gte - multilingual - baseオープンソース多言語文章埋め込みモデル - 無料でデプロイ可能、50種類以上の言語の類似度計算をサポート

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Gte Multilingual Base

Alibaba-NLPによって開発

GTE Multilingual Base は50以上の言語をサポートする多言語文埋め込みモデルで、文類似度計算などのタスクに適しています。

テキスト埋め込み

Transformers

複数言語対応オープンソースライセンス:Apache-2.0 #多言語文類似度 #密ベクトル検索 #言語間テキストマッチング

ダウンロード数 1.2M

リリース時間 : 7/20/2024

モデル概要

このモデルはTransformerアーキテクチャに基づく多言語文埋め込みモデルで、異なる言語の文を統一されたベクトル空間にマッピングし、言語間の文類似度計算や情報検索を容易にします。

モデル特徴

多言語サポート

50以上の言語の文埋め込みをサポートし、言語間の意味理解を実現

多機能タスク適応

文類似度、クラスタリング、分類、検索など様々な自然言語処理タスクに適用可能

高性能

複数のベンチマークテストで優れた性能指標を示す

モデル能力

文類似度計算

テキストクラスタリング

テキスト分類

情報検索

テキスト再ランキング

バイリンガルテキストマイニング

使用事例

情報検索

言語間ドキュメント検索

異なる言語のドキュメントコレクションから関連ドキュメントを検索

AlloprofRetrievalテストでNDCG@10が53.638を達成

テキスト分類

製品レビュー分類

多言語の製品レビューを感情分類

AmazonPolarityClassificationで精度80.72%を達成

文類似度

言語間文マッチング

異なる言語の文間の意味的類似度を計算

BIOSSESテストでSpearman相関係数81.21を達成

tags:

mteb
sentence-transformers
transformers
multilingual
sentence-similarity license: apache-2.0 language:
af
ar
az
be
bg
bn
ca
ceb
cs
cy
da
de
el
en
es
et
eu
fa
fi
fr
gl
gu
he
hi
hr
ht
hu
hy
id
is
it
ja
jv
ka
kk
km
kn
ko
ky
lo
lt
lv
mk
ml
mn
mr
ms
my
ne
nl
'no'
pa
pl
pt
qu
ro
ru
si
sk
sl
so
sq
sr
sv
sw
ta
te
th
tl
tr
uk
ur
vi
yo
zh model-index:
name: gte-multilingual-base (dense) results:
- task: type: Clustering dataset: type: PL-MTEB/8tags-clustering name: MTEB 8TagsClustering config: default split: test revision: None metrics:
  - type: v_measure value: 33.66681726329994
- task: type: STS dataset: type: C-MTEB/AFQMC name: MTEB AFQMC config: default split: validation revision: b44c3b011063adb25877c13823db83bb193913c4 metrics:
  - type: cos_sim_spearman value: 43.54760696384009
- task: type: STS dataset: type: C-MTEB/ATEC name: MTEB ATEC config: default split: test revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865 metrics:
  - type: cos_sim_spearman value: 48.91186363417501
- task: type: Classification dataset: type: PL-MTEB/allegro-reviews name: MTEB AllegroReviews config: default split: test revision: None metrics:
  - type: accuracy value: 41.689860834990064
- task: type: Clustering dataset: type: lyon-nlp/alloprof name: MTEB AlloProfClusteringP2P config: default split: test revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b metrics:
  - type: v_measure value: 54.20241337977897
- task: type: Clustering dataset: type: lyon-nlp/alloprof name: MTEB AlloProfClusteringS2S config: default split: test revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b metrics:
  - type: v_measure value: 44.34083695608643
- task: type: Reranking dataset: type: lyon-nlp/mteb-fr-reranking-alloprof-s2p name: MTEB AlloprofReranking config: default split: test revision: 666fdacebe0291776e86f29345663dfaf80a0db9 metrics:
  - type: map value: 64.91495250072002
- task: type: Retrieval dataset: type: lyon-nlp/alloprof name: MTEB AlloprofRetrieval config: default split: test revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b metrics:
  - type: ndcg_at_10 value: 53.638
- task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics:
  - type: accuracy value: 75.95522388059702
- task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics:
  - type: accuracy value: 80.717625
- task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics:
  - type: accuracy value: 43.64199999999999
- task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (de) config: de split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics:
  - type: accuracy value: 40.108
- task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (es) config: es split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics:
  - type: accuracy value: 40.169999999999995
- task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (fr) config: fr split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics:
  - type: accuracy value: 39.56799999999999
- task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (ja) config: ja split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics:
  - type: accuracy value: 35.75000000000001
- task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (zh) config: zh split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics:
  - type: accuracy value: 33.342000000000006
- task: type: Retrieval dataset: type: mteb/arguana name: MTEB ArguAna config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics:
  - type: ndcg_at_10 value: 58.231
- task: type: Retrieval dataset: type: clarin-knext/arguana-pl name: MTEB ArguAna-PL config: default split: test revision: 63fc86750af76253e8c760fc9e534bbf24d260a2 metrics:
  - type: ndcg_at_10 value: 53.166000000000004
- task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics:
  - type: v_measure value: 46.01900557959478
- task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics:
  - type: v_measure value: 41.06626465345723
- task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics:
  - type: map value: 61.87514497610431
- task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics:
  - type: cos_sim_spearman value: 81.21450112991194
- task: type: STS dataset: type: C-MTEB/BQ name: MTEB BQ config: default split: test revision: e3dda5e115e487b39ec7e618c0c6a29137052a55 metrics:
  - type: cos_sim_spearman value: 51.71589543397271
- task: type: Retrieval dataset: type: maastrichtlawtech/bsard name: MTEB BSARDRetrieval config: default split: test revision: 5effa1b9b5fa3b0f9e12523e6e43e5f86a6e6d59 metrics:
  - type: ndcg_at_10 value: 26.115
- task: type: BitextMining dataset: type: mteb/bucc-bitext-mining name: MTEB BUCC (de-en) config: de-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics:
  - type: f1 value: 98.6169102296451
- task: type: BitextMining dataset: type: mteb/bucc-bitext-mining name: MTEB BUCC (fr-en) config: fr-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics:
  - type: f1 value: 97.89603052314916
- task: type: BitextMining dataset: type: mteb/bucc-bitext-mining name: MTEB BUCC (ru-en) config: ru-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics:
  - type: f1 value: 97.12388869645537
- task: type: BitextMining dataset: type: mteb/bucc-bitext-mining name: MTEB BUCC (zh-en) config: zh-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics:
  - type: f1 value: 98.15692469720906
- task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics:
  - type: accuracy value: 85.36038961038962
- task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics:
  - type: v_measure value: 37.5903826674123
- task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics:
  - type: v_measure value: 34.21474277151329
- task: type: Classification dataset: type: PL-MTEB/cbd name: MTEB CBD config: default split: test revision: None metrics:
  - type: accuracy value: 62.519999999999996
- task: type: PairClassification dataset: type: PL-MTEB/cdsce-pairclassification name: MTEB CDSC-E config: default split: test revision: None metrics:
  - type: cos_sim_ap value: 74.90132799162956
- task: type: STS dataset: type: PL-MTEB/cdscr-sts name: MTEB CDSC-R config: default split: test revision: None metrics:
  - type: cos_sim_spearman value: 90.30727955142524
- task: type: Clustering dataset: type: C-MTEB/CLSClusteringP2P name: MTEB CLSClusteringP2P config: default split: test revision: 4b6227591c6c1a73bc76b1055f3b7f3588e72476 metrics:
  - type: v_measure value: 37.94850105022274
- task: type: Clustering dataset: type: C-MTEB/CLSClusteringS2S name: MTEB CLSClusteringS2S config: default split: test revision: e458b3f5414b62b7f9f83499ac1f5497ae2e869f metrics:
  - type: v_measure value: 38.11958675421534
- task: type: Reranking dataset: type: C-MTEB/CMedQAv1-reranking name: MTEB CMedQAv1 config: default split: test revision: 8d7f1e942507dac42dc58017c1a001c3717da7df metrics:
  - type: map value: 86.10950950485399
- task: type: Reranking dataset: type: C-MTEB/CMedQAv2-reranking name: MTEB CMedQAv2 config: default split: test revision: 23d186750531a14a0357ca22cd92d712fd512ea0 metrics:
  - type: map value: 87.28038294231966
- task: type: Retrieval dataset: type: mteb/cqadupstack-android name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: f46a197baaae43b4f621051089b82a364682dfeb metrics:
  - type: ndcg_at_10 value: 47.099000000000004
- task: type: Retrieval dataset: type: mteb/cqadupstack-english name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 metrics:
  - type: ndcg_at_10 value: 45.973000000000006
- task: type: Retrieval dataset: type: mteb/cqadupstack-gaming name: MTEB CQADupstackGamingRetrieval config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics:
  - type: ndcg_at_10 value: 55.606
- task: type: Retrieval dataset: type: mteb/cqadupstack-gis name: MTEB CQADupstackGisRetrieval config: default split: test revision: 5003b3064772da1887988e05400cf3806fe491f2 metrics:
  - type: ndcg_at_10 value: 36.638
- task: type: Retrieval dataset: type: mteb/cqadupstack-mathematica name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: 90fceea13679c63fe563ded68f3b6f06e50061de metrics:
  - type: ndcg_at_10 value: 30.711
- task: type: Retrieval dataset: type: mteb/cqadupstack-physics name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 metrics:
  - type: ndcg_at_10 value: 44.523
- task: type: Retrieval dataset: type: mteb/cqadupstack-programmers name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 metrics:
  - type: ndcg_at_10 value: 37.940000000000005
- task: type: Retrieval dataset: type: mteb/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics:
  - type: ndcg_at_10 value: 38.12183333333333
- task: type: Retrieval dataset: type: mteb/cqadupstack-stats name: MTEB CQADupstackStatsRetrieval config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a metrics:
  - type: ndcg_at_10 value: 32.684000000000005
- task: type: Retrieval dataset: type: mteb/cqadupstack-tex name: MTEB CQADupstackTexRetrieval config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 metrics:
  - type: ndcg_at_10 value: 26.735
- task: type: Retrieval dataset: type: mteb/cqadupstack-unix name: MTEB CQADupstackUnixRetrieval config: default split: test revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 metrics:
  - type: ndcg_at_10 value: 36.933
- task: type: Retrieval dataset: type: mteb/cqadupstack-webmasters name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics:
  - type: ndcg_at_10 value: 33.747
- task: type: Retrieval dataset: type: mteb/cqadupstack-wordpress name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics:
  - type: ndcg_at_10 value: 28.872999999999998
- task: type: Retrieval dataset: type: mteb/climate-fever name: MTEB ClimateFEVER config: default split: test revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 metrics:
  - type: ndcg_at_10 value: 34.833
- task: type: Retrieval dataset: type: C-MTEB/CmedqaRetrieval name: MTEB CmedqaRetrieval config: default split: dev revision: cd540c506dae1cf9e9a59c3e06f42030d54e7301 metrics:
  - type: ndcg_at_10 value: 43.78
- task: type: PairClassification dataset: type: C-MTEB/CMNLI name: MTEB Cmnli config: default split: validation revision: 41bc36f332156f7adc9e38f53777c959b2ae9766 metrics:
  - type: cos_sim_ap value: 84.00640599186677
- task: type: Retrieval dataset: type: C-MTEB/CovidRetrieval name: MTEB CovidRetrieval config: default split: dev revision: 1271c7809071a13532e05f25fb53511ffce77117 metrics:
  - type: ndcg_at_10 value: 80.60000000000001
- task: type: Retrieval dataset: type: mteb/dbpedia name: MTEB DBPedia config: default split: test revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 metrics:
  - type: ndcg_at_10 value: 40.116
- task: type: Retrieval dataset: type: clarin-knext/dbpedia-pl name: MTEB DBPedia-PL config: default split: test revision: 76afe41d9af165cc40999fcaa92312b8b012064a metrics:
  - type: ndcg_at_10 value: 32.498
- task: type: Retrieval dataset: type: C-MTEB/DuRetrieval name: MTEB DuRetrieval config: default split: dev revision: a1a333e290fe30b10f3f56498e3a0d911a693ced metrics:
  - type: ndcg_at_10 value: 87.547
- task: type: Retrieval dataset: type: C-MTEB/EcomRetrieval name: MTEB EcomRetrieval config: default split: dev revision: 687de13dc7294d6fd9be10c6945f9e8fec8166b9 metrics:
  - type: ndcg_at_10 value: 64.85
- task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics:
  - type: accuracy value: 47.949999999999996
- task: type: Retrieval dataset: type: mteb/fever name: MTEB FEVER config: default split: test revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 metrics:
  - type: ndcg_at_10 value: 92.111
- task: type: Retrieval dataset: type: clarin-knext/fiqa-pl name: MTEB FiQA-PL config: default split: test revision: 2e535829717f8bf9dc829b7f911cc5bbd4e6608e metrics:
  - type: ndcg_at_10 value: 28.962
- task: type: Retrieval dataset: type: mteb/fiqa name: MTEB FiQA2018 config: default split: test revision: 27a168819829fe9bcd655c2df245fb19452e8e06 metrics:
  - type: ndcg_at_10 value: 45.005
- task: type: Clustering dataset: type: lyon-nlp/clustering-hal-s2s name: MTEB HALClusteringS2S config: default split: test revision: e06ebbbb123f8144bef1a5d18796f3dec9ae2915 metrics:
  - type: v_measure value: 25.133776435657595
- task: type: Retrieval dataset: type: mteb/hotpotqa name: MTEB HotpotQA config: default split: test revision: ab518f4d6fcca38d87c25209f94beba119d02014 metrics:
  - type: ndcg_at_10 value: 63.036
- task: type: Retrieval dataset: type: clarin-knext/hotpotqa-pl name: MTEB HotpotQA-PL config: default split: test revision: a0bd479ac97b4ccb5bd6ce320c415d0bb4beb907 metrics:
  - type: ndcg_at_10 value: 56.904999999999994
- task: type: Classification dataset: type: C-MTEB/IFlyTek-classification name: MTEB IFlyTek config: default split: validation revision: 421605374b29664c5fc098418fe20ada9bd55f8a metrics:
  - type: accuracy value: 44.59407464409388
- task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics:
  - type: accuracy value: 74.912
- task: type: Classification dataset: type: C-MTEB/JDReview-classification name: MTEB JDReview config: default split: test revision: b7c64bd89eb87f8ded463478346f76731f07bf8b metrics:
  - type: accuracy value: 79.26829268292683
- task: type: STS dataset: type: C-MTEB/LCQMC name: MTEB LCQMC config: default split: test revision: 17f9b096f80380fce5ed12a9be8be7784b337daf metrics:
  - type: cos_sim_spearman value: 74.8601229809791
- task: type: Clustering dataset: type: mlsum name: MTEB MLSUMClusteringP2P config: default split: test revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7 metrics:
  - type: v_measure value: 42.331902754246556
- task: type: Clustering dataset: type: mlsum name: MTEB MLSUMClusteringS2S config: default split: test revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7 metrics:
  - type: v_measure value: 40.92029335502153
- task: type: Reranking dataset: type: C-MTEB/Mmarco-reranking name: MTEB MMarcoReranking config: default split: dev revision: 8e0c766dbe9e16e1d221116a3f36795fbade07f6 metrics:
  - type: map value: 32.19266316591337
- task: type: Retrieval dataset: type: C-MTEB/MMarcoRetrieval name: MTEB MMarcoRetrieval config: default split: dev revision: 539bbde593d947e2a124ba72651aafc09eb33fc2 metrics:
  - type: ndcg_at_10 value: 79.346
- task: type: Retrieval dataset: type: mteb/msmarco name: MTEB MSMARCO config: default split: dev revision: c5a29a104738b98a9e76336939199e264163d4a0 metrics:
  - type: ndcg_at_10 value: 39.922999999999995
- task: type: Retrieval dataset: type: clarin-knext/msmarco-pl name: MTEB MSMARCO-PL config: default split: test revision: 8634c07806d5cce3a6138e260e59b81760a0a640 metrics:
  - type: ndcg_at_10 value: 55.620999999999995
- task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics:
  - type: accuracy value: 92.53989968080255
- task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (de) config: de split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics:
  - type: accuracy value: 88.26993519301212
- task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (es) config: es split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics:
  - type: accuracy value: 90.87725150100067
- task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (fr) config: fr split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics:
  - type: accuracy value: 87.48512370811149
- task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (hi) config: hi split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics:
  - type: accuracy value: 89.45141627823591
- task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (th) config: th split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics:
  - type: accuracy value: 83.45750452079565
- task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics:
  - type: accuracy value: 72.57637938896488
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- task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (nl) config: nl split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics:
  - type: accuracy value: 71.95696032279758
- task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (pl) config: pl split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics:
  - type: accuracy value: 70.11768661735036
- task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (pt) config: pt split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics:
  - type: accuracy value: 71.86953597848016
- task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (ro) config: ro split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics:
  - type: accuracy value: 68.51042367182247
- task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (ru) config: ru split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics:
  - type: accuracy value: 71.65097511768661
- task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (sl) config: sl split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics:
  - type: accuracy value: 66.81573638197713
- task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (sq) config: sq split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics:
  - type: accuracy value: 65.26227303295225
- task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (sv) config: sv split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics:
  - type: accuracy value: 72.51513113651646
- task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (sw) config: sw split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics:
  - type: accuracy value: 58.29858776059179
- task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (ta) config: ta split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics:
  - type: accuracy value: 62.72696704774714
- task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (te) config: te split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics:
  - type: accuracy value: 66.57700067249496
- task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (th) config: th split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics:
  - type: accuracy value: 68.22797579018157
- task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (tl) config: tl split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics:
  - type: accuracy value: 61.97041022192333
- task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (tr) config: tr split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics:
  - type: accuracy value: 70.72629455279085
- task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (ur) config: ur split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics:
  - type: accuracy value: 63.16072629455278
- task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (vi) config: vi split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics:
  - type: accuracy value: 67.92199058507062
- task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (zh-CN) config: zh-CN split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics:
  - type: accuracy value: 74.40484196368527
- task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (zh-TW) config: zh-TW split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics:
  - type: accuracy value: 71.61398789509079
- task: type: Retrieval dataset: type: C-MTEB/MedicalRetrieval name: MTEB MedicalRetrieval config: default split: dev revision: 2039188fb5800a9803ba5048df7b76e6fb151fc6 metrics:
  - type: ndcg_at_10 value: 61.934999999999995
- task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics:
  - type: v_measure value: 33.052031054565205
- task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics:
  - type: v_measure value: 31.969909524076794
- task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics:
  - type: map value: 31.7530992892652
- task: type: Retrieval dataset: type: jinaai/mintakaqa name: MTEB MintakaRetrieval (fr) config: fr split: test revision: efa78cc2f74bbcd21eff2261f9e13aebe40b814e metrics:
  - type: ndcg_at_10 value: 34.705999999999996
- task: type: Retrieval dataset: type: Shitao/MLDR name: MTEB MultiLongDocRetrieval (ar) config: ar split: test revision: None metrics:
  - type: ndcg_at_10 value: 55.166000000000004
- task: type: Retrieval dataset: type: Shitao/MLDR name: MTEB MultiLongDocRetrieval (de) config: de split: test revision: None metrics:
  - type: ndcg_at_10 value: 55.155
- task: type: Retrieval dataset: type: Shitao/MLDR name: MTEB MultiLongDocRetrieval (en) config: en split: test revision: None metrics:
  - type: ndcg_at_10 value: 50.993
- task: type: Retrieval dataset: type: Shitao/MLDR name: MTEB MultiLongDocRetrieval (es) config: es split: test revision: None metrics:
  - type: ndcg_at_10 value: 81.228
- task: type: Retrieval dataset: type: Shitao/MLDR name: MTEB MultiLongDocRetrieval (fr) config: fr split: test revision: None metrics:
  - type: ndcg_at_10 value: 76.19
- task: type: Retrieval dataset: type: Shitao/MLDR name: MTEB MultiLongDocRetrieval (hi) config: hi split: test revision: None metrics:
  - type: ndcg_at_10 value: 45.206
- task: type: Retrieval dataset: type: Shitao/MLDR name: MTEB MultiLongDocRetrieval (it) config: it split: test revision: None metrics:
  - type: ndcg_at_10 value: 66.741
- task: type: Retrieval dataset: type: Shitao/MLDR name: MTEB MultiLongDocRetrieval (ja) config: ja split: test revision: None metrics:
  - type: ndcg_at_10 value: 52.111
- task: type: Retrieval dataset: type: Shitao/MLDR name: MTEB MultiLongDocRetrieval (ko) config: ko split: test revision: None metrics:
  - type: ndcg_at_10 value: 46.733000000000004
- task: type: Retrieval dataset: type: Shitao/MLDR name: MTEB MultiLongDocRetrieval (pt) config: pt split: test revision: None metrics:
  - type: ndcg_at_10 value: 79.105
- task: type: Retrieval dataset: type: Shitao/MLDR name: MTEB MultiLongDocRetrieval (ru) config: ru split: test revision: None metrics:
  - type: ndcg_at_10 value: 64.21
- task: type: Retrieval dataset: type: Shitao/MLDR name: MTEB MultiLongDocRetrieval (th) config: th split: test revision: None metrics:
  - type: ndcg_at_10 value: 35.467
- task: type: Retrieval dataset: type: Shitao/MLDR name: MTEB MultiLongDocRetrieval (zh) config: zh split: test revision: None metrics:
  - type: ndcg_at_10 value: 27.419
- task: type: Classification dataset: type: C-MTEB/MultilingualSentiment-classification name: MTEB MultilingualSentiment config: default split: validation revision: 46958b007a63fdbf239b7672c25d0bea67b5ea1a metrics:
  - type: accuracy value: 61.02000000000001
- task: type: Retrieval dataset: type: mteb/nfcorpus name: MTEB NFCorpus config: default split: test revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 metrics:
  - type: ndcg_at_10 value: 36.65
- task: type: Retrieval dataset: type: clarin-knext/nfcorpus-pl name: MTEB NFCorpus-PL config: default split: test revision: 9a6f9567fda928260afed2de480d79c98bf0bec0 metrics:
  - type: ndcg_at_10 value: 26.831
- task: type: Retrieval dataset: type: mteb/nq name: MTEB NQ config: default split: test revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 metrics:
  - type: ndcg_at_10 value: 58.111000000000004
- task: type: Retrieval dataset: type: clarin-knext/nq-pl name: MTEB NQ-PL config: default split: test revision: f171245712cf85dd4700b06bef18001578d0ca8d metrics:
  - type: ndcg_at_10 value: 43.126999999999995
- task: type: PairClassification dataset: type: C-MTEB/OCNLI name: MTEB Ocnli config: default split: validation revision: 66e76a618a34d6d565d5538088562851e6daa7ec metrics:
  - type: cos_sim_ap value: 72.67630697316041
- task: type: Classification dataset: type: C-MTEB/OnlineShopping-classification name: MTEB OnlineShopping config: default split: test revision: e610f2ebd179a8fda30ae534c3878750a96db120 metrics:
  - type: accuracy value: 84.85000000000001
- task: type: PairClassification dataset: type: GEM/opusparcus name: MTEB OpusparcusPC (fr) config: fr split: test revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a metrics:
  - type: cos_sim_ap value: 100
- task: type: Classification dataset: type: laugustyniak/abusive-clauses-pl name: MTEB PAC config: default split: test revision: None metrics:
  - type: accuracy value: 65.99189110918043
- task: type: STS dataset: type: C-MTEB/PAWSX name: MTEB PAWSX config: default split: test revision: 9c6a90e430ac22b5779fb019a23e820b11a8b5e1 metrics:
  - type: cos_sim_spearman value: 16.124364530596228
- task: type: PairClassification dataset: type: PL-MTEB/ppc-pairclassification name: MTEB PPC config: default split: test revision: None metrics:
  - type: cos_sim_ap value: 92.43431057460192
- task: type: PairClassification dataset: type: PL-MTEB/psc-pairclassification name: MTEB PSC config: default split: test revision: None metrics:
  - type: cos_sim_ap value: 99.06090138049724
- task: type: PairClassification dataset: type: paws-x name: MTEB PawsX (fr) config: fr split: test revision: 8a04d940a42cd40658986fdd8e3da561533a3646 metrics:
  - type: cos_sim_ap value: 58.9314954874314
- task: type: Classification dataset: type: PL-MTEB/polemo2_in name: MTEB PolEmo2.0-IN config: default split: test revision: None metrics:
  - type: accuracy value: 69.59833795013851
- task: type: Classification dataset: type: PL-MTEB/polemo2_out name: MTEB PolEmo2.0-OUT config: default split: test revision: None metrics:
  - type: accuracy value: 44.73684210526315
- task: type: STS dataset: type: C-MTEB/QBQTC name: MTEB QBQTC config: default split: test revision: 790b0510dc52b1553e8c49f3d2afb48c0e5c48b7 metrics:
  - type: cos_sim_spearman value: 39.36450754137984
- task: type: Retrieval dataset: type: clarin-knext/quora-pl name: MTEB Quora-PL config: default split: test revision: 0be27e93455051e531182b85e85e425aba12e9d4 metrics:
  - type: ndcg_at_10 value: 80.76299999999999
- task: type: Retrieval dataset: type: mteb/quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics:
  - type: ndcg_at_10 value: 88.022
- task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics:
  - type: v_measure value: 55.719165988934385
- task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics:
  - type: v_measure value: 62.25390069273025
- task: type: Retrieval dataset: type: mteb/scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics:
  - type: ndcg_at_10 value: 18.243000000000002
- task: type: Retrieval dataset: type: clarin-knext/scidocs-pl name: MTEB SCIDOCS-PL config: default split: test revision: 45452b03f05560207ef19149545f168e596c9337 metrics:
  - type: ndcg_at_10 value: 14.219000000000001
- task: type: PairClassification dataset: type: PL-MTEB/sicke-pl-pairclassification name: MTEB SICK-E-PL config: default split: test revision: None metrics:
  - type: cos_sim_ap value: 75.4022630307816
- task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics:
  - type: cos_sim_spearman value: 79.34269390198548
- task: type: STS dataset: type: PL-MTEB/sickr-pl-sts name: MTEB SICK-R-PL config: default split: test revision: None metrics:
  - type: cos_sim_spearman value: 74.0651660446132
- task: type: STS dataset: type: Lajavaness/SICK-fr name: MTEB SICKFr config: default split: test revision: e077ab4cf4774a1e36d86d593b150422fafd8e8a metrics:
  - type: cos_sim_spearman value: 78.62693119733123
- task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics:
  - type: cos_sim_spearman value: 77.50660544631359
- task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics:
  - type: cos_sim_spearman value: 85.55415077723738
- task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics:
  - type: cos_sim_spearman value: 81.67550814479077
- task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics:
  - type: cos_sim_spearman value: 88.94601412322764
- task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics:
  - type: cos_sim_spearman value: 84.33844259337481
- task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (ko-ko) config: ko-ko split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics:
  - type: cos_sim_spearman value: 81.58650681159105
- task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (ar-ar) config: ar-ar split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics:
  - type: cos_sim_spearman value: 78.82472265884256
- task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-ar) config: en-ar split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics:
  - type: cos_sim_spearman value: 76.43637938260397
- task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-de) config: en-de split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics:
  - type: cos_sim_spearman value: 84.71008299464059
- 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_spearman value: 88.88074713413747
- task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-tr) config: en-tr split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics:
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- task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (es-en) config: es-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics:
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- task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (es-es) config: es-es split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics:
  - type: cos_sim_spearman value: 87.03931621433031
- task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (fr-en) config: fr-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics:
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gte-multilingual-base

The gte-multilingual-base model is the latest in the GTE (General Text Embedding) family of models, featuring several key attributes:

High Performance: Achieves state-of-the-art (SOTA) results in multilingual retrieval tasks and multi-task representation model evaluations when compared to models of similar size.
Training Architecture: Trained using an encoder-only transformers architecture, resulting in a smaller model size. Unlike previous models based on decode-only LLM architecture (e.g., gte-qwen2-1.5b-instruct), this model has lower hardware requirements for inference, offering a 10x increase in inference speed.
Long Context: Supports text lengths up to 8192 tokens.
Multilingual Capability: Supports over 70 languages.
Elastic Dense Embedding: Support elastic output dense representation while maintaining the effectiveness of downstream tasks, which significantly reduces storage costs and improves execution efficiency.
Sparse Vectors: In addition to dense representations, it can also generate sparse vectors.

Paper: mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval

Model Information

Model Size: 305M
Embedding Dimension: 768
Max Input Tokens: 8192

Usage

It is recommended to install xformers and enable unpadding for acceleration, refer to enable-unpadding-and-xformers.
How to use it offline: new-impl/discussions/2
How to use with TEI: refs/pr/7

Get Dense Embeddings with Transformers

# Requires transformers>=4.36.0

import torch.nn.functional as F
from transformers import AutoModel, AutoTokenizer

input_texts = [
    "what is the capital of China?",
    "how to implement quick sort in python?",
    "北京",
    "快排算法介绍"
]

model_name_or_path = 'Alibaba-NLP/gte-multilingual-base'
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = AutoModel.from_pretrained(model_name_or_path, trust_remote_code=True)

# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=8192, padding=True, truncation=True, return_tensors='pt')

outputs = model(**batch_dict)

dimension=768 # The output dimension of the output embedding, should be in [128, 768]
embeddings = outputs.last_hidden_state[:, 0][:dimension]

embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:1] @ embeddings[1:].T) * 100
print(scores.tolist())

# [[0.3016996383666992, 0.7503870129585266, 0.3203084468841553]]

Use with sentence-transformers

# Requires sentence-transformers>=3.0.0

from sentence_transformers import SentenceTransformer

input_texts = [
    "what is the capital of China?",
    "how to implement quick sort in python?",
    "北京",
    "快排算法介绍"
]

model_name_or_path="Alibaba-NLP/gte-multilingual-base"
model = SentenceTransformer(model_name_or_path, trust_remote_code=True)
embeddings = model.encode(input_texts, normalize_embeddings=True) # embeddings.shape (4, 768)

# sim scores
scores = model.similarity(embeddings[:1], embeddings[1:])

print(scores.tolist())
# [[0.301699697971344, 0.7503870129585266, 0.32030850648880005]]

Use with infinity

Usage via docker and infinity, MIT Licensed.

docker run --gpus all -v $PWD/data:/app/.cache -p "7997":"7997" \
michaelf34/infinity:0.0.69 \
v2 --model-id Alibaba-NLP/gte-multilingual-base --revision "main" --dtype float16 --batch-size 32 --device cuda --engine torch --port 7997

Use with custom code to get dense embeddings and sparse token weights

# You can find the script gte_embedding.py in https://huggingface.co/Alibaba-NLP/gte-multilingual-base/blob/main/scripts/gte_embedding.py

from gte_embedding import GTEEmbeddidng

model_name_or_path = 'Alibaba-NLP/gte-multilingual-base'
model = GTEEmbeddidng(model_name_or_path)
query = "中国的首都在哪儿"

docs = [
    "what is the capital of China?",
    "how to implement quick sort in python?",
    "北京",
    "快排算法介绍"
]

embs = model.encode(docs, return_dense=True,return_sparse=True)
print('dense_embeddings vecs', embs['dense_embeddings'])
print('token_weights', embs['token_weights'])
pairs = [(query, doc) for doc in docs]
dense_scores = model.compute_scores(pairs, dense_weight=1.0, sparse_weight=0.0)
sparse_scores = model.compute_scores(pairs, dense_weight=0.0, sparse_weight=1.0)
hybrid_scores = model.compute_scores(pairs, dense_weight=1.0, sparse_weight=0.3)

print('dense_scores', dense_scores)
print('sparse_scores', sparse_scores)
print('hybrid_scores', hybrid_scores)

# dense_scores [0.85302734375, 0.257568359375, 0.76953125, 0.325439453125]
# sparse_scores [0.0, 0.0, 4.600879669189453, 1.570279598236084]
# hybrid_scores [0.85302734375, 0.257568359375, 2.1497951507568356, 0.7965233325958252]

Evaluation

We validated the performance of the gte-multilingual-base model on multiple downstream tasks, including multilingual retrieval, cross-lingual retrieval, long text retrieval, and general text representation evaluation on the MTEB Leaderboard, among others.

Retrieval Task

Retrieval results on MIRACL and MLDR (multilingual), MKQA (crosslingual), BEIR and LoCo (English).

Detail results on MLDR

Detail results on LoCo

MTEB

Results on MTEB English, Chinese, French, Polish

More detailed experimental results can be found in the paper.

Cloud API Services

In addition to the open-source GTE series models, GTE series models are also available as commercial API services on Alibaba Cloud.

Embedding Models: Three versions of the text embedding models are available: text-embedding-v1/v2/v3, with v3 being the latest API service.
ReRank Models: The gte-rerank model service is available.

Note that the models behind the commercial APIs are not entirely identical to the open-source models.

Citation

If you find our paper or models helpful, please consider cite:

@inproceedings{zhang2024mgte,
  title={mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval},
  author={Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Wen and Dai, Ziqi and Tang, Jialong and Lin, Huan and Yang, Baosong and Xie, Pengjun and Huang, Fei and others},
  booktitle={Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track},
  pages={1393--1412},
  year={2024}
}