|
--- |
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tags: |
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- mteb |
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model-index: |
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- name: embed-english-v3.0 |
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results: |
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- 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). |
