--- tags: - mteb model-index: - name: embed-multilingual-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: 77.85074626865672 - type: ap value: 41.53151744002314 - type: f1 value: 71.94656880817726 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 95.600375 - type: ap value: 93.57882128753579 - type: f1 value: 95.59945484944305 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 49.794 - type: f1 value: 48.740439663130985 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: ndcg_at_10 value: 55.105000000000004 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 48.15653426568874 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 40.78876256237919 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 62.12873500780318 - type: mrr value: 75.87037769863255 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 86.01183720167818 - type: cos_sim_spearman value: 85.00916590717613 - type: euclidean_pearson value: 84.072733561361 - type: euclidean_spearman value: 85.00916590717613 - type: manhattan_pearson value: 83.89233507343208 - type: manhattan_spearman value: 84.87482549674115 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 86.09415584415584 - type: f1 value: 86.05173549773973 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 40.49773000165541 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 36.909633073998876 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 49.481 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 47.449999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 59.227 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 37.729 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 29.673 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 44.278 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 43.218 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 40.63741666666667 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 33.341 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 29.093999999999998 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 40.801 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 40.114 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 33.243 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: ndcg_at_10 value: 29.958000000000002 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: ndcg_at_10 value: 41.004000000000005 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 48.150000000000006 - type: f1 value: 43.69803436468346 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: ndcg_at_10 value: 88.532 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: ndcg_at_10 value: 44.105 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: ndcg_at_10 value: 70.612 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 93.9672 - type: ap value: 90.72947025321227 - type: f1 value: 93.96271599852622 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: test revision: None metrics: - type: ndcg_at_10 value: 43.447 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 94.92476060191517 - type: f1 value: 94.69383758972194 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 78.8873689010488 - type: f1 value: 62.537485052253885 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 74.51244115669132 - type: f1 value: 72.40074466830153 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 79.00470746469401 - type: f1 value: 79.03758200183096 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 36.183215937303736 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 33.443759055792135 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 32.58713095176127 - type: mrr value: 33.7326038566206 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: ndcg_at_10 value: 36.417 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: ndcg_at_10 value: 63.415 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 88.924 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 58.10997801688676 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 65.02444843766075 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: ndcg_at_10 value: 19.339000000000002 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 86.61540076033945 - type: cos_sim_spearman value: 82.1820253476181 - type: euclidean_pearson value: 83.73901215845989 - type: euclidean_spearman value: 82.182021064594 - type: manhattan_pearson value: 83.76685139192031 - type: manhattan_spearman value: 82.14074705306663 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 85.62241109228789 - type: cos_sim_spearman value: 77.62042143066208 - type: euclidean_pearson value: 82.77237785274072 - type: euclidean_spearman value: 77.62042142290566 - type: manhattan_pearson value: 82.70945589621266 - type: manhattan_spearman value: 77.57245632826351 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 84.8307075352031 - type: cos_sim_spearman value: 85.15620774806095 - type: euclidean_pearson value: 84.21956724564915 - type: euclidean_spearman value: 85.15620774806095 - type: manhattan_pearson value: 84.0677597021641 - type: manhattan_spearman value: 85.02572172855729 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 83.33749463516592 - type: cos_sim_spearman value: 80.01967438481185 - type: euclidean_pearson value: 82.16884494022196 - type: euclidean_spearman value: 80.01967218194336 - type: manhattan_pearson value: 81.94431512413773 - type: manhattan_spearman value: 79.81636247503731 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 88.2070761097028 - type: cos_sim_spearman value: 88.92297656560552 - type: euclidean_pearson value: 87.95961374550303 - type: euclidean_spearman value: 88.92298798854765 - type: manhattan_pearson value: 87.85515971478168 - type: manhattan_spearman value: 88.8100644762342 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 85.48103354546488 - type: cos_sim_spearman value: 86.91850928862898 - type: euclidean_pearson value: 86.06766986527145 - type: euclidean_spearman value: 86.91850928862898 - type: manhattan_pearson value: 86.02705585360717 - type: manhattan_spearman value: 86.86666545434721 - 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: 90.30267248880148 - type: cos_sim_spearman value: 90.08752166657892 - type: euclidean_pearson value: 90.4697525265135 - type: euclidean_spearman value: 90.08752166657892 - type: manhattan_pearson value: 90.57174978064741 - type: manhattan_spearman value: 90.212834942229 - 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: 67.10616236380835 - type: cos_sim_spearman value: 66.81483164137016 - type: euclidean_pearson value: 68.48505128040803 - type: euclidean_spearman value: 66.81483164137016 - type: manhattan_pearson value: 68.46133268524885 - type: manhattan_spearman value: 66.83684227990202 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 87.12768629069949 - type: cos_sim_spearman value: 88.78683817318573 - type: euclidean_pearson value: 88.47603251297261 - type: euclidean_spearman value: 88.78683817318573 - type: manhattan_pearson value: 88.46483630890225 - type: manhattan_spearman value: 88.76593424921617 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 84.30886658431281 - type: mrr value: 95.5964251797585 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - type: ndcg_at_10 value: 70.04599999999999 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.87524752475248 - type: cos_sim_ap value: 96.79160651306724 - type: cos_sim_f1 value: 93.57798165137615 - type: cos_sim_precision value: 95.42619542619542 - type: cos_sim_recall value: 91.8 - type: dot_accuracy value: 99.87524752475248 - type: dot_ap value: 96.79160651306724 - type: dot_f1 value: 93.57798165137615 - type: dot_precision value: 95.42619542619542 - type: dot_recall value: 91.8 - type: euclidean_accuracy value: 99.87524752475248 - type: euclidean_ap value: 96.79160651306724 - type: euclidean_f1 value: 93.57798165137615 - type: euclidean_precision value: 95.42619542619542 - type: euclidean_recall value: 91.8 - type: manhattan_accuracy value: 99.87326732673267 - type: manhattan_ap value: 96.7574606340297 - type: manhattan_f1 value: 93.45603271983639 - type: manhattan_precision value: 95.60669456066945 - type: manhattan_recall value: 91.4 - type: max_accuracy value: 99.87524752475248 - type: max_ap value: 96.79160651306724 - type: max_f1 value: 93.57798165137615 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 68.12288811917144 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 35.22267280169542 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 52.39780995606098 - type: mrr value: 53.26826563958916 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 31.15118979569649 - type: cos_sim_spearman value: 30.99428921914572 - type: dot_pearson value: 31.151189338601924 - type: dot_spearman value: 30.99428921914572 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: ndcg_at_10 value: 83.372 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics: - type: ndcg_at_10 value: 32.698 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 71.1998 - type: ap value: 14.646205259325157 - type: f1 value: 54.96172518137252 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 62.176004527447645 - type: f1 value: 62.48549068096645 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 50.13767789739772 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 86.38016331882935 - type: cos_sim_ap value: 75.1635976260804 - type: cos_sim_f1 value: 69.29936305732484 - type: cos_sim_precision value: 66.99507389162561 - type: cos_sim_recall value: 71.76781002638522 - type: dot_accuracy value: 86.38016331882935 - type: dot_ap value: 75.16359359202374 - type: dot_f1 value: 69.29936305732484 - type: dot_precision value: 66.99507389162561 - type: dot_recall value: 71.76781002638522 - type: euclidean_accuracy value: 86.38016331882935 - type: euclidean_ap value: 75.16360246558416 - type: euclidean_f1 value: 69.29936305732484 - type: euclidean_precision value: 66.99507389162561 - type: euclidean_recall value: 71.76781002638522 - type: manhattan_accuracy value: 86.27883411813792 - type: manhattan_ap value: 75.02872038741897 - type: manhattan_f1 value: 69.29256284011403 - type: manhattan_precision value: 68.07535641547861 - type: manhattan_recall value: 70.55408970976254 - type: max_accuracy value: 86.38016331882935 - type: max_ap value: 75.16360246558416 - type: max_f1 value: 69.29936305732484 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 89.39729110878255 - type: cos_sim_ap value: 86.48560260020555 - type: cos_sim_f1 value: 79.35060602690982 - type: cos_sim_precision value: 76.50632549496105 - type: cos_sim_recall value: 82.41453649522637 - type: dot_accuracy value: 89.39729110878255 - type: dot_ap value: 86.48559829915334 - type: dot_f1 value: 79.35060602690982 - type: dot_precision value: 76.50632549496105 - type: dot_recall value: 82.41453649522637 - type: euclidean_accuracy value: 89.39729110878255 - type: euclidean_ap value: 86.48559993122497 - type: euclidean_f1 value: 79.35060602690982 - type: euclidean_precision value: 76.50632549496105 - type: euclidean_recall value: 82.41453649522637 - type: manhattan_accuracy value: 89.36042224550782 - type: manhattan_ap value: 86.47238558562499 - type: manhattan_f1 value: 79.24500641378047 - type: manhattan_precision value: 75.61726236273344 - type: manhattan_recall value: 83.23837388358484 - type: max_accuracy value: 89.39729110878255 - type: max_ap value: 86.48560260020555 - type: max_f1 value: 79.35060602690982 --- # Cohere embed-multilingual-v3.0 This repository contains the tokenizer for the Cohere `embed-multilingual-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-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-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).