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README.md

@@ -1,2851 +1,348 @@
 ---
-library_name: sentence-transformers
-pipeline_tag: sentence-similarity
 tags:
-- feature-extraction
-- sentence-similarity
-- mteb
-- transformers
-- transformers.js
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: Add a navigation menu text
+- text: Change to square format
+- text: Change the button text
+- text: Make the object stand alone
+- text: Mirror the logo vertically
+metrics:
+- accuracy
+pipeline_tag: text-classification
+library_name: setfit
+inference: true
+base_model: nomic-ai/nomic-embed-text-v1.5
 model-index:
-- name: epoch_0_model
+- name: SetFit with nomic-ai/nomic-embed-text-v1.5
   results:
   - task:
-      type: Classification
+      type: text-classification
+      name: Text Classification
     dataset:
-      type: mteb/amazon_counterfactual
-      name: MTEB AmazonCounterfactualClassification (en)
-      config: en
+      name: Unknown
+      type: unknown
       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
+      value: 0.5353535353535354
+      name: Accuracy
 ---
 
-# nomic-embed-text-v1.5: Resizable Production Embeddings with Matryoshka Representation Learning  
-
-[Blog](https://www.nomic.ai/blog/posts/nomic-embed-text-v1) | [Technical Report](https://arxiv.org/abs/2402.01613) | [AWS SageMaker](https://aws.amazon.com/marketplace/seller-profile?id=seller-tpqidcj54zawi) | [Nomic Platform](https://atlas.nomic.ai)
-
-**Exciting Update!**: `nomic-embed-text-v1.5` is now multimodal! [nomic-embed-vision-v1.5](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-v1.5", 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-v1.5", 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-v1.5", 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-v1.5", 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
+# SetFit with nomic-ai/nomic-embed-text-v1.5
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [nomic-ai/nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
+
+The model has been trained using an efficient few-shot learning technique that involves:
+
+1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
+2. Training a classification head with features from the fine-tuned Sentence Transformer.
+
+## Model Details
+
+### Model Description
+- **Model Type:** SetFit
+- **Sentence Transformer body:** [nomic-ai/nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5)
+- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
+- **Maximum Sequence Length:** 8192 tokens
+- **Number of Classes:** 63 classes
+<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
+- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
+- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
+
+### Model Labels
+| Label | Examples                                                                                                                                                                            |
+|:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 0     | <ul><li>'Add a corporate presentation background'</li><li>'Insert a modern icon set for the design'</li><li>'Add a mountain landscape background to the page'</li></ul>             |
+| 1     | <ul><li>'Find me some shape options for this design'</li><li>'I need some professional-looking assets'</li><li>'Can you recommend some images that would work well here?'</li></ul> |
+| 2     | <ul><li>'Add a date and time for the event'</li><li>'Insert a disclaimer text'</li><li>'Add a navigation menu text'</li></ul>                                                       |
+| 3     | <ul><li>'Distribute the icons evenly'</li><li>'Align all the text elements to the left'</li><li>'Align the footer elements'</li></ul>                                               |
+| 4     | <ul><li>'Make the button pulse'</li><li>'Add a flip animation'</li><li>'Make the text glow'</li></ul>                                                                               |
+| 5     | <ul><li>'Make everything fade in gradually'</li><li>'Make the page bounce in from the top'</li><li>'Add a spiral animation to the page'</li></ul>                                   |
+| 6     | <ul><li>'Change the building color'</li><li>'Remove the unwanted text overlay'</li><li>'Add a party hat to the dog'</li></ul>                                                       |
+| 7     | <ul><li>'Remove the draft image'</li><li>'Delete the backup copy'</li><li>'Remove the unwanted image'</li></ul>                                                                     |
+| 8     | <ul><li>'What image editing tools do you have?'</li><li>'How do I create a template?'</li><li>'How can I align elements properly?'</li></ul>                                        |
+| 9     | <ul><li>'Distribute the buttons around the center image'</li><li>'Place the elements in a circular arrangement'</li><li>'Arrange the images in a circular layout'</li></ul>         |
+| 10    | <ul><li>'Duplicate the design'</li><li>'Make a second version'</li><li>'Create a new version'</li></ul>                                                                             |
+| 11    | <ul><li>'Duplicate the icon and move it'</li><li>'Duplicate the text and align it differently'</li><li>'Copy the image and apply a filter'</li></ul>                                |
+| 12    | <ul><li>'Duplicate the logo to page 3'</li><li>'Copy the text to the last page'</li><li>'Copy the navigation to the next page'</li></ul>                                            |
+| 13    | <ul><li>'Fix the typographic errors'</li><li>'Improve the text flow'</li><li>'Fix the font consistency'</li></ul>                                                                   |
+| 14    | <ul><li>'Mirror the icon horizontally'</li><li>'Mirror the logo vertically'</li><li>'Flip the image horizontally'</li></ul>                                                         |
+| 15    | <ul><li>'Create a photo of a futuristic city'</li><li>'Generate a picture of a tropical beach'</li><li>'Generate a picture of a cat playing with yarn'</li></ul>                    |
+| 16    | <ul><li>'Create a card for a birthday party'</li><li>'Create a flyer for a happy birthday party'</li><li>'Generate an Instagram post for a birthday'</li></ul>                      |
+| 17    | <ul><li>'Group the navigation elements'</li><li>'Combine the shape and text'</li><li>'Combine the image and overlay'</li></ul>                                                      |
+| 18    | <ul><li>'Move the shape to the bottom'</li><li>'Position the icon at (0, 0)'</li><li>'Move the shape to coordinates (100, 100)'</li></ul>                                           |
+| 19    | <ul><li>'Add a sepia tone effect'</li><li>'Apply a modern filter'</li><li>'Make the image black and white'</li></ul>                                                                |
+| 20    | <ul><li>'Suggest some shape designs'</li><li>'Show me pattern options'</li><li>'Find me border designs'</li></ul>                                                                   |
+| 21    | <ul><li>'Restore the previous opacity'</li><li>'Redo the text edit'</li><li>'Restore the previous color'</li></ul>                                                                  |
+| 22    | <ul><li>'Make the image have no background'</li><li>'Remove the background from the animal'</li><li>'Remove the background from the item'</li></ul>                                 |
+| 23    | <ul><li>'Delete the unwanted text'</li><li>'Remove the person from the background'</li><li>'Remove the graffiti'</li></ul>                                                          |
+| 24    | <ul><li>'Replace the illustration'</li><li>'Change the background image'</li><li>'Change the product photo'</li></ul>                                                               |
+| 25    | <ul><li>'Change the button text'</li><li>'Update the navigation text'</li><li>'Update the description'</li></ul>                                                                    |
+| 26    | <ul><li>'Remove all modifications'</li><li>'Restore the original colors'</li><li>'Remove all effects from the image'</li></ul>                                                      |
+| 27    | <ul><li>'Scale the text up'</li><li>'Make the shape smaller'</li><li>'Reduce the shape size'</li></ul>                                                                              |
+| 28    | <ul><li>'Change to square format'</li><li>'Change to poster size'</li><li>'Make the page smaller'</li></ul>                                                                         |
+| 29    | <ul><li>'Rotate the text 45 degrees'</li><li>'Rotate the text 15 degrees'</li><li>'Turn the image counterclockwise'</li></ul>                                                       |
+| 30    | <ul><li>'Distribute the shapes randomly'</li><li>'Distribute the leaves randomly on the page'</li><li>'Scatter the confetti around the design'</li></ul>                            |
+| 31    | <ul><li>'Select the sidebar elements'</li><li>'Select the background image'</li><li>'Select the footer content'</li></ul>                                                           |
+| 32    | <ul><li>'Change to a dark background'</li><li>'Make the background transparent'</li><li>'Change to a neutral background'</li></ul>                                                  |
+| 33    | <ul><li>'Set the blend mode to soft light'</li><li>'Set the blend mode to darken'</li><li>'Set the blend mode to normal'</li></ul>                                                  |
+| 34    | <ul><li>'Add a depth of field blur'</li><li>'Add a directional blur'</li><li>'Add a soft blur effect'</li></ul>                                                                     |
+| 35    | <ul><li>'Change the border style to solid'</li><li>'Change the border to dotted'</li><li>'Add a double border to the image'</li></ul>                                               |
+| 36    | <ul><li>'Brighten the shadows'</li><li>'Make the image brighter'</li><li>'Brighten the highlights'</li></ul>                                                                        |
+| 37    | <ul><li>'Bring the title to the front'</li><li>'Bring the shape to the front'</li><li>'Move the logo to the top layer'</li></ul>                                                    |
+| 38    | <ul><li>'Make the image more intense'</li><li>'Enhance the contrast ratio'</li><li>'Increase the tonal range'</li></ul>                                                             |
+| 39    | <ul><li>'Make the image round'</li><li>'Make the image rectangular'</li><li>'Crop to a heart shape'</li></ul>                                                                       |
+| 40    | <ul><li>'Add a sharp drop shadow'</li><li>'Add a gradient shadow'</li><li>'Add a hard shadow edge'</li></ul>                                                                        |
+| 41    | <ul><li>'Fill the shape with orange'</li><li>'Fill the element with brown'</li><li>'Change the shape color to blue'</li></ul>                                                       |
+| 42    | <ul><li>'Make the text more readable'</li><li>'Increase the heading size'</li><li>'Make the description smaller'</li></ul>                                                          |
+| 43    | <ul><li>'Make the text bold and italic'</li><li>'Add strikethrough to the text'</li><li>'Add bold to the title'</li></ul>                                                           |
+| 44    | <ul><li>'Use a contemporary font'</li><li>'Use a professional font'</li><li>'Use a serif font for the heading'</li></ul>                                                            |
+| 45    | <ul><li>'Make the bright areas brighter'</li><li>'Make the highlights more prominent'</li><li>'Enhance the bright spots'</li></ul>                                                  |
+| 46    | <ul><li>'Set the image as background layer'</li><li>'Set the picture as background fill'</li><li>'Make the photo cover the background'</li></ul>                                    |
+| 47    | <ul><li>'Reduce the character spacing'</li><li>'Add letter spacing to the logo'</li><li>'Increase spacing between characters'</li></ul>                                             |
+| 48    | <ul><li>'Make the lines tighter'</li><li>'Spread out the text lines'</li><li>'Increase the paragraph spacing'</li></ul>                                                             |
+| 49    | <ul><li>'Reduce the opacity of the overlay'</li><li>'Increase the transparency of the image'</li><li>'Make the shape more opaque'</li></ul>                                         |
+| 50    | <ul><li>'Make the paragraphs closer together'</li><li>'Increase the text block spacing'</li><li>'Reduce the paragraph spacing'</li></ul>                                            |
+| 51    | <ul><li>'Make the colors more intense'</li><li>'Increase the color depth'</li><li>'Make the image more colorful'</li></ul>                                                          |
+| 52    | <ul><li>'Darken the shadows in the image'</li><li>'Increase the shadow intensity'</li><li>'Enhance the shadow depth'</li></ul>                                                      |
+| 53    | <ul><li>'Enhance the image clarity'</li><li>'Increase the image sharpness'</li><li>'Sharpen the image details'</li></ul>                                                            |
+| 54    | <ul><li>'Center the button text'</li><li>'Justify the paragraph text'</li><li>'Center the title text'</li></ul>                                                                     |
+| 55    | <ul><li>'Create a border around the text'</li><li>'Add a glow effect behind the text'</li><li>'Add a colored background to the text'</li></ul>                                      |
+| 56    | <ul><li>'Create text in a radial pattern'</li><li>'Create text that follows a circle'</li><li>'Create text that follows a shape'</li></ul>                                          |
+| 57    | <ul><li>'Convert to a bulleted list'</li><li>'Make the text into bullet points'</li><li>'Make the text into a list with bullets'</li></ul>                                          |
+| 58    | <ul><li>'Create a soft shadow behind the text'</li><li>'Add a dramatic shadow effect'</li><li>'Create a shadow for the text'</li></ul>                                              |
+| 59    | <ul><li>'Add warm undertones to the photo'</li><li>'Add warm color grading'</li><li>'Make the photo more golden hour'</li></ul>                                                     |
+| 60    | <ul><li>'I need to add my own image'</li><li>'Open the image upload tool'</li><li>'Show me how to upload files'</li></ul>                                                           |
+| 61    | <ul><li>'Revert the color change'</li><li>'Undo the last modification'</li><li>'Undo the text edit'</li></ul>                                                                       |
+| 62    | <ul><li>'Separate the grouped components'</li><li>'Ungroup the combined elements'</li><li>'Break up the grouped objects'</li></ul>                                                  |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.5354   |
+
+## Uses
+
+### Direct Use for Inference
+
+First install the SetFit library:
 
-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-v1.5', trust_remote_code=True)
-+ model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1.5', trust_remote_code=True, rotary_scaling_factor=2)
-```
-
-### Transformers.js
-
-```js
-import { pipeline, layer_norm } from '@huggingface/transformers';
-
-// Create a feature extraction pipeline
-const extractor = await pipeline('feature-extraction', 'nomic-ai/nomic-embed-text-v1.5');
-
-// 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());
+```bash
+pip install setfit
 ```
 
-
-## 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 
+Then you can load this model and run inference.
 
 ```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,
-)
+from setfit import SetFitModel
 
-print(output)
+# Download from the 🤗 Hub
+model = SetFitModel.from_pretrained("setfit_model_id")
+# Run inference
+preds = model("Change the button text")
 ```
 
-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
+<!--
+### Downstream Use
+
+*List how someone could finetune this model on their own dataset.*
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Set Metrics
+| Training set | Min | Median | Max |
+|:-------------|:----|:-------|:----|
+| Word count   | 3   | 5.1778 | 10  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0     | 5                     |
+| 1     | 5                     |
+| 2     | 5                     |
+| 3     | 5                     |
+| 4     | 5                     |
+| 5     | 5                     |
+| 6     | 5                     |
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+| 59    | 5                     |
+| 60    | 5                     |
+| 61    | 5                     |
+| 62    | 5                     |
+
+### Training Hyperparameters
+- batch_size: (64, 64)
+- num_epochs: (1, 1)
+- max_steps: -1
+- sampling_strategy: oversampling
+- body_learning_rate: (2e-05, 1e-05)
+- head_learning_rate: 0.01
+- loss: CosineSimilarityLoss
+- distance_metric: cosine_distance
+- margin: 0.25
+- end_to_end: False
+- use_amp: False
+- warmup_proportion: 0.1
+- l2_weight: 0.01
+- seed: 42
+- eval_max_steps: -1
+- load_best_model_at_end: False
+
+### Training Results
+| Epoch  | Step | Training Loss | Validation Loss |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0007 | 1    | 0.1545        | -               |
+| 0.0328 | 50   | 0.1187        | -               |
+| 0.0655 | 100  | 0.0521        | -               |
+| 0.0983 | 150  | 0.0208        | -               |
+| 0.1311 | 200  | 0.0123        | -               |
+| 0.1638 | 250  | 0.0096        | -               |
+| 0.1966 | 300  | 0.0056        | -               |
+| 0.2294 | 350  | 0.0036        | -               |
+| 0.2621 | 400  | 0.0027        | -               |
+| 0.2949 | 450  | 0.0017        | -               |
+| 0.3277 | 500  | 0.0007        | -               |
+| 0.3604 | 550  | 0.0009        | -               |
+| 0.3932 | 600  | 0.0009        | -               |
+| 0.4260 | 650  | 0.0003        | -               |
+| 0.4587 | 700  | 0.0003        | -               |
+| 0.4915 | 750  | 0.0004        | -               |
+| 0.5242 | 800  | 0.0004        | -               |
+| 0.5570 | 850  | 0.0002        | -               |
+| 0.5898 | 900  | 0.0001        | -               |
+| 0.6225 | 950  | 0.0001        | -               |
+| 0.6553 | 1000 | 0.0001        | -               |
+| 0.6881 | 1050 | 0.0001        | -               |
+| 0.7208 | 1100 | 0.0001        | -               |
+| 0.7536 | 1150 | 0.0001        | -               |
+| 0.7864 | 1200 | 0.0001        | -               |
+| 0.8191 | 1250 | 0.0001        | -               |
+| 0.8519 | 1300 | 0.0001        | -               |
+| 0.8847 | 1350 | 0.0001        | -               |
+| 0.9174 | 1400 | 0.0001        | -               |
+| 0.9502 | 1450 | 0.0001        | -               |
+| 0.9830 | 1500 | 0.0001        | -               |
+
+### Framework Versions
+- Python: 3.12.11
+- SetFit: 1.1.3
+- Sentence Transformers: 5.1.0
+- Transformers: 4.54.1
+- PyTorch: 2.7.1
+- Datasets: 4.0.0
+- Tokenizers: 0.21.4
+
+## Citation
+
+### BibTeX
+```bibtex
+@article{https://doi.org/10.48550/arxiv.2209.11055,
+    doi = {10.48550/ARXIV.2209.11055},
+    url = {https://arxiv.org/abs/2209.11055},
+    author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
+    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
+    title = {Efficient Few-Shot Learning Without Prompts},
+    publisher = {arXiv},
+    year = {2022},
+    copyright = {Creative Commons Attribution 4.0 International}
+}
 ```
 
-## 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     |
-
+<!--
+## Glossary
 
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/607997c83a565c15675055b3/CRnaHV-c2wMUMZKw72q85.png)
+*Clearly define terms in order to be accessible across audiences.*
+-->
 
-## Training
-Click the Nomic Atlas map below to visualize a 5M sample of our contrastive pretraining data!
+<!--
+## Model Card Authors
 
-[![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)
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
 
-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.
+<!--
+## Model Card Contact
 
-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}
-}
-```
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config_setfit.json

@@ -1,68 +1,68 @@
 {
   "normalize_embeddings": false,
   "labels": [
-    "addAsset",
-    "addAssetsUIAction",
-    "addText",
-    "align",
-    "applyAnimationObjectPreset",
-    "applyPageLevelAnimation",
-    "changeContentInImage",
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-    "setCropShape",
-    "setDropShadow",
-    "setFillColor",
-    "setFontSize",
-    "setFontStyle",
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-    "setHighlight",
-    "setImageAsBackground",
-    "setLetterSpacing",
-    "setLineSpacing",
-    "setOpacity",
-    "setParagraphSpacing",
-    "setSaturation",
-    "setShadow",
-    "setSharpness",
-    "setTextAlignment",
-    "setTextDecorationEffect",
-    "setTextLayout",
-    "setTextListStyle",
-    "setTextShadow",
-    "setWarmth",
-    "showUploadUI",
-    "undo",
-    "ungroup"
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   ]
 }

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