Create README.md

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: final_base_results
+  results:
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_counterfactual
+      name: MTEB AmazonCounterfactualClassification (en)
+      config: en
+      split: test
+      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
+    metrics:
+    - type: accuracy
+      value: 86.2089552238806
+    - type: ap
+      value: 55.76273850794966
+    - type: f1
+      value: 81.26104211414781
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_polarity
+      name: MTEB AmazonPolarityClassification
+      config: default
+      split: test
+      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
+    metrics:
+    - type: accuracy
+      value: 88.35995000000001
+    - type: ap
+      value: 84.18839957309655
+    - type: f1
+      value: 88.317619250081
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_reviews_multi
+      name: MTEB AmazonReviewsClassification (en)
+      config: en
+      split: test
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+    metrics:
+    - type: accuracy
+      value: 44.64
+    - type: f1
+      value: 42.48663956478136
+  - task:
+      type: Retrieval
+    dataset:
+      type: arguana
+      name: MTEB ArguAna
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 27.383000000000003
+    - type: map_at_10
+      value: 43.024
+    - type: map_at_100
+      value: 44.023
+    - type: map_at_1000
+      value: 44.025999999999996
+    - type: map_at_3
+      value: 37.684
+    - type: map_at_5
+      value: 40.884
+    - type: mrr_at_1
+      value: 28.094
+    - type: mrr_at_10
+      value: 43.315
+    - type: mrr_at_100
+      value: 44.313
+    - type: mrr_at_1000
+      value: 44.317
+    - type: mrr_at_3
+      value: 37.862
+    - type: mrr_at_5
+      value: 41.155
+    - type: ndcg_at_1
+      value: 27.383000000000003
+    - type: ndcg_at_10
+      value: 52.032000000000004
+    - type: ndcg_at_100
+      value: 56.19499999999999
+    - type: ndcg_at_1000
+      value: 56.272
+    - type: ndcg_at_3
+      value: 41.166000000000004
+    - type: ndcg_at_5
+      value: 46.92
+    - type: precision_at_1
+      value: 27.383000000000003
+    - type: precision_at_10
+      value: 8.087
+    - type: precision_at_100
+      value: 0.989
+    - type: precision_at_1000
+      value: 0.099
+    - type: precision_at_3
+      value: 17.093
+    - type: precision_at_5
+      value: 13.044
+    - type: recall_at_1
+      value: 27.383000000000003
+    - type: recall_at_10
+      value: 80.868
+    - type: recall_at_100
+      value: 98.86200000000001
+    - type: recall_at_1000
+      value: 99.431
+    - type: recall_at_3
+      value: 51.28
+    - type: recall_at_5
+      value: 65.22
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-p2p
+      name: MTEB ArxivClusteringP2P
+      config: default
+      split: test
+      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
+    metrics:
+    - type: v_measure
+      value: 39.68441054431849
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-s2s
+      name: MTEB ArxivClusteringS2S
+      config: default
+      split: test
+      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
+    metrics:
+    - type: v_measure
+      value: 29.188539728343844
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/askubuntudupquestions-reranking
+      name: MTEB AskUbuntuDupQuestions
+      config: default
+      split: test
+      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
+    metrics:
+    - type: map
+      value: 63.173362687519784
+    - type: mrr
+      value: 76.18860748362133
+  - task:
+      type: STS
+    dataset:
+      type: mteb/biosses-sts
+      name: MTEB BIOSSES
+      config: default
+      split: test
+      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
+    metrics:
+    - type: cos_sim_spearman
+      value: 82.30789953771232
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/banking77
+      name: MTEB Banking77Classification
+      config: default
+      split: test
+      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
+    metrics:
+    - type: accuracy
+      value: 77.03571428571428
+    - type: f1
+      value: 75.87384305045917
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-p2p
+      name: MTEB BiorxivClusteringP2P
+      config: default
+      split: test
+      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
+    metrics:
+    - type: v_measure
+      value: 32.98041170516364
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-s2s
+      name: MTEB BiorxivClusteringS2S
+      config: default
+      split: test
+      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
+    metrics:
+    - type: v_measure
+      value: 25.71652988451154
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackAndroidRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 33.739999999999995
+    - type: map_at_10
+      value: 46.197
+    - type: map_at_100
+      value: 47.814
+    - type: map_at_1000
+      value: 47.934
+    - type: map_at_3
+      value: 43.091
+    - type: map_at_5
+      value: 44.81
+    - type: mrr_at_1
+      value: 41.059
+    - type: mrr_at_10
+      value: 52.292
+    - type: mrr_at_100
+      value: 52.978
+    - type: mrr_at_1000
+      value: 53.015
+    - type: mrr_at_3
+      value: 49.976
+    - type: mrr_at_5
+      value: 51.449999999999996
+    - type: ndcg_at_1
+      value: 41.059
+    - type: ndcg_at_10
+      value: 52.608
+    - type: ndcg_at_100
+      value: 57.965
+    - type: ndcg_at_1000
+      value: 59.775999999999996
+    - type: ndcg_at_3
+      value: 48.473
+    - type: ndcg_at_5
+      value: 50.407999999999994
+    - type: precision_at_1
+      value: 41.059
+    - type: precision_at_10
+      value: 9.943
+    - type: precision_at_100
+      value: 1.6070000000000002
+    - type: precision_at_1000
+      value: 0.20500000000000002
+    - type: precision_at_3
+      value: 23.413999999999998
+    - type: precision_at_5
+      value: 16.481
+    - type: recall_at_1
+      value: 33.739999999999995
+    - type: recall_at_10
+      value: 63.888999999999996
+    - type: recall_at_100
+      value: 85.832
+    - type: recall_at_1000
+      value: 97.475
+    - type: recall_at_3
+      value: 51.953
+    - type: recall_at_5
+      value: 57.498000000000005
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackEnglishRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 31.169999999999998
+    - type: map_at_10
+      value: 41.455
+    - type: map_at_100
+      value: 42.716
+    - type: map_at_1000
+      value: 42.847
+    - type: map_at_3
+      value: 38.568999999999996
+    - type: map_at_5
+      value: 40.099000000000004
+    - type: mrr_at_1
+      value: 39.427
+    - type: mrr_at_10
+      value: 47.818
+    - type: mrr_at_100
+      value: 48.519
+    - type: mrr_at_1000
+      value: 48.558
+    - type: mrr_at_3
+      value: 45.86
+    - type: mrr_at_5
+      value: 46.936
+    - type: ndcg_at_1
+      value: 39.427
+    - type: ndcg_at_10
+      value: 47.181
+    - type: ndcg_at_100
+      value: 51.737
+    - type: ndcg_at_1000
+      value: 53.74
+    - type: ndcg_at_3
+      value: 43.261
+    - type: ndcg_at_5
+      value: 44.891
+    - type: precision_at_1
+      value: 39.427
+    - type: precision_at_10
+      value: 8.847
+    - type: precision_at_100
+      value: 1.425
+    - type: precision_at_1000
+      value: 0.189
+    - type: precision_at_3
+      value: 20.785999999999998
+    - type: precision_at_5
+      value: 14.560999999999998
+    - type: recall_at_1
+      value: 31.169999999999998
+    - type: recall_at_10
+      value: 56.971000000000004
+    - type: recall_at_100
+      value: 76.31400000000001
+    - type: recall_at_1000
+      value: 88.93900000000001
+    - type: recall_at_3
+      value: 45.208
+    - type: recall_at_5
+      value: 49.923
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGamingRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 39.682
+    - type: map_at_10
+      value: 52.766000000000005
+    - type: map_at_100
+      value: 53.84100000000001
+    - type: map_at_1000
+      value: 53.898
+    - type: map_at_3
+      value: 49.291000000000004
+    - type: map_at_5
+      value: 51.365
+    - type: mrr_at_1
+      value: 45.266
+    - type: mrr_at_10
+      value: 56.093
+    - type: mrr_at_100
+      value: 56.763
+    - type: mrr_at_1000
+      value: 56.793000000000006
+    - type: mrr_at_3
+      value: 53.668000000000006
+    - type: mrr_at_5
+      value: 55.1
+    - type: ndcg_at_1
+      value: 45.266
+    - type: ndcg_at_10
+      value: 58.836
+    - type: ndcg_at_100
+      value: 62.863
+    - type: ndcg_at_1000
+      value: 63.912
+    - type: ndcg_at_3
+      value: 53.19199999999999
+    - type: ndcg_at_5
+      value: 56.125
+    - type: precision_at_1
+      value: 45.266
+    - type: precision_at_10
+      value: 9.492
+    - type: precision_at_100
+      value: 1.236
+    - type: precision_at_1000
+      value: 0.13699999999999998
+    - type: precision_at_3
+      value: 23.762
+    - type: precision_at_5
+      value: 16.414
+    - type: recall_at_1
+      value: 39.682
+    - type: recall_at_10
+      value: 73.233
+    - type: recall_at_100
+      value: 90.335
+    - type: recall_at_1000
+      value: 97.452
+    - type: recall_at_3
+      value: 58.562000000000005
+    - type: recall_at_5
+      value: 65.569
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGisRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 26.743
+    - type: map_at_10
+      value: 34.016000000000005
+    - type: map_at_100
+      value: 35.028999999999996
+    - type: map_at_1000
+      value: 35.113
+    - type: map_at_3
+      value: 31.763
+    - type: map_at_5
+      value: 33.013999999999996
+    - type: mrr_at_1
+      value: 28.927000000000003
+    - type: mrr_at_10
+      value: 36.32
+    - type: mrr_at_100
+      value: 37.221
+    - type: mrr_at_1000
+      value: 37.281
+    - type: mrr_at_3
+      value: 34.105000000000004
+    - type: mrr_at_5
+      value: 35.371
+    - type: ndcg_at_1
+      value: 28.927000000000003
+    - type: ndcg_at_10
+      value: 38.474000000000004
+    - type: ndcg_at_100
+      value: 43.580000000000005
+    - type: ndcg_at_1000
+      value: 45.64
+    - type: ndcg_at_3
+      value: 34.035
+    - type: ndcg_at_5
+      value: 36.186
+    - type: precision_at_1
+      value: 28.927000000000003
+    - type: precision_at_10
+      value: 5.74
+    - type: precision_at_100
+      value: 0.8710000000000001
+    - type: precision_at_1000
+      value: 0.108
+    - type: precision_at_3
+      value: 14.124
+    - type: precision_at_5
+      value: 9.74
+    - type: recall_at_1
+      value: 26.743
+    - type: recall_at_10
+      value: 49.955
+    - type: recall_at_100
+      value: 73.904
+    - type: recall_at_1000
+      value: 89.133
+    - type: recall_at_3
+      value: 38.072
+    - type: recall_at_5
+      value: 43.266
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackMathematicaRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 16.928
+    - type: map_at_10
+      value: 23.549
+    - type: map_at_100
+      value: 24.887
+    - type: map_at_1000
+      value: 25.018
+    - type: map_at_3
+      value: 21.002000000000002
+    - type: map_at_5
+      value: 22.256
+    - type: mrr_at_1
+      value: 21.02
+    - type: mrr_at_10
+      value: 27.898
+    - type: mrr_at_100
+      value: 29.018
+    - type: mrr_at_1000
+      value: 29.099999999999998
+    - type: mrr_at_3
+      value: 25.456
+    - type: mrr_at_5
+      value: 26.625
+    - type: ndcg_at_1
+      value: 21.02
+    - type: ndcg_at_10
+      value: 28.277
+    - type: ndcg_at_100
+      value: 34.54
+    - type: ndcg_at_1000
+      value: 37.719
+    - type: ndcg_at_3
+      value: 23.707
+    - type: ndcg_at_5
+      value: 25.482
+    - type: precision_at_1
+      value: 21.02
+    - type: precision_at_10
+      value: 5.361
+    - type: precision_at_100
+      value: 0.9809999999999999
+    - type: precision_at_1000
+      value: 0.13899999999999998
+    - type: precision_at_3
+      value: 11.401
+    - type: precision_at_5
+      value: 8.209
+    - type: recall_at_1
+      value: 16.928
+    - type: recall_at_10
+      value: 38.601
+    - type: recall_at_100
+      value: 65.759
+    - type: recall_at_1000
+      value: 88.543
+    - type: recall_at_3
+      value: 25.556
+    - type: recall_at_5
+      value: 30.447000000000003
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackPhysicsRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 28.549000000000003
+    - type: map_at_10
+      value: 38.426
+    - type: map_at_100
+      value: 39.845000000000006
+    - type: map_at_1000
+      value: 39.956
+    - type: map_at_3
+      value: 35.372
+    - type: map_at_5
+      value: 37.204
+    - type: mrr_at_1
+      value: 35.034
+    - type: mrr_at_10
+      value: 44.041000000000004
+    - type: mrr_at_100
+      value: 44.95
+    - type: mrr_at_1000
+      value: 44.997
+    - type: mrr_at_3
+      value: 41.498000000000005
+    - type: mrr_at_5
+      value: 43.077
+    - type: ndcg_at_1
+      value: 35.034
+    - type: ndcg_at_10
+      value: 44.218
+    - type: ndcg_at_100
+      value: 49.958000000000006
+    - type: ndcg_at_1000
+      value: 52.019000000000005
+    - type: ndcg_at_3
+      value: 39.34
+    - type: ndcg_at_5
+      value: 41.892
+    - type: precision_at_1
+      value: 35.034
+    - type: precision_at_10
+      value: 7.911
+    - type: precision_at_100
+      value: 1.26
+    - type: precision_at_1000
+      value: 0.16
+    - type: precision_at_3
+      value: 18.511
+    - type: precision_at_5
+      value: 13.205
+    - type: recall_at_1
+      value: 28.549000000000003
+    - type: recall_at_10
+      value: 56.035999999999994
+    - type: recall_at_100
+      value: 79.701
+    - type: recall_at_1000
+      value: 93.149
+    - type: recall_at_3
+      value: 42.275
+    - type: recall_at_5
+      value: 49.097
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackProgrammersRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 29.391000000000002
+    - type: map_at_10
+      value: 39.48
+    - type: map_at_100
+      value: 40.727000000000004
+    - type: map_at_1000
+      value: 40.835
+    - type: map_at_3
+      value: 36.234
+    - type: map_at_5
+      value: 37.877
+    - type: mrr_at_1
+      value: 35.959
+    - type: mrr_at_10
+      value: 44.726
+    - type: mrr_at_100
+      value: 45.531
+    - type: mrr_at_1000
+      value: 45.582
+    - type: mrr_at_3
+      value: 42.047000000000004
+    - type: mrr_at_5
+      value: 43.611
+    - type: ndcg_at_1
+      value: 35.959
+    - type: ndcg_at_10
+      value: 45.303
+    - type: ndcg_at_100
+      value: 50.683
+    - type: ndcg_at_1000
+      value: 52.818
+    - type: ndcg_at_3
+      value: 39.987
+    - type: ndcg_at_5
+      value: 42.243
+    - type: precision_at_1
+      value: 35.959
+    - type: precision_at_10
+      value: 8.241999999999999
+    - type: precision_at_100
+      value: 1.274
+    - type: precision_at_1000
+      value: 0.163
+    - type: precision_at_3
+      value: 18.836
+    - type: precision_at_5
+      value: 13.196
+    - type: recall_at_1
+      value: 29.391000000000002
+    - type: recall_at_10
+      value: 57.364000000000004
+    - type: recall_at_100
+      value: 80.683
+    - type: recall_at_1000
+      value: 94.918
+    - type: recall_at_3
+      value: 42.263
+    - type: recall_at_5
+      value: 48.634
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 26.791749999999997
+    - type: map_at_10
+      value: 35.75541666666667
+    - type: map_at_100
+      value: 37.00791666666667
+    - type: map_at_1000
+      value: 37.12408333333333
+    - type: map_at_3
+      value: 33.02966666666667
+    - type: map_at_5
+      value: 34.56866666666667
+    - type: mrr_at_1
+      value: 31.744333333333337
+    - type: mrr_at_10
+      value: 39.9925
+    - type: mrr_at_100
+      value: 40.86458333333333
+    - type: mrr_at_1000
+      value: 40.92175000000001
+    - type: mrr_at_3
+      value: 37.68183333333334
+    - type: mrr_at_5
+      value: 39.028499999999994
+    - type: ndcg_at_1
+      value: 31.744333333333337
+    - type: ndcg_at_10
+      value: 40.95008333333334
+    - type: ndcg_at_100
+      value: 46.25966666666667
+    - type: ndcg_at_1000
+      value: 48.535333333333334
+    - type: ndcg_at_3
+      value: 36.43333333333333
+    - type: ndcg_at_5
+      value: 38.602333333333334
+    - type: precision_at_1
+      value: 31.744333333333337
+    - type: precision_at_10
+      value: 7.135166666666666
+    - type: precision_at_100
+      value: 1.1535833333333334
+    - type: precision_at_1000
+      value: 0.15391666666666665
+    - type: precision_at_3
+      value: 16.713
+    - type: precision_at_5
+      value: 11.828416666666666
+    - type: recall_at_1
+      value: 26.791749999999997
+    - type: recall_at_10
+      value: 51.98625
+    - type: recall_at_100
+      value: 75.30358333333334
+    - type: recall_at_1000
+      value: 91.05433333333333
+    - type: recall_at_3
+      value: 39.39583333333333
+    - type: recall_at_5
+      value: 45.05925
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackStatsRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 22.219
+    - type: map_at_10
+      value: 29.162
+    - type: map_at_100
+      value: 30.049999999999997
+    - type: map_at_1000
+      value: 30.144
+    - type: map_at_3
+      value: 27.204
+    - type: map_at_5
+      value: 28.351
+    - type: mrr_at_1
+      value: 25.153
+    - type: mrr_at_10
+      value: 31.814999999999998
+    - type: mrr_at_100
+      value: 32.573
+    - type: mrr_at_1000
+      value: 32.645
+    - type: mrr_at_3
+      value: 29.934
+    - type: mrr_at_5
+      value: 30.946
+    - type: ndcg_at_1
+      value: 25.153
+    - type: ndcg_at_10
+      value: 33.099000000000004
+    - type: ndcg_at_100
+      value: 37.768
+    - type: ndcg_at_1000
+      value: 40.331
+    - type: ndcg_at_3
+      value: 29.473
+    - type: ndcg_at_5
+      value: 31.206
+    - type: precision_at_1
+      value: 25.153
+    - type: precision_at_10
+      value: 5.183999999999999
+    - type: precision_at_100
+      value: 0.8170000000000001
+    - type: precision_at_1000
+      value: 0.11100000000000002
+    - type: precision_at_3
+      value: 12.831999999999999
+    - type: precision_at_5
+      value: 8.895999999999999
+    - type: recall_at_1
+      value: 22.219
+    - type: recall_at_10
+      value: 42.637
+    - type: recall_at_100
+      value: 64.704
+    - type: recall_at_1000
+      value: 83.963
+    - type: recall_at_3
+      value: 32.444
+    - type: recall_at_5
+      value: 36.802
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackTexRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 17.427999999999997
+    - type: map_at_10
+      value: 24.029
+    - type: map_at_100
+      value: 25.119999999999997
+    - type: map_at_1000
+      value: 25.257
+    - type: map_at_3
+      value: 22.016
+    - type: map_at_5
+      value: 23.143
+    - type: mrr_at_1
+      value: 21.129
+    - type: mrr_at_10
+      value: 27.750000000000004
+    - type: mrr_at_100
+      value: 28.666999999999998
+    - type: mrr_at_1000
+      value: 28.754999999999995
+    - type: mrr_at_3
+      value: 25.849
+    - type: mrr_at_5
+      value: 26.939999999999998
+    - type: ndcg_at_1
+      value: 21.129
+    - type: ndcg_at_10
+      value: 28.203
+    - type: ndcg_at_100
+      value: 33.44
+    - type: ndcg_at_1000
+      value: 36.61
+    - type: ndcg_at_3
+      value: 24.648999999999997
+    - type: ndcg_at_5
+      value: 26.316
+    - type: precision_at_1
+      value: 21.129
+    - type: precision_at_10
+      value: 5.055
+    - type: precision_at_100
+      value: 0.909
+    - type: precision_at_1000
+      value: 0.13699999999999998
+    - type: precision_at_3
+      value: 11.666
+    - type: precision_at_5
+      value: 8.3
+    - type: recall_at_1
+      value: 17.427999999999997
+    - type: recall_at_10
+      value: 36.923
+    - type: recall_at_100
+      value: 60.606
+    - type: recall_at_1000
+      value: 83.19
+    - type: recall_at_3
+      value: 26.845000000000002
+    - type: recall_at_5
+      value: 31.247000000000003
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackUnixRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 26.457000000000004
+    - type: map_at_10
+      value: 35.228
+    - type: map_at_100
+      value: 36.475
+    - type: map_at_1000
+      value: 36.585
+    - type: map_at_3
+      value: 32.444
+    - type: map_at_5
+      value: 34.046
+    - type: mrr_at_1
+      value: 30.784
+    - type: mrr_at_10
+      value: 39.133
+    - type: mrr_at_100
+      value: 40.11
+    - type: mrr_at_1000
+      value: 40.169
+    - type: mrr_at_3
+      value: 36.692
+    - type: mrr_at_5
+      value: 38.17
+    - type: ndcg_at_1
+      value: 30.784
+    - type: ndcg_at_10
+      value: 40.358
+    - type: ndcg_at_100
+      value: 46.119
+    - type: ndcg_at_1000
+      value: 48.428
+    - type: ndcg_at_3
+      value: 35.504000000000005
+    - type: ndcg_at_5
+      value: 37.864
+    - type: precision_at_1
+      value: 30.784
+    - type: precision_at_10
+      value: 6.800000000000001
+    - type: precision_at_100
+      value: 1.083
+    - type: precision_at_1000
+      value: 0.13899999999999998
+    - type: precision_at_3
+      value: 15.920000000000002
+    - type: precision_at_5
+      value: 11.437
+    - type: recall_at_1
+      value: 26.457000000000004
+    - type: recall_at_10
+      value: 51.845
+    - type: recall_at_100
+      value: 77.046
+    - type: recall_at_1000
+      value: 92.892
+    - type: recall_at_3
+      value: 38.89
+    - type: recall_at_5
+      value: 44.688
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWebmastersRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 29.378999999999998
+    - type: map_at_10
+      value: 37.373
+    - type: map_at_100
+      value: 39.107
+    - type: map_at_1000
+      value: 39.317
+    - type: map_at_3
+      value: 34.563
+    - type: map_at_5
+      value: 36.173
+    - type: mrr_at_1
+      value: 35.178
+    - type: mrr_at_10
+      value: 42.44
+    - type: mrr_at_100
+      value: 43.434
+    - type: mrr_at_1000
+      value: 43.482
+    - type: mrr_at_3
+      value: 39.987
+    - type: mrr_at_5
+      value: 41.370000000000005
+    - type: ndcg_at_1
+      value: 35.178
+    - type: ndcg_at_10
+      value: 42.82
+    - type: ndcg_at_100
+      value: 48.935
+    - type: ndcg_at_1000
+      value: 51.28
+    - type: ndcg_at_3
+      value: 38.562999999999995
+    - type: ndcg_at_5
+      value: 40.687
+    - type: precision_at_1
+      value: 35.178
+    - type: precision_at_10
+      value: 7.945
+    - type: precision_at_100
+      value: 1.524
+    - type: precision_at_1000
+      value: 0.242
+    - type: precision_at_3
+      value: 17.721
+    - type: precision_at_5
+      value: 12.925
+    - type: recall_at_1
+      value: 29.378999999999998
+    - type: recall_at_10
+      value: 52.141999999999996
+    - type: recall_at_100
+      value: 79.49000000000001
+    - type: recall_at_1000
+      value: 93.782
+    - type: recall_at_3
+      value: 39.579
+    - type: recall_at_5
+      value: 45.462
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWordpressRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 19.814999999999998
+    - type: map_at_10
+      value: 27.383999999999997
+    - type: map_at_100
+      value: 28.483999999999998
+    - type: map_at_1000
+      value: 28.585
+    - type: map_at_3
+      value: 24.807000000000002
+    - type: map_at_5
+      value: 26.485999999999997
+    - type: mrr_at_1
+      value: 21.996
+    - type: mrr_at_10
+      value: 29.584
+    - type: mrr_at_100
+      value: 30.611
+    - type: mrr_at_1000
+      value: 30.684
+    - type: mrr_at_3
+      value: 27.11
+    - type: mrr_at_5
+      value: 28.746
+    - type: ndcg_at_1
+      value: 21.996
+    - type: ndcg_at_10
+      value: 32.024
+    - type: ndcg_at_100
+      value: 37.528
+    - type: ndcg_at_1000
+      value: 40.150999999999996
+    - type: ndcg_at_3
+      value: 27.016000000000002
+    - type: ndcg_at_5
+      value: 29.927999999999997
+    - type: precision_at_1
+      value: 21.996
+    - type: precision_at_10
+      value: 5.102
+    - type: precision_at_100
+      value: 0.856
+    - type: precision_at_1000
+      value: 0.117
+    - type: precision_at_3
+      value: 11.583
+    - type: precision_at_5
+      value: 8.577
+    - type: recall_at_1
+      value: 19.814999999999998
+    - type: recall_at_10
+      value: 44.239
+    - type: recall_at_100
+      value: 69.269
+    - type: recall_at_1000
+      value: 89.216
+    - type: recall_at_3
+      value: 31.102999999999998
+    - type: recall_at_5
+      value: 38.078
+  - task:
+      type: Retrieval
+    dataset:
+      type: climate-fever
+      name: MTEB ClimateFEVER
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 11.349
+    - type: map_at_10
+      value: 19.436
+    - type: map_at_100
+      value: 21.282999999999998
+    - type: map_at_1000
+      value: 21.479
+    - type: map_at_3
+      value: 15.841
+    - type: map_at_5
+      value: 17.558
+    - type: mrr_at_1
+      value: 25.863000000000003
+    - type: mrr_at_10
+      value: 37.218
+    - type: mrr_at_100
+      value: 38.198
+    - type: mrr_at_1000
+      value: 38.236
+    - type: mrr_at_3
+      value: 33.409
+    - type: mrr_at_5
+      value: 35.602000000000004
+    - type: ndcg_at_1
+      value: 25.863000000000003
+    - type: ndcg_at_10
+      value: 27.953
+    - type: ndcg_at_100
+      value: 35.327
+    - type: ndcg_at_1000
+      value: 38.708999999999996
+    - type: ndcg_at_3
+      value: 21.985
+    - type: ndcg_at_5
+      value: 23.957
+    - type: precision_at_1
+      value: 25.863000000000003
+    - type: precision_at_10
+      value: 8.99
+    - type: precision_at_100
+      value: 1.6889999999999998
+    - type: precision_at_1000
+      value: 0.232
+    - type: precision_at_3
+      value: 16.308
+    - type: precision_at_5
+      value: 12.912
+    - type: recall_at_1
+      value: 11.349
+    - type: recall_at_10
+      value: 34.581
+    - type: recall_at_100
+      value: 60.178
+    - type: recall_at_1000
+      value: 78.88199999999999
+    - type: recall_at_3
+      value: 20.041999999999998
+    - type: recall_at_5
+      value: 25.458
+  - task:
+      type: Retrieval
+    dataset:
+      type: dbpedia-entity
+      name: MTEB DBPedia
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 7.893
+    - type: map_at_10
+      value: 15.457
+    - type: map_at_100
+      value: 20.905
+    - type: map_at_1000
+      value: 22.116
+    - type: map_at_3
+      value: 11.593
+    - type: map_at_5
+      value: 13.134
+    - type: mrr_at_1
+      value: 57.49999999999999
+    - type: mrr_at_10
+      value: 65.467
+    - type: mrr_at_100
+      value: 66.022
+    - type: mrr_at_1000
+      value: 66.039
+    - type: mrr_at_3
+      value: 63.458000000000006
+    - type: mrr_at_5
+      value: 64.546
+    - type: ndcg_at_1
+      value: 45.875
+    - type: ndcg_at_10
+      value: 33.344
+    - type: ndcg_at_100
+      value: 36.849
+    - type: ndcg_at_1000
+      value: 44.03
+    - type: ndcg_at_3
+      value: 37.504
+    - type: ndcg_at_5
+      value: 34.892
+    - type: precision_at_1
+      value: 57.49999999999999
+    - type: precision_at_10
+      value: 25.95
+    - type: precision_at_100
+      value: 7.89
+    - type: precision_at_1000
+      value: 1.669
+    - type: precision_at_3
+      value: 40.333000000000006
+    - type: precision_at_5
+      value: 33.050000000000004
+    - type: recall_at_1
+      value: 7.893
+    - type: recall_at_10
+      value: 20.724999999999998
+    - type: recall_at_100
+      value: 42.516
+    - type: recall_at_1000
+      value: 65.822
+    - type: recall_at_3
+      value: 12.615000000000002
+    - type: recall_at_5
+      value: 15.482000000000001
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/emotion
+      name: MTEB EmotionClassification
+      config: default
+      split: test
+      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
+    metrics:
+    - type: accuracy
+      value: 51.760000000000005
+    - type: f1
+      value: 45.51690565701713
+  - task:
+      type: Retrieval
+    dataset:
+      type: fever
+      name: MTEB FEVER
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 53.882
+    - type: map_at_10
+      value: 65.902
+    - type: map_at_100
+      value: 66.33
+    - type: map_at_1000
+      value: 66.348
+    - type: map_at_3
+      value: 63.75999999999999
+    - type: map_at_5
+      value: 65.181
+    - type: mrr_at_1
+      value: 58.041
+    - type: mrr_at_10
+      value: 70.133
+    - type: mrr_at_100
+      value: 70.463
+    - type: mrr_at_1000
+      value: 70.47
+    - type: mrr_at_3
+      value: 68.164
+    - type: mrr_at_5
+      value: 69.465
+    - type: ndcg_at_1
+      value: 58.041
+    - type: ndcg_at_10
+      value: 71.84700000000001
+    - type: ndcg_at_100
+      value: 73.699
+    - type: ndcg_at_1000
+      value: 74.06700000000001
+    - type: ndcg_at_3
+      value: 67.855
+    - type: ndcg_at_5
+      value: 70.203
+    - type: precision_at_1
+      value: 58.041
+    - type: precision_at_10
+      value: 9.427000000000001
+    - type: precision_at_100
+      value: 1.049
+    - type: precision_at_1000
+      value: 0.11
+    - type: precision_at_3
+      value: 27.278000000000002
+    - type: precision_at_5
+      value: 17.693
+    - type: recall_at_1
+      value: 53.882
+    - type: recall_at_10
+      value: 85.99
+    - type: recall_at_100
+      value: 94.09100000000001
+    - type: recall_at_1000
+      value: 96.612
+    - type: recall_at_3
+      value: 75.25
+    - type: recall_at_5
+      value: 80.997
+  - task:
+      type: Retrieval
+    dataset:
+      type: fiqa
+      name: MTEB FiQA2018
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 19.165
+    - type: map_at_10
+      value: 31.845000000000002
+    - type: map_at_100
+      value: 33.678999999999995
+    - type: map_at_1000
+      value: 33.878
+    - type: map_at_3
+      value: 27.881
+    - type: map_at_5
+      value: 30.049999999999997
+    - type: mrr_at_1
+      value: 38.272
+    - type: mrr_at_10
+      value: 47.04
+    - type: mrr_at_100
+      value: 47.923
+    - type: mrr_at_1000
+      value: 47.973
+    - type: mrr_at_3
+      value: 44.985
+    - type: mrr_at_5
+      value: 46.150000000000006
+    - type: ndcg_at_1
+      value: 38.272
+    - type: ndcg_at_10
+      value: 39.177
+    - type: ndcg_at_100
+      value: 45.995000000000005
+    - type: ndcg_at_1000
+      value: 49.312
+    - type: ndcg_at_3
+      value: 36.135
+    - type: ndcg_at_5
+      value: 36.936
+    - type: precision_at_1
+      value: 38.272
+    - type: precision_at_10
+      value: 10.926
+    - type: precision_at_100
+      value: 1.809
+    - type: precision_at_1000
+      value: 0.23700000000000002
+    - type: precision_at_3
+      value: 24.331
+    - type: precision_at_5
+      value: 17.747
+    - type: recall_at_1
+      value: 19.165
+    - type: recall_at_10
+      value: 45.103
+    - type: recall_at_100
+      value: 70.295
+    - type: recall_at_1000
+      value: 90.592
+    - type: recall_at_3
+      value: 32.832
+    - type: recall_at_5
+      value: 37.905
+  - task:
+      type: Retrieval
+    dataset:
+      type: hotpotqa
+      name: MTEB HotpotQA
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 32.397
+    - type: map_at_10
+      value: 44.83
+    - type: map_at_100
+      value: 45.716
+    - type: map_at_1000
+      value: 45.797
+    - type: map_at_3
+      value: 41.955999999999996
+    - type: map_at_5
+      value: 43.736999999999995
+    - type: mrr_at_1
+      value: 64.794
+    - type: mrr_at_10
+      value: 71.866
+    - type: mrr_at_100
+      value: 72.22
+    - type: mrr_at_1000
+      value: 72.238
+    - type: mrr_at_3
+      value: 70.416
+    - type: mrr_at_5
+      value: 71.304
+    - type: ndcg_at_1
+      value: 64.794
+    - type: ndcg_at_10
+      value: 54.186
+    - type: ndcg_at_100
+      value: 57.623000000000005
+    - type: ndcg_at_1000
+      value: 59.302
+    - type: ndcg_at_3
+      value: 49.703
+    - type: ndcg_at_5
+      value: 52.154999999999994
+    - type: precision_at_1
+      value: 64.794
+    - type: precision_at_10
+      value: 11.219
+    - type: precision_at_100
+      value: 1.394
+    - type: precision_at_1000
+      value: 0.16199999999999998
+    - type: precision_at_3
+      value: 30.767
+    - type: precision_at_5
+      value: 20.397000000000002
+    - type: recall_at_1
+      value: 32.397
+    - type: recall_at_10
+      value: 56.096999999999994
+    - type: recall_at_100
+      value: 69.696
+    - type: recall_at_1000
+      value: 80.88499999999999
+    - type: recall_at_3
+      value: 46.150999999999996
+    - type: recall_at_5
+      value: 50.993
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/imdb
+      name: MTEB ImdbClassification
+      config: default
+      split: test
+      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
+    metrics:
+    - type: accuracy
+      value: 81.1744
+    - type: ap
+      value: 75.44973697032414
+    - type: f1
+      value: 81.09901117955782
+  - task:
+      type: Retrieval
+    dataset:
+      type: msmarco
+      name: MTEB MSMARCO
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 19.519000000000002
+    - type: map_at_10
+      value: 31.025000000000002
+    - type: map_at_100
+      value: 32.275999999999996
+    - type: map_at_1000
+      value: 32.329
+    - type: map_at_3
+      value: 27.132
+    - type: map_at_5
+      value: 29.415999999999997
+    - type: mrr_at_1
+      value: 20.115
+    - type: mrr_at_10
+      value: 31.569000000000003
+    - type: mrr_at_100
+      value: 32.768
+    - type: mrr_at_1000
+      value: 32.816
+    - type: mrr_at_3
+      value: 27.748
+    - type: mrr_at_5
+      value: 29.956
+    - type: ndcg_at_1
+      value: 20.115
+    - type: ndcg_at_10
+      value: 37.756
+    - type: ndcg_at_100
+      value: 43.858000000000004
+    - type: ndcg_at_1000
+      value: 45.199
+    - type: ndcg_at_3
+      value: 29.818
+    - type: ndcg_at_5
+      value: 33.875
+    - type: precision_at_1
+      value: 20.115
+    - type: precision_at_10
+      value: 6.122
+    - type: precision_at_100
+      value: 0.919
+    - type: precision_at_1000
+      value: 0.10300000000000001
+    - type: precision_at_3
+      value: 12.794
+    - type: precision_at_5
+      value: 9.731
+    - type: recall_at_1
+      value: 19.519000000000002
+    - type: recall_at_10
+      value: 58.62500000000001
+    - type: recall_at_100
+      value: 86.99
+    - type: recall_at_1000
+      value: 97.268
+    - type: recall_at_3
+      value: 37.002
+    - type: recall_at_5
+      value: 46.778
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_domain
+      name: MTEB MTOPDomainClassification (en)
+      config: en
+      split: test
+      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
+    metrics:
+    - type: accuracy
+      value: 93.71865025079799
+    - type: f1
+      value: 93.38906173610519
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_intent
+      name: MTEB MTOPIntentClassification (en)
+      config: en
+      split: test
+      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
+    metrics:
+    - type: accuracy
+      value: 70.2576379388965
+    - type: f1
+      value: 49.20405830249464
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_intent
+      name: MTEB MassiveIntentClassification (en)
+      config: en
+      split: test
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+    metrics:
+    - type: accuracy
+      value: 67.48486886348351
+    - type: f1
+      value: 64.92199176095157
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (en)
+      config: en
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 72.59246805648958
+    - type: f1
+      value: 72.1222026389164
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-p2p
+      name: MTEB MedrxivClusteringP2P
+      config: default
+      split: test
+      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
+    metrics:
+    - type: v_measure
+      value: 30.887642595096825
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-s2s
+      name: MTEB MedrxivClusteringS2S
+      config: default
+      split: test
+      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
+    metrics:
+    - type: v_measure
+      value: 28.3764418784054
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/mind_small
+      name: MTEB MindSmallReranking
+      config: default
+      split: test
+      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
+    metrics:
+    - type: map
+      value: 31.81544126336991
+    - type: mrr
+      value: 32.82666576268031
+  - task:
+      type: Retrieval
+    dataset:
+      type: nfcorpus
+      name: MTEB NFCorpus
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 5.185
+    - type: map_at_10
+      value: 11.158
+    - type: map_at_100
+      value: 14.041
+    - type: map_at_1000
+      value: 15.360999999999999
+    - type: map_at_3
+      value: 8.417
+    - type: map_at_5
+      value: 9.378
+    - type: mrr_at_1
+      value: 44.582
+    - type: mrr_at_10
+      value: 53.083999999999996
+    - type: mrr_at_100
+      value: 53.787
+    - type: mrr_at_1000
+      value: 53.824000000000005
+    - type: mrr_at_3
+      value: 51.187000000000005
+    - type: mrr_at_5
+      value: 52.379
+    - type: ndcg_at_1
+      value: 42.57
+    - type: ndcg_at_10
+      value: 31.593
+    - type: ndcg_at_100
+      value: 29.093999999999998
+    - type: ndcg_at_1000
+      value: 37.909
+    - type: ndcg_at_3
+      value: 37.083
+    - type: ndcg_at_5
+      value: 34.397
+    - type: precision_at_1
+      value: 43.963
+    - type: precision_at_10
+      value: 23.498
+    - type: precision_at_100
+      value: 7.6160000000000005
+    - type: precision_at_1000
+      value: 2.032
+    - type: precision_at_3
+      value: 34.572
+    - type: precision_at_5
+      value: 29.412
+    - type: recall_at_1
+      value: 5.185
+    - type: recall_at_10
+      value: 15.234
+    - type: recall_at_100
+      value: 29.49
+    - type: recall_at_1000
+      value: 62.273999999999994
+    - type: recall_at_3
+      value: 9.55
+    - type: recall_at_5
+      value: 11.103
+  - task:
+      type: Retrieval
+    dataset:
+      type: nq
+      name: MTEB NQ
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 23.803
+    - type: map_at_10
+      value: 38.183
+    - type: map_at_100
+      value: 39.421
+    - type: map_at_1000
+      value: 39.464
+    - type: map_at_3
+      value: 33.835
+    - type: map_at_5
+      value: 36.327
+    - type: mrr_at_1
+      value: 26.68
+    - type: mrr_at_10
+      value: 40.439
+    - type: mrr_at_100
+      value: 41.415
+    - type: mrr_at_1000
+      value: 41.443999999999996
+    - type: mrr_at_3
+      value: 36.612
+    - type: mrr_at_5
+      value: 38.877
+    - type: ndcg_at_1
+      value: 26.68
+    - type: ndcg_at_10
+      value: 45.882
+    - type: ndcg_at_100
+      value: 51.227999999999994
+    - type: ndcg_at_1000
+      value: 52.207
+    - type: ndcg_at_3
+      value: 37.511
+    - type: ndcg_at_5
+      value: 41.749
+    - type: precision_at_1
+      value: 26.68
+    - type: precision_at_10
+      value: 7.9750000000000005
+    - type: precision_at_100
+      value: 1.0959999999999999
+    - type: precision_at_1000
+      value: 0.11900000000000001
+    - type: precision_at_3
+      value: 17.449
+    - type: precision_at_5
+      value: 12.897
+    - type: recall_at_1
+      value: 23.803
+    - type: recall_at_10
+      value: 67.152
+    - type: recall_at_100
+      value: 90.522
+    - type: recall_at_1000
+      value: 97.743
+    - type: recall_at_3
+      value: 45.338
+    - type: recall_at_5
+      value: 55.106
+  - task:
+      type: Retrieval
+    dataset:
+      type: quora
+      name: MTEB QuoraRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 70.473
+    - type: map_at_10
+      value: 84.452
+    - type: map_at_100
+      value: 85.101
+    - type: map_at_1000
+      value: 85.115
+    - type: map_at_3
+      value: 81.435
+    - type: map_at_5
+      value: 83.338
+    - type: mrr_at_1
+      value: 81.19
+    - type: mrr_at_10
+      value: 87.324
+    - type: mrr_at_100
+      value: 87.434
+    - type: mrr_at_1000
+      value: 87.435
+    - type: mrr_at_3
+      value: 86.31
+    - type: mrr_at_5
+      value: 87.002
+    - type: ndcg_at_1
+      value: 81.21000000000001
+    - type: ndcg_at_10
+      value: 88.19
+    - type: ndcg_at_100
+      value: 89.44
+    - type: ndcg_at_1000
+      value: 89.526
+    - type: ndcg_at_3
+      value: 85.237
+    - type: ndcg_at_5
+      value: 86.892
+    - type: precision_at_1
+      value: 81.21000000000001
+    - type: precision_at_10
+      value: 13.417000000000002
+    - type: precision_at_100
+      value: 1.537
+    - type: precision_at_1000
+      value: 0.157
+    - type: precision_at_3
+      value: 37.31
+    - type: precision_at_5
+      value: 24.59
+    - type: recall_at_1
+      value: 70.473
+    - type: recall_at_10
+      value: 95.367
+    - type: recall_at_100
+      value: 99.616
+    - type: recall_at_1000
+      value: 99.996
+    - type: recall_at_3
+      value: 86.936
+    - type: recall_at_5
+      value: 91.557
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering
+      name: MTEB RedditClustering
+      config: default
+      split: test
+      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
+    metrics:
+    - type: v_measure
+      value: 59.25776525253911
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering-p2p
+      name: MTEB RedditClusteringP2P
+      config: default
+      split: test
+      revision: 282350215ef01743dc01b456c7f5241fa8937f16
+    metrics:
+    - type: v_measure
+      value: 63.22135271663078
+  - task:
+      type: Retrieval
+    dataset:
+      type: scidocs
+      name: MTEB SCIDOCS
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 4.003
+    - type: map_at_10
+      value: 10.062999999999999
+    - type: map_at_100
+      value: 11.854000000000001
+    - type: map_at_1000
+      value: 12.145999999999999
+    - type: map_at_3
+      value: 7.242
+    - type: map_at_5
+      value: 8.652999999999999
+    - type: mrr_at_1
+      value: 19.7
+    - type: mrr_at_10
+      value: 29.721999999999998
+    - type: mrr_at_100
+      value: 30.867
+    - type: mrr_at_1000
+      value: 30.944
+    - type: mrr_at_3
+      value: 26.683
+    - type: mrr_at_5
+      value: 28.498
+    - type: ndcg_at_1
+      value: 19.7
+    - type: ndcg_at_10
+      value: 17.095
+    - type: ndcg_at_100
+      value: 24.375
+    - type: ndcg_at_1000
+      value: 29.831000000000003
+    - type: ndcg_at_3
+      value: 16.305
+    - type: ndcg_at_5
+      value: 14.291
+    - type: precision_at_1
+      value: 19.7
+    - type: precision_at_10
+      value: 8.799999999999999
+    - type: precision_at_100
+      value: 1.9349999999999998
+    - type: precision_at_1000
+      value: 0.32399999999999995
+    - type: precision_at_3
+      value: 15.2
+    - type: precision_at_5
+      value: 12.540000000000001
+    - type: recall_at_1
+      value: 4.003
+    - type: recall_at_10
+      value: 17.877000000000002
+    - type: recall_at_100
+      value: 39.217
+    - type: recall_at_1000
+      value: 65.862
+    - type: recall_at_3
+      value: 9.242
+    - type: recall_at_5
+      value: 12.715000000000002
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sickr-sts
+      name: MTEB SICK-R
+      config: default
+      split: test
+      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
+    metrics:
+    - type: cos_sim_spearman
+      value: 80.25888668589654
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts12-sts
+      name: MTEB STS12
+      config: default
+      split: test
+      revision: a0d554a64d88156834ff5ae9920b964011b16384
+    metrics:
+    - type: cos_sim_spearman
+      value: 77.02037527837669
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts13-sts
+      name: MTEB STS13
+      config: default
+      split: test
+      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
+    metrics:
+    - type: cos_sim_spearman
+      value: 86.58432681008449
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts14-sts
+      name: MTEB STS14
+      config: default
+      split: test
+      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
+    metrics:
+    - type: cos_sim_spearman
+      value: 81.31697756099051
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts15-sts
+      name: MTEB STS15
+      config: default
+      split: test
+      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
+    metrics:
+    - type: cos_sim_spearman
+      value: 88.18867599667057
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts16-sts
+      name: MTEB STS16
+      config: default
+      split: test
+      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
+    metrics:
+    - type: cos_sim_spearman
+      value: 84.87853941747623
+  - 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: 89.46479925383916
+  - 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: 66.45272113649146
+  - task:
+      type: STS
+    dataset:
+      type: mteb/stsbenchmark-sts
+      name: MTEB STSBenchmark
+      config: default
+      split: test
+      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
+    metrics:
+    - type: cos_sim_spearman
+      value: 86.43357313527851
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/scidocs-reranking
+      name: MTEB SciDocsRR
+      config: default
+      split: test
+      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
+    metrics:
+    - type: map
+      value: 78.82761687254882
+    - type: mrr
+      value: 93.46223674655047
+  - task:
+      type: Retrieval
+    dataset:
+      type: scifact
+      name: MTEB SciFact
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 44.583
+    - type: map_at_10
+      value: 52.978
+    - type: map_at_100
+      value: 53.803
+    - type: map_at_1000
+      value: 53.839999999999996
+    - type: map_at_3
+      value: 50.03300000000001
+    - type: map_at_5
+      value: 51.939
+    - type: mrr_at_1
+      value: 47.0
+    - type: mrr_at_10
+      value: 54.730000000000004
+    - type: mrr_at_100
+      value: 55.31399999999999
+    - type: mrr_at_1000
+      value: 55.346
+    - type: mrr_at_3
+      value: 52.0
+    - type: mrr_at_5
+      value: 53.783
+    - type: ndcg_at_1
+      value: 47.0
+    - type: ndcg_at_10
+      value: 57.82899999999999
+    - type: ndcg_at_100
+      value: 61.49400000000001
+    - type: ndcg_at_1000
+      value: 62.676
+    - type: ndcg_at_3
+      value: 52.373000000000005
+    - type: ndcg_at_5
+      value: 55.481
+    - type: precision_at_1
+      value: 47.0
+    - type: precision_at_10
+      value: 7.867
+    - type: precision_at_100
+      value: 0.997
+    - type: precision_at_1000
+      value: 0.11
+    - type: precision_at_3
+      value: 20.556
+    - type: precision_at_5
+      value: 14.066999999999998
+    - type: recall_at_1
+      value: 44.583
+    - type: recall_at_10
+      value: 71.172
+    - type: recall_at_100
+      value: 87.7
+    - type: recall_at_1000
+      value: 97.333
+    - type: recall_at_3
+      value: 56.511
+    - type: recall_at_5
+      value: 64.206
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/sprintduplicatequestions-pairclassification
+      name: MTEB SprintDuplicateQuestions
+      config: default
+      split: test
+      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
+    metrics:
+    - type: cos_sim_accuracy
+      value: 99.66237623762376
+    - type: cos_sim_ap
+      value: 90.35465126226322
+    - type: cos_sim_f1
+      value: 82.44575936883628
+    - type: cos_sim_precision
+      value: 81.32295719844358
+    - type: cos_sim_recall
+      value: 83.6
+    - type: dot_accuracy
+      value: 99.66237623762376
+    - type: dot_ap
+      value: 90.35464287920453
+    - type: dot_f1
+      value: 82.44575936883628
+    - type: dot_precision
+      value: 81.32295719844358
+    - type: dot_recall
+      value: 83.6
+    - type: euclidean_accuracy
+      value: 99.66237623762376
+    - type: euclidean_ap
+      value: 90.3546512622632
+    - type: euclidean_f1
+      value: 82.44575936883628
+    - type: euclidean_precision
+      value: 81.32295719844358
+    - type: euclidean_recall
+      value: 83.6
+    - type: manhattan_accuracy
+      value: 99.65940594059406
+    - type: manhattan_ap
+      value: 90.29220174849843
+    - type: manhattan_f1
+      value: 82.4987605354487
+    - type: manhattan_precision
+      value: 81.80924287118977
+    - type: manhattan_recall
+      value: 83.2
+    - type: max_accuracy
+      value: 99.66237623762376
+    - type: max_ap
+      value: 90.35465126226322
+    - type: max_f1
+      value: 82.4987605354487
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering
+      name: MTEB StackExchangeClustering
+      config: default
+      split: test
+      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
+    metrics:
+    - type: v_measure
+      value: 65.0394225901397
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering-p2p
+      name: MTEB StackExchangeClusteringP2P
+      config: default
+      split: test
+      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
+    metrics:
+    - type: v_measure
+      value: 35.27954189859326
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/stackoverflowdupquestions-reranking
+      name: MTEB StackOverflowDupQuestions
+      config: default
+      split: test
+      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
+    metrics:
+    - type: map
+      value: 50.99055979974896
+    - type: mrr
+      value: 51.82745257193787
+  - task:
+      type: Summarization
+    dataset:
+      type: mteb/summeval
+      name: MTEB SummEval
+      config: default
+      split: test
+      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
+    metrics:
+    - type: cos_sim_pearson
+      value: 30.21655465344237
+    - type: cos_sim_spearman
+      value: 29.853205339630172
+    - type: dot_pearson
+      value: 30.216540628083564
+    - type: dot_spearman
+      value: 29.868978894753027
+  - task:
+      type: Retrieval
+    dataset:
+      type: trec-covid
+      name: MTEB TRECCOVID
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 0.2
+    - type: map_at_10
+      value: 1.398
+    - type: map_at_100
+      value: 7.406
+    - type: map_at_1000
+      value: 18.401
+    - type: map_at_3
+      value: 0.479
+    - type: map_at_5
+      value: 0.772
+    - type: mrr_at_1
+      value: 70.0
+    - type: mrr_at_10
+      value: 79.25999999999999
+    - type: mrr_at_100
+      value: 79.25999999999999
+    - type: mrr_at_1000
+      value: 79.25999999999999
+    - type: mrr_at_3
+      value: 77.333
+    - type: mrr_at_5
+      value: 78.133
+    - type: ndcg_at_1
+      value: 63.0
+    - type: ndcg_at_10
+      value: 58.548
+    - type: ndcg_at_100
+      value: 45.216
+    - type: ndcg_at_1000
+      value: 41.149
+    - type: ndcg_at_3
+      value: 60.641999999999996
+    - type: ndcg_at_5
+      value: 61.135
+    - type: precision_at_1
+      value: 70.0
+    - type: precision_at_10
+      value: 64.0
+    - type: precision_at_100
+      value: 46.92
+    - type: precision_at_1000
+      value: 18.642
+    - type: precision_at_3
+      value: 64.667
+    - type: precision_at_5
+      value: 66.4
+    - type: recall_at_1
+      value: 0.2
+    - type: recall_at_10
+      value: 1.6729999999999998
+    - type: recall_at_100
+      value: 10.856
+    - type: recall_at_1000
+      value: 38.964999999999996
+    - type: recall_at_3
+      value: 0.504
+    - type: recall_at_5
+      value: 0.852
+  - task:
+      type: Retrieval
+    dataset:
+      type: webis-touche2020
+      name: MTEB Touche2020
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 1.6629999999999998
+    - type: map_at_10
+      value: 8.601
+    - type: map_at_100
+      value: 14.354
+    - type: map_at_1000
+      value: 15.927
+    - type: map_at_3
+      value: 4.1930000000000005
+    - type: map_at_5
+      value: 5.655
+    - type: mrr_at_1
+      value: 18.367
+    - type: mrr_at_10
+      value: 34.466
+    - type: mrr_at_100
+      value: 35.235
+    - type: mrr_at_1000
+      value: 35.27
+    - type: mrr_at_3
+      value: 28.571
+    - type: mrr_at_5
+      value: 31.531
+    - type: ndcg_at_1
+      value: 14.285999999999998
+    - type: ndcg_at_10
+      value: 20.374
+    - type: ndcg_at_100
+      value: 33.532000000000004
+    - type: ndcg_at_1000
+      value: 45.561
+    - type: ndcg_at_3
+      value: 18.442
+    - type: ndcg_at_5
+      value: 18.076
+    - type: precision_at_1
+      value: 18.367
+    - type: precision_at_10
+      value: 20.204
+    - type: precision_at_100
+      value: 7.489999999999999
+    - type: precision_at_1000
+      value: 1.5630000000000002
+    - type: precision_at_3
+      value: 21.769
+    - type: precision_at_5
+      value: 20.408
+    - type: recall_at_1
+      value: 1.6629999999999998
+    - type: recall_at_10
+      value: 15.549
+    - type: recall_at_100
+      value: 47.497
+    - type: recall_at_1000
+      value: 84.524
+    - type: recall_at_3
+      value: 5.289
+    - type: recall_at_5
+      value: 8.035
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/toxic_conversations_50k
+      name: MTEB ToxicConversationsClassification
+      config: default
+      split: test
+      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
+    metrics:
+    - type: accuracy
+      value: 71.8194
+    - type: ap
+      value: 14.447702451658554
+    - type: f1
+      value: 55.13659412856185
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/tweet_sentiment_extraction
+      name: MTEB TweetSentimentExtractionClassification
+      config: default
+      split: test
+      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
+    metrics:
+    - type: accuracy
+      value: 63.310696095076416
+    - type: f1
+      value: 63.360434851097814
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/twentynewsgroups-clustering
+      name: MTEB TwentyNewsgroupsClustering
+      config: default
+      split: test
+      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
+    metrics:
+    - type: v_measure
+      value: 51.30677907335145
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twittersemeval2015-pairclassification
+      name: MTEB TwitterSemEval2015
+      config: default
+      split: test
+      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
+    metrics:
+    - type: cos_sim_accuracy
+      value: 86.12386004649221
+    - type: cos_sim_ap
+      value: 73.99096426215495
+    - type: cos_sim_f1
+      value: 68.18416968442834
+    - type: cos_sim_precision
+      value: 66.86960933536275
+    - type: cos_sim_recall
+      value: 69.55145118733509
+    - type: dot_accuracy
+      value: 86.12386004649221
+    - type: dot_ap
+      value: 73.99096813038672
+    - type: dot_f1
+      value: 68.18416968442834
+    - type: dot_precision
+      value: 66.86960933536275
+    - type: dot_recall
+      value: 69.55145118733509
+    - type: euclidean_accuracy
+      value: 86.12386004649221
+    - type: euclidean_ap
+      value: 73.99095984980165
+    - type: euclidean_f1
+      value: 68.18416968442834
+    - type: euclidean_precision
+      value: 66.86960933536275
+    - type: euclidean_recall
+      value: 69.55145118733509
+    - type: manhattan_accuracy
+      value: 86.09405734040651
+    - type: manhattan_ap
+      value: 73.96825745608601
+    - type: manhattan_f1
+      value: 68.13888179729383
+    - type: manhattan_precision
+      value: 65.99901088031652
+    - type: manhattan_recall
+      value: 70.42216358839049
+    - type: max_accuracy
+      value: 86.12386004649221
+    - type: max_ap
+      value: 73.99096813038672
+    - type: max_f1
+      value: 68.18416968442834
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twitterurlcorpus-pairclassification
+      name: MTEB TwitterURLCorpus
+      config: default
+      split: test
+      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
+    metrics:
+    - type: cos_sim_accuracy
+      value: 88.99367407924865
+    - type: cos_sim_ap
+      value: 86.19720829843081
+    - type: cos_sim_f1
+      value: 78.39889075384951
+    - type: cos_sim_precision
+      value: 74.5110278818144
+    - type: cos_sim_recall
+      value: 82.71481367416075
+    - type: dot_accuracy
+      value: 88.99367407924865
+    - type: dot_ap
+      value: 86.19718471454047
+    - type: dot_f1
+      value: 78.39889075384951
+    - type: dot_precision
+      value: 74.5110278818144
+    - type: dot_recall
+      value: 82.71481367416075
+    - type: euclidean_accuracy
+      value: 88.99367407924865
+    - type: euclidean_ap
+      value: 86.1972021422436
+    - type: euclidean_f1
+      value: 78.39889075384951
+    - type: euclidean_precision
+      value: 74.5110278818144
+    - type: euclidean_recall
+      value: 82.71481367416075
+    - type: manhattan_accuracy
+      value: 88.95680521597392
+    - type: manhattan_ap
+      value: 86.16659921351506
+    - type: manhattan_f1
+      value: 78.39125971550081
+    - type: manhattan_precision
+      value: 74.82502799552073
+    - type: manhattan_recall
+      value: 82.31444410224823
+    - type: max_accuracy
+      value: 88.99367407924865
+    - type: max_ap
+      value: 86.19720829843081
+    - type: max_f1
+      value: 78.39889075384951
+---
+
+# hkunlp/instructor-base
+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!
+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** ****************************
+
+* 01/21: We released a new [checkpoint](https://huggingface.co/hkunlp/instructor-base) 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-base) 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-base')
+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)
+```