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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). |