--- tags: - mteb model-index: - name: embed-english-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: 78.62686567164178 - type: ap value: 43.50072127690769 - type: f1 value: 73.12414870629323 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification 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: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 51.016000000000005 - type: f1 value: 48.9266470039522 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: ndcg_at_10 value: 50.806 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 46.19304218375896 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 37.57785041962193 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 60.11396377106911 - type: mrr value: 72.9068284746955 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES 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: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 79.69805194805193 - type: f1 value: 79.07431143559548 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 38.973417975095934 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 34.51608057107556 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 46.615 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 45.383 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 57.062999999999995 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 37.201 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 27.473 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 41.868 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 42.059000000000005 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 38.885416666666664 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 32.134 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 28.052 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 38.237 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 37.875 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 32.665 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: ndcg_at_10 value: 28.901 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: ndcg_at_10 value: 41.028 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 52.745 - type: f1 value: 46.432564522368054 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: ndcg_at_10 value: 87.64 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: ndcg_at_10 value: 38.834999999999994 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: ndcg_at_10 value: 66.793 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification 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: type: msmarco name: MTEB MSMARCO config: default split: test revision: None metrics: - type: ndcg_at_10 value: 41.325 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 93.62517099863202 - type: f1 value: 93.3852826127328 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 64.93388052895577 - type: f1 value: 48.035548201830366 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 70.01344989912577 - type: f1 value: 68.01236893966525 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 76.34498991257564 - type: f1 value: 75.72876911765213 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 37.66326759167091 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 33.53562430544494 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 31.86814320224619 - type: mrr value: 33.02567757581291 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: ndcg_at_10 value: 33.649 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: ndcg_at_10 value: 57.994 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 88.115 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 53.4970929237201 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 63.59086757472922 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: ndcg_at_10 value: 18.098 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 85.05019841005287 - 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: type: mteb/sts12-sts name: MTEB STS12 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: type: mteb/sts13-sts name: MTEB STS13 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: type: mteb/sts14-sts name: MTEB STS14 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: type: mteb/sts15-sts name: MTEB STS15 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: type: mteb/sts16-sts name: MTEB STS16 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: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-en) 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: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (en) 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: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark 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: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 81.62235389081138 - type: mrr value: 94.65811965811966 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - type: ndcg_at_10 value: 66.687 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions 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: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 62.938055854344455 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 36.479716154538224 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 50.75827388766867 - type: mrr value: 51.65291305916306 - task: type: Summarization dataset: type: mteb/summeval name: 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: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: ndcg_at_10 value: 79.364 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics: - type: ndcg_at_10 value: 31.927 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification 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: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 64.72269383135257 - type: f1 value: 64.70143593421479 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 46.06343037695152 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 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: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus 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).