - type: recall_at_100
+      value: 72.841
+    - type: recall_at_1000
+      value: 89.535
+    - type: recall_at_3
+      value: 36.087
+    - type: recall_at_5
+      value: 40.346
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 24.79425
+    - type: map_at_10
+      value: 33.12033333333333
+    - type: map_at_100
+      value: 34.221333333333334
+    - type: map_at_1000
+      value: 34.3435
+    - type: map_at_3
+      value: 30.636583333333338
+    - type: map_at_5
+      value: 31.974083333333326
+    - type: mrr_at_1
+      value: 29.242416666666664
+    - type: mrr_at_10
+      value: 37.11675
+    - type: mrr_at_100
+      value: 37.93783333333334
+    - type: mrr_at_1000
+      value: 38.003083333333336
+    - type: mrr_at_3
+      value: 34.904666666666664
+    - type: mrr_at_5
+      value: 36.12916666666667
+    - type: ndcg_at_1
+      value: 29.242416666666664
+    - type: ndcg_at_10
+      value: 38.03416666666667
+    - type: ndcg_at_100
+      value: 42.86674999999999
+    - type: ndcg_at_1000
+      value: 45.34550000000001
+    - type: ndcg_at_3
+      value: 33.76466666666666
+    - type: ndcg_at_5
+      value: 35.668666666666674
+    - type: precision_at_1
+      value: 29.242416666666664
+    - type: precision_at_10
+      value: 6.589833333333334
+    - type: precision_at_100
+      value: 1.0693333333333332
+    - type: precision_at_1000
+      value: 0.14641666666666667
+    - type: precision_at_3
+      value: 15.430749999999998
+    - type: precision_at_5
+      value: 10.833833333333333
+    - type: recall_at_1
+      value: 24.79425
+    - type: recall_at_10
+      value: 48.582916666666655
+    - type: recall_at_100
+      value: 69.88499999999999
+    - type: recall_at_1000
+      value: 87.211
+    - type: recall_at_3
+      value: 36.625499999999995
+    - type: recall_at_5
+      value: 41.553999999999995
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackStatsRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 22.767
+    - type: map_at_10
+      value: 28.450999999999997
+    - type: map_at_100
+      value: 29.332
+    - type: map_at_1000
+      value: 29.426000000000002
+    - type: map_at_3
+      value: 26.379
+    - type: map_at_5
+      value: 27.584999999999997
+    - type: mrr_at_1
+      value: 25.46
+    - type: mrr_at_10
+      value: 30.974
+    - type: mrr_at_100
+      value: 31.784000000000002
+    - type: mrr_at_1000
+      value: 31.857999999999997
+    - type: mrr_at_3
+      value: 28.962
+    - type: mrr_at_5
+      value: 30.066
+    - type: ndcg_at_1
+      value: 25.46
+    - type: ndcg_at_10
+      value: 32.041
+    - type: ndcg_at_100
+      value: 36.522
+    - type: ndcg_at_1000
+      value: 39.101
+    - type: ndcg_at_3
+      value: 28.152
+    - type: ndcg_at_5
+      value: 30.03
+    - type: precision_at_1
+      value: 25.46
+    - type: precision_at_10
+      value: 4.893
+    - type: precision_at_100
+      value: 0.77
+    - type: precision_at_1000
+      value: 0.107
+    - type: precision_at_3
+      value: 11.605
+    - type: precision_at_5
+      value: 8.19
+    - type: recall_at_1
+      value: 22.767
+    - type: recall_at_10
+      value: 40.71
+    - type: recall_at_100
+      value: 61.334999999999994
+    - type: recall_at_1000
+      value: 80.567
+    - type: recall_at_3
+      value: 30.198000000000004
+    - type: recall_at_5
+      value: 34.803
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackTexRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 16.722
+    - type: map_at_10
+      value: 22.794
+    - type: map_at_100
+      value: 23.7
+    - type: map_at_1000
+      value: 23.822
+    - type: map_at_3
+      value: 20.781
+    - type: map_at_5
+      value: 22.024
+    - type: mrr_at_1
+      value: 20.061999999999998
+    - type: mrr_at_10
+      value: 26.346999999999998
+    - type: mrr_at_100
+      value: 27.153
+    - type: mrr_at_1000
+      value: 27.233
+    - type: mrr_at_3
+      value: 24.375
+    - type: mrr_at_5
+      value: 25.593
+    - type: ndcg_at_1
+      value: 20.061999999999998
+    - type: ndcg_at_10
+      value: 26.785999999999998
+    - type: ndcg_at_100
+      value: 31.319999999999997
+    - type: ndcg_at_1000
+      value: 34.346
+    - type: ndcg_at_3
+      value: 23.219
+    - type: ndcg_at_5
+      value: 25.107000000000003
+    - type: precision_at_1
+      value: 20.061999999999998
+    - type: precision_at_10
+      value: 4.78
+    - type: precision_at_100
+      value: 0.83
+    - type: precision_at_1000
+      value: 0.125
+    - type: precision_at_3
+      value: 10.874
+    - type: precision_at_5
+      value: 7.956
+    - type: recall_at_1
+      value: 16.722
+    - type: recall_at_10
+      value: 35.204
+    - type: recall_at_100
+      value: 55.797
+    - type: recall_at_1000
+      value: 77.689
+    - type: recall_at_3
+      value: 25.245
+    - type: recall_at_5
+      value: 30.115
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackUnixRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 24.842
+    - type: map_at_10
+      value: 32.917
+    - type: map_at_100
+      value: 33.961000000000006
+    - type: map_at_1000
+      value: 34.069
+    - type: map_at_3
+      value: 30.595
+    - type: map_at_5
+      value: 31.837
+    - type: mrr_at_1
+      value: 29.011
+    - type: mrr_at_10
+      value: 36.977
+    - type: mrr_at_100
+      value: 37.814
+    - type: mrr_at_1000
+      value: 37.885999999999996
+    - type: mrr_at_3
+      value: 34.966
+    - type: mrr_at_5
+      value: 36.043
+    - type: ndcg_at_1
+      value: 29.011
+    - type: ndcg_at_10
+      value: 37.735
+    - type: ndcg_at_100
+      value: 42.683
+    - type: ndcg_at_1000
+      value: 45.198
+    - type: ndcg_at_3
+      value: 33.650000000000006
+    - type: ndcg_at_5
+      value: 35.386
+    - type: precision_at_1
+      value: 29.011
+    - type: precision_at_10
+      value: 6.259
+    - type: precision_at_100
+      value: 0.984
+    - type: precision_at_1000
+      value: 0.13
+    - type: precision_at_3
+      value: 15.329999999999998
+    - type: precision_at_5
+      value: 10.541
+    - type: recall_at_1
+      value: 24.842
+    - type: recall_at_10
+      value: 48.304
+    - type: recall_at_100
+      value: 70.04899999999999
+    - type: recall_at_1000
+      value: 87.82600000000001
+    - type: recall_at_3
+      value: 36.922
+    - type: recall_at_5
+      value: 41.449999999999996
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWebmastersRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 24.252000000000002
+    - type: map_at_10
+      value: 32.293
+    - type: map_at_100
+      value: 33.816
+    - type: map_at_1000
+      value: 34.053
+    - type: map_at_3
+      value: 29.781999999999996
+    - type: map_at_5
+      value: 31.008000000000003
+    - type: mrr_at_1
+      value: 29.051
+    - type: mrr_at_10
+      value: 36.722
+    - type: mrr_at_100
+      value: 37.663000000000004
+    - type: mrr_at_1000
+      value: 37.734
+    - type: mrr_at_3
+      value: 34.354
+    - type: mrr_at_5
+      value: 35.609
+    - type: ndcg_at_1
+      value: 29.051
+    - type: ndcg_at_10
+      value: 37.775999999999996
+    - type: ndcg_at_100
+      value: 43.221
+    - type: ndcg_at_1000
+      value: 46.116
+    - type: ndcg_at_3
+      value: 33.403
+    - type: ndcg_at_5
+      value: 35.118
+    - type: precision_at_1
+      value: 29.051
+    - type: precision_at_10
+      value: 7.332
+    - type: precision_at_100
+      value: 1.49
+    - type: precision_at_1000
+      value: 0.23600000000000002
+    - type: precision_at_3
+      value: 15.415000000000001
+    - type: precision_at_5
+      value: 11.107
+    - type: recall_at_1
+      value: 24.252000000000002
+    - type: recall_at_10
+      value: 47.861
+    - type: recall_at_100
+      value: 72.21600000000001
+    - type: recall_at_1000
+      value: 90.886
+    - type: recall_at_3
+      value: 35.533
+    - type: recall_at_5
+      value: 39.959
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWordpressRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 20.025000000000002
+    - type: map_at_10
+      value: 27.154
+    - type: map_at_100
+      value: 28.118
+    - type: map_at_1000
+      value: 28.237000000000002
+    - type: map_at_3
+      value: 25.017
+    - type: map_at_5
+      value: 25.832
+    - type: mrr_at_1
+      value: 21.627
+    - type: mrr_at_10
+      value: 28.884999999999998
+    - type: mrr_at_100
+      value: 29.741
+    - type: mrr_at_1000
+      value: 29.831999999999997
+    - type: mrr_at_3
+      value: 26.741
+    - type: mrr_at_5
+      value: 27.628000000000004
+    - type: ndcg_at_1
+      value: 21.627
+    - type: ndcg_at_10
+      value: 31.436999999999998
+    - type: ndcg_at_100
+      value: 36.181000000000004
+    - type: ndcg_at_1000
+      value: 38.986
+    - type: ndcg_at_3
+      value: 27.025
+    - type: ndcg_at_5
+      value: 28.436
+    - type: precision_at_1
+      value: 21.627
+    - type: precision_at_10
+      value: 5.009
+    - type: precision_at_100
+      value: 0.7929999999999999
+    - type: precision_at_1000
+      value: 0.11299999999999999
+    - type: precision_at_3
+      value: 11.522
+    - type: precision_at_5
+      value: 7.763000000000001
+    - type: recall_at_1
+      value: 20.025000000000002
+    - type: recall_at_10
+      value: 42.954
+    - type: recall_at_100
+      value: 64.67500000000001
+    - type: recall_at_1000
+      value: 85.301
+    - type: recall_at_3
+      value: 30.892999999999997
+    - type: recall_at_5
+      value: 34.288000000000004
+  - task:
+      type: Retrieval
+    dataset:
+      type: climate-fever
+      name: MTEB ClimateFEVER
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 10.079
+    - type: map_at_10
+      value: 16.930999999999997
+    - type: map_at_100
+      value: 18.398999999999997
+    - type: map_at_1000
+      value: 18.561
+    - type: map_at_3
+      value: 14.294
+    - type: map_at_5
+      value: 15.579
+    - type: mrr_at_1
+      value: 22.606
+    - type: mrr_at_10
+      value: 32.513
+    - type: mrr_at_100
+      value: 33.463
+    - type: mrr_at_1000
+      value: 33.513999999999996
+    - type: mrr_at_3
+      value: 29.479
+    - type: mrr_at_5
+      value: 31.3
+    - type: ndcg_at_1
+      value: 22.606
+    - type: ndcg_at_10
+      value: 24.053
+    - type: ndcg_at_100
+      value: 30.258000000000003
+    - type: ndcg_at_1000
+      value: 33.516
+    - type: ndcg_at_3
+      value: 19.721
+    - type: ndcg_at_5
+      value: 21.144
+    - type: precision_at_1
+      value: 22.606
+    - type: precision_at_10
+      value: 7.55
+    - type: precision_at_100
+      value: 1.399
+    - type: precision_at_1000
+      value: 0.2
+    - type: precision_at_3
+      value: 14.701
+    - type: precision_at_5
+      value: 11.192
+    - type: recall_at_1
+      value: 10.079
+    - type: recall_at_10
+      value: 28.970000000000002
+    - type: recall_at_100
+      value: 50.805
+    - type: recall_at_1000
+      value: 69.378
+    - type: recall_at_3
+      value: 18.199
+    - type: recall_at_5
+      value: 22.442
+  - task:
+      type: Retrieval
+    dataset:
+      type: dbpedia-entity
+      name: MTEB DBPedia
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 7.794
+    - type: map_at_10
+      value: 15.165999999999999
+    - type: map_at_100
+      value: 20.508000000000003
+    - type: map_at_1000
+      value: 21.809
+    - type: map_at_3
+      value: 11.568000000000001
+    - type: map_at_5
+      value: 13.059000000000001
+    - type: mrr_at_1
+      value: 56.49999999999999
+    - type: mrr_at_10
+      value: 65.90899999999999
+    - type: mrr_at_100
+      value: 66.352
+    - type: mrr_at_1000
+      value: 66.369
+    - type: mrr_at_3
+      value: 64.0
+    - type: mrr_at_5
+      value: 65.10000000000001
+    - type: ndcg_at_1
+      value: 44.25
+    - type: ndcg_at_10
+      value: 32.649
+    - type: ndcg_at_100
+      value: 36.668
+    - type: ndcg_at_1000
+      value: 43.918
+    - type: ndcg_at_3
+      value: 37.096000000000004
+    - type: ndcg_at_5
+      value: 34.048
+    - type: precision_at_1
+      value: 56.49999999999999
+    - type: precision_at_10
+      value: 25.45
+    - type: precision_at_100
+      value: 8.055
+    - type: precision_at_1000
+      value: 1.7489999999999999
+    - type: precision_at_3
+      value: 41.0
+    - type: precision_at_5
+      value: 32.85
+    - type: recall_at_1
+      value: 7.794
+    - type: recall_at_10
+      value: 20.101
+    - type: recall_at_100
+      value: 42.448
+    - type: recall_at_1000
+      value: 65.88000000000001
+    - type: recall_at_3
+      value: 12.753
+    - type: recall_at_5
+      value: 15.307
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/emotion
+      name: MTEB EmotionClassification
+      config: default
+      split: test
+      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
+    metrics:
+    - type: accuracy
+      value: 44.01
+    - type: f1
+      value: 38.659680951114964
+  - task:
+      type: Retrieval
+    dataset:
+      type: fever
+      name: MTEB FEVER
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 49.713
+    - type: map_at_10
+      value: 61.79
+    - type: map_at_100
+      value: 62.28
+    - type: map_at_1000
+      value: 62.297000000000004
+    - type: map_at_3
+      value: 59.361
+    - type: map_at_5
+      value: 60.92100000000001
+    - type: mrr_at_1
+      value: 53.405
+    - type: mrr_at_10
+      value: 65.79899999999999
+    - type: mrr_at_100
+      value: 66.219
+    - type: mrr_at_1000
+      value: 66.227
+    - type: mrr_at_3
+      value: 63.431000000000004
+    - type: mrr_at_5
+      value: 64.98
+    - type: ndcg_at_1
+      value: 53.405
+    - type: ndcg_at_10
+      value: 68.01899999999999
+    - type: ndcg_at_100
+      value: 70.197
+    - type: ndcg_at_1000
+      value: 70.571
+    - type: ndcg_at_3
+      value: 63.352
+    - type: ndcg_at_5
+      value: 66.018
+    - type: precision_at_1
+      value: 53.405
+    - type: precision_at_10
+      value: 9.119
+    - type: precision_at_100
+      value: 1.03
+    - type: precision_at_1000
+      value: 0.107
+    - type: precision_at_3
+      value: 25.602999999999998
+    - type: precision_at_5
+      value: 16.835
+    - type: recall_at_1
+      value: 49.713
+    - type: recall_at_10
+      value: 83.306
+    - type: recall_at_100
+      value: 92.92
+    - type: recall_at_1000
+      value: 95.577
+    - type: recall_at_3
+      value: 70.798
+    - type: recall_at_5
+      value: 77.254
+  - task:
+      type: Retrieval
+    dataset:
+      type: fiqa
+      name: MTEB FiQA2018
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 15.310000000000002
+    - type: map_at_10
+      value: 26.204
+    - type: map_at_100
+      value: 27.932000000000002
+    - type: map_at_1000
+      value: 28.121000000000002
+    - type: map_at_3
+      value: 22.481
+    - type: map_at_5
+      value: 24.678
+    - type: mrr_at_1
+      value: 29.784
+    - type: mrr_at_10
+      value: 39.582
+    - type: mrr_at_100
+      value: 40.52
+    - type: mrr_at_1000
+      value: 40.568
+    - type: mrr_at_3
+      value: 37.114000000000004
+    - type: mrr_at_5
+      value: 38.596000000000004
+    - type: ndcg_at_1
+      value: 29.784
+    - type: ndcg_at_10
+      value: 33.432
+    - type: ndcg_at_100
+      value: 40.281
+    - type: ndcg_at_1000
+      value: 43.653999999999996
+    - type: ndcg_at_3
+      value: 29.612
+    - type: ndcg_at_5
+      value: 31.223
+    - type: precision_at_1
+      value: 29.784
+    - type: precision_at_10
+      value: 9.645
+    - type: precision_at_100
+      value: 1.645
+    - type: precision_at_1000
+      value: 0.22499999999999998
+    - type: precision_at_3
+      value: 20.165
+    - type: precision_at_5
+      value: 15.401000000000002
+    - type: recall_at_1
+      value: 15.310000000000002
+    - type: recall_at_10
+      value: 40.499
+    - type: recall_at_100
+      value: 66.643
+    - type: recall_at_1000
+      value: 87.059
+    - type: recall_at_3
+      value: 27.492
+    - type: recall_at_5
+      value: 33.748
+  - task:
+      type: Retrieval
+    dataset:
+      type: hotpotqa
+      name: MTEB HotpotQA
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 33.599000000000004
+    - type: map_at_10
+      value: 47.347
+    - type: map_at_100
+      value: 48.191
+    - type: map_at_1000
+      value: 48.263
+    - type: map_at_3
+      value: 44.698
+    - type: map_at_5
+      value: 46.278999999999996
+    - type: mrr_at_1
+      value: 67.19800000000001
+    - type: mrr_at_10
+      value: 74.054
+    - type: mrr_at_100
+      value: 74.376
+    - type: mrr_at_1000
+      value: 74.392
+    - type: mrr_at_3
+      value: 72.849
+    - type: mrr_at_5
+      value: 73.643
+    - type: ndcg_at_1
+      value: 67.19800000000001
+    - type: ndcg_at_10
+      value: 56.482
+    - type: ndcg_at_100
+      value: 59.694
+    - type: ndcg_at_1000
+      value: 61.204
+    - type: ndcg_at_3
+      value: 52.43299999999999
+    - type: ndcg_at_5
+      value: 54.608000000000004
+    - type: precision_at_1
+      value: 67.19800000000001
+    - type: precision_at_10
+      value: 11.613999999999999
+    - type: precision_at_100
+      value: 1.415
+    - type: precision_at_1000
+      value: 0.16199999999999998
+    - type: precision_at_3
+      value: 32.726
+    - type: precision_at_5
+      value: 21.349999999999998
+    - type: recall_at_1
+      value: 33.599000000000004
+    - type: recall_at_10
+      value: 58.069
+    - type: recall_at_100
+      value: 70.736
+    - type: recall_at_1000
+      value: 80.804
+    - type: recall_at_3
+      value: 49.088
+    - type: recall_at_5
+      value: 53.376000000000005
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/imdb
+      name: MTEB ImdbClassification
+      config: default
+      split: test
+      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
+    metrics:
+    - type: accuracy
+      value: 73.64359999999999
+    - type: ap
+      value: 67.54685976014599
+    - type: f1
+      value: 73.55148707559482
+  - task:
+      type: Retrieval
+    dataset:
+      type: msmarco
+      name: MTEB MSMARCO
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 19.502
+    - type: map_at_10
+      value: 30.816
+    - type: map_at_100
+      value: 32.007999999999996
+    - type: map_at_1000
+      value: 32.067
+    - type: map_at_3
+      value: 27.215
+    - type: map_at_5
+      value: 29.304000000000002
+    - type: mrr_at_1
+      value: 20.072000000000003
+    - type: mrr_at_10
+      value: 31.406
+    - type: mrr_at_100
+      value: 32.549
+    - type: mrr_at_1000
+      value: 32.602
+    - type: mrr_at_3
+      value: 27.839000000000002
+    - type: mrr_at_5
+      value: 29.926000000000002
+    - type: ndcg_at_1
+      value: 20.086000000000002
+    - type: ndcg_at_10
+      value: 37.282
+    - type: ndcg_at_100
+      value: 43.206
+    - type: ndcg_at_1000
+      value: 44.690000000000005
+    - type: ndcg_at_3
+      value: 29.932
+    - type: ndcg_at_5
+      value: 33.668
+    - type: precision_at_1
+      value: 20.086000000000002
+    - type: precision_at_10
+      value: 5.961
+    - type: precision_at_100
+      value: 0.898
+    - type: precision_at_1000
+      value: 0.10200000000000001
+    - type: precision_at_3
+      value: 12.856000000000002
+    - type: precision_at_5
+      value: 9.596
+    - type: recall_at_1
+      value: 19.502
+    - type: recall_at_10
+      value: 57.182
+    - type: recall_at_100
+      value: 84.952
+    - type: recall_at_1000
+      value: 96.34700000000001
+    - type: recall_at_3
+      value: 37.193
+    - type: recall_at_5
+      value: 46.157
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_domain
+      name: MTEB MTOPDomainClassification (en)
+      config: en
+      split: test
+      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
+    metrics:
+    - type: accuracy
+      value: 93.96488828089375
+    - type: f1
+      value: 93.32119260543482
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_intent
+      name: MTEB MTOPIntentClassification (en)
+      config: en
+      split: test
+      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
+    metrics:
+    - type: accuracy
+      value: 72.4965800273598
+    - type: f1
+      value: 49.34896217536082
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_intent
+      name: MTEB MassiveIntentClassification (en)
+      config: en
+      split: test
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+    metrics:
+    - type: accuracy
+      value: 67.60928043039678
+    - type: f1
+      value: 64.34244712074538
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (en)
+      config: en
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 69.75453934095493
+    - type: f1
+      value: 68.39224867489249
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-p2p
+      name: MTEB MedrxivClusteringP2P
+      config: default
+      split: test
+      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
+    metrics:
+    - type: v_measure
+      value: 31.862573504920082
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-s2s
+      name: MTEB MedrxivClusteringS2S
+      config: default
+      split: test
+      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
+    metrics:
+    - type: v_measure
+      value: 27.511123551196803
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/mind_small
+      name: MTEB MindSmallReranking
+      config: default
+      split: test
+      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
+    metrics:
+    - type: map
+      value: 30.99145104942086
+    - type: mrr
+      value: 32.03606480418627
+  - task:
+      type: Retrieval
+    dataset:
+      type: nfcorpus
+      name: MTEB NFCorpus
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 5.015
+    - type: map_at_10
+      value: 11.054
+    - type: map_at_100
+      value: 13.773
+    - type: map_at_1000
+      value: 15.082999999999998
+    - type: map_at_3
+      value: 8.253
+    - type: map_at_5
+      value: 9.508999999999999
+    - type: mrr_at_1
+      value: 42.105
+    - type: mrr_at_10
+      value: 50.44499999999999
+    - type: mrr_at_100
+      value: 51.080000000000005
+    - type: mrr_at_1000
+      value: 51.129999999999995
+    - type: mrr_at_3
+      value: 48.555
+    - type: mrr_at_5
+      value: 49.84
+    - type: ndcg_at_1
+      value: 40.402
+    - type: ndcg_at_10
+      value: 30.403000000000002
+    - type: ndcg_at_100
+      value: 28.216
+    - type: ndcg_at_1000
+      value: 37.021
+    - type: ndcg_at_3
+      value: 35.53
+    - type: ndcg_at_5
+      value: 33.202999999999996
+    - type: precision_at_1
+      value: 42.105
+    - type: precision_at_10
+      value: 22.353
+    - type: precision_at_100
+      value: 7.266
+    - type: precision_at_1000
+      value: 2.011
+    - type: precision_at_3
+      value: 32.921
+    - type: precision_at_5
+      value: 28.297
+    - type: recall_at_1
+      value: 5.015
+    - type: recall_at_10
+      value: 14.393
+    - type: recall_at_100
+      value: 28.893
+    - type: recall_at_1000
+      value: 60.18
+    - type: recall_at_3
+      value: 9.184000000000001
+    - type: recall_at_5
+      value: 11.39
+  - task:
+      type: Retrieval
+    dataset:
+      type: nq
+      name: MTEB NQ
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 29.524
+    - type: map_at_10
+      value: 44.182
+    - type: map_at_100
+      value: 45.228
+    - type: map_at_1000
+      value: 45.265
+    - type: map_at_3
+      value: 39.978
+    - type: map_at_5
+      value: 42.482
+    - type: mrr_at_1
+      value: 33.256
+    - type: mrr_at_10
+      value: 46.661
+    - type: mrr_at_100
+      value: 47.47
+    - type: mrr_at_1000
+      value: 47.496
+    - type: mrr_at_3
+      value: 43.187999999999995
+    - type: mrr_at_5
+      value: 45.330999999999996
+    - type: ndcg_at_1
+      value: 33.227000000000004
+    - type: ndcg_at_10
+      value: 51.589
+    - type: ndcg_at_100
+      value: 56.043
+    - type: ndcg_at_1000
+      value: 56.937000000000005
+    - type: ndcg_at_3
+      value: 43.751
+    - type: ndcg_at_5
+      value: 47.937000000000005
+    - type: precision_at_1
+      value: 33.227000000000004
+    - type: precision_at_10
+      value: 8.556999999999999
+    - type: precision_at_100
+      value: 1.103
+    - type: precision_at_1000
+      value: 0.11900000000000001
+    - type: precision_at_3
+      value: 19.921
+    - type: precision_at_5
+      value: 14.396999999999998
+    - type: recall_at_1
+      value: 29.524
+    - type: recall_at_10
+      value: 71.615
+    - type: recall_at_100
+      value: 91.056
+    - type: recall_at_1000
+      value: 97.72800000000001
+    - type: recall_at_3
+      value: 51.451
+    - type: recall_at_5
+      value: 61.119
+  - task:
+      type: Retrieval
+    dataset:
+      type: quora
+      name: MTEB QuoraRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 69.596
+    - type: map_at_10
+      value: 83.281
+    - type: map_at_100
+      value: 83.952
+    - type: map_at_1000
+      value: 83.97200000000001
+    - type: map_at_3
+      value: 80.315
+    - type: map_at_5
+      value: 82.223
+    - type: mrr_at_1
+      value: 80.17
+    - type: mrr_at_10
+      value: 86.522
+    - type: mrr_at_100
+      value: 86.644
+    - type: mrr_at_1000
+      value: 86.64500000000001
+    - type: mrr_at_3
+      value: 85.438
+    - type: mrr_at_5
+      value: 86.21799999999999
+    - type: ndcg_at_1
+      value: 80.19
+    - type: ndcg_at_10
+      value: 87.19
+    - type: ndcg_at_100
+      value: 88.567
+    - type: ndcg_at_1000
+      value: 88.70400000000001
+    - type: ndcg_at_3
+      value: 84.17999999999999
+    - type: ndcg_at_5
+      value: 85.931
+    - type: precision_at_1
+      value: 80.19
+    - type: precision_at_10
+      value: 13.209000000000001
+    - type: precision_at_100
+      value: 1.518
+    - type: precision_at_1000
+      value: 0.157
+    - type: precision_at_3
+      value: 36.717
+    - type: precision_at_5
+      value: 24.248
+    - type: recall_at_1
+      value: 69.596
+    - type: recall_at_10
+      value: 94.533
+    - type: recall_at_100
+      value: 99.322
+    - type: recall_at_1000
+      value: 99.965
+    - type: recall_at_3
+      value: 85.911
+    - type: recall_at_5
+      value: 90.809
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering
+      name: MTEB RedditClustering
+      config: default
+      split: test
+      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
+    metrics:
+    - type: v_measure
+      value: 49.27650627571912
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering-p2p
+      name: MTEB RedditClusteringP2P
+      config: default
+      split: test
+      revision: 282350215ef01743dc01b456c7f5241fa8937f16
+    metrics:
+    - type: v_measure
+      value: 57.08550946534183
+  - task:
+      type: Retrieval
+    dataset:
+      type: scidocs
+      name: MTEB SCIDOCS
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 4.568
+    - type: map_at_10
+      value: 10.862
+    - type: map_at_100
+      value: 12.757
+    - type: map_at_1000
+      value: 13.031
+    - type: map_at_3
+      value: 7.960000000000001
+    - type: map_at_5
+      value: 9.337
+    - type: mrr_at_1
+      value: 22.5
+    - type: mrr_at_10
+      value: 32.6
+    - type: mrr_at_100
+      value: 33.603
+    - type: mrr_at_1000
+      value: 33.672000000000004
+    - type: mrr_at_3
+      value: 29.299999999999997
+    - type: mrr_at_5
+      value: 31.25
+    - type: ndcg_at_1
+      value: 22.5
+    - type: ndcg_at_10
+      value: 18.605
+    - type: ndcg_at_100
+      value: 26.029999999999998
+    - type: ndcg_at_1000
+      value: 31.256
+    - type: ndcg_at_3
+      value: 17.873
+    - type: ndcg_at_5
+      value: 15.511
+    - type: precision_at_1
+      value: 22.5
+    - type: precision_at_10
+      value: 9.58
+    - type: precision_at_100
+      value: 2.033
+    - type: precision_at_1000
+      value: 0.33
+    - type: precision_at_3
+      value: 16.633
+    - type: precision_at_5
+      value: 13.54
+    - type: recall_at_1
+      value: 4.568
+    - type: recall_at_10
+      value: 19.402
+    - type: recall_at_100
+      value: 41.277
+    - type: recall_at_1000
+      value: 66.963
+    - type: recall_at_3
+      value: 10.112
+    - type: recall_at_5
+      value: 13.712
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sickr-sts
+      name: MTEB SICK-R
+      config: default
+      split: test
+      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
+    metrics:
+    - type: cos_sim_pearson
+      value: 83.31992291680787
+    - type: cos_sim_spearman
+      value: 76.7212346922664
+    - type: euclidean_pearson
+      value: 80.42189271706478
+    - type: euclidean_spearman
+      value: 76.7212342532493
+    - type: manhattan_pearson
+      value: 80.33171093031578
+    - type: manhattan_spearman
+      value: 76.63192883074694
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts12-sts
+      name: MTEB STS12
+      config: default
+      split: test
+      revision: a0d554a64d88156834ff5ae9920b964011b16384
+    metrics:
+    - type: cos_sim_pearson
+      value: 83.16654278886763
+    - type: cos_sim_spearman
+      value: 73.66390263429565
+    - type: euclidean_pearson
+      value: 79.7485360086639
+    - type: euclidean_spearman
+      value: 73.66389870373436
+    - type: manhattan_pearson
+      value: 79.73652237443706
+    - type: manhattan_spearman
+      value: 73.65296117151647
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts13-sts
+      name: MTEB STS13
+      config: default
+      split: test
+      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
+    metrics:
+    - type: cos_sim_pearson
+      value: 82.40389689929246
+    - type: cos_sim_spearman
+      value: 83.29727595993955
+    - type: euclidean_pearson
+      value: 82.23970587854079
+    - type: euclidean_spearman
+      value: 83.29727595993955
+    - type: manhattan_pearson
+      value: 82.18823600831897
+    - type: manhattan_spearman
+      value: 83.20746192209594
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts14-sts
+      name: MTEB STS14
+      config: default
+      split: test
+      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
+    metrics:
+    - type: cos_sim_pearson
+      value: 81.73505246913413
+    - type: cos_sim_spearman
+      value: 79.1686548248754
+    - type: euclidean_pearson
+      value: 80.48889135993412
+    - type: euclidean_spearman
+      value: 79.16864112930354
+    - type: manhattan_pearson
+      value: 80.40720651057302
+    - type: manhattan_spearman
+      value: 79.0640155089286
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts15-sts
+      name: MTEB STS15
+      config: default
+      split: test
+      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
+    metrics:
+    - type: cos_sim_pearson
+      value: 86.3953512879065
+    - type: cos_sim_spearman
+      value: 87.29947322714338
+    - type: euclidean_pearson
+      value: 86.59759438529645
+    - type: euclidean_spearman
+      value: 87.29947511092824
+    - type: manhattan_pearson
+      value: 86.52097806169155
+    - type: manhattan_spearman
+      value: 87.22987242146534
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts16-sts
+      name: MTEB STS16
+      config: default
+      split: test
+      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
+    metrics:
+    - type: cos_sim_pearson
+      value: 82.48565753792056
+    - type: cos_sim_spearman
+      value: 83.6049720319893
+    - type: euclidean_pearson
+      value: 82.56452023172913
+    - type: euclidean_spearman
+      value: 83.60490168191697
+    - type: manhattan_pearson
+      value: 82.58079941137872
+    - type: manhattan_spearman
+      value: 83.60975807374051
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts17-crosslingual-sts
+      name: MTEB STS17 (en-en)
+      config: en-en
+      split: test
+      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
+    metrics:
+    - type: cos_sim_pearson
+      value: 88.18239976618212
+    - type: cos_sim_spearman
+      value: 88.23061724730616
+    - type: euclidean_pearson
+      value: 87.78482472776658
+    - type: euclidean_spearman
+      value: 88.23061724730616
+    - type: manhattan_pearson
+      value: 87.75059641730239
+    - type: manhattan_spearman
+      value: 88.22527413524622
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts22-crosslingual-sts
+      name: MTEB STS22 (en)
+      config: en
+      split: test
+      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
+    metrics:
+    - type: cos_sim_pearson
+      value: 63.42816418706765
+    - type: cos_sim_spearman
+      value: 63.4569864520124
+    - type: euclidean_pearson
+      value: 64.35405409953853
+    - type: euclidean_spearman
+      value: 63.4569864520124
+    - type: manhattan_pearson
+      value: 63.96649236073056
+    - type: manhattan_spearman
+      value: 63.01448583722708
+  - task:
+      type: STS
+    dataset:
+      type: mteb/stsbenchmark-sts
+      name: MTEB STSBenchmark
+      config: default
+      split: test
+      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
+    metrics:
+    - type: cos_sim_pearson
+      value: 83.41659638047614
+    - type: cos_sim_spearman
+      value: 84.03893866106175
+    - type: euclidean_pearson
+      value: 84.2251203953798
+    - type: euclidean_spearman
+      value: 84.03893866106175
+    - type: manhattan_pearson
+      value: 84.22733643205514
+    - type: manhattan_spearman
+      value: 84.06504411263612
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/scidocs-reranking
+      name: MTEB SciDocsRR
+      config: default
+      split: test
+      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
+    metrics:
+    - type: map
+      value: 79.75608022582414
+    - type: mrr
+      value: 94.0947732369301
+  - task:
+      type: Retrieval
+    dataset:
+      type: scifact
+      name: MTEB SciFact
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 50.161
+    - type: map_at_10
+      value: 59.458999999999996
+    - type: map_at_100
+      value: 60.156
+    - type: map_at_1000
+      value: 60.194
+    - type: map_at_3
+      value: 56.45400000000001
+    - type: map_at_5
+      value: 58.165
+    - type: mrr_at_1
+      value: 53.333
+    - type: mrr_at_10
+      value: 61.050000000000004
+    - type: mrr_at_100
+      value: 61.586
+    - type: mrr_at_1000
+      value: 61.624
+    - type: mrr_at_3
+      value: 58.889
+    - type: mrr_at_5
+      value: 60.122
+    - type: ndcg_at_1
+      value: 53.333
+    - type: ndcg_at_10
+      value: 63.888999999999996
+    - type: ndcg_at_100
+      value: 66.963
+    - type: ndcg_at_1000
+      value: 68.062
+    - type: ndcg_at_3
+      value: 59.01
+    - type: ndcg_at_5
+      value: 61.373999999999995
+    - type: precision_at_1
+      value: 53.333
+    - type: precision_at_10
+      value: 8.633000000000001
+    - type: precision_at_100
+      value: 1.027
+    - type: precision_at_1000
+      value: 0.11199999999999999
+    - type: precision_at_3
+      value: 23.111
+    - type: precision_at_5
+      value: 15.467
+    - type: recall_at_1
+      value: 50.161
+    - type: recall_at_10
+      value: 75.922
+    - type: recall_at_100
+      value: 90.0
+    - type: recall_at_1000
+      value: 98.667
+    - type: recall_at_3
+      value: 62.90599999999999
+    - type: recall_at_5
+      value: 68.828
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/sprintduplicatequestions-pairclassification
+      name: MTEB SprintDuplicateQuestions
+      config: default
+      split: test
+      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
+    metrics:
+    - type: cos_sim_accuracy
+      value: 99.81188118811882
+    - type: cos_sim_ap
+      value: 95.11619225962413
+    - type: cos_sim_f1
+      value: 90.35840484603736
+    - type: cos_sim_precision
+      value: 91.23343527013252
+    - type: cos_sim_recall
+      value: 89.5
+    - type: dot_accuracy
+      value: 99.81188118811882
+    - type: dot_ap
+      value: 95.11619225962413
+    - type: dot_f1
+      value: 90.35840484603736
+    - type: dot_precision
+      value: 91.23343527013252
+    - type: dot_recall
+      value: 89.5
+    - type: euclidean_accuracy
+      value: 99.81188118811882
+    - type: euclidean_ap
+      value: 95.11619225962413
+    - type: euclidean_f1
+      value: 90.35840484603736
+    - type: euclidean_precision
+      value: 91.23343527013252
+    - type: euclidean_recall
+      value: 89.5
+    - type: manhattan_accuracy
+      value: 99.80891089108911
+    - type: manhattan_ap
+      value: 95.07294266220966
+    - type: manhattan_f1
+      value: 90.21794221996959
+    - type: manhattan_precision
+      value: 91.46968139773895
+    - type: manhattan_recall
+      value: 89.0
+    - type: max_accuracy
+      value: 99.81188118811882
+    - type: max_ap
+      value: 95.11619225962413
+    - type: max_f1
+      value: 90.35840484603736
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering
+      name: MTEB StackExchangeClustering
+      config: default
+      split: test
+      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
+    metrics:
+    - type: v_measure
+      value: 55.3481874105239
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering-p2p
+      name: MTEB StackExchangeClusteringP2P
+      config: default
+      split: test
+      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
+    metrics:
+    - type: v_measure
+      value: 34.421291695525
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/stackoverflowdupquestions-reranking
+      name: MTEB StackOverflowDupQuestions
+      config: default
+      split: test
+      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
+    metrics:
+    - type: map
+      value: 49.98746633276634
+    - type: mrr
+      value: 50.63143249724133
+  - task:
+      type: Summarization
+    dataset:
+      type: mteb/summeval
+      name: MTEB SummEval
+      config: default
+      split: test
+      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
+    metrics:
+    - type: cos_sim_pearson
+      value: 31.009961979844036
+    - type: cos_sim_spearman
+      value: 30.558416108881044
+    - type: dot_pearson
+      value: 31.009964941134253
+    - type: dot_spearman
+      value: 30.545760761761393
+  - task:
+      type: Retrieval
+    dataset:
+      type: trec-covid
+      name: MTEB TRECCOVID
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 0.207
+    - type: map_at_10
+      value: 1.6
+    - type: map_at_100
+      value: 8.594
+    - type: map_at_1000
+      value: 20.213
+    - type: map_at_3
+      value: 0.585
+    - type: map_at_5
+      value: 0.9039999999999999
+    - type: mrr_at_1
+      value: 78.0
+    - type: mrr_at_10
+      value: 87.4
+    - type: mrr_at_100
+      value: 87.4
+    - type: mrr_at_1000
+      value: 87.4
+    - type: mrr_at_3
+      value: 86.667
+    - type: mrr_at_5
+      value: 87.06700000000001
+    - type: ndcg_at_1
+      value: 73.0
+    - type: ndcg_at_10
+      value: 65.18
+    - type: ndcg_at_100
+      value: 49.631
+    - type: ndcg_at_1000
+      value: 43.498999999999995
+    - type: ndcg_at_3
+      value: 71.83800000000001
+    - type: ndcg_at_5
+      value: 69.271
+    - type: precision_at_1
+      value: 78.0
+    - type: precision_at_10
+      value: 69.19999999999999
+    - type: precision_at_100
+      value: 50.980000000000004
+    - type: precision_at_1000
+      value: 19.426
+    - type: precision_at_3
+      value: 77.333
+    - type: precision_at_5
+      value: 74.0
+    - type: recall_at_1
+      value: 0.207
+    - type: recall_at_10
+      value: 1.822
+    - type: recall_at_100
+      value: 11.849
+    - type: recall_at_1000
+      value: 40.492
+    - type: recall_at_3
+      value: 0.622
+    - type: recall_at_5
+      value: 0.9809999999999999
+  - task:
+      type: Retrieval
+    dataset:
+      type: webis-touche2020
+      name: MTEB Touche2020
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 2.001
+    - type: map_at_10
+      value: 10.376000000000001
+    - type: map_at_100
+      value: 16.936999999999998
+    - type: map_at_1000
+      value: 18.615000000000002
+    - type: map_at_3
+      value: 5.335999999999999
+    - type: map_at_5
+      value: 7.374
+    - type: mrr_at_1
+      value: 20.408
+    - type: mrr_at_10
+      value: 38.29
+    - type: mrr_at_100
+      value: 39.33
+    - type: mrr_at_1000
+      value: 39.347
+    - type: mrr_at_3
+      value: 32.993
+    - type: mrr_at_5
+      value: 36.973
+    - type: ndcg_at_1
+      value: 17.347
+    - type: ndcg_at_10
+      value: 23.515
+    - type: ndcg_at_100
+      value: 37.457
+    - type: ndcg_at_1000
+      value: 49.439
+    - type: ndcg_at_3
+      value: 22.762999999999998
+    - type: ndcg_at_5
+      value: 22.622
+    - type: precision_at_1
+      value: 20.408
+    - type: precision_at_10
+      value: 22.448999999999998
+    - type: precision_at_100
+      value: 8.184
+    - type: precision_at_1000
+      value: 1.608
+    - type: precision_at_3
+      value: 25.85
+    - type: precision_at_5
+      value: 25.306
+    - type: recall_at_1
+      value: 2.001
+    - type: recall_at_10
+      value: 17.422
+    - type: recall_at_100
+      value: 51.532999999999994
+    - type: recall_at_1000
+      value: 87.466
+    - type: recall_at_3
+      value: 6.861000000000001
+    - type: recall_at_5
+      value: 10.502
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/toxic_conversations_50k
+      name: MTEB ToxicConversationsClassification
+      config: default
+      split: test
+      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
+    metrics:
+    - type: accuracy
+      value: 71.54419999999999
+    - type: ap
+      value: 14.372170450843907
+    - type: f1
+      value: 54.94420257390529
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/tweet_sentiment_extraction
+      name: MTEB TweetSentimentExtractionClassification
+      config: default
+      split: test
+      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
+    metrics:
+    - type: accuracy
+      value: 59.402942840973395
+    - type: f1
+      value: 59.4166538875571
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/twentynewsgroups-clustering
+      name: MTEB TwentyNewsgroupsClustering
+      config: default
+      split: test
+      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
+    metrics:
+    - type: v_measure
+      value: 41.569064336457906
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twittersemeval2015-pairclassification
+      name: MTEB TwitterSemEval2015
+      config: default
+      split: test
+      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
+    metrics:
+    - type: cos_sim_accuracy
+      value: 85.31322644096085
+    - type: cos_sim_ap
+      value: 72.14518894837381
+    - type: cos_sim_f1
+      value: 66.67489813557229
+    - type: cos_sim_precision
+      value: 62.65954977953121
+    - type: cos_sim_recall
+      value: 71.2401055408971
+    - type: dot_accuracy
+      value: 85.31322644096085
+    - type: dot_ap
+      value: 72.14521480685293
+    - type: dot_f1
+      value: 66.67489813557229
+    - type: dot_precision
+      value: 62.65954977953121
+    - type: dot_recall
+      value: 71.2401055408971
+    - type: euclidean_accuracy
+      value: 85.31322644096085
+    - type: euclidean_ap
+      value: 72.14520820485349
+    - type: euclidean_f1
+      value: 66.67489813557229
+    - type: euclidean_precision
+      value: 62.65954977953121
+    - type: euclidean_recall
+      value: 71.2401055408971
+    - type: manhattan_accuracy
+      value: 85.21785778148656
+    - type: manhattan_ap
+      value: 72.01177147657364
+    - type: manhattan_f1
+      value: 66.62594673833374
+    - type: manhattan_precision
+      value: 62.0336669699727
+    - type: manhattan_recall
+      value: 71.95250659630607
+    - type: max_accuracy
+      value: 85.31322644096085
+    - type: max_ap
+      value: 72.14521480685293
+    - type: max_f1
+      value: 66.67489813557229
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twitterurlcorpus-pairclassification
+      name: MTEB TwitterURLCorpus
+      config: default
+      split: test
+      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
+    metrics:
+    - type: cos_sim_accuracy
+      value: 89.12756626693057
+    - type: cos_sim_ap
+      value: 86.05430786440826
+    - type: cos_sim_f1
+      value: 78.27759692216631
+    - type: cos_sim_precision
+      value: 75.33466248931929
+    - type: cos_sim_recall
+      value: 81.45980905451185
+    - type: dot_accuracy
+      value: 89.12950673341872
+    - type: dot_ap
+      value: 86.05431161145492
+    - type: dot_f1
+      value: 78.27759692216631
+    - type: dot_precision
+      value: 75.33466248931929
+    - type: dot_recall
+      value: 81.45980905451185
+    - type: euclidean_accuracy
+      value: 89.12756626693057
+    - type: euclidean_ap
+      value: 86.05431303247397
+    - type: euclidean_f1
+      value: 78.27759692216631
+    - type: euclidean_precision
+      value: 75.33466248931929
+    - type: euclidean_recall
+      value: 81.45980905451185
+    - type: manhattan_accuracy
+      value: 89.04994760740482
+    - type: manhattan_ap
+      value: 86.00860610892074
+    - type: manhattan_f1
+      value: 78.1846776005392
+    - type: manhattan_precision
+      value: 76.10438839480975
+    - type: manhattan_recall
+      value: 80.3818909762858
+    - type: max_accuracy
+      value: 89.12950673341872
+    - type: max_ap
+      value: 86.05431303247397
+    - type: max_f1
+      value: 78.27759692216631
+---
+<!-- TODO: add evaluation results here -->
+<br><br>
+
+<p align="center">
+<img src="https://huggingface.co/datasets/jinaai/documentation-images/resolve/main/logo.webp" alt="Jina AI: Your Search Foundation, Supercharged!" 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-small-en` is to use Jina AI's [Embedding API](https://jina.ai/embeddings/).
+
+
+## Intended Usage & Model Info
+
+`jina-embeddings-v2-small-en` is an English, monolingual **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.
+The backbone `jina-bert-v2-small-en` is pretrained on the C4 dataset.
+The model is further trained on Jina AI's collection of more than 400 millions of sentence pairs and hard negatives.
+These pairs were obtained from various domains and were carefully selected through a thorough cleaning process.
+
+The embedding model was trained using 512 sequence length, but extrapolates to 8k sequence length (or even longer) thanks to ALiBi.
+This makes our model useful for a range of use cases, especially when processing long documents is needed, including long document retrieval, semantic textual similarity, text reranking, recommendation, RAG and LLM-based generative search, etc.
+
+This model has 33 million parameters, which enables lightning-fast and memory efficient inference, while still delivering impressive performance.
+Additionally, we provide the following embedding models:
+
+- [`jina-embeddings-v2-small-en`](https://huggingface.co/jinaai/jina-embeddings-v2-small-en): 33 million parameters **(you are here)**.
+- [`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.
+- [`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).
+
+## Data & Parameters
+
+Jina Embeddings V2 [technical report](https://arxiv.org/abs/2310.19923)
+
+## 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?', 'What is the current weather like today?']
+
+tokenizer = AutoTokenizer.from_pretrained('jinaai/jina-embeddings-v2-small-en')
+model = AutoModel.from_pretrained('jinaai/jina-embeddings-v2-small-en', trust_remote_code=True)
+
+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
+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-small-en', trust_remote_code=True) # trust_remote_code is needed to use the encode method
+embeddings = model.encode(['How is the weather today?', 'What is the current weather like today?'])
+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
+)
+```
+
+The latest sentence-transformers also supports Jina embeddings:
+
+```python
+!pip install -U sentence-transformers
+from sentence_transformers import SentenceTransformer
+from sentence_transformers.util import cos_sim
+
+model = SentenceTransformer(
+    "jinaai/jina-embeddings-v2-small-en", # 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?',
+    'What is the current weather like today?'
+])
+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).
+
+## RAG Performance
+
+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">
+
+## Plans
+
+1. Bilingual embedding models supporting more European & Asian languages, including Spanish, French, Italian and Japanese.
+2. Multimodal embedding models enable Multimodal RAG applications.
+3. High-performt rerankers.
+
+## 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-en 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', ...
+```
+
+## 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:
+
+```
+@misc{günther2023jina,
+      title={Jina Embeddings 2: 8192-Token General-Purpose Text Embeddings for Long Documents}, 
+      author={Michael Günther and Jackmin Ong and Isabelle Mohr and Alaeddine Abdessalem and Tanguy Abel and Mohammad Kalim Akram and Susana Guzman and Georgios Mastrapas and Saba Sturua and Bo Wang and Maximilian Werk and Nan Wang and Han Xiao},
+      year={2023},
+      eprint={2310.19923},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```

config.json

@@ -0,0 +1,36 @@
+{
+  "_name_or_path": "jinaai/jina-embeddings-v2-small-en",
+  "architectures": [
+    "JinaBertModel"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "attn_implementation": null,
+  "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",
+    "AutoModelForSequenceClassification": "jinaai/jina-bert-implementation--modeling_bert.JinaBertForSequenceClassification"
+  },
+  "classifier_dropout": null,
+  "emb_pooler": "mean",
+  "feed_forward_type": "geglu",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 512,
+  "initializer_range": 0.02,
+  "intermediate_size": 2048,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 8192,
+  "model_max_length": 8192,
+  "model_type": "bert",
+  "num_attention_heads": 8,
+  "num_hidden_layers": 4,
+  "pad_token_id": 0,
+  "position_embedding_type": "alibi",
+  "torch_dtype": "float32",
+  "transformers_version": "4.48.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30528
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.4.1",
+    "transformers": "4.48.1",
+    "pytorch": "2.6.0+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:89980b047a2c2baeabafc878fd66061305a6a6a62092375a9192d326ce89a089
+size 130769960

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 8192,
+  "do_lower_case": false
+}

special_tokens_map.json

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

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 8192,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}