Upload 13 files

1_Pooling/config.json

@@ -0,0 +1,9 @@
+{
+    "word_embedding_dimension": 768,
+    "pooling_mode_cls_token": false,
+    "pooling_mode_mean_tokens": true,
+    "pooling_mode_max_tokens": false,
+    "pooling_mode_mean_sqrt_len_tokens": false,
+    "pooling_mode_weightedmean_tokens": false,
+    "pooling_mode_lasttoken": false
+  }

README.md

@@ -1,3 +1,2851 @@
 ---
+library_name: sentence-transformers
+pipeline_tag: sentence-similarity
+tags:
+- feature-extraction
+- sentence-similarity
+- mteb
+- transformers
+- transformers.js
+model-index:
+- name: epoch_0_model
+  results:
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_counterfactual
+      name: MTEB AmazonCounterfactualClassification (en)
+      config: en
+      split: test
+      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
+    metrics:
+    - type: accuracy
+      value: 75.20895522388058
+    - type: ap
+      value: 38.57605549557802
+    - type: f1
+      value: 69.35586565857854
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_polarity
+      name: MTEB AmazonPolarityClassification
+      config: default
+      split: test
+      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
+    metrics:
+    - type: accuracy
+      value: 91.8144
+    - type: ap
+      value: 88.65222882032363
+    - type: f1
+      value: 91.80426301643274
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_reviews_multi
+      name: MTEB AmazonReviewsClassification (en)
+      config: en
+      split: test
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+    metrics:
+    - type: accuracy
+      value: 47.162000000000006
+    - type: f1
+      value: 46.59329642263158
+  - task:
+      type: Retrieval
+    dataset:
+      type: arguana
+      name: MTEB ArguAna
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 24.253
+    - type: map_at_10
+      value: 38.962
+    - type: map_at_100
+      value: 40.081
+    - type: map_at_1000
+      value: 40.089000000000006
+    - type: map_at_3
+      value: 33.499
+    - type: map_at_5
+      value: 36.351
+    - type: mrr_at_1
+      value: 24.609
+    - type: mrr_at_10
+      value: 39.099000000000004
+    - type: mrr_at_100
+      value: 40.211000000000006
+    - type: mrr_at_1000
+      value: 40.219
+    - type: mrr_at_3
+      value: 33.677
+    - type: mrr_at_5
+      value: 36.469
+    - type: ndcg_at_1
+      value: 24.253
+    - type: ndcg_at_10
+      value: 48.010999999999996
+    - type: ndcg_at_100
+      value: 52.756
+    - type: ndcg_at_1000
+      value: 52.964999999999996
+    - type: ndcg_at_3
+      value: 36.564
+    - type: ndcg_at_5
+      value: 41.711999999999996
+    - type: precision_at_1
+      value: 24.253
+    - type: precision_at_10
+      value: 7.738
+    - type: precision_at_100
+      value: 0.98
+    - type: precision_at_1000
+      value: 0.1
+    - type: precision_at_3
+      value: 15.149000000000001
+    - type: precision_at_5
+      value: 11.593
+    - type: recall_at_1
+      value: 24.253
+    - type: recall_at_10
+      value: 77.383
+    - type: recall_at_100
+      value: 98.009
+    - type: recall_at_1000
+      value: 99.644
+    - type: recall_at_3
+      value: 45.448
+    - type: recall_at_5
+      value: 57.965999999999994
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-p2p
+      name: MTEB ArxivClusteringP2P
+      config: default
+      split: test
+      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
+    metrics:
+    - type: v_measure
+      value: 45.69069567851087
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-s2s
+      name: MTEB ArxivClusteringS2S
+      config: default
+      split: test
+      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
+    metrics:
+    - type: v_measure
+      value: 36.35185490976283
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/askubuntudupquestions-reranking
+      name: MTEB AskUbuntuDupQuestions
+      config: default
+      split: test
+      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
+    metrics:
+    - type: map
+      value: 61.71274951450321
+    - type: mrr
+      value: 76.06032625423207
+  - task:
+      type: STS
+    dataset:
+      type: mteb/biosses-sts
+      name: MTEB BIOSSES
+      config: default
+      split: test
+      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
+    metrics:
+    - type: cos_sim_pearson
+      value: 86.73980520022269
+    - type: cos_sim_spearman
+      value: 84.24649792685918
+    - type: euclidean_pearson
+      value: 85.85197641158186
+    - type: euclidean_spearman
+      value: 84.24649792685918
+    - type: manhattan_pearson
+      value: 86.26809552711346
+    - type: manhattan_spearman
+      value: 84.56397504030865
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/banking77
+      name: MTEB Banking77Classification
+      config: default
+      split: test
+      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
+    metrics:
+    - type: accuracy
+      value: 84.25324675324674
+    - type: f1
+      value: 84.17872280892557
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-p2p
+      name: MTEB BiorxivClusteringP2P
+      config: default
+      split: test
+      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
+    metrics:
+    - type: v_measure
+      value: 38.770253446400886
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-s2s
+      name: MTEB BiorxivClusteringS2S
+      config: default
+      split: test
+      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
+    metrics:
+    - type: v_measure
+      value: 32.94307095497281
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackAndroidRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 32.164
+    - type: map_at_10
+      value: 42.641
+    - type: map_at_100
+      value: 43.947
+    - type: map_at_1000
+      value: 44.074999999999996
+    - type: map_at_3
+      value: 39.592
+    - type: map_at_5
+      value: 41.204
+    - type: mrr_at_1
+      value: 39.628
+    - type: mrr_at_10
+      value: 48.625
+    - type: mrr_at_100
+      value: 49.368
+    - type: mrr_at_1000
+      value: 49.413000000000004
+    - type: mrr_at_3
+      value: 46.400000000000006
+    - type: mrr_at_5
+      value: 47.68
+    - type: ndcg_at_1
+      value: 39.628
+    - type: ndcg_at_10
+      value: 48.564
+    - type: ndcg_at_100
+      value: 53.507000000000005
+    - type: ndcg_at_1000
+      value: 55.635999999999996
+    - type: ndcg_at_3
+      value: 44.471
+    - type: ndcg_at_5
+      value: 46.137
+    - type: precision_at_1
+      value: 39.628
+    - type: precision_at_10
+      value: 8.856
+    - type: precision_at_100
+      value: 1.429
+    - type: precision_at_1000
+      value: 0.191
+    - type: precision_at_3
+      value: 21.268
+    - type: precision_at_5
+      value: 14.649000000000001
+    - type: recall_at_1
+      value: 32.164
+    - type: recall_at_10
+      value: 59.609
+    - type: recall_at_100
+      value: 80.521
+    - type: recall_at_1000
+      value: 94.245
+    - type: recall_at_3
+      value: 46.521
+    - type: recall_at_5
+      value: 52.083999999999996
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackEnglishRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 31.526
+    - type: map_at_10
+      value: 41.581
+    - type: map_at_100
+      value: 42.815999999999995
+    - type: map_at_1000
+      value: 42.936
+    - type: map_at_3
+      value: 38.605000000000004
+    - type: map_at_5
+      value: 40.351
+    - type: mrr_at_1
+      value: 39.489999999999995
+    - type: mrr_at_10
+      value: 47.829
+    - type: mrr_at_100
+      value: 48.512
+    - type: mrr_at_1000
+      value: 48.552
+    - type: mrr_at_3
+      value: 45.754
+    - type: mrr_at_5
+      value: 46.986
+    - type: ndcg_at_1
+      value: 39.489999999999995
+    - type: ndcg_at_10
+      value: 47.269
+    - type: ndcg_at_100
+      value: 51.564
+    - type: ndcg_at_1000
+      value: 53.53099999999999
+    - type: ndcg_at_3
+      value: 43.301
+    - type: ndcg_at_5
+      value: 45.239000000000004
+    - type: precision_at_1
+      value: 39.489999999999995
+    - type: precision_at_10
+      value: 8.93
+    - type: precision_at_100
+      value: 1.415
+    - type: precision_at_1000
+      value: 0.188
+    - type: precision_at_3
+      value: 20.892
+    - type: precision_at_5
+      value: 14.865999999999998
+    - type: recall_at_1
+      value: 31.526
+    - type: recall_at_10
+      value: 56.76
+    - type: recall_at_100
+      value: 75.029
+    - type: recall_at_1000
+      value: 87.491
+    - type: recall_at_3
+      value: 44.786
+    - type: recall_at_5
+      value: 50.254
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGamingRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 40.987
+    - type: map_at_10
+      value: 52.827
+    - type: map_at_100
+      value: 53.751000000000005
+    - type: map_at_1000
+      value: 53.81
+    - type: map_at_3
+      value: 49.844
+    - type: map_at_5
+      value: 51.473
+    - type: mrr_at_1
+      value: 46.833999999999996
+    - type: mrr_at_10
+      value: 56.389
+    - type: mrr_at_100
+      value: 57.003
+    - type: mrr_at_1000
+      value: 57.034
+    - type: mrr_at_3
+      value: 54.17999999999999
+    - type: mrr_at_5
+      value: 55.486999999999995
+    - type: ndcg_at_1
+      value: 46.833999999999996
+    - type: ndcg_at_10
+      value: 58.372
+    - type: ndcg_at_100
+      value: 62.068
+    - type: ndcg_at_1000
+      value: 63.288
+    - type: ndcg_at_3
+      value: 53.400000000000006
+    - type: ndcg_at_5
+      value: 55.766000000000005
+    - type: precision_at_1
+      value: 46.833999999999996
+    - type: precision_at_10
+      value: 9.191
+    - type: precision_at_100
+      value: 1.192
+    - type: precision_at_1000
+      value: 0.134
+    - type: precision_at_3
+      value: 23.448
+    - type: precision_at_5
+      value: 15.862000000000002
+    - type: recall_at_1
+      value: 40.987
+    - type: recall_at_10
+      value: 71.146
+    - type: recall_at_100
+      value: 87.035
+    - type: recall_at_1000
+      value: 95.633
+    - type: recall_at_3
+      value: 58.025999999999996
+    - type: recall_at_5
+      value: 63.815999999999995
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGisRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 24.587
+    - type: map_at_10
+      value: 33.114
+    - type: map_at_100
+      value: 34.043
+    - type: map_at_1000
+      value: 34.123999999999995
+    - type: map_at_3
+      value: 30.45
+    - type: map_at_5
+      value: 31.813999999999997
+    - type: mrr_at_1
+      value: 26.554
+    - type: mrr_at_10
+      value: 35.148
+    - type: mrr_at_100
+      value: 35.926
+    - type: mrr_at_1000
+      value: 35.991
+    - type: mrr_at_3
+      value: 32.599000000000004
+    - type: mrr_at_5
+      value: 33.893
+    - type: ndcg_at_1
+      value: 26.554
+    - type: ndcg_at_10
+      value: 38.132
+    - type: ndcg_at_100
+      value: 42.78
+    - type: ndcg_at_1000
+      value: 44.919
+    - type: ndcg_at_3
+      value: 32.833
+    - type: ndcg_at_5
+      value: 35.168
+    - type: precision_at_1
+      value: 26.554
+    - type: precision_at_10
+      value: 5.921
+    - type: precision_at_100
+      value: 0.8659999999999999
+    - type: precision_at_1000
+      value: 0.109
+    - type: precision_at_3
+      value: 13.861
+    - type: precision_at_5
+      value: 9.605
+    - type: recall_at_1
+      value: 24.587
+    - type: recall_at_10
+      value: 51.690000000000005
+    - type: recall_at_100
+      value: 73.428
+    - type: recall_at_1000
+      value: 89.551
+    - type: recall_at_3
+      value: 37.336999999999996
+    - type: recall_at_5
+      value: 43.047000000000004
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackMathematicaRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 16.715
+    - type: map_at_10
+      value: 24.251
+    - type: map_at_100
+      value: 25.326999999999998
+    - type: map_at_1000
+      value: 25.455
+    - type: map_at_3
+      value: 21.912000000000003
+    - type: map_at_5
+      value: 23.257
+    - type: mrr_at_1
+      value: 20.274
+    - type: mrr_at_10
+      value: 28.552
+    - type: mrr_at_100
+      value: 29.42
+    - type: mrr_at_1000
+      value: 29.497
+    - type: mrr_at_3
+      value: 26.14
+    - type: mrr_at_5
+      value: 27.502
+    - type: ndcg_at_1
+      value: 20.274
+    - type: ndcg_at_10
+      value: 29.088
+    - type: ndcg_at_100
+      value: 34.293
+    - type: ndcg_at_1000
+      value: 37.271
+    - type: ndcg_at_3
+      value: 24.708
+    - type: ndcg_at_5
+      value: 26.809
+    - type: precision_at_1
+      value: 20.274
+    - type: precision_at_10
+      value: 5.361
+    - type: precision_at_100
+      value: 0.915
+    - type: precision_at_1000
+      value: 0.13
+    - type: precision_at_3
+      value: 11.733
+    - type: precision_at_5
+      value: 8.556999999999999
+    - type: recall_at_1
+      value: 16.715
+    - type: recall_at_10
+      value: 39.587
+    - type: recall_at_100
+      value: 62.336000000000006
+    - type: recall_at_1000
+      value: 83.453
+    - type: recall_at_3
+      value: 27.839999999999996
+    - type: recall_at_5
+      value: 32.952999999999996
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackPhysicsRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 28.793000000000003
+    - type: map_at_10
+      value: 38.582
+    - type: map_at_100
+      value: 39.881
+    - type: map_at_1000
+      value: 39.987
+    - type: map_at_3
+      value: 35.851
+    - type: map_at_5
+      value: 37.289
+    - type: mrr_at_1
+      value: 34.455999999999996
+    - type: mrr_at_10
+      value: 43.909
+    - type: mrr_at_100
+      value: 44.74
+    - type: mrr_at_1000
+      value: 44.786
+    - type: mrr_at_3
+      value: 41.659
+    - type: mrr_at_5
+      value: 43.010999999999996
+    - type: ndcg_at_1
+      value: 34.455999999999996
+    - type: ndcg_at_10
+      value: 44.266
+    - type: ndcg_at_100
+      value: 49.639
+    - type: ndcg_at_1000
+      value: 51.644
+    - type: ndcg_at_3
+      value: 39.865
+    - type: ndcg_at_5
+      value: 41.887
+    - type: precision_at_1
+      value: 34.455999999999996
+    - type: precision_at_10
+      value: 7.843999999999999
+    - type: precision_at_100
+      value: 1.243
+    - type: precision_at_1000
+      value: 0.158
+    - type: precision_at_3
+      value: 18.831999999999997
+    - type: precision_at_5
+      value: 13.147
+    - type: recall_at_1
+      value: 28.793000000000003
+    - type: recall_at_10
+      value: 55.68300000000001
+    - type: recall_at_100
+      value: 77.99000000000001
+    - type: recall_at_1000
+      value: 91.183
+    - type: recall_at_3
+      value: 43.293
+    - type: recall_at_5
+      value: 48.618
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackProgrammersRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 25.907000000000004
+    - type: map_at_10
+      value: 35.519
+    - type: map_at_100
+      value: 36.806
+    - type: map_at_1000
+      value: 36.912
+    - type: map_at_3
+      value: 32.748
+    - type: map_at_5
+      value: 34.232
+    - type: mrr_at_1
+      value: 31.621
+    - type: mrr_at_10
+      value: 40.687
+    - type: mrr_at_100
+      value: 41.583
+    - type: mrr_at_1000
+      value: 41.638999999999996
+    - type: mrr_at_3
+      value: 38.527
+    - type: mrr_at_5
+      value: 39.612
+    - type: ndcg_at_1
+      value: 31.621
+    - type: ndcg_at_10
+      value: 41.003
+    - type: ndcg_at_100
+      value: 46.617999999999995
+    - type: ndcg_at_1000
+      value: 48.82
+    - type: ndcg_at_3
+      value: 36.542
+    - type: ndcg_at_5
+      value: 38.368
+    - type: precision_at_1
+      value: 31.621
+    - type: precision_at_10
+      value: 7.396999999999999
+    - type: precision_at_100
+      value: 1.191
+    - type: precision_at_1000
+      value: 0.153
+    - type: precision_at_3
+      value: 17.39
+    - type: precision_at_5
+      value: 12.1
+    - type: recall_at_1
+      value: 25.907000000000004
+    - type: recall_at_10
+      value: 52.115
+    - type: recall_at_100
+      value: 76.238
+    - type: recall_at_1000
+      value: 91.218
+    - type: recall_at_3
+      value: 39.417
+    - type: recall_at_5
+      value: 44.435
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 25.732166666666668
+    - type: map_at_10
+      value: 34.51616666666667
+    - type: map_at_100
+      value: 35.67241666666666
+    - type: map_at_1000
+      value: 35.78675
+    - type: map_at_3
+      value: 31.953416666666662
+    - type: map_at_5
+      value: 33.333
+    - type: mrr_at_1
+      value: 30.300166666666673
+    - type: mrr_at_10
+      value: 38.6255
+    - type: mrr_at_100
+      value: 39.46183333333334
+    - type: mrr_at_1000
+      value: 39.519999999999996
+    - type: mrr_at_3
+      value: 36.41299999999999
+    - type: mrr_at_5
+      value: 37.6365
+    - type: ndcg_at_1
+      value: 30.300166666666673
+    - type: ndcg_at_10
+      value: 39.61466666666667
+    - type: ndcg_at_100
+      value: 44.60808333333334
+    - type: ndcg_at_1000
+      value: 46.91708333333334
+    - type: ndcg_at_3
+      value: 35.26558333333333
+    - type: ndcg_at_5
+      value: 37.220000000000006
+    - type: precision_at_1
+      value: 30.300166666666673
+    - type: precision_at_10
+      value: 6.837416666666667
+    - type: precision_at_100
+      value: 1.10425
+    - type: precision_at_1000
+      value: 0.14875
+    - type: precision_at_3
+      value: 16.13716666666667
+    - type: precision_at_5
+      value: 11.2815
+    - type: recall_at_1
+      value: 25.732166666666668
+    - type: recall_at_10
+      value: 50.578916666666665
+    - type: recall_at_100
+      value: 72.42183333333334
+    - type: recall_at_1000
+      value: 88.48766666666667
+    - type: recall_at_3
+      value: 38.41325
+    - type: recall_at_5
+      value: 43.515750000000004
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackStatsRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 23.951
+    - type: map_at_10
+      value: 30.974
+    - type: map_at_100
+      value: 31.804
+    - type: map_at_1000
+      value: 31.900000000000002
+    - type: map_at_3
+      value: 28.762
+    - type: map_at_5
+      value: 29.94
+    - type: mrr_at_1
+      value: 26.534000000000002
+    - type: mrr_at_10
+      value: 33.553
+    - type: mrr_at_100
+      value: 34.297
+    - type: mrr_at_1000
+      value: 34.36
+    - type: mrr_at_3
+      value: 31.391000000000002
+    - type: mrr_at_5
+      value: 32.525999999999996
+    - type: ndcg_at_1
+      value: 26.534000000000002
+    - type: ndcg_at_10
+      value: 35.112
+    - type: ndcg_at_100
+      value: 39.28
+    - type: ndcg_at_1000
+      value: 41.723
+    - type: ndcg_at_3
+      value: 30.902
+    - type: ndcg_at_5
+      value: 32.759
+    - type: precision_at_1
+      value: 26.534000000000002
+    - type: precision_at_10
+      value: 5.445
+    - type: precision_at_100
+      value: 0.819
+    - type: precision_at_1000
+      value: 0.11
+    - type: precision_at_3
+      value: 12.986
+    - type: precision_at_5
+      value: 9.049
+    - type: recall_at_1
+      value: 23.951
+    - type: recall_at_10
+      value: 45.24
+    - type: recall_at_100
+      value: 64.12299999999999
+    - type: recall_at_1000
+      value: 82.28999999999999
+    - type: recall_at_3
+      value: 33.806000000000004
+    - type: recall_at_5
+      value: 38.277
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackTexRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 16.829
+    - type: map_at_10
+      value: 23.684
+    - type: map_at_100
+      value: 24.683
+    - type: map_at_1000
+      value: 24.81
+    - type: map_at_3
+      value: 21.554000000000002
+    - type: map_at_5
+      value: 22.768
+    - type: mrr_at_1
+      value: 20.096
+    - type: mrr_at_10
+      value: 27.230999999999998
+    - type: mrr_at_100
+      value: 28.083999999999996
+    - type: mrr_at_1000
+      value: 28.166000000000004
+    - type: mrr_at_3
+      value: 25.212
+    - type: mrr_at_5
+      value: 26.32
+    - type: ndcg_at_1
+      value: 20.096
+    - type: ndcg_at_10
+      value: 27.989000000000004
+    - type: ndcg_at_100
+      value: 32.847
+    - type: ndcg_at_1000
+      value: 35.896
+    - type: ndcg_at_3
+      value: 24.116
+    - type: ndcg_at_5
+      value: 25.964
+    - type: precision_at_1
+      value: 20.096
+    - type: precision_at_10
+      value: 5
+    - type: precision_at_100
+      value: 0.8750000000000001
+    - type: precision_at_1000
+      value: 0.131
+    - type: precision_at_3
+      value: 11.207
+    - type: precision_at_5
+      value: 8.08
+    - type: recall_at_1
+      value: 16.829
+    - type: recall_at_10
+      value: 37.407000000000004
+    - type: recall_at_100
+      value: 59.101000000000006
+    - type: recall_at_1000
+      value: 81.024
+    - type: recall_at_3
+      value: 26.739
+    - type: recall_at_5
+      value: 31.524
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackUnixRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 24.138
+    - type: map_at_10
+      value: 32.275999999999996
+    - type: map_at_100
+      value: 33.416000000000004
+    - type: map_at_1000
+      value: 33.527
+    - type: map_at_3
+      value: 29.854000000000003
+    - type: map_at_5
+      value: 31.096
+    - type: mrr_at_1
+      value: 28.450999999999997
+    - type: mrr_at_10
+      value: 36.214
+    - type: mrr_at_100
+      value: 37.134
+    - type: mrr_at_1000
+      value: 37.198
+    - type: mrr_at_3
+      value: 34.001999999999995
+    - type: mrr_at_5
+      value: 35.187000000000005
+    - type: ndcg_at_1
+      value: 28.450999999999997
+    - type: ndcg_at_10
+      value: 37.166
+    - type: ndcg_at_100
+      value: 42.454
+    - type: ndcg_at_1000
+      value: 44.976
+    - type: ndcg_at_3
+      value: 32.796
+    - type: ndcg_at_5
+      value: 34.631
+    - type: precision_at_1
+      value: 28.450999999999997
+    - type: precision_at_10
+      value: 6.241
+    - type: precision_at_100
+      value: 0.9950000000000001
+    - type: precision_at_1000
+      value: 0.133
+    - type: precision_at_3
+      value: 14.801
+    - type: precision_at_5
+      value: 10.280000000000001
+    - type: recall_at_1
+      value: 24.138
+    - type: recall_at_10
+      value: 48.111
+    - type: recall_at_100
+      value: 71.245
+    - type: recall_at_1000
+      value: 88.986
+    - type: recall_at_3
+      value: 36.119
+    - type: recall_at_5
+      value: 40.846
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWebmastersRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 23.244
+    - type: map_at_10
+      value: 31.227
+    - type: map_at_100
+      value: 33.007
+    - type: map_at_1000
+      value: 33.223
+    - type: map_at_3
+      value: 28.924
+    - type: map_at_5
+      value: 30.017
+    - type: mrr_at_1
+      value: 27.668
+    - type: mrr_at_10
+      value: 35.524
+    - type: mrr_at_100
+      value: 36.699
+    - type: mrr_at_1000
+      value: 36.759
+    - type: mrr_at_3
+      value: 33.366
+    - type: mrr_at_5
+      value: 34.552
+    - type: ndcg_at_1
+      value: 27.668
+    - type: ndcg_at_10
+      value: 36.381
+    - type: ndcg_at_100
+      value: 43.062
+    - type: ndcg_at_1000
+      value: 45.656
+    - type: ndcg_at_3
+      value: 32.501999999999995
+    - type: ndcg_at_5
+      value: 34.105999999999995
+    - type: precision_at_1
+      value: 27.668
+    - type: precision_at_10
+      value: 6.798
+    - type: precision_at_100
+      value: 1.492
+    - type: precision_at_1000
+      value: 0.234
+    - type: precision_at_3
+      value: 15.152
+    - type: precision_at_5
+      value: 10.791
+    - type: recall_at_1
+      value: 23.244
+    - type: recall_at_10
+      value: 45.979
+    - type: recall_at_100
+      value: 74.822
+    - type: recall_at_1000
+      value: 91.078
+    - type: recall_at_3
+      value: 34.925
+    - type: recall_at_5
+      value: 39.126
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWordpressRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 19.945
+    - type: map_at_10
+      value: 27.517999999999997
+    - type: map_at_100
+      value: 28.588
+    - type: map_at_1000
+      value: 28.682000000000002
+    - type: map_at_3
+      value: 25.345000000000002
+    - type: map_at_5
+      value: 26.555
+    - type: mrr_at_1
+      value: 21.996
+    - type: mrr_at_10
+      value: 29.845
+    - type: mrr_at_100
+      value: 30.775999999999996
+    - type: mrr_at_1000
+      value: 30.845
+    - type: mrr_at_3
+      value: 27.726
+    - type: mrr_at_5
+      value: 28.882
+    - type: ndcg_at_1
+      value: 21.996
+    - type: ndcg_at_10
+      value: 32.034
+    - type: ndcg_at_100
+      value: 37.185
+    - type: ndcg_at_1000
+      value: 39.645
+    - type: ndcg_at_3
+      value: 27.750999999999998
+    - type: ndcg_at_5
+      value: 29.805999999999997
+    - type: precision_at_1
+      value: 21.996
+    - type: precision_at_10
+      value: 5.065
+    - type: precision_at_100
+      value: 0.819
+    - type: precision_at_1000
+      value: 0.11399999999999999
+    - type: precision_at_3
+      value: 12.076
+    - type: precision_at_5
+      value: 8.392
+    - type: recall_at_1
+      value: 19.945
+    - type: recall_at_10
+      value: 43.62
+    - type: recall_at_100
+      value: 67.194
+    - type: recall_at_1000
+      value: 85.7
+    - type: recall_at_3
+      value: 32.15
+    - type: recall_at_5
+      value: 37.208999999999996
+  - task:
+      type: Retrieval
+    dataset:
+      type: climate-fever
+      name: MTEB ClimateFEVER
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 18.279
+    - type: map_at_10
+      value: 31.052999999999997
+    - type: map_at_100
+      value: 33.125
+    - type: map_at_1000
+      value: 33.306000000000004
+    - type: map_at_3
+      value: 26.208
+    - type: map_at_5
+      value: 28.857
+    - type: mrr_at_1
+      value: 42.671
+    - type: mrr_at_10
+      value: 54.557
+    - type: mrr_at_100
+      value: 55.142
+    - type: mrr_at_1000
+      value: 55.169000000000004
+    - type: mrr_at_3
+      value: 51.488
+    - type: mrr_at_5
+      value: 53.439
+    - type: ndcg_at_1
+      value: 42.671
+    - type: ndcg_at_10
+      value: 41.276
+    - type: ndcg_at_100
+      value: 48.376000000000005
+    - type: ndcg_at_1000
+      value: 51.318
+    - type: ndcg_at_3
+      value: 35.068
+    - type: ndcg_at_5
+      value: 37.242
+    - type: precision_at_1
+      value: 42.671
+    - type: precision_at_10
+      value: 12.638
+    - type: precision_at_100
+      value: 2.045
+    - type: precision_at_1000
+      value: 0.26
+    - type: precision_at_3
+      value: 26.08
+    - type: precision_at_5
+      value: 19.805
+    - type: recall_at_1
+      value: 18.279
+    - type: recall_at_10
+      value: 46.946
+    - type: recall_at_100
+      value: 70.97200000000001
+    - type: recall_at_1000
+      value: 87.107
+    - type: recall_at_3
+      value: 31.147999999999996
+    - type: recall_at_5
+      value: 38.099
+  - task:
+      type: Retrieval
+    dataset:
+      type: dbpedia-entity
+      name: MTEB DBPedia
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 8.573
+    - type: map_at_10
+      value: 19.747
+    - type: map_at_100
+      value: 28.205000000000002
+    - type: map_at_1000
+      value: 29.831000000000003
+    - type: map_at_3
+      value: 14.109
+    - type: map_at_5
+      value: 16.448999999999998
+    - type: mrr_at_1
+      value: 71
+    - type: mrr_at_10
+      value: 77.68599999999999
+    - type: mrr_at_100
+      value: 77.995
+    - type: mrr_at_1000
+      value: 78.00200000000001
+    - type: mrr_at_3
+      value: 76.292
+    - type: mrr_at_5
+      value: 77.029
+    - type: ndcg_at_1
+      value: 59.12500000000001
+    - type: ndcg_at_10
+      value: 43.9
+    - type: ndcg_at_100
+      value: 47.863
+    - type: ndcg_at_1000
+      value: 54.848
+    - type: ndcg_at_3
+      value: 49.803999999999995
+    - type: ndcg_at_5
+      value: 46.317
+    - type: precision_at_1
+      value: 71
+    - type: precision_at_10
+      value: 34.4
+    - type: precision_at_100
+      value: 11.063
+    - type: precision_at_1000
+      value: 1.989
+    - type: precision_at_3
+      value: 52.333
+    - type: precision_at_5
+      value: 43.7
+    - type: recall_at_1
+      value: 8.573
+    - type: recall_at_10
+      value: 25.615
+    - type: recall_at_100
+      value: 53.385000000000005
+    - type: recall_at_1000
+      value: 75.46000000000001
+    - type: recall_at_3
+      value: 15.429
+    - type: recall_at_5
+      value: 19.357
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/emotion
+      name: MTEB EmotionClassification
+      config: default
+      split: test
+      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
+    metrics:
+    - type: accuracy
+      value: 47.989999999999995
+    - type: f1
+      value: 42.776314451497555
+  - task:
+      type: Retrieval
+    dataset:
+      type: fever
+      name: MTEB FEVER
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 74.13499999999999
+    - type: map_at_10
+      value: 82.825
+    - type: map_at_100
+      value: 83.096
+    - type: map_at_1000
+      value: 83.111
+    - type: map_at_3
+      value: 81.748
+    - type: map_at_5
+      value: 82.446
+    - type: mrr_at_1
+      value: 79.553
+    - type: mrr_at_10
+      value: 86.654
+    - type: mrr_at_100
+      value: 86.774
+    - type: mrr_at_1000
+      value: 86.778
+    - type: mrr_at_3
+      value: 85.981
+    - type: mrr_at_5
+      value: 86.462
+    - type: ndcg_at_1
+      value: 79.553
+    - type: ndcg_at_10
+      value: 86.345
+    - type: ndcg_at_100
+      value: 87.32
+    - type: ndcg_at_1000
+      value: 87.58200000000001
+    - type: ndcg_at_3
+      value: 84.719
+    - type: ndcg_at_5
+      value: 85.677
+    - type: precision_at_1
+      value: 79.553
+    - type: precision_at_10
+      value: 10.402000000000001
+    - type: precision_at_100
+      value: 1.1119999999999999
+    - type: precision_at_1000
+      value: 0.11499999999999999
+    - type: precision_at_3
+      value: 32.413
+    - type: precision_at_5
+      value: 20.138
+    - type: recall_at_1
+      value: 74.13499999999999
+    - type: recall_at_10
+      value: 93.215
+    - type: recall_at_100
+      value: 97.083
+    - type: recall_at_1000
+      value: 98.732
+    - type: recall_at_3
+      value: 88.79
+    - type: recall_at_5
+      value: 91.259
+  - task:
+      type: Retrieval
+    dataset:
+      type: fiqa
+      name: MTEB FiQA2018
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 18.298000000000002
+    - type: map_at_10
+      value: 29.901
+    - type: map_at_100
+      value: 31.528
+    - type: map_at_1000
+      value: 31.713
+    - type: map_at_3
+      value: 25.740000000000002
+    - type: map_at_5
+      value: 28.227999999999998
+    - type: mrr_at_1
+      value: 36.728
+    - type: mrr_at_10
+      value: 45.401
+    - type: mrr_at_100
+      value: 46.27
+    - type: mrr_at_1000
+      value: 46.315
+    - type: mrr_at_3
+      value: 42.978
+    - type: mrr_at_5
+      value: 44.29
+    - type: ndcg_at_1
+      value: 36.728
+    - type: ndcg_at_10
+      value: 37.456
+    - type: ndcg_at_100
+      value: 43.832
+    - type: ndcg_at_1000
+      value: 47
+    - type: ndcg_at_3
+      value: 33.694
+    - type: ndcg_at_5
+      value: 35.085
+    - type: precision_at_1
+      value: 36.728
+    - type: precision_at_10
+      value: 10.386
+    - type: precision_at_100
+      value: 1.701
+    - type: precision_at_1000
+      value: 0.22599999999999998
+    - type: precision_at_3
+      value: 22.479
+    - type: precision_at_5
+      value: 16.605
+    - type: recall_at_1
+      value: 18.298000000000002
+    - type: recall_at_10
+      value: 44.369
+    - type: recall_at_100
+      value: 68.098
+    - type: recall_at_1000
+      value: 87.21900000000001
+    - type: recall_at_3
+      value: 30.215999999999998
+    - type: recall_at_5
+      value: 36.861
+  - task:
+      type: Retrieval
+    dataset:
+      type: hotpotqa
+      name: MTEB HotpotQA
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 39.568
+    - type: map_at_10
+      value: 65.061
+    - type: map_at_100
+      value: 65.896
+    - type: map_at_1000
+      value: 65.95100000000001
+    - type: map_at_3
+      value: 61.831
+    - type: map_at_5
+      value: 63.849000000000004
+    - type: mrr_at_1
+      value: 79.136
+    - type: mrr_at_10
+      value: 84.58200000000001
+    - type: mrr_at_100
+      value: 84.765
+    - type: mrr_at_1000
+      value: 84.772
+    - type: mrr_at_3
+      value: 83.684
+    - type: mrr_at_5
+      value: 84.223
+    - type: ndcg_at_1
+      value: 79.136
+    - type: ndcg_at_10
+      value: 72.622
+    - type: ndcg_at_100
+      value: 75.539
+    - type: ndcg_at_1000
+      value: 76.613
+    - type: ndcg_at_3
+      value: 68.065
+    - type: ndcg_at_5
+      value: 70.58
+    - type: precision_at_1
+      value: 79.136
+    - type: precision_at_10
+      value: 15.215
+    - type: precision_at_100
+      value: 1.7500000000000002
+    - type: precision_at_1000
+      value: 0.189
+    - type: precision_at_3
+      value: 44.011
+    - type: precision_at_5
+      value: 28.388999999999996
+    - type: recall_at_1
+      value: 39.568
+    - type: recall_at_10
+      value: 76.077
+    - type: recall_at_100
+      value: 87.481
+    - type: recall_at_1000
+      value: 94.56400000000001
+    - type: recall_at_3
+      value: 66.01599999999999
+    - type: recall_at_5
+      value: 70.97200000000001
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/imdb
+      name: MTEB ImdbClassification
+      config: default
+      split: test
+      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
+    metrics:
+    - type: accuracy
+      value: 85.312
+    - type: ap
+      value: 80.36296867333715
+    - type: f1
+      value: 85.26613311552218
+  - task:
+      type: Retrieval
+    dataset:
+      type: msmarco
+      name: MTEB MSMARCO
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 23.363999999999997
+    - type: map_at_10
+      value: 35.711999999999996
+    - type: map_at_100
+      value: 36.876999999999995
+    - type: map_at_1000
+      value: 36.923
+    - type: map_at_3
+      value: 32.034
+    - type: map_at_5
+      value: 34.159
+    - type: mrr_at_1
+      value: 24.04
+    - type: mrr_at_10
+      value: 36.345
+    - type: mrr_at_100
+      value: 37.441
+    - type: mrr_at_1000
+      value: 37.480000000000004
+    - type: mrr_at_3
+      value: 32.713
+    - type: mrr_at_5
+      value: 34.824
+    - type: ndcg_at_1
+      value: 24.026
+    - type: ndcg_at_10
+      value: 42.531
+    - type: ndcg_at_100
+      value: 48.081
+    - type: ndcg_at_1000
+      value: 49.213
+    - type: ndcg_at_3
+      value: 35.044
+    - type: ndcg_at_5
+      value: 38.834
+    - type: precision_at_1
+      value: 24.026
+    - type: precision_at_10
+      value: 6.622999999999999
+    - type: precision_at_100
+      value: 0.941
+    - type: precision_at_1000
+      value: 0.104
+    - type: precision_at_3
+      value: 14.909
+    - type: precision_at_5
+      value: 10.871
+    - type: recall_at_1
+      value: 23.363999999999997
+    - type: recall_at_10
+      value: 63.426
+    - type: recall_at_100
+      value: 88.96300000000001
+    - type: recall_at_1000
+      value: 97.637
+    - type: recall_at_3
+      value: 43.095
+    - type: recall_at_5
+      value: 52.178000000000004
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_domain
+      name: MTEB MTOPDomainClassification (en)
+      config: en
+      split: test
+      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
+    metrics:
+    - type: accuracy
+      value: 93.0095759233926
+    - type: f1
+      value: 92.78387794667408
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_intent
+      name: MTEB MTOPIntentClassification (en)
+      config: en
+      split: test
+      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
+    metrics:
+    - type: accuracy
+      value: 75.0296397628819
+    - type: f1
+      value: 58.45699589820874
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_intent
+      name: MTEB MassiveIntentClassification (en)
+      config: en
+      split: test
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+    metrics:
+    - type: accuracy
+      value: 73.45662407531944
+    - type: f1
+      value: 71.42364781421813
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (en)
+      config: en
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 77.07800941492937
+    - type: f1
+      value: 77.22799045640845
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-p2p
+      name: MTEB MedrxivClusteringP2P
+      config: default
+      split: test
+      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
+    metrics:
+    - type: v_measure
+      value: 34.531234379250606
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-s2s
+      name: MTEB MedrxivClusteringS2S
+      config: default
+      split: test
+      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
+    metrics:
+    - type: v_measure
+      value: 30.941490381193802
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/mind_small
+      name: MTEB MindSmallReranking
+      config: default
+      split: test
+      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
+    metrics:
+    - type: map
+      value: 30.3115090856725
+    - type: mrr
+      value: 31.290667638675757
+  - task:
+      type: Retrieval
+    dataset:
+      type: nfcorpus
+      name: MTEB NFCorpus
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 5.465
+    - type: map_at_10
+      value: 13.03
+    - type: map_at_100
+      value: 16.057
+    - type: map_at_1000
+      value: 17.49
+    - type: map_at_3
+      value: 9.553
+    - type: map_at_5
+      value: 11.204
+    - type: mrr_at_1
+      value: 43.653
+    - type: mrr_at_10
+      value: 53.269
+    - type: mrr_at_100
+      value: 53.72
+    - type: mrr_at_1000
+      value: 53.761
+    - type: mrr_at_3
+      value: 50.929
+    - type: mrr_at_5
+      value: 52.461
+    - type: ndcg_at_1
+      value: 42.26
+    - type: ndcg_at_10
+      value: 34.673
+    - type: ndcg_at_100
+      value: 30.759999999999998
+    - type: ndcg_at_1000
+      value: 39.728
+    - type: ndcg_at_3
+      value: 40.349000000000004
+    - type: ndcg_at_5
+      value: 37.915
+    - type: precision_at_1
+      value: 43.653
+    - type: precision_at_10
+      value: 25.789
+    - type: precision_at_100
+      value: 7.754999999999999
+    - type: precision_at_1000
+      value: 2.07
+    - type: precision_at_3
+      value: 38.596000000000004
+    - type: precision_at_5
+      value: 33.251
+    - type: recall_at_1
+      value: 5.465
+    - type: recall_at_10
+      value: 17.148
+    - type: recall_at_100
+      value: 29.768
+    - type: recall_at_1000
+      value: 62.239
+    - type: recall_at_3
+      value: 10.577
+    - type: recall_at_5
+      value: 13.315
+  - task:
+      type: Retrieval
+    dataset:
+      type: nq
+      name: MTEB NQ
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 37.008
+    - type: map_at_10
+      value: 52.467
+    - type: map_at_100
+      value: 53.342999999999996
+    - type: map_at_1000
+      value: 53.366
+    - type: map_at_3
+      value: 48.412
+    - type: map_at_5
+      value: 50.875
+    - type: mrr_at_1
+      value: 41.541
+    - type: mrr_at_10
+      value: 54.967
+    - type: mrr_at_100
+      value: 55.611
+    - type: mrr_at_1000
+      value: 55.627
+    - type: mrr_at_3
+      value: 51.824999999999996
+    - type: mrr_at_5
+      value: 53.763000000000005
+    - type: ndcg_at_1
+      value: 41.541
+    - type: ndcg_at_10
+      value: 59.724999999999994
+    - type: ndcg_at_100
+      value: 63.38700000000001
+    - type: ndcg_at_1000
+      value: 63.883
+    - type: ndcg_at_3
+      value: 52.331
+    - type: ndcg_at_5
+      value: 56.327000000000005
+    - type: precision_at_1
+      value: 41.541
+    - type: precision_at_10
+      value: 9.447
+    - type: precision_at_100
+      value: 1.1520000000000001
+    - type: precision_at_1000
+      value: 0.12
+    - type: precision_at_3
+      value: 23.262
+    - type: precision_at_5
+      value: 16.314999999999998
+    - type: recall_at_1
+      value: 37.008
+    - type: recall_at_10
+      value: 79.145
+    - type: recall_at_100
+      value: 94.986
+    - type: recall_at_1000
+      value: 98.607
+    - type: recall_at_3
+      value: 60.277
+    - type: recall_at_5
+      value: 69.407
+  - task:
+      type: Retrieval
+    dataset:
+      type: quora
+      name: MTEB QuoraRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 70.402
+    - type: map_at_10
+      value: 84.181
+    - type: map_at_100
+      value: 84.796
+    - type: map_at_1000
+      value: 84.81400000000001
+    - type: map_at_3
+      value: 81.209
+    - type: map_at_5
+      value: 83.085
+    - type: mrr_at_1
+      value: 81.02000000000001
+    - type: mrr_at_10
+      value: 87.263
+    - type: mrr_at_100
+      value: 87.36
+    - type: mrr_at_1000
+      value: 87.36
+    - type: mrr_at_3
+      value: 86.235
+    - type: mrr_at_5
+      value: 86.945
+    - type: ndcg_at_1
+      value: 81.01
+    - type: ndcg_at_10
+      value: 87.99900000000001
+    - type: ndcg_at_100
+      value: 89.217
+    - type: ndcg_at_1000
+      value: 89.33
+    - type: ndcg_at_3
+      value: 85.053
+    - type: ndcg_at_5
+      value: 86.703
+    - type: precision_at_1
+      value: 81.01
+    - type: precision_at_10
+      value: 13.336
+    - type: precision_at_100
+      value: 1.52
+    - type: precision_at_1000
+      value: 0.156
+    - type: precision_at_3
+      value: 37.14
+    - type: precision_at_5
+      value: 24.44
+    - type: recall_at_1
+      value: 70.402
+    - type: recall_at_10
+      value: 95.214
+    - type: recall_at_100
+      value: 99.438
+    - type: recall_at_1000
+      value: 99.928
+    - type: recall_at_3
+      value: 86.75699999999999
+    - type: recall_at_5
+      value: 91.44099999999999
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering
+      name: MTEB RedditClustering
+      config: default
+      split: test
+      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
+    metrics:
+    - type: v_measure
+      value: 56.51721502758904
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering-p2p
+      name: MTEB RedditClusteringP2P
+      config: default
+      split: test
+      revision: 282350215ef01743dc01b456c7f5241fa8937f16
+    metrics:
+    - type: v_measure
+      value: 61.054808572333016
+  - task:
+      type: Retrieval
+    dataset:
+      type: scidocs
+      name: MTEB SCIDOCS
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 4.578
+    - type: map_at_10
+      value: 11.036999999999999
+    - type: map_at_100
+      value: 12.879999999999999
+    - type: map_at_1000
+      value: 13.150999999999998
+    - type: map_at_3
+      value: 8.133
+    - type: map_at_5
+      value: 9.559
+    - type: mrr_at_1
+      value: 22.6
+    - type: mrr_at_10
+      value: 32.68
+    - type: mrr_at_100
+      value: 33.789
+    - type: mrr_at_1000
+      value: 33.854
+    - type: mrr_at_3
+      value: 29.7
+    - type: mrr_at_5
+      value: 31.480000000000004
+    - type: ndcg_at_1
+      value: 22.6
+    - type: ndcg_at_10
+      value: 18.616
+    - type: ndcg_at_100
+      value: 25.883
+    - type: ndcg_at_1000
+      value: 30.944
+    - type: ndcg_at_3
+      value: 18.136
+    - type: ndcg_at_5
+      value: 15.625
+    - type: precision_at_1
+      value: 22.6
+    - type: precision_at_10
+      value: 9.48
+    - type: precision_at_100
+      value: 1.991
+    - type: precision_at_1000
+      value: 0.321
+    - type: precision_at_3
+      value: 16.8
+    - type: precision_at_5
+      value: 13.54
+    - type: recall_at_1
+      value: 4.578
+    - type: recall_at_10
+      value: 19.213
+    - type: recall_at_100
+      value: 40.397
+    - type: recall_at_1000
+      value: 65.2
+    - type: recall_at_3
+      value: 10.208
+    - type: recall_at_5
+      value: 13.718
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sickr-sts
+      name: MTEB SICK-R
+      config: default
+      split: test
+      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
+    metrics:
+    - type: cos_sim_pearson
+      value: 83.44288351714071
+    - type: cos_sim_spearman
+      value: 79.37995604564952
+    - type: euclidean_pearson
+      value: 81.1078874670718
+    - type: euclidean_spearman
+      value: 79.37995905980499
+    - type: manhattan_pearson
+      value: 81.03697527288986
+    - type: manhattan_spearman
+      value: 79.33490235296236
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts12-sts
+      name: MTEB STS12
+      config: default
+      split: test
+      revision: a0d554a64d88156834ff5ae9920b964011b16384
+    metrics:
+    - type: cos_sim_pearson
+      value: 84.95557650436523
+    - type: cos_sim_spearman
+      value: 78.5190672399868
+    - type: euclidean_pearson
+      value: 81.58064025904707
+    - type: euclidean_spearman
+      value: 78.5190672399868
+    - type: manhattan_pearson
+      value: 81.52857930619889
+    - type: manhattan_spearman
+      value: 78.50421361308034
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts13-sts
+      name: MTEB STS13
+      config: default
+      split: test
+      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
+    metrics:
+    - type: cos_sim_pearson
+      value: 84.79128416228737
+    - type: cos_sim_spearman
+      value: 86.05402451477147
+    - type: euclidean_pearson
+      value: 85.46280267054289
+    - type: euclidean_spearman
+      value: 86.05402451477147
+    - type: manhattan_pearson
+      value: 85.46278563858236
+    - type: manhattan_spearman
+      value: 86.08079590861004
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts14-sts
+      name: MTEB STS14
+      config: default
+      split: test
+      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
+    metrics:
+    - type: cos_sim_pearson
+      value: 83.20623089568763
+    - type: cos_sim_spearman
+      value: 81.53786907061009
+    - type: euclidean_pearson
+      value: 82.82272250091494
+    - type: euclidean_spearman
+      value: 81.53786907061009
+    - type: manhattan_pearson
+      value: 82.78850494027013
+    - type: manhattan_spearman
+      value: 81.5135618083407
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts15-sts
+      name: MTEB STS15
+      config: default
+      split: test
+      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
+    metrics:
+    - type: cos_sim_pearson
+      value: 85.46366618397936
+    - type: cos_sim_spearman
+      value: 86.96566013336908
+    - type: euclidean_pearson
+      value: 86.62651697548931
+    - type: euclidean_spearman
+      value: 86.96565526364454
+    - type: manhattan_pearson
+      value: 86.58812160258009
+    - type: manhattan_spearman
+      value: 86.9336484321288
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts16-sts
+      name: MTEB STS16
+      config: default
+      split: test
+      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
+    metrics:
+    - type: cos_sim_pearson
+      value: 82.51858358641559
+    - type: cos_sim_spearman
+      value: 84.7652527954999
+    - type: euclidean_pearson
+      value: 84.23914783766861
+    - type: euclidean_spearman
+      value: 84.7652527954999
+    - type: manhattan_pearson
+      value: 84.22749648503171
+    - type: manhattan_spearman
+      value: 84.74527996746386
+  - 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: 87.28026563313065
+    - type: cos_sim_spearman
+      value: 87.46928143824915
+    - type: euclidean_pearson
+      value: 88.30558762000372
+    - type: euclidean_spearman
+      value: 87.46928143824915
+    - type: manhattan_pearson
+      value: 88.10513330809331
+    - type: manhattan_spearman
+      value: 87.21069787834173
+  - 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: 62.376497134587375
+    - type: cos_sim_spearman
+      value: 65.0159550112516
+    - type: euclidean_pearson
+      value: 65.64572120879598
+    - type: euclidean_spearman
+      value: 65.0159550112516
+    - type: manhattan_pearson
+      value: 65.88143604989976
+    - type: manhattan_spearman
+      value: 65.17547297222434
+  - task:
+      type: STS
+    dataset:
+      type: mteb/stsbenchmark-sts
+      name: MTEB STSBenchmark
+      config: default
+      split: test
+      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
+    metrics:
+    - type: cos_sim_pearson
+      value: 84.22876368947644
+    - type: cos_sim_spearman
+      value: 85.46935577445318
+    - type: euclidean_pearson
+      value: 85.32830231392005
+    - type: euclidean_spearman
+      value: 85.46935577445318
+    - type: manhattan_pearson
+      value: 85.30353211758495
+    - type: manhattan_spearman
+      value: 85.42821085956945
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/scidocs-reranking
+      name: MTEB SciDocsRR
+      config: default
+      split: test
+      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
+    metrics:
+    - type: map
+      value: 80.60986667767133
+    - type: mrr
+      value: 94.29432314236236
+  - task:
+      type: Retrieval
+    dataset:
+      type: scifact
+      name: MTEB SciFact
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 54.528
+    - type: map_at_10
+      value: 65.187
+    - type: map_at_100
+      value: 65.62599999999999
+    - type: map_at_1000
+      value: 65.657
+    - type: map_at_3
+      value: 62.352
+    - type: map_at_5
+      value: 64.025
+    - type: mrr_at_1
+      value: 57.333
+    - type: mrr_at_10
+      value: 66.577
+    - type: mrr_at_100
+      value: 66.88
+    - type: mrr_at_1000
+      value: 66.908
+    - type: mrr_at_3
+      value: 64.556
+    - type: mrr_at_5
+      value: 65.739
+    - type: ndcg_at_1
+      value: 57.333
+    - type: ndcg_at_10
+      value: 70.275
+    - type: ndcg_at_100
+      value: 72.136
+    - type: ndcg_at_1000
+      value: 72.963
+    - type: ndcg_at_3
+      value: 65.414
+    - type: ndcg_at_5
+      value: 67.831
+    - type: precision_at_1
+      value: 57.333
+    - type: precision_at_10
+      value: 9.5
+    - type: precision_at_100
+      value: 1.057
+    - type: precision_at_1000
+      value: 0.11199999999999999
+    - type: precision_at_3
+      value: 25.778000000000002
+    - type: precision_at_5
+      value: 17.2
+    - type: recall_at_1
+      value: 54.528
+    - type: recall_at_10
+      value: 84.356
+    - type: recall_at_100
+      value: 92.833
+    - type: recall_at_1000
+      value: 99.333
+    - type: recall_at_3
+      value: 71.283
+    - type: recall_at_5
+      value: 77.14999999999999
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/sprintduplicatequestions-pairclassification
+      name: MTEB SprintDuplicateQuestions
+      config: default
+      split: test
+      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
+    metrics:
+    - type: cos_sim_accuracy
+      value: 99.74158415841585
+    - type: cos_sim_ap
+      value: 92.90048959850317
+    - type: cos_sim_f1
+      value: 86.35650810245687
+    - type: cos_sim_precision
+      value: 90.4709748083242
+    - type: cos_sim_recall
+      value: 82.6
+    - type: dot_accuracy
+      value: 99.74158415841585
+    - type: dot_ap
+      value: 92.90048959850317
+    - type: dot_f1
+      value: 86.35650810245687
+    - type: dot_precision
+      value: 90.4709748083242
+    - type: dot_recall
+      value: 82.6
+    - type: euclidean_accuracy
+      value: 99.74158415841585
+    - type: euclidean_ap
+      value: 92.90048959850317
+    - type: euclidean_f1
+      value: 86.35650810245687
+    - type: euclidean_precision
+      value: 90.4709748083242
+    - type: euclidean_recall
+      value: 82.6
+    - type: manhattan_accuracy
+      value: 99.74158415841585
+    - type: manhattan_ap
+      value: 92.87344692947894
+    - type: manhattan_f1
+      value: 86.38497652582159
+    - type: manhattan_precision
+      value: 90.29443838604145
+    - type: manhattan_recall
+      value: 82.8
+    - type: max_accuracy
+      value: 99.74158415841585
+    - type: max_ap
+      value: 92.90048959850317
+    - type: max_f1
+      value: 86.38497652582159
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering
+      name: MTEB StackExchangeClustering
+      config: default
+      split: test
+      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
+    metrics:
+    - type: v_measure
+      value: 63.191648770424216
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering-p2p
+      name: MTEB StackExchangeClusteringP2P
+      config: default
+      split: test
+      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
+    metrics:
+    - type: v_measure
+      value: 34.02944668730218
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/stackoverflowdupquestions-reranking
+      name: MTEB StackOverflowDupQuestions
+      config: default
+      split: test
+      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
+    metrics:
+    - type: map
+      value: 50.466386167525265
+    - type: mrr
+      value: 51.19071492233257
+  - task:
+      type: Summarization
+    dataset:
+      type: mteb/summeval
+      name: MTEB SummEval
+      config: default
+      split: test
+      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
+    metrics:
+    - type: cos_sim_pearson
+      value: 30.198022505886435
+    - type: cos_sim_spearman
+      value: 30.40170257939193
+    - type: dot_pearson
+      value: 30.198015316402614
+    - type: dot_spearman
+      value: 30.40170257939193
+  - task:
+      type: Retrieval
+    dataset:
+      type: trec-covid
+      name: MTEB TRECCOVID
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 0.242
+    - type: map_at_10
+      value: 2.17
+    - type: map_at_100
+      value: 12.221
+    - type: map_at_1000
+      value: 28.63
+    - type: map_at_3
+      value: 0.728
+    - type: map_at_5
+      value: 1.185
+    - type: mrr_at_1
+      value: 94
+    - type: mrr_at_10
+      value: 97
+    - type: mrr_at_100
+      value: 97
+    - type: mrr_at_1000
+      value: 97
+    - type: mrr_at_3
+      value: 97
+    - type: mrr_at_5
+      value: 97
+    - type: ndcg_at_1
+      value: 89
+    - type: ndcg_at_10
+      value: 82.30499999999999
+    - type: ndcg_at_100
+      value: 61.839999999999996
+    - type: ndcg_at_1000
+      value: 53.381
+    - type: ndcg_at_3
+      value: 88.877
+    - type: ndcg_at_5
+      value: 86.05199999999999
+    - type: precision_at_1
+      value: 94
+    - type: precision_at_10
+      value: 87
+    - type: precision_at_100
+      value: 63.38
+    - type: precision_at_1000
+      value: 23.498
+    - type: precision_at_3
+      value: 94
+    - type: precision_at_5
+      value: 92
+    - type: recall_at_1
+      value: 0.242
+    - type: recall_at_10
+      value: 2.302
+    - type: recall_at_100
+      value: 14.979000000000001
+    - type: recall_at_1000
+      value: 49.638
+    - type: recall_at_3
+      value: 0.753
+    - type: recall_at_5
+      value: 1.226
+  - task:
+      type: Retrieval
+    dataset:
+      type: webis-touche2020
+      name: MTEB Touche2020
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 3.006
+    - type: map_at_10
+      value: 11.805
+    - type: map_at_100
+      value: 18.146
+    - type: map_at_1000
+      value: 19.788
+    - type: map_at_3
+      value: 5.914
+    - type: map_at_5
+      value: 8.801
+    - type: mrr_at_1
+      value: 40.816
+    - type: mrr_at_10
+      value: 56.36600000000001
+    - type: mrr_at_100
+      value: 56.721999999999994
+    - type: mrr_at_1000
+      value: 56.721999999999994
+    - type: mrr_at_3
+      value: 52.041000000000004
+    - type: mrr_at_5
+      value: 54.796
+    - type: ndcg_at_1
+      value: 37.755
+    - type: ndcg_at_10
+      value: 29.863
+    - type: ndcg_at_100
+      value: 39.571
+    - type: ndcg_at_1000
+      value: 51.385999999999996
+    - type: ndcg_at_3
+      value: 32.578
+    - type: ndcg_at_5
+      value: 32.351
+    - type: precision_at_1
+      value: 40.816
+    - type: precision_at_10
+      value: 26.531
+    - type: precision_at_100
+      value: 7.796
+    - type: precision_at_1000
+      value: 1.555
+    - type: precision_at_3
+      value: 32.653
+    - type: precision_at_5
+      value: 33.061
+    - type: recall_at_1
+      value: 3.006
+    - type: recall_at_10
+      value: 18.738
+    - type: recall_at_100
+      value: 48.058
+    - type: recall_at_1000
+      value: 83.41300000000001
+    - type: recall_at_3
+      value: 7.166
+    - type: recall_at_5
+      value: 12.102
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/toxic_conversations_50k
+      name: MTEB ToxicConversationsClassification
+      config: default
+      split: test
+      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
+    metrics:
+    - type: accuracy
+      value: 71.4178
+    - type: ap
+      value: 14.648781342150446
+    - type: f1
+      value: 55.07299194946378
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/tweet_sentiment_extraction
+      name: MTEB TweetSentimentExtractionClassification
+      config: default
+      split: test
+      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
+    metrics:
+    - type: accuracy
+      value: 60.919637804187886
+    - type: f1
+      value: 61.24122013967399
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/twentynewsgroups-clustering
+      name: MTEB TwentyNewsgroupsClustering
+      config: default
+      split: test
+      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
+    metrics:
+    - type: v_measure
+      value: 49.207896583685695
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twittersemeval2015-pairclassification
+      name: MTEB TwitterSemEval2015
+      config: default
+      split: test
+      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
+    metrics:
+    - type: cos_sim_accuracy
+      value: 86.23114978840078
+    - type: cos_sim_ap
+      value: 74.26624727825818
+    - type: cos_sim_f1
+      value: 68.72377190817083
+    - type: cos_sim_precision
+      value: 64.56400742115028
+    - type: cos_sim_recall
+      value: 73.45646437994723
+    - type: dot_accuracy
+      value: 86.23114978840078
+    - type: dot_ap
+      value: 74.26624032659652
+    - type: dot_f1
+      value: 68.72377190817083
+    - type: dot_precision
+      value: 64.56400742115028
+    - type: dot_recall
+      value: 73.45646437994723
+    - type: euclidean_accuracy
+      value: 86.23114978840078
+    - type: euclidean_ap
+      value: 74.26624714480556
+    - type: euclidean_f1
+      value: 68.72377190817083
+    - type: euclidean_precision
+      value: 64.56400742115028
+    - type: euclidean_recall
+      value: 73.45646437994723
+    - type: manhattan_accuracy
+      value: 86.16558383501221
+    - type: manhattan_ap
+      value: 74.2091943976357
+    - type: manhattan_f1
+      value: 68.64221520524654
+    - type: manhattan_precision
+      value: 63.59135913591359
+    - type: manhattan_recall
+      value: 74.5646437994723
+    - type: max_accuracy
+      value: 86.23114978840078
+    - type: max_ap
+      value: 74.26624727825818
+    - type: max_f1
+      value: 68.72377190817083
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twitterurlcorpus-pairclassification
+      name: MTEB TwitterURLCorpus
+      config: default
+      split: test
+      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
+    metrics:
+    - type: cos_sim_accuracy
+      value: 89.3681841114604
+    - type: cos_sim_ap
+      value: 86.65166387498546
+    - type: cos_sim_f1
+      value: 79.02581944698774
+    - type: cos_sim_precision
+      value: 75.35796605434099
+    - type: cos_sim_recall
+      value: 83.06898675700647
+    - type: dot_accuracy
+      value: 89.3681841114604
+    - type: dot_ap
+      value: 86.65166019802056
+    - type: dot_f1
+      value: 79.02581944698774
+    - type: dot_precision
+      value: 75.35796605434099
+    - type: dot_recall
+      value: 83.06898675700647
+    - type: euclidean_accuracy
+      value: 89.3681841114604
+    - type: euclidean_ap
+      value: 86.65166462876266
+    - type: euclidean_f1
+      value: 79.02581944698774
+    - type: euclidean_precision
+      value: 75.35796605434099
+    - type: euclidean_recall
+      value: 83.06898675700647
+    - type: manhattan_accuracy
+      value: 89.36624364497226
+    - type: manhattan_ap
+      value: 86.65076471274106
+    - type: manhattan_f1
+      value: 79.07408783532733
+    - type: manhattan_precision
+      value: 76.41102972856527
+    - type: manhattan_recall
+      value: 81.92947336002464
+    - type: max_accuracy
+      value: 89.3681841114604
+    - type: max_ap
+      value: 86.65166462876266
+    - type: max_f1
+      value: 79.07408783532733
 license: apache-2.0
+language:
+- en
 ---
+
+# nomic-embed-text-v1.5: Resizable Production Embeddings with Matryoshka Representation Learning  
+
+**Exciting Update!**: `nomic-embed-text-v1.5` is now multimodal! [nomic-embed-vision-v1](https://huggingface.co/nomic-ai/nomic-embed-vision-v1.5) is aligned to the embedding space of `nomic-embed-text-v1.5`, meaning any text embedding is multimodal!
+
+## Usage
+
+**Important**: the text prompt *must* include a *task instruction prefix*, instructing the model which task is being performed. 
+
+For example, if you are implementing a RAG application, you embed your documents as `search_document: <text here>` and embed your user queries as `search_query: <text here>`.
+
+## Task instruction prefixes
+
+### `search_document`
+
+#### Purpose: embed texts as documents from a dataset
+
+This prefix is used for embedding texts as documents, for example as documents for a RAG index.
+
+```python
+from sentence_transformers import SentenceTransformer
+
+model = SentenceTransformer("nomic-ai/nomic-embed-text-v1", trust_remote_code=True)
+sentences = ['search_document: TSNE is a dimensionality reduction algorithm created by Laurens van Der Maaten']
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
+### `search_query`
+
+#### Purpose: embed texts as questions to answer
+
+This prefix is used for embedding texts as questions that documents from a dataset could resolve, for example as queries to be answered by a RAG application.
+
+```python
+from sentence_transformers import SentenceTransformer
+
+model = SentenceTransformer("nomic-ai/nomic-embed-text-v1", trust_remote_code=True)
+sentences = ['search_query: Who is Laurens van Der Maaten?']
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
+### `clustering`
+
+#### Purpose: embed texts to group them into clusters
+
+This prefix is used for embedding texts in order to group them into clusters, discover common topics, or remove semantic duplicates.
+
+```python
+from sentence_transformers import SentenceTransformer
+
+model = SentenceTransformer("nomic-ai/nomic-embed-text-v1", trust_remote_code=True)
+sentences = ['clustering: the quick brown fox']
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
+### `classification`
+
+#### Purpose: embed texts to classify them
+
+This prefix is used for embedding texts into vectors that will be used as features for a classification model
+
+```python
+from sentence_transformers import SentenceTransformer
+
+model = SentenceTransformer("nomic-ai/nomic-embed-text-v1", trust_remote_code=True)
+sentences = ['classification: the quick brown fox']
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
+
+### Sentence Transformers
+```python
+import torch.nn.functional as F
+from sentence_transformers import SentenceTransformer
+
+matryoshka_dim = 512
+
+model = SentenceTransformer("nomic-ai/nomic-embed-text-v1.5", trust_remote_code=True)
+sentences = ['search_query: What is TSNE?', 'search_query: Who is Laurens van der Maaten?']
+embeddings = model.encode(sentences, convert_to_tensor=True)
+embeddings = F.layer_norm(embeddings, normalized_shape=(embeddings.shape[1],))
+embeddings = embeddings[:, :matryoshka_dim]
+embeddings = F.normalize(embeddings, p=2, dim=1)
+print(embeddings)
+```
+
+### Transformers
+
+```diff
+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 = ['search_query: What is TSNE?', 'search_query: Who is Laurens van der Maaten?']
+
+tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
+model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1.5', trust_remote_code=True, safe_serialization=True)
+model.eval()
+
+encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
+
++ matryoshka_dim = 512
+
+with torch.no_grad():
+    model_output = model(**encoded_input)
+
+embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
++ embeddings = F.layer_norm(embeddings, normalized_shape=(embeddings.shape[1],))
++ embeddings = embeddings[:, :matryoshka_dim]
+embeddings = F.normalize(embeddings, p=2, dim=1)
+print(embeddings)
+```
+
+The model natively supports scaling of the sequence length past 2048 tokens. To do so, 
+
+```diff
+- tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
++ tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', model_max_length=8192)
+
+
+- model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1', trust_remote_code=True)
++ model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1', trust_remote_code=True, rotary_scaling_factor=2)
+```
+
+### Transformers.js
+
+```js
+import { pipeline, layer_norm } from '@xenova/transformers';
+
+// Create a feature extraction pipeline
+const extractor = await pipeline('feature-extraction', 'nomic-ai/nomic-embed-text-v1.5', {
+    quantized: false, // Comment out this line to use the quantized version
+});
+
+// Define sentences
+const texts = ['search_query: What is TSNE?', 'search_query: Who is Laurens van der Maaten?'];
+
+// Compute sentence embeddings
+let embeddings = await extractor(texts, { pooling: 'mean' });
+console.log(embeddings); // Tensor of shape [2, 768]
+
+const matryoshka_dim = 512;
+embeddings = layer_norm(embeddings, [embeddings.dims[1]])
+    .slice(null, [0, matryoshka_dim])
+    .normalize(2, -1);
+console.log(embeddings.tolist());
+```
+
+
+## Nomic API
+
+The easiest way to use Nomic Embed is through the Nomic Embedding API.
+
+Generating embeddings with the `nomic` Python client is as easy as 
+
+```python
+from nomic import embed
+
+output = embed.text(
+    texts=['Nomic Embedding API', '#keepAIOpen'],
+    model='nomic-embed-text-v1.5',
+    task_type='search_document',
+    dimensionality=256,
+)
+
+print(output)
+```
+
+For more information, see the [API reference](https://docs.nomic.ai/reference/endpoints/nomic-embed-text)
+
+
+## Infinity
+
+Usage with [Infinity](https://github.com/michaelfeil/infinity).
+
+```bash
+docker run --gpus all -v $PWD/data:/app/.cache -e HF_TOKEN=$HF_TOKEN -p "7997":"7997" \
+michaelf34/infinity:0.0.70 \
+v2 --model-id nomic-ai/nomic-embed-text-v1.5 --revision "main" --dtype float16 --batch-size 8 --engine torch --port 7997 --no-bettertransformer
+```
+
+## Adjusting Dimensionality
+
+`nomic-embed-text-v1.5` is an improvement upon [Nomic Embed](https://huggingface.co/nomic-ai/nomic-embed-text-v1) that utilizes [Matryoshka Representation Learning](https://arxiv.org/abs/2205.13147) which gives developers the flexibility to trade off the embedding size for a negligible reduction in performance.
+
+
+| Name                             | SeqLen | Dimension | MTEB      |
+| :-------------------------------:| :----- | :-------- | :------:  |
+| nomic-embed-text-v1              | 8192   |  768      | **62.39** |
+| nomic-embed-text-v1.5            | 8192   |  768      | 62.28     |
+| nomic-embed-text-v1.5            | 8192   |  512      | 61.96     |
+| nomic-embed-text-v1.5            | 8192   |  256      | 61.04     |
+| nomic-embed-text-v1.5            | 8192   |  128      | 59.34     |
+| nomic-embed-text-v1.5            | 8192   |  64       | 56.10     |
+
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/607997c83a565c15675055b3/CRnaHV-c2wMUMZKw72q85.png)
+
+## Training
+Click the Nomic Atlas map below to visualize a 5M sample of our contrastive pretraining data!
+
+[![image/webp](https://cdn-uploads.huggingface.co/production/uploads/607997c83a565c15675055b3/pjhJhuNyRfPagRd_c_iUz.webp)](https://atlas.nomic.ai/map/nomic-text-embed-v1-5m-sample)
+
+We train our embedder using a multi-stage training pipeline. Starting from a long-context [BERT model](https://huggingface.co/nomic-ai/nomic-bert-2048),
+the first unsupervised contrastive stage trains on a dataset generated from weakly related text pairs, such as question-answer pairs from forums like StackExchange and Quora, title-body pairs from Amazon reviews, and summarizations from news articles.
+
+In the second finetuning stage, higher quality labeled datasets such as search queries and answers from web searches are leveraged. Data curation and hard-example mining is crucial in this stage.
+
+For more details, see the Nomic Embed [Technical Report](https://static.nomic.ai/reports/2024_Nomic_Embed_Text_Technical_Report.pdf) and corresponding [blog post](https://blog.nomic.ai/posts/nomic-embed-matryoshka).
+
+Training data to train the models is released in its entirety. For more details, see the `contrastors` [repository](https://github.com/nomic-ai/contrastors)
+
+
+# Join the Nomic Community
+
+- Nomic: [https://nomic.ai](https://nomic.ai)
+- Discord: [https://discord.gg/myY5YDR8z8](https://discord.gg/myY5YDR8z8)
+- Twitter: [https://twitter.com/nomic_ai](https://twitter.com/nomic_ai)
+
+
+# Citation
+
+If you find the model, dataset, or training code useful, please cite our work
+
+```bibtex
+@misc{nussbaum2024nomic,
+      title={Nomic Embed: Training a Reproducible Long Context Text Embedder}, 
+      author={Zach Nussbaum and John X. Morris and Brandon Duderstadt and Andriy Mulyar},
+      year={2024},
+      eprint={2402.01613},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```

config.json

@@ -0,0 +1,57 @@
+{
+  "activation_function": "swiglu",
+  "architectures": [
+    "NomicBertModel"
+  ],
+  "attn_pdrop": 0.0,
+  "auto_map": {                                                                                                                                                                                                                 
+    "AutoConfig": "nomic-ai/nomic-bert-2048--configuration_hf_nomic_bert.NomicBertConfig",
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+}

config_sentence_transformers.json

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+{
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+      "transformers": "4.37.2",
+      "pytorch": "2.1.0+cu121"
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model.safetensors

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modules.json

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onnx/model.onnx

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sentence_bert_config.json

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+    "do_lower_case": false,
+    "model_args": {
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+    }
+}

special_tokens_map.json

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+}

tokenizer_config.json

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