model

README.md

@@ -1,3 +1,2762 @@
----
-license: apache-2.0
----
+---
+language:
+- en
+library_name: transformers
+license: apache-2.0
+model-index:
+- name: gte-base-en-v1.5
+  results:
+  - dataset:
+      config: en
+      name: MTEB AmazonCounterfactualClassification (en)
+      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
+      split: test
+      type: mteb/amazon_counterfactual
+    metrics:
+    - type: accuracy
+      value: 74.7910447761194
+    - type: ap
+      value: 37.053785713650626
+    - type: f1
+      value: 68.51101510998551
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB AmazonPolarityClassification
+      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
+      split: test
+      type: mteb/amazon_polarity
+    metrics:
+    - type: accuracy
+      value: 93.016875
+    - type: ap
+      value: 89.17750268426342
+    - type: f1
+      value: 92.9970977240524
+    task:
+      type: Classification
+  - dataset:
+      config: en
+      name: MTEB AmazonReviewsClassification (en)
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+      split: test
+      type: mteb/amazon_reviews_multi
+    metrics:
+    - type: accuracy
+      value: 53.312000000000005
+    - type: f1
+      value: 52.98175784163017
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB ArguAna
+      revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
+      split: test
+      type: mteb/arguana
+    metrics:
+    - type: map_at_1
+      value: 38.193
+    - type: map_at_10
+      value: 54.848
+    - type: map_at_100
+      value: 55.388000000000005
+    - type: map_at_1000
+      value: 55.388999999999996
+    - type: map_at_3
+      value: 50.427
+    - type: map_at_5
+      value: 53.105000000000004
+    - type: mrr_at_1
+      value: 39.047
+    - type: mrr_at_10
+      value: 55.153
+    - type: mrr_at_100
+      value: 55.686
+    - type: mrr_at_1000
+      value: 55.688
+    - type: mrr_at_3
+      value: 50.676
+    - type: mrr_at_5
+      value: 53.417
+    - type: ndcg_at_1
+      value: 38.193
+    - type: ndcg_at_10
+      value: 63.486
+    - type: ndcg_at_100
+      value: 65.58
+    - type: ndcg_at_1000
+      value: 65.61
+    - type: ndcg_at_3
+      value: 54.494
+    - type: ndcg_at_5
+      value: 59.339
+    - type: precision_at_1
+      value: 38.193
+    - type: precision_at_10
+      value: 9.075
+    - type: precision_at_100
+      value: 0.9939999999999999
+    - type: precision_at_1000
+      value: 0.1
+    - type: precision_at_3
+      value: 22.096
+    - type: precision_at_5
+      value: 15.619
+    - type: recall_at_1
+      value: 38.193
+    - type: recall_at_10
+      value: 90.754
+    - type: recall_at_100
+      value: 99.431
+    - type: recall_at_1000
+      value: 99.644
+    - type: recall_at_3
+      value: 66.28699999999999
+    - type: recall_at_5
+      value: 78.094
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB ArxivClusteringP2P
+      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
+      split: test
+      type: mteb/arxiv-clustering-p2p
+    metrics:
+    - type: v_measure
+      value: 47.508221208908964
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB ArxivClusteringS2S
+      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
+      split: test
+      type: mteb/arxiv-clustering-s2s
+    metrics:
+    - type: v_measure
+      value: 42.04668382560096
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB AskUbuntuDupQuestions
+      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
+      split: test
+      type: mteb/askubuntudupquestions-reranking
+    metrics:
+    - type: map
+      value: 61.828759903716815
+    - type: mrr
+      value: 74.37343358395991
+    task:
+      type: Reranking
+  - dataset:
+      config: default
+      name: MTEB BIOSSES
+      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
+      split: test
+      type: mteb/biosses-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 85.03673698773017
+    - type: cos_sim_spearman
+      value: 83.6470866785058
+    - type: euclidean_pearson
+      value: 82.64048673096565
+    - type: euclidean_spearman
+      value: 83.63142367101115
+    - type: manhattan_pearson
+      value: 82.71493099760228
+    - type: manhattan_spearman
+      value: 83.60491704294326
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB Banking77Classification
+      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
+      split: test
+      type: mteb/banking77
+    metrics:
+    - type: accuracy
+      value: 86.73376623376623
+    - type: f1
+      value: 86.70294049278262
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB BiorxivClusteringP2P
+      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
+      split: test
+      type: mteb/biorxiv-clustering-p2p
+    metrics:
+    - type: v_measure
+      value: 40.31923804167062
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB BiorxivClusteringS2S
+      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
+      split: test
+      type: mteb/biorxiv-clustering-s2s
+    metrics:
+    - type: v_measure
+      value: 37.552547125348454
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB CQADupstackAndroidRetrieval
+      revision: f46a197baaae43b4f621051089b82a364682dfeb
+      split: test
+      type: mteb/cqadupstack-android
+    metrics:
+    - type: map_at_1
+      value: 30.567
+    - type: map_at_10
+      value: 41.269
+    - type: map_at_100
+      value: 42.689
+    - type: map_at_1000
+      value: 42.84
+    - type: map_at_3
+      value: 37.567
+    - type: map_at_5
+      value: 39.706
+    - type: mrr_at_1
+      value: 37.053000000000004
+    - type: mrr_at_10
+      value: 46.900999999999996
+    - type: mrr_at_100
+      value: 47.662
+    - type: mrr_at_1000
+      value: 47.713
+    - type: mrr_at_3
+      value: 43.801
+    - type: mrr_at_5
+      value: 45.689
+    - type: ndcg_at_1
+      value: 37.053000000000004
+    - type: ndcg_at_10
+      value: 47.73
+    - type: ndcg_at_100
+      value: 53.128
+    - type: ndcg_at_1000
+      value: 55.300000000000004
+    - type: ndcg_at_3
+      value: 42.046
+    - type: ndcg_at_5
+      value: 44.782
+    - type: precision_at_1
+      value: 37.053000000000004
+    - type: precision_at_10
+      value: 9.142
+    - type: precision_at_100
+      value: 1.485
+    - type: precision_at_1000
+      value: 0.197
+    - type: precision_at_3
+      value: 20.076
+    - type: precision_at_5
+      value: 14.535
+    - type: recall_at_1
+      value: 30.567
+    - type: recall_at_10
+      value: 60.602999999999994
+    - type: recall_at_100
+      value: 83.22800000000001
+    - type: recall_at_1000
+      value: 96.696
+    - type: recall_at_3
+      value: 44.336999999999996
+    - type: recall_at_5
+      value: 51.949
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB CQADupstackEnglishRetrieval
+      revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
+      split: test
+      type: mteb/cqadupstack-english
+    metrics:
+    - type: map_at_1
+      value: 28.538000000000004
+    - type: map_at_10
+      value: 38.757999999999996
+    - type: map_at_100
+      value: 40.129
+    - type: map_at_1000
+      value: 40.262
+    - type: map_at_3
+      value: 35.866
+    - type: map_at_5
+      value: 37.417
+    - type: mrr_at_1
+      value: 36.051
+    - type: mrr_at_10
+      value: 44.868
+    - type: mrr_at_100
+      value: 45.568999999999996
+    - type: mrr_at_1000
+      value: 45.615
+    - type: mrr_at_3
+      value: 42.558
+    - type: mrr_at_5
+      value: 43.883
+    - type: ndcg_at_1
+      value: 36.051
+    - type: ndcg_at_10
+      value: 44.584
+    - type: ndcg_at_100
+      value: 49.356
+    - type: ndcg_at_1000
+      value: 51.39
+    - type: ndcg_at_3
+      value: 40.389
+    - type: ndcg_at_5
+      value: 42.14
+    - type: precision_at_1
+      value: 36.051
+    - type: precision_at_10
+      value: 8.446
+    - type: precision_at_100
+      value: 1.411
+    - type: precision_at_1000
+      value: 0.19
+    - type: precision_at_3
+      value: 19.639
+    - type: precision_at_5
+      value: 13.796
+    - type: recall_at_1
+      value: 28.538000000000004
+    - type: recall_at_10
+      value: 54.99000000000001
+    - type: recall_at_100
+      value: 75.098
+    - type: recall_at_1000
+      value: 87.848
+    - type: recall_at_3
+      value: 42.236000000000004
+    - type: recall_at_5
+      value: 47.377
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB CQADupstackGamingRetrieval
+      revision: 4885aa143210c98657558c04aaf3dc47cfb54340
+      split: test
+      type: mteb/cqadupstack-gaming
+    metrics:
+    - type: map_at_1
+      value: 37.188
+    - type: map_at_10
+      value: 50.861000000000004
+    - type: map_at_100
+      value: 51.917
+    - type: map_at_1000
+      value: 51.964999999999996
+    - type: map_at_3
+      value: 47.144000000000005
+    - type: map_at_5
+      value: 49.417
+    - type: mrr_at_1
+      value: 42.571
+    - type: mrr_at_10
+      value: 54.086999999999996
+    - type: mrr_at_100
+      value: 54.739000000000004
+    - type: mrr_at_1000
+      value: 54.762
+    - type: mrr_at_3
+      value: 51.285000000000004
+    - type: mrr_at_5
+      value: 53.0
+    - type: ndcg_at_1
+      value: 42.571
+    - type: ndcg_at_10
+      value: 57.282
+    - type: ndcg_at_100
+      value: 61.477000000000004
+    - type: ndcg_at_1000
+      value: 62.426
+    - type: ndcg_at_3
+      value: 51.0
+    - type: ndcg_at_5
+      value: 54.346000000000004
+    - type: precision_at_1
+      value: 42.571
+    - type: precision_at_10
+      value: 9.467
+    - type: precision_at_100
+      value: 1.2550000000000001
+    - type: precision_at_1000
+      value: 0.13799999999999998
+    - type: precision_at_3
+      value: 23.114
+    - type: precision_at_5
+      value: 16.250999999999998
+    - type: recall_at_1
+      value: 37.188
+    - type: recall_at_10
+      value: 73.068
+    - type: recall_at_100
+      value: 91.203
+    - type: recall_at_1000
+      value: 97.916
+    - type: recall_at_3
+      value: 56.552
+    - type: recall_at_5
+      value: 64.567
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB CQADupstackGisRetrieval
+      revision: 5003b3064772da1887988e05400cf3806fe491f2
+      split: test
+      type: mteb/cqadupstack-gis
+    metrics:
+    - type: map_at_1
+      value: 25.041000000000004
+    - type: map_at_10
+      value: 33.86
+    - type: map_at_100
+      value: 34.988
+    - type: map_at_1000
+      value: 35.064
+    - type: map_at_3
+      value: 31.049
+    - type: map_at_5
+      value: 32.845
+    - type: mrr_at_1
+      value: 26.893
+    - type: mrr_at_10
+      value: 35.594
+    - type: mrr_at_100
+      value: 36.617
+    - type: mrr_at_1000
+      value: 36.671
+    - type: mrr_at_3
+      value: 33.051
+    - type: mrr_at_5
+      value: 34.61
+    - type: ndcg_at_1
+      value: 26.893
+    - type: ndcg_at_10
+      value: 38.674
+    - type: ndcg_at_100
+      value: 44.178
+    - type: ndcg_at_1000
+      value: 46.089999999999996
+    - type: ndcg_at_3
+      value: 33.485
+    - type: ndcg_at_5
+      value: 36.402
+    - type: precision_at_1
+      value: 26.893
+    - type: precision_at_10
+      value: 5.989
+    - type: precision_at_100
+      value: 0.918
+    - type: precision_at_1000
+      value: 0.11100000000000002
+    - type: precision_at_3
+      value: 14.2
+    - type: precision_at_5
+      value: 10.26
+    - type: recall_at_1
+      value: 25.041000000000004
+    - type: recall_at_10
+      value: 51.666000000000004
+    - type: recall_at_100
+      value: 76.896
+    - type: recall_at_1000
+      value: 91.243
+    - type: recall_at_3
+      value: 38.035999999999994
+    - type: recall_at_5
+      value: 44.999
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB CQADupstackMathematicaRetrieval
+      revision: 90fceea13679c63fe563ded68f3b6f06e50061de
+      split: test
+      type: mteb/cqadupstack-mathematica
+    metrics:
+    - type: map_at_1
+      value: 15.909999999999998
+    - type: map_at_10
+      value: 23.901
+    - type: map_at_100
+      value: 25.165
+    - type: map_at_1000
+      value: 25.291000000000004
+    - type: map_at_3
+      value: 21.356
+    - type: map_at_5
+      value: 22.816
+    - type: mrr_at_1
+      value: 20.025000000000002
+    - type: mrr_at_10
+      value: 28.382
+    - type: mrr_at_100
+      value: 29.465000000000003
+    - type: mrr_at_1000
+      value: 29.535
+    - type: mrr_at_3
+      value: 25.933
+    - type: mrr_at_5
+      value: 27.332
+    - type: ndcg_at_1
+      value: 20.025000000000002
+    - type: ndcg_at_10
+      value: 29.099000000000004
+    - type: ndcg_at_100
+      value: 35.127
+    - type: ndcg_at_1000
+      value: 38.096000000000004
+    - type: ndcg_at_3
+      value: 24.464
+    - type: ndcg_at_5
+      value: 26.709
+    - type: precision_at_1
+      value: 20.025000000000002
+    - type: precision_at_10
+      value: 5.398
+    - type: precision_at_100
+      value: 0.9690000000000001
+    - type: precision_at_1000
+      value: 0.13699999999999998
+    - type: precision_at_3
+      value: 11.774
+    - type: precision_at_5
+      value: 8.632
+    - type: recall_at_1
+      value: 15.909999999999998
+    - type: recall_at_10
+      value: 40.672000000000004
+    - type: recall_at_100
+      value: 66.855
+    - type: recall_at_1000
+      value: 87.922
+    - type: recall_at_3
+      value: 28.069
+    - type: recall_at_5
+      value: 33.812
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB CQADupstackPhysicsRetrieval
+      revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
+      split: test
+      type: mteb/cqadupstack-physics
+    metrics:
+    - type: map_at_1
+      value: 30.175
+    - type: map_at_10
+      value: 41.36
+    - type: map_at_100
+      value: 42.701
+    - type: map_at_1000
+      value: 42.817
+    - type: map_at_3
+      value: 37.931
+    - type: map_at_5
+      value: 39.943
+    - type: mrr_at_1
+      value: 35.611
+    - type: mrr_at_10
+      value: 46.346
+    - type: mrr_at_100
+      value: 47.160000000000004
+    - type: mrr_at_1000
+      value: 47.203
+    - type: mrr_at_3
+      value: 43.712
+    - type: mrr_at_5
+      value: 45.367000000000004
+    - type: ndcg_at_1
+      value: 35.611
+    - type: ndcg_at_10
+      value: 47.532000000000004
+    - type: ndcg_at_100
+      value: 53.003
+    - type: ndcg_at_1000
+      value: 55.007
+    - type: ndcg_at_3
+      value: 42.043
+    - type: ndcg_at_5
+      value: 44.86
+    - type: precision_at_1
+      value: 35.611
+    - type: precision_at_10
+      value: 8.624
+    - type: precision_at_100
+      value: 1.332
+    - type: precision_at_1000
+      value: 0.169
+    - type: precision_at_3
+      value: 20.083000000000002
+    - type: precision_at_5
+      value: 14.437
+    - type: recall_at_1
+      value: 30.175
+    - type: recall_at_10
+      value: 60.5
+    - type: recall_at_100
+      value: 83.399
+    - type: recall_at_1000
+      value: 96.255
+    - type: recall_at_3
+      value: 45.448
+    - type: recall_at_5
+      value: 52.432
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB CQADupstackProgrammersRetrieval
+      revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
+      split: test
+      type: mteb/cqadupstack-programmers
+    metrics:
+    - type: map_at_1
+      value: 22.467000000000002
+    - type: map_at_10
+      value: 33.812999999999995
+    - type: map_at_100
+      value: 35.248000000000005
+    - type: map_at_1000
+      value: 35.359
+    - type: map_at_3
+      value: 30.316
+    - type: map_at_5
+      value: 32.233000000000004
+    - type: mrr_at_1
+      value: 28.310999999999996
+    - type: mrr_at_10
+      value: 38.979
+    - type: mrr_at_100
+      value: 39.937
+    - type: mrr_at_1000
+      value: 39.989999999999995
+    - type: mrr_at_3
+      value: 36.244
+    - type: mrr_at_5
+      value: 37.871
+    - type: ndcg_at_1
+      value: 28.310999999999996
+    - type: ndcg_at_10
+      value: 40.282000000000004
+    - type: ndcg_at_100
+      value: 46.22
+    - type: ndcg_at_1000
+      value: 48.507
+    - type: ndcg_at_3
+      value: 34.596
+    - type: ndcg_at_5
+      value: 37.267
+    - type: precision_at_1
+      value: 28.310999999999996
+    - type: precision_at_10
+      value: 7.831
+    - type: precision_at_100
+      value: 1.257
+    - type: precision_at_1000
+      value: 0.164
+    - type: precision_at_3
+      value: 17.275
+    - type: precision_at_5
+      value: 12.556999999999999
+    - type: recall_at_1
+      value: 22.467000000000002
+    - type: recall_at_10
+      value: 54.14099999999999
+    - type: recall_at_100
+      value: 79.593
+    - type: recall_at_1000
+      value: 95.063
+    - type: recall_at_3
+      value: 38.539
+    - type: recall_at_5
+      value: 45.403
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB CQADupstackRetrieval
+      revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
+      split: test
+      type: mteb/cqadupstack
+    metrics:
+    - type: map_at_1
+      value: 24.18591666666667
+    - type: map_at_10
+      value: 33.84258333333333
+    - type: map_at_100
+      value: 35.11391666666666
+    - type: map_at_1000
+      value: 35.23258333333333
+    - type: map_at_3
+      value: 30.764249999999997
+    - type: map_at_5
+      value: 32.52333333333334
+    - type: mrr_at_1
+      value: 28.54733333333333
+    - type: mrr_at_10
+      value: 37.81725
+    - type: mrr_at_100
+      value: 38.716499999999996
+    - type: mrr_at_1000
+      value: 38.77458333333333
+    - type: mrr_at_3
+      value: 35.157833333333336
+    - type: mrr_at_5
+      value: 36.69816666666667
+    - type: ndcg_at_1
+      value: 28.54733333333333
+    - type: ndcg_at_10
+      value: 39.51508333333334
+    - type: ndcg_at_100
+      value: 44.95316666666666
+    - type: ndcg_at_1000
+      value: 47.257083333333334
+    - type: ndcg_at_3
+      value: 34.205833333333324
+    - type: ndcg_at_5
+      value: 36.78266666666667
+    - type: precision_at_1
+      value: 28.54733333333333
+    - type: precision_at_10
+      value: 7.082583333333334
+    - type: precision_at_100
+      value: 1.1590833333333332
+    - type: precision_at_1000
+      value: 0.15516666666666662
+    - type: precision_at_3
+      value: 15.908750000000001
+    - type: precision_at_5
+      value: 11.505416666666669
+    - type: recall_at_1
+      value: 24.18591666666667
+    - type: recall_at_10
+      value: 52.38758333333333
+    - type: recall_at_100
+      value: 76.13666666666667
+    - type: recall_at_1000
+      value: 91.99066666666667
+    - type: recall_at_3
+      value: 37.78333333333334
+    - type: recall_at_5
+      value: 44.30141666666666
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB CQADupstackStatsRetrieval
+      revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
+      split: test
+      type: mteb/cqadupstack-stats
+    metrics:
+    - type: map_at_1
+      value: 21.975
+    - type: map_at_10
+      value: 29.781000000000002
+    - type: map_at_100
+      value: 30.847
+    - type: map_at_1000
+      value: 30.94
+    - type: map_at_3
+      value: 27.167
+    - type: map_at_5
+      value: 28.633999999999997
+    - type: mrr_at_1
+      value: 24.387
+    - type: mrr_at_10
+      value: 32.476
+    - type: mrr_at_100
+      value: 33.337
+    - type: mrr_at_1000
+      value: 33.403
+    - type: mrr_at_3
+      value: 29.881999999999998
+    - type: mrr_at_5
+      value: 31.339
+    - type: ndcg_at_1
+      value: 24.387
+    - type: ndcg_at_10
+      value: 34.596
+    - type: ndcg_at_100
+      value: 39.635
+    - type: ndcg_at_1000
+      value: 42.079
+    - type: ndcg_at_3
+      value: 29.516
+    - type: ndcg_at_5
+      value: 31.959
+    - type: precision_at_1
+      value: 24.387
+    - type: precision_at_10
+      value: 5.6129999999999995
+    - type: precision_at_100
+      value: 0.8909999999999999
+    - type: precision_at_1000
+      value: 0.117
+    - type: precision_at_3
+      value: 12.73
+    - type: precision_at_5
+      value: 9.171999999999999
+    - type: recall_at_1
+      value: 21.975
+    - type: recall_at_10
+      value: 46.826
+    - type: recall_at_100
+      value: 69.554
+    - type: recall_at_1000
+      value: 87.749
+    - type: recall_at_3
+      value: 33.016
+    - type: recall_at_5
+      value: 38.97
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB CQADupstackTexRetrieval
+      revision: 46989137a86843e03a6195de44b09deda022eec7
+      split: test
+      type: mteb/cqadupstack-tex
+    metrics:
+    - type: map_at_1
+      value: 15.614
+    - type: map_at_10
+      value: 22.927
+    - type: map_at_100
+      value: 24.185000000000002
+    - type: map_at_1000
+      value: 24.319
+    - type: map_at_3
+      value: 20.596
+    - type: map_at_5
+      value: 21.854000000000003
+    - type: mrr_at_1
+      value: 18.858
+    - type: mrr_at_10
+      value: 26.535999999999998
+    - type: mrr_at_100
+      value: 27.582
+    - type: mrr_at_1000
+      value: 27.665
+    - type: mrr_at_3
+      value: 24.295
+    - type: mrr_at_5
+      value: 25.532
+    - type: ndcg_at_1
+      value: 18.858
+    - type: ndcg_at_10
+      value: 27.583000000000002
+    - type: ndcg_at_100
+      value: 33.635
+    - type: ndcg_at_1000
+      value: 36.647
+    - type: ndcg_at_3
+      value: 23.348
+    - type: ndcg_at_5
+      value: 25.257
+    - type: precision_at_1
+      value: 18.858
+    - type: precision_at_10
+      value: 5.158
+    - type: precision_at_100
+      value: 0.964
+    - type: precision_at_1000
+      value: 0.13999999999999999
+    - type: precision_at_3
+      value: 11.092
+    - type: precision_at_5
+      value: 8.1
+    - type: recall_at_1
+      value: 15.614
+    - type: recall_at_10
+      value: 37.916
+    - type: recall_at_100
+      value: 65.205
+    - type: recall_at_1000
+      value: 86.453
+    - type: recall_at_3
+      value: 26.137
+    - type: recall_at_5
+      value: 31.087999999999997
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB CQADupstackUnixRetrieval
+      revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
+      split: test
+      type: mteb/cqadupstack-unix
+    metrics:
+    - type: map_at_1
+      value: 23.078000000000003
+    - type: map_at_10
+      value: 31.941999999999997
+    - type: map_at_100
+      value: 33.196999999999996
+    - type: map_at_1000
+      value: 33.303
+    - type: map_at_3
+      value: 28.927000000000003
+    - type: map_at_5
+      value: 30.707
+    - type: mrr_at_1
+      value: 26.866
+    - type: mrr_at_10
+      value: 35.557
+    - type: mrr_at_100
+      value: 36.569
+    - type: mrr_at_1000
+      value: 36.632
+    - type: mrr_at_3
+      value: 32.897999999999996
+    - type: mrr_at_5
+      value: 34.437
+    - type: ndcg_at_1
+      value: 26.866
+    - type: ndcg_at_10
+      value: 37.372
+    - type: ndcg_at_100
+      value: 43.248
+    - type: ndcg_at_1000
+      value: 45.632
+    - type: ndcg_at_3
+      value: 31.852999999999998
+    - type: ndcg_at_5
+      value: 34.582
+    - type: precision_at_1
+      value: 26.866
+    - type: precision_at_10
+      value: 6.511
+    - type: precision_at_100
+      value: 1.078
+    - type: precision_at_1000
+      value: 0.13899999999999998
+    - type: precision_at_3
+      value: 14.582999999999998
+    - type: precision_at_5
+      value: 10.634
+    - type: recall_at_1
+      value: 23.078000000000003
+    - type: recall_at_10
+      value: 50.334
+    - type: recall_at_100
+      value: 75.787
+    - type: recall_at_1000
+      value: 92.485
+    - type: recall_at_3
+      value: 35.386
+    - type: recall_at_5
+      value: 42.225
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB CQADupstackWebmastersRetrieval
+      revision: 160c094312a0e1facb97e55eeddb698c0abe3571
+      split: test
+      type: mteb/cqadupstack-webmasters
+    metrics:
+    - type: map_at_1
+      value: 22.203999999999997
+    - type: map_at_10
+      value: 31.276
+    - type: map_at_100
+      value: 32.844
+    - type: map_at_1000
+      value: 33.062999999999995
+    - type: map_at_3
+      value: 27.733999999999998
+    - type: map_at_5
+      value: 29.64
+    - type: mrr_at_1
+      value: 27.272999999999996
+    - type: mrr_at_10
+      value: 36.083
+    - type: mrr_at_100
+      value: 37.008
+    - type: mrr_at_1000
+      value: 37.076
+    - type: mrr_at_3
+      value: 33.004
+    - type: mrr_at_5
+      value: 34.664
+    - type: ndcg_at_1
+      value: 27.272999999999996
+    - type: ndcg_at_10
+      value: 37.763000000000005
+    - type: ndcg_at_100
+      value: 43.566
+    - type: ndcg_at_1000
+      value: 46.356
+    - type: ndcg_at_3
+      value: 31.673000000000002
+    - type: ndcg_at_5
+      value: 34.501
+    - type: precision_at_1
+      value: 27.272999999999996
+    - type: precision_at_10
+      value: 7.470000000000001
+    - type: precision_at_100
+      value: 1.502
+    - type: precision_at_1000
+      value: 0.24
+    - type: precision_at_3
+      value: 14.756
+    - type: precision_at_5
+      value: 11.225
+    - type: recall_at_1
+      value: 22.203999999999997
+    - type: recall_at_10
+      value: 51.437999999999995
+    - type: recall_at_100
+      value: 76.845
+    - type: recall_at_1000
+      value: 94.38600000000001
+    - type: recall_at_3
+      value: 34.258
+    - type: recall_at_5
+      value: 41.512
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB CQADupstackWordpressRetrieval
+      revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
+      split: test
+      type: mteb/cqadupstack-wordpress
+    metrics:
+    - type: map_at_1
+      value: 17.474
+    - type: map_at_10
+      value: 26.362999999999996
+    - type: map_at_100
+      value: 27.456999999999997
+    - type: map_at_1000
+      value: 27.567999999999998
+    - type: map_at_3
+      value: 23.518
+    - type: map_at_5
+      value: 25.068
+    - type: mrr_at_1
+      value: 18.669
+    - type: mrr_at_10
+      value: 27.998
+    - type: mrr_at_100
+      value: 28.953
+    - type: mrr_at_1000
+      value: 29.03
+    - type: mrr_at_3
+      value: 25.230999999999998
+    - type: mrr_at_5
+      value: 26.654
+    - type: ndcg_at_1
+      value: 18.669
+    - type: ndcg_at_10
+      value: 31.684
+    - type: ndcg_at_100
+      value: 36.864999999999995
+    - type: ndcg_at_1000
+      value: 39.555
+    - type: ndcg_at_3
+      value: 26.057000000000002
+    - type: ndcg_at_5
+      value: 28.587
+    - type: precision_at_1
+      value: 18.669
+    - type: precision_at_10
+      value: 5.3420000000000005
+    - type: precision_at_100
+      value: 0.847
+    - type: precision_at_1000
+      value: 0.12
+    - type: precision_at_3
+      value: 11.583
+    - type: precision_at_5
+      value: 8.466
+    - type: recall_at_1
+      value: 17.474
+    - type: recall_at_10
+      value: 46.497
+    - type: recall_at_100
+      value: 69.977
+    - type: recall_at_1000
+      value: 89.872
+    - type: recall_at_3
+      value: 31.385999999999996
+    - type: recall_at_5
+      value: 37.283
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB ClimateFEVER
+      revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
+      split: test
+      type: mteb/climate-fever
+    metrics:
+    - type: map_at_1
+      value: 17.173
+    - type: map_at_10
+      value: 30.407
+    - type: map_at_100
+      value: 32.528
+    - type: map_at_1000
+      value: 32.698
+    - type: map_at_3
+      value: 25.523
+    - type: map_at_5
+      value: 28.038
+    - type: mrr_at_1
+      value: 38.958
+    - type: mrr_at_10
+      value: 51.515
+    - type: mrr_at_100
+      value: 52.214000000000006
+    - type: mrr_at_1000
+      value: 52.237
+    - type: mrr_at_3
+      value: 48.502
+    - type: mrr_at_5
+      value: 50.251000000000005
+    - type: ndcg_at_1
+      value: 38.958
+    - type: ndcg_at_10
+      value: 40.355000000000004
+    - type: ndcg_at_100
+      value: 47.68
+    - type: ndcg_at_1000
+      value: 50.370000000000005
+    - type: ndcg_at_3
+      value: 33.946
+    - type: ndcg_at_5
+      value: 36.057
+    - type: precision_at_1
+      value: 38.958
+    - type: precision_at_10
+      value: 12.508
+    - type: precision_at_100
+      value: 2.054
+    - type: precision_at_1000
+      value: 0.256
+    - type: precision_at_3
+      value: 25.581
+    - type: precision_at_5
+      value: 19.256999999999998
+    - type: recall_at_1
+      value: 17.173
+    - type: recall_at_10
+      value: 46.967
+    - type: recall_at_100
+      value: 71.47200000000001
+    - type: recall_at_1000
+      value: 86.238
+    - type: recall_at_3
+      value: 30.961
+    - type: recall_at_5
+      value: 37.539
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB DBPedia
+      revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
+      split: test
+      type: mteb/dbpedia
+    metrics:
+    - type: map_at_1
+      value: 8.999
+    - type: map_at_10
+      value: 18.989
+    - type: map_at_100
+      value: 26.133
+    - type: map_at_1000
+      value: 27.666
+    - type: map_at_3
+      value: 13.918
+    - type: map_at_5
+      value: 16.473
+    - type: mrr_at_1
+      value: 66.25
+    - type: mrr_at_10
+      value: 74.161
+    - type: mrr_at_100
+      value: 74.516
+    - type: mrr_at_1000
+      value: 74.524
+    - type: mrr_at_3
+      value: 72.875
+    - type: mrr_at_5
+      value: 73.613
+    - type: ndcg_at_1
+      value: 54.37499999999999
+    - type: ndcg_at_10
+      value: 39.902
+    - type: ndcg_at_100
+      value: 44.212
+    - type: ndcg_at_1000
+      value: 51.62
+    - type: ndcg_at_3
+      value: 45.193
+    - type: ndcg_at_5
+      value: 42.541000000000004
+    - type: precision_at_1
+      value: 66.25
+    - type: precision_at_10
+      value: 30.425
+    - type: precision_at_100
+      value: 9.754999999999999
+    - type: precision_at_1000
+      value: 2.043
+    - type: precision_at_3
+      value: 48.25
+    - type: precision_at_5
+      value: 40.65
+    - type: recall_at_1
+      value: 8.999
+    - type: recall_at_10
+      value: 24.133
+    - type: recall_at_100
+      value: 49.138999999999996
+    - type: recall_at_1000
+      value: 72.639
+    - type: recall_at_3
+      value: 15.287999999999998
+    - type: recall_at_5
+      value: 19.415
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB EmotionClassification
+      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
+      split: test
+      type: mteb/emotion
+    metrics:
+    - type: accuracy
+      value: 46.38999999999999
+    - type: f1
+      value: 41.444205512055234
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB FEVER
+      revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
+      split: test
+      type: mteb/fever
+    metrics:
+    - type: map_at_1
+      value: 87.35000000000001
+    - type: map_at_10
+      value: 92.837
+    - type: map_at_100
+      value: 92.996
+    - type: map_at_1000
+      value: 93.006
+    - type: map_at_3
+      value: 92.187
+    - type: map_at_5
+      value: 92.595
+    - type: mrr_at_1
+      value: 93.864
+    - type: mrr_at_10
+      value: 96.723
+    - type: mrr_at_100
+      value: 96.72500000000001
+    - type: mrr_at_1000
+      value: 96.72500000000001
+    - type: mrr_at_3
+      value: 96.64
+    - type: mrr_at_5
+      value: 96.71499999999999
+    - type: ndcg_at_1
+      value: 93.864
+    - type: ndcg_at_10
+      value: 94.813
+    - type: ndcg_at_100
+      value: 95.243
+    - type: ndcg_at_1000
+      value: 95.38600000000001
+    - type: ndcg_at_3
+      value: 94.196
+    - type: ndcg_at_5
+      value: 94.521
+    - type: precision_at_1
+      value: 93.864
+    - type: precision_at_10
+      value: 10.951
+    - type: precision_at_100
+      value: 1.1400000000000001
+    - type: precision_at_1000
+      value: 0.117
+    - type: precision_at_3
+      value: 35.114000000000004
+    - type: precision_at_5
+      value: 21.476
+    - type: recall_at_1
+      value: 87.35000000000001
+    - type: recall_at_10
+      value: 96.941
+    - type: recall_at_100
+      value: 98.397
+    - type: recall_at_1000
+      value: 99.21600000000001
+    - type: recall_at_3
+      value: 95.149
+    - type: recall_at_5
+      value: 96.131
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB FiQA2018
+      revision: 27a168819829fe9bcd655c2df245fb19452e8e06
+      split: test
+      type: mteb/fiqa
+    metrics:
+    - type: map_at_1
+      value: 24.476
+    - type: map_at_10
+      value: 40.11
+    - type: map_at_100
+      value: 42.229
+    - type: map_at_1000
+      value: 42.378
+    - type: map_at_3
+      value: 34.512
+    - type: map_at_5
+      value: 38.037
+    - type: mrr_at_1
+      value: 47.839999999999996
+    - type: mrr_at_10
+      value: 57.053
+    - type: mrr_at_100
+      value: 57.772
+    - type: mrr_at_1000
+      value: 57.799
+    - type: mrr_at_3
+      value: 54.552
+    - type: mrr_at_5
+      value: 56.011
+    - type: ndcg_at_1
+      value: 47.839999999999996
+    - type: ndcg_at_10
+      value: 48.650999999999996
+    - type: ndcg_at_100
+      value: 55.681000000000004
+    - type: ndcg_at_1000
+      value: 57.979
+    - type: ndcg_at_3
+      value: 43.923
+    - type: ndcg_at_5
+      value: 46.037
+    - type: precision_at_1
+      value: 47.839999999999996
+    - type: precision_at_10
+      value: 13.395000000000001
+    - type: precision_at_100
+      value: 2.0660000000000003
+    - type: precision_at_1000
+      value: 0.248
+    - type: precision_at_3
+      value: 29.064
+    - type: precision_at_5
+      value: 22.006
+    - type: recall_at_1
+      value: 24.476
+    - type: recall_at_10
+      value: 56.216
+    - type: recall_at_100
+      value: 81.798
+    - type: recall_at_1000
+      value: 95.48299999999999
+    - type: recall_at_3
+      value: 39.357
+    - type: recall_at_5
+      value: 47.802
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB HotpotQA
+      revision: ab518f4d6fcca38d87c25209f94beba119d02014
+      split: test
+      type: mteb/hotpotqa
+    metrics:
+    - type: map_at_1
+      value: 42.728
+    - type: map_at_10
+      value: 57.737
+    - type: map_at_100
+      value: 58.531
+    - type: map_at_1000
+      value: 58.594
+    - type: map_at_3
+      value: 54.869
+    - type: map_at_5
+      value: 56.55
+    - type: mrr_at_1
+      value: 85.456
+    - type: mrr_at_10
+      value: 90.062
+    - type: mrr_at_100
+      value: 90.159
+    - type: mrr_at_1000
+      value: 90.16
+    - type: mrr_at_3
+      value: 89.37899999999999
+    - type: mrr_at_5
+      value: 89.81
+    - type: ndcg_at_1
+      value: 85.456
+    - type: ndcg_at_10
+      value: 67.755
+    - type: ndcg_at_100
+      value: 70.341
+    - type: ndcg_at_1000
+      value: 71.538
+    - type: ndcg_at_3
+      value: 63.735
+    - type: ndcg_at_5
+      value: 65.823
+    - type: precision_at_1
+      value: 85.456
+    - type: precision_at_10
+      value: 13.450000000000001
+    - type: precision_at_100
+      value: 1.545
+    - type: precision_at_1000
+      value: 0.16999999999999998
+    - type: precision_at_3
+      value: 38.861000000000004
+    - type: precision_at_5
+      value: 24.964
+    - type: recall_at_1
+      value: 42.728
+    - type: recall_at_10
+      value: 67.252
+    - type: recall_at_100
+      value: 77.265
+    - type: recall_at_1000
+      value: 85.246
+    - type: recall_at_3
+      value: 58.292
+    - type: recall_at_5
+      value: 62.41100000000001
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB ImdbClassification
+      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
+      split: test
+      type: mteb/imdb
+    metrics:
+    - type: accuracy
+      value: 87.4836
+    - type: ap
+      value: 82.29552224030336
+    - type: f1
+      value: 87.42791432227448
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB MSMARCO
+      revision: c5a29a104738b98a9e76336939199e264163d4a0
+      split: dev
+      type: mteb/msmarco
+    metrics:
+    - type: map_at_1
+      value: 23.015
+    - type: map_at_10
+      value: 35.621
+    - type: map_at_100
+      value: 36.809
+    - type: map_at_1000
+      value: 36.853
+    - type: map_at_3
+      value: 31.832
+    - type: map_at_5
+      value: 34.006
+    - type: mrr_at_1
+      value: 23.738999999999997
+    - type: mrr_at_10
+      value: 36.309999999999995
+    - type: mrr_at_100
+      value: 37.422
+    - type: mrr_at_1000
+      value: 37.461
+    - type: mrr_at_3
+      value: 32.592999999999996
+    - type: mrr_at_5
+      value: 34.736
+    - type: ndcg_at_1
+      value: 23.724999999999998
+    - type: ndcg_at_10
+      value: 42.617
+    - type: ndcg_at_100
+      value: 48.217999999999996
+    - type: ndcg_at_1000
+      value: 49.309
+    - type: ndcg_at_3
+      value: 34.905
+    - type: ndcg_at_5
+      value: 38.769
+    - type: precision_at_1
+      value: 23.724999999999998
+    - type: precision_at_10
+      value: 6.689
+    - type: precision_at_100
+      value: 0.9480000000000001
+    - type: precision_at_1000
+      value: 0.104
+    - type: precision_at_3
+      value: 14.89
+    - type: precision_at_5
+      value: 10.897
+    - type: recall_at_1
+      value: 23.015
+    - type: recall_at_10
+      value: 64.041
+    - type: recall_at_100
+      value: 89.724
+    - type: recall_at_1000
+      value: 98.00999999999999
+    - type: recall_at_3
+      value: 43.064
+    - type: recall_at_5
+      value: 52.31099999999999
+    task:
+      type: Retrieval
+  - dataset:
+      config: en
+      name: MTEB MTOPDomainClassification (en)
+      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
+      split: test
+      type: mteb/mtop_domain
+    metrics:
+    - type: accuracy
+      value: 96.49794801641588
+    - type: f1
+      value: 96.28931114498003
+    task:
+      type: Classification
+  - dataset:
+      config: en
+      name: MTEB MTOPIntentClassification (en)
+      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
+      split: test
+      type: mteb/mtop_intent
+    metrics:
+    - type: accuracy
+      value: 82.81121751025992
+    - type: f1
+      value: 63.18740125901853
+    task:
+      type: Classification
+  - dataset:
+      config: en
+      name: MTEB MassiveIntentClassification (en)
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+      split: test
+      type: mteb/amazon_massive_intent
+    metrics:
+    - type: accuracy
+      value: 77.66644250168123
+    - type: f1
+      value: 74.93211186867839
+    task:
+      type: Classification
+  - dataset:
+      config: en
+      name: MTEB MassiveScenarioClassification (en)
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+      split: test
+      type: mteb/amazon_massive_scenario
+    metrics:
+    - type: accuracy
+      value: 81.77202420981843
+    - type: f1
+      value: 81.63681969283554
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB MedrxivClusteringP2P
+      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
+      split: test
+      type: mteb/medrxiv-clustering-p2p
+    metrics:
+    - type: v_measure
+      value: 34.596687684870645
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB MedrxivClusteringS2S
+      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
+      split: test
+      type: mteb/medrxiv-clustering-s2s
+    metrics:
+    - type: v_measure
+      value: 32.26965660101405
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB MindSmallReranking
+      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
+      split: test
+      type: mteb/mind_small
+    metrics:
+    - type: map
+      value: 31.33619694846802
+    - type: mrr
+      value: 32.53719657720334
+    task:
+      type: Reranking
+  - dataset:
+      config: default
+      name: MTEB NFCorpus
+      revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
+      split: test
+      type: mteb/nfcorpus
+    metrics:
+    - type: map_at_1
+      value: 6.0729999999999995
+    - type: map_at_10
+      value: 13.245999999999999
+    - type: map_at_100
+      value: 16.747999999999998
+    - type: map_at_1000
+      value: 18.163
+    - type: map_at_3
+      value: 10.064
+    - type: map_at_5
+      value: 11.513
+    - type: mrr_at_1
+      value: 49.536
+    - type: mrr_at_10
+      value: 58.092
+    - type: mrr_at_100
+      value: 58.752
+    - type: mrr_at_1000
+      value: 58.78
+    - type: mrr_at_3
+      value: 56.398
+    - type: mrr_at_5
+      value: 57.389
+    - type: ndcg_at_1
+      value: 47.059
+    - type: ndcg_at_10
+      value: 35.881
+    - type: ndcg_at_100
+      value: 32.751999999999995
+    - type: ndcg_at_1000
+      value: 41.498000000000005
+    - type: ndcg_at_3
+      value: 42.518
+    - type: ndcg_at_5
+      value: 39.550999999999995
+    - type: precision_at_1
+      value: 49.536
+    - type: precision_at_10
+      value: 26.316
+    - type: precision_at_100
+      value: 8.084
+    - type: precision_at_1000
+      value: 2.081
+    - type: precision_at_3
+      value: 39.938
+    - type: precision_at_5
+      value: 34.056
+    - type: recall_at_1
+      value: 6.0729999999999995
+    - type: recall_at_10
+      value: 16.593
+    - type: recall_at_100
+      value: 32.883
+    - type: recall_at_1000
+      value: 64.654
+    - type: recall_at_3
+      value: 11.174000000000001
+    - type: recall_at_5
+      value: 13.528
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB NQ
+      revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
+      split: test
+      type: mteb/nq
+    metrics:
+    - type: map_at_1
+      value: 30.043
+    - type: map_at_10
+      value: 45.318999999999996
+    - type: map_at_100
+      value: 46.381
+    - type: map_at_1000
+      value: 46.412
+    - type: map_at_3
+      value: 40.941
+    - type: map_at_5
+      value: 43.662
+    - type: mrr_at_1
+      value: 33.98
+    - type: mrr_at_10
+      value: 47.870000000000005
+    - type: mrr_at_100
+      value: 48.681999999999995
+    - type: mrr_at_1000
+      value: 48.703
+    - type: mrr_at_3
+      value: 44.341
+    - type: mrr_at_5
+      value: 46.547
+    - type: ndcg_at_1
+      value: 33.98
+    - type: ndcg_at_10
+      value: 52.957
+    - type: ndcg_at_100
+      value: 57.434
+    - type: ndcg_at_1000
+      value: 58.103
+    - type: ndcg_at_3
+      value: 44.896
+    - type: ndcg_at_5
+      value: 49.353
+    - type: precision_at_1
+      value: 33.98
+    - type: precision_at_10
+      value: 8.786
+    - type: precision_at_100
+      value: 1.1280000000000001
+    - type: precision_at_1000
+      value: 0.11900000000000001
+    - type: precision_at_3
+      value: 20.577
+    - type: precision_at_5
+      value: 14.942
+    - type: recall_at_1
+      value: 30.043
+    - type: recall_at_10
+      value: 73.593
+    - type: recall_at_100
+      value: 93.026
+    - type: recall_at_1000
+      value: 97.943
+    - type: recall_at_3
+      value: 52.955
+    - type: recall_at_5
+      value: 63.132
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB QuoraRetrieval
+      revision: None
+      split: test
+      type: mteb/quora
+    metrics:
+    - type: map_at_1
+      value: 70.808
+    - type: map_at_10
+      value: 84.675
+    - type: map_at_100
+      value: 85.322
+    - type: map_at_1000
+      value: 85.33800000000001
+    - type: map_at_3
+      value: 81.68900000000001
+    - type: map_at_5
+      value: 83.543
+    - type: mrr_at_1
+      value: 81.5
+    - type: mrr_at_10
+      value: 87.59700000000001
+    - type: mrr_at_100
+      value: 87.705
+    - type: mrr_at_1000
+      value: 87.70599999999999
+    - type: mrr_at_3
+      value: 86.607
+    - type: mrr_at_5
+      value: 87.289
+    - type: ndcg_at_1
+      value: 81.51
+    - type: ndcg_at_10
+      value: 88.41799999999999
+    - type: ndcg_at_100
+      value: 89.644
+    - type: ndcg_at_1000
+      value: 89.725
+    - type: ndcg_at_3
+      value: 85.49900000000001
+    - type: ndcg_at_5
+      value: 87.078
+    - type: precision_at_1
+      value: 81.51
+    - type: precision_at_10
+      value: 13.438
+    - type: precision_at_100
+      value: 1.532
+    - type: precision_at_1000
+      value: 0.157
+    - type: precision_at_3
+      value: 37.363
+    - type: precision_at_5
+      value: 24.57
+    - type: recall_at_1
+      value: 70.808
+    - type: recall_at_10
+      value: 95.575
+    - type: recall_at_100
+      value: 99.667
+    - type: recall_at_1000
+      value: 99.98899999999999
+    - type: recall_at_3
+      value: 87.223
+    - type: recall_at_5
+      value: 91.682
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB RedditClustering
+      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
+      split: test
+      type: mteb/reddit-clustering
+    metrics:
+    - type: v_measure
+      value: 58.614831329137715
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB RedditClusteringP2P
+      revision: 282350215ef01743dc01b456c7f5241fa8937f16
+      split: test
+      type: mteb/reddit-clustering-p2p
+    metrics:
+    - type: v_measure
+      value: 66.86580408560826
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB SCIDOCS
+      revision: None
+      split: test
+      type: mteb/scidocs
+    metrics:
+    - type: map_at_1
+      value: 5.093
+    - type: map_at_10
+      value: 13.014000000000001
+    - type: map_at_100
+      value: 15.412999999999998
+    - type: map_at_1000
+      value: 15.756999999999998
+    - type: map_at_3
+      value: 9.216000000000001
+    - type: map_at_5
+      value: 11.036999999999999
+    - type: mrr_at_1
+      value: 25.1
+    - type: mrr_at_10
+      value: 37.133
+    - type: mrr_at_100
+      value: 38.165
+    - type: mrr_at_1000
+      value: 38.198
+    - type: mrr_at_3
+      value: 33.217
+    - type: mrr_at_5
+      value: 35.732
+    - type: ndcg_at_1
+      value: 25.1
+    - type: ndcg_at_10
+      value: 21.918000000000003
+    - type: ndcg_at_100
+      value: 30.983
+    - type: ndcg_at_1000
+      value: 36.629
+    - type: ndcg_at_3
+      value: 20.544999999999998
+    - type: ndcg_at_5
+      value: 18.192
+    - type: precision_at_1
+      value: 25.1
+    - type: precision_at_10
+      value: 11.44
+    - type: precision_at_100
+      value: 2.459
+    - type: precision_at_1000
+      value: 0.381
+    - type: precision_at_3
+      value: 19.267
+    - type: precision_at_5
+      value: 16.16
+    - type: recall_at_1
+      value: 5.093
+    - type: recall_at_10
+      value: 23.215
+    - type: recall_at_100
+      value: 49.902
+    - type: recall_at_1000
+      value: 77.403
+    - type: recall_at_3
+      value: 11.733
+    - type: recall_at_5
+      value: 16.372999999999998
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB SICK-R
+      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
+      split: test
+      type: mteb/sickr-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 82.9365442977452
+    - type: cos_sim_spearman
+      value: 79.36960687383745
+    - type: euclidean_pearson
+      value: 79.6045204840714
+    - type: euclidean_spearman
+      value: 79.26382712751337
+    - type: manhattan_pearson
+      value: 79.4805084789529
+    - type: manhattan_spearman
+      value: 79.21847863209523
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB STS12
+      revision: a0d554a64d88156834ff5ae9920b964011b16384
+      split: test
+      type: mteb/sts12-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 83.27906192961453
+    - type: cos_sim_spearman
+      value: 74.38364712099211
+    - type: euclidean_pearson
+      value: 78.54358927241223
+    - type: euclidean_spearman
+      value: 74.22185560806376
+    - type: manhattan_pearson
+      value: 78.50904327377751
+    - type: manhattan_spearman
+      value: 74.2627500781748
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB STS13
+      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
+      split: test
+      type: mteb/sts13-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 84.66863742649639
+    - type: cos_sim_spearman
+      value: 84.70630905216271
+    - type: euclidean_pearson
+      value: 84.64498334705334
+    - type: euclidean_spearman
+      value: 84.87204770690148
+    - type: manhattan_pearson
+      value: 84.65774227976077
+    - type: manhattan_spearman
+      value: 84.91251851797985
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB STS14
+      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
+      split: test
+      type: mteb/sts14-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 83.1577763924467
+    - type: cos_sim_spearman
+      value: 80.10314039230198
+    - type: euclidean_pearson
+      value: 81.51346991046043
+    - type: euclidean_spearman
+      value: 80.08678485109435
+    - type: manhattan_pearson
+      value: 81.57058914661894
+    - type: manhattan_spearman
+      value: 80.1516230725106
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB STS15
+      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
+      split: test
+      type: mteb/sts15-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 86.40310839662533
+    - type: cos_sim_spearman
+      value: 87.16293477217867
+    - type: euclidean_pearson
+      value: 86.50688711184775
+    - type: euclidean_spearman
+      value: 87.08651444923031
+    - type: manhattan_pearson
+      value: 86.54674677557857
+    - type: manhattan_spearman
+      value: 87.15079017870971
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB STS16
+      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
+      split: test
+      type: mteb/sts16-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 84.32886275207817
+    - type: cos_sim_spearman
+      value: 85.0190460590732
+    - type: euclidean_pearson
+      value: 84.42553652784679
+    - type: euclidean_spearman
+      value: 85.20027364279328
+    - type: manhattan_pearson
+      value: 84.42926246281078
+    - type: manhattan_spearman
+      value: 85.20187419804306
+    task:
+      type: STS
+  - dataset:
+      config: en-en
+      name: MTEB STS17 (en-en)
+      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
+      split: test
+      type: mteb/sts17-crosslingual-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 90.76732216967812
+    - type: cos_sim_spearman
+      value: 90.63701653633909
+    - type: euclidean_pearson
+      value: 90.26678186114682
+    - type: euclidean_spearman
+      value: 90.67288073455427
+    - type: manhattan_pearson
+      value: 90.20772020584582
+    - type: manhattan_spearman
+      value: 90.60764863983702
+    task:
+      type: STS
+  - dataset:
+      config: en
+      name: MTEB STS22 (en)
+      revision: eea2b4fe26a775864c896887d910b76a8098ad3f
+      split: test
+      type: mteb/sts22-crosslingual-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 69.09280387698125
+    - type: cos_sim_spearman
+      value: 68.62743151172162
+    - type: euclidean_pearson
+      value: 69.89386398104689
+    - type: euclidean_spearman
+      value: 68.71191066733556
+    - type: manhattan_pearson
+      value: 69.92516500604872
+    - type: manhattan_spearman
+      value: 68.80452846992576
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB STSBenchmark
+      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
+      split: test
+      type: mteb/stsbenchmark-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 86.13178592019887
+    - type: cos_sim_spearman
+      value: 86.03947178806887
+    - type: euclidean_pearson
+      value: 85.87029414285313
+    - type: euclidean_spearman
+      value: 86.04960843306998
+    - type: manhattan_pearson
+      value: 85.92946858580146
+    - type: manhattan_spearman
+      value: 86.12575341860442
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB SciDocsRR
+      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
+      split: test
+      type: mteb/scidocs-reranking
+    metrics:
+    - type: map
+      value: 85.16657063002837
+    - type: mrr
+      value: 95.73671063867141
+    task:
+      type: Reranking
+  - dataset:
+      config: default
+      name: MTEB SciFact
+      revision: 0228b52cf27578f30900b9e5271d331663a030d7
+      split: test
+      type: mteb/scifact
+    metrics:
+    - type: map_at_1
+      value: 63.510999999999996
+    - type: map_at_10
+      value: 72.76899999999999
+    - type: map_at_100
+      value: 73.303
+    - type: map_at_1000
+      value: 73.32499999999999
+    - type: map_at_3
+      value: 70.514
+    - type: map_at_5
+      value: 71.929
+    - type: mrr_at_1
+      value: 66.333
+    - type: mrr_at_10
+      value: 73.75
+    - type: mrr_at_100
+      value: 74.119
+    - type: mrr_at_1000
+      value: 74.138
+    - type: mrr_at_3
+      value: 72.222
+    - type: mrr_at_5
+      value: 73.122
+    - type: ndcg_at_1
+      value: 66.333
+    - type: ndcg_at_10
+      value: 76.774
+    - type: ndcg_at_100
+      value: 78.78500000000001
+    - type: ndcg_at_1000
+      value: 79.254
+    - type: ndcg_at_3
+      value: 73.088
+    - type: ndcg_at_5
+      value: 75.002
+    - type: precision_at_1
+      value: 66.333
+    - type: precision_at_10
+      value: 9.833
+    - type: precision_at_100
+      value: 1.093
+    - type: precision_at_1000
+      value: 0.11299999999999999
+    - type: precision_at_3
+      value: 28.222
+    - type: precision_at_5
+      value: 18.333
+    - type: recall_at_1
+      value: 63.510999999999996
+    - type: recall_at_10
+      value: 87.98899999999999
+    - type: recall_at_100
+      value: 96.5
+    - type: recall_at_1000
+      value: 100.0
+    - type: recall_at_3
+      value: 77.86699999999999
+    - type: recall_at_5
+      value: 82.73899999999999
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB SprintDuplicateQuestions
+      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
+      split: test
+      type: mteb/sprintduplicatequestions-pairclassification
+    metrics:
+    - type: cos_sim_accuracy
+      value: 99.78514851485149
+    - type: cos_sim_ap
+      value: 94.94214383862038
+    - type: cos_sim_f1
+      value: 89.02255639097744
+    - type: cos_sim_precision
+      value: 89.2462311557789
+    - type: cos_sim_recall
+      value: 88.8
+    - type: dot_accuracy
+      value: 99.78217821782178
+    - type: dot_ap
+      value: 94.69965247836805
+    - type: dot_f1
+      value: 88.78695208970439
+    - type: dot_precision
+      value: 90.54054054054053
+    - type: dot_recall
+      value: 87.1
+    - type: euclidean_accuracy
+      value: 99.78118811881188
+    - type: euclidean_ap
+      value: 94.9865187695411
+    - type: euclidean_f1
+      value: 88.99950223992036
+    - type: euclidean_precision
+      value: 88.60257680872151
+    - type: euclidean_recall
+      value: 89.4
+    - type: manhattan_accuracy
+      value: 99.78811881188119
+    - type: manhattan_ap
+      value: 95.0021236766459
+    - type: manhattan_f1
+      value: 89.12071535022356
+    - type: manhattan_precision
+      value: 88.54886475814413
+    - type: manhattan_recall
+      value: 89.7
+    - type: max_accuracy
+      value: 99.78811881188119
+    - type: max_ap
+      value: 95.0021236766459
+    - type: max_f1
+      value: 89.12071535022356
+    task:
+      type: PairClassification
+  - dataset:
+      config: default
+      name: MTEB StackExchangeClustering
+      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
+      split: test
+      type: mteb/stackexchange-clustering
+    metrics:
+    - type: v_measure
+      value: 68.93190546593995
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB StackExchangeClusteringP2P
+      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
+      split: test
+      type: mteb/stackexchange-clustering-p2p
+    metrics:
+    - type: v_measure
+      value: 37.602808534760655
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB StackOverflowDupQuestions
+      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
+      split: test
+      type: mteb/stackoverflowdupquestions-reranking
+    metrics:
+    - type: map
+      value: 52.29214480978073
+    - type: mrr
+      value: 53.123169722434426
+    task:
+      type: Reranking
+  - dataset:
+      config: default
+      name: MTEB SummEval
+      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
+      split: test
+      type: mteb/summeval
+    metrics:
+    - type: cos_sim_pearson
+      value: 30.967800769650022
+    - type: cos_sim_spearman
+      value: 31.168490040206926
+    - type: dot_pearson
+      value: 30.888603021128553
+    - type: dot_spearman
+      value: 31.028241262520385
+    task:
+      type: Summarization
+  - dataset:
+      config: default
+      name: MTEB TRECCOVID
+      revision: None
+      split: test
+      type: mteb/trec-covid
+    metrics:
+    - type: map_at_1
+      value: 0.22300000000000003
+    - type: map_at_10
+      value: 1.781
+    - type: map_at_100
+      value: 9.905999999999999
+    - type: map_at_1000
+      value: 23.455000000000002
+    - type: map_at_3
+      value: 0.569
+    - type: map_at_5
+      value: 0.918
+    - type: mrr_at_1
+      value: 84.0
+    - type: mrr_at_10
+      value: 91.067
+    - type: mrr_at_100
+      value: 91.067
+    - type: mrr_at_1000
+      value: 91.067
+    - type: mrr_at_3
+      value: 90.667
+    - type: mrr_at_5
+      value: 91.067
+    - type: ndcg_at_1
+      value: 78.0
+    - type: ndcg_at_10
+      value: 73.13499999999999
+    - type: ndcg_at_100
+      value: 55.32
+    - type: ndcg_at_1000
+      value: 49.532
+    - type: ndcg_at_3
+      value: 73.715
+    - type: ndcg_at_5
+      value: 72.74199999999999
+    - type: precision_at_1
+      value: 84.0
+    - type: precision_at_10
+      value: 78.8
+    - type: precision_at_100
+      value: 56.32
+    - type: precision_at_1000
+      value: 21.504
+    - type: precision_at_3
+      value: 77.333
+    - type: precision_at_5
+      value: 78.0
+    - type: recall_at_1
+      value: 0.22300000000000003
+    - type: recall_at_10
+      value: 2.049
+    - type: recall_at_100
+      value: 13.553
+    - type: recall_at_1000
+      value: 46.367999999999995
+    - type: recall_at_3
+      value: 0.604
+    - type: recall_at_5
+      value: 1.015
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB Touche2020
+      revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
+      split: test
+      type: mteb/touche2020
+    metrics:
+    - type: map_at_1
+      value: 3.0380000000000003
+    - type: map_at_10
+      value: 10.188
+    - type: map_at_100
+      value: 16.395
+    - type: map_at_1000
+      value: 18.024
+    - type: map_at_3
+      value: 6.236
+    - type: map_at_5
+      value: 7.276000000000001
+    - type: mrr_at_1
+      value: 34.694
+    - type: mrr_at_10
+      value: 46.292
+    - type: mrr_at_100
+      value: 47.446
+    - type: mrr_at_1000
+      value: 47.446
+    - type: mrr_at_3
+      value: 41.156
+    - type: mrr_at_5
+      value: 44.32
+    - type: ndcg_at_1
+      value: 32.653
+    - type: ndcg_at_10
+      value: 25.219
+    - type: ndcg_at_100
+      value: 37.802
+    - type: ndcg_at_1000
+      value: 49.274
+    - type: ndcg_at_3
+      value: 28.605999999999998
+    - type: ndcg_at_5
+      value: 26.21
+    - type: precision_at_1
+      value: 34.694
+    - type: precision_at_10
+      value: 21.837
+    - type: precision_at_100
+      value: 7.776
+    - type: precision_at_1000
+      value: 1.522
+    - type: precision_at_3
+      value: 28.571
+    - type: precision_at_5
+      value: 25.306
+    - type: recall_at_1
+      value: 3.0380000000000003
+    - type: recall_at_10
+      value: 16.298000000000002
+    - type: recall_at_100
+      value: 48.712
+    - type: recall_at_1000
+      value: 83.16799999999999
+    - type: recall_at_3
+      value: 7.265000000000001
+    - type: recall_at_5
+      value: 9.551
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB ToxicConversationsClassification
+      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
+      split: test
+      type: mteb/toxic_conversations_50k
+    metrics:
+    - type: accuracy
+      value: 83.978
+    - type: ap
+      value: 24.751887949330015
+    - type: f1
+      value: 66.8685134049279
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB TweetSentimentExtractionClassification
+      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
+      split: test
+      type: mteb/tweet_sentiment_extraction
+    metrics:
+    - type: accuracy
+      value: 61.573288058856825
+    - type: f1
+      value: 61.973261751726604
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB TwentyNewsgroupsClustering
+      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
+      split: test
+      type: mteb/twentynewsgroups-clustering
+    metrics:
+    - type: v_measure
+      value: 48.75483298792469
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB TwitterSemEval2015
+      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
+      split: test
+      type: mteb/twittersemeval2015-pairclassification
+    metrics:
+    - type: cos_sim_accuracy
+      value: 86.36824223639506
+    - type: cos_sim_ap
+      value: 75.53126388573047
+    - type: cos_sim_f1
+      value: 67.9912831688245
+    - type: cos_sim_precision
+      value: 66.11817501869858
+    - type: cos_sim_recall
+      value: 69.9736147757256
+    - type: dot_accuracy
+      value: 86.39804494248078
+    - type: dot_ap
+      value: 75.27598891718046
+    - type: dot_f1
+      value: 67.91146284159763
+    - type: dot_precision
+      value: 63.90505003490807
+    - type: dot_recall
+      value: 72.45382585751979
+    - type: euclidean_accuracy
+      value: 86.36228169517793
+    - type: euclidean_ap
+      value: 75.51438087434647
+    - type: euclidean_f1
+      value: 68.02370523061066
+    - type: euclidean_precision
+      value: 66.46525679758308
+    - type: euclidean_recall
+      value: 69.65699208443272
+    - type: manhattan_accuracy
+      value: 86.46361089586935
+    - type: manhattan_ap
+      value: 75.50800785730111
+    - type: manhattan_f1
+      value: 67.9220437187253
+    - type: manhattan_precision
+      value: 67.79705573080967
+    - type: manhattan_recall
+      value: 68.04749340369392
+    - type: max_accuracy
+      value: 86.46361089586935
+    - type: max_ap
+      value: 75.53126388573047
+    - type: max_f1
+      value: 68.02370523061066
+    task:
+      type: PairClassification
+  - dataset:
+      config: default
+      name: MTEB TwitterURLCorpus
+      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
+      split: test
+      type: mteb/twitterurlcorpus-pairclassification
+    metrics:
+    - type: cos_sim_accuracy
+      value: 88.80350836341057
+    - type: cos_sim_ap
+      value: 85.51101933260743
+    - type: cos_sim_f1
+      value: 77.9152271629704
+    - type: cos_sim_precision
+      value: 75.27815662910056
+    - type: cos_sim_recall
+      value: 80.74376347397599
+    - type: dot_accuracy
+      value: 88.84425815966158
+    - type: dot_ap
+      value: 85.49726945962519
+    - type: dot_f1
+      value: 77.94445269567801
+    - type: dot_precision
+      value: 75.27251864601261
+    - type: dot_recall
+      value: 80.81305820757623
+    - type: euclidean_accuracy
+      value: 88.80350836341057
+    - type: euclidean_ap
+      value: 85.4882880790211
+    - type: euclidean_f1
+      value: 77.87063284615103
+    - type: euclidean_precision
+      value: 74.61022927689595
+    - type: euclidean_recall
+      value: 81.42901139513397
+    - type: manhattan_accuracy
+      value: 88.7161873714441
+    - type: manhattan_ap
+      value: 85.45753871906821
+    - type: manhattan_f1
+      value: 77.8686401480111
+    - type: manhattan_precision
+      value: 74.95903683123174
+    - type: manhattan_recall
+      value: 81.01324299353249
+    - type: max_accuracy
+      value: 88.84425815966158
+    - type: max_ap
+      value: 85.51101933260743
+    - type: max_f1
+      value: 77.94445269567801
+    task:
+      type: PairClassification
+tags:
+- sentence-transformers
+- gte
+- mteb
+- transformers.js
+- sentence-similarity
+- onnx
+- teradata
+
+---
+# A Teradata Vantage compatible Embeddings Model
+
+# Alibaba-NLP/gte-base-en-v1.5
+
+## Overview of this Model
+
+An Embedding Model which maps text (sentence/ paragraphs) into a vector.  The [Alibaba-NLP/gte-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) model well known for its effectiveness in capturing semantic meanings in text data. It's a state-of-the-art model trained on a large corpus, capable of generating high-quality text embeddings.
+
+- 136.78M params (Sizes in ONNX format - "fp32": 530.23MB, "int8": 139.38MB, "uint8": 139.38MB)
+- 8192 maximum input tokens 
+- 768 dimensions of output vector
+- Licence: apache-2.0. The released models can be used for commercial purposes free of charge.
+- Reference to Original Model: https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5
+
+
+## Quickstart: Deploying this Model in Teradata Vantage
+
+We have pre-converted the model into the ONNX format compatible with BYOM 6.0, eliminating the need for manual conversion. 
+
+**Note:** Ensure you have access to a Teradata Database with BYOM 6.0 installed.
+
+To get started, clone the pre-converted model directly from the Teradata HuggingFace repository.
+
+
+```python
+
+import teradataml as tdml
+import getpass
+from huggingface_hub import hf_hub_download
+
+model_name = "gte-base-en-v1.5"
+number_dimensions_output = 768
+model_file_name = "model.onnx"
+
+# Step 1: Download Model from Teradata HuggingFace Page
+
+hf_hub_download(repo_id=f"Teradata/{model_name}", filename=f"onnx/{model_file_name}", local_dir="./")
+hf_hub_download(repo_id=f"Teradata/{model_name}", filename=f"tokenizer.json", local_dir="./")
+
+# Step 2: Create Connection to Vantage
+
+tdml.create_context(host = input('enter your hostname'), 
+                    username=input('enter your username'), 
+                    password = getpass.getpass("enter your password"))
+
+# Step 3: Load Models into Vantage
+# a) Embedding model
+tdml.save_byom(model_id = model_name, # must be unique in the models table
+               model_file = model_file_name,
+               table_name = 'embeddings_models' )
+# b) Tokenizer
+tdml.save_byom(model_id = model_name, # must be unique in the models table
+              model_file = 'tokenizer.json',
+              table_name = 'embeddings_tokenizers') 
+
+# Step 4: Test ONNXEmbeddings Function
+# Note that ONNXEmbeddings expects the 'payload' column to be 'txt'. 
+# If it has got a different name, just rename it in a subquery/CTE.
+input_table = "emails.emails"
+embeddings_query = f"""
+SELECT 
+        *
+from mldb.ONNXEmbeddings(
+        on {input_table} as InputTable
+        on (select * from embeddings_models where model_id = '{model_name}') as ModelTable DIMENSION
+        on (select model as tokenizer from embeddings_tokenizers where model_id = '{model_name}') as TokenizerTable DIMENSION
+        using
+            Accumulate('id', 'txt') 
+            ModelOutputTensor('sentence_embedding')
+            EnableMemoryCheck('false')
+            OutputFormat('FLOAT32({number_dimensions_output})')
+            OverwriteCachedModel('true')
+    ) a 
+"""
+DF_embeddings = tdml.DataFrame.from_query(embeddings_query)
+DF_embeddings
+```
+
+
+
+## What Can I Do with the Embeddings?
+
+Teradata Vantage includes pre-built in-database functions to process embeddings further. Explore the following examples:
+
+- **Semantic Clustering with TD_KMeans:** [Semantic Clustering Python Notebook](https://github.com/Teradata/jupyter-demos/blob/main/UseCases/Language_Models_InVantage/Semantic_Clustering_Python.ipynb)
+- **Semantic Distance with TD_VectorDistance:** [Semantic Similarity Python Notebook](https://github.com/Teradata/jupyter-demos/blob/main/UseCases/Language_Models_InVantage/Semantic_Similarity_Python.ipynb)
+- **RAG-Based Application with TD_VectorDistance:** [RAG and Bedrock Query PDF Notebook](https://github.com/Teradata/jupyter-demos/blob/main/UseCases/Language_Models_InVantage/RAG_and_Bedrock_QueryPDF.ipynb)
+
+
+## Deep Dive into Model Conversion to ONNX
+
+**The steps below outline how we converted the open-source Hugging Face model into an ONNX file compatible with the in-database ONNXEmbeddings function.** 
+
+You do not need to perform these steps—they are provided solely for documentation and transparency. However, they may be helpful if you wish to convert another model to the required format.
+
+
+### Part 1. Importing and Converting Model using optimum
+
+We start by importing the pre-trained [Alibaba-NLP/gte-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) model from Hugging Face.
+
+To enhance performance and ensure compatibility with various execution environments, we'll use the [Optimum](https://github.com/huggingface/optimum) utility to convert the model into the ONNX (Open Neural Network Exchange) format. 
+
+After conversion to ONNX, we are fixing the opset in the ONNX file for compatibility with ONNX runtime used in Teradata Vantage
+
+We are generating ONNX files for multiple different precisions: fp32, int8, uint8
+
+You can find the detailed conversion steps in the file [convert.py](./convert.py)
+
+### Part 2. Running the model in Python with onnxruntime & compare results
+
+Once the fixes are applied, we proceed to test the correctness of the ONNX model by calculating cosine similarity between two texts using native SentenceTransformers and ONNX runtime, comparing the results.
+
+If the results are identical, it confirms that the ONNX model gives the same result as the native models, validating its correctness and suitability for further use in the database.
+
+
+```python
+import onnxruntime as rt
+
+from sentence_transformers.util import cos_sim
+from sentence_transformers import SentenceTransformer
+
+import transformers
+
+
+sentences_1 = 'How is the weather today?'
+sentences_2 = 'What is the current weather like today?'
+
+# Calculate ONNX result
+tokenizer = transformers.AutoTokenizer.from_pretrained("Alibaba-NLP/gte-base-en-v1.5")
+predef_sess = rt.InferenceSession("onnx/model.onnx")
+
+enc1 = tokenizer(sentences_1)
+embeddings_1_onnx = predef_sess.run(None,     {"input_ids": [enc1.input_ids], 
+     "attention_mask": [enc1.attention_mask]})
+
+enc2 = tokenizer(sentences_2)
+embeddings_2_onnx = predef_sess.run(None,     {"input_ids": [enc2.input_ids], 
+     "attention_mask": [enc2.attention_mask]})
+
+
+# Calculate embeddings with SentenceTransformer
+model = SentenceTransformer(model_id, trust_remote_code=True)
+embeddings_1_sentence_transformer = model.encode(sentences_1, normalize_embeddings=True, trust_remote_code=True)
+embeddings_2_sentence_transformer = model.encode(sentences_2, normalize_embeddings=True, trust_remote_code=True)
+
+# Compare results
+print("Cosine similiarity for embeddings calculated with ONNX:" + str(cos_sim(embeddings_1_onnx[1][0], embeddings_2_onnx[1][0])))
+print("Cosine similiarity for embeddings calculated with SentenceTransformer:" + str(cos_sim(embeddings_1_sentence_transformer, embeddings_2_sentence_transformer)))
+```
+
+You can find the detailed ONNX vs. SentenceTransformer result comparison steps in the file [test_local.py](./test_local.py)
+

config.json

@@ -0,0 +1,46 @@
+{
+  "_attn_implementation_autoset": true,
+  "_name_or_path": "Alibaba-NLP/gte-base-en-v1.5",
+  "architectures": [
+    "NewModel"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "auto_map": {
+    "AutoConfig": "Alibaba-NLP/new-impl--configuration.NewConfig",
+    "AutoModel": "Alibaba-NLP/new-impl--modeling.NewModel",
+    "AutoModelForMaskedLM": "Alibaba-NLP/new-impl--modeling.NewForMaskedLM",
+    "AutoModelForMultipleChoice": "Alibaba-NLP/new-impl--modeling.NewForMultipleChoice",
+    "AutoModelForQuestionAnswering": "Alibaba-NLP/new-impl--modeling.NewForQuestionAnswering",
+    "AutoModelForSequenceClassification": "Alibaba-NLP/new-impl--modeling.NewForSequenceClassification",
+    "AutoModelForTokenClassification": "Alibaba-NLP/new-impl--modeling.NewForTokenClassification"
+  },
+  "classifier_dropout": null,
+  "export_model_type": "transformer",
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "layer_norm_type": "layer_norm",
+  "logn_attention_clip1": false,
+  "logn_attention_scale": false,
+  "max_position_embeddings": 8192,
+  "model_type": "new",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pack_qkv": true,
+  "pad_token_id": 0,
+  "position_embedding_type": "rope",
+  "rope_scaling": {
+    "factor": 2.0,
+    "type": "ntk"
+  },
+  "rope_theta": 500000,
+  "torch_dtype": "float32",
+  "transformers_version": "4.47.1",
+  "type_vocab_size": 0,
+  "unpad_inputs": false,
+  "use_memory_efficient_attention": false,
+  "vocab_size": 30528
+}

conversion_config.json

@@ -0,0 +1,12 @@
+{
+    "model_id": "Alibaba-NLP/gte-base-en-v1.5",
+    "number_of_generated_embeddings": 768,
+    "precision_to_filename_map": {
+        "fp32": "onnx/model.onnx",
+        "int8": "onnx/model_int8.onnx", 
+        "uint8": "onnx/model_uint8.onnx"
+        
+    },
+    "opset": 16,
+    "IR": 8
+}

convert.py

@@ -0,0 +1,51 @@
+import os
+import json
+import shutil
+
+from optimum.exporters.onnx import main_export
+import onnx
+from onnxconverter_common import float16
+import onnxruntime as rt
+from onnxruntime.tools.onnx_model_utils import *
+from onnxruntime.quantization import quantize_dynamic, QuantType
+
+with open('conversion_config.json') as json_file:
+    conversion_config = json.load(json_file)
+
+
+    model_id = conversion_config["model_id"]
+    number_of_generated_embeddings = conversion_config["number_of_generated_embeddings"]
+    precision_to_filename_map = conversion_config["precision_to_filename_map"]
+    opset = conversion_config["opset"]
+    IR = conversion_config["IR"]
+
+    
+    op = onnx.OperatorSetIdProto()
+    op.version = opset
+    
+    
+    if not os.path.exists("onnx"):
+        os.makedirs("onnx")
+
+    print("Exporting the main model version")
+
+    main_export(model_name_or_path=model_id, output="./",  opset=opset, trust_remote_code=True, task="feature-extraction", dtype="fp32")
+    
+    if "fp32" in precision_to_filename_map:
+        print("Exporting the fp32 onnx file...")
+        
+        shutil.copyfile('model.onnx', precision_to_filename_map["fp32"])
+        
+        print("Done\n\n")
+
+    if "int8" in precision_to_filename_map:
+        print("Quantizing fp32 model to int8...")
+        quantize_dynamic("model.onnx",  precision_to_filename_map["int8"], weight_type=QuantType.QInt8)
+        print("Done\n\n")
+        
+    if "uint8" in precision_to_filename_map:
+        print("Quantizing fp32 model to uint8...")
+        quantize_dynamic("model.onnx", precision_to_filename_map["uint8"], weight_type=QuantType.QUInt8)
+        print("Done\n\n")
+        
+    os.remove("model.onnx")

onnx/model.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:40aa468bfe2194dd285fbf4ca50fb6311c4c0e8e7e177554bec81e50355425ef
+size 555983762

onnx/model_int8.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:96fa2a0af4ba91b889bea073e4360a83b2e26ae239e80a59ab38f0e920135d20
+size 146153111

onnx/model_uint8.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5265123a73ec3fede01fa801ebd7c96177b4264d3ce677d1bc4d6332465b61c1
+size 146153133

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

test_local.py

@@ -0,0 +1,49 @@
+import onnxruntime as rt
+
+from sentence_transformers.util import cos_sim
+from sentence_transformers import SentenceTransformer
+
+import transformers
+
+import gc
+import json
+
+
+with open('conversion_config.json') as json_file:
+    conversion_config = json.load(json_file)
+
+
+    model_id = conversion_config["model_id"]
+    number_of_generated_embeddings = conversion_config["number_of_generated_embeddings"]
+    precision_to_filename_map = conversion_config["precision_to_filename_map"]
+    
+    sentences_1 = 'How is the weather today?'
+    sentences_2 = 'What is the current weather like today?'
+    
+    print(f"Testing on cosine similiarity between sentences: \n'{sentences_1}'\n'{sentences_2}'\n\n\n")
+    
+    tokenizer = transformers.AutoTokenizer.from_pretrained("./")
+    enc1 = tokenizer(sentences_1)
+    enc2 = tokenizer(sentences_2)
+    
+    for precision, file_name in precision_to_filename_map.items():
+    
+        
+        onnx_session = rt.InferenceSession(file_name)
+        embeddings_1_onnx = onnx_session.run(None,     {"input_ids": [enc1.input_ids], 
+             "attention_mask": [enc1.attention_mask]})[1][0]
+    
+        embeddings_2_onnx = onnx_session.run(None,     {"input_ids": [enc2.input_ids], 
+             "attention_mask": [enc2.attention_mask]})[1][0]
+    
+        del onnx_session
+        gc.collect()
+        print(f'Cosine similiarity for ONNX model with precision "{precision}" is {str(cos_sim(embeddings_1_onnx, embeddings_2_onnx))}')
+    
+    
+    
+    
+    model = SentenceTransformer(model_id, trust_remote_code=True)
+    embeddings_1_sentence_transformer = model.encode(sentences_1, normalize_embeddings=True, trust_remote_code=True)
+    embeddings_2_sentence_transformer = model.encode(sentences_2, normalize_embeddings=True, trust_remote_code=True)
+    print('Cosine similiarity for original sentence transformer model is '+str(cos_sim(embeddings_1_sentence_transformer, embeddings_2_sentence_transformer)))

test_teradata.py

@@ -0,0 +1,106 @@
+import sys
+import teradataml as tdml
+from tabulate import tabulate
+
+import json
+
+
+with open('conversion_config.json') as json_file:
+    conversion_config = json.load(json_file)
+
+
+    model_id = conversion_config["model_id"]
+    number_of_generated_embeddings = conversion_config["number_of_generated_embeddings"]
+    precision_to_filename_map = conversion_config["precision_to_filename_map"]
+    
+    host = sys.argv[1]
+    username = sys.argv[2]
+    password = sys.argv[3]
+    
+    print("Setting up connection to teradata...")
+    tdml.create_context(host = host, username = username, password = password)
+    print("Done\n\n")
+    
+    
+    print("Deploying tokenizer...")
+    try:
+        tdml.db_drop_table('tokenizer_table')
+    except:
+        print("Can't drop tokenizers table - it's not existing")
+    tdml.save_byom('tokenizer',
+                  'tokenizer.json',
+                  'tokenizer_table')
+    print("Done\n\n")
+    
+    print("Testing models...")
+    try:
+        tdml.db_drop_table('model_table')
+    except:
+        print("Can't drop models table - it's not existing")
+    
+    for precision, file_name in precision_to_filename_map.items():
+        print(f"Deploying {precision} model...")
+        tdml.save_byom(precision,
+                      file_name,
+                      'model_table')
+        print(f"Model {precision} is deployed\n")
+    
+        print(f"Calculating embeddings with {precision} model...")
+        try:
+            tdml.db_drop_table('emails_embeddings_store')
+        except:
+            print("Can't drop embeddings table - it's not existing")
+        
+        tdml.execute_sql(f"""
+            create volatile table emails_embeddings_store as (
+                select 
+                    *
+            from mldb.ONNXEmbeddings(
+                    on emails.emails as InputTable
+                    on (select * from model_table where model_id = '{precision}') as ModelTable DIMENSION
+                    on (select model as tokenizer from tokenizer_table where model_id = 'tokenizer') as TokenizerTable DIMENSION
+               
+                    using
+                        Accumulate('id', 'txt') 
+                        ModelOutputTensor('sentence_embedding')
+                        EnableMemoryCheck('false')
+                        OutputFormat('FLOAT32({number_of_generated_embeddings})')
+                        OverwriteCachedModel('true')
+                ) a 
+        ) with data on commit preserve rows
+        
+        """)
+        print("Embeddings calculated")
+        print(f"Testing semantic search with cosine similiarity on the output of the model with precision '{precision}'...")
+        tdf_embeddings_store = tdml.DataFrame('emails_embeddings_store')
+        tdf_embeddings_store_tgt = tdf_embeddings_store[tdf_embeddings_store.id == 3]
+        
+        tdf_embeddings_store_ref = tdf_embeddings_store[tdf_embeddings_store.id != 3]
+        
+        cos_sim_pd = tdml.DataFrame.from_query(f"""
+            SELECT 
+                dt.target_id, 
+                dt.reference_id,
+                e_tgt.txt as target_txt,
+                e_ref.txt as reference_txt,
+                (1.0 - dt.distance) as similiarity 
+            FROM
+                TD_VECTORDISTANCE (
+                    ON ({tdf_embeddings_store_tgt.show_query()}) AS TargetTable
+                    ON ({tdf_embeddings_store_ref.show_query()}) AS ReferenceTable DIMENSION
+                    USING
+                        TargetIDColumn('id')
+                        TargetFeatureColumns('[emb_0:emb_{number_of_generated_embeddings - 1}]')
+                        RefIDColumn('id')
+                        RefFeatureColumns('[emb_0:emb_{number_of_generated_embeddings - 1}]')
+                        DistanceMeasure('cosine')
+                        topk(3)
+                ) AS dt
+            JOIN emails.emails e_tgt on e_tgt.id = dt.target_id
+            JOIN emails.emails e_ref on e_ref.id = dt.reference_id;
+            """).to_pandas()
+        print(tabulate(cos_sim_pd, headers='keys', tablefmt='fancy_grid'))
+        print("Done\n\n")
+    
+    
+    tdml.remove_context()

tokenizer_config.json

@@ -0,0 +1,63 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "max_length": 512,
+  "model_max_length": 32768,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}