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.gitattributes

@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text
+unigram.json filter=lfs diff=lfs merge=lfs -text
+all.jsonl filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

@@ -0,0 +1,7 @@
+{
+  "word_embedding_dimension": 384,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false
+}

README.md

@@ -0,0 +1,3085 @@
+---
+pipeline_tag: sentence-similarity
+license: apache-2.0
+tags:
+- text2vec
+- feature-extraction
+- sentence-similarity
+- transformers
+- mteb
+datasets:
+- >-
+  https://huggingface.co/datasets/shibing624/nli-zh-all/tree/main/text2vec-base-multilingual-dataset
+language:
+- zh
+- en
+- de
+- fr
+- it
+- nl
+- pt
+- pl
+- ru
+metrics:
+- spearmanr
+library_name: transformers
+model-index:
+- name: text2vec-base-multilingual
+  results:
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_counterfactual
+      name: MTEB AmazonCounterfactualClassification (en)
+      config: en
+      split: test
+      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
+    metrics:
+    - type: accuracy
+      value: 70.97014925373134
+    - type: ap
+      value: 33.95151328318672
+    - type: f1
+      value: 65.14740155705596
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_counterfactual
+      name: MTEB AmazonCounterfactualClassification (de)
+      config: de
+      split: test
+      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
+    metrics:
+    - type: accuracy
+      value: 68.69379014989293
+    - type: ap
+      value: 79.68277579733802
+    - type: f1
+      value: 66.54960052336921
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_counterfactual
+      name: MTEB AmazonCounterfactualClassification (en-ext)
+      config: en-ext
+      split: test
+      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
+    metrics:
+    - type: accuracy
+      value: 70.90704647676162
+    - type: ap
+      value: 20.747518928580437
+    - type: f1
+      value: 58.64365465884924
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_counterfactual
+      name: MTEB AmazonCounterfactualClassification (ja)
+      config: ja
+      split: test
+      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
+    metrics:
+    - type: accuracy
+      value: 61.605995717344754
+    - type: ap
+      value: 14.135974879487028
+    - type: f1
+      value: 49.980224800472136
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_polarity
+      name: MTEB AmazonPolarityClassification
+      config: default
+      split: test
+      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
+    metrics:
+    - type: accuracy
+      value: 66.103375
+    - type: ap
+      value: 61.10087197664471
+    - type: f1
+      value: 65.75198509894145
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_reviews_multi
+      name: MTEB AmazonReviewsClassification (en)
+      config: en
+      split: test
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+    metrics:
+    - type: accuracy
+      value: 33.134
+    - type: f1
+      value: 32.7905397597083
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_reviews_multi
+      name: MTEB AmazonReviewsClassification (de)
+      config: de
+      split: test
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+    metrics:
+    - type: accuracy
+      value: 33.388
+    - type: f1
+      value: 33.190561196873084
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_reviews_multi
+      name: MTEB AmazonReviewsClassification (es)
+      config: es
+      split: test
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+    metrics:
+    - type: accuracy
+      value: 34.824
+    - type: f1
+      value: 34.297290157740726
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_reviews_multi
+      name: MTEB AmazonReviewsClassification (fr)
+      config: fr
+      split: test
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+    metrics:
+    - type: accuracy
+      value: 33.449999999999996
+    - type: f1
+      value: 33.08017234412433
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_reviews_multi
+      name: MTEB AmazonReviewsClassification (ja)
+      config: ja
+      split: test
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+    metrics:
+    - type: accuracy
+      value: 30.046
+    - type: f1
+      value: 29.857141661482228
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_reviews_multi
+      name: MTEB AmazonReviewsClassification (zh)
+      config: zh
+      split: test
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+    metrics:
+    - type: accuracy
+      value: 32.522
+    - type: f1
+      value: 31.854699911472174
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-p2p
+      name: MTEB ArxivClusteringP2P
+      config: default
+      split: test
+      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
+    metrics:
+    - type: v_measure
+      value: 32.31918856561886
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-s2s
+      name: MTEB ArxivClusteringS2S
+      config: default
+      split: test
+      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
+    metrics:
+    - type: v_measure
+      value: 25.503481615956137
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/askubuntudupquestions-reranking
+      name: MTEB AskUbuntuDupQuestions
+      config: default
+      split: test
+      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
+    metrics:
+    - type: map
+      value: 57.91471462820568
+    - type: mrr
+      value: 71.82990370663501
+  - task:
+      type: STS
+    dataset:
+      type: mteb/biosses-sts
+      name: MTEB BIOSSES
+      config: default
+      split: test
+      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
+    metrics:
+    - type: cos_sim_pearson
+      value: 68.83853315193127
+    - type: cos_sim_spearman
+      value: 66.16174850417771
+    - type: euclidean_pearson
+      value: 56.65313897263153
+    - type: euclidean_spearman
+      value: 52.69156205876939
+    - type: manhattan_pearson
+      value: 56.97282154658304
+    - type: manhattan_spearman
+      value: 53.167476517261015
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/banking77
+      name: MTEB Banking77Classification
+      config: default
+      split: test
+      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
+    metrics:
+    - type: accuracy
+      value: 78.08441558441558
+    - type: f1
+      value: 77.99825264827898
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-p2p
+      name: MTEB BiorxivClusteringP2P
+      config: default
+      split: test
+      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
+    metrics:
+    - type: v_measure
+      value: 28.98583420521256
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-s2s
+      name: MTEB BiorxivClusteringS2S
+      config: default
+      split: test
+      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
+    metrics:
+    - type: v_measure
+      value: 23.195091778460892
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/emotion
+      name: MTEB EmotionClassification
+      config: default
+      split: test
+      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
+    metrics:
+    - type: accuracy
+      value: 43.35
+    - type: f1
+      value: 38.80269436557695
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/imdb
+      name: MTEB ImdbClassification
+      config: default
+      split: test
+      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
+    metrics:
+    - type: accuracy
+      value: 59.348
+    - type: ap
+      value: 55.75065220262251
+    - type: f1
+      value: 58.72117519082607
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_domain
+      name: MTEB MTOPDomainClassification (en)
+      config: en
+      split: test
+      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
+    metrics:
+    - type: accuracy
+      value: 81.04879160966712
+    - type: f1
+      value: 80.86889779192701
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_domain
+      name: MTEB MTOPDomainClassification (de)
+      config: de
+      split: test
+      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
+    metrics:
+    - type: accuracy
+      value: 78.59397013243168
+    - type: f1
+      value: 77.09902761555972
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_domain
+      name: MTEB MTOPDomainClassification (es)
+      config: es
+      split: test
+      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
+    metrics:
+    - type: accuracy
+      value: 79.24282855236824
+    - type: f1
+      value: 78.75883867079015
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_domain
+      name: MTEB MTOPDomainClassification (fr)
+      config: fr
+      split: test
+      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
+    metrics:
+    - type: accuracy
+      value: 76.16661446915127
+    - type: f1
+      value: 76.30204722831901
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_domain
+      name: MTEB MTOPDomainClassification (hi)
+      config: hi
+      split: test
+      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
+    metrics:
+    - type: accuracy
+      value: 78.74506991753317
+    - type: f1
+      value: 77.50560442779701
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_domain
+      name: MTEB MTOPDomainClassification (th)
+      config: th
+      split: test
+      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
+    metrics:
+    - type: accuracy
+      value: 77.67088607594937
+    - type: f1
+      value: 77.21442956887493
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_intent
+      name: MTEB MTOPIntentClassification (en)
+      config: en
+      split: test
+      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
+    metrics:
+    - type: accuracy
+      value: 62.786137710898316
+    - type: f1
+      value: 46.23474201126368
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_intent
+      name: MTEB MTOPIntentClassification (de)
+      config: de
+      split: test
+      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
+    metrics:
+    - type: accuracy
+      value: 55.285996055226825
+    - type: f1
+      value: 37.98039513682919
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_intent
+      name: MTEB MTOPIntentClassification (es)
+      config: es
+      split: test
+      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
+    metrics:
+    - type: accuracy
+      value: 58.67911941294196
+    - type: f1
+      value: 40.541410807124954
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_intent
+      name: MTEB MTOPIntentClassification (fr)
+      config: fr
+      split: test
+      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
+    metrics:
+    - type: accuracy
+      value: 53.257124960851854
+    - type: f1
+      value: 38.42982319259366
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_intent
+      name: MTEB MTOPIntentClassification (hi)
+      config: hi
+      split: test
+      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
+    metrics:
+    - type: accuracy
+      value: 59.62352097525995
+    - type: f1
+      value: 41.28886486568534
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_intent
+      name: MTEB MTOPIntentClassification (th)
+      config: th
+      split: test
+      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
+    metrics:
+    - type: accuracy
+      value: 58.799276672694404
+    - type: f1
+      value: 43.68379466247341
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_intent
+      name: MTEB MassiveIntentClassification (af)
+      config: af
+      split: test
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+    metrics:
+    - type: accuracy
+      value: 45.42030934767989
+    - type: f1
+      value: 44.12201543566376
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_intent
+      name: MTEB MassiveIntentClassification (am)
+      config: am
+      split: test
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+    metrics:
+    - type: accuracy
+      value: 37.67652992602556
+    - type: f1
+      value: 35.422091900843164
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_intent
+      name: MTEB MassiveIntentClassification (ar)
+      config: ar
+      split: test
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+    metrics:
+    - type: accuracy
+      value: 45.02353732347007
+    - type: f1
+      value: 41.852484084738194
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_intent
+      name: MTEB MassiveIntentClassification (az)
+      config: az
+      split: test
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+    metrics:
+    - type: accuracy
+      value: 48.70880968392737
+    - type: f1
+      value: 46.904360615435046
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_intent
+      name: MTEB MassiveIntentClassification (bn)
+      config: bn
+      split: test
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+    metrics:
+    - type: accuracy
+      value: 43.78950907868191
+    - type: f1
+      value: 41.58872353920405
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_intent
+      name: MTEB MassiveIntentClassification (cy)
+      config: cy
+      split: test
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+    metrics:
+    - type: accuracy
+      value: 28.759246805648957
+    - type: f1
+      value: 27.41182001374226
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_intent
+      name: MTEB MassiveIntentClassification (da)
+      config: da
+      split: test
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+    metrics:
+    - type: accuracy
+      value: 56.74176193678547
+    - type: f1
+      value: 53.82727354182497
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_intent
+      name: MTEB MassiveIntentClassification (de)
+      config: de
+      split: test
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+    metrics:
+    - type: accuracy
+      value: 51.55682582380632
+    - type: f1
+      value: 49.41963627941866
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_intent
+      name: MTEB MassiveIntentClassification (el)
+      config: el
+      split: test
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+    metrics:
+    - type: accuracy
+      value: 56.46940147948891
+    - type: f1
+      value: 55.28178711367465
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_intent
+      name: MTEB MassiveIntentClassification (en)
+      config: en
+      split: test
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+    metrics:
+    - type: accuracy
+      value: 63.83322125084063
+    - type: f1
+      value: 61.836172900845554
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_intent
+      name: MTEB MassiveIntentClassification (es)
+      config: es
+      split: test
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+    metrics:
+    - type: accuracy
+      value: 58.27505043712172
+    - type: f1
+      value: 57.642436374361154
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_intent
+      name: MTEB MassiveIntentClassification (fa)
+      config: fa
+      split: test
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+    metrics:
+    - type: accuracy
+      value: 59.05178211163417
+    - type: f1
+      value: 56.858998820504056
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_intent
+      name: MTEB MassiveIntentClassification (fi)
+      config: fi
+      split: test
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+    metrics:
+    - type: accuracy
+      value: 57.357094821788834
+    - type: f1
+      value: 54.79711189260453
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_intent
+      name: MTEB MassiveIntentClassification (fr)
+      config: fr
+      split: test
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+    metrics:
+    - type: accuracy
+      value: 58.79959650302623
+    - type: f1
+      value: 57.59158671719513
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_intent
+      name: MTEB MassiveIntentClassification (he)
+      config: he
+      split: test
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+    metrics:
+    - type: accuracy
+      value: 51.1768661735037
+    - type: f1
+      value: 48.886397276270515
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_intent
+      name: MTEB MassiveIntentClassification (hi)
+      config: hi
+      split: test
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+    metrics:
+    - type: accuracy
+      value: 57.06455951580362
+    - type: f1
+      value: 55.01530952684585
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_intent
+      name: MTEB MassiveIntentClassification (hu)
+      config: hu
+      split: test
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+    metrics:
+    - type: accuracy
+      value: 58.3591123066577
+    - type: f1
+      value: 55.9277783370191
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_intent
+      name: MTEB MassiveIntentClassification (hy)
+      config: hy
+      split: test
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+    metrics:
+    - type: accuracy
+      value: 52.108271687962336
+    - type: f1
+      value: 51.195023400664596
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_intent
+      name: MTEB MassiveIntentClassification (id)
+      config: id
+      split: test
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+    metrics:
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+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (jv)
+      config: jv
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 36.12642905178212
+    - type: f1
+      value: 35.334393048479484
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (ka)
+      config: ka
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 50.26899798251513
+    - type: f1
+      value: 49.041065960139434
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (km)
+      config: km
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 44.24344317417619
+    - type: f1
+      value: 42.42177854872125
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (kn)
+      config: kn
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 47.370544720914594
+    - type: f1
+      value: 46.589722581465324
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (ko)
+      config: ko
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 58.89038332212508
+    - type: f1
+      value: 57.753607921990394
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (lv)
+      config: lv
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 56.506388702084756
+    - type: f1
+      value: 56.0485860423295
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (ml)
+      config: ml
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 50.06388702084734
+    - type: f1
+      value: 50.109364641824584
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (mn)
+      config: mn
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 55.053799596503026
+    - type: f1
+      value: 54.490665705666686
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (ms)
+      config: ms
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 59.77135171486213
+    - type: f1
+      value: 58.2808650158803
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (my)
+      config: my
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 55.71620712844654
+    - type: f1
+      value: 53.863034882475304
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (nb)
+      config: nb
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 60.26227303295225
+    - type: f1
+      value: 59.86604657147016
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (nl)
+      config: nl
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 63.3759246805649
+    - type: f1
+      value: 62.45257339288533
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (pl)
+      config: pl
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 62.552118359112306
+    - type: f1
+      value: 61.354449605776765
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (pt)
+      config: pt
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 62.40753194351043
+    - type: f1
+      value: 61.98779889528889
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (ro)
+      config: ro
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 60.68258238063214
+    - type: f1
+      value: 60.59973978976571
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (ru)
+      config: ru
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 62.31002017484868
+    - type: f1
+      value: 62.412312268503655
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (sl)
+      config: sl
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 61.429051782111635
+    - type: f1
+      value: 61.60095590401424
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (sq)
+      config: sq
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 62.229320780094156
+    - type: f1
+      value: 61.02251426747547
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (sv)
+      config: sv
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 64.42501681237391
+    - type: f1
+      value: 63.461494430605235
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (sw)
+      config: sw
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 38.51714862138534
+    - type: f1
+      value: 37.12466722986362
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (ta)
+      config: ta
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 46.99731002017485
+    - type: f1
+      value: 45.859147049984834
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (te)
+      config: te
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 51.01882985877605
+    - type: f1
+      value: 49.01040173136056
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (th)
+      config: th
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 63.234700739744454
+    - type: f1
+      value: 62.732294595214746
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (tl)
+      config: tl
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 38.72225958305312
+    - type: f1
+      value: 36.603231928120906
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (tr)
+      config: tr
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 64.48554135843982
+    - type: f1
+      value: 63.97380562022752
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (ur)
+      config: ur
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 56.7955615332885
+    - type: f1
+      value: 55.95308241204802
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (vi)
+      config: vi
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 57.06455951580362
+    - type: f1
+      value: 56.95570494066693
+  - 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: 65.8338937457969
+    - type: f1
+      value: 65.6778746906008
+  - 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: 63.369199731002034
+    - type: f1
+      value: 63.527650116059945
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-p2p
+      name: MTEB MedrxivClusteringP2P
+      config: default
+      split: test
+      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
+    metrics:
+    - type: v_measure
+      value: 29.442504112215538
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-s2s
+      name: MTEB MedrxivClusteringS2S
+      config: default
+      split: test
+      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
+    metrics:
+    - type: v_measure
+      value: 26.16062814161053
+  - task:
+      type: Retrieval
+    dataset:
+      type: quora
+      name: MTEB QuoraRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 65.319
+    - type: map_at_10
+      value: 78.72
+    - type: map_at_100
+      value: 79.44600000000001
+    - type: map_at_1000
+      value: 79.469
+    - type: map_at_3
+      value: 75.693
+    - type: map_at_5
+      value: 77.537
+    - type: mrr_at_1
+      value: 75.24
+    - type: mrr_at_10
+      value: 82.304
+    - type: mrr_at_100
+      value: 82.485
+    - type: mrr_at_1000
+      value: 82.489
+    - type: mrr_at_3
+      value: 81.002
+    - type: mrr_at_5
+      value: 81.817
+    - type: ndcg_at_1
+      value: 75.26
+    - type: ndcg_at_10
+      value: 83.07
+    - type: ndcg_at_100
+      value: 84.829
+    - type: ndcg_at_1000
+      value: 85.087
+    - type: ndcg_at_3
+      value: 79.67699999999999
+    - type: ndcg_at_5
+      value: 81.42
+    - type: precision_at_1
+      value: 75.26
+    - type: precision_at_10
+      value: 12.697
+    - type: precision_at_100
+      value: 1.4829999999999999
+    - type: precision_at_1000
+      value: 0.154
+    - type: precision_at_3
+      value: 34.849999999999994
+    - type: precision_at_5
+      value: 23.054
+    - type: recall_at_1
+      value: 65.319
+    - type: recall_at_10
+      value: 91.551
+    - type: recall_at_100
+      value: 98.053
+    - type: recall_at_1000
+      value: 99.516
+    - type: recall_at_3
+      value: 81.819
+    - type: recall_at_5
+      value: 86.66199999999999
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering
+      name: MTEB RedditClustering
+      config: default
+      split: test
+      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
+    metrics:
+    - type: v_measure
+      value: 31.249791587189996
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering-p2p
+      name: MTEB RedditClusteringP2P
+      config: default
+      split: test
+      revision: 282350215ef01743dc01b456c7f5241fa8937f16
+    metrics:
+    - type: v_measure
+      value: 43.302922383029816
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sickr-sts
+      name: MTEB SICK-R
+      config: default
+      split: test
+      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
+    metrics:
+    - type: cos_sim_pearson
+      value: 84.80670811345861
+    - type: cos_sim_spearman
+      value: 79.97373018384307
+    - type: euclidean_pearson
+      value: 83.40205934125837
+    - type: euclidean_spearman
+      value: 79.73331008251854
+    - type: manhattan_pearson
+      value: 83.3320983393412
+    - type: manhattan_spearman
+      value: 79.677919746045
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts12-sts
+      name: MTEB STS12
+      config: default
+      split: test
+      revision: a0d554a64d88156834ff5ae9920b964011b16384
+    metrics:
+    - type: cos_sim_pearson
+      value: 86.3816087627948
+    - type: cos_sim_spearman
+      value: 80.91314664846955
+    - type: euclidean_pearson
+      value: 85.10603071031096
+    - type: euclidean_spearman
+      value: 79.42663939501841
+    - type: manhattan_pearson
+      value: 85.16096376014066
+    - type: manhattan_spearman
+      value: 79.51936545543191
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts13-sts
+      name: MTEB STS13
+      config: default
+      split: test
+      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
+    metrics:
+    - type: cos_sim_pearson
+      value: 80.44665329940209
+    - type: cos_sim_spearman
+      value: 82.86479010707745
+    - type: euclidean_pearson
+      value: 84.06719627734672
+    - type: euclidean_spearman
+      value: 84.9356099976297
+    - type: manhattan_pearson
+      value: 84.10370009572624
+    - type: manhattan_spearman
+      value: 84.96828040546536
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts14-sts
+      name: MTEB STS14
+      config: default
+      split: test
+      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
+    metrics:
+    - type: cos_sim_pearson
+      value: 86.05704260568437
+    - type: cos_sim_spearman
+      value: 87.36399473803172
+    - type: euclidean_pearson
+      value: 86.8895170159388
+    - type: euclidean_spearman
+      value: 87.16246440866921
+    - type: manhattan_pearson
+      value: 86.80814774538997
+    - type: manhattan_spearman
+      value: 87.09320142699522
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts15-sts
+      name: MTEB STS15
+      config: default
+      split: test
+      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
+    metrics:
+    - type: cos_sim_pearson
+      value: 85.97825118945852
+    - type: cos_sim_spearman
+      value: 88.31438033558268
+    - type: euclidean_pearson
+      value: 87.05174694758092
+    - type: euclidean_spearman
+      value: 87.80659468392355
+    - type: manhattan_pearson
+      value: 86.98831322198717
+    - type: manhattan_spearman
+      value: 87.72820615049285
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts16-sts
+      name: MTEB STS16
+      config: default
+      split: test
+      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
+    metrics:
+    - type: cos_sim_pearson
+      value: 78.68745420126719
+    - type: cos_sim_spearman
+      value: 81.6058424699445
+    - type: euclidean_pearson
+      value: 81.16540133861879
+    - type: euclidean_spearman
+      value: 81.86377535458067
+    - type: manhattan_pearson
+      value: 81.13813317937021
+    - type: manhattan_spearman
+      value: 81.87079962857256
+  - 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_pearson
+      value: 68.06192660936868
+    - type: cos_sim_spearman
+      value: 68.2376353514075
+    - type: euclidean_pearson
+      value: 60.68326946956215
+    - type: euclidean_spearman
+      value: 59.19352349785952
+    - type: manhattan_pearson
+      value: 60.6592944683418
+    - type: manhattan_spearman
+      value: 59.167534419270865
+  - 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_pearson
+      value: 76.78098264855684
+    - type: cos_sim_spearman
+      value: 78.02670452969812
+    - type: euclidean_pearson
+      value: 77.26694463661255
+    - type: euclidean_spearman
+      value: 77.47007626009587
+    - type: manhattan_pearson
+      value: 77.25070088632027
+    - type: manhattan_spearman
+      value: 77.36368265830724
+  - 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_pearson
+      value: 78.45418506379532
+    - type: cos_sim_spearman
+      value: 78.60412019902428
+    - type: euclidean_pearson
+      value: 79.90303710850512
+    - type: euclidean_spearman
+      value: 78.67123625004957
+    - type: manhattan_pearson
+      value: 80.09189580897753
+    - type: manhattan_spearman
+      value: 79.02484481441483
+  - 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_pearson
+      value: 82.35556731232779
+    - type: cos_sim_spearman
+      value: 81.48249735354844
+    - type: euclidean_pearson
+      value: 81.66748026636621
+    - type: euclidean_spearman
+      value: 80.35571574338547
+    - type: manhattan_pearson
+      value: 81.38214732806365
+    - type: manhattan_spearman
+      value: 79.9018202958774
+  - 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: 86.4527703176897
+    - type: cos_sim_spearman
+      value: 85.81084095829584
+    - type: euclidean_pearson
+      value: 86.43489162324457
+    - type: euclidean_spearman
+      value: 85.27110976093296
+    - type: manhattan_pearson
+      value: 86.43674259444512
+    - type: manhattan_spearman
+      value: 85.05719308026032
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts17-crosslingual-sts
+      name: MTEB STS17 (en-tr)
+      config: en-tr
+      split: test
+      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
+    metrics:
+    - type: cos_sim_pearson
+      value: 76.00411240034492
+    - type: cos_sim_spearman
+      value: 76.33887356560854
+    - type: euclidean_pearson
+      value: 76.81730660019446
+    - type: euclidean_spearman
+      value: 75.04432185451306
+    - type: manhattan_pearson
+      value: 77.22298813168995
+    - type: manhattan_spearman
+      value: 75.56420330256725
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts17-crosslingual-sts
+      name: MTEB STS17 (es-en)
+      config: es-en
+      split: test
+      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
+    metrics:
+    - type: cos_sim_pearson
+      value: 79.1447136836213
+    - type: cos_sim_spearman
+      value: 81.80823850788917
+    - type: euclidean_pearson
+      value: 80.84505734814422
+    - type: euclidean_spearman
+      value: 81.714168092736
+    - type: manhattan_pearson
+      value: 80.84713816174187
+    - type: manhattan_spearman
+      value: 81.61267814749516
+  - 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_pearson
+      value: 87.01257457052873
+    - type: cos_sim_spearman
+      value: 87.91146458004216
+    - type: euclidean_pearson
+      value: 88.36771859717994
+    - type: euclidean_spearman
+      value: 87.73182474597515
+    - type: manhattan_pearson
+      value: 88.26551451003671
+    - type: manhattan_spearman
+      value: 87.71675151388992
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts17-crosslingual-sts
+      name: MTEB STS17 (fr-en)
+      config: fr-en
+      split: test
+      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
+    metrics:
+    - type: cos_sim_pearson
+      value: 79.20121618382373
+    - type: cos_sim_spearman
+      value: 78.05794691968603
+    - type: euclidean_pearson
+      value: 79.93819925682054
+    - type: euclidean_spearman
+      value: 78.00586118701553
+    - type: manhattan_pearson
+      value: 80.05598625820885
+    - type: manhattan_spearman
+      value: 78.04802948866832
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts17-crosslingual-sts
+      name: MTEB STS17 (it-en)
+      config: it-en
+      split: test
+      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
+    metrics:
+    - type: cos_sim_pearson
+      value: 81.51743373871778
+    - type: cos_sim_spearman
+      value: 80.98266651818703
+    - type: euclidean_pearson
+      value: 81.11875722505269
+    - type: euclidean_spearman
+      value: 79.45188413284538
+    - type: manhattan_pearson
+      value: 80.7988457619225
+    - type: manhattan_spearman
+      value: 79.49643569311485
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts17-crosslingual-sts
+      name: MTEB STS17 (nl-en)
+      config: nl-en
+      split: test
+      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
+    metrics:
+    - type: cos_sim_pearson
+      value: 81.78679924046351
+    - type: cos_sim_spearman
+      value: 80.9986574147117
+    - type: euclidean_pearson
+      value: 82.09130079135713
+    - type: euclidean_spearman
+      value: 80.66215667390159
+    - type: manhattan_pearson
+      value: 82.0328610549654
+    - type: manhattan_spearman
+      value: 80.31047226932408
+  - 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: 58.08082172994642
+    - type: cos_sim_spearman
+      value: 62.9940530222459
+    - type: euclidean_pearson
+      value: 58.47927303460365
+    - type: euclidean_spearman
+      value: 60.8440317609258
+    - type: manhattan_pearson
+      value: 58.32438211697841
+    - type: manhattan_spearman
+      value: 60.69642636776064
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts22-crosslingual-sts
+      name: MTEB STS22 (de)
+      config: de
+      split: test
+      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
+    metrics:
+    - type: cos_sim_pearson
+      value: 33.83985707464123
+    - type: cos_sim_spearman
+      value: 46.89093209603036
+    - type: euclidean_pearson
+      value: 34.63602187576556
+    - type: euclidean_spearman
+      value: 46.31087228200712
+    - type: manhattan_pearson
+      value: 34.66899391543166
+    - type: manhattan_spearman
+      value: 46.33049538425276
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts22-crosslingual-sts
+      name: MTEB STS22 (es)
+      config: es
+      split: test
+      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
+    metrics:
+    - type: cos_sim_pearson
+      value: 51.61315965767736
+    - type: cos_sim_spearman
+      value: 58.9434266730386
+    - type: euclidean_pearson
+      value: 50.35885602217862
+    - type: euclidean_spearman
+      value: 58.238679883286025
+    - type: manhattan_pearson
+      value: 53.01732044381151
+    - type: manhattan_spearman
+      value: 58.10482351761412
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts22-crosslingual-sts
+      name: MTEB STS22 (pl)
+      config: pl
+      split: test
+      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
+    metrics:
+    - type: cos_sim_pearson
+      value: 26.771738440430177
+    - type: cos_sim_spearman
+      value: 34.807259227816054
+    - type: euclidean_pearson
+      value: 17.82657835823811
+    - type: euclidean_spearman
+      value: 34.27912898498941
+    - type: manhattan_pearson
+      value: 19.121527758886312
+    - type: manhattan_spearman
+      value: 34.4940050226265
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts22-crosslingual-sts
+      name: MTEB STS22 (tr)
+      config: tr
+      split: test
+      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
+    metrics:
+    - type: cos_sim_pearson
+      value: 52.8354704676683
+    - type: cos_sim_spearman
+      value: 57.28629534815841
+    - type: euclidean_pearson
+      value: 54.10329332004385
+    - type: euclidean_spearman
+      value: 58.15030615859976
+    - type: manhattan_pearson
+      value: 55.42372087433115
+    - type: manhattan_spearman
+      value: 57.52270736584036
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts22-crosslingual-sts
+      name: MTEB STS22 (ar)
+      config: ar
+      split: test
+      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
+    metrics:
+    - type: cos_sim_pearson
+      value: 31.01976557986924
+    - type: cos_sim_spearman
+      value: 54.506959483927616
+    - type: euclidean_pearson
+      value: 36.917863022119086
+    - type: euclidean_spearman
+      value: 53.750194241538566
+    - type: manhattan_pearson
+      value: 37.200177833241085
+    - type: manhattan_spearman
+      value: 53.507659188082535
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts22-crosslingual-sts
+      name: MTEB STS22 (ru)
+      config: ru
+      split: test
+      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
+    metrics:
+    - type: cos_sim_pearson
+      value: 46.38635647225934
+    - type: cos_sim_spearman
+      value: 54.50892732637536
+    - type: euclidean_pearson
+      value: 40.8331015184763
+    - type: euclidean_spearman
+      value: 53.142903182230924
+    - type: manhattan_pearson
+      value: 43.07655692906317
+    - type: manhattan_spearman
+      value: 53.5833474125901
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts22-crosslingual-sts
+      name: MTEB STS22 (zh)
+      config: zh
+      split: test
+      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
+    metrics:
+    - type: cos_sim_pearson
+      value: 60.52525456662916
+    - type: cos_sim_spearman
+      value: 63.23975489531082
+    - type: euclidean_pearson
+      value: 58.989191722317514
+    - type: euclidean_spearman
+      value: 62.536326639863894
+    - type: manhattan_pearson
+      value: 61.32982866201855
+    - type: manhattan_spearman
+      value: 63.068262822520516
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts22-crosslingual-sts
+      name: MTEB STS22 (fr)
+      config: fr
+      split: test
+      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
+    metrics:
+    - type: cos_sim_pearson
+      value: 59.63798684577696
+    - type: cos_sim_spearman
+      value: 74.09937723367189
+    - type: euclidean_pearson
+      value: 63.77494904383906
+    - type: euclidean_spearman
+      value: 71.15932571292481
+    - type: manhattan_pearson
+      value: 63.69646122775205
+    - type: manhattan_spearman
+      value: 70.54960698541632
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts22-crosslingual-sts
+      name: MTEB STS22 (de-en)
+      config: de-en
+      split: test
+      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
+    metrics:
+    - type: cos_sim_pearson
+      value: 36.50262468726711
+    - type: cos_sim_spearman
+      value: 45.00322499674274
+    - type: euclidean_pearson
+      value: 32.58759216581778
+    - type: euclidean_spearman
+      value: 40.13720951315429
+    - type: manhattan_pearson
+      value: 34.88422299605277
+    - type: manhattan_spearman
+      value: 40.63516862200963
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts22-crosslingual-sts
+      name: MTEB STS22 (es-en)
+      config: es-en
+      split: test
+      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
+    metrics:
+    - type: cos_sim_pearson
+      value: 56.498552617040275
+    - type: cos_sim_spearman
+      value: 67.71358426124443
+    - type: euclidean_pearson
+      value: 57.16474781778287
+    - type: euclidean_spearman
+      value: 65.721515493531
+    - type: manhattan_pearson
+      value: 59.25227610738926
+    - type: manhattan_spearman
+      value: 65.89743680340739
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts22-crosslingual-sts
+      name: MTEB STS22 (it)
+      config: it
+      split: test
+      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
+    metrics:
+    - type: cos_sim_pearson
+      value: 55.97978814727984
+    - type: cos_sim_spearman
+      value: 65.85821395092104
+    - type: euclidean_pearson
+      value: 59.11117270978519
+    - type: euclidean_spearman
+      value: 64.50062069934965
+    - type: manhattan_pearson
+      value: 59.4436213778161
+    - type: manhattan_spearman
+      value: 64.4003273074382
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts22-crosslingual-sts
+      name: MTEB STS22 (pl-en)
+      config: pl-en
+      split: test
+      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
+    metrics:
+    - type: cos_sim_pearson
+      value: 58.00873192515712
+    - type: cos_sim_spearman
+      value: 60.167708809138745
+    - type: euclidean_pearson
+      value: 56.91950637760252
+    - type: euclidean_spearman
+      value: 58.50593399441014
+    - type: manhattan_pearson
+      value: 58.683747352584994
+    - type: manhattan_spearman
+      value: 59.38110066799761
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts22-crosslingual-sts
+      name: MTEB STS22 (zh-en)
+      config: zh-en
+      split: test
+      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
+    metrics:
+    - type: cos_sim_pearson
+      value: 54.26020658151187
+    - type: cos_sim_spearman
+      value: 61.29236187204147
+    - type: euclidean_pearson
+      value: 55.993896804147056
+    - type: euclidean_spearman
+      value: 58.654928232615354
+    - type: manhattan_pearson
+      value: 56.612492816099426
+    - type: manhattan_spearman
+      value: 58.65144067094258
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts22-crosslingual-sts
+      name: MTEB STS22 (es-it)
+      config: es-it
+      split: test
+      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
+    metrics:
+    - type: cos_sim_pearson
+      value: 49.13817835368122
+    - type: cos_sim_spearman
+      value: 50.78524216975442
+    - type: euclidean_pearson
+      value: 46.56046454501862
+    - type: euclidean_spearman
+      value: 50.3935060082369
+    - type: manhattan_pearson
+      value: 48.0232348418531
+    - type: manhattan_spearman
+      value: 50.79528358464199
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts22-crosslingual-sts
+      name: MTEB STS22 (de-fr)
+      config: de-fr
+      split: test
+      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
+    metrics:
+    - type: cos_sim_pearson
+      value: 44.274388638585286
+    - type: cos_sim_spearman
+      value: 49.43124017389838
+    - type: euclidean_pearson
+      value: 42.45909582681174
+    - type: euclidean_spearman
+      value: 49.661383797129055
+    - type: manhattan_pearson
+      value: 42.5771970142383
+    - type: manhattan_spearman
+      value: 50.14423414390715
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts22-crosslingual-sts
+      name: MTEB STS22 (de-pl)
+      config: de-pl
+      split: test
+      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
+    metrics:
+    - type: cos_sim_pearson
+      value: 26.119500839749776
+    - type: cos_sim_spearman
+      value: 39.324070169024424
+    - type: euclidean_pearson
+      value: 35.83247077201831
+    - type: euclidean_spearman
+      value: 42.61903924348457
+    - type: manhattan_pearson
+      value: 35.50415034487894
+    - type: manhattan_spearman
+      value: 41.87998075949351
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts22-crosslingual-sts
+      name: MTEB STS22 (fr-pl)
+      config: fr-pl
+      split: test
+      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
+    metrics:
+    - type: cos_sim_pearson
+      value: 72.62575835691209
+    - type: cos_sim_spearman
+      value: 73.24670207647144
+    - type: euclidean_pearson
+      value: 78.07793323914657
+    - type: euclidean_spearman
+      value: 73.24670207647144
+    - type: manhattan_pearson
+      value: 77.51429306378206
+    - type: manhattan_spearman
+      value: 73.24670207647144
+  - task:
+      type: STS
+    dataset:
+      type: mteb/stsbenchmark-sts
+      name: MTEB STSBenchmark
+      config: default
+      split: test
+      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
+    metrics:
+    - type: cos_sim_pearson
+      value: 84.09375596849891
+    - type: cos_sim_spearman
+      value: 86.44881302053585
+    - type: euclidean_pearson
+      value: 84.71259163967213
+    - type: euclidean_spearman
+      value: 85.63661992344069
+    - type: manhattan_pearson
+      value: 84.64466537502614
+    - type: manhattan_spearman
+      value: 85.53769949940238
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/scidocs-reranking
+      name: MTEB SciDocsRR
+      config: default
+      split: test
+      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
+    metrics:
+    - type: map
+      value: 70.2056154684549
+    - type: mrr
+      value: 89.52703161036494
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/sprintduplicatequestions-pairclassification
+      name: MTEB SprintDuplicateQuestions
+      config: default
+      split: test
+      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
+    metrics:
+    - type: cos_sim_accuracy
+      value: 99.57623762376238
+    - type: cos_sim_ap
+      value: 83.53051588811371
+    - type: cos_sim_f1
+      value: 77.72704211060375
+    - type: cos_sim_precision
+      value: 78.88774459320288
+    - type: cos_sim_recall
+      value: 76.6
+    - type: dot_accuracy
+      value: 99.06435643564356
+    - type: dot_ap
+      value: 27.003124923857463
+    - type: dot_f1
+      value: 34.125269978401725
+    - type: dot_precision
+      value: 37.08920187793427
+    - type: dot_recall
+      value: 31.6
+    - type: euclidean_accuracy
+      value: 99.61485148514852
+    - type: euclidean_ap
+      value: 85.47332647001774
+    - type: euclidean_f1
+      value: 80.0808897876643
+    - type: euclidean_precision
+      value: 80.98159509202453
+    - type: euclidean_recall
+      value: 79.2
+    - type: manhattan_accuracy
+      value: 99.61683168316831
+    - type: manhattan_ap
+      value: 85.41969859598552
+    - type: manhattan_f1
+      value: 79.77755308392315
+    - type: manhattan_precision
+      value: 80.67484662576688
+    - type: manhattan_recall
+      value: 78.9
+    - type: max_accuracy
+      value: 99.61683168316831
+    - type: max_ap
+      value: 85.47332647001774
+    - type: max_f1
+      value: 80.0808897876643
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering
+      name: MTEB StackExchangeClustering
+      config: default
+      split: test
+      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
+    metrics:
+    - type: v_measure
+      value: 34.35688940053467
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering-p2p
+      name: MTEB StackExchangeClusteringP2P
+      config: default
+      split: test
+      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
+    metrics:
+    - type: v_measure
+      value: 30.64427069276576
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/stackoverflowdupquestions-reranking
+      name: MTEB StackOverflowDupQuestions
+      config: default
+      split: test
+      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
+    metrics:
+    - type: map
+      value: 44.89500754900078
+    - type: mrr
+      value: 45.33215558950853
+  - task:
+      type: Summarization
+    dataset:
+      type: mteb/summeval
+      name: MTEB SummEval
+      config: default
+      split: test
+      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
+    metrics:
+    - type: cos_sim_pearson
+      value: 30.653069624224084
+    - type: cos_sim_spearman
+      value: 30.10187112430319
+    - type: dot_pearson
+      value: 28.966278202103666
+    - type: dot_spearman
+      value: 28.342234095507767
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/toxic_conversations_50k
+      name: MTEB ToxicConversationsClassification
+      config: default
+      split: test
+      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
+    metrics:
+    - type: accuracy
+      value: 65.96839999999999
+    - type: ap
+      value: 11.846327590186444
+    - type: f1
+      value: 50.518102944693574
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/tweet_sentiment_extraction
+      name: MTEB TweetSentimentExtractionClassification
+      config: default
+      split: test
+      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
+    metrics:
+    - type: accuracy
+      value: 55.220713073005086
+    - type: f1
+      value: 55.47856175692088
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/twentynewsgroups-clustering
+      name: MTEB TwentyNewsgroupsClustering
+      config: default
+      split: test
+      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
+    metrics:
+    - type: v_measure
+      value: 31.581473892235877
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twittersemeval2015-pairclassification
+      name: MTEB TwitterSemEval2015
+      config: default
+      split: test
+      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
+    metrics:
+    - type: cos_sim_accuracy
+      value: 82.94093103653812
+    - type: cos_sim_ap
+      value: 62.48963249213361
+    - type: cos_sim_f1
+      value: 58.9541137429912
+    - type: cos_sim_precision
+      value: 52.05091937765205
+    - type: cos_sim_recall
+      value: 67.96833773087072
+    - type: dot_accuracy
+      value: 78.24998509864696
+    - type: dot_ap
+      value: 40.82371294480071
+    - type: dot_f1
+      value: 44.711163153786096
+    - type: dot_precision
+      value: 35.475379374419326
+    - type: dot_recall
+      value: 60.4485488126649
+    - type: euclidean_accuracy
+      value: 83.13166835548668
+    - type: euclidean_ap
+      value: 63.459878609769774
+    - type: euclidean_f1
+      value: 60.337199569532466
+    - type: euclidean_precision
+      value: 55.171659741963694
+    - type: euclidean_recall
+      value: 66.56992084432719
+    - type: manhattan_accuracy
+      value: 83.00649698992669
+    - type: manhattan_ap
+      value: 63.263161177904905
+    - type: manhattan_f1
+      value: 60.17122874713614
+    - type: manhattan_precision
+      value: 55.40750610703975
+    - type: manhattan_recall
+      value: 65.8311345646438
+    - type: max_accuracy
+      value: 83.13166835548668
+    - type: max_ap
+      value: 63.459878609769774
+    - type: max_f1
+      value: 60.337199569532466
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twitterurlcorpus-pairclassification
+      name: MTEB TwitterURLCorpus
+      config: default
+      split: test
+      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
+    metrics:
+    - type: cos_sim_accuracy
+      value: 87.80416812201653
+    - type: cos_sim_ap
+      value: 83.45540469219863
+    - type: cos_sim_f1
+      value: 75.58836427422892
+    - type: cos_sim_precision
+      value: 71.93934335002783
+    - type: cos_sim_recall
+      value: 79.62734832152756
+    - type: dot_accuracy
+      value: 83.04226336011176
+    - type: dot_ap
+      value: 70.63007268018524
+    - type: dot_f1
+      value: 65.35980325765405
+    - type: dot_precision
+      value: 60.84677151768532
+    - type: dot_recall
+      value: 70.59593470896212
+    - type: euclidean_accuracy
+      value: 87.60430007373773
+    - type: euclidean_ap
+      value: 83.10068502536592
+    - type: euclidean_f1
+      value: 75.02510506936439
+    - type: euclidean_precision
+      value: 72.56637168141593
+    - type: euclidean_recall
+      value: 77.65629812134279
+    - type: manhattan_accuracy
+      value: 87.60041914076145
+    - type: manhattan_ap
+      value: 83.05480769911229
+    - type: manhattan_f1
+      value: 74.98522895125554
+    - type: manhattan_precision
+      value: 72.04797047970479
+    - type: manhattan_recall
+      value: 78.17215891592238
+    - type: max_accuracy
+      value: 87.80416812201653
+    - type: max_ap
+      value: 83.45540469219863
+    - type: max_f1
+      value: 75.58836427422892
+---
+# shibing624/text2vec-base-multilingual
+This is a CoSENT(Cosine Sentence) model: shibing624/text2vec-base-multilingual.
+
+It maps sentences to a 384 dimensional dense vector space and can be used for tasks 
+like sentence embeddings, text matching or semantic search.
+
+
+
+- training dataset: https://huggingface.co/datasets/shibing624/nli-zh-all/tree/main/text2vec-base-multilingual-dataset
+- base model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
+- max_seq_length: 256
+- best epoch: 4
+- sentence embedding dim: 384
+
+## Evaluation
+For an automated evaluation of this model, see the *Evaluation Benchmark*: [text2vec](https://github.com/shibing624/text2vec)
+## Languages
+Available languages are: de, en, es, fr, it, nl, pl, pt, ru, zh
+
+### Release Models
+
+| Arch       | BaseModel                                                    | Model                                                                                                                                             | ATEC  |  BQ   | LCQMC | PAWSX | STS-B | SOHU-dd | SOHU-dc |    Avg    |  QPS  |
+|:-----------|:-------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------|:-----:|:-----:|:-----:|:-----:|:-----:|:-------:|:-------:|:---------:|:-----:|
+| Word2Vec   | word2vec                                                     | [w2v-light-tencent-chinese](https://ai.tencent.com/ailab/nlp/en/download.html)                                                                    | 20.00 | 31.49 | 59.46 | 2.57  | 55.78 |  55.04  |  20.70  |   35.03   | 23769 |
+| SBERT      | xlm-roberta-base                                             | [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) | 18.42 | 38.52 | 63.96 | 10.14 | 78.90 |  63.01  |  52.28  |   46.46   | 3138  |
+| Instructor | hfl/chinese-roberta-wwm-ext                                  | [moka-ai/m3e-base](https://huggingface.co/moka-ai/m3e-base)                                                                                       | 41.27 | 63.81 | 74.87 | 12.20 | 76.96 |  75.83  |  60.55  |   57.93   | 2980  |
+| CoSENT     | hfl/chinese-macbert-base                                     | [shibing624/text2vec-base-chinese](https://huggingface.co/shibing624/text2vec-base-chinese)                                                       | 31.93 | 42.67 | 70.16 | 17.21 | 79.30 |  70.27  |  50.42  |   51.61   | 3008  |
+| CoSENT     | hfl/chinese-lert-large                                       | [GanymedeNil/text2vec-large-chinese](https://huggingface.co/GanymedeNil/text2vec-large-chinese)                                                   | 32.61 | 44.59 | 69.30 | 14.51 | 79.44 |  73.01  |  59.04  |   53.12   | 2092  |
+| CoSENT     | nghuyong/ernie-3.0-base-zh                                   | [shibing624/text2vec-base-chinese-sentence](https://huggingface.co/shibing624/text2vec-base-chinese-sentence)                                     | 43.37 | 61.43 | 73.48 | 38.90 | 78.25 |  70.60  |  53.08  |   59.87   | 3089  |
+| CoSENT     | nghuyong/ernie-3.0-base-zh                                   | [shibing624/text2vec-base-chinese-paraphrase](https://huggingface.co/shibing624/text2vec-base-chinese-paraphrase)                                 | 44.89 | 63.58 | 74.24 | 40.90 | 78.93 |  76.70  |  63.30  | **63.08** | 3066  |
+| CoSENT     | sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2  | [shibing624/text2vec-base-multilingual](https://huggingface.co/shibing624/text2vec-base-multilingual)                                             | 32.39 | 50.33 | 65.64 | 32.56 | 74.45 |  68.88  |  51.17  |   53.67   | 4004  |
+
+
+Illustrate:
+- Result evaluation index: spearman coefficient
+- The `shibing624/text2vec-base-chinese` model is trained using the CoSENT method. It is trained on Chinese STS-B data based on `hfl/chinese-macbert-base` and has achieved good results in the Chinese STS-B test set evaluation. , run [examples/training_sup_text_matching_model.py](https://github.com/shibing624/text2vec/blob/master/examples/training_sup_text_matching_model.py) code to train the model, the model file has been uploaded to HF model hub, Chinese universal semantic matching task Recommended Use
+- The `shibing624/text2vec-base-chinese-sentence` model is trained using the CoSENT method and is based on the manually selected Chinese STS data set of `nghuyong/ernie-3.0-base-zh` [shibing624/nli-zh-all/ text2vec-base-chinese-sentence-dataset](https://huggingface.co/datasets/shibing624/nli-zh-all/tree/main/text2vec-base-chinese-sentence-dataset), and is used in various Chinese NLI test set evaluation has achieved good results. Run the [examples/training_sup_text_matching_model_jsonl_data.py](https://github.com/shibing624/text2vec/blob/master/examples/training_sup_text_matching_model_jsonl_data.py) code to train the model, and the model file has been uploaded to HF model hub, recommended for Chinese s2s (sentence vs sentence) semantic matching tasks
+- The `shibing624/text2vec-base-chinese-paraphrase` model is trained using the CoSENT method and is based on the manually selected Chinese STS data set of `nghuyong/ernie-3.0-base-zh` [shibing624/nli-zh-all/ text2vec-base-chinese-paraphrase-dataset](https://huggingface.co/datasets/shibing624/nli-zh-all/tree/main/text2vec-base-chinese-paraphrase-dataset), the data set is relative to [shibing624 /nli-zh-all/text2vec-base-chinese-sentence-dataset](https://huggingface.co/datasets/shibing624/nli-zh-all/tree/main/text2vec-base-chinese-sentence-dataset) s2p (sentence to paraphrase) data was added to strengthen its long text representation capabilities, and the evaluation on each Chinese NLI test set reached SOTA, running [examples/training_sup_text_matching_model_jsonl_data.py](https://github.com/shibing624/text2vec /blob/master/examples/training_sup_text_matching_model_jsonl_data.py) code can train the model. The model file has been uploaded to HF model hub. It is recommended for Chinese s2p (sentence vs paragraph) semantic matching tasks.
+- The `shibing624/text2vec-base-multilingual` model is trained using the CoSENT method and is based on the manually selected multilingual STS data set of `sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2` [shibing624/nli-zh -all/text2vec-base-multilingual-dataset](https://huggingface.co/datasets/shibing624/nli-zh-all/tree/main/text2vec-base-multilingual-dataset) trained and tested in Chinese and English The set evaluation effect is improved compared to the original model. Run the [examples/training_sup_text_matching_model_jsonl_data.py](https://github.com/shibing624/text2vec/blob/master/examples/training_sup_text_matching_model_jsonl_data.py) code to train the model, and the model file has been uploaded. HF model hub, recommended for multi-language semantic matching tasks
+- `w2v-light-tencent-chinese` is the Word2Vec model of Tencent word vector, which is loaded and used by CPU. It is suitable for Chinese text matching tasks and cold start situations where data is missing.
+- The GPU test environment of QPS is Tesla V100 with 32GB memory.
+
+Model training experiment report: [Experiment report](https://github.com/shibing624/text2vec/blob/master/docs/model_report.md)
+
+## Usage (text2vec)
+Using this model becomes easy when you have [text2vec](https://github.com/shibing624/text2vec) installed:
+
+```
+pip install -U text2vec
+```
+
+Then you can use the model like this:
+
+```python
+from text2vec import SentenceModel
+sentences = ['如何更换花呗绑定银行卡', 'How to replace the Huabei bundled bank card']
+
+model = SentenceModel('shibing624/text2vec-base-multilingual')
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
+## Usage (HuggingFace Transformers)
+Without [text2vec](https://github.com/shibing624/text2vec), you can use the model like this: 
+
+First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
+
+Install transformers:
+```
+pip install transformers
+```
+
+Then load model and predict:
+```python
+from transformers import AutoTokenizer, AutoModel
+import torch
+
+# Mean Pooling - Take attention mask into account for correct averaging
+def mean_pooling(model_output, attention_mask):
+    token_embeddings = model_output[0]  # First element of model_output contains all token embeddings
+    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+
+# Load model from HuggingFace Hub
+tokenizer = AutoTokenizer.from_pretrained('shibing624/text2vec-base-multilingual')
+model = AutoModel.from_pretrained('shibing624/text2vec-base-multilingual')
+sentences = ['如何更换花呗绑定银行卡', 'How to replace the Huabei bundled bank card']
+# Tokenize sentences
+encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
+
+# Compute token embeddings
+with torch.no_grad():
+    model_output = model(**encoded_input)
+# Perform pooling. In this case, mean pooling.
+sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+print("Sentence embeddings:")
+print(sentence_embeddings)
+```
+
+## Usage (sentence-transformers)
+[sentence-transformers](https://github.com/UKPLab/sentence-transformers) is a popular library to compute dense vector representations for sentences.
+
+Install sentence-transformers:
+```
+pip install -U sentence-transformers
+```
+
+Then load model and predict:
+
+```python
+from sentence_transformers import SentenceTransformer
+
+m = SentenceTransformer("shibing624/text2vec-base-multilingual")
+sentences = ['如何更换花呗绑定银行卡', 'How to replace the Huabei bundled bank card']
+
+sentence_embeddings = m.encode(sentences)
+print("Sentence embeddings:")
+print(sentence_embeddings)
+```
+
+
+## Full Model Architecture
+```
+CoSENT(
+  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_mean_tokens': True})
+)
+```
+
+
+## Intended uses
+
+Our model is intented to be used as a sentence and short paragraph encoder. Given an input text, it ouptuts a vector which captures 
+the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks.
+
+By default, input text longer than 256 word pieces is truncated.
+
+
+## Training procedure
+
+### Pre-training 
+
+We use the pretrained [`sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2`](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) model. 
+Please refer to the model card for more detailed information about the pre-training procedure.
+
+### Fine-tuning 
+
+We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each 
+possible sentence pairs from the batch.
+We then apply the rank loss by comparing with true pairs and false pairs.
+
+
+## Citing & Authors
+This model was trained by [text2vec](https://github.com/shibing624/text2vec). 
+        
+If you find this model helpful, feel free to cite:
+```bibtex 
+@software{text2vec,
+  author = {Ming Xu},
+  title = {text2vec: A Tool for Text to Vector},
+  year = {2023},
+  url = {https://github.com/shibing624/text2vec},
+}
+```

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