transformers mteb model-index endpoints_compatible region:us
---
tags:
- mteb
model-index:
- name: embed-multilingual-light-v3.0
  results:
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonCounterfactualClassification (en)
      type: mteb/amazon_counterfactual
      config: en
      split: test
      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
    metrics:
    - type: accuracy
      value: 70.02985074626865
    - type: ap
      value: 33.228065779544146
    - type: f1
      value: 64.27173953207297
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonPolarityClassification
      type: mteb/amazon_polarity
      config: default
      split: test
      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
    metrics:
    - type: accuracy
      value: 90.701225
    - type: ap
      value: 87.07178174251762
    - type: f1
      value: 90.69168484877625
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonReviewsClassification (en)
      type: mteb/amazon_reviews_multi
      config: en
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 46.550000000000004
    - type: f1
      value: 44.7233215588199
  - task:
      type: Retrieval
    dataset:
      name: MTEB ArguAna
      type: arguana
      config: default
      split: test
      revision: None
    metrics:
    - type: ndcg_at_10
      value: 53.369
  - task:
      type: Clustering
    dataset:
      name: MTEB ArxivClusteringP2P
      type: mteb/arxiv-clustering-p2p
      config: default
      split: test
      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
    metrics:
    - type: v_measure
      value: 44.206988765030744
  - task:
      type: Clustering
    dataset:
      name: MTEB ArxivClusteringS2S
      type: mteb/arxiv-clustering-s2s
      config: default
      split: test
      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
    metrics:
    - type: v_measure
      value: 33.913737041277
  - task:
      type: Reranking
    dataset:
      name: MTEB AskUbuntuDupQuestions
      type: mteb/askubuntudupquestions-reranking
      config: default
      split: test
      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
    metrics:
    - type: map
      value: 58.544257541214925
    - type: mrr
      value: 72.07151651057468
  - task:
      type: STS
    dataset:
      name: MTEB BIOSSES
      type: mteb/biosses-sts
      config: default
      split: test
      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
    metrics:
    - type: cos_sim_pearson
      value: 84.79582115243736
    - type: cos_sim_spearman
      value: 84.01396250789998
    - type: euclidean_pearson
      value: 83.90766476102458
    - type: euclidean_spearman
      value: 84.01396250789998
    - type: manhattan_pearson
      value: 84.75071274784274
    - type: manhattan_spearman
      value: 85.02482891467078
  - task:
      type: Classification
    dataset:
      name: MTEB Banking77Classification
      type: mteb/banking77
      config: default
      split: test
      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
    metrics:
    - type: accuracy
      value: 78.12337662337663
    - type: f1
      value: 77.48610340227478
  - task:
      type: Clustering
    dataset:
      name: MTEB BiorxivClusteringP2P
      type: mteb/biorxiv-clustering-p2p
      config: default
      split: test
      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
    metrics:
    - type: v_measure
      value: 38.68268504601174
  - task:
      type: Clustering
    dataset:
      name: MTEB BiorxivClusteringS2S
      type: mteb/biorxiv-clustering-s2s
      config: default
      split: test
      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
    metrics:
    - type: v_measure
      value: 32.20870648143671
  - task:
      type: Retrieval
    dataset:
      name: MTEB CQADupstackAndroidRetrieval
      type: BeIR/cqadupstack
      config: default
      split: test
      revision: None
    metrics:
    - type: ndcg_at_10
      value: 46.259
    - type: ndcg_at_10
      value: 44.555
    - type: ndcg_at_10
      value: 56.564
    - type: ndcg_at_10
      value: 36.162
    - type: ndcg_at_10
      value: 26.185000000000002
    - type: ndcg_at_10
      value: 41.547
    - type: ndcg_at_10
      value: 39.042
    - type: ndcg_at_10
      value: 38.086999999999996
    - type: ndcg_at_10
      value: 32.088
    - type: ndcg_at_10
      value: 27.006999999999998
    - type: ndcg_at_10
      value: 37.336999999999996
    - type: ndcg_at_10
      value: 38.011
    - type: ndcg_at_10
      value: 32.287
  - task:
      type: Retrieval
    dataset:
      name: MTEB ClimateFEVER
      type: climate-fever
      config: default
      split: test
      revision: None
    metrics:
    - type: ndcg_at_10
      value: 24.804000000000002
  - task:
      type: Retrieval
    dataset:
      name: MTEB DBPedia
      type: dbpedia-entity
      config: default
      split: test
      revision: None
    metrics:
    - type: ndcg_at_10
      value: 38.055
  - task:
      type: Classification
    dataset:
      name: MTEB EmotionClassification
      type: mteb/emotion
      config: default
      split: test
      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
    metrics:
    - type: accuracy
      value: 46.665
    - type: f1
      value: 40.77568559660878
  - task:
      type: Retrieval
    dataset:
      name: MTEB FEVER
      type: fever
      config: default
      split: test
      revision: None
    metrics:
    - type: ndcg_at_10
      value: 85.52499999999999
  - task:
      type: Retrieval
    dataset:
      name: MTEB FiQA2018
      type: fiqa
      config: default
      split: test
      revision: None
    metrics:
    - type: ndcg_at_10
      value: 36.161
  - task:
      type: Retrieval
    dataset:
      name: MTEB HotpotQA
      type: hotpotqa
      config: default
      split: test
      revision: None
    metrics:
    - type: ndcg_at_10
      value: 66.878
  - task:
      type: Classification
    dataset:
      name: MTEB ImdbClassification
      type: mteb/imdb
      config: default
      split: test
      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
    metrics:
    - type: accuracy
      value: 85.6372
    - type: ap
      value: 80.54846874011302
    - type: f1
      value: 85.61438421821343
  - task:
      type: Retrieval
    dataset:
      name: MTEB MSMARCO
      type: msmarco
      config: default
      split: test
      revision: None
    metrics:
    - type: ndcg_at_10
      value: 40.487
  - task:
      type: Classification
    dataset:
      name: MTEB MTOPDomainClassification (en)
      type: mteb/mtop_domain
      config: en
      split: test
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
    metrics:
    - type: accuracy
      value: 91.8559051527588
    - type: f1
      value: 91.6271749996447
  - task:
      type: Classification
    dataset:
      name: MTEB MTOPIntentClassification (en)
      type: mteb/mtop_intent
      config: en
      split: test
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
    metrics:
    - type: accuracy
      value: 62.17738258093936
    - type: f1
      value: 45.80307070449218
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (en)
      type: mteb/amazon_massive_intent
      config: en
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 67.42434431741762
    - type: f1
      value: 65.39580264698957
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveScenarioClassification (en)
      type: mteb/amazon_massive_scenario
      config: en
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 72.60928043039677
    - type: f1
      value: 72.30912915707411
  - task:
      type: Clustering
    dataset:
      name: MTEB MedrxivClusteringP2P
      type: mteb/medrxiv-clustering-p2p
      config: default
      split: test
      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
    metrics:
    - type: v_measure
      value: 35.17967476592229
  - task:
      type: Clustering
    dataset:
      name: MTEB MedrxivClusteringS2S
      type: mteb/medrxiv-clustering-s2s
      config: default
      split: test
      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
    metrics:
    - type: v_measure
      value: 30.993641089208683
  - task:
      type: Reranking
    dataset:
      name: MTEB MindSmallReranking
      type: mteb/mind_small
      config: default
      split: test
      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
    metrics:
    - type: map
      value: 31.362481813275295
    - type: mrr
      value: 32.43717742343303
  - task:
      type: Retrieval
    dataset:
      name: MTEB NFCorpus
      type: nfcorpus
      config: default
      split: test
      revision: None
    metrics:
    - type: ndcg_at_10
      value: 32.123000000000005
  - task:
      type: Retrieval
    dataset:
      name: MTEB NQ
      type: nq
      config: default
      split: test
      revision: None
    metrics:
    - type: ndcg_at_10
      value: 55.51199999999999
  - task:
      type: Retrieval
    dataset:
      name: MTEB QuoraRetrieval
      type: quora
      config: default
      split: test
      revision: None
    metrics:
    - type: ndcg_at_10
      value: 87.847
  - task:
      type: Clustering
    dataset:
      name: MTEB RedditClustering
      type: mteb/reddit-clustering
      config: default
      split: test
      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
    metrics:
    - type: v_measure
      value: 49.4973643968247
  - task:
      type: Clustering
    dataset:
      name: MTEB RedditClusteringP2P
      type: mteb/reddit-clustering-p2p
      config: default
      split: test
      revision: 282350215ef01743dc01b456c7f5241fa8937f16
    metrics:
    - type: v_measure
      value: 60.2135284243427
  - task:
      type: Retrieval
    dataset:
      name: MTEB SCIDOCS
      type: scidocs
      config: default
      split: test
      revision: None
    metrics:
    - type: ndcg_at_10
      value: 17.1
  - task:
      type: STS
    dataset:
      name: MTEB SICK-R
      type: mteb/sickr-sts
      config: default
      split: test
      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
    metrics:
    - type: cos_sim_pearson
      value: 83.7330191296952
    - type: cos_sim_spearman
      value: 77.03523134004043
    - type: euclidean_pearson
      value: 80.86067787185137
    - type: euclidean_spearman
      value: 77.03522959536473
    - type: manhattan_pearson
      value: 80.76089708603587
    - type: manhattan_spearman
      value: 76.86245377437302
  - task:
      type: STS
    dataset:
      name: MTEB STS12
      type: mteb/sts12-sts
      config: default
      split: test
      revision: a0d554a64d88156834ff5ae9920b964011b16384
    metrics:
    - type: cos_sim_pearson
      value: 80.46387812633851
    - type: cos_sim_spearman
      value: 73.21878234127571
    - type: euclidean_pearson
      value: 76.82160699895033
    - type: euclidean_spearman
      value: 73.21878234127571
    - type: manhattan_pearson
      value: 76.75657006349886
    - type: manhattan_spearman
      value: 73.19160258034827
  - task:
      type: STS
    dataset:
      name: MTEB STS13
      type: mteb/sts13-sts
      config: default
      split: test
      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
    metrics:
    - type: cos_sim_pearson
      value: 79.06411399119807
    - type: cos_sim_spearman
      value: 79.49916779764082
    - type: euclidean_pearson
      value: 79.3356521660954
    - type: euclidean_spearman
      value: 79.49916779764082
    - type: manhattan_pearson
      value: 79.04971532119936
    - type: manhattan_spearman
      value: 79.16859911220654
  - task:
      type: STS
    dataset:
      name: MTEB STS14
      type: mteb/sts14-sts
      config: default
      split: test
      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
    metrics:
    - type: cos_sim_pearson
      value: 80.6940934994372
    - type: cos_sim_spearman
      value: 76.9552055757283
    - type: euclidean_pearson
      value: 79.52818133592284
    - type: euclidean_spearman
      value: 76.9552055757283
    - type: manhattan_pearson
      value: 79.35220459438406
    - type: manhattan_spearman
      value: 76.85314462036561
  - task:
      type: STS
    dataset:
      name: MTEB STS15
      type: mteb/sts15-sts
      config: default
      split: test
      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
    metrics:
    - type: cos_sim_pearson
      value: 85.58608774451231
    - type: cos_sim_spearman
      value: 86.42805701554927
    - type: euclidean_pearson
      value: 86.01117122595934
    - type: euclidean_spearman
      value: 86.42805701554927
    - type: manhattan_pearson
      value: 86.01345208923057
    - type: manhattan_spearman
      value: 86.43179450307953
  - task:
      type: STS
    dataset:
      name: MTEB STS16
      type: mteb/sts16-sts
      config: default
      split: test
      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
    metrics:
    - type: cos_sim_pearson
      value: 83.18733039014667
    - type: cos_sim_spearman
      value: 84.3339529564109
    - type: euclidean_pearson
      value: 83.54530885349595
    - type: euclidean_spearman
      value: 84.3339529564109
    - type: manhattan_pearson
      value: 83.47015931913937
    - type: manhattan_spearman
      value: 84.22564786654777
  - task:
      type: STS
    dataset:
      name: MTEB STS17 (en-en)
      type: mteb/sts17-crosslingual-sts
      config: en-en
      split: test
      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
    metrics:
    - type: cos_sim_pearson
      value: 87.88402211340522
    - type: cos_sim_spearman
      value: 88.6693290310468
    - type: euclidean_pearson
      value: 88.24947476618257
    - type: euclidean_spearman
      value: 88.6693290310468
    - type: manhattan_pearson
      value: 88.24496656367964
    - type: manhattan_spearman
      value: 88.52029848819545
  - task:
      type: STS
    dataset:
      name: MTEB STS22 (en)
      type: mteb/sts22-crosslingual-sts
      config: en
      split: test
      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
    metrics:
    - type: cos_sim_pearson
      value: 64.96467575926597
    - type: cos_sim_spearman
      value: 65.30666900046252
    - type: euclidean_pearson
      value: 66.58031971340725
    - type: euclidean_spearman
      value: 65.30666900046252
    - type: manhattan_pearson
      value: 66.56530433327998
    - type: manhattan_spearman
      value: 65.42121899024113
  - task:
      type: STS
    dataset:
      name: MTEB STSBenchmark
      type: mteb/stsbenchmark-sts
      config: default
      split: test
      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
    metrics:
    - type: cos_sim_pearson
      value: 85.31047656296519
    - type: cos_sim_spearman
      value: 85.46101092708824
    - type: euclidean_pearson
      value: 85.75896623084044
    - type: euclidean_spearman
      value: 85.46101092708824
    - type: manhattan_pearson
      value: 85.57323880630182
    - type: manhattan_spearman
      value: 85.23375523080594
  - task:
      type: Reranking
    dataset:
      name: MTEB SciDocsRR
      type: mteb/scidocs-reranking
      config: default
      split: test
      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
    metrics:
    - type: map
      value: 79.89731978284804
    - type: mrr
      value: 94.28980424078465
  - task:
      type: Retrieval
    dataset:
      name: MTEB SciFact
      type: scifact
      config: default
      split: test
      revision: None
    metrics:
    - type: ndcg_at_10
      value: 67.95
  - task:
      type: PairClassification
    dataset:
      name: MTEB SprintDuplicateQuestions
      type: mteb/sprintduplicatequestions-pairclassification
      config: default
      split: test
      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
    metrics:
    - type: cos_sim_accuracy
      value: 99.85643564356435
    - type: cos_sim_ap
      value: 96.59618618212247
    - type: cos_sim_f1
      value: 92.6221335992024
    - type: cos_sim_precision
      value: 92.34592445328032
    - type: cos_sim_recall
      value: 92.9
    - type: dot_accuracy
      value: 99.85643564356435
    - type: dot_ap
      value: 96.5961861821225
    - type: dot_f1
      value: 92.6221335992024
    - type: dot_precision
      value: 92.34592445328032
    - type: dot_recall
      value: 92.9
    - type: euclidean_accuracy
      value: 99.85643564356435
    - type: euclidean_ap
      value: 96.5961861821225
    - type: euclidean_f1
      value: 92.6221335992024
    - type: euclidean_precision
      value: 92.34592445328032
    - type: euclidean_recall
      value: 92.9
    - type: manhattan_accuracy
      value: 99.85841584158416
    - type: manhattan_ap
      value: 96.5578240948512
    - type: manhattan_f1
      value: 92.71523178807946
    - type: manhattan_precision
      value: 94.4963655244029
    - type: manhattan_recall
      value: 91.0
    - type: max_accuracy
      value: 99.85841584158416
    - type: max_ap
      value: 96.5961861821225
    - type: max_f1
      value: 92.71523178807946
  - task:
      type: Clustering
    dataset:
      name: MTEB StackExchangeClustering
      type: mteb/stackexchange-clustering
      config: default
      split: test
      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
    metrics:
    - type: v_measure
      value: 60.84750068050385
  - task:
      type: Clustering
    dataset:
      name: MTEB StackExchangeClusteringP2P
      type: mteb/stackexchange-clustering-p2p
      config: default
      split: test
      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
    metrics:
    - type: v_measure
      value: 33.96844721192451
  - task:
      type: Reranking
    dataset:
      name: MTEB StackOverflowDupQuestions
      type: mteb/stackoverflowdupquestions-reranking
      config: default
      split: test
      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
    metrics:
    - type: map
      value: 50.454280909595205
    - type: mrr
      value: 51.24249320940497
  - task:
      type: Summarization
    dataset:
      name: MTEB SummEval
      type: mteb/summeval
      config: default
      split: test
      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
    metrics:
    - type: cos_sim_pearson
      value: 29.998438678552517
    - type: cos_sim_spearman
      value: 30.409482543506876
    - type: dot_pearson
      value: 29.998443850173224
    - type: dot_spearman
      value: 30.409482543506876
  - task:
      type: Retrieval
    dataset:
      name: MTEB TRECCOVID
      type: trec-covid
      config: default
      split: test
      revision: None
    metrics:
    - type: ndcg_at_10
      value: 78.93
  - task:
      type: Retrieval
    dataset:
      name: MTEB Touche2020
      type: webis-touche2020
      config: default
      split: test
      revision: None
    metrics:
    - type: ndcg_at_10
      value: 29.482999999999997
  - task:
      type: Classification
    dataset:
      name: MTEB ToxicConversationsClassification
      type: mteb/toxic_conversations_50k
      config: default
      split: test
      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
    metrics:
    - type: accuracy
      value: 70.65859999999999
    - type: ap
      value: 15.03693738050973
    - type: f1
      value: 54.94379403846167
  - task:
      type: Classification
    dataset:
      name: MTEB TweetSentimentExtractionClassification
      type: mteb/tweet_sentiment_extraction
      config: default
      split: test
      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
    metrics:
    - type: accuracy
      value: 64.4567062818336
    - type: f1
      value: 64.48980729427107
  - task:
      type: Clustering
    dataset:
      name: MTEB TwentyNewsgroupsClustering
      type: mteb/twentynewsgroups-clustering
      config: default
      split: test
      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
    metrics:
    - type: v_measure
      value: 42.08554991843959
  - task:
      type: PairClassification
    dataset:
      name: MTEB TwitterSemEval2015
      type: mteb/twittersemeval2015-pairclassification
      config: default
      split: test
      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
    metrics:
    - type: cos_sim_accuracy
      value: 84.75293556654945
    - type: cos_sim_ap
      value: 69.40551043272129
    - type: cos_sim_f1
      value: 65.56335231034026
    - type: cos_sim_precision
      value: 65.79856497475419
    - type: cos_sim_recall
      value: 65.32981530343008
    - type: dot_accuracy
      value: 84.75293556654945
    - type: dot_ap
      value: 69.40550704470631
    - type: dot_f1
      value: 65.56335231034026
    - type: dot_precision
      value: 65.79856497475419
    - type: dot_recall
      value: 65.32981530343008
    - type: euclidean_accuracy
      value: 84.75293556654945
    - type: euclidean_ap
      value: 69.4055136381454
    - type: euclidean_f1
      value: 65.56335231034026
    - type: euclidean_precision
      value: 65.79856497475419
    - type: euclidean_recall
      value: 65.32981530343008
    - type: manhattan_accuracy
      value: 84.6337247422066
    - type: manhattan_ap
      value: 69.13628354134198
    - type: manhattan_f1
      value: 65.46998180715585
    - type: manhattan_precision
      value: 60.58361391694726
    - type: manhattan_recall
      value: 71.21372031662268
    - type: max_accuracy
      value: 84.75293556654945
    - type: max_ap
      value: 69.4055136381454
    - type: max_f1
      value: 65.56335231034026
  - task:
      type: PairClassification
    dataset:
      name: MTEB TwitterURLCorpus
      type: mteb/twitterurlcorpus-pairclassification
      config: default
      split: test
      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
    metrics:
    - type: cos_sim_accuracy
      value: 89.04800714091667
    - type: cos_sim_ap
      value: 85.84596325009252
    - type: cos_sim_f1
      value: 78.39228527221042
    - type: cos_sim_precision
      value: 73.58643518205768
    - type: cos_sim_recall
      value: 83.86972590083154
    - type: dot_accuracy
      value: 89.04800714091667
    - type: dot_ap
      value: 85.8459646697087
    - type: dot_f1
      value: 78.39228527221042
    - type: dot_precision
      value: 73.58643518205768
    - type: dot_recall
      value: 83.86972590083154
    - type: euclidean_accuracy
      value: 89.04800714091667
    - type: euclidean_ap
      value: 85.84596376376919
    - type: euclidean_f1
      value: 78.39228527221042
    - type: euclidean_precision
      value: 73.58643518205768
    - type: euclidean_recall
      value: 83.86972590083154
    - type: manhattan_accuracy
      value: 89.0266620095471
    - type: manhattan_ap
      value: 85.80124417850608
    - type: manhattan_f1
      value: 78.37817859254879
    - type: manhattan_precision
      value: 75.36963321012226
    - type: manhattan_recall
      value: 81.63689559593472
    - type: max_accuracy
      value: 89.04800714091667
    - type: max_ap
      value: 85.8459646697087
    - type: max_f1
      value: 78.39228527221042
---


# Cohere embed-multilingual-light-v3.0

This repository contains the tokenizer for the Cohere `embed-multilingual-light-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-multilingual-light-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-multilingual-light-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 and nearly 0.5B Non-English training pairs from 100+ languages. 

Evaluation results can be found in the [Embed V3.0 Benchmark Results spreadsheet](https://docs.google.com/spreadsheets/d/1w7gnHWMDBdEUrmHgSfDnGHJgVQE5aOiXCCwO3uNH_mI/edit?usp=sharing).