|
--- |
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tags: |
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- mteb |
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model-index: |
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- name: embed-english-light-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: 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). |
