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---
language: []
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dataset_size:100K<n<1M
- loss:AnglELoss
base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
widget:
- source_sentence: 有些人在路上溜达。
sentences:
- Folk går
- Otururken gitar çalan adam.
- ארה"ב קבעה שסוריה השתמשה בנשק כימי
- source_sentence: 緬甸以前稱為緬甸。
sentences:
- 缅甸以前叫缅甸。
- This is very contradictory.
- 한 남자가 아기를 안고 의자에 앉아 잠들어 있다.
- source_sentence: אדם כותב.
sentences:
- האדם כותב.
- questa non è una risposta.
- 7 שוטרים נהרגו ו-4 שוטרים נפצעו.
- source_sentence: הם מפחדים.
sentences:
- liên quan đến rủi ro đáng kể;
- A man is playing a guitar.
- A man is playing a piano.
- source_sentence: 一个女人正在洗澡。
sentences:
- A woman is taking a bath.
- En jente børster håret sitt
- אדם מחלק תפוח אדמה.
pipeline_tag: sentence-similarity
model-index:
- name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev
type: sts-dev
metrics:
- type: pearson_cosine
value: 0.9551466915019567
name: Pearson Cosine
- type: spearman_cosine
value: 0.9592676437617756
name: Spearman Cosine
- type: pearson_manhattan
value: 0.9270103565661432
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.9382925369644322
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.9278315400036575
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.9393641949848517
name: Spearman Euclidean
- type: pearson_dot
value: 0.8760113280718741
name: Pearson Dot
- type: spearman_dot
value: 0.8864509380027734
name: Spearman Dot
- type: pearson_max
value: 0.9551466915019567
name: Pearson Max
- type: spearman_max
value: 0.9592676437617756
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test
type: sts-test
metrics:
- type: pearson_cosine
value: 0.9479585032380113
name: Pearson Cosine
- type: spearman_cosine
value: 0.9514910354916427
name: Spearman Cosine
- type: pearson_manhattan
value: 0.925192141913064
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.9351648026362221
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.9258239806908134
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.9363652577900217
name: Spearman Euclidean
- type: pearson_dot
value: 0.8442947652156254
name: Pearson Dot
- type: spearman_dot
value: 0.8435104766124126
name: Spearman Dot
- type: pearson_max
value: 0.9479585032380113
name: Pearson Max
- type: spearman_max
value: 0.9514910354916427
name: Spearman Max
- type: pearson_cosine
value: 0.9725274765440489
name: Pearson Cosine
- type: spearman_cosine
value: 0.9766335692570665
name: Spearman Cosine
- type: pearson_manhattan
value: 0.9382317294386867
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.948654920505423
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.9392057529290415
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.9500099103637895
name: Spearman Euclidean
- type: pearson_dot
value: 0.8531236460319379
name: Pearson Dot
- type: spearman_dot
value: 0.8611492409185547
name: Spearman Dot
- type: pearson_max
value: 0.9725274765440489
name: Pearson Max
- type: spearman_max
value: 0.9766335692570665
name: Spearman Max
- type: pearson_cosine
value: 0.8026922386812214
name: Pearson Cosine
- type: spearman_cosine
value: 0.8124393788492182
name: Spearman Cosine
- type: pearson_manhattan
value: 0.7839394479918361
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.7899571854314883
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.7835912695413444
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.7920219916708612
name: Spearman Euclidean
- type: pearson_dot
value: 0.7698701769634279
name: Pearson Dot
- type: spearman_dot
value: 0.781996122357711
name: Spearman Dot
- type: pearson_max
value: 0.8026922386812214
name: Pearson Max
- type: spearman_max
value: 0.8124393788492182
name: Spearman Max
- type: pearson_cosine
value: 0.7795928581740468
name: Pearson Cosine
- type: spearman_cosine
value: 0.7703365842088069
name: Spearman Cosine
- type: pearson_manhattan
value: 0.7903764226370217
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.7829879213871844
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.7911863454505806
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.7841695636601043
name: Spearman Euclidean
- type: pearson_dot
value: 0.7077312955932407
name: Pearson Dot
- type: spearman_dot
value: 0.6914225616023565
name: Spearman Dot
- type: pearson_max
value: 0.7911863454505806
name: Pearson Max
- type: spearman_max
value: 0.7841695636601043
name: Spearman Max
- type: pearson_cosine
value: 0.9112700251605085
name: Pearson Cosine
- type: spearman_cosine
value: 0.9109414091487618
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8969826303560867
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8934356058163047
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8986106629139636
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8954517657266873
name: Spearman Euclidean
- type: pearson_dot
value: 0.884386067267308
name: Pearson Dot
- type: spearman_dot
value: 0.8922685778872441
name: Spearman Dot
- type: pearson_max
value: 0.9112700251605085
name: Pearson Max
- type: spearman_max
value: 0.9109414091487618
name: Spearman Max
- type: pearson_cosine
value: 0.9361870787330656
name: Pearson Cosine
- type: spearman_cosine
value: 0.9378741534997558
name: Spearman Cosine
- type: pearson_manhattan
value: 0.9230051982649123
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.9244721677465636
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.9230904520135751
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.9251248730902872
name: Spearman Euclidean
- type: pearson_dot
value: 0.9069963151228692
name: Pearson Dot
- type: spearman_dot
value: 0.9185797530151516
name: Spearman Dot
- type: pearson_max
value: 0.9361870787330656
name: Pearson Max
- type: spearman_max
value: 0.9378741534997558
name: Spearman Max
- type: pearson_cosine
value: 0.8048757108412675
name: Pearson Cosine
- type: spearman_cosine
value: 0.7987027653005363
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8017660413612523
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.7828168153285264
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8006665075585622
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.7824761741785664
name: Spearman Euclidean
- type: pearson_dot
value: 0.7894710045147775
name: Pearson Dot
- type: spearman_dot
value: 0.7819409907917216
name: Spearman Dot
- type: pearson_max
value: 0.8048757108412675
name: Pearson Max
- type: spearman_max
value: 0.7987027653005363
name: Spearman Max
- type: pearson_cosine
value: 0.8520160385093393
name: Pearson Cosine
- type: spearman_cosine
value: 0.8553203530552356
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8464006282913296
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8409514527398295
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8467543977447098
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8458591066828018
name: Spearman Euclidean
- type: pearson_dot
value: 0.8093136598158064
name: Pearson Dot
- type: spearman_dot
value: 0.8153571493902085
name: Spearman Dot
- type: pearson_max
value: 0.8520160385093393
name: Pearson Max
- type: spearman_max
value: 0.8553203530552356
name: Spearman Max
- type: pearson_cosine
value: 0.8751983236341568
name: Pearson Cosine
- type: spearman_cosine
value: 0.872701191632785
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8744834146908832
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8661385734785878
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.874802989814616
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8668384026485944
name: Spearman Euclidean
- type: pearson_dot
value: 0.8603441420083793
name: Pearson Dot
- type: spearman_dot
value: 0.8519571499551175
name: Spearman Dot
- type: pearson_max
value: 0.8751983236341568
name: Pearson Max
- type: spearman_max
value: 0.872701191632785
name: Spearman Max
- type: pearson_cosine
value: 0.9082404991830442
name: Pearson Cosine
- type: spearman_cosine
value: 0.9067607122592818
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8908378724095692
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.885184918244054
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8907567800603056
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8850799779856109
name: Spearman Euclidean
- type: pearson_dot
value: 0.8888621290344544
name: Pearson Dot
- type: spearman_dot
value: 0.8965880419316619
name: Spearman Dot
- type: pearson_max
value: 0.9082404991830442
name: Pearson Max
- type: spearman_max
value: 0.9067607122592818
name: Spearman Max
- type: pearson_cosine
value: 0.9249796814520836
name: Pearson Cosine
- type: spearman_cosine
value: 0.9246785886944904
name: Spearman Cosine
- type: pearson_manhattan
value: 0.9083667986520362
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.90288714821411
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.9115880396459031
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.9083794061358542
name: Spearman Euclidean
- type: pearson_dot
value: 0.9000889923763985
name: Pearson Dot
- type: spearman_dot
value: 0.9070443969139744
name: Spearman Dot
- type: pearson_max
value: 0.9249796814520836
name: Pearson Max
- type: spearman_max
value: 0.9246785886944904
name: Spearman Max
- type: pearson_cosine
value: 0.9133091498737149
name: Pearson Cosine
- type: spearman_cosine
value: 0.9114826394926738
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8977113793113364
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8933433506440468
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8979058595014344
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8937323599537337
name: Spearman Euclidean
- type: pearson_dot
value: 0.891219202934611
name: Pearson Dot
- type: spearman_dot
value: 0.8987764114969254
name: Spearman Dot
- type: pearson_max
value: 0.9133091498737149
name: Pearson Max
- type: spearman_max
value: 0.9114826394926738
name: Spearman Max
- type: pearson_cosine
value: 0.8984578585216539
name: Pearson Cosine
- type: spearman_cosine
value: 0.8451542547285167
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8714879175346363
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8451542547285167
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8809190484217423
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8451542547285167
name: Spearman Euclidean
- type: pearson_dot
value: 0.8537957222589418
name: Pearson Dot
- type: spearman_dot
value: 0.8451542547285167
name: Spearman Dot
- type: pearson_max
value: 0.8984578585216539
name: Pearson Max
- type: spearman_max
value: 0.8451542547285167
name: Spearman Max
- type: pearson_cosine
value: 0.6494815112978085
name: Pearson Cosine
- type: spearman_cosine
value: 0.6385354535483773
name: Spearman Cosine
- type: pearson_manhattan
value: 0.6429493098908716
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.6473666993823523
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.6442945700268683
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.6444758519763731
name: Spearman Euclidean
- type: pearson_dot
value: 0.6128358976757747
name: Pearson Dot
- type: spearman_dot
value: 0.6108258021881942
name: Spearman Dot
- type: pearson_max
value: 0.6494815112978085
name: Pearson Max
- type: spearman_max
value: 0.6473666993823523
name: Spearman Max
- type: pearson_cosine
value: 0.7441341150359049
name: Pearson Cosine
- type: spearman_cosine
value: 0.7518021273920814
name: Spearman Cosine
- type: pearson_manhattan
value: 0.7339108684091178
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.7367402927783612
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.7336764576613932
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.734241088471987
name: Spearman Euclidean
- type: pearson_dot
value: 0.6886320720189693
name: Pearson Dot
- type: spearman_dot
value: 0.698561864698337
name: Spearman Dot
- type: pearson_max
value: 0.7441341150359049
name: Pearson Max
- type: spearman_max
value: 0.7518021273920814
name: Spearman Max
- type: pearson_cosine
value: 0.6278594754203957
name: Pearson Cosine
- type: spearman_cosine
value: 0.6319430830291571
name: Spearman Cosine
- type: pearson_manhattan
value: 0.543548091135791
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- type: spearman_manhattan
value: 0.6002053211770223
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- type: pearson_euclidean
value: 0.5399866615749636
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- type: spearman_euclidean
value: 0.5955360076924765
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- type: pearson_dot
value: 0.5657998544710718
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- type: spearman_dot
value: 0.6068611192160528
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- type: pearson_max
value: 0.6278594754203957
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- type: spearman_max
value: 0.6319430830291571
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value: 0.7778538763931996
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- type: spearman_cosine
value: 0.7875616631597785
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value: 0.7425757616272681
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value: 0.7789392103102715
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value: 0.780583955651507
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value: 0.7214423493083364
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value: 0.7489073787091952
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value: 0.7778538763931996
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value: 0.7875616631597785
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value: 0.526790729806662
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value: 0.5774252131250034
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value: 0.41713442172065224
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- type: spearman_manhattan
value: 0.5599676717727231
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- type: pearson_euclidean
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value: 0.603366556372183
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- type: spearman_manhattan
value: 0.6167955278711116
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value: 0.5737511662255662
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value: 0.49058760093828624
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value: 0.5762095703672849
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value: 0.4306984514506903
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value: 0.5470683854030187
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value: 0.571787148920759
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value: 0.5500811027014174
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value: 0.5695499775959532
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value: 0.3571900232473057
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value: 0.4335552432730643
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value: 0.20808854264339055
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- type: spearman_manhattan
value: 0.4354537154533896
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- type: pearson_euclidean
value: 0.208616390027902
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- type: spearman_euclidean
value: 0.440246452767669
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- type: pearson_dot
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value: 0.3706905558756734
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value: 0.3571900232473057
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value: 0.440246452767669
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value: 0.6863427356006826
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value: 0.6620948502618977
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value: 0.6428578762643233
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value: 0.6483663123081533
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value: 0.6424050032110411
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- type: spearman_euclidean
value: 0.6485902628925195
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- type: pearson_dot
value: 0.6352371374824808
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value: 0.6159110999161411
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value: 0.6863427356006826
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- type: spearman_max
value: 0.6620948502618977
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value: 0.7570295008280781
name: Pearson Cosine
- type: spearman_cosine
value: 0.7510805416538202
name: Spearman Cosine
- type: pearson_manhattan
value: 0.7191097960855934
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.7140422377894933
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- type: pearson_euclidean
value: 0.7204228437397647
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- type: spearman_euclidean
value: 0.7257632200250398
name: Spearman Euclidean
- type: pearson_dot
value: 0.7144336778935939
name: Pearson Dot
- type: spearman_dot
value: 0.7284199759984302
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- type: pearson_max
value: 0.7570295008280781
name: Pearson Max
- type: spearman_max
value: 0.7510805416538202
name: Spearman Max
- type: pearson_cosine
value: 0.6502825737911098
name: Pearson Cosine
- type: spearman_cosine
value: 0.6624635951676386
name: Spearman Cosine
- type: pearson_manhattan
value: 0.647419285100459
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.6589805549915764
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.6516956762905051
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.6667221229271868
name: Spearman Euclidean
- type: pearson_dot
value: 0.5646710115576599
name: Pearson Dot
- type: spearman_dot
value: 0.570198719868156
name: Spearman Dot
- type: pearson_max
value: 0.6516956762905051
name: Pearson Max
- type: spearman_max
value: 0.6667221229271868
name: Spearman Max
- type: pearson_cosine
value: 0.6774230420538705
name: Pearson Cosine
- type: spearman_cosine
value: 0.6537294853166558
name: Spearman Cosine
- type: pearson_manhattan
value: 0.6824702119604247
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.6324707043840341
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.6905615468119815
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.640725065351179
name: Spearman Euclidean
- type: pearson_dot
value: 0.5834798827905125
name: Pearson Dot
- type: spearman_dot
value: 0.5962447037764929
name: Spearman Dot
- type: pearson_max
value: 0.6905615468119815
name: Pearson Max
- type: spearman_max
value: 0.6537294853166558
name: Spearman Max
- type: pearson_cosine
value: 0.6709478850576526
name: Pearson Cosine
- type: spearman_cosine
value: 0.6847049462613332
name: Spearman Cosine
- type: pearson_manhattan
value: 0.6612883666796053
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.6906896123993531
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.66070522554664
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.6880796473119815
name: Spearman Euclidean
- type: pearson_dot
value: 0.609762034287328
name: Pearson Dot
- type: spearman_dot
value: 0.6194587632000961
name: Spearman Dot
- type: pearson_max
value: 0.6709478850576526
name: Pearson Max
- type: spearman_max
value: 0.6906896123993531
name: Spearman Max
- type: pearson_cosine
value: 0.5977420246846783
name: Pearson Cosine
- type: spearman_cosine
value: 0.5798716781400349
name: Spearman Cosine
- type: pearson_manhattan
value: 0.5974348978243684
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.5952597125560467
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.5949256850264925
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.5935900431326085
name: Spearman Euclidean
- type: pearson_dot
value: 0.5042542872226021
name: Pearson Dot
- type: spearman_dot
value: 0.4968394689744579
name: Spearman Dot
- type: pearson_max
value: 0.5977420246846783
name: Pearson Max
- type: spearman_max
value: 0.5952597125560467
name: Spearman Max
- type: pearson_cosine
value: 0.45623521030042163
name: Pearson Cosine
- type: spearman_cosine
value: 0.44220332625465214
name: Spearman Cosine
- type: pearson_manhattan
value: 0.4154787596532877
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.3836945296053597
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.4111357738180186
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.3821548244303783
name: Spearman Euclidean
- type: pearson_dot
value: 0.48625234725541483
name: Pearson Dot
- type: spearman_dot
value: 0.5302744622635869
name: Spearman Dot
- type: pearson_max
value: 0.48625234725541483
name: Pearson Max
- type: spearman_max
value: 0.5302744622635869
name: Spearman Max
- type: pearson_cosine
value: 0.5929570742517215
name: Pearson Cosine
- type: spearman_cosine
value: 0.6266361518449931
name: Spearman Cosine
- type: pearson_manhattan
value: 0.5608268850302591
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.6228972623939251
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.5579847474929831
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.6202030126844109
name: Spearman Euclidean
- type: pearson_dot
value: 0.4578333834889949
name: Pearson Dot
- type: spearman_dot
value: 0.5628471668594075
name: Spearman Dot
- type: pearson_max
value: 0.5929570742517215
name: Pearson Max
- type: spearman_max
value: 0.6266361518449931
name: Spearman Max
---
# SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) <!-- at revision 79f2382ceacceacdf38563d7c5d16b9ff8d725d6 -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("Gameselo/STS-multilingual-mpnet-base-v2")
# Run inference
sentences = [
'一个女人正在洗澡。',
'A woman is taking a bath.',
'En jente børster håret sitt',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Semantic Similarity
* Dataset: `sts-dev`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.9551 |
| **spearman_cosine** | **0.9593** |
| pearson_manhattan | 0.927 |
| spearman_manhattan | 0.9383 |
| pearson_euclidean | 0.9278 |
| spearman_euclidean | 0.9394 |
| pearson_dot | 0.876 |
| spearman_dot | 0.8865 |
| pearson_max | 0.9551 |
| spearman_max | 0.9593 |
#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.948 |
| **spearman_cosine** | **0.9515** |
| pearson_manhattan | 0.9252 |
| spearman_manhattan | 0.9352 |
| pearson_euclidean | 0.9258 |
| spearman_euclidean | 0.9364 |
| pearson_dot | 0.8443 |
| spearman_dot | 0.8435 |
| pearson_max | 0.948 |
| spearman_max | 0.9515 |
#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.9725 |
| **spearman_cosine** | **0.9766** |
| pearson_manhattan | 0.9382 |
| spearman_manhattan | 0.9487 |
| pearson_euclidean | 0.9392 |
| spearman_euclidean | 0.95 |
| pearson_dot | 0.8531 |
| spearman_dot | 0.8611 |
| pearson_max | 0.9725 |
| spearman_max | 0.9766 |
#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8027 |
| **spearman_cosine** | **0.8124** |
| pearson_manhattan | 0.7839 |
| spearman_manhattan | 0.79 |
| pearson_euclidean | 0.7836 |
| spearman_euclidean | 0.792 |
| pearson_dot | 0.7699 |
| spearman_dot | 0.782 |
| pearson_max | 0.8027 |
| spearman_max | 0.8124 |
#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.7796 |
| **spearman_cosine** | **0.7703** |
| pearson_manhattan | 0.7904 |
| spearman_manhattan | 0.783 |
| pearson_euclidean | 0.7912 |
| spearman_euclidean | 0.7842 |
| pearson_dot | 0.7077 |
| spearman_dot | 0.6914 |
| pearson_max | 0.7912 |
| spearman_max | 0.7842 |
#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.9113 |
| **spearman_cosine** | **0.9109** |
| pearson_manhattan | 0.897 |
| spearman_manhattan | 0.8934 |
| pearson_euclidean | 0.8986 |
| spearman_euclidean | 0.8955 |
| pearson_dot | 0.8844 |
| spearman_dot | 0.8923 |
| pearson_max | 0.9113 |
| spearman_max | 0.9109 |
#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.9362 |
| **spearman_cosine** | **0.9379** |
| pearson_manhattan | 0.923 |
| spearman_manhattan | 0.9245 |
| pearson_euclidean | 0.9231 |
| spearman_euclidean | 0.9251 |
| pearson_dot | 0.907 |
| spearman_dot | 0.9186 |
| pearson_max | 0.9362 |
| spearman_max | 0.9379 |
#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8049 |
| **spearman_cosine** | **0.7987** |
| pearson_manhattan | 0.8018 |
| spearman_manhattan | 0.7828 |
| pearson_euclidean | 0.8007 |
| spearman_euclidean | 0.7825 |
| pearson_dot | 0.7895 |
| spearman_dot | 0.7819 |
| pearson_max | 0.8049 |
| spearman_max | 0.7987 |
#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.852 |
| **spearman_cosine** | **0.8553** |
| pearson_manhattan | 0.8464 |
| spearman_manhattan | 0.841 |
| pearson_euclidean | 0.8468 |
| spearman_euclidean | 0.8459 |
| pearson_dot | 0.8093 |
| spearman_dot | 0.8154 |
| pearson_max | 0.852 |
| spearman_max | 0.8553 |
#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8752 |
| **spearman_cosine** | **0.8727** |
| pearson_manhattan | 0.8745 |
| spearman_manhattan | 0.8661 |
| pearson_euclidean | 0.8748 |
| spearman_euclidean | 0.8668 |
| pearson_dot | 0.8603 |
| spearman_dot | 0.852 |
| pearson_max | 0.8752 |
| spearman_max | 0.8727 |
#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.9082 |
| **spearman_cosine** | **0.9068** |
| pearson_manhattan | 0.8908 |
| spearman_manhattan | 0.8852 |
| pearson_euclidean | 0.8908 |
| spearman_euclidean | 0.8851 |
| pearson_dot | 0.8889 |
| spearman_dot | 0.8966 |
| pearson_max | 0.9082 |
| spearman_max | 0.9068 |
#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.925 |
| **spearman_cosine** | **0.9247** |
| pearson_manhattan | 0.9084 |
| spearman_manhattan | 0.9029 |
| pearson_euclidean | 0.9116 |
| spearman_euclidean | 0.9084 |
| pearson_dot | 0.9001 |
| spearman_dot | 0.907 |
| pearson_max | 0.925 |
| spearman_max | 0.9247 |
#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.9133 |
| **spearman_cosine** | **0.9115** |
| pearson_manhattan | 0.8977 |
| spearman_manhattan | 0.8933 |
| pearson_euclidean | 0.8979 |
| spearman_euclidean | 0.8937 |
| pearson_dot | 0.8912 |
| spearman_dot | 0.8988 |
| pearson_max | 0.9133 |
| spearman_max | 0.9115 |
#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8985 |
| **spearman_cosine** | **0.8452** |
| pearson_manhattan | 0.8715 |
| spearman_manhattan | 0.8452 |
| pearson_euclidean | 0.8809 |
| spearman_euclidean | 0.8452 |
| pearson_dot | 0.8538 |
| spearman_dot | 0.8452 |
| pearson_max | 0.8985 |
| spearman_max | 0.8452 |
#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.6495 |
| **spearman_cosine** | **0.6385** |
| pearson_manhattan | 0.6429 |
| spearman_manhattan | 0.6474 |
| pearson_euclidean | 0.6443 |
| spearman_euclidean | 0.6445 |
| pearson_dot | 0.6128 |
| spearman_dot | 0.6108 |
| pearson_max | 0.6495 |
| spearman_max | 0.6474 |
#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.7441 |
| **spearman_cosine** | **0.7518** |
| pearson_manhattan | 0.7339 |
| spearman_manhattan | 0.7367 |
| pearson_euclidean | 0.7337 |
| spearman_euclidean | 0.7342 |
| pearson_dot | 0.6886 |
| spearman_dot | 0.6986 |
| pearson_max | 0.7441 |
| spearman_max | 0.7518 |
#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.6279 |
| **spearman_cosine** | **0.6319** |
| pearson_manhattan | 0.5435 |
| spearman_manhattan | 0.6002 |
| pearson_euclidean | 0.54 |
| spearman_euclidean | 0.5955 |
| pearson_dot | 0.5658 |
| spearman_dot | 0.6069 |
| pearson_max | 0.6279 |
| spearman_max | 0.6319 |
#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.7779 |
| **spearman_cosine** | **0.7876** |
| pearson_manhattan | 0.7426 |
| spearman_manhattan | 0.7789 |
| pearson_euclidean | 0.7437 |
| spearman_euclidean | 0.7806 |
| pearson_dot | 0.7214 |
| spearman_dot | 0.7489 |
| pearson_max | 0.7779 |
| spearman_max | 0.7876 |
#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.5268 |
| **spearman_cosine** | **0.5774** |
| pearson_manhattan | 0.4171 |
| spearman_manhattan | 0.56 |
| pearson_euclidean | 0.4219 |
| spearman_euclidean | 0.5665 |
| pearson_dot | 0.4981 |
| spearman_dot | 0.5367 |
| pearson_max | 0.5268 |
| spearman_max | 0.5774 |
#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.6306 |
| **spearman_cosine** | **0.6384** |
| pearson_manhattan | 0.6034 |
| spearman_manhattan | 0.6168 |
| pearson_euclidean | 0.6081 |
| spearman_euclidean | 0.622 |
| pearson_dot | 0.5767 |
| spearman_dot | 0.5831 |
| pearson_max | 0.6306 |
| spearman_max | 0.6384 |
#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.5568 |
| **spearman_cosine** | **0.5867** |
| pearson_manhattan | 0.4924 |
| spearman_manhattan | 0.5738 |
| pearson_euclidean | 0.4906 |
| spearman_euclidean | 0.5762 |
| pearson_dot | 0.4307 |
| spearman_dot | 0.5471 |
| pearson_max | 0.5568 |
| spearman_max | 0.5867 |
#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:----------|
| pearson_cosine | 0.5776 |
| **spearman_cosine** | **0.575** |
| pearson_manhattan | 0.5718 |
| spearman_manhattan | 0.5501 |
| pearson_euclidean | 0.5695 |
| spearman_euclidean | 0.5532 |
| pearson_dot | 0.5315 |
| spearman_dot | 0.5191 |
| pearson_max | 0.5776 |
| spearman_max | 0.575 |
#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.3572 |
| **spearman_cosine** | **0.4336** |
| pearson_manhattan | 0.2081 |
| spearman_manhattan | 0.4355 |
| pearson_euclidean | 0.2086 |
| spearman_euclidean | 0.4402 |
| pearson_dot | 0.2234 |
| spearman_dot | 0.3707 |
| pearson_max | 0.3572 |
| spearman_max | 0.4402 |
#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.6863 |
| **spearman_cosine** | **0.6621** |
| pearson_manhattan | 0.6429 |
| spearman_manhattan | 0.6484 |
| pearson_euclidean | 0.6424 |
| spearman_euclidean | 0.6486 |
| pearson_dot | 0.6352 |
| spearman_dot | 0.6159 |
| pearson_max | 0.6863 |
| spearman_max | 0.6621 |
#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.757 |
| **spearman_cosine** | **0.7511** |
| pearson_manhattan | 0.7191 |
| spearman_manhattan | 0.714 |
| pearson_euclidean | 0.7204 |
| spearman_euclidean | 0.7258 |
| pearson_dot | 0.7144 |
| spearman_dot | 0.7284 |
| pearson_max | 0.757 |
| spearman_max | 0.7511 |
#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.6503 |
| **spearman_cosine** | **0.6625** |
| pearson_manhattan | 0.6474 |
| spearman_manhattan | 0.659 |
| pearson_euclidean | 0.6517 |
| spearman_euclidean | 0.6667 |
| pearson_dot | 0.5647 |
| spearman_dot | 0.5702 |
| pearson_max | 0.6517 |
| spearman_max | 0.6667 |
#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.6774 |
| **spearman_cosine** | **0.6537** |
| pearson_manhattan | 0.6825 |
| spearman_manhattan | 0.6325 |
| pearson_euclidean | 0.6906 |
| spearman_euclidean | 0.6407 |
| pearson_dot | 0.5835 |
| spearman_dot | 0.5962 |
| pearson_max | 0.6906 |
| spearman_max | 0.6537 |
#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.6709 |
| **spearman_cosine** | **0.6847** |
| pearson_manhattan | 0.6613 |
| spearman_manhattan | 0.6907 |
| pearson_euclidean | 0.6607 |
| spearman_euclidean | 0.6881 |
| pearson_dot | 0.6098 |
| spearman_dot | 0.6195 |
| pearson_max | 0.6709 |
| spearman_max | 0.6907 |
#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.5977 |
| **spearman_cosine** | **0.5799** |
| pearson_manhattan | 0.5974 |
| spearman_manhattan | 0.5953 |
| pearson_euclidean | 0.5949 |
| spearman_euclidean | 0.5936 |
| pearson_dot | 0.5043 |
| spearman_dot | 0.4968 |
| pearson_max | 0.5977 |
| spearman_max | 0.5953 |
#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.4562 |
| **spearman_cosine** | **0.4422** |
| pearson_manhattan | 0.4155 |
| spearman_manhattan | 0.3837 |
| pearson_euclidean | 0.4111 |
| spearman_euclidean | 0.3822 |
| pearson_dot | 0.4863 |
| spearman_dot | 0.5303 |
| pearson_max | 0.4863 |
| spearman_max | 0.5303 |
#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.593 |
| **spearman_cosine** | **0.6266** |
| pearson_manhattan | 0.5608 |
| spearman_manhattan | 0.6229 |
| pearson_euclidean | 0.558 |
| spearman_euclidean | 0.6202 |
| pearson_dot | 0.4578 |
| spearman_dot | 0.5628 |
| pearson_max | 0.593 |
| spearman_max | 0.6266 |
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 226,547 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 | label |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 3 tokens</li><li>mean: 20.05 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 19.94 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 1.92</li><li>max: 398.6</li></ul> |
* Samples:
| sentence_0 | sentence_1 | label |
|:-------------------------------------------------------------------|:----------------------------------------------------------------|:---------------------------------|
| <code>Bir kadın makineye dikiş dikiyor.</code> | <code>Bir kadın biraz et ekiyor.</code> | <code>0.12</code> |
| <code>Snowden 'gegeven vluchtelingendocument door Ecuador'.</code> | <code>Snowden staat op het punt om uit Moskou te vliegen</code> | <code>0.24000000953674316</code> |
| <code>Czarny pies idzie mostem przez wodę</code> | <code>Czarny pies nie idzie mostem przez wodę</code> | <code>0.74000000954</code> |
* Loss: [<code>AnglELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#angleloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_angle_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 256
- `per_device_eval_batch_size`: 256
- `num_train_epochs`: 10
- `multi_dataset_batch_sampler`: round_robin
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 256
- `per_device_eval_batch_size`: 256
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `num_train_epochs`: 10
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
</details>
### Training Logs
| Epoch | Step | Training Loss | sts-dev_spearman_cosine | sts-test_spearman_cosine |
|:------:|:----:|:-------------:|:-----------------------:|:------------------------:|
| 0.5650 | 500 | 10.9426 | - | - |
| 1.0 | 885 | - | 0.9202 | - |
| 1.1299 | 1000 | 9.7184 | - | - |
| 1.6949 | 1500 | 9.5348 | - | - |
| 2.0 | 1770 | - | 0.9400 | - |
| 2.2599 | 2000 | 9.4412 | - | - |
| 2.8249 | 2500 | 9.3097 | - | - |
| 3.0 | 2655 | - | 0.9489 | - |
| 3.3898 | 3000 | 9.2357 | - | - |
| 3.9548 | 3500 | 9.1594 | - | - |
| 4.0 | 3540 | - | 0.9528 | - |
| 4.5198 | 4000 | 9.0963 | - | - |
| 5.0 | 4425 | - | 0.9553 | - |
| 5.0847 | 4500 | 9.0382 | - | - |
| 5.6497 | 5000 | 8.9837 | - | - |
| 6.0 | 5310 | - | 0.9567 | - |
| 6.2147 | 5500 | 8.9403 | - | - |
| 6.7797 | 6000 | 8.8841 | - | - |
| 7.0 | 6195 | - | 0.9581 | - |
| 7.3446 | 6500 | 8.8513 | - | - |
| 7.9096 | 7000 | 8.81 | - | - |
| 8.0 | 7080 | - | 0.9582 | - |
| 8.4746 | 7500 | 8.8069 | - | - |
| 9.0 | 7965 | - | 0.9589 | - |
| 9.0395 | 8000 | 8.7616 | - | - |
| 9.6045 | 8500 | 8.7521 | - | - |
| 10.0 | 8850 | - | 0.9593 | 0.6266 |
### Framework Versions
- Python: 3.9.7
- Sentence Transformers: 3.0.0
- Transformers: 4.40.1
- PyTorch: 2.3.0+cu121
- Accelerate: 0.29.3
- Datasets: 2.19.0
- Tokenizers: 0.19.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### AnglELoss
```bibtex
@misc{li2023angleoptimized,
title={AnglE-optimized Text Embeddings},
author={Xianming Li and Jing Li},
year={2023},
eprint={2309.12871},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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