model

.gitattributes

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 *.gz filter=lfs diff=lfs merge=lfs -text
 *.h5 filter=lfs diff=lfs merge=lfs -text
 *.joblib filter=lfs diff=lfs merge=lfs -text
+*.json filter=lfs diff=lfs merge=lfs -text
 *.lfs.* filter=lfs diff=lfs merge=lfs -text
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 *.model filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,3 +1,2764 @@
----
-license: apache-2.0
----
+---
+datasets:
+- allenai/c4
+language:
+- en
+library_name: transformers
+license: apache-2.0
+model-index:
+- name: gte-large-en-v1.5
+  results:
+  - dataset:
+      config: en
+      name: MTEB AmazonCounterfactualClassification (en)
+      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
+      split: test
+      type: mteb/amazon_counterfactual
+    metrics:
+    - type: accuracy
+      value: 73.01492537313432
+    - type: ap
+      value: 35.05341696659522
+    - type: f1
+      value: 66.71270310883853
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB AmazonPolarityClassification
+      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
+      split: test
+      type: mteb/amazon_polarity
+    metrics:
+    - type: accuracy
+      value: 93.97189999999999
+    - type: ap
+      value: 90.5952493948908
+    - type: f1
+      value: 93.95848137716877
+    task:
+      type: Classification
+  - dataset:
+      config: en
+      name: MTEB AmazonReviewsClassification (en)
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+      split: test
+      type: mteb/amazon_reviews_multi
+    metrics:
+    - type: accuracy
+      value: 54.196
+    - type: f1
+      value: 53.80122334012787
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB ArguAna
+      revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
+      split: test
+      type: mteb/arguana
+    metrics:
+    - type: map_at_1
+      value: 47.297
+    - type: map_at_10
+      value: 64.303
+    - type: map_at_100
+      value: 64.541
+    - type: map_at_1000
+      value: 64.541
+    - type: map_at_3
+      value: 60.728
+    - type: map_at_5
+      value: 63.114000000000004
+    - type: mrr_at_1
+      value: 48.435
+    - type: mrr_at_10
+      value: 64.657
+    - type: mrr_at_100
+      value: 64.901
+    - type: mrr_at_1000
+      value: 64.901
+    - type: mrr_at_3
+      value: 61.06
+    - type: mrr_at_5
+      value: 63.514
+    - type: ndcg_at_1
+      value: 47.297
+    - type: ndcg_at_10
+      value: 72.107
+    - type: ndcg_at_100
+      value: 72.963
+    - type: ndcg_at_1000
+      value: 72.963
+    - type: ndcg_at_3
+      value: 65.063
+    - type: ndcg_at_5
+      value: 69.352
+    - type: precision_at_1
+      value: 47.297
+    - type: precision_at_10
+      value: 9.623
+    - type: precision_at_100
+      value: 0.996
+    - type: precision_at_1000
+      value: 0.1
+    - type: precision_at_3
+      value: 25.865
+    - type: precision_at_5
+      value: 17.596
+    - type: recall_at_1
+      value: 47.297
+    - type: recall_at_10
+      value: 96.23
+    - type: recall_at_100
+      value: 99.644
+    - type: recall_at_1000
+      value: 99.644
+    - type: recall_at_3
+      value: 77.596
+    - type: recall_at_5
+      value: 87.98
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB ArxivClusteringP2P
+      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
+      split: test
+      type: mteb/arxiv-clustering-p2p
+    metrics:
+    - type: v_measure
+      value: 48.467787861077475
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB ArxivClusteringS2S
+      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
+      split: test
+      type: mteb/arxiv-clustering-s2s
+    metrics:
+    - type: v_measure
+      value: 43.39198391914257
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB AskUbuntuDupQuestions
+      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
+      split: test
+      type: mteb/askubuntudupquestions-reranking
+    metrics:
+    - type: map
+      value: 63.12794820591384
+    - type: mrr
+      value: 75.9331442641692
+    task:
+      type: Reranking
+  - dataset:
+      config: default
+      name: MTEB BIOSSES
+      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
+      split: test
+      type: mteb/biosses-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 87.85062993863319
+    - type: cos_sim_spearman
+      value: 85.39049989733459
+    - type: euclidean_pearson
+      value: 86.00222680278333
+    - type: euclidean_spearman
+      value: 85.45556162077396
+    - type: manhattan_pearson
+      value: 85.88769871785621
+    - type: manhattan_spearman
+      value: 85.11760211290839
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB Banking77Classification
+      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
+      split: test
+      type: mteb/banking77
+    metrics:
+    - type: accuracy
+      value: 87.32792207792208
+    - type: f1
+      value: 87.29132945999555
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB BiorxivClusteringP2P
+      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
+      split: test
+      type: mteb/biorxiv-clustering-p2p
+    metrics:
+    - type: v_measure
+      value: 40.5779328301945
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB BiorxivClusteringS2S
+      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
+      split: test
+      type: mteb/biorxiv-clustering-s2s
+    metrics:
+    - type: v_measure
+      value: 37.94425623865118
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB CQADupstackAndroidRetrieval
+      revision: f46a197baaae43b4f621051089b82a364682dfeb
+      split: test
+      type: mteb/cqadupstack-android
+    metrics:
+    - type: map_at_1
+      value: 32.978
+    - type: map_at_10
+      value: 44.45
+    - type: map_at_100
+      value: 46.19
+    - type: map_at_1000
+      value: 46.303
+    - type: map_at_3
+      value: 40.849000000000004
+    - type: map_at_5
+      value: 42.55
+    - type: mrr_at_1
+      value: 40.629
+    - type: mrr_at_10
+      value: 50.848000000000006
+    - type: mrr_at_100
+      value: 51.669
+    - type: mrr_at_1000
+      value: 51.705
+    - type: mrr_at_3
+      value: 47.997
+    - type: mrr_at_5
+      value: 49.506
+    - type: ndcg_at_1
+      value: 40.629
+    - type: ndcg_at_10
+      value: 51.102000000000004
+    - type: ndcg_at_100
+      value: 57.159000000000006
+    - type: ndcg_at_1000
+      value: 58.669000000000004
+    - type: ndcg_at_3
+      value: 45.738
+    - type: ndcg_at_5
+      value: 47.632999999999996
+    - type: precision_at_1
+      value: 40.629
+    - type: precision_at_10
+      value: 9.700000000000001
+    - type: precision_at_100
+      value: 1.5970000000000002
+    - type: precision_at_1000
+      value: 0.202
+    - type: precision_at_3
+      value: 21.698
+    - type: precision_at_5
+      value: 15.393
+    - type: recall_at_1
+      value: 32.978
+    - type: recall_at_10
+      value: 63.711
+    - type: recall_at_100
+      value: 88.39399999999999
+    - type: recall_at_1000
+      value: 97.513
+    - type: recall_at_3
+      value: 48.025
+    - type: recall_at_5
+      value: 53.52
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB CQADupstackEnglishRetrieval
+      revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
+      split: test
+      type: mteb/cqadupstack-english
+    metrics:
+    - type: map_at_1
+      value: 30.767
+    - type: map_at_10
+      value: 42.195
+    - type: map_at_100
+      value: 43.541999999999994
+    - type: map_at_1000
+      value: 43.673
+    - type: map_at_3
+      value: 38.561
+    - type: map_at_5
+      value: 40.532000000000004
+    - type: mrr_at_1
+      value: 38.79
+    - type: mrr_at_10
+      value: 48.021
+    - type: mrr_at_100
+      value: 48.735
+    - type: mrr_at_1000
+      value: 48.776
+    - type: mrr_at_3
+      value: 45.594
+    - type: mrr_at_5
+      value: 46.986
+    - type: ndcg_at_1
+      value: 38.79
+    - type: ndcg_at_10
+      value: 48.468
+    - type: ndcg_at_100
+      value: 53.037
+    - type: ndcg_at_1000
+      value: 55.001999999999995
+    - type: ndcg_at_3
+      value: 43.409
+    - type: ndcg_at_5
+      value: 45.654
+    - type: precision_at_1
+      value: 38.79
+    - type: precision_at_10
+      value: 9.452
+    - type: precision_at_100
+      value: 1.518
+    - type: precision_at_1000
+      value: 0.201
+    - type: precision_at_3
+      value: 21.21
+    - type: precision_at_5
+      value: 15.171999999999999
+    - type: recall_at_1
+      value: 30.767
+    - type: recall_at_10
+      value: 60.118
+    - type: recall_at_100
+      value: 79.271
+    - type: recall_at_1000
+      value: 91.43299999999999
+    - type: recall_at_3
+      value: 45.36
+    - type: recall_at_5
+      value: 51.705
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB CQADupstackGamingRetrieval
+      revision: 4885aa143210c98657558c04aaf3dc47cfb54340
+      split: test
+      type: mteb/cqadupstack-gaming
+    metrics:
+    - type: map_at_1
+      value: 40.007
+    - type: map_at_10
+      value: 53.529
+    - type: map_at_100
+      value: 54.602
+    - type: map_at_1000
+      value: 54.647
+    - type: map_at_3
+      value: 49.951
+    - type: map_at_5
+      value: 52.066
+    - type: mrr_at_1
+      value: 45.705
+    - type: mrr_at_10
+      value: 56.745000000000005
+    - type: mrr_at_100
+      value: 57.43899999999999
+    - type: mrr_at_1000
+      value: 57.462999999999994
+    - type: mrr_at_3
+      value: 54.25299999999999
+    - type: mrr_at_5
+      value: 55.842000000000006
+    - type: ndcg_at_1
+      value: 45.705
+    - type: ndcg_at_10
+      value: 59.809
+    - type: ndcg_at_100
+      value: 63.837999999999994
+    - type: ndcg_at_1000
+      value: 64.729
+    - type: ndcg_at_3
+      value: 53.994
+    - type: ndcg_at_5
+      value: 57.028
+    - type: precision_at_1
+      value: 45.705
+    - type: precision_at_10
+      value: 9.762
+    - type: precision_at_100
+      value: 1.275
+    - type: precision_at_1000
+      value: 0.13899999999999998
+    - type: precision_at_3
+      value: 24.368000000000002
+    - type: precision_at_5
+      value: 16.84
+    - type: recall_at_1
+      value: 40.007
+    - type: recall_at_10
+      value: 75.017
+    - type: recall_at_100
+      value: 91.99000000000001
+    - type: recall_at_1000
+      value: 98.265
+    - type: recall_at_3
+      value: 59.704
+    - type: recall_at_5
+      value: 67.109
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB CQADupstackGisRetrieval
+      revision: 5003b3064772da1887988e05400cf3806fe491f2
+      split: test
+      type: mteb/cqadupstack-gis
+    metrics:
+    - type: map_at_1
+      value: 26.639000000000003
+    - type: map_at_10
+      value: 35.926
+    - type: map_at_100
+      value: 37.126999999999995
+    - type: map_at_1000
+      value: 37.202
+    - type: map_at_3
+      value: 32.989000000000004
+    - type: map_at_5
+      value: 34.465
+    - type: mrr_at_1
+      value: 28.475
+    - type: mrr_at_10
+      value: 37.7
+    - type: mrr_at_100
+      value: 38.753
+    - type: mrr_at_1000
+      value: 38.807
+    - type: mrr_at_3
+      value: 35.066
+    - type: mrr_at_5
+      value: 36.512
+    - type: ndcg_at_1
+      value: 28.475
+    - type: ndcg_at_10
+      value: 41.245
+    - type: ndcg_at_100
+      value: 46.814
+    - type: ndcg_at_1000
+      value: 48.571
+    - type: ndcg_at_3
+      value: 35.528999999999996
+    - type: ndcg_at_5
+      value: 38.066
+    - type: precision_at_1
+      value: 28.475
+    - type: precision_at_10
+      value: 6.497
+    - type: precision_at_100
+      value: 0.9650000000000001
+    - type: precision_at_1000
+      value: 0.11499999999999999
+    - type: precision_at_3
+      value: 15.065999999999999
+    - type: precision_at_5
+      value: 10.599
+    - type: recall_at_1
+      value: 26.639000000000003
+    - type: recall_at_10
+      value: 55.759
+    - type: recall_at_100
+      value: 80.913
+    - type: recall_at_1000
+      value: 93.929
+    - type: recall_at_3
+      value: 40.454
+    - type: recall_at_5
+      value: 46.439
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB CQADupstackMathematicaRetrieval
+      revision: 90fceea13679c63fe563ded68f3b6f06e50061de
+      split: test
+      type: mteb/cqadupstack-mathematica
+    metrics:
+    - type: map_at_1
+      value: 15.767999999999999
+    - type: map_at_10
+      value: 24.811
+    - type: map_at_100
+      value: 26.064999999999998
+    - type: map_at_1000
+      value: 26.186999999999998
+    - type: map_at_3
+      value: 21.736
+    - type: map_at_5
+      value: 23.283
+    - type: mrr_at_1
+      value: 19.527
+    - type: mrr_at_10
+      value: 29.179
+    - type: mrr_at_100
+      value: 30.153999999999996
+    - type: mrr_at_1000
+      value: 30.215999999999998
+    - type: mrr_at_3
+      value: 26.223000000000003
+    - type: mrr_at_5
+      value: 27.733999999999998
+    - type: ndcg_at_1
+      value: 19.527
+    - type: ndcg_at_10
+      value: 30.786
+    - type: ndcg_at_100
+      value: 36.644
+    - type: ndcg_at_1000
+      value: 39.440999999999995
+    - type: ndcg_at_3
+      value: 24.958
+    - type: ndcg_at_5
+      value: 27.392
+    - type: precision_at_1
+      value: 19.527
+    - type: precision_at_10
+      value: 5.995
+    - type: precision_at_100
+      value: 1.03
+    - type: precision_at_1000
+      value: 0.14100000000000001
+    - type: precision_at_3
+      value: 12.520999999999999
+    - type: precision_at_5
+      value: 9.129
+    - type: recall_at_1
+      value: 15.767999999999999
+    - type: recall_at_10
+      value: 44.824000000000005
+    - type: recall_at_100
+      value: 70.186
+    - type: recall_at_1000
+      value: 89.934
+    - type: recall_at_3
+      value: 28.607
+    - type: recall_at_5
+      value: 34.836
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB CQADupstackPhysicsRetrieval
+      revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
+      split: test
+      type: mteb/cqadupstack-physics
+    metrics:
+    - type: map_at_1
+      value: 31.952
+    - type: map_at_10
+      value: 44.438
+    - type: map_at_100
+      value: 45.778
+    - type: map_at_1000
+      value: 45.883
+    - type: map_at_3
+      value: 41.044000000000004
+    - type: map_at_5
+      value: 42.986000000000004
+    - type: mrr_at_1
+      value: 39.172000000000004
+    - type: mrr_at_10
+      value: 49.76
+    - type: mrr_at_100
+      value: 50.583999999999996
+    - type: mrr_at_1000
+      value: 50.621
+    - type: mrr_at_3
+      value: 47.353
+    - type: mrr_at_5
+      value: 48.739
+    - type: ndcg_at_1
+      value: 39.172000000000004
+    - type: ndcg_at_10
+      value: 50.760000000000005
+    - type: ndcg_at_100
+      value: 56.084
+    - type: ndcg_at_1000
+      value: 57.865
+    - type: ndcg_at_3
+      value: 45.663
+    - type: ndcg_at_5
+      value: 48.178
+    - type: precision_at_1
+      value: 39.172000000000004
+    - type: precision_at_10
+      value: 9.22
+    - type: precision_at_100
+      value: 1.387
+    - type: precision_at_1000
+      value: 0.17099999999999999
+    - type: precision_at_3
+      value: 21.976000000000003
+    - type: precision_at_5
+      value: 15.457
+    - type: recall_at_1
+      value: 31.952
+    - type: recall_at_10
+      value: 63.900999999999996
+    - type: recall_at_100
+      value: 85.676
+    - type: recall_at_1000
+      value: 97.03699999999999
+    - type: recall_at_3
+      value: 49.781
+    - type: recall_at_5
+      value: 56.330000000000005
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB CQADupstackProgrammersRetrieval
+      revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
+      split: test
+      type: mteb/cqadupstack-programmers
+    metrics:
+    - type: map_at_1
+      value: 25.332
+    - type: map_at_10
+      value: 36.874
+    - type: map_at_100
+      value: 38.340999999999994
+    - type: map_at_1000
+      value: 38.452
+    - type: map_at_3
+      value: 33.068
+    - type: map_at_5
+      value: 35.324
+    - type: mrr_at_1
+      value: 30.822
+    - type: mrr_at_10
+      value: 41.641
+    - type: mrr_at_100
+      value: 42.519
+    - type: mrr_at_1000
+      value: 42.573
+    - type: mrr_at_3
+      value: 38.413000000000004
+    - type: mrr_at_5
+      value: 40.542
+    - type: ndcg_at_1
+      value: 30.822
+    - type: ndcg_at_10
+      value: 43.414
+    - type: ndcg_at_100
+      value: 49.196
+    - type: ndcg_at_1000
+      value: 51.237
+    - type: ndcg_at_3
+      value: 37.230000000000004
+    - type: ndcg_at_5
+      value: 40.405
+    - type: precision_at_1
+      value: 30.822
+    - type: precision_at_10
+      value: 8.379
+    - type: precision_at_100
+      value: 1.315
+    - type: precision_at_1000
+      value: 0.168
+    - type: precision_at_3
+      value: 18.417
+    - type: precision_at_5
+      value: 13.744
+    - type: recall_at_1
+      value: 25.332
+    - type: recall_at_10
+      value: 57.774
+    - type: recall_at_100
+      value: 82.071
+    - type: recall_at_1000
+      value: 95.60600000000001
+    - type: recall_at_3
+      value: 40.722
+    - type: recall_at_5
+      value: 48.754999999999995
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB CQADupstackRetrieval
+      revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
+      split: test
+      type: mteb/cqadupstack
+    metrics:
+    - type: map_at_1
+      value: 25.91033333333334
+    - type: map_at_10
+      value: 36.23225000000001
+    - type: map_at_100
+      value: 37.55766666666667
+    - type: map_at_1000
+      value: 37.672583333333336
+    - type: map_at_3
+      value: 32.95666666666667
+    - type: map_at_5
+      value: 34.73375
+    - type: mrr_at_1
+      value: 30.634
+    - type: mrr_at_10
+      value: 40.19449999999999
+    - type: mrr_at_100
+      value: 41.099250000000005
+    - type: mrr_at_1000
+      value: 41.15091666666667
+    - type: mrr_at_3
+      value: 37.4615
+    - type: mrr_at_5
+      value: 39.00216666666667
+    - type: ndcg_at_1
+      value: 30.634
+    - type: ndcg_at_10
+      value: 42.162166666666664
+    - type: ndcg_at_100
+      value: 47.60708333333333
+    - type: ndcg_at_1000
+      value: 49.68616666666666
+    - type: ndcg_at_3
+      value: 36.60316666666666
+    - type: ndcg_at_5
+      value: 39.15616666666668
+    - type: precision_at_1
+      value: 30.634
+    - type: precision_at_10
+      value: 7.6193333333333335
+    - type: precision_at_100
+      value: 1.2198333333333333
+    - type: precision_at_1000
+      value: 0.15975000000000003
+    - type: precision_at_3
+      value: 17.087
+    - type: precision_at_5
+      value: 12.298333333333334
+    - type: recall_at_1
+      value: 25.91033333333334
+    - type: recall_at_10
+      value: 55.67300000000001
+    - type: recall_at_100
+      value: 79.20608333333334
+    - type: recall_at_1000
+      value: 93.34866666666667
+    - type: recall_at_3
+      value: 40.34858333333333
+    - type: recall_at_5
+      value: 46.834083333333325
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB CQADupstackStatsRetrieval
+      revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
+      split: test
+      type: mteb/cqadupstack-stats
+    metrics:
+    - type: map_at_1
+      value: 25.006
+    - type: map_at_10
+      value: 32.177
+    - type: map_at_100
+      value: 33.324999999999996
+    - type: map_at_1000
+      value: 33.419
+    - type: map_at_3
+      value: 29.952
+    - type: map_at_5
+      value: 31.095
+    - type: mrr_at_1
+      value: 28.066999999999997
+    - type: mrr_at_10
+      value: 34.995
+    - type: mrr_at_100
+      value: 35.978
+    - type: mrr_at_1000
+      value: 36.042
+    - type: mrr_at_3
+      value: 33.103
+    - type: mrr_at_5
+      value: 34.001
+    - type: ndcg_at_1
+      value: 28.066999999999997
+    - type: ndcg_at_10
+      value: 36.481
+    - type: ndcg_at_100
+      value: 42.022999999999996
+    - type: ndcg_at_1000
+      value: 44.377
+    - type: ndcg_at_3
+      value: 32.394
+    - type: ndcg_at_5
+      value: 34.108
+    - type: precision_at_1
+      value: 28.066999999999997
+    - type: precision_at_10
+      value: 5.736
+    - type: precision_at_100
+      value: 0.9259999999999999
+    - type: precision_at_1000
+      value: 0.12
+    - type: precision_at_3
+      value: 13.804
+    - type: precision_at_5
+      value: 9.508999999999999
+    - type: recall_at_1
+      value: 25.006
+    - type: recall_at_10
+      value: 46.972
+    - type: recall_at_100
+      value: 72.138
+    - type: recall_at_1000
+      value: 89.479
+    - type: recall_at_3
+      value: 35.793
+    - type: recall_at_5
+      value: 39.947
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB CQADupstackTexRetrieval
+      revision: 46989137a86843e03a6195de44b09deda022eec7
+      split: test
+      type: mteb/cqadupstack-tex
+    metrics:
+    - type: map_at_1
+      value: 16.07
+    - type: map_at_10
+      value: 24.447
+    - type: map_at_100
+      value: 25.685999999999996
+    - type: map_at_1000
+      value: 25.813999999999997
+    - type: map_at_3
+      value: 21.634
+    - type: map_at_5
+      value: 23.133
+    - type: mrr_at_1
+      value: 19.580000000000002
+    - type: mrr_at_10
+      value: 28.127999999999997
+    - type: mrr_at_100
+      value: 29.119
+    - type: mrr_at_1000
+      value: 29.192
+    - type: mrr_at_3
+      value: 25.509999999999998
+    - type: mrr_at_5
+      value: 26.878
+    - type: ndcg_at_1
+      value: 19.580000000000002
+    - type: ndcg_at_10
+      value: 29.804000000000002
+    - type: ndcg_at_100
+      value: 35.555
+    - type: ndcg_at_1000
+      value: 38.421
+    - type: ndcg_at_3
+      value: 24.654999999999998
+    - type: ndcg_at_5
+      value: 26.881
+    - type: precision_at_1
+      value: 19.580000000000002
+    - type: precision_at_10
+      value: 5.736
+    - type: precision_at_100
+      value: 1.005
+    - type: precision_at_1000
+      value: 0.145
+    - type: precision_at_3
+      value: 12.033000000000001
+    - type: precision_at_5
+      value: 8.871
+    - type: recall_at_1
+      value: 16.07
+    - type: recall_at_10
+      value: 42.364000000000004
+    - type: recall_at_100
+      value: 68.01899999999999
+    - type: recall_at_1000
+      value: 88.122
+    - type: recall_at_3
+      value: 27.846
+    - type: recall_at_5
+      value: 33.638
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB CQADupstackUnixRetrieval
+      revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
+      split: test
+      type: mteb/cqadupstack-unix
+    metrics:
+    - type: map_at_1
+      value: 26.365
+    - type: map_at_10
+      value: 36.591
+    - type: map_at_100
+      value: 37.730000000000004
+    - type: map_at_1000
+      value: 37.84
+    - type: map_at_3
+      value: 33.403
+    - type: map_at_5
+      value: 35.272999999999996
+    - type: mrr_at_1
+      value: 30.503999999999998
+    - type: mrr_at_10
+      value: 39.940999999999995
+    - type: mrr_at_100
+      value: 40.818
+    - type: mrr_at_1000
+      value: 40.876000000000005
+    - type: mrr_at_3
+      value: 37.065
+    - type: mrr_at_5
+      value: 38.814
+    - type: ndcg_at_1
+      value: 30.503999999999998
+    - type: ndcg_at_10
+      value: 42.185
+    - type: ndcg_at_100
+      value: 47.416000000000004
+    - type: ndcg_at_1000
+      value: 49.705
+    - type: ndcg_at_3
+      value: 36.568
+    - type: ndcg_at_5
+      value: 39.416000000000004
+    - type: precision_at_1
+      value: 30.503999999999998
+    - type: precision_at_10
+      value: 7.276000000000001
+    - type: precision_at_100
+      value: 1.118
+    - type: precision_at_1000
+      value: 0.14300000000000002
+    - type: precision_at_3
+      value: 16.729
+    - type: precision_at_5
+      value: 12.107999999999999
+    - type: recall_at_1
+      value: 26.365
+    - type: recall_at_10
+      value: 55.616
+    - type: recall_at_100
+      value: 78.129
+    - type: recall_at_1000
+      value: 93.95599999999999
+    - type: recall_at_3
+      value: 40.686
+    - type: recall_at_5
+      value: 47.668
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB CQADupstackWebmastersRetrieval
+      revision: 160c094312a0e1facb97e55eeddb698c0abe3571
+      split: test
+      type: mteb/cqadupstack-webmasters
+    metrics:
+    - type: map_at_1
+      value: 22.750999999999998
+    - type: map_at_10
+      value: 33.446
+    - type: map_at_100
+      value: 35.235
+    - type: map_at_1000
+      value: 35.478
+    - type: map_at_3
+      value: 29.358
+    - type: map_at_5
+      value: 31.525
+    - type: mrr_at_1
+      value: 27.668
+    - type: mrr_at_10
+      value: 37.694
+    - type: mrr_at_100
+      value: 38.732
+    - type: mrr_at_1000
+      value: 38.779
+    - type: mrr_at_3
+      value: 34.223
+    - type: mrr_at_5
+      value: 36.08
+    - type: ndcg_at_1
+      value: 27.668
+    - type: ndcg_at_10
+      value: 40.557
+    - type: ndcg_at_100
+      value: 46.605999999999995
+    - type: ndcg_at_1000
+      value: 48.917
+    - type: ndcg_at_3
+      value: 33.677
+    - type: ndcg_at_5
+      value: 36.85
+    - type: precision_at_1
+      value: 27.668
+    - type: precision_at_10
+      value: 8.3
+    - type: precision_at_100
+      value: 1.6260000000000001
+    - type: precision_at_1000
+      value: 0.253
+    - type: precision_at_3
+      value: 16.008
+    - type: precision_at_5
+      value: 12.292
+    - type: recall_at_1
+      value: 22.750999999999998
+    - type: recall_at_10
+      value: 55.643
+    - type: recall_at_100
+      value: 82.151
+    - type: recall_at_1000
+      value: 95.963
+    - type: recall_at_3
+      value: 36.623
+    - type: recall_at_5
+      value: 44.708
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB CQADupstackWordpressRetrieval
+      revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
+      split: test
+      type: mteb/cqadupstack-wordpress
+    metrics:
+    - type: map_at_1
+      value: 17.288999999999998
+    - type: map_at_10
+      value: 25.903
+    - type: map_at_100
+      value: 27.071
+    - type: map_at_1000
+      value: 27.173000000000002
+    - type: map_at_3
+      value: 22.935
+    - type: map_at_5
+      value: 24.573
+    - type: mrr_at_1
+      value: 18.669
+    - type: mrr_at_10
+      value: 27.682000000000002
+    - type: mrr_at_100
+      value: 28.691
+    - type: mrr_at_1000
+      value: 28.761
+    - type: mrr_at_3
+      value: 24.738
+    - type: mrr_at_5
+      value: 26.392
+    - type: ndcg_at_1
+      value: 18.669
+    - type: ndcg_at_10
+      value: 31.335
+    - type: ndcg_at_100
+      value: 36.913000000000004
+    - type: ndcg_at_1000
+      value: 39.300000000000004
+    - type: ndcg_at_3
+      value: 25.423000000000002
+    - type: ndcg_at_5
+      value: 28.262999999999998
+    - type: precision_at_1
+      value: 18.669
+    - type: precision_at_10
+      value: 5.379
+    - type: precision_at_100
+      value: 0.876
+    - type: precision_at_1000
+      value: 0.11900000000000001
+    - type: precision_at_3
+      value: 11.214
+    - type: precision_at_5
+      value: 8.466
+    - type: recall_at_1
+      value: 17.288999999999998
+    - type: recall_at_10
+      value: 46.377
+    - type: recall_at_100
+      value: 71.53500000000001
+    - type: recall_at_1000
+      value: 88.947
+    - type: recall_at_3
+      value: 30.581999999999997
+    - type: recall_at_5
+      value: 37.354
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB ClimateFEVER
+      revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
+      split: test
+      type: mteb/climate-fever
+    metrics:
+    - type: map_at_1
+      value: 21.795
+    - type: map_at_10
+      value: 37.614999999999995
+    - type: map_at_100
+      value: 40.037
+    - type: map_at_1000
+      value: 40.184999999999995
+    - type: map_at_3
+      value: 32.221
+    - type: map_at_5
+      value: 35.154999999999994
+    - type: mrr_at_1
+      value: 50.358000000000004
+    - type: mrr_at_10
+      value: 62.129
+    - type: mrr_at_100
+      value: 62.613
+    - type: mrr_at_1000
+      value: 62.62
+    - type: mrr_at_3
+      value: 59.272999999999996
+    - type: mrr_at_5
+      value: 61.138999999999996
+    - type: ndcg_at_1
+      value: 50.358000000000004
+    - type: ndcg_at_10
+      value: 48.362
+    - type: ndcg_at_100
+      value: 55.932
+    - type: ndcg_at_1000
+      value: 58.062999999999995
+    - type: ndcg_at_3
+      value: 42.111
+    - type: ndcg_at_5
+      value: 44.063
+    - type: precision_at_1
+      value: 50.358000000000004
+    - type: precision_at_10
+      value: 14.677999999999999
+    - type: precision_at_100
+      value: 2.2950000000000004
+    - type: precision_at_1000
+      value: 0.271
+    - type: precision_at_3
+      value: 31.77
+    - type: precision_at_5
+      value: 23.375
+    - type: recall_at_1
+      value: 21.795
+    - type: recall_at_10
+      value: 53.846000000000004
+    - type: recall_at_100
+      value: 78.952
+    - type: recall_at_1000
+      value: 90.41900000000001
+    - type: recall_at_3
+      value: 37.257
+    - type: recall_at_5
+      value: 44.661
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB DBPedia
+      revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
+      split: test
+      type: mteb/dbpedia
+    metrics:
+    - type: map_at_1
+      value: 9.728
+    - type: map_at_10
+      value: 22.691
+    - type: map_at_100
+      value: 31.734
+    - type: map_at_1000
+      value: 33.464
+    - type: map_at_3
+      value: 16.273
+    - type: map_at_5
+      value: 19.016
+    - type: mrr_at_1
+      value: 73.25
+    - type: mrr_at_10
+      value: 80.782
+    - type: mrr_at_100
+      value: 81.01899999999999
+    - type: mrr_at_1000
+      value: 81.021
+    - type: mrr_at_3
+      value: 79.583
+    - type: mrr_at_5
+      value: 80.146
+    - type: ndcg_at_1
+      value: 59.62499999999999
+    - type: ndcg_at_10
+      value: 46.304
+    - type: ndcg_at_100
+      value: 51.23
+    - type: ndcg_at_1000
+      value: 58.048
+    - type: ndcg_at_3
+      value: 51.541000000000004
+    - type: ndcg_at_5
+      value: 48.635
+    - type: precision_at_1
+      value: 73.25
+    - type: precision_at_10
+      value: 36.375
+    - type: precision_at_100
+      value: 11.53
+    - type: precision_at_1000
+      value: 2.23
+    - type: precision_at_3
+      value: 55.583000000000006
+    - type: precision_at_5
+      value: 47.15
+    - type: recall_at_1
+      value: 9.728
+    - type: recall_at_10
+      value: 28.793999999999997
+    - type: recall_at_100
+      value: 57.885
+    - type: recall_at_1000
+      value: 78.759
+    - type: recall_at_3
+      value: 17.79
+    - type: recall_at_5
+      value: 21.733
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB EmotionClassification
+      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
+      split: test
+      type: mteb/emotion
+    metrics:
+    - type: accuracy
+      value: 46.775
+    - type: f1
+      value: 41.89794273264891
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB FEVER
+      revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
+      split: test
+      type: mteb/fever
+    metrics:
+    - type: map_at_1
+      value: 85.378
+    - type: map_at_10
+      value: 91.51
+    - type: map_at_100
+      value: 91.666
+    - type: map_at_1000
+      value: 91.676
+    - type: map_at_3
+      value: 90.757
+    - type: map_at_5
+      value: 91.277
+    - type: mrr_at_1
+      value: 91.839
+    - type: mrr_at_10
+      value: 95.49
+    - type: mrr_at_100
+      value: 95.493
+    - type: mrr_at_1000
+      value: 95.493
+    - type: mrr_at_3
+      value: 95.345
+    - type: mrr_at_5
+      value: 95.47200000000001
+    - type: ndcg_at_1
+      value: 91.839
+    - type: ndcg_at_10
+      value: 93.806
+    - type: ndcg_at_100
+      value: 94.255
+    - type: ndcg_at_1000
+      value: 94.399
+    - type: ndcg_at_3
+      value: 93.027
+    - type: ndcg_at_5
+      value: 93.51
+    - type: precision_at_1
+      value: 91.839
+    - type: precision_at_10
+      value: 10.93
+    - type: precision_at_100
+      value: 1.1400000000000001
+    - type: precision_at_1000
+      value: 0.117
+    - type: precision_at_3
+      value: 34.873
+    - type: precision_at_5
+      value: 21.44
+    - type: recall_at_1
+      value: 85.378
+    - type: recall_at_10
+      value: 96.814
+    - type: recall_at_100
+      value: 98.386
+    - type: recall_at_1000
+      value: 99.21600000000001
+    - type: recall_at_3
+      value: 94.643
+    - type: recall_at_5
+      value: 95.976
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB FiQA2018
+      revision: 27a168819829fe9bcd655c2df245fb19452e8e06
+      split: test
+      type: mteb/fiqa
+    metrics:
+    - type: map_at_1
+      value: 32.190000000000005
+    - type: map_at_10
+      value: 53.605000000000004
+    - type: map_at_100
+      value: 55.550999999999995
+    - type: map_at_1000
+      value: 55.665
+    - type: map_at_3
+      value: 46.62
+    - type: map_at_5
+      value: 50.517999999999994
+    - type: mrr_at_1
+      value: 60.34
+    - type: mrr_at_10
+      value: 70.775
+    - type: mrr_at_100
+      value: 71.238
+    - type: mrr_at_1000
+      value: 71.244
+    - type: mrr_at_3
+      value: 68.72399999999999
+    - type: mrr_at_5
+      value: 69.959
+    - type: ndcg_at_1
+      value: 60.34
+    - type: ndcg_at_10
+      value: 63.226000000000006
+    - type: ndcg_at_100
+      value: 68.60300000000001
+    - type: ndcg_at_1000
+      value: 69.901
+    - type: ndcg_at_3
+      value: 58.048
+    - type: ndcg_at_5
+      value: 59.789
+    - type: precision_at_1
+      value: 60.34
+    - type: precision_at_10
+      value: 17.130000000000003
+    - type: precision_at_100
+      value: 2.29
+    - type: precision_at_1000
+      value: 0.256
+    - type: precision_at_3
+      value: 38.323
+    - type: precision_at_5
+      value: 27.87
+    - type: recall_at_1
+      value: 32.190000000000005
+    - type: recall_at_10
+      value: 73.041
+    - type: recall_at_100
+      value: 91.31
+    - type: recall_at_1000
+      value: 98.104
+    - type: recall_at_3
+      value: 53.70399999999999
+    - type: recall_at_5
+      value: 62.358999999999995
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB HotpotQA
+      revision: ab518f4d6fcca38d87c25209f94beba119d02014
+      split: test
+      type: mteb/hotpotqa
+    metrics:
+    - type: map_at_1
+      value: 43.511
+    - type: map_at_10
+      value: 58.15
+    - type: map_at_100
+      value: 58.95399999999999
+    - type: map_at_1000
+      value: 59.018
+    - type: map_at_3
+      value: 55.31700000000001
+    - type: map_at_5
+      value: 57.04900000000001
+    - type: mrr_at_1
+      value: 87.022
+    - type: mrr_at_10
+      value: 91.32000000000001
+    - type: mrr_at_100
+      value: 91.401
+    - type: mrr_at_1000
+      value: 91.403
+    - type: mrr_at_3
+      value: 90.77
+    - type: mrr_at_5
+      value: 91.156
+    - type: ndcg_at_1
+      value: 87.022
+    - type: ndcg_at_10
+      value: 68.183
+    - type: ndcg_at_100
+      value: 70.781
+    - type: ndcg_at_1000
+      value: 72.009
+    - type: ndcg_at_3
+      value: 64.334
+    - type: ndcg_at_5
+      value: 66.449
+    - type: precision_at_1
+      value: 87.022
+    - type: precision_at_10
+      value: 13.406
+    - type: precision_at_100
+      value: 1.542
+    - type: precision_at_1000
+      value: 0.17099999999999999
+    - type: precision_at_3
+      value: 39.023
+    - type: precision_at_5
+      value: 25.080000000000002
+    - type: recall_at_1
+      value: 43.511
+    - type: recall_at_10
+      value: 67.02900000000001
+    - type: recall_at_100
+      value: 77.11
+    - type: recall_at_1000
+      value: 85.294
+    - type: recall_at_3
+      value: 58.535000000000004
+    - type: recall_at_5
+      value: 62.70099999999999
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB ImdbClassification
+      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
+      split: test
+      type: mteb/imdb
+    metrics:
+    - type: accuracy
+      value: 92.0996
+    - type: ap
+      value: 87.86206089096373
+    - type: f1
+      value: 92.07554547510763
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB MSMARCO
+      revision: c5a29a104738b98a9e76336939199e264163d4a0
+      split: dev
+      type: mteb/msmarco
+    metrics:
+    - type: map_at_1
+      value: 23.179
+    - type: map_at_10
+      value: 35.86
+    - type: map_at_100
+      value: 37.025999999999996
+    - type: map_at_1000
+      value: 37.068
+    - type: map_at_3
+      value: 31.921
+    - type: map_at_5
+      value: 34.172000000000004
+    - type: mrr_at_1
+      value: 23.926
+    - type: mrr_at_10
+      value: 36.525999999999996
+    - type: mrr_at_100
+      value: 37.627
+    - type: mrr_at_1000
+      value: 37.665
+    - type: mrr_at_3
+      value: 32.653
+    - type: mrr_at_5
+      value: 34.897
+    - type: ndcg_at_1
+      value: 23.910999999999998
+    - type: ndcg_at_10
+      value: 42.927
+    - type: ndcg_at_100
+      value: 48.464
+    - type: ndcg_at_1000
+      value: 49.533
+    - type: ndcg_at_3
+      value: 34.910000000000004
+    - type: ndcg_at_5
+      value: 38.937
+    - type: precision_at_1
+      value: 23.910999999999998
+    - type: precision_at_10
+      value: 6.758
+    - type: precision_at_100
+      value: 0.9520000000000001
+    - type: precision_at_1000
+      value: 0.104
+    - type: precision_at_3
+      value: 14.838000000000001
+    - type: precision_at_5
+      value: 10.934000000000001
+    - type: recall_at_1
+      value: 23.179
+    - type: recall_at_10
+      value: 64.622
+    - type: recall_at_100
+      value: 90.135
+    - type: recall_at_1000
+      value: 98.301
+    - type: recall_at_3
+      value: 42.836999999999996
+    - type: recall_at_5
+      value: 52.512
+    task:
+      type: Retrieval
+  - dataset:
+      config: en
+      name: MTEB MTOPDomainClassification (en)
+      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
+      split: test
+      type: mteb/mtop_domain
+    metrics:
+    - type: accuracy
+      value: 96.59598723210215
+    - type: f1
+      value: 96.41913500001952
+    task:
+      type: Classification
+  - dataset:
+      config: en
+      name: MTEB MTOPIntentClassification (en)
+      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
+      split: test
+      type: mteb/mtop_intent
+    metrics:
+    - type: accuracy
+      value: 82.89557683538533
+    - type: f1
+      value: 63.379319722356264
+    task:
+      type: Classification
+  - dataset:
+      config: en
+      name: MTEB MassiveIntentClassification (en)
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+      split: test
+      type: mteb/amazon_massive_intent
+    metrics:
+    - type: accuracy
+      value: 78.93745796906524
+    - type: f1
+      value: 75.71616541785902
+    task:
+      type: Classification
+  - dataset:
+      config: en
+      name: MTEB MassiveScenarioClassification (en)
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+      split: test
+      type: mteb/amazon_massive_scenario
+    metrics:
+    - type: accuracy
+      value: 81.41223940820443
+    - type: f1
+      value: 81.2877893719078
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB MedrxivClusteringP2P
+      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
+      split: test
+      type: mteb/medrxiv-clustering-p2p
+    metrics:
+    - type: v_measure
+      value: 35.03682528325662
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB MedrxivClusteringS2S
+      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
+      split: test
+      type: mteb/medrxiv-clustering-s2s
+    metrics:
+    - type: v_measure
+      value: 32.942529406124
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB MindSmallReranking
+      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
+      split: test
+      type: mteb/mind_small
+    metrics:
+    - type: map
+      value: 31.459949660460317
+    - type: mrr
+      value: 32.70509582031616
+    task:
+      type: Reranking
+  - dataset:
+      config: default
+      name: MTEB NFCorpus
+      revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
+      split: test
+      type: mteb/nfcorpus
+    metrics:
+    - type: map_at_1
+      value: 6.497
+    - type: map_at_10
+      value: 13.843
+    - type: map_at_100
+      value: 17.713
+    - type: map_at_1000
+      value: 19.241
+    - type: map_at_3
+      value: 10.096
+    - type: map_at_5
+      value: 11.85
+    - type: mrr_at_1
+      value: 48.916
+    - type: mrr_at_10
+      value: 57.764
+    - type: mrr_at_100
+      value: 58.251
+    - type: mrr_at_1000
+      value: 58.282999999999994
+    - type: mrr_at_3
+      value: 55.623999999999995
+    - type: mrr_at_5
+      value: 57.018
+    - type: ndcg_at_1
+      value: 46.594
+    - type: ndcg_at_10
+      value: 36.945
+    - type: ndcg_at_100
+      value: 34.06
+    - type: ndcg_at_1000
+      value: 43.05
+    - type: ndcg_at_3
+      value: 41.738
+    - type: ndcg_at_5
+      value: 39.330999999999996
+    - type: precision_at_1
+      value: 48.916
+    - type: precision_at_10
+      value: 27.43
+    - type: precision_at_100
+      value: 8.616
+    - type: precision_at_1000
+      value: 2.155
+    - type: precision_at_3
+      value: 39.112
+    - type: precision_at_5
+      value: 33.808
+    - type: recall_at_1
+      value: 6.497
+    - type: recall_at_10
+      value: 18.163
+    - type: recall_at_100
+      value: 34.566
+    - type: recall_at_1000
+      value: 67.15
+    - type: recall_at_3
+      value: 11.100999999999999
+    - type: recall_at_5
+      value: 14.205000000000002
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB NQ
+      revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
+      split: test
+      type: mteb/nq
+    metrics:
+    - type: map_at_1
+      value: 31.916
+    - type: map_at_10
+      value: 48.123
+    - type: map_at_100
+      value: 49.103
+    - type: map_at_1000
+      value: 49.131
+    - type: map_at_3
+      value: 43.711
+    - type: map_at_5
+      value: 46.323
+    - type: mrr_at_1
+      value: 36.181999999999995
+    - type: mrr_at_10
+      value: 50.617999999999995
+    - type: mrr_at_100
+      value: 51.329
+    - type: mrr_at_1000
+      value: 51.348000000000006
+    - type: mrr_at_3
+      value: 47.010999999999996
+    - type: mrr_at_5
+      value: 49.175000000000004
+    - type: ndcg_at_1
+      value: 36.181999999999995
+    - type: ndcg_at_10
+      value: 56.077999999999996
+    - type: ndcg_at_100
+      value: 60.037
+    - type: ndcg_at_1000
+      value: 60.63499999999999
+    - type: ndcg_at_3
+      value: 47.859
+    - type: ndcg_at_5
+      value: 52.178999999999995
+    - type: precision_at_1
+      value: 36.181999999999995
+    - type: precision_at_10
+      value: 9.284
+    - type: precision_at_100
+      value: 1.149
+    - type: precision_at_1000
+      value: 0.121
+    - type: precision_at_3
+      value: 22.006999999999998
+    - type: precision_at_5
+      value: 15.695
+    - type: recall_at_1
+      value: 31.916
+    - type: recall_at_10
+      value: 77.771
+    - type: recall_at_100
+      value: 94.602
+    - type: recall_at_1000
+      value: 98.967
+    - type: recall_at_3
+      value: 56.528
+    - type: recall_at_5
+      value: 66.527
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB QuoraRetrieval
+      revision: None
+      split: test
+      type: mteb/quora
+    metrics:
+    - type: map_at_1
+      value: 71.486
+    - type: map_at_10
+      value: 85.978
+    - type: map_at_100
+      value: 86.587
+    - type: map_at_1000
+      value: 86.598
+    - type: map_at_3
+      value: 83.04899999999999
+    - type: map_at_5
+      value: 84.857
+    - type: mrr_at_1
+      value: 82.32000000000001
+    - type: mrr_at_10
+      value: 88.64
+    - type: mrr_at_100
+      value: 88.702
+    - type: mrr_at_1000
+      value: 88.702
+    - type: mrr_at_3
+      value: 87.735
+    - type: mrr_at_5
+      value: 88.36
+    - type: ndcg_at_1
+      value: 82.34
+    - type: ndcg_at_10
+      value: 89.67
+    - type: ndcg_at_100
+      value: 90.642
+    - type: ndcg_at_1000
+      value: 90.688
+    - type: ndcg_at_3
+      value: 86.932
+    - type: ndcg_at_5
+      value: 88.408
+    - type: precision_at_1
+      value: 82.34
+    - type: precision_at_10
+      value: 13.675999999999998
+    - type: precision_at_100
+      value: 1.544
+    - type: precision_at_1000
+      value: 0.157
+    - type: precision_at_3
+      value: 38.24
+    - type: precision_at_5
+      value: 25.068
+    - type: recall_at_1
+      value: 71.486
+    - type: recall_at_10
+      value: 96.844
+    - type: recall_at_100
+      value: 99.843
+    - type: recall_at_1000
+      value: 99.996
+    - type: recall_at_3
+      value: 88.92099999999999
+    - type: recall_at_5
+      value: 93.215
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB RedditClustering
+      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
+      split: test
+      type: mteb/reddit-clustering
+    metrics:
+    - type: v_measure
+      value: 59.75758437908334
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB RedditClusteringP2P
+      revision: 282350215ef01743dc01b456c7f5241fa8937f16
+      split: test
+      type: mteb/reddit-clustering-p2p
+    metrics:
+    - type: v_measure
+      value: 68.03497914092789
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB SCIDOCS
+      revision: None
+      split: test
+      type: mteb/scidocs
+    metrics:
+    - type: map_at_1
+      value: 5.808
+    - type: map_at_10
+      value: 16.059
+    - type: map_at_100
+      value: 19.048000000000002
+    - type: map_at_1000
+      value: 19.43
+    - type: map_at_3
+      value: 10.953
+    - type: map_at_5
+      value: 13.363
+    - type: mrr_at_1
+      value: 28.7
+    - type: mrr_at_10
+      value: 42.436
+    - type: mrr_at_100
+      value: 43.599
+    - type: mrr_at_1000
+      value: 43.62
+    - type: mrr_at_3
+      value: 38.45
+    - type: mrr_at_5
+      value: 40.89
+    - type: ndcg_at_1
+      value: 28.7
+    - type: ndcg_at_10
+      value: 26.346000000000004
+    - type: ndcg_at_100
+      value: 36.758
+    - type: ndcg_at_1000
+      value: 42.113
+    - type: ndcg_at_3
+      value: 24.254
+    - type: ndcg_at_5
+      value: 21.506
+    - type: precision_at_1
+      value: 28.7
+    - type: precision_at_10
+      value: 13.969999999999999
+    - type: precision_at_100
+      value: 2.881
+    - type: precision_at_1000
+      value: 0.414
+    - type: precision_at_3
+      value: 22.933
+    - type: precision_at_5
+      value: 19.220000000000002
+    - type: recall_at_1
+      value: 5.808
+    - type: recall_at_10
+      value: 28.310000000000002
+    - type: recall_at_100
+      value: 58.475
+    - type: recall_at_1000
+      value: 84.072
+    - type: recall_at_3
+      value: 13.957
+    - type: recall_at_5
+      value: 19.515
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB SICK-R
+      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
+      split: test
+      type: mteb/sickr-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 82.39274129958557
+    - type: cos_sim_spearman
+      value: 79.78021235170053
+    - type: euclidean_pearson
+      value: 79.35335401300166
+    - type: euclidean_spearman
+      value: 79.7271870968275
+    - type: manhattan_pearson
+      value: 79.35256263340601
+    - type: manhattan_spearman
+      value: 79.76036386976321
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB STS12
+      revision: a0d554a64d88156834ff5ae9920b964011b16384
+      split: test
+      type: mteb/sts12-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 83.99130429246708
+    - type: cos_sim_spearman
+      value: 73.88322811171203
+    - type: euclidean_pearson
+      value: 80.7569419170376
+    - type: euclidean_spearman
+      value: 73.82542155409597
+    - type: manhattan_pearson
+      value: 80.79468183847625
+    - type: manhattan_spearman
+      value: 73.87027144047784
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB STS13
+      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
+      split: test
+      type: mteb/sts13-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 84.88548789489907
+    - type: cos_sim_spearman
+      value: 85.07535893847255
+    - type: euclidean_pearson
+      value: 84.6637222061494
+    - type: euclidean_spearman
+      value: 85.14200626702456
+    - type: manhattan_pearson
+      value: 84.75327892344734
+    - type: manhattan_spearman
+      value: 85.24406181838596
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB STS14
+      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
+      split: test
+      type: mteb/sts14-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 82.88140039325008
+    - type: cos_sim_spearman
+      value: 79.61211268112362
+    - type: euclidean_pearson
+      value: 81.29639728816458
+    - type: euclidean_spearman
+      value: 79.51284578041442
+    - type: manhattan_pearson
+      value: 81.3381797137111
+    - type: manhattan_spearman
+      value: 79.55683684039808
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB STS15
+      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
+      split: test
+      type: mteb/sts15-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 85.16716737270485
+    - type: cos_sim_spearman
+      value: 86.14823841857738
+    - type: euclidean_pearson
+      value: 85.36325733440725
+    - type: euclidean_spearman
+      value: 86.04919691402029
+    - type: manhattan_pearson
+      value: 85.3147511385052
+    - type: manhattan_spearman
+      value: 86.00676205857764
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB STS16
+      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
+      split: test
+      type: mteb/sts16-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 80.34266645861588
+    - type: cos_sim_spearman
+      value: 81.59914035005882
+    - type: euclidean_pearson
+      value: 81.15053076245988
+    - type: euclidean_spearman
+      value: 81.52776915798489
+    - type: manhattan_pearson
+      value: 81.1819647418673
+    - type: manhattan_spearman
+      value: 81.57479527353556
+    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: 89.38263326821439
+    - type: cos_sim_spearman
+      value: 89.10946308202642
+    - type: euclidean_pearson
+      value: 88.87831312540068
+    - type: euclidean_spearman
+      value: 89.03615865973664
+    - type: manhattan_pearson
+      value: 88.79835539970384
+    - type: manhattan_spearman
+      value: 88.9766156339753
+    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: 70.1574915581685
+    - type: cos_sim_spearman
+      value: 70.59144980004054
+    - type: euclidean_pearson
+      value: 71.43246306918755
+    - type: euclidean_spearman
+      value: 70.5544189562984
+    - type: manhattan_pearson
+      value: 71.4071414609503
+    - type: manhattan_spearman
+      value: 70.31799126163712
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB STSBenchmark
+      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
+      split: test
+      type: mteb/stsbenchmark-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 83.36215796635351
+    - type: cos_sim_spearman
+      value: 83.07276756467208
+    - type: euclidean_pearson
+      value: 83.06690453635584
+    - type: euclidean_spearman
+      value: 82.9635366303289
+    - type: manhattan_pearson
+      value: 83.04994049700815
+    - type: manhattan_spearman
+      value: 82.98120125356036
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB SciDocsRR
+      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
+      split: test
+      type: mteb/scidocs-reranking
+    metrics:
+    - type: map
+      value: 86.92530011616722
+    - type: mrr
+      value: 96.21826793395421
+    task:
+      type: Reranking
+  - dataset:
+      config: default
+      name: MTEB SciFact
+      revision: 0228b52cf27578f30900b9e5271d331663a030d7
+      split: test
+      type: mteb/scifact
+    metrics:
+    - type: map_at_1
+      value: 65.75
+    - type: map_at_10
+      value: 77.701
+    - type: map_at_100
+      value: 78.005
+    - type: map_at_1000
+      value: 78.006
+    - type: map_at_3
+      value: 75.48
+    - type: map_at_5
+      value: 76.927
+    - type: mrr_at_1
+      value: 68.333
+    - type: mrr_at_10
+      value: 78.511
+    - type: mrr_at_100
+      value: 78.704
+    - type: mrr_at_1000
+      value: 78.704
+    - type: mrr_at_3
+      value: 77
+    - type: mrr_at_5
+      value: 78.083
+    - type: ndcg_at_1
+      value: 68.333
+    - type: ndcg_at_10
+      value: 82.42699999999999
+    - type: ndcg_at_100
+      value: 83.486
+    - type: ndcg_at_1000
+      value: 83.511
+    - type: ndcg_at_3
+      value: 78.96300000000001
+    - type: ndcg_at_5
+      value: 81.028
+    - type: precision_at_1
+      value: 68.333
+    - type: precision_at_10
+      value: 10.667
+    - type: precision_at_100
+      value: 1.127
+    - type: precision_at_1000
+      value: 0.11299999999999999
+    - type: precision_at_3
+      value: 31.333
+    - type: precision_at_5
+      value: 20.133000000000003
+    - type: recall_at_1
+      value: 65.75
+    - type: recall_at_10
+      value: 95.578
+    - type: recall_at_100
+      value: 99.833
+    - type: recall_at_1000
+      value: 100
+    - type: recall_at_3
+      value: 86.506
+    - type: recall_at_5
+      value: 91.75
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB SprintDuplicateQuestions
+      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
+      split: test
+      type: mteb/sprintduplicatequestions-pairclassification
+    metrics:
+    - type: cos_sim_accuracy
+      value: 99.75247524752476
+    - type: cos_sim_ap
+      value: 94.16065078045173
+    - type: cos_sim_f1
+      value: 87.22986247544205
+    - type: cos_sim_precision
+      value: 85.71428571428571
+    - type: cos_sim_recall
+      value: 88.8
+    - type: dot_accuracy
+      value: 99.74554455445545
+    - type: dot_ap
+      value: 93.90633887037264
+    - type: dot_f1
+      value: 86.9873417721519
+    - type: dot_precision
+      value: 88.1025641025641
+    - type: dot_recall
+      value: 85.9
+    - type: euclidean_accuracy
+      value: 99.75247524752476
+    - type: euclidean_ap
+      value: 94.17466319018055
+    - type: euclidean_f1
+      value: 87.3405299313052
+    - type: euclidean_precision
+      value: 85.74181117533719
+    - type: euclidean_recall
+      value: 89
+    - type: manhattan_accuracy
+      value: 99.75445544554455
+    - type: manhattan_ap
+      value: 94.27688371923577
+    - type: manhattan_f1
+      value: 87.74002954209749
+    - type: manhattan_precision
+      value: 86.42095053346266
+    - type: manhattan_recall
+      value: 89.1
+    - type: max_accuracy
+      value: 99.75445544554455
+    - type: max_ap
+      value: 94.27688371923577
+    - type: max_f1
+      value: 87.74002954209749
+    task:
+      type: PairClassification
+  - dataset:
+      config: default
+      name: MTEB StackExchangeClustering
+      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
+      split: test
+      type: mteb/stackexchange-clustering
+    metrics:
+    - type: v_measure
+      value: 71.26500637517056
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB StackExchangeClusteringP2P
+      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
+      split: test
+      type: mteb/stackexchange-clustering-p2p
+    metrics:
+    - type: v_measure
+      value: 39.17507906280528
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB StackOverflowDupQuestions
+      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
+      split: test
+      type: mteb/stackoverflowdupquestions-reranking
+    metrics:
+    - type: map
+      value: 52.4848744828509
+    - type: mrr
+      value: 53.33678168236992
+    task:
+      type: Reranking
+  - dataset:
+      config: default
+      name: MTEB SummEval
+      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
+      split: test
+      type: mteb/summeval
+    metrics:
+    - type: cos_sim_pearson
+      value: 30.599864323827887
+    - type: cos_sim_spearman
+      value: 30.91116204665598
+    - type: dot_pearson
+      value: 30.82637894269936
+    - type: dot_spearman
+      value: 30.957573868416066
+    task:
+      type: Summarization
+  - dataset:
+      config: default
+      name: MTEB TRECCOVID
+      revision: None
+      split: test
+      type: mteb/trec-covid
+    metrics:
+    - type: map_at_1
+      value: 0.23600000000000002
+    - type: map_at_10
+      value: 1.892
+    - type: map_at_100
+      value: 11.586
+    - type: map_at_1000
+      value: 27.761999999999997
+    - type: map_at_3
+      value: 0.653
+    - type: map_at_5
+      value: 1.028
+    - type: mrr_at_1
+      value: 88
+    - type: mrr_at_10
+      value: 94
+    - type: mrr_at_100
+      value: 94
+    - type: mrr_at_1000
+      value: 94
+    - type: mrr_at_3
+      value: 94
+    - type: mrr_at_5
+      value: 94
+    - type: ndcg_at_1
+      value: 82
+    - type: ndcg_at_10
+      value: 77.48899999999999
+    - type: ndcg_at_100
+      value: 60.141
+    - type: ndcg_at_1000
+      value: 54.228
+    - type: ndcg_at_3
+      value: 82.358
+    - type: ndcg_at_5
+      value: 80.449
+    - type: precision_at_1
+      value: 88
+    - type: precision_at_10
+      value: 82.19999999999999
+    - type: precision_at_100
+      value: 61.760000000000005
+    - type: precision_at_1000
+      value: 23.684
+    - type: precision_at_3
+      value: 88
+    - type: precision_at_5
+      value: 85.6
+    - type: recall_at_1
+      value: 0.23600000000000002
+    - type: recall_at_10
+      value: 2.117
+    - type: recall_at_100
+      value: 14.985000000000001
+    - type: recall_at_1000
+      value: 51.107
+    - type: recall_at_3
+      value: 0.688
+    - type: recall_at_5
+      value: 1.1039999999999999
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB Touche2020
+      revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
+      split: test
+      type: mteb/touche2020
+    metrics:
+    - type: map_at_1
+      value: 2.3040000000000003
+    - type: map_at_10
+      value: 9.025
+    - type: map_at_100
+      value: 15.312999999999999
+    - type: map_at_1000
+      value: 16.954
+    - type: map_at_3
+      value: 4.981
+    - type: map_at_5
+      value: 6.32
+    - type: mrr_at_1
+      value: 24.490000000000002
+    - type: mrr_at_10
+      value: 39.835
+    - type: mrr_at_100
+      value: 40.8
+    - type: mrr_at_1000
+      value: 40.8
+    - type: mrr_at_3
+      value: 35.034
+    - type: mrr_at_5
+      value: 37.687
+    - type: ndcg_at_1
+      value: 22.448999999999998
+    - type: ndcg_at_10
+      value: 22.545
+    - type: ndcg_at_100
+      value: 35.931999999999995
+    - type: ndcg_at_1000
+      value: 47.665
+    - type: ndcg_at_3
+      value: 23.311
+    - type: ndcg_at_5
+      value: 22.421
+    - type: precision_at_1
+      value: 24.490000000000002
+    - type: precision_at_10
+      value: 20.408
+    - type: precision_at_100
+      value: 7.815999999999999
+    - type: precision_at_1000
+      value: 1.553
+    - type: precision_at_3
+      value: 25.169999999999998
+    - type: precision_at_5
+      value: 23.265
+    - type: recall_at_1
+      value: 2.3040000000000003
+    - type: recall_at_10
+      value: 15.693999999999999
+    - type: recall_at_100
+      value: 48.917
+    - type: recall_at_1000
+      value: 84.964
+    - type: recall_at_3
+      value: 6.026
+    - type: recall_at_5
+      value: 9.066
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB ToxicConversationsClassification
+      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
+      split: test
+      type: mteb/toxic_conversations_50k
+    metrics:
+    - type: accuracy
+      value: 82.6074
+    - type: ap
+      value: 23.187467098602013
+    - type: f1
+      value: 65.36829506379657
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB TweetSentimentExtractionClassification
+      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
+      split: test
+      type: mteb/tweet_sentiment_extraction
+    metrics:
+    - type: accuracy
+      value: 63.16355404640635
+    - type: f1
+      value: 63.534725639863346
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB TwentyNewsgroupsClustering
+      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
+      split: test
+      type: mteb/twentynewsgroups-clustering
+    metrics:
+    - type: v_measure
+      value: 50.91004094411276
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB TwitterSemEval2015
+      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
+      split: test
+      type: mteb/twittersemeval2015-pairclassification
+    metrics:
+    - type: cos_sim_accuracy
+      value: 86.55301901412649
+    - type: cos_sim_ap
+      value: 75.25312618556728
+    - type: cos_sim_f1
+      value: 68.76561719140429
+    - type: cos_sim_precision
+      value: 65.3061224489796
+    - type: cos_sim_recall
+      value: 72.61213720316623
+    - type: dot_accuracy
+      value: 86.29671574178936
+    - type: dot_ap
+      value: 75.11910195501207
+    - type: dot_f1
+      value: 68.44048376830045
+    - type: dot_precision
+      value: 66.12546125461255
+    - type: dot_recall
+      value: 70.92348284960423
+    - type: euclidean_accuracy
+      value: 86.5828217202122
+    - type: euclidean_ap
+      value: 75.22986344900924
+    - type: euclidean_f1
+      value: 68.81267797449549
+    - type: euclidean_precision
+      value: 64.8238861674831
+    - type: euclidean_recall
+      value: 73.3245382585752
+    - type: manhattan_accuracy
+      value: 86.61262442629791
+    - type: manhattan_ap
+      value: 75.24401608557328
+    - type: manhattan_f1
+      value: 68.80473982483257
+    - type: manhattan_precision
+      value: 67.21187720181177
+    - type: manhattan_recall
+      value: 70.47493403693932
+    - type: max_accuracy
+      value: 86.61262442629791
+    - type: max_ap
+      value: 75.25312618556728
+    - type: max_f1
+      value: 68.81267797449549
+    task:
+      type: PairClassification
+  - dataset:
+      config: default
+      name: MTEB TwitterURLCorpus
+      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
+      split: test
+      type: mteb/twitterurlcorpus-pairclassification
+    metrics:
+    - type: cos_sim_accuracy
+      value: 88.10688089416696
+    - type: cos_sim_ap
+      value: 84.17862178779863
+    - type: cos_sim_f1
+      value: 76.17305208781748
+    - type: cos_sim_precision
+      value: 71.31246641590543
+    - type: cos_sim_recall
+      value: 81.74468740375731
+    - type: dot_accuracy
+      value: 88.1844995536927
+    - type: dot_ap
+      value: 84.33816725235876
+    - type: dot_f1
+      value: 76.43554032918746
+    - type: dot_precision
+      value: 74.01557767200346
+    - type: dot_recall
+      value: 79.0190945488143
+    - type: euclidean_accuracy
+      value: 88.07001203089223
+    - type: euclidean_ap
+      value: 84.12267000814985
+    - type: euclidean_f1
+      value: 76.12232600180778
+    - type: euclidean_precision
+      value: 74.50604541433205
+    - type: euclidean_recall
+      value: 77.81028641823221
+    - type: manhattan_accuracy
+      value: 88.06419063142779
+    - type: manhattan_ap
+      value: 84.11648917164187
+    - type: manhattan_f1
+      value: 76.20579953925474
+    - type: manhattan_precision
+      value: 72.56772755762935
+    - type: manhattan_recall
+      value: 80.22790267939637
+    - type: max_accuracy
+      value: 88.1844995536927
+    - type: max_ap
+      value: 84.33816725235876
+    - type: max_f1
+      value: 76.43554032918746
+    task:
+      type: PairClassification
+tags:
+- sentence-transformers
+- gte
+- mteb
+- transformers.js
+- sentence-similarity
+- onnx
+- teradata
+
+---
+# A Teradata Vantage compatible Embeddings Model
+
+# Alibaba-NLP/gte-large-en-v1.5
+
+## Overview of this Model
+
+An Embedding Model which maps text (sentence/ paragraphs) into a vector.  The [Alibaba-NLP/gte-large-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-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.
+
+- 434.14M params (Sizes in ONNX format - "fp32": 1664.98MB, "int8": 424.0MB, "uint8": 424.0MB)
+- 8192 maximum input tokens 
+- 1024 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-large-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-large-en-v1.5"
+number_dimensions_output = 1024
+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-large-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-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-large-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 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1ad27385d728efc552e2a2c42e1b15353554990ed47f6e161bdc9073caaaec5b
+size 1575

conversion_config.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9398aec0d299affc8b33da6610e7a0e7711bf3d762e8a556e26f5ed48c47f40f
+size 291

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")

convert.py.bk

@@ -0,0 +1,79 @@
+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
+from huggingface_hub import hf_hub_download
+import transformers
+
+
+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
+
+    print("Exporting tokenizer...")
+
+    tokenizer = transformers.AutoTokenizer.from_pretrained(model_id)
+    tokenizer.save_pretrained("./")
+
+    print("Done\n\n")
+    
+    
+    if not os.path.exists("onnx"):
+        os.makedirs("onnx")
+    
+    if "fp32" in precision_to_filename_map:
+        print("Exporting the fp32 onnx file...")
+
+        filename = precision_to_filename_map['fp32']
+ 
+        hf_hub_download(repo_id=model_id, filename=filename, local_dir = "./")
+        model = onnx.load(filename)
+        model_fixed = onnx.helper.make_model(model.graph, ir_version = IR, opset_imports = [op]) #to be sure that we have compatible opset and IR version
+        onnx.save(model_fixed, filename)
+
+        print("Done\n\n")
+
+
+    if "int8" in precision_to_filename_map:
+        print("Exporting the int8 onnx file...")
+
+
+        filename = precision_to_filename_map['int8']
+ 
+        hf_hub_download(repo_id=model_id, filename=filename, local_dir = "./")
+        model = onnx.load(filename)
+        model_fixed = onnx.helper.make_model(model.graph, ir_version = IR, opset_imports = [op]) #to be sure that we have compatible opset and IR version
+        onnx.save(model_fixed, filename)
+
+        
+        print("Done\n\n")
+        
+    if "uint8" in precision_to_filename_map:
+        print("Exporting the uint8 onnx file...")
+
+        filename = precision_to_filename_map['uint8']
+ 
+        hf_hub_download(repo_id=model_id, filename=filename, local_dir = "./")
+        model = onnx.load(filename)
+        model_fixed = onnx.helper.make_model(model.graph, ir_version = IR, opset_imports = [op]) #to be sure that we have compatible opset and IR version
+        onnx.save(model_fixed, filename)
+
+        
+        print("Done\n\n")
+        

onnx/model.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:06ed0e91abae57c89a61c2a1717c166900dbd987b6ed457a540ea47706b32b50
+size 1745854634

onnx/model_int8.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4a1fcd18e147eb97c14830c8570bc019399edbfb34185a60159f946e19cb360b
+size 444595156

onnx/model_uint8.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:be182274e8d591c683a5f43a7361998e88454cc6955708257e9104b5908ad546
+size 444595204

special_tokens_map.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5d5b662e421ea9fac075174bb0688ee0d9431699900b90662acd44b2a350503a
+size 695

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.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cb374d6bc042c22455946f4e09a89d29882a199fdaf8fb25be00dc8b8857a448
+size 711661

tokenizer_config.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:19fa014d59708114b5913495534d97efaa3316a4f9227e87861d9c7e0840df2e
+size 1414