--- tags: - mteb model-index: - name: embed-multilingual-light-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: 70.02985074626865 - type: ap value: 33.228065779544146 - type: f1 value: 64.27173953207297 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 90.701225 - type: ap value: 87.07178174251762 - type: f1 value: 90.69168484877625 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 46.550000000000004 - type: f1 value: 44.7233215588199 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: ndcg_at_10 value: 53.369 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 44.206988765030744 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 33.913737041277 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 58.544257541214925 - type: mrr value: 72.07151651057468 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 84.79582115243736 - type: cos_sim_spearman value: 84.01396250789998 - type: euclidean_pearson value: 83.90766476102458 - type: euclidean_spearman value: 84.01396250789998 - type: manhattan_pearson value: 84.75071274784274 - type: manhattan_spearman value: 85.02482891467078 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 78.12337662337663 - type: f1 value: 77.48610340227478 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 38.68268504601174 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 32.20870648143671 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 46.259 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 44.555 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 56.564 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 36.162 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 26.185000000000002 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 41.547 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 39.042 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 38.086999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 32.088 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 27.006999999999998 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 37.336999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 38.011 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 32.287 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: ndcg_at_10 value: 24.804000000000002 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: ndcg_at_10 value: 38.055 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 46.665 - type: f1 value: 40.77568559660878 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: ndcg_at_10 value: 85.52499999999999 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: ndcg_at_10 value: 36.161 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: ndcg_at_10 value: 66.878 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 85.6372 - type: ap value: 80.54846874011302 - type: f1 value: 85.61438421821343 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: test revision: None metrics: - type: ndcg_at_10 value: 40.487 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 91.8559051527588 - type: f1 value: 91.6271749996447 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 62.17738258093936 - type: f1 value: 45.80307070449218 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 67.42434431741762 - type: f1 value: 65.39580264698957 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 72.60928043039677 - type: f1 value: 72.30912915707411 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 35.17967476592229 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 30.993641089208683 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 31.362481813275295 - type: mrr value: 32.43717742343303 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: ndcg_at_10 value: 32.123000000000005 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: ndcg_at_10 value: 55.51199999999999 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 87.847 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 49.4973643968247 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 60.2135284243427 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: ndcg_at_10 value: 17.1 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 83.7330191296952 - type: cos_sim_spearman value: 77.03523134004043 - type: euclidean_pearson value: 80.86067787185137 - type: euclidean_spearman value: 77.03522959536473 - type: manhattan_pearson value: 80.76089708603587 - type: manhattan_spearman value: 76.86245377437302 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 80.46387812633851 - type: cos_sim_spearman value: 73.21878234127571 - type: euclidean_pearson value: 76.82160699895033 - type: euclidean_spearman value: 73.21878234127571 - type: manhattan_pearson value: 76.75657006349886 - type: manhattan_spearman value: 73.19160258034827 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 79.06411399119807 - type: cos_sim_spearman value: 79.49916779764082 - type: euclidean_pearson value: 79.3356521660954 - type: euclidean_spearman value: 79.49916779764082 - type: manhattan_pearson value: 79.04971532119936 - type: manhattan_spearman value: 79.16859911220654 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 80.6940934994372 - type: cos_sim_spearman value: 76.9552055757283 - type: euclidean_pearson value: 79.52818133592284 - type: euclidean_spearman value: 76.9552055757283 - type: manhattan_pearson value: 79.35220459438406 - type: manhattan_spearman value: 76.85314462036561 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 85.58608774451231 - type: cos_sim_spearman value: 86.42805701554927 - type: euclidean_pearson value: 86.01117122595934 - type: euclidean_spearman value: 86.42805701554927 - type: manhattan_pearson value: 86.01345208923057 - type: manhattan_spearman value: 86.43179450307953 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 83.18733039014667 - type: cos_sim_spearman value: 84.3339529564109 - type: euclidean_pearson value: 83.54530885349595 - type: euclidean_spearman value: 84.3339529564109 - type: manhattan_pearson value: 83.47015931913937 - type: manhattan_spearman value: 84.22564786654777 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-en) config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 87.88402211340522 - type: cos_sim_spearman value: 88.6693290310468 - type: euclidean_pearson value: 88.24947476618257 - type: euclidean_spearman value: 88.6693290310468 - type: manhattan_pearson value: 88.24496656367964 - type: manhattan_spearman value: 88.52029848819545 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (en) config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 64.96467575926597 - type: cos_sim_spearman value: 65.30666900046252 - type: euclidean_pearson value: 66.58031971340725 - type: euclidean_spearman value: 65.30666900046252 - type: manhattan_pearson value: 66.56530433327998 - type: manhattan_spearman value: 65.42121899024113 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 85.31047656296519 - type: cos_sim_spearman value: 85.46101092708824 - type: euclidean_pearson value: 85.75896623084044 - type: euclidean_spearman value: 85.46101092708824 - type: manhattan_pearson value: 85.57323880630182 - type: manhattan_spearman value: 85.23375523080594 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 79.89731978284804 - type: mrr value: 94.28980424078465 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - type: ndcg_at_10 value: 67.95 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.85643564356435 - type: cos_sim_ap value: 96.59618618212247 - type: cos_sim_f1 value: 92.6221335992024 - type: cos_sim_precision value: 92.34592445328032 - type: cos_sim_recall value: 92.9 - type: dot_accuracy value: 99.85643564356435 - type: dot_ap value: 96.5961861821225 - type: dot_f1 value: 92.6221335992024 - type: dot_precision value: 92.34592445328032 - type: dot_recall value: 92.9 - type: euclidean_accuracy value: 99.85643564356435 - type: euclidean_ap value: 96.5961861821225 - type: euclidean_f1 value: 92.6221335992024 - type: euclidean_precision value: 92.34592445328032 - type: euclidean_recall value: 92.9 - type: manhattan_accuracy value: 99.85841584158416 - type: manhattan_ap value: 96.5578240948512 - type: manhattan_f1 value: 92.71523178807946 - type: manhattan_precision value: 94.4963655244029 - type: manhattan_recall value: 91.0 - type: max_accuracy value: 99.85841584158416 - type: max_ap value: 96.5961861821225 - type: max_f1 value: 92.71523178807946 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 60.84750068050385 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 33.96844721192451 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 50.454280909595205 - type: mrr value: 51.24249320940497 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 29.998438678552517 - type: cos_sim_spearman value: 30.409482543506876 - type: dot_pearson value: 29.998443850173224 - type: dot_spearman value: 30.409482543506876 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: ndcg_at_10 value: 78.93 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics: - type: ndcg_at_10 value: 29.482999999999997 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 70.65859999999999 - type: ap value: 15.03693738050973 - type: f1 value: 54.94379403846167 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 64.4567062818336 - type: f1 value: 64.48980729427107 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 42.08554991843959 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 84.75293556654945 - type: cos_sim_ap value: 69.40551043272129 - type: cos_sim_f1 value: 65.56335231034026 - type: cos_sim_precision value: 65.79856497475419 - type: cos_sim_recall value: 65.32981530343008 - type: dot_accuracy value: 84.75293556654945 - type: dot_ap value: 69.40550704470631 - type: dot_f1 value: 65.56335231034026 - type: dot_precision value: 65.79856497475419 - type: dot_recall value: 65.32981530343008 - type: euclidean_accuracy value: 84.75293556654945 - type: euclidean_ap value: 69.4055136381454 - type: euclidean_f1 value: 65.56335231034026 - type: euclidean_precision value: 65.79856497475419 - type: euclidean_recall value: 65.32981530343008 - type: manhattan_accuracy value: 84.6337247422066 - type: manhattan_ap value: 69.13628354134198 - type: manhattan_f1 value: 65.46998180715585 - type: manhattan_precision value: 60.58361391694726 - type: manhattan_recall value: 71.21372031662268 - type: max_accuracy value: 84.75293556654945 - type: max_ap value: 69.4055136381454 - type: max_f1 value: 65.56335231034026 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 89.04800714091667 - type: cos_sim_ap value: 85.84596325009252 - type: cos_sim_f1 value: 78.39228527221042 - type: cos_sim_precision value: 73.58643518205768 - type: cos_sim_recall value: 83.86972590083154 - type: dot_accuracy value: 89.04800714091667 - type: dot_ap value: 85.8459646697087 - type: dot_f1 value: 78.39228527221042 - type: dot_precision value: 73.58643518205768 - type: dot_recall value: 83.86972590083154 - type: euclidean_accuracy value: 89.04800714091667 - type: euclidean_ap value: 85.84596376376919 - type: euclidean_f1 value: 78.39228527221042 - type: euclidean_precision value: 73.58643518205768 - type: euclidean_recall value: 83.86972590083154 - type: manhattan_accuracy value: 89.0266620095471 - type: manhattan_ap value: 85.80124417850608 - type: manhattan_f1 value: 78.37817859254879 - type: manhattan_precision value: 75.36963321012226 - type: manhattan_recall value: 81.63689559593472 - type: max_accuracy value: 89.04800714091667 - type: max_ap value: 85.8459646697087 - type: max_f1 value: 78.39228527221042 --- # Cohere embed-multilingual-light-v3.0 This repository contains the tokenizer for the Cohere `embed-multilingual-light-v3.0` model. See our blogpost [Cohere Embed V3](https://txt.cohere.com/introducing-embed-v3/) for more details on this model. You can use the embedding model either via the Cohere API, AWS SageMaker or in your private deployments. ## Usage Cohere API The following code snippet shows the usage of the Cohere API. Install the cohere SDK via: ``` pip install -U cohere ``` Get your free API key on: www.cohere.com ```python # This snippet shows and example how to use the Cohere Embed V3 models for semantic search. # Make sure to have the Cohere SDK in at least v4.30 install: pip install -U cohere # Get your API key from: www.cohere.com import cohere import numpy as np cohere_key = "{YOUR_COHERE_API_KEY}" #Get your API key from www.cohere.com co = cohere.Client(cohere_key) docs = ["The capital of France is Paris", "PyTorch is a machine learning framework based on the Torch library.", "The average cat lifespan is between 13-17 years"] #Encode your documents with input type 'search_document' doc_emb = co.embed(docs, input_type="search_document", model="embed-multilingual-light-v3.0").embeddings doc_emb = np.asarray(doc_emb) #Encode your query with input type 'search_query' query = "What is Pytorch" query_emb = co.embed([query], input_type="search_query", model="embed-multilingual-light-v3.0").embeddings query_emb = np.asarray(query_emb) query_emb.shape #Compute the dot product between query embedding and document embedding scores = np.dot(query_emb, doc_emb.T)[0] #Find the highest scores max_idx = np.argsort(-scores) print(f"Query: {query}") for idx in max_idx: print(f"Score: {scores[idx]:.2f}") print(docs[idx]) print("--------") ``` ## Usage AWS SageMaker The embedding model can be privately deployed in your AWS Cloud using our [AWS SageMaker marketplace offering](https://aws.amazon.com/marketplace/pp/prodview-z6huxszcqc25i). It runs privately in your VPC, with latencies as low as 5ms for query encoding. ## Usage AWS Bedrock Soon the model will also be available via AWS Bedrock. Stay tuned ## Private Deployment You want to run the model on your own hardware? [Contact Sales](https://cohere.com/contact-sales) to learn more. ## Supported Languages This model was trained on nearly 1B English training pairs and nearly 0.5B Non-English training pairs from 100+ languages. Evaluation results can be found in the [Embed V3.0 Benchmark Results spreadsheet](https://docs.google.com/spreadsheets/d/1w7gnHWMDBdEUrmHgSfDnGHJgVQE5aOiXCCwO3uNH_mI/edit?usp=sharing).