--- 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).