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README.md

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+---
+tags:
+- mteb
+model-index:
+- name: embed-english-v3.0
+  results:
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_counterfactual
+      name: MTEB AmazonCounterfactualClassification (en)
+      config: en
+      split: test
+      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
+    metrics:
+    - type: accuracy
+      value: 81.29850746268656
+    - type: ap
+      value: 46.181772245676136
+    - type: f1
+      value: 75.47731234579823
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_polarity
+      name: MTEB AmazonPolarityClassification
+      config: default
+      split: test
+      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
+    metrics:
+    - type: accuracy
+      value: 95.61824999999999
+    - type: ap
+      value: 93.22525741797098
+    - type: f1
+      value: 95.61627312544859
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_reviews_multi
+      name: MTEB AmazonReviewsClassification (en)
+      config: en
+      split: test
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+    metrics:
+    - type: accuracy
+      value: 51.72
+    - type: f1
+      value: 50.529480725642465
+  - task:
+      type: Retrieval
+    dataset:
+      type: arguana
+      name: MTEB ArguAna
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_10
+      value: 61.521
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-p2p
+      name: MTEB ArxivClusteringP2P
+      config: default
+      split: test
+      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
+    metrics:
+    - type: v_measure
+      value: 49.173332266218914
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-s2s
+      name: MTEB ArxivClusteringS2S
+      config: default
+      split: test
+      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
+    metrics:
+    - type: v_measure
+      value: 42.1800504937582
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/askubuntudupquestions-reranking
+      name: MTEB AskUbuntuDupQuestions
+      config: default
+      split: test
+      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
+    metrics:
+    - type: map
+      value: 61.69942465283367
+    - type: mrr
+      value: 73.8089741898606
+  - task:
+      type: STS
+    dataset:
+      type: mteb/biosses-sts
+      name: MTEB BIOSSES
+      config: default
+      split: test
+      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
+    metrics:
+    - type: cos_sim_pearson
+      value: 85.1805709775319
+    - type: cos_sim_spearman
+      value: 83.50310749422796
+    - type: euclidean_pearson
+      value: 83.57134970408762
+    - type: euclidean_spearman
+      value: 83.50310749422796
+    - type: manhattan_pearson
+      value: 83.422472116232
+    - type: manhattan_spearman
+      value: 83.35611619312422
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/banking77
+      name: MTEB Banking77Classification
+      config: default
+      split: test
+      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
+    metrics:
+    - type: accuracy
+      value: 85.52922077922078
+    - type: f1
+      value: 85.48530911742581
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-p2p
+      name: MTEB BiorxivClusteringP2P
+      config: default
+      split: test
+      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
+    metrics:
+    - type: v_measure
+      value: 40.95750155360001
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-s2s
+      name: MTEB BiorxivClusteringS2S
+      config: default
+      split: test
+      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
+    metrics:
+    - type: v_measure
+      value: 37.25334765305169
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackAndroidRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_10
+      value: 50.037
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackEnglishRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_10
+      value: 49.089
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGamingRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_10
+      value: 60.523
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGisRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_10
+      value: 39.293
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackMathematicaRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_10
+      value: 30.414
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackPhysicsRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_10
+      value: 43.662
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackProgrammersRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_10
+      value: 43.667
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_10
+      value: 41.53158333333334
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackStatsRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_10
+      value: 35.258
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackTexRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_10
+      value: 30.866
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackUnixRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_10
+      value: 40.643
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWebmastersRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_10
+      value: 40.663
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWordpressRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_10
+      value: 34.264
+  - task:
+      type: Retrieval
+    dataset:
+      type: climate-fever
+      name: MTEB ClimateFEVER
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_10
+      value: 38.433
+  - task:
+      type: Retrieval
+    dataset:
+      type: dbpedia-entity
+      name: MTEB DBPedia
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_10
+      value: 43.36
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/emotion
+      name: MTEB EmotionClassification
+      config: default
+      split: test
+      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
+    metrics:
+    - type: accuracy
+      value: 51.574999999999996
+    - type: f1
+      value: 46.84362123583929
+  - task:
+      type: Retrieval
+    dataset:
+      type: fever
+      name: MTEB FEVER
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_10
+      value: 88.966
+  - task:
+      type: Retrieval
+    dataset:
+      type: fiqa
+      name: MTEB FiQA2018
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_10
+      value: 42.189
+  - task:
+      type: Retrieval
+    dataset:
+      type: hotpotqa
+      name: MTEB HotpotQA
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_10
+      value: 70.723
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/imdb
+      name: MTEB ImdbClassification
+      config: default
+      split: test
+      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
+    metrics:
+    - type: accuracy
+      value: 93.56920000000001
+    - type: ap
+      value: 90.56104192134326
+    - type: f1
+      value: 93.56471146876505
+  - task:
+      type: Retrieval
+    dataset:
+      type: msmarco
+      name: MTEB MSMARCO
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_10
+      value: 42.931000000000004
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_domain
+      name: MTEB MTOPDomainClassification (en)
+      config: en
+      split: test
+      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
+    metrics:
+    - type: accuracy
+      value: 94.88372093023256
+    - type: f1
+      value: 94.64417024711646
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_intent
+      name: MTEB MTOPIntentClassification (en)
+      config: en
+      split: test
+      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
+    metrics:
+    - type: accuracy
+      value: 76.52302781577748
+    - type: f1
+      value: 59.52848723786157
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_intent
+      name: MTEB MassiveIntentClassification (en)
+      config: en
+      split: test
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+    metrics:
+    - type: accuracy
+      value: 73.84330867518494
+    - type: f1
+      value: 72.18121296285702
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (en)
+      config: en
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 78.73907195696033
+    - type: f1
+      value: 78.86079300338558
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-p2p
+      name: MTEB MedrxivClusteringP2P
+      config: default
+      split: test
+      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
+    metrics:
+    - type: v_measure
+      value: 37.40673427491627
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-s2s
+      name: MTEB MedrxivClusteringS2S
+      config: default
+      split: test
+      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
+    metrics:
+    - type: v_measure
+      value: 33.38936252583581
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/mind_small
+      name: MTEB MindSmallReranking
+      config: default
+      split: test
+      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
+    metrics:
+    - type: map
+      value: 32.67317850167471
+    - type: mrr
+      value: 33.9334102169254
+  - task:
+      type: Retrieval
+    dataset:
+      type: nfcorpus
+      name: MTEB NFCorpus
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_10
+      value: 38.574000000000005
+  - task:
+      type: Retrieval
+    dataset:
+      type: nq
+      name: MTEB NQ
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_10
+      value: 61.556
+  - task:
+      type: Retrieval
+    dataset:
+      type: quora
+      name: MTEB QuoraRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_10
+      value: 88.722
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering
+      name: MTEB RedditClustering
+      config: default
+      split: test
+      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
+    metrics:
+    - type: v_measure
+      value: 58.45790556534654
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering-p2p
+      name: MTEB RedditClusteringP2P
+      config: default
+      split: test
+      revision: 282350215ef01743dc01b456c7f5241fa8937f16
+    metrics:
+    - type: v_measure
+      value: 66.35141658656822
+  - task:
+      type: Retrieval
+    dataset:
+      type: scidocs
+      name: MTEB SCIDOCS
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_10
+      value: 20.314
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sickr-sts
+      name: MTEB SICK-R
+      config: default
+      split: test
+      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
+    metrics:
+    - type: cos_sim_pearson
+      value: 85.49945063881191
+    - type: cos_sim_spearman
+      value: 81.27177640994141
+    - type: euclidean_pearson
+      value: 82.74613694646263
+    - type: euclidean_spearman
+      value: 81.2717795980493
+    - type: manhattan_pearson
+      value: 82.75268512220467
+    - type: manhattan_spearman
+      value: 81.28362006796547
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts12-sts
+      name: MTEB STS12
+      config: default
+      split: test
+      revision: a0d554a64d88156834ff5ae9920b964011b16384
+    metrics:
+    - type: cos_sim_pearson
+      value: 83.17562591888526
+    - type: cos_sim_spearman
+      value: 74.37099514810372
+    - type: euclidean_pearson
+      value: 79.97392043583372
+    - type: euclidean_spearman
+      value: 74.37103618585903
+    - type: manhattan_pearson
+      value: 80.00641585184354
+    - type: manhattan_spearman
+      value: 74.35403985608939
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts13-sts
+      name: MTEB STS13
+      config: default
+      split: test
+      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
+    metrics:
+    - type: cos_sim_pearson
+      value: 84.96937598668538
+    - type: cos_sim_spearman
+      value: 85.20181466598035
+    - type: euclidean_pearson
+      value: 84.51715977112744
+    - type: euclidean_spearman
+      value: 85.20181466598035
+    - type: manhattan_pearson
+      value: 84.45150037846719
+    - type: manhattan_spearman
+      value: 85.12338939049123
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts14-sts
+      name: MTEB STS14
+      config: default
+      split: test
+      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
+    metrics:
+    - type: cos_sim_pearson
+      value: 84.58787775650663
+    - type: cos_sim_spearman
+      value: 80.97859876561874
+    - type: euclidean_pearson
+      value: 83.38711461294801
+    - type: euclidean_spearman
+      value: 80.97859876561874
+    - type: manhattan_pearson
+      value: 83.34934127987394
+    - type: manhattan_spearman
+      value: 80.9556224835537
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts15-sts
+      name: MTEB STS15
+      config: default
+      split: test
+      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
+    metrics:
+    - type: cos_sim_pearson
+      value: 88.57387982528677
+    - type: cos_sim_spearman
+      value: 89.22666720704161
+    - type: euclidean_pearson
+      value: 88.50953296228646
+    - type: euclidean_spearman
+      value: 89.22666720704161
+    - type: manhattan_pearson
+      value: 88.45343635855095
+    - type: manhattan_spearman
+      value: 89.1638631562071
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts16-sts
+      name: MTEB STS16
+      config: default
+      split: test
+      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
+    metrics:
+    - type: cos_sim_pearson
+      value: 85.26071496425682
+    - type: cos_sim_spearman
+      value: 86.31740966379304
+    - type: euclidean_pearson
+      value: 85.85515938268887
+    - type: euclidean_spearman
+      value: 86.31740966379304
+    - type: manhattan_pearson
+      value: 85.80077191882177
+    - type: manhattan_spearman
+      value: 86.27885602957302
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts17-crosslingual-sts
+      name: MTEB STS17 (en-en)
+      config: en-en
+      split: test
+      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
+    metrics:
+    - type: cos_sim_pearson
+      value: 90.41413251495673
+    - type: cos_sim_spearman
+      value: 90.3370719075361
+    - type: euclidean_pearson
+      value: 90.5785973346113
+    - type: euclidean_spearman
+      value: 90.3370719075361
+    - type: manhattan_pearson
+      value: 90.5278703024898
+    - type: manhattan_spearman
+      value: 90.23870483011629
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts22-crosslingual-sts
+      name: MTEB STS22 (en)
+      config: en
+      split: test
+      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
+    metrics:
+    - type: cos_sim_pearson
+      value: 66.1571023517868
+    - type: cos_sim_spearman
+      value: 66.42297916256133
+    - type: euclidean_pearson
+      value: 67.55835224919745
+    - type: euclidean_spearman
+      value: 66.42297916256133
+    - type: manhattan_pearson
+      value: 67.40537247802385
+    - type: manhattan_spearman
+      value: 66.26259339863576
+  - task:
+      type: STS
+    dataset:
+      type: mteb/stsbenchmark-sts
+      name: MTEB STSBenchmark
+      config: default
+      split: test
+      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
+    metrics:
+    - type: cos_sim_pearson
+      value: 87.4251695055504
+    - type: cos_sim_spearman
+      value: 88.54881886307972
+    - type: euclidean_pearson
+      value: 88.54094330250571
+    - type: euclidean_spearman
+      value: 88.54881886307972
+    - type: manhattan_pearson
+      value: 88.49069549839685
+    - type: manhattan_spearman
+      value: 88.49149164694148
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/scidocs-reranking
+      name: MTEB SciDocsRR
+      config: default
+      split: test
+      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
+    metrics:
+    - type: map
+      value: 85.19974508901711
+    - type: mrr
+      value: 95.95137342686361
+  - task:
+      type: Retrieval
+    dataset:
+      type: scifact
+      name: MTEB SciFact
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_10
+      value: 71.825
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/sprintduplicatequestions-pairclassification
+      name: MTEB SprintDuplicateQuestions
+      config: default
+      split: test
+      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
+    metrics:
+    - type: cos_sim_accuracy
+      value: 99.85346534653465
+    - type: cos_sim_ap
+      value: 96.2457455868878
+    - type: cos_sim_f1
+      value: 92.49492900608519
+    - type: cos_sim_precision
+      value: 93.82716049382715
+    - type: cos_sim_recall
+      value: 91.2
+    - type: dot_accuracy
+      value: 99.85346534653465
+    - type: dot_ap
+      value: 96.24574558688776
+    - type: dot_f1
+      value: 92.49492900608519
+    - type: dot_precision
+      value: 93.82716049382715
+    - type: dot_recall
+      value: 91.2
+    - type: euclidean_accuracy
+      value: 99.85346534653465
+    - type: euclidean_ap
+      value: 96.2457455868878
+    - type: euclidean_f1
+      value: 92.49492900608519
+    - type: euclidean_precision
+      value: 93.82716049382715
+    - type: euclidean_recall
+      value: 91.2
+    - type: manhattan_accuracy
+      value: 99.85643564356435
+    - type: manhattan_ap
+      value: 96.24594126679709
+    - type: manhattan_f1
+      value: 92.63585576434738
+    - type: manhattan_precision
+      value: 94.11764705882352
+    - type: manhattan_recall
+      value: 91.2
+    - type: max_accuracy
+      value: 99.85643564356435
+    - type: max_ap
+      value: 96.24594126679709
+    - type: max_f1
+      value: 92.63585576434738
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering
+      name: MTEB StackExchangeClustering
+      config: default
+      split: test
+      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
+    metrics:
+    - type: v_measure
+      value: 68.41861859721674
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering-p2p
+      name: MTEB StackExchangeClusteringP2P
+      config: default
+      split: test
+      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
+    metrics:
+    - type: v_measure
+      value: 37.51202861563424
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/stackoverflowdupquestions-reranking
+      name: MTEB StackOverflowDupQuestions
+      config: default
+      split: test
+      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
+    metrics:
+    - type: map
+      value: 52.48207537634766
+    - type: mrr
+      value: 53.36204747050335
+  - task:
+      type: Summarization
+    dataset:
+      type: mteb/summeval
+      name: MTEB SummEval
+      config: default
+      split: test
+      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
+    metrics:
+    - type: cos_sim_pearson
+      value: 30.397150340510397
+    - type: cos_sim_spearman
+      value: 30.180928192386
+    - type: dot_pearson
+      value: 30.397148822378796
+    - type: dot_spearman
+      value: 30.180928192386
+  - task:
+      type: Retrieval
+    dataset:
+      type: trec-covid
+      name: MTEB TRECCOVID
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_10
+      value: 81.919
+  - task:
+      type: Retrieval
+    dataset:
+      type: webis-touche2020
+      name: MTEB Touche2020
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_10
+      value: 32.419
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/toxic_conversations_50k
+      name: MTEB ToxicConversationsClassification
+      config: default
+      split: test
+      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
+    metrics:
+    - type: accuracy
+      value: 72.613
+    - type: ap
+      value: 15.696112954573444
+    - type: f1
+      value: 56.30148693392767
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/tweet_sentiment_extraction
+      name: MTEB TweetSentimentExtractionClassification
+      config: default
+      split: test
+      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
+    metrics:
+    - type: accuracy
+      value: 62.02037351443125
+    - type: f1
+      value: 62.31189055427593
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/twentynewsgroups-clustering
+      name: MTEB TwentyNewsgroupsClustering
+      config: default
+      split: test
+      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
+    metrics:
+    - type: v_measure
+      value: 50.64186455543417
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twittersemeval2015-pairclassification
+      name: MTEB TwitterSemEval2015
+      config: default
+      split: test
+      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
+    metrics:
+    - type: cos_sim_accuracy
+      value: 86.27883411813792
+    - type: cos_sim_ap
+      value: 74.80076733774258
+    - type: cos_sim_f1
+      value: 68.97989210397255
+    - type: cos_sim_precision
+      value: 64.42968392120935
+    - type: cos_sim_recall
+      value: 74.22163588390501
+    - type: dot_accuracy
+      value: 86.27883411813792
+    - type: dot_ap
+      value: 74.80076608107143
+    - type: dot_f1
+      value: 68.97989210397255
+    - type: dot_precision
+      value: 64.42968392120935
+    - type: dot_recall
+      value: 74.22163588390501
+    - type: euclidean_accuracy
+      value: 86.27883411813792
+    - type: euclidean_ap
+      value: 74.80076820459502
+    - type: euclidean_f1
+      value: 68.97989210397255
+    - type: euclidean_precision
+      value: 64.42968392120935
+    - type: euclidean_recall
+      value: 74.22163588390501
+    - type: manhattan_accuracy
+      value: 86.23711032961793
+    - type: manhattan_ap
+      value: 74.73958348950038
+    - type: manhattan_f1
+      value: 68.76052948255115
+    - type: manhattan_precision
+      value: 63.207964601769916
+    - type: manhattan_recall
+      value: 75.3825857519789
+    - type: max_accuracy
+      value: 86.27883411813792
+    - type: max_ap
+      value: 74.80076820459502
+    - type: max_f1
+      value: 68.97989210397255
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twitterurlcorpus-pairclassification
+      name: MTEB TwitterURLCorpus
+      config: default
+      split: test
+      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
+    metrics:
+    - type: cos_sim_accuracy
+      value: 89.09263787014399
+    - type: cos_sim_ap
+      value: 86.46378381763645
+    - type: cos_sim_f1
+      value: 78.67838784176413
+    - type: cos_sim_precision
+      value: 76.20868812238419
+    - type: cos_sim_recall
+      value: 81.3135201724669
+    - type: dot_accuracy
+      value: 89.09263787014399
+    - type: dot_ap
+      value: 86.46378353247907
+    - type: dot_f1
+      value: 78.67838784176413
+    - type: dot_precision
+      value: 76.20868812238419
+    - type: dot_recall
+      value: 81.3135201724669
+    - type: euclidean_accuracy
+      value: 89.09263787014399
+    - type: euclidean_ap
+      value: 86.46378511891255
+    - type: euclidean_f1
+      value: 78.67838784176413
+    - type: euclidean_precision
+      value: 76.20868812238419
+    - type: euclidean_recall
+      value: 81.3135201724669
+    - type: manhattan_accuracy
+      value: 89.09069740365584
+    - type: manhattan_ap
+      value: 86.44864502475154
+    - type: manhattan_f1
+      value: 78.67372818141132
+    - type: manhattan_precision
+      value: 76.29484953703704
+    - type: manhattan_recall
+      value: 81.20572836464429
+    - type: max_accuracy
+      value: 89.09263787014399
+    - type: max_ap
+      value: 86.46378511891255
+    - type: max_f1
+      value: 78.67838784176413
+---
+
+
+# Cohere embed-english-v3.0
+
+This repository contains the tokenizer for the Cohere `embed-english-v3.0` model. See our blogpost [Cohere Embed V3](https://txt.cohere.com/introducing-embed-v3/) for more details on this model.
+
+You can use the embedding model either via the Cohere API, AWS SageMaker or in your private deployments.
+
+## Usage Cohere API
+
+The following code snippet shows the usage of the Cohere API. Install the cohere SDK via:
+```
+pip install -U cohere
+```
+
+Get your free API key on: www.cohere.com
+
+
+```python
+# This snippet shows and example how to use the Cohere Embed V3 models for semantic search.
+# Make sure to have the Cohere SDK in at least v4.30 install: pip install -U cohere 
+# Get your API key from: www.cohere.com
+import cohere
+import numpy as np
+
+cohere_key = "{YOUR_COHERE_API_KEY}"   #Get your API key from www.cohere.com
+co = cohere.Client(cohere_key)
+
+docs = ["The capital of France is Paris",
+        "PyTorch is a machine learning framework based on the Torch library.",
+        "The average cat lifespan is between 13-17 years"]
+
+
+#Encode your documents with input type 'search_document'
+doc_emb = co.embed(docs, input_type="search_document", model="embed-english-v3.0").embeddings
+doc_emb = np.asarray(doc_emb)
+
+
+#Encode your query with input type 'search_query'
+query = "What is Pytorch"
+query_emb = co.embed([query], input_type="search_query", model="embed-english-v3.0").embeddings
+query_emb = np.asarray(query_emb)
+query_emb.shape
+
+#Compute the dot product between query embedding and document embedding
+scores = np.dot(query_emb, doc_emb.T)[0]
+
+#Find the highest scores
+max_idx = np.argsort(-scores)
+
+print(f"Query: {query}")
+for idx in max_idx:
+  print(f"Score: {scores[idx]:.2f}")
+  print(docs[idx])
+  print("--------")
+```
+
+## Usage AWS SageMaker
+The embedding model can be privately deployed in your AWS Cloud using our [AWS SageMaker marketplace offering](https://aws.amazon.com/marketplace/pp/prodview-z6huxszcqc25i). It runs privately in your VPC, with latencies as low as 5ms for query encoding.
+
+## Usage AWS Bedrock
+Soon the model will also be available via AWS Bedrock. Stay tuned
+
+## Private Deployment
+You want to run the model on your own hardware? [Contact Sales](https://cohere.com/contact-sales) to learn more.
+
+## Supported Languages
+This model was trained on nearly 1B English training pairs. 
+
+Evaluation results can be found in the [Embed V3.0 Benchmark Results spreadsheet](https://docs.google.com/spreadsheets/d/1w7gnHWMDBdEUrmHgSfDnGHJgVQE5aOiXCCwO3uNH_mI/edit?usp=sharing).

added_tokens.json

@@ -0,0 +1,5 @@
+{
+  "[COHERE_CLUSTERING_ID]": 30524,
+  "[COHERE_SEARCH_DOCUMENT_ID]": 30522,
+  "[COHERE_SEARCH_QUERY_ID]": 30523
+}

config.json

@@ -0,0 +1,4 @@
+{
+	"n_positions": 512,
+	"hidden_dim": 1024
+}

special_tokens_map.json

@@ -0,0 +1,7 @@
+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

@@ -0,0 +1,20 @@
+{
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "max_length": 512,
+  "model_max_length": 512,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}