transformers mteb model-index endpoints_compatible region:us
---
tags:
- mteb
model-index:
- name: embed-english-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: 78.62686567164178
    - type: ap
      value: 43.50072127690769
    - type: f1
      value: 73.12414870629323
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonPolarityClassification
      type: mteb/amazon_polarity
      config: default
      split: test
      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
    metrics:
    - type: accuracy
      value: 94.795
    - type: ap
      value: 92.14178233328848
    - type: f1
      value: 94.79269356571955
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonReviewsClassification (en)
      type: mteb/amazon_reviews_multi
      config: en
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 51.016000000000005
    - type: f1
      value: 48.9266470039522
  - task:
      type: Retrieval
    dataset:
      name: MTEB ArguAna
      type: arguana
      config: default
      split: test
      revision: None
    metrics:
    - type: ndcg_at_10
      value: 50.806
  - task:
      type: Clustering
    dataset:
      name: MTEB ArxivClusteringP2P
      type: mteb/arxiv-clustering-p2p
      config: default
      split: test
      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
    metrics:
    - type: v_measure
      value: 46.19304218375896
  - task:
      type: Clustering
    dataset:
      name: MTEB ArxivClusteringS2S
      type: mteb/arxiv-clustering-s2s
      config: default
      split: test
      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
    metrics:
    - type: v_measure
      value: 37.57785041962193
  - task:
      type: Reranking
    dataset:
      name: MTEB AskUbuntuDupQuestions
      type: mteb/askubuntudupquestions-reranking
      config: default
      split: test
      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
    metrics:
    - type: map
      value: 60.11396377106911
    - type: mrr
      value: 72.9068284746955
  - task:
      type: STS
    dataset:
      name: MTEB BIOSSES
      type: mteb/biosses-sts
      config: default
      split: test
      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
    metrics:
    - type: cos_sim_pearson
      value: 82.59354737468067
    - type: cos_sim_spearman
      value: 81.71933190993215
    - type: euclidean_pearson
      value: 81.39212345994983
    - type: euclidean_spearman
      value: 81.71933190993215
    - type: manhattan_pearson
      value: 81.29257414603093
    - type: manhattan_spearman
      value: 81.80246633432691
  - task:
      type: Classification
    dataset:
      name: MTEB Banking77Classification
      type: mteb/banking77
      config: default
      split: test
      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
    metrics:
    - type: accuracy
      value: 79.69805194805193
    - type: f1
      value: 79.07431143559548
  - task:
      type: Clustering
    dataset:
      name: MTEB BiorxivClusteringP2P
      type: mteb/biorxiv-clustering-p2p
      config: default
      split: test
      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
    metrics:
    - type: v_measure
      value: 38.973417975095934
  - task:
      type: Clustering
    dataset:
      name: MTEB BiorxivClusteringS2S
      type: mteb/biorxiv-clustering-s2s
      config: default
      split: test
      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
    metrics:
    - type: v_measure
      value: 34.51608057107556
  - task:
      type: Retrieval
    dataset:
      name: MTEB CQADupstackAndroidRetrieval
      type: BeIR/cqadupstack
      config: default
      split: test
      revision: None
    metrics:
    - type: ndcg_at_10
      value: 46.615
    - type: ndcg_at_10
      value: 45.383
    - type: ndcg_at_10
      value: 57.062999999999995
    - type: ndcg_at_10
      value: 37.201
    - type: ndcg_at_10
      value: 27.473
    - type: ndcg_at_10
      value: 41.868
    - type: ndcg_at_10
      value: 42.059000000000005
    - type: ndcg_at_10
      value: 38.885416666666664
    - type: ndcg_at_10
      value: 32.134
    - type: ndcg_at_10
      value: 28.052
    - type: ndcg_at_10
      value: 38.237
    - type: ndcg_at_10
      value: 37.875
    - type: ndcg_at_10
      value: 32.665
  - task:
      type: Retrieval
    dataset:
      name: MTEB ClimateFEVER
      type: climate-fever
      config: default
      split: test
      revision: None
    metrics:
    - type: ndcg_at_10
      value: 28.901
  - task:
      type: Retrieval
    dataset:
      name: MTEB DBPedia
      type: dbpedia-entity
      config: default
      split: test
      revision: None
    metrics:
    - type: ndcg_at_10
      value: 41.028
  - task:
      type: Classification
    dataset:
      name: MTEB EmotionClassification
      type: mteb/emotion
      config: default
      split: test
      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
    metrics:
    - type: accuracy
      value: 52.745
    - type: f1
      value: 46.432564522368054
  - task:
      type: Retrieval
    dataset:
      name: MTEB FEVER
      type: fever
      config: default
      split: test
      revision: None
    metrics:
    - type: ndcg_at_10
      value: 87.64
  - task:
      type: Retrieval
    dataset:
      name: MTEB FiQA2018
      type: fiqa
      config: default
      split: test
      revision: None
    metrics:
    - type: ndcg_at_10
      value: 38.834999999999994
  - task:
      type: Retrieval
    dataset:
      name: MTEB HotpotQA
      type: hotpotqa
      config: default
      split: test
      revision: None
    metrics:
    - type: ndcg_at_10
      value: 66.793
  - task:
      type: Classification
    dataset:
      name: MTEB ImdbClassification
      type: mteb/imdb
      config: default
      split: test
      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
    metrics:
    - type: accuracy
      value: 92.16680000000001
    - type: ap
      value: 88.9326260956379
    - type: f1
      value: 92.16197209455585
  - task:
      type: Retrieval
    dataset:
      name: MTEB MSMARCO
      type: msmarco
      config: default
      split: test
      revision: None
    metrics:
    - type: ndcg_at_10
      value: 41.325
  - task:
      type: Classification
    dataset:
      name: MTEB MTOPDomainClassification (en)
      type: mteb/mtop_domain
      config: en
      split: test
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
    metrics:
    - type: accuracy
      value: 93.62517099863202
    - type: f1
      value: 93.3852826127328
  - task:
      type: Classification
    dataset:
      name: MTEB MTOPIntentClassification (en)
      type: mteb/mtop_intent
      config: en
      split: test
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
    metrics:
    - type: accuracy
      value: 64.93388052895577
    - type: f1
      value: 48.035548201830366
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (en)
      type: mteb/amazon_massive_intent
      config: en
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 70.01344989912577
    - type: f1
      value: 68.01236893966525
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveScenarioClassification (en)
      type: mteb/amazon_massive_scenario
      config: en
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 76.34498991257564
    - type: f1
      value: 75.72876911765213
  - task:
      type: Clustering
    dataset:
      name: MTEB MedrxivClusteringP2P
      type: mteb/medrxiv-clustering-p2p
      config: default
      split: test
      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
    metrics:
    - type: v_measure
      value: 37.66326759167091
  - task:
      type: Clustering
    dataset:
      name: MTEB MedrxivClusteringS2S
      type: mteb/medrxiv-clustering-s2s
      config: default
      split: test
      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
    metrics:
    - type: v_measure
      value: 33.53562430544494
  - task:
      type: Reranking
    dataset:
      name: MTEB MindSmallReranking
      type: mteb/mind_small
      config: default
      split: test
      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
    metrics:
    - type: map
      value: 31.86814320224619
    - type: mrr
      value: 33.02567757581291
  - task:
      type: Retrieval
    dataset:
      name: MTEB NFCorpus
      type: nfcorpus
      config: default
      split: test
      revision: None
    metrics:
    - type: ndcg_at_10
      value: 33.649
  - task:
      type: Retrieval
    dataset:
      name: MTEB NQ
      type: nq
      config: default
      split: test
      revision: None
    metrics:
    - type: ndcg_at_10
      value: 57.994
  - task:
      type: Retrieval
    dataset:
      name: MTEB QuoraRetrieval
      type: quora
      config: default
      split: test
      revision: None
    metrics:
    - type: ndcg_at_10
      value: 88.115
  - task:
      type: Clustering
    dataset:
      name: MTEB RedditClustering
      type: mteb/reddit-clustering
      config: default
      split: test
      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
    metrics:
    - type: v_measure
      value: 53.4970929237201
  - task:
      type: Clustering
    dataset:
      name: MTEB RedditClusteringP2P
      type: mteb/reddit-clustering-p2p
      config: default
      split: test
      revision: 282350215ef01743dc01b456c7f5241fa8937f16
    metrics:
    - type: v_measure
      value: 63.59086757472922
  - task:
      type: Retrieval
    dataset:
      name: MTEB SCIDOCS
      type: scidocs
      config: default
      split: test
      revision: None
    metrics:
    - type: ndcg_at_10
      value: 18.098
  - task:
      type: STS
    dataset:
      name: MTEB SICK-R
      type: mteb/sickr-sts
      config: default
      split: test
      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
    metrics:
    - type: cos_sim_pearson
      value: 85.05019841005287
    - type: cos_sim_spearman
      value: 79.65240734965128
    - type: euclidean_pearson
      value: 82.33894047327843
    - type: euclidean_spearman
      value: 79.65240666088022
    - type: manhattan_pearson
      value: 82.33098051639543
    - type: manhattan_spearman
      value: 79.5592521956291
  - task:
      type: STS
    dataset:
      name: MTEB STS12
      type: mteb/sts12-sts
      config: default
      split: test
      revision: a0d554a64d88156834ff5ae9920b964011b16384
    metrics:
    - type: cos_sim_pearson
      value: 81.28561469269728
    - type: cos_sim_spearman
      value: 72.6022866501722
    - type: euclidean_pearson
      value: 77.89616448619745
    - type: euclidean_spearman
      value: 72.6022866429173
    - type: manhattan_pearson
      value: 77.9073648819866
    - type: manhattan_spearman
      value: 72.6928162672852
  - task:
      type: STS
    dataset:
      name: MTEB STS13
      type: mteb/sts13-sts
      config: default
      split: test
      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
    metrics:
    - type: cos_sim_pearson
      value: 82.48271297318195
    - type: cos_sim_spearman
      value: 82.87639489647019
    - type: euclidean_pearson
      value: 82.24654676315204
    - type: euclidean_spearman
      value: 82.87642765399856
    - type: manhattan_pearson
      value: 82.19673632886851
    - type: manhattan_spearman
      value: 82.822727205448
  - task:
      type: STS
    dataset:
      name: MTEB STS14
      type: mteb/sts14-sts
      config: default
      split: test
      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
    metrics:
    - type: cos_sim_pearson
      value: 83.74140104895864
    - type: cos_sim_spearman
      value: 79.74024708732993
    - type: euclidean_pearson
      value: 82.50081856448949
    - type: euclidean_spearman
      value: 79.74024708732993
    - type: manhattan_pearson
      value: 82.36588991657912
    - type: manhattan_spearman
      value: 79.59022658604357
  - task:
      type: STS
    dataset:
      name: MTEB STS15
      type: mteb/sts15-sts
      config: default
      split: test
      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
    metrics:
    - type: cos_sim_pearson
      value: 86.30124436614311
    - type: cos_sim_spearman
      value: 86.97688974734349
    - type: euclidean_pearson
      value: 86.36868875097032
    - type: euclidean_spearman
      value: 86.97688974734349
    - type: manhattan_pearson
      value: 86.37787059133234
    - type: manhattan_spearman
      value: 86.96666693570158
  - task:
      type: STS
    dataset:
      name: MTEB STS16
      type: mteb/sts16-sts
      config: default
      split: test
      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
    metrics:
    - type: cos_sim_pearson
      value: 83.27590066451398
    - type: cos_sim_spearman
      value: 84.40811627278994
    - type: euclidean_pearson
      value: 83.77341566536141
    - type: euclidean_spearman
      value: 84.40811627278994
    - type: manhattan_pearson
      value: 83.72567664904311
    - type: manhattan_spearman
      value: 84.42172336387632
  - 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: 89.13791942173916
    - type: cos_sim_spearman
      value: 89.22016928873572
    - type: euclidean_pearson
      value: 89.43583792557924
    - type: euclidean_spearman
      value: 89.22016928873572
    - type: manhattan_pearson
      value: 89.47307915863284
    - type: manhattan_spearman
      value: 89.20752264220539
  - 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: 64.92003328655028
    - type: cos_sim_spearman
      value: 65.42027229611072
    - type: euclidean_pearson
      value: 66.68765284942059
    - type: euclidean_spearman
      value: 65.42027229611072
    - type: manhattan_pearson
      value: 66.85383496796447
    - type: manhattan_spearman
      value: 65.53490117706689
  - task:
      type: STS
    dataset:
      name: MTEB STSBenchmark
      type: mteb/stsbenchmark-sts
      config: default
      split: test
      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
    metrics:
    - type: cos_sim_pearson
      value: 85.97445894753297
    - type: cos_sim_spearman
      value: 86.57651994952795
    - type: euclidean_pearson
      value: 86.7061296897819
    - type: euclidean_spearman
      value: 86.57651994952795
    - type: manhattan_pearson
      value: 86.66411668551642
    - type: manhattan_spearman
      value: 86.53200653755397
  - task:
      type: Reranking
    dataset:
      name: MTEB SciDocsRR
      type: mteb/scidocs-reranking
      config: default
      split: test
      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
    metrics:
    - type: map
      value: 81.62235389081138
    - type: mrr
      value: 94.65811965811966
  - task:
      type: Retrieval
    dataset:
      name: MTEB SciFact
      type: scifact
      config: default
      split: test
      revision: None
    metrics:
    - type: ndcg_at_10
      value: 66.687
  - task:
      type: PairClassification
    dataset:
      name: MTEB SprintDuplicateQuestions
      type: mteb/sprintduplicatequestions-pairclassification
      config: default
      split: test
      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
    metrics:
    - type: cos_sim_accuracy
      value: 99.86435643564356
    - type: cos_sim_ap
      value: 96.59150882873165
    - type: cos_sim_f1
      value: 93.07030854830552
    - type: cos_sim_precision
      value: 94.16581371545547
    - type: cos_sim_recall
      value: 92.0
    - type: dot_accuracy
      value: 99.86435643564356
    - type: dot_ap
      value: 96.59150882873165
    - type: dot_f1
      value: 93.07030854830552
    - type: dot_precision
      value: 94.16581371545547
    - type: dot_recall
      value: 92.0
    - type: euclidean_accuracy
      value: 99.86435643564356
    - type: euclidean_ap
      value: 96.59150882873162
    - type: euclidean_f1
      value: 93.07030854830552
    - type: euclidean_precision
      value: 94.16581371545547
    - type: euclidean_recall
      value: 92.0
    - type: manhattan_accuracy
      value: 99.86336633663366
    - type: manhattan_ap
      value: 96.58123246795022
    - type: manhattan_f1
      value: 92.9591836734694
    - type: manhattan_precision
      value: 94.89583333333333
    - type: manhattan_recall
      value: 91.10000000000001
    - type: max_accuracy
      value: 99.86435643564356
    - type: max_ap
      value: 96.59150882873165
    - type: max_f1
      value: 93.07030854830552
  - task:
      type: Clustering
    dataset:
      name: MTEB StackExchangeClustering
      type: mteb/stackexchange-clustering
      config: default
      split: test
      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
    metrics:
    - type: v_measure
      value: 62.938055854344455
  - task:
      type: Clustering
    dataset:
      name: MTEB StackExchangeClusteringP2P
      type: mteb/stackexchange-clustering-p2p
      config: default
      split: test
      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
    metrics:
    - type: v_measure
      value: 36.479716154538224
  - task:
      type: Reranking
    dataset:
      name: MTEB StackOverflowDupQuestions
      type: mteb/stackoverflowdupquestions-reranking
      config: default
      split: test
      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
    metrics:
    - type: map
      value: 50.75827388766867
    - type: mrr
      value: 51.65291305916306
  - task:
      type: Summarization
    dataset:
      name: MTEB SummEval
      type: mteb/summeval
      config: default
      split: test
      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
    metrics:
    - type: cos_sim_pearson
      value: 31.81419421090782
    - type: cos_sim_spearman
      value: 31.287464634068492
    - type: dot_pearson
      value: 31.814195589790177
    - type: dot_spearman
      value: 31.287464634068492
  - task:
      type: Retrieval
    dataset:
      name: MTEB TRECCOVID
      type: trec-covid
      config: default
      split: test
      revision: None
    metrics:
    - type: ndcg_at_10
      value: 79.364
  - task:
      type: Retrieval
    dataset:
      name: MTEB Touche2020
      type: webis-touche2020
      config: default
      split: test
      revision: None
    metrics:
    - type: ndcg_at_10
      value: 31.927
  - task:
      type: Classification
    dataset:
      name: MTEB ToxicConversationsClassification
      type: mteb/toxic_conversations_50k
      config: default
      split: test
      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
    metrics:
    - type: accuracy
      value: 73.0414
    - type: ap
      value: 16.06723077348852
    - type: f1
      value: 56.73470421774399
  - task:
      type: Classification
    dataset:
      name: MTEB TweetSentimentExtractionClassification
      type: mteb/tweet_sentiment_extraction
      config: default
      split: test
      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
    metrics:
    - type: accuracy
      value: 64.72269383135257
    - type: f1
      value: 64.70143593421479
  - task:
      type: Clustering
    dataset:
      name: MTEB TwentyNewsgroupsClustering
      type: mteb/twentynewsgroups-clustering
      config: default
      split: test
      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
    metrics:
    - type: v_measure
      value: 46.06343037695152
  - task:
      type: PairClassification
    dataset:
      name: MTEB TwitterSemEval2015
      type: mteb/twittersemeval2015-pairclassification
      config: default
      split: test
      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
    metrics:
    - type: cos_sim_accuracy
      value: 85.59337187816654
    - type: cos_sim_ap
      value: 72.23331527941706
    - type: cos_sim_f1
      value: 67.22915138175593
    - type: cos_sim_precision
      value: 62.64813126709207
    - type: cos_sim_recall
      value: 72.53298153034301
    - type: dot_accuracy
      value: 85.59337187816654
    - type: dot_ap
      value: 72.23332517262921
    - type: dot_f1
      value: 67.22915138175593
    - type: dot_precision
      value: 62.64813126709207
    - type: dot_recall
      value: 72.53298153034301
    - type: euclidean_accuracy
      value: 85.59337187816654
    - type: euclidean_ap
      value: 72.23331029091486
    - type: euclidean_f1
      value: 67.22915138175593
    - type: euclidean_precision
      value: 62.64813126709207
    - type: euclidean_recall
      value: 72.53298153034301
    - type: manhattan_accuracy
      value: 85.4622399713894
    - type: manhattan_ap
      value: 72.05180729774357
    - type: manhattan_f1
      value: 67.12683347713546
    - type: manhattan_precision
      value: 62.98866527874162
    - type: manhattan_recall
      value: 71.84696569920844
    - type: max_accuracy
      value: 85.59337187816654
    - type: max_ap
      value: 72.23332517262921
    - type: max_f1
      value: 67.22915138175593
  - task:
      type: PairClassification
    dataset:
      name: MTEB TwitterURLCorpus
      type: mteb/twitterurlcorpus-pairclassification
      config: default
      split: test
      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
    metrics:
    - type: cos_sim_accuracy
      value: 89.08681647067955
    - type: cos_sim_ap
      value: 86.31913876322757
    - type: cos_sim_f1
      value: 78.678007640741
    - type: cos_sim_precision
      value: 73.95988616343678
    - type: cos_sim_recall
      value: 84.03911302740991
    - type: dot_accuracy
      value: 89.08681647067955
    - type: dot_ap
      value: 86.31913976395484
    - type: dot_f1
      value: 78.678007640741
    - type: dot_precision
      value: 73.95988616343678
    - type: dot_recall
      value: 84.03911302740991
    - type: euclidean_accuracy
      value: 89.08681647067955
    - type: euclidean_ap
      value: 86.31913869004254
    - type: euclidean_f1
      value: 78.678007640741
    - type: euclidean_precision
      value: 73.95988616343678
    - type: euclidean_recall
      value: 84.03911302740991
    - type: manhattan_accuracy
      value: 89.06547133930997
    - type: manhattan_ap
      value: 86.24122868846949
    - type: manhattan_f1
      value: 78.74963094183643
    - type: manhattan_precision
      value: 75.62375956903884
    - type: manhattan_recall
      value: 82.14505697566985
    - type: max_accuracy
      value: 89.08681647067955
    - type: max_ap
      value: 86.31913976395484
    - type: max_f1
      value: 78.74963094183643
---


# Cohere embed-english-light-v3.0

This repository contains the tokenizer for the Cohere `embed-english-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-english-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-english-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. 

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