README.md

---
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
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.6002053211770223
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.5399866615749636
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.5955360076924765
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.5657998544710718
      name: Pearson Dot
    - type: spearman_dot
      value: 0.6068611192160528
      name: Spearman Dot
    - type: pearson_max
      value: 0.6278594754203957
      name: Pearson Max
    - type: spearman_max
      value: 0.6319430830291571
      name: Spearman Max
    - type: pearson_cosine
      value: 0.7778538763931996
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.7875616631597785
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.7425757616272681
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.7789392103102715
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.7437054735775576
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.780583955651507
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.7214423493083364
      name: Pearson Dot
    - type: spearman_dot
      value: 0.7489073787091952
      name: Spearman Dot
    - type: pearson_max
      value: 0.7778538763931996
      name: Pearson Max
    - type: spearman_max
      value: 0.7875616631597785
      name: Spearman Max
    - type: pearson_cosine
      value: 0.526790729806662
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.5774252131250034
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.41713442172065224
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.5599676717727231
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.42192411421528214
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.5665444422359257
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.49809047501575476
      name: Pearson Dot
    - type: spearman_dot
      value: 0.5367148143234142
      name: Spearman Dot
    - type: pearson_max
      value: 0.526790729806662
      name: Pearson Max
    - type: spearman_max
      value: 0.5774252131250034
      name: Spearman Max
    - type: pearson_cosine
      value: 0.6306061651851392
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.6383757017928495
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.603366556372183
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.6167955278711116
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.6081018686388112
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.6219639110001453
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.5767081284665276
      name: Pearson Dot
    - type: spearman_dot
      value: 0.5831358067917275
      name: Spearman Dot
    - type: pearson_max
      value: 0.6306061651851392
      name: Pearson Max
    - type: spearman_max
      value: 0.6383757017928495
      name: Spearman Max
    - type: pearson_cosine
      value: 0.5568482062575557
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.5866853707548388
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.49244450938868833
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.5737511662255662
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.49058760093828624
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.5762095703672849
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.4306984514506903
      name: Pearson Dot
    - type: spearman_dot
      value: 0.5470683854030187
      name: Spearman Dot
    - type: pearson_max
      value: 0.5568482062575557
      name: Pearson Max
    - type: spearman_max
      value: 0.5866853707548388
      name: Spearman Max
    - type: pearson_cosine
      value: 0.5776222742798018
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.5749790581441845
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.571787148920759
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.5500811027014174
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.5695499775959532
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.5532223379017994
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.53146407233978
      name: Pearson Dot
    - type: spearman_dot
      value: 0.5190797374963447
      name: Spearman Dot
    - type: pearson_max
      value: 0.5776222742798018
      name: Pearson Max
    - type: spearman_max
      value: 0.5749790581441845
      name: Spearman Max
    - type: pearson_cosine
      value: 0.3571900232473057
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.4335552432730643
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.20808854264339055
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.4354537154533896
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.208616390027902
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.440246452767669
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.22336496195751424
      name: Pearson Dot
    - type: spearman_dot
      value: 0.3706905558756734
      name: Spearman Dot
    - type: pearson_max
      value: 0.3571900232473057
      name: Pearson Max
    - type: spearman_max
      value: 0.440246452767669
      name: Spearman Max
    - type: pearson_cosine
      value: 0.6863427356006826
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.6620948502618977
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.6428578762643233
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.6483663123081533
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.6424050032110411
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.6485902628925195
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.6352371374824808
      name: Pearson Dot
    - type: spearman_dot
      value: 0.6159110999161411
      name: Spearman Dot
    - type: pearson_max
      value: 0.6863427356006826
      name: Pearson Max
    - type: spearman_max
      value: 0.6620948502618977
      name: Spearman Max
    - type: pearson_cosine
      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
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.7204228437397647
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.7257632200250398
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.7144336778935939
      name: Pearson Dot
    - type: spearman_dot
      value: 0.7284199759984302
      name: Spearman Dot
    - 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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