first

.gitignore

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+checkpoint-*/

1_INSTRUCTOR_Pooling/config.json

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+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false
+}

2_Dense/config.json

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+{"in_features": 1024, "out_features": 768, "bias": false, "activation_function": "torch.nn.modules.linear.Identity"}

2_Dense/pytorch_model.bin

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+oid sha256:4d53c2f35145f368d9f330035fb571d52b01be3df07759a8459ea4b8e18f4c98
+size 3146603

README.md

@@ -0,0 +1,2610 @@
+---
+pipeline_tag: sentence-similarity
+tags:
+- text-embedding
+- embeddings
+- information-retrieval
+- beir
+- text-classification
+- language-model
+- text-clustering
+- text-semantic-similarity
+- text-evaluation
+- prompt-retrieval
+- text-reranking
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- transformers
+- t5
+- English
+- Sentence Similarity
+- natural_questions
+- ms_marco
+- fever
+- hotpot_qa
+- mteb
+language: en
+inference: false
+license: apache-2.0
+model-index:
+- name: INSTRUCTOR
+  results:
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_counterfactual
+      name: MTEB AmazonCounterfactualClassification (en)
+      config: en
+      split: test
+      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
+    metrics:
+    - type: accuracy
+      value: 88.13432835820896
+    - type: ap
+      value: 59.298209334395665
+    - type: f1
+      value: 83.31769058643586
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_polarity
+      name: MTEB AmazonPolarityClassification
+      config: default
+      split: test
+      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
+    metrics:
+    - type: accuracy
+      value: 91.526375
+    - type: ap
+      value: 88.16327709705504
+    - type: f1
+      value: 91.51095801287843
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_reviews_multi
+      name: MTEB AmazonReviewsClassification (en)
+      config: en
+      split: test
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+    metrics:
+    - type: accuracy
+      value: 47.856
+    - type: f1
+      value: 45.41490917650942
+  - task:
+      type: Retrieval
+    dataset:
+      type: arguana
+      name: MTEB ArguAna
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 31.223
+    - type: map_at_10
+      value: 47.947
+    - type: map_at_100
+      value: 48.742000000000004
+    - type: map_at_1000
+      value: 48.745
+    - type: map_at_3
+      value: 43.137
+    - type: map_at_5
+      value: 45.992
+    - type: mrr_at_1
+      value: 32.432
+    - type: mrr_at_10
+      value: 48.4
+    - type: mrr_at_100
+      value: 49.202
+    - type: mrr_at_1000
+      value: 49.205
+    - type: mrr_at_3
+      value: 43.551
+    - type: mrr_at_5
+      value: 46.467999999999996
+    - type: ndcg_at_1
+      value: 31.223
+    - type: ndcg_at_10
+      value: 57.045
+    - type: ndcg_at_100
+      value: 60.175
+    - type: ndcg_at_1000
+      value: 60.233000000000004
+    - type: ndcg_at_3
+      value: 47.171
+    - type: ndcg_at_5
+      value: 52.322
+    - type: precision_at_1
+      value: 31.223
+    - type: precision_at_10
+      value: 8.599
+    - type: precision_at_100
+      value: 0.991
+    - type: precision_at_1000
+      value: 0.1
+    - type: precision_at_3
+      value: 19.63
+    - type: precision_at_5
+      value: 14.282
+    - type: recall_at_1
+      value: 31.223
+    - type: recall_at_10
+      value: 85.989
+    - type: recall_at_100
+      value: 99.075
+    - type: recall_at_1000
+      value: 99.502
+    - type: recall_at_3
+      value: 58.89
+    - type: recall_at_5
+      value: 71.408
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-p2p
+      name: MTEB ArxivClusteringP2P
+      config: default
+      split: test
+      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
+    metrics:
+    - type: v_measure
+      value: 43.1621946393635
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-s2s
+      name: MTEB ArxivClusteringS2S
+      config: default
+      split: test
+      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
+    metrics:
+    - type: v_measure
+      value: 32.56417132407894
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/askubuntudupquestions-reranking
+      name: MTEB AskUbuntuDupQuestions
+      config: default
+      split: test
+      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
+    metrics:
+    - type: map
+      value: 64.29539304390207
+    - type: mrr
+      value: 76.44484017060196
+  - task:
+      type: STS
+    dataset:
+      type: mteb/biosses-sts
+      name: MTEB BIOSSES
+      config: default
+      split: test
+      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
+    metrics:
+    - type: cos_sim_spearman
+      value: 84.38746499431112
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/banking77
+      name: MTEB Banking77Classification
+      config: default
+      split: test
+      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
+    metrics:
+    - type: accuracy
+      value: 78.51298701298701
+    - type: f1
+      value: 77.49041754069235
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-p2p
+      name: MTEB BiorxivClusteringP2P
+      config: default
+      split: test
+      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
+    metrics:
+    - type: v_measure
+      value: 37.61848554098577
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-s2s
+      name: MTEB BiorxivClusteringS2S
+      config: default
+      split: test
+      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
+    metrics:
+    - type: v_measure
+      value: 31.32623280148178
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackAndroidRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 35.803000000000004
+    - type: map_at_10
+      value: 48.848
+    - type: map_at_100
+      value: 50.5
+    - type: map_at_1000
+      value: 50.602999999999994
+    - type: map_at_3
+      value: 45.111000000000004
+    - type: map_at_5
+      value: 47.202
+    - type: mrr_at_1
+      value: 44.635000000000005
+    - type: mrr_at_10
+      value: 55.593
+    - type: mrr_at_100
+      value: 56.169999999999995
+    - type: mrr_at_1000
+      value: 56.19499999999999
+    - type: mrr_at_3
+      value: 53.361999999999995
+    - type: mrr_at_5
+      value: 54.806999999999995
+    - type: ndcg_at_1
+      value: 44.635000000000005
+    - type: ndcg_at_10
+      value: 55.899
+    - type: ndcg_at_100
+      value: 60.958
+    - type: ndcg_at_1000
+      value: 62.302
+    - type: ndcg_at_3
+      value: 51.051
+    - type: ndcg_at_5
+      value: 53.351000000000006
+    - type: precision_at_1
+      value: 44.635000000000005
+    - type: precision_at_10
+      value: 10.786999999999999
+    - type: precision_at_100
+      value: 1.6580000000000001
+    - type: precision_at_1000
+      value: 0.213
+    - type: precision_at_3
+      value: 24.893
+    - type: precision_at_5
+      value: 17.740000000000002
+    - type: recall_at_1
+      value: 35.803000000000004
+    - type: recall_at_10
+      value: 68.657
+    - type: recall_at_100
+      value: 89.77199999999999
+    - type: recall_at_1000
+      value: 97.67
+    - type: recall_at_3
+      value: 54.066
+    - type: recall_at_5
+      value: 60.788
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackEnglishRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 33.706
+    - type: map_at_10
+      value: 44.896
+    - type: map_at_100
+      value: 46.299
+    - type: map_at_1000
+      value: 46.44
+    - type: map_at_3
+      value: 41.721000000000004
+    - type: map_at_5
+      value: 43.486000000000004
+    - type: mrr_at_1
+      value: 41.592
+    - type: mrr_at_10
+      value: 50.529
+    - type: mrr_at_100
+      value: 51.22
+    - type: mrr_at_1000
+      value: 51.258
+    - type: mrr_at_3
+      value: 48.205999999999996
+    - type: mrr_at_5
+      value: 49.528
+    - type: ndcg_at_1
+      value: 41.592
+    - type: ndcg_at_10
+      value: 50.77199999999999
+    - type: ndcg_at_100
+      value: 55.383
+    - type: ndcg_at_1000
+      value: 57.288
+    - type: ndcg_at_3
+      value: 46.324
+    - type: ndcg_at_5
+      value: 48.346000000000004
+    - type: precision_at_1
+      value: 41.592
+    - type: precision_at_10
+      value: 9.516
+    - type: precision_at_100
+      value: 1.541
+    - type: precision_at_1000
+      value: 0.2
+    - type: precision_at_3
+      value: 22.399
+    - type: precision_at_5
+      value: 15.770999999999999
+    - type: recall_at_1
+      value: 33.706
+    - type: recall_at_10
+      value: 61.353
+    - type: recall_at_100
+      value: 80.182
+    - type: recall_at_1000
+      value: 91.896
+    - type: recall_at_3
+      value: 48.204
+    - type: recall_at_5
+      value: 53.89699999999999
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGamingRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 44.424
+    - type: map_at_10
+      value: 57.169000000000004
+    - type: map_at_100
+      value: 58.202
+    - type: map_at_1000
+      value: 58.242000000000004
+    - type: map_at_3
+      value: 53.825
+    - type: map_at_5
+      value: 55.714
+    - type: mrr_at_1
+      value: 50.470000000000006
+    - type: mrr_at_10
+      value: 60.489000000000004
+    - type: mrr_at_100
+      value: 61.096
+    - type: mrr_at_1000
+      value: 61.112
+    - type: mrr_at_3
+      value: 58.192
+    - type: mrr_at_5
+      value: 59.611999999999995
+    - type: ndcg_at_1
+      value: 50.470000000000006
+    - type: ndcg_at_10
+      value: 63.071999999999996
+    - type: ndcg_at_100
+      value: 66.964
+    - type: ndcg_at_1000
+      value: 67.659
+    - type: ndcg_at_3
+      value: 57.74399999999999
+    - type: ndcg_at_5
+      value: 60.367000000000004
+    - type: precision_at_1
+      value: 50.470000000000006
+    - type: precision_at_10
+      value: 10.019
+    - type: precision_at_100
+      value: 1.29
+    - type: precision_at_1000
+      value: 0.13899999999999998
+    - type: precision_at_3
+      value: 25.558999999999997
+    - type: precision_at_5
+      value: 17.467
+    - type: recall_at_1
+      value: 44.424
+    - type: recall_at_10
+      value: 77.02
+    - type: recall_at_100
+      value: 93.738
+    - type: recall_at_1000
+      value: 98.451
+    - type: recall_at_3
+      value: 62.888
+    - type: recall_at_5
+      value: 69.138
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGisRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 26.294
+    - type: map_at_10
+      value: 34.503
+    - type: map_at_100
+      value: 35.641
+    - type: map_at_1000
+      value: 35.724000000000004
+    - type: map_at_3
+      value: 31.753999999999998
+    - type: map_at_5
+      value: 33.190999999999995
+    - type: mrr_at_1
+      value: 28.362
+    - type: mrr_at_10
+      value: 36.53
+    - type: mrr_at_100
+      value: 37.541000000000004
+    - type: mrr_at_1000
+      value: 37.602000000000004
+    - type: mrr_at_3
+      value: 33.917
+    - type: mrr_at_5
+      value: 35.358000000000004
+    - type: ndcg_at_1
+      value: 28.362
+    - type: ndcg_at_10
+      value: 39.513999999999996
+    - type: ndcg_at_100
+      value: 44.815
+    - type: ndcg_at_1000
+      value: 46.839
+    - type: ndcg_at_3
+      value: 34.02
+    - type: ndcg_at_5
+      value: 36.522
+    - type: precision_at_1
+      value: 28.362
+    - type: precision_at_10
+      value: 6.101999999999999
+    - type: precision_at_100
+      value: 0.9129999999999999
+    - type: precision_at_1000
+      value: 0.11399999999999999
+    - type: precision_at_3
+      value: 14.161999999999999
+    - type: precision_at_5
+      value: 9.966
+    - type: recall_at_1
+      value: 26.294
+    - type: recall_at_10
+      value: 53.098
+    - type: recall_at_100
+      value: 76.877
+    - type: recall_at_1000
+      value: 91.834
+    - type: recall_at_3
+      value: 38.266
+    - type: recall_at_5
+      value: 44.287
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackMathematicaRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 16.407
+    - type: map_at_10
+      value: 25.185999999999996
+    - type: map_at_100
+      value: 26.533
+    - type: map_at_1000
+      value: 26.657999999999998
+    - type: map_at_3
+      value: 22.201999999999998
+    - type: map_at_5
+      value: 23.923
+    - type: mrr_at_1
+      value: 20.522000000000002
+    - type: mrr_at_10
+      value: 29.522
+    - type: mrr_at_100
+      value: 30.644
+    - type: mrr_at_1000
+      value: 30.713
+    - type: mrr_at_3
+      value: 26.679000000000002
+    - type: mrr_at_5
+      value: 28.483000000000004
+    - type: ndcg_at_1
+      value: 20.522000000000002
+    - type: ndcg_at_10
+      value: 30.656
+    - type: ndcg_at_100
+      value: 36.864999999999995
+    - type: ndcg_at_1000
+      value: 39.675
+    - type: ndcg_at_3
+      value: 25.319000000000003
+    - type: ndcg_at_5
+      value: 27.992
+    - type: precision_at_1
+      value: 20.522000000000002
+    - type: precision_at_10
+      value: 5.795999999999999
+    - type: precision_at_100
+      value: 1.027
+    - type: precision_at_1000
+      value: 0.13999999999999999
+    - type: precision_at_3
+      value: 12.396
+    - type: precision_at_5
+      value: 9.328
+    - type: recall_at_1
+      value: 16.407
+    - type: recall_at_10
+      value: 43.164
+    - type: recall_at_100
+      value: 69.695
+    - type: recall_at_1000
+      value: 89.41900000000001
+    - type: recall_at_3
+      value: 28.634999999999998
+    - type: recall_at_5
+      value: 35.308
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackPhysicsRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 30.473
+    - type: map_at_10
+      value: 41.676
+    - type: map_at_100
+      value: 43.120999999999995
+    - type: map_at_1000
+      value: 43.230000000000004
+    - type: map_at_3
+      value: 38.306000000000004
+    - type: map_at_5
+      value: 40.355999999999995
+    - type: mrr_at_1
+      value: 37.536
+    - type: mrr_at_10
+      value: 47.643
+    - type: mrr_at_100
+      value: 48.508
+    - type: mrr_at_1000
+      value: 48.551
+    - type: mrr_at_3
+      value: 45.348
+    - type: mrr_at_5
+      value: 46.744
+    - type: ndcg_at_1
+      value: 37.536
+    - type: ndcg_at_10
+      value: 47.823
+    - type: ndcg_at_100
+      value: 53.395
+    - type: ndcg_at_1000
+      value: 55.271
+    - type: ndcg_at_3
+      value: 42.768
+    - type: ndcg_at_5
+      value: 45.373000000000005
+    - type: precision_at_1
+      value: 37.536
+    - type: precision_at_10
+      value: 8.681
+    - type: precision_at_100
+      value: 1.34
+    - type: precision_at_1000
+      value: 0.165
+    - type: precision_at_3
+      value: 20.468
+    - type: precision_at_5
+      value: 14.495
+    - type: recall_at_1
+      value: 30.473
+    - type: recall_at_10
+      value: 60.092999999999996
+    - type: recall_at_100
+      value: 82.733
+    - type: recall_at_1000
+      value: 94.875
+    - type: recall_at_3
+      value: 45.734
+    - type: recall_at_5
+      value: 52.691
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackProgrammersRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 29.976000000000003
+    - type: map_at_10
+      value: 41.097
+    - type: map_at_100
+      value: 42.547000000000004
+    - type: map_at_1000
+      value: 42.659000000000006
+    - type: map_at_3
+      value: 37.251
+    - type: map_at_5
+      value: 39.493
+    - type: mrr_at_1
+      value: 37.557
+    - type: mrr_at_10
+      value: 46.605000000000004
+    - type: mrr_at_100
+      value: 47.487
+    - type: mrr_at_1000
+      value: 47.54
+    - type: mrr_at_3
+      value: 43.721
+    - type: mrr_at_5
+      value: 45.411
+    - type: ndcg_at_1
+      value: 37.557
+    - type: ndcg_at_10
+      value: 47.449000000000005
+    - type: ndcg_at_100
+      value: 53.052
+    - type: ndcg_at_1000
+      value: 55.010999999999996
+    - type: ndcg_at_3
+      value: 41.439
+    - type: ndcg_at_5
+      value: 44.292
+    - type: precision_at_1
+      value: 37.557
+    - type: precision_at_10
+      value: 8.847
+    - type: precision_at_100
+      value: 1.357
+    - type: precision_at_1000
+      value: 0.16999999999999998
+    - type: precision_at_3
+      value: 20.091
+    - type: precision_at_5
+      value: 14.384
+    - type: recall_at_1
+      value: 29.976000000000003
+    - type: recall_at_10
+      value: 60.99099999999999
+    - type: recall_at_100
+      value: 84.245
+    - type: recall_at_1000
+      value: 96.97200000000001
+    - type: recall_at_3
+      value: 43.794
+    - type: recall_at_5
+      value: 51.778999999999996
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 28.099166666666665
+    - type: map_at_10
+      value: 38.1365
+    - type: map_at_100
+      value: 39.44491666666667
+    - type: map_at_1000
+      value: 39.55858333333334
+    - type: map_at_3
+      value: 35.03641666666666
+    - type: map_at_5
+      value: 36.79833333333334
+    - type: mrr_at_1
+      value: 33.39966666666667
+    - type: mrr_at_10
+      value: 42.42583333333333
+    - type: mrr_at_100
+      value: 43.28575
+    - type: mrr_at_1000
+      value: 43.33741666666667
+    - type: mrr_at_3
+      value: 39.94975
+    - type: mrr_at_5
+      value: 41.41633333333334
+    - type: ndcg_at_1
+      value: 33.39966666666667
+    - type: ndcg_at_10
+      value: 43.81741666666667
+    - type: ndcg_at_100
+      value: 49.08166666666667
+    - type: ndcg_at_1000
+      value: 51.121166666666674
+    - type: ndcg_at_3
+      value: 38.73575
+    - type: ndcg_at_5
+      value: 41.18158333333333
+    - type: precision_at_1
+      value: 33.39966666666667
+    - type: precision_at_10
+      value: 7.738916666666667
+    - type: precision_at_100
+      value: 1.2265833333333331
+    - type: precision_at_1000
+      value: 0.15983333333333336
+    - type: precision_at_3
+      value: 17.967416666666665
+    - type: precision_at_5
+      value: 12.78675
+    - type: recall_at_1
+      value: 28.099166666666665
+    - type: recall_at_10
+      value: 56.27049999999999
+    - type: recall_at_100
+      value: 78.93291666666667
+    - type: recall_at_1000
+      value: 92.81608333333334
+    - type: recall_at_3
+      value: 42.09775
+    - type: recall_at_5
+      value: 48.42533333333334
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackStatsRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 23.663
+    - type: map_at_10
+      value: 30.377
+    - type: map_at_100
+      value: 31.426
+    - type: map_at_1000
+      value: 31.519000000000002
+    - type: map_at_3
+      value: 28.069
+    - type: map_at_5
+      value: 29.256999999999998
+    - type: mrr_at_1
+      value: 26.687
+    - type: mrr_at_10
+      value: 33.107
+    - type: mrr_at_100
+      value: 34.055
+    - type: mrr_at_1000
+      value: 34.117999999999995
+    - type: mrr_at_3
+      value: 31.058000000000003
+    - type: mrr_at_5
+      value: 32.14
+    - type: ndcg_at_1
+      value: 26.687
+    - type: ndcg_at_10
+      value: 34.615
+    - type: ndcg_at_100
+      value: 39.776
+    - type: ndcg_at_1000
+      value: 42.05
+    - type: ndcg_at_3
+      value: 30.322
+    - type: ndcg_at_5
+      value: 32.157000000000004
+    - type: precision_at_1
+      value: 26.687
+    - type: precision_at_10
+      value: 5.491
+    - type: precision_at_100
+      value: 0.877
+    - type: precision_at_1000
+      value: 0.11499999999999999
+    - type: precision_at_3
+      value: 13.139000000000001
+    - type: precision_at_5
+      value: 9.049
+    - type: recall_at_1
+      value: 23.663
+    - type: recall_at_10
+      value: 45.035
+    - type: recall_at_100
+      value: 68.554
+    - type: recall_at_1000
+      value: 85.077
+    - type: recall_at_3
+      value: 32.982
+    - type: recall_at_5
+      value: 37.688
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackTexRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 17.403
+    - type: map_at_10
+      value: 25.197000000000003
+    - type: map_at_100
+      value: 26.355
+    - type: map_at_1000
+      value: 26.487
+    - type: map_at_3
+      value: 22.733
+    - type: map_at_5
+      value: 24.114
+    - type: mrr_at_1
+      value: 21.37
+    - type: mrr_at_10
+      value: 29.091
+    - type: mrr_at_100
+      value: 30.018
+    - type: mrr_at_1000
+      value: 30.096
+    - type: mrr_at_3
+      value: 26.887
+    - type: mrr_at_5
+      value: 28.157
+    - type: ndcg_at_1
+      value: 21.37
+    - type: ndcg_at_10
+      value: 30.026000000000003
+    - type: ndcg_at_100
+      value: 35.416
+    - type: ndcg_at_1000
+      value: 38.45
+    - type: ndcg_at_3
+      value: 25.764
+    - type: ndcg_at_5
+      value: 27.742
+    - type: precision_at_1
+      value: 21.37
+    - type: precision_at_10
+      value: 5.609
+    - type: precision_at_100
+      value: 0.9860000000000001
+    - type: precision_at_1000
+      value: 0.14300000000000002
+    - type: precision_at_3
+      value: 12.423
+    - type: precision_at_5
+      value: 9.009
+    - type: recall_at_1
+      value: 17.403
+    - type: recall_at_10
+      value: 40.573
+    - type: recall_at_100
+      value: 64.818
+    - type: recall_at_1000
+      value: 86.53699999999999
+    - type: recall_at_3
+      value: 28.493000000000002
+    - type: recall_at_5
+      value: 33.660000000000004
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackUnixRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 28.639
+    - type: map_at_10
+      value: 38.951
+    - type: map_at_100
+      value: 40.238
+    - type: map_at_1000
+      value: 40.327
+    - type: map_at_3
+      value: 35.842
+    - type: map_at_5
+      value: 37.617
+    - type: mrr_at_1
+      value: 33.769
+    - type: mrr_at_10
+      value: 43.088
+    - type: mrr_at_100
+      value: 44.03
+    - type: mrr_at_1000
+      value: 44.072
+    - type: mrr_at_3
+      value: 40.656
+    - type: mrr_at_5
+      value: 42.138999999999996
+    - type: ndcg_at_1
+      value: 33.769
+    - type: ndcg_at_10
+      value: 44.676
+    - type: ndcg_at_100
+      value: 50.416000000000004
+    - type: ndcg_at_1000
+      value: 52.227999999999994
+    - type: ndcg_at_3
+      value: 39.494
+    - type: ndcg_at_5
+      value: 42.013
+    - type: precision_at_1
+      value: 33.769
+    - type: precision_at_10
+      value: 7.668
+    - type: precision_at_100
+      value: 1.18
+    - type: precision_at_1000
+      value: 0.145
+    - type: precision_at_3
+      value: 18.221
+    - type: precision_at_5
+      value: 12.966
+    - type: recall_at_1
+      value: 28.639
+    - type: recall_at_10
+      value: 57.687999999999995
+    - type: recall_at_100
+      value: 82.541
+    - type: recall_at_1000
+      value: 94.896
+    - type: recall_at_3
+      value: 43.651
+    - type: recall_at_5
+      value: 49.925999999999995
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWebmastersRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 29.57
+    - type: map_at_10
+      value: 40.004
+    - type: map_at_100
+      value: 41.75
+    - type: map_at_1000
+      value: 41.97
+    - type: map_at_3
+      value: 36.788
+    - type: map_at_5
+      value: 38.671
+    - type: mrr_at_1
+      value: 35.375
+    - type: mrr_at_10
+      value: 45.121
+    - type: mrr_at_100
+      value: 45.994
+    - type: mrr_at_1000
+      value: 46.04
+    - type: mrr_at_3
+      value: 42.227
+    - type: mrr_at_5
+      value: 43.995
+    - type: ndcg_at_1
+      value: 35.375
+    - type: ndcg_at_10
+      value: 46.392
+    - type: ndcg_at_100
+      value: 52.196
+    - type: ndcg_at_1000
+      value: 54.274
+    - type: ndcg_at_3
+      value: 41.163
+    - type: ndcg_at_5
+      value: 43.813
+    - type: precision_at_1
+      value: 35.375
+    - type: precision_at_10
+      value: 8.676
+    - type: precision_at_100
+      value: 1.678
+    - type: precision_at_1000
+      value: 0.253
+    - type: precision_at_3
+      value: 19.104
+    - type: precision_at_5
+      value: 13.913
+    - type: recall_at_1
+      value: 29.57
+    - type: recall_at_10
+      value: 58.779
+    - type: recall_at_100
+      value: 83.337
+    - type: recall_at_1000
+      value: 95.979
+    - type: recall_at_3
+      value: 44.005
+    - type: recall_at_5
+      value: 50.975
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWordpressRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 20.832
+    - type: map_at_10
+      value: 29.733999999999998
+    - type: map_at_100
+      value: 30.727
+    - type: map_at_1000
+      value: 30.843999999999998
+    - type: map_at_3
+      value: 26.834999999999997
+    - type: map_at_5
+      value: 28.555999999999997
+    - type: mrr_at_1
+      value: 22.921
+    - type: mrr_at_10
+      value: 31.791999999999998
+    - type: mrr_at_100
+      value: 32.666000000000004
+    - type: mrr_at_1000
+      value: 32.751999999999995
+    - type: mrr_at_3
+      value: 29.144
+    - type: mrr_at_5
+      value: 30.622
+    - type: ndcg_at_1
+      value: 22.921
+    - type: ndcg_at_10
+      value: 34.915
+    - type: ndcg_at_100
+      value: 39.744
+    - type: ndcg_at_1000
+      value: 42.407000000000004
+    - type: ndcg_at_3
+      value: 29.421000000000003
+    - type: ndcg_at_5
+      value: 32.211
+    - type: precision_at_1
+      value: 22.921
+    - type: precision_at_10
+      value: 5.675
+    - type: precision_at_100
+      value: 0.872
+    - type: precision_at_1000
+      value: 0.121
+    - type: precision_at_3
+      value: 12.753999999999998
+    - type: precision_at_5
+      value: 9.353
+    - type: recall_at_1
+      value: 20.832
+    - type: recall_at_10
+      value: 48.795
+    - type: recall_at_100
+      value: 70.703
+    - type: recall_at_1000
+      value: 90.187
+    - type: recall_at_3
+      value: 34.455000000000005
+    - type: recall_at_5
+      value: 40.967
+  - task:
+      type: Retrieval
+    dataset:
+      type: climate-fever
+      name: MTEB ClimateFEVER
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 10.334
+    - type: map_at_10
+      value: 19.009999999999998
+    - type: map_at_100
+      value: 21.129
+    - type: map_at_1000
+      value: 21.328
+    - type: map_at_3
+      value: 15.152
+    - type: map_at_5
+      value: 17.084
+    - type: mrr_at_1
+      value: 23.453
+    - type: mrr_at_10
+      value: 36.099
+    - type: mrr_at_100
+      value: 37.069
+    - type: mrr_at_1000
+      value: 37.104
+    - type: mrr_at_3
+      value: 32.096000000000004
+    - type: mrr_at_5
+      value: 34.451
+    - type: ndcg_at_1
+      value: 23.453
+    - type: ndcg_at_10
+      value: 27.739000000000004
+    - type: ndcg_at_100
+      value: 35.836
+    - type: ndcg_at_1000
+      value: 39.242
+    - type: ndcg_at_3
+      value: 21.263
+    - type: ndcg_at_5
+      value: 23.677
+    - type: precision_at_1
+      value: 23.453
+    - type: precision_at_10
+      value: 9.199
+    - type: precision_at_100
+      value: 1.791
+    - type: precision_at_1000
+      value: 0.242
+    - type: precision_at_3
+      value: 16.2
+    - type: precision_at_5
+      value: 13.147
+    - type: recall_at_1
+      value: 10.334
+    - type: recall_at_10
+      value: 35.177
+    - type: recall_at_100
+      value: 63.009
+    - type: recall_at_1000
+      value: 81.938
+    - type: recall_at_3
+      value: 19.914
+    - type: recall_at_5
+      value: 26.077
+  - task:
+      type: Retrieval
+    dataset:
+      type: dbpedia-entity
+      name: MTEB DBPedia
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 8.212
+    - type: map_at_10
+      value: 17.386
+    - type: map_at_100
+      value: 24.234
+    - type: map_at_1000
+      value: 25.724999999999998
+    - type: map_at_3
+      value: 12.727
+    - type: map_at_5
+      value: 14.785
+    - type: mrr_at_1
+      value: 59.25
+    - type: mrr_at_10
+      value: 68.687
+    - type: mrr_at_100
+      value: 69.133
+    - type: mrr_at_1000
+      value: 69.14099999999999
+    - type: mrr_at_3
+      value: 66.917
+    - type: mrr_at_5
+      value: 67.742
+    - type: ndcg_at_1
+      value: 48.625
+    - type: ndcg_at_10
+      value: 36.675999999999995
+    - type: ndcg_at_100
+      value: 41.543
+    - type: ndcg_at_1000
+      value: 49.241
+    - type: ndcg_at_3
+      value: 41.373
+    - type: ndcg_at_5
+      value: 38.707
+    - type: precision_at_1
+      value: 59.25
+    - type: precision_at_10
+      value: 28.525
+    - type: precision_at_100
+      value: 9.027000000000001
+    - type: precision_at_1000
+      value: 1.8339999999999999
+    - type: precision_at_3
+      value: 44.833
+    - type: precision_at_5
+      value: 37.35
+    - type: recall_at_1
+      value: 8.212
+    - type: recall_at_10
+      value: 23.188
+    - type: recall_at_100
+      value: 48.613
+    - type: recall_at_1000
+      value: 73.093
+    - type: recall_at_3
+      value: 14.419
+    - type: recall_at_5
+      value: 17.798
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/emotion
+      name: MTEB EmotionClassification
+      config: default
+      split: test
+      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
+    metrics:
+    - type: accuracy
+      value: 52.725
+    - type: f1
+      value: 46.50743309855908
+  - task:
+      type: Retrieval
+    dataset:
+      type: fever
+      name: MTEB FEVER
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 55.086
+    - type: map_at_10
+      value: 66.914
+    - type: map_at_100
+      value: 67.321
+    - type: map_at_1000
+      value: 67.341
+    - type: map_at_3
+      value: 64.75800000000001
+    - type: map_at_5
+      value: 66.189
+    - type: mrr_at_1
+      value: 59.28600000000001
+    - type: mrr_at_10
+      value: 71.005
+    - type: mrr_at_100
+      value: 71.304
+    - type: mrr_at_1000
+      value: 71.313
+    - type: mrr_at_3
+      value: 69.037
+    - type: mrr_at_5
+      value: 70.35
+    - type: ndcg_at_1
+      value: 59.28600000000001
+    - type: ndcg_at_10
+      value: 72.695
+    - type: ndcg_at_100
+      value: 74.432
+    - type: ndcg_at_1000
+      value: 74.868
+    - type: ndcg_at_3
+      value: 68.72200000000001
+    - type: ndcg_at_5
+      value: 71.081
+    - type: precision_at_1
+      value: 59.28600000000001
+    - type: precision_at_10
+      value: 9.499
+    - type: precision_at_100
+      value: 1.052
+    - type: precision_at_1000
+      value: 0.11100000000000002
+    - type: precision_at_3
+      value: 27.503
+    - type: precision_at_5
+      value: 17.854999999999997
+    - type: recall_at_1
+      value: 55.086
+    - type: recall_at_10
+      value: 86.453
+    - type: recall_at_100
+      value: 94.028
+    - type: recall_at_1000
+      value: 97.052
+    - type: recall_at_3
+      value: 75.821
+    - type: recall_at_5
+      value: 81.6
+  - task:
+      type: Retrieval
+    dataset:
+      type: fiqa
+      name: MTEB FiQA2018
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 22.262999999999998
+    - type: map_at_10
+      value: 37.488
+    - type: map_at_100
+      value: 39.498
+    - type: map_at_1000
+      value: 39.687
+    - type: map_at_3
+      value: 32.529
+    - type: map_at_5
+      value: 35.455
+    - type: mrr_at_1
+      value: 44.907000000000004
+    - type: mrr_at_10
+      value: 53.239000000000004
+    - type: mrr_at_100
+      value: 54.086
+    - type: mrr_at_1000
+      value: 54.122
+    - type: mrr_at_3
+      value: 51.235
+    - type: mrr_at_5
+      value: 52.415
+    - type: ndcg_at_1
+      value: 44.907000000000004
+    - type: ndcg_at_10
+      value: 45.446
+    - type: ndcg_at_100
+      value: 52.429
+    - type: ndcg_at_1000
+      value: 55.169000000000004
+    - type: ndcg_at_3
+      value: 41.882000000000005
+    - type: ndcg_at_5
+      value: 43.178
+    - type: precision_at_1
+      value: 44.907000000000004
+    - type: precision_at_10
+      value: 12.931999999999999
+    - type: precision_at_100
+      value: 2.025
+    - type: precision_at_1000
+      value: 0.248
+    - type: precision_at_3
+      value: 28.652
+    - type: precision_at_5
+      value: 21.204
+    - type: recall_at_1
+      value: 22.262999999999998
+    - type: recall_at_10
+      value: 52.447
+    - type: recall_at_100
+      value: 78.045
+    - type: recall_at_1000
+      value: 94.419
+    - type: recall_at_3
+      value: 38.064
+    - type: recall_at_5
+      value: 44.769
+  - task:
+      type: Retrieval
+    dataset:
+      type: hotpotqa
+      name: MTEB HotpotQA
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 32.519
+    - type: map_at_10
+      value: 45.831
+    - type: map_at_100
+      value: 46.815
+    - type: map_at_1000
+      value: 46.899
+    - type: map_at_3
+      value: 42.836
+    - type: map_at_5
+      value: 44.65
+    - type: mrr_at_1
+      value: 65.037
+    - type: mrr_at_10
+      value: 72.16
+    - type: mrr_at_100
+      value: 72.51100000000001
+    - type: mrr_at_1000
+      value: 72.53
+    - type: mrr_at_3
+      value: 70.682
+    - type: mrr_at_5
+      value: 71.54599999999999
+    - type: ndcg_at_1
+      value: 65.037
+    - type: ndcg_at_10
+      value: 55.17999999999999
+    - type: ndcg_at_100
+      value: 58.888
+    - type: ndcg_at_1000
+      value: 60.648
+    - type: ndcg_at_3
+      value: 50.501
+    - type: ndcg_at_5
+      value: 52.977
+    - type: precision_at_1
+      value: 65.037
+    - type: precision_at_10
+      value: 11.530999999999999
+    - type: precision_at_100
+      value: 1.4460000000000002
+    - type: precision_at_1000
+      value: 0.168
+    - type: precision_at_3
+      value: 31.483
+    - type: precision_at_5
+      value: 20.845
+    - type: recall_at_1
+      value: 32.519
+    - type: recall_at_10
+      value: 57.657000000000004
+    - type: recall_at_100
+      value: 72.30199999999999
+    - type: recall_at_1000
+      value: 84.024
+    - type: recall_at_3
+      value: 47.225
+    - type: recall_at_5
+      value: 52.113
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/imdb
+      name: MTEB ImdbClassification
+      config: default
+      split: test
+      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
+    metrics:
+    - type: accuracy
+      value: 88.3168
+    - type: ap
+      value: 83.80165516037135
+    - type: f1
+      value: 88.29942471066407
+  - task:
+      type: Retrieval
+    dataset:
+      type: msmarco
+      name: MTEB MSMARCO
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 20.724999999999998
+    - type: map_at_10
+      value: 32.736
+    - type: map_at_100
+      value: 33.938
+    - type: map_at_1000
+      value: 33.991
+    - type: map_at_3
+      value: 28.788000000000004
+    - type: map_at_5
+      value: 31.016
+    - type: mrr_at_1
+      value: 21.361
+    - type: mrr_at_10
+      value: 33.323
+    - type: mrr_at_100
+      value: 34.471000000000004
+    - type: mrr_at_1000
+      value: 34.518
+    - type: mrr_at_3
+      value: 29.453000000000003
+    - type: mrr_at_5
+      value: 31.629
+    - type: ndcg_at_1
+      value: 21.361
+    - type: ndcg_at_10
+      value: 39.649
+    - type: ndcg_at_100
+      value: 45.481
+    - type: ndcg_at_1000
+      value: 46.775
+    - type: ndcg_at_3
+      value: 31.594
+    - type: ndcg_at_5
+      value: 35.543
+    - type: precision_at_1
+      value: 21.361
+    - type: precision_at_10
+      value: 6.3740000000000006
+    - type: precision_at_100
+      value: 0.931
+    - type: precision_at_1000
+      value: 0.104
+    - type: precision_at_3
+      value: 13.514999999999999
+    - type: precision_at_5
+      value: 10.100000000000001
+    - type: recall_at_1
+      value: 20.724999999999998
+    - type: recall_at_10
+      value: 61.034
+    - type: recall_at_100
+      value: 88.062
+    - type: recall_at_1000
+      value: 97.86399999999999
+    - type: recall_at_3
+      value: 39.072
+    - type: recall_at_5
+      value: 48.53
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_domain
+      name: MTEB MTOPDomainClassification (en)
+      config: en
+      split: test
+      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
+    metrics:
+    - type: accuracy
+      value: 93.8919288645691
+    - type: f1
+      value: 93.57059586398059
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_intent
+      name: MTEB MTOPIntentClassification (en)
+      config: en
+      split: test
+      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
+    metrics:
+    - type: accuracy
+      value: 67.97993616051072
+    - type: f1
+      value: 48.244319183606535
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_intent
+      name: MTEB MassiveIntentClassification (en)
+      config: en
+      split: test
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+    metrics:
+    - type: accuracy
+      value: 68.90047074646941
+    - type: f1
+      value: 66.48999056063725
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (en)
+      config: en
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 73.34566240753195
+    - type: f1
+      value: 73.54164154290658
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-p2p
+      name: MTEB MedrxivClusteringP2P
+      config: default
+      split: test
+      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
+    metrics:
+    - type: v_measure
+      value: 34.21866934757011
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-s2s
+      name: MTEB MedrxivClusteringS2S
+      config: default
+      split: test
+      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
+    metrics:
+    - type: v_measure
+      value: 32.000936217235534
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/mind_small
+      name: MTEB MindSmallReranking
+      config: default
+      split: test
+      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
+    metrics:
+    - type: map
+      value: 31.68189362520352
+    - type: mrr
+      value: 32.69603637784303
+  - task:
+      type: Retrieval
+    dataset:
+      type: nfcorpus
+      name: MTEB NFCorpus
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 6.078
+    - type: map_at_10
+      value: 12.671
+    - type: map_at_100
+      value: 16.291
+    - type: map_at_1000
+      value: 17.855999999999998
+    - type: map_at_3
+      value: 9.610000000000001
+    - type: map_at_5
+      value: 11.152
+    - type: mrr_at_1
+      value: 43.963
+    - type: mrr_at_10
+      value: 53.173
+    - type: mrr_at_100
+      value: 53.718999999999994
+    - type: mrr_at_1000
+      value: 53.756
+    - type: mrr_at_3
+      value: 50.980000000000004
+    - type: mrr_at_5
+      value: 52.42
+    - type: ndcg_at_1
+      value: 42.415000000000006
+    - type: ndcg_at_10
+      value: 34.086
+    - type: ndcg_at_100
+      value: 32.545
+    - type: ndcg_at_1000
+      value: 41.144999999999996
+    - type: ndcg_at_3
+      value: 39.434999999999995
+    - type: ndcg_at_5
+      value: 37.888
+    - type: precision_at_1
+      value: 43.653
+    - type: precision_at_10
+      value: 25.014999999999997
+    - type: precision_at_100
+      value: 8.594
+    - type: precision_at_1000
+      value: 2.169
+    - type: precision_at_3
+      value: 37.049
+    - type: precision_at_5
+      value: 33.065
+    - type: recall_at_1
+      value: 6.078
+    - type: recall_at_10
+      value: 16.17
+    - type: recall_at_100
+      value: 34.512
+    - type: recall_at_1000
+      value: 65.447
+    - type: recall_at_3
+      value: 10.706
+    - type: recall_at_5
+      value: 13.158
+  - task:
+      type: Retrieval
+    dataset:
+      type: nq
+      name: MTEB NQ
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 27.378000000000004
+    - type: map_at_10
+      value: 42.178
+    - type: map_at_100
+      value: 43.32
+    - type: map_at_1000
+      value: 43.358000000000004
+    - type: map_at_3
+      value: 37.474000000000004
+    - type: map_at_5
+      value: 40.333000000000006
+    - type: mrr_at_1
+      value: 30.823
+    - type: mrr_at_10
+      value: 44.626
+    - type: mrr_at_100
+      value: 45.494
+    - type: mrr_at_1000
+      value: 45.519
+    - type: mrr_at_3
+      value: 40.585
+    - type: mrr_at_5
+      value: 43.146
+    - type: ndcg_at_1
+      value: 30.794
+    - type: ndcg_at_10
+      value: 50.099000000000004
+    - type: ndcg_at_100
+      value: 54.900999999999996
+    - type: ndcg_at_1000
+      value: 55.69499999999999
+    - type: ndcg_at_3
+      value: 41.238
+    - type: ndcg_at_5
+      value: 46.081
+    - type: precision_at_1
+      value: 30.794
+    - type: precision_at_10
+      value: 8.549
+    - type: precision_at_100
+      value: 1.124
+    - type: precision_at_1000
+      value: 0.12
+    - type: precision_at_3
+      value: 18.926000000000002
+    - type: precision_at_5
+      value: 14.16
+    - type: recall_at_1
+      value: 27.378000000000004
+    - type: recall_at_10
+      value: 71.842
+    - type: recall_at_100
+      value: 92.565
+    - type: recall_at_1000
+      value: 98.402
+    - type: recall_at_3
+      value: 49.053999999999995
+    - type: recall_at_5
+      value: 60.207
+  - task:
+      type: Retrieval
+    dataset:
+      type: quora
+      name: MTEB QuoraRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 70.557
+    - type: map_at_10
+      value: 84.729
+    - type: map_at_100
+      value: 85.369
+    - type: map_at_1000
+      value: 85.382
+    - type: map_at_3
+      value: 81.72
+    - type: map_at_5
+      value: 83.613
+    - type: mrr_at_1
+      value: 81.3
+    - type: mrr_at_10
+      value: 87.488
+    - type: mrr_at_100
+      value: 87.588
+    - type: mrr_at_1000
+      value: 87.589
+    - type: mrr_at_3
+      value: 86.53
+    - type: mrr_at_5
+      value: 87.18599999999999
+    - type: ndcg_at_1
+      value: 81.28999999999999
+    - type: ndcg_at_10
+      value: 88.442
+    - type: ndcg_at_100
+      value: 89.637
+    - type: ndcg_at_1000
+      value: 89.70700000000001
+    - type: ndcg_at_3
+      value: 85.55199999999999
+    - type: ndcg_at_5
+      value: 87.154
+    - type: precision_at_1
+      value: 81.28999999999999
+    - type: precision_at_10
+      value: 13.489999999999998
+    - type: precision_at_100
+      value: 1.54
+    - type: precision_at_1000
+      value: 0.157
+    - type: precision_at_3
+      value: 37.553
+    - type: precision_at_5
+      value: 24.708
+    - type: recall_at_1
+      value: 70.557
+    - type: recall_at_10
+      value: 95.645
+    - type: recall_at_100
+      value: 99.693
+    - type: recall_at_1000
+      value: 99.995
+    - type: recall_at_3
+      value: 87.359
+    - type: recall_at_5
+      value: 91.89699999999999
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering
+      name: MTEB RedditClustering
+      config: default
+      split: test
+      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
+    metrics:
+    - type: v_measure
+      value: 63.65060114776209
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering-p2p
+      name: MTEB RedditClusteringP2P
+      config: default
+      split: test
+      revision: 282350215ef01743dc01b456c7f5241fa8937f16
+    metrics:
+    - type: v_measure
+      value: 64.63271250680617
+  - task:
+      type: Retrieval
+    dataset:
+      type: scidocs
+      name: MTEB SCIDOCS
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 4.263
+    - type: map_at_10
+      value: 10.801
+    - type: map_at_100
+      value: 12.888
+    - type: map_at_1000
+      value: 13.224
+    - type: map_at_3
+      value: 7.362
+    - type: map_at_5
+      value: 9.149000000000001
+    - type: mrr_at_1
+      value: 21
+    - type: mrr_at_10
+      value: 31.416
+    - type: mrr_at_100
+      value: 32.513
+    - type: mrr_at_1000
+      value: 32.58
+    - type: mrr_at_3
+      value: 28.116999999999997
+    - type: mrr_at_5
+      value: 29.976999999999997
+    - type: ndcg_at_1
+      value: 21
+    - type: ndcg_at_10
+      value: 18.551000000000002
+    - type: ndcg_at_100
+      value: 26.657999999999998
+    - type: ndcg_at_1000
+      value: 32.485
+    - type: ndcg_at_3
+      value: 16.834
+    - type: ndcg_at_5
+      value: 15.204999999999998
+    - type: precision_at_1
+      value: 21
+    - type: precision_at_10
+      value: 9.84
+    - type: precision_at_100
+      value: 2.16
+    - type: precision_at_1000
+      value: 0.35500000000000004
+    - type: precision_at_3
+      value: 15.667
+    - type: precision_at_5
+      value: 13.62
+    - type: recall_at_1
+      value: 4.263
+    - type: recall_at_10
+      value: 19.922
+    - type: recall_at_100
+      value: 43.808
+    - type: recall_at_1000
+      value: 72.14500000000001
+    - type: recall_at_3
+      value: 9.493
+    - type: recall_at_5
+      value: 13.767999999999999
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sickr-sts
+      name: MTEB SICK-R
+      config: default
+      split: test
+      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
+    metrics:
+    - type: cos_sim_spearman
+      value: 81.27446313317233
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts12-sts
+      name: MTEB STS12
+      config: default
+      split: test
+      revision: a0d554a64d88156834ff5ae9920b964011b16384
+    metrics:
+    - type: cos_sim_spearman
+      value: 76.27963301217527
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts13-sts
+      name: MTEB STS13
+      config: default
+      split: test
+      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
+    metrics:
+    - type: cos_sim_spearman
+      value: 88.18495048450949
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts14-sts
+      name: MTEB STS14
+      config: default
+      split: test
+      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
+    metrics:
+    - type: cos_sim_spearman
+      value: 81.91982338692046
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts15-sts
+      name: MTEB STS15
+      config: default
+      split: test
+      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
+    metrics:
+    - type: cos_sim_spearman
+      value: 89.00896818385291
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts16-sts
+      name: MTEB STS16
+      config: default
+      split: test
+      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
+    metrics:
+    - type: cos_sim_spearman
+      value: 85.48814644586132
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts17-crosslingual-sts
+      name: MTEB STS17 (en-en)
+      config: en-en
+      split: test
+      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
+    metrics:
+    - type: cos_sim_spearman
+      value: 90.30116926966582
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts22-crosslingual-sts
+      name: MTEB STS22 (en)
+      config: en
+      split: test
+      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
+    metrics:
+    - type: cos_sim_spearman
+      value: 67.74132963032342
+  - task:
+      type: STS
+    dataset:
+      type: mteb/stsbenchmark-sts
+      name: MTEB STSBenchmark
+      config: default
+      split: test
+      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
+    metrics:
+    - type: cos_sim_spearman
+      value: 86.87741355780479
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/scidocs-reranking
+      name: MTEB SciDocsRR
+      config: default
+      split: test
+      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
+    metrics:
+    - type: map
+      value: 82.0019012295875
+    - type: mrr
+      value: 94.70267024188593
+  - task:
+      type: Retrieval
+    dataset:
+      type: scifact
+      name: MTEB SciFact
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 50.05
+    - type: map_at_10
+      value: 59.36
+    - type: map_at_100
+      value: 59.967999999999996
+    - type: map_at_1000
+      value: 60.023
+    - type: map_at_3
+      value: 56.515
+    - type: map_at_5
+      value: 58.272999999999996
+    - type: mrr_at_1
+      value: 53
+    - type: mrr_at_10
+      value: 61.102000000000004
+    - type: mrr_at_100
+      value: 61.476
+    - type: mrr_at_1000
+      value: 61.523
+    - type: mrr_at_3
+      value: 58.778
+    - type: mrr_at_5
+      value: 60.128
+    - type: ndcg_at_1
+      value: 53
+    - type: ndcg_at_10
+      value: 64.43100000000001
+    - type: ndcg_at_100
+      value: 66.73599999999999
+    - type: ndcg_at_1000
+      value: 68.027
+    - type: ndcg_at_3
+      value: 59.279
+    - type: ndcg_at_5
+      value: 61.888
+    - type: precision_at_1
+      value: 53
+    - type: precision_at_10
+      value: 8.767
+    - type: precision_at_100
+      value: 1.01
+    - type: precision_at_1000
+      value: 0.11100000000000002
+    - type: precision_at_3
+      value: 23.444000000000003
+    - type: precision_at_5
+      value: 15.667
+    - type: recall_at_1
+      value: 50.05
+    - type: recall_at_10
+      value: 78.511
+    - type: recall_at_100
+      value: 88.5
+    - type: recall_at_1000
+      value: 98.333
+    - type: recall_at_3
+      value: 64.117
+    - type: recall_at_5
+      value: 70.867
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/sprintduplicatequestions-pairclassification
+      name: MTEB SprintDuplicateQuestions
+      config: default
+      split: test
+      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
+    metrics:
+    - type: cos_sim_accuracy
+      value: 99.72178217821782
+    - type: cos_sim_ap
+      value: 93.0728601593541
+    - type: cos_sim_f1
+      value: 85.6727976766699
+    - type: cos_sim_precision
+      value: 83.02063789868667
+    - type: cos_sim_recall
+      value: 88.5
+    - type: dot_accuracy
+      value: 99.72178217821782
+    - type: dot_ap
+      value: 93.07287396168348
+    - type: dot_f1
+      value: 85.6727976766699
+    - type: dot_precision
+      value: 83.02063789868667
+    - type: dot_recall
+      value: 88.5
+    - type: euclidean_accuracy
+      value: 99.72178217821782
+    - type: euclidean_ap
+      value: 93.07285657982895
+    - type: euclidean_f1
+      value: 85.6727976766699
+    - type: euclidean_precision
+      value: 83.02063789868667
+    - type: euclidean_recall
+      value: 88.5
+    - type: manhattan_accuracy
+      value: 99.72475247524753
+    - type: manhattan_ap
+      value: 93.02792973059809
+    - type: manhattan_f1
+      value: 85.7727737973388
+    - type: manhattan_precision
+      value: 87.84067085953879
+    - type: manhattan_recall
+      value: 83.8
+    - type: max_accuracy
+      value: 99.72475247524753
+    - type: max_ap
+      value: 93.07287396168348
+    - type: max_f1
+      value: 85.7727737973388
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering
+      name: MTEB StackExchangeClustering
+      config: default
+      split: test
+      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
+    metrics:
+    - type: v_measure
+      value: 68.77583615550819
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering-p2p
+      name: MTEB StackExchangeClusteringP2P
+      config: default
+      split: test
+      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
+    metrics:
+    - type: v_measure
+      value: 36.151636938606956
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/stackoverflowdupquestions-reranking
+      name: MTEB StackOverflowDupQuestions
+      config: default
+      split: test
+      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
+    metrics:
+    - type: map
+      value: 52.16607939471187
+    - type: mrr
+      value: 52.95172046091163
+  - task:
+      type: Summarization
+    dataset:
+      type: mteb/summeval
+      name: MTEB SummEval
+      config: default
+      split: test
+      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
+    metrics:
+    - type: cos_sim_pearson
+      value: 31.314646669495666
+    - type: cos_sim_spearman
+      value: 31.83562491439455
+    - type: dot_pearson
+      value: 31.314590842874157
+    - type: dot_spearman
+      value: 31.83363065810437
+  - task:
+      type: Retrieval
+    dataset:
+      type: trec-covid
+      name: MTEB TRECCOVID
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 0.198
+    - type: map_at_10
+      value: 1.3010000000000002
+    - type: map_at_100
+      value: 7.2139999999999995
+    - type: map_at_1000
+      value: 20.179
+    - type: map_at_3
+      value: 0.528
+    - type: map_at_5
+      value: 0.8019999999999999
+    - type: mrr_at_1
+      value: 72
+    - type: mrr_at_10
+      value: 83.39999999999999
+    - type: mrr_at_100
+      value: 83.39999999999999
+    - type: mrr_at_1000
+      value: 83.39999999999999
+    - type: mrr_at_3
+      value: 81.667
+    - type: mrr_at_5
+      value: 83.06700000000001
+    - type: ndcg_at_1
+      value: 66
+    - type: ndcg_at_10
+      value: 58.059000000000005
+    - type: ndcg_at_100
+      value: 44.316
+    - type: ndcg_at_1000
+      value: 43.147000000000006
+    - type: ndcg_at_3
+      value: 63.815999999999995
+    - type: ndcg_at_5
+      value: 63.005
+    - type: precision_at_1
+      value: 72
+    - type: precision_at_10
+      value: 61.4
+    - type: precision_at_100
+      value: 45.62
+    - type: precision_at_1000
+      value: 19.866
+    - type: precision_at_3
+      value: 70
+    - type: precision_at_5
+      value: 68.8
+    - type: recall_at_1
+      value: 0.198
+    - type: recall_at_10
+      value: 1.517
+    - type: recall_at_100
+      value: 10.587
+    - type: recall_at_1000
+      value: 41.233
+    - type: recall_at_3
+      value: 0.573
+    - type: recall_at_5
+      value: 0.907
+  - task:
+      type: Retrieval
+    dataset:
+      type: webis-touche2020
+      name: MTEB Touche2020
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 1.894
+    - type: map_at_10
+      value: 8.488999999999999
+    - type: map_at_100
+      value: 14.445
+    - type: map_at_1000
+      value: 16.078
+    - type: map_at_3
+      value: 4.589
+    - type: map_at_5
+      value: 6.019
+    - type: mrr_at_1
+      value: 22.448999999999998
+    - type: mrr_at_10
+      value: 39.82
+    - type: mrr_at_100
+      value: 40.752
+    - type: mrr_at_1000
+      value: 40.771
+    - type: mrr_at_3
+      value: 34.354
+    - type: mrr_at_5
+      value: 37.721
+    - type: ndcg_at_1
+      value: 19.387999999999998
+    - type: ndcg_at_10
+      value: 21.563
+    - type: ndcg_at_100
+      value: 33.857
+    - type: ndcg_at_1000
+      value: 46.199
+    - type: ndcg_at_3
+      value: 22.296
+    - type: ndcg_at_5
+      value: 21.770999999999997
+    - type: precision_at_1
+      value: 22.448999999999998
+    - type: precision_at_10
+      value: 19.796
+    - type: precision_at_100
+      value: 7.142999999999999
+    - type: precision_at_1000
+      value: 1.541
+    - type: precision_at_3
+      value: 24.490000000000002
+    - type: precision_at_5
+      value: 22.448999999999998
+    - type: recall_at_1
+      value: 1.894
+    - type: recall_at_10
+      value: 14.931
+    - type: recall_at_100
+      value: 45.524
+    - type: recall_at_1000
+      value: 83.243
+    - type: recall_at_3
+      value: 5.712
+    - type: recall_at_5
+      value: 8.386000000000001
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/toxic_conversations_50k
+      name: MTEB ToxicConversationsClassification
+      config: default
+      split: test
+      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
+    metrics:
+    - type: accuracy
+      value: 71.049
+    - type: ap
+      value: 13.85116971310922
+    - type: f1
+      value: 54.37504302487686
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/tweet_sentiment_extraction
+      name: MTEB TweetSentimentExtractionClassification
+      config: default
+      split: test
+      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
+    metrics:
+    - type: accuracy
+      value: 64.1312959818902
+    - type: f1
+      value: 64.11413877009383
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/twentynewsgroups-clustering
+      name: MTEB TwentyNewsgroupsClustering
+      config: default
+      split: test
+      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
+    metrics:
+    - type: v_measure
+      value: 54.13103431861502
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twittersemeval2015-pairclassification
+      name: MTEB TwitterSemEval2015
+      config: default
+      split: test
+      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
+    metrics:
+    - type: cos_sim_accuracy
+      value: 87.327889372355
+    - type: cos_sim_ap
+      value: 77.42059895975699
+    - type: cos_sim_f1
+      value: 71.02706903250873
+    - type: cos_sim_precision
+      value: 69.75324344950394
+    - type: cos_sim_recall
+      value: 72.34828496042216
+    - type: dot_accuracy
+      value: 87.327889372355
+    - type: dot_ap
+      value: 77.4209479346677
+    - type: dot_f1
+      value: 71.02706903250873
+    - type: dot_precision
+      value: 69.75324344950394
+    - type: dot_recall
+      value: 72.34828496042216
+    - type: euclidean_accuracy
+      value: 87.327889372355
+    - type: euclidean_ap
+      value: 77.42096495861037
+    - type: euclidean_f1
+      value: 71.02706903250873
+    - type: euclidean_precision
+      value: 69.75324344950394
+    - type: euclidean_recall
+      value: 72.34828496042216
+    - type: manhattan_accuracy
+      value: 87.31000774870358
+    - type: manhattan_ap
+      value: 77.38930750711619
+    - type: manhattan_f1
+      value: 71.07935314027831
+    - type: manhattan_precision
+      value: 67.70957726295677
+    - type: manhattan_recall
+      value: 74.80211081794195
+    - type: max_accuracy
+      value: 87.327889372355
+    - type: max_ap
+      value: 77.42096495861037
+    - type: max_f1
+      value: 71.07935314027831
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twitterurlcorpus-pairclassification
+      name: MTEB TwitterURLCorpus
+      config: default
+      split: test
+      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
+    metrics:
+    - type: cos_sim_accuracy
+      value: 89.58939729110878
+    - type: cos_sim_ap
+      value: 87.17594155025475
+    - type: cos_sim_f1
+      value: 79.21146953405018
+    - type: cos_sim_precision
+      value: 76.8918527109307
+    - type: cos_sim_recall
+      value: 81.67539267015707
+    - type: dot_accuracy
+      value: 89.58939729110878
+    - type: dot_ap
+      value: 87.17593963273593
+    - type: dot_f1
+      value: 79.21146953405018
+    - type: dot_precision
+      value: 76.8918527109307
+    - type: dot_recall
+      value: 81.67539267015707
+    - type: euclidean_accuracy
+      value: 89.58939729110878
+    - type: euclidean_ap
+      value: 87.17592466925834
+    - type: euclidean_f1
+      value: 79.21146953405018
+    - type: euclidean_precision
+      value: 76.8918527109307
+    - type: euclidean_recall
+      value: 81.67539267015707
+    - type: manhattan_accuracy
+      value: 89.62626615438352
+    - type: manhattan_ap
+      value: 87.16589873161546
+    - type: manhattan_f1
+      value: 79.25143598295348
+    - type: manhattan_precision
+      value: 76.39494177323712
+    - type: manhattan_recall
+      value: 82.32984293193716
+    - type: max_accuracy
+      value: 89.62626615438352
+    - type: max_ap
+      value: 87.17594155025475
+    - type: max_f1
+      value: 79.25143598295348
+---
+
+# hkunlp/instructor-large
+We introduce **Instructor**👨‍🏫, an instruction-finetuned text embedding model that can generate text embeddings tailored to any task (e.g., classification, retrieval, clustering, text evaluation, etc.) and domains (e.g., science, finance, etc.) ***by simply providing the task instruction, without any finetuning***. Instructor👨‍ achieves sota on 70 diverse embedding tasks ([MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard))!
+The model is easy to use with **our customized** `sentence-transformer` library. For more details, check out [our paper](https://arxiv.org/abs/2212.09741) and [project page](https://instructor-embedding.github.io/)! 
+
+**************************** **Updates** ****************************
+
+* 12/28: We released a new [checkpoint](https://huggingface.co/hkunlp/instructor-large) trained with hard negatives, which gives better performance.
+* 12/21: We released our [paper](https://arxiv.org/abs/2212.09741), [code](https://github.com/HKUNLP/instructor-embedding), [checkpoint](https://huggingface.co/hkunlp/instructor-large) and [project page](https://instructor-embedding.github.io/)! Check them out!
+
+## Quick start
+<hr />
+
+## Installation
+```bash
+pip install InstructorEmbedding
+```
+
+## Compute your customized embeddings
+Then you can use the model like this to calculate domain-specific and task-aware embeddings:
+```python
+from InstructorEmbedding import INSTRUCTOR
+model = INSTRUCTOR('hkunlp/instructor-large')
+sentence = "3D ActionSLAM: wearable person tracking in multi-floor environments"
+instruction = "Represent the Science title:"
+embeddings = model.encode([[instruction,sentence]])
+print(embeddings)
+```
+
+## Use cases
+<hr />
+
+## Calculate embeddings for your customized texts
+If you want to calculate customized embeddings for specific sentences, you may follow the unified template to write instructions: 
+
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Represent the `domain` `text_type` for `task_objective`:
+* `domain` is optional, and it specifies the domain of the text, e.g., science, finance, medicine, etc.
+* `text_type` is required, and it specifies the encoding unit, e.g., sentence, document, paragraph, etc.
+* `task_objective` is optional, and it specifies the objective of embedding, e.g., retrieve a document, classify the sentence, etc.
+
+## Calculate Sentence similarities
+You can further use the model to compute similarities between two groups of sentences, with **customized embeddings**.
+```python
+from sklearn.metrics.pairwise import cosine_similarity
+sentences_a = [['Represent the Science sentence: ','Parton energy loss in QCD matter'], 
+               ['Represent the Financial statement: ','The Federal Reserve on Wednesday raised its benchmark interest rate.']]
+sentences_b = [['Represent the Science sentence: ','The Chiral Phase Transition in Dissipative Dynamics'],
+               ['Represent the Financial statement: ','The funds rose less than 0.5 per cent on Friday']]
+embeddings_a = model.encode(sentences_a)
+embeddings_b = model.encode(sentences_b)
+similarities = cosine_similarity(embeddings_a,embeddings_b)
+print(similarities)
+```
+
+## Information Retrieval
+You can also use **customized embeddings** for information retrieval.
+```python
+import numpy as np
+from sklearn.metrics.pairwise import cosine_similarity
+query  = [['Represent the Wikipedia question for retrieving supporting documents: ','where is the food stored in a yam plant']]
+corpus = [['Represent the Wikipedia document for retrieval: ','Capitalism has been dominant in the Western world since the end of feudalism, but most feel[who?] that the term "mixed economies" more precisely describes most contemporary economies, due to their containing both private-owned and state-owned enterprises. In capitalism, prices determine the demand-supply scale. For example, higher demand for certain goods and services lead to higher prices and lower demand for certain goods lead to lower prices.'],
+          ['Represent the Wikipedia document for retrieval: ',"The disparate impact theory is especially controversial under the Fair Housing Act because the Act regulates many activities relating to housing, insurance, and mortgage loans—and some scholars have argued that the theory's use under the Fair Housing Act, combined with extensions of the Community Reinvestment Act, contributed to rise of sub-prime lending and the crash of the U.S. housing market and ensuing global economic recession"],
+          ['Represent the Wikipedia document for retrieval: ','Disparate impact in United States labor law refers to practices in employment, housing, and other areas that adversely affect one group of people of a protected characteristic more than another, even though rules applied by employers or landlords are formally neutral. Although the protected classes vary by statute, most federal civil rights laws protect based on race, color, religion, national origin, and sex as protected traits, and some laws include disability status and other traits as well.']]
+query_embeddings = model.encode(query)
+corpus_embeddings = model.encode(corpus)
+similarities = cosine_similarity(query_embeddings,corpus_embeddings)
+retrieved_doc_id = np.argmax(similarities)
+print(retrieved_doc_id)
+```
+
+## Clustering
+Use **customized embeddings** for clustering texts in groups.
+```python
+import sklearn.cluster
+sentences = [['Represent the Medicine sentence for clustering: ','Dynamical Scalar Degree of Freedom in Horava-Lifshitz Gravity'],
+             ['Represent the Medicine sentence for clustering: ','Comparison of Atmospheric Neutrino Flux Calculations at Low Energies'],
+             ['Represent the Medicine sentence for clustering: ','Fermion Bags in the Massive Gross-Neveu Model'],
+             ['Represent the Medicine sentence for clustering: ',"QCD corrections to Associated t-tbar-H production at the Tevatron"],
+             ['Represent the Medicine sentence for clustering: ','A New Analysis of the R Measurements: Resonance Parameters of the Higher,  Vector States of Charmonium']]
+embeddings = model.encode(sentences)
+clustering_model = sklearn.cluster.MiniBatchKMeans(n_clusters=2)
+clustering_model.fit(embeddings)
+cluster_assignment = clustering_model.labels_
+print(cluster_assignment)
+```

config.json

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+{
+  "_name_or_path": "result/hkunlp_instructor-large/",
+  "architectures": [
+    "T5EncoderModel"
+  ],
+  "d_ff": 4096,
+  "d_kv": 64,
+  "d_model": 1024,
+  "decoder_start_token_id": 0,
+  "dense_act_fn": "relu",
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+  "eos_token_id": 1,
+  "feed_forward_proj": "relu",
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+  "is_gated_act": false,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "t5",
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+  "num_heads": 16,
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+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_max_distance": 128,
+  "relative_attention_num_buckets": 32,
+  "task_specific_params": {
+    "summarization": {
+      "early_stopping": true,
+      "length_penalty": 2.0,
+      "max_length": 200,
+      "min_length": 30,
+      "no_repeat_ngram_size": 3,
+      "num_beams": 4,
+      "prefix": "summarize: "
+    },
+    "translation_en_to_de": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to German: "
+    },
+    "translation_en_to_fr": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to French: "
+    },
+    "translation_en_to_ro": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to Romanian: "
+    }
+  },
+  "torch_dtype": "float32",
+  "transformers_version": "4.20.0",
+  "use_cache": true,
+  "vocab_size": 32128
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "2.2.0",
+    "transformers": "4.7.0",
+    "pytorch": "1.9.0+cu102"
+  }
+}

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "InstructorEmbedding.instructor"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_INSTRUCTOR_Pooling",
+    "type": "InstructorEmbedding.instructor"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Dense",
+    "type": "sentence_transformers.models.Dense"
+  },
+  {
+    "idx": 3,
+    "name": "3",
+    "path": "3_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

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