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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:86648
- loss:MSELoss
widget:
- source_sentence: Familienberaterin
sentences:
- electric power station operator
- venue booker & promoter
- betrieblicher Aus- und Weiterbildner/betriebliche Aus- und Weiterbildnerin
- source_sentence: high school RS teacher
sentences:
- infantryman
- Schnellbedienungsrestaurantteamleiter
- drill setup operator
- source_sentence: lighting designer
sentences:
- software support manager
- 直升机维护协调员
- bus maintenance supervisor
- source_sentence: 机场消防员
sentences:
- Flake操作员
- técnico en gestión de residuos peligrosos/técnica en gestión de residuos peligrosos
- 专门学校老师
- source_sentence: Entwicklerin für mobile Anwendungen
sentences:
- fashion design expert
- Mergers-and-Acquisitions-Analyst/Mergers-and-Acquisitions-Analystin
- commercial bid manager
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@20
- cosine_accuracy@50
- cosine_accuracy@100
- cosine_accuracy@150
- cosine_accuracy@200
- cosine_precision@1
- cosine_precision@20
- cosine_precision@50
- cosine_precision@100
- cosine_precision@150
- cosine_precision@200
- cosine_recall@1
- cosine_recall@20
- cosine_recall@50
- cosine_recall@100
- cosine_recall@150
- cosine_recall@200
- cosine_ndcg@1
- cosine_ndcg@20
- cosine_ndcg@50
- cosine_ndcg@100
- cosine_ndcg@150
- cosine_ndcg@200
- cosine_mrr@1
- cosine_mrr@20
- cosine_mrr@50
- cosine_mrr@100
- cosine_mrr@150
- cosine_mrr@200
- cosine_map@1
- cosine_map@20
- cosine_map@50
- cosine_map@100
- cosine_map@150
- cosine_map@200
- cosine_map@500
model-index:
- name: SentenceTransformer
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: full en
type: full_en
metrics:
- type: cosine_accuracy@1
value: 0.6476190476190476
name: Cosine Accuracy@1
- type: cosine_accuracy@20
value: 0.9714285714285714
name: Cosine Accuracy@20
- type: cosine_accuracy@50
value: 0.9904761904761905
name: Cosine Accuracy@50
- type: cosine_accuracy@100
value: 0.9904761904761905
name: Cosine Accuracy@100
- type: cosine_accuracy@150
value: 0.9904761904761905
name: Cosine Accuracy@150
- type: cosine_accuracy@200
value: 0.9904761904761905
name: Cosine Accuracy@200
- type: cosine_precision@1
value: 0.6476190476190476
name: Cosine Precision@1
- type: cosine_precision@20
value: 0.47952380952380946
name: Cosine Precision@20
- type: cosine_precision@50
value: 0.28838095238095235
name: Cosine Precision@50
- type: cosine_precision@100
value: 0.17304761904761906
name: Cosine Precision@100
- type: cosine_precision@150
value: 0.12444444444444444
name: Cosine Precision@150
- type: cosine_precision@200
value: 0.09857142857142859
name: Cosine Precision@200
- type: cosine_recall@1
value: 0.06609801577496094
name: Cosine Recall@1
- type: cosine_recall@20
value: 0.5122224752770898
name: Cosine Recall@20
- type: cosine_recall@50
value: 0.6835205863376973
name: Cosine Recall@50
- type: cosine_recall@100
value: 0.7899550177449521
name: Cosine Recall@100
- type: cosine_recall@150
value: 0.8399901051245952
name: Cosine Recall@150
- type: cosine_recall@200
value: 0.875868212220809
name: Cosine Recall@200
- type: cosine_ndcg@1
value: 0.6476190476190476
name: Cosine Ndcg@1
- type: cosine_ndcg@20
value: 0.6467537144833913
name: Cosine Ndcg@20
- type: cosine_ndcg@50
value: 0.6579566361404572
name: Cosine Ndcg@50
- type: cosine_ndcg@100
value: 0.7095129047395976
name: Cosine Ndcg@100
- type: cosine_ndcg@150
value: 0.7310060454392588
name: Cosine Ndcg@150
- type: cosine_ndcg@200
value: 0.746053293561821
name: Cosine Ndcg@200
- type: cosine_mrr@1
value: 0.6476190476190476
name: Cosine Mrr@1
- type: cosine_mrr@20
value: 0.7901817137111254
name: Cosine Mrr@20
- type: cosine_mrr@50
value: 0.7909547501984476
name: Cosine Mrr@50
- type: cosine_mrr@100
value: 0.7909547501984476
name: Cosine Mrr@100
- type: cosine_mrr@150
value: 0.7909547501984476
name: Cosine Mrr@150
- type: cosine_mrr@200
value: 0.7909547501984476
name: Cosine Mrr@200
- type: cosine_map@1
value: 0.6476190476190476
name: Cosine Map@1
- type: cosine_map@20
value: 0.5025649155749793
name: Cosine Map@20
- type: cosine_map@50
value: 0.48398477448194993
name: Cosine Map@50
- type: cosine_map@100
value: 0.5117703759309522
name: Cosine Map@100
- type: cosine_map@150
value: 0.520199435224254
name: Cosine Map@150
- type: cosine_map@200
value: 0.5249113393002316
name: Cosine Map@200
- type: cosine_map@500
value: 0.5304170344184883
name: Cosine Map@500
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: full es
type: full_es
metrics:
- type: cosine_accuracy@1
value: 0.11891891891891893
name: Cosine Accuracy@1
- type: cosine_accuracy@20
value: 1.0
name: Cosine Accuracy@20
- type: cosine_accuracy@50
value: 1.0
name: Cosine Accuracy@50
- type: cosine_accuracy@100
value: 1.0
name: Cosine Accuracy@100
- type: cosine_accuracy@150
value: 1.0
name: Cosine Accuracy@150
- type: cosine_accuracy@200
value: 1.0
name: Cosine Accuracy@200
- type: cosine_precision@1
value: 0.11891891891891893
name: Cosine Precision@1
- type: cosine_precision@20
value: 0.5267567567567567
name: Cosine Precision@20
- type: cosine_precision@50
value: 0.3437837837837838
name: Cosine Precision@50
- type: cosine_precision@100
value: 0.21897297297297297
name: Cosine Precision@100
- type: cosine_precision@150
value: 0.1658018018018018
name: Cosine Precision@150
- type: cosine_precision@200
value: 0.1332972972972973
name: Cosine Precision@200
- type: cosine_recall@1
value: 0.0035840147528632613
name: Cosine Recall@1
- type: cosine_recall@20
value: 0.35407760203362965
name: Cosine Recall@20
- type: cosine_recall@50
value: 0.5097999383006715
name: Cosine Recall@50
- type: cosine_recall@100
value: 0.6076073817878247
name: Cosine Recall@100
- type: cosine_recall@150
value: 0.6705429838138021
name: Cosine Recall@150
- type: cosine_recall@200
value: 0.7125464731776301
name: Cosine Recall@200
- type: cosine_ndcg@1
value: 0.11891891891891893
name: Cosine Ndcg@1
- type: cosine_ndcg@20
value: 0.5708144272431339
name: Cosine Ndcg@20
- type: cosine_ndcg@50
value: 0.535516963498245
name: Cosine Ndcg@50
- type: cosine_ndcg@100
value: 0.558980163264909
name: Cosine Ndcg@100
- type: cosine_ndcg@150
value: 0.5900024611410689
name: Cosine Ndcg@150
- type: cosine_ndcg@200
value: 0.609478782549869
name: Cosine Ndcg@200
- type: cosine_mrr@1
value: 0.11891891891891893
name: Cosine Mrr@1
- type: cosine_mrr@20
value: 0.5531531531531532
name: Cosine Mrr@20
- type: cosine_mrr@50
value: 0.5531531531531532
name: Cosine Mrr@50
- type: cosine_mrr@100
value: 0.5531531531531532
name: Cosine Mrr@100
- type: cosine_mrr@150
value: 0.5531531531531532
name: Cosine Mrr@150
- type: cosine_mrr@200
value: 0.5531531531531532
name: Cosine Mrr@200
- type: cosine_map@1
value: 0.11891891891891893
name: Cosine Map@1
- type: cosine_map@20
value: 0.4379349002801489
name: Cosine Map@20
- type: cosine_map@50
value: 0.3739269627118989
name: Cosine Map@50
- type: cosine_map@100
value: 0.37629843599877466
name: Cosine Map@100
- type: cosine_map@150
value: 0.3891828650842837
name: Cosine Map@150
- type: cosine_map@200
value: 0.39584338663408436
name: Cosine Map@200
- type: cosine_map@500
value: 0.4062909401616274
name: Cosine Map@500
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: full de
type: full_de
metrics:
- type: cosine_accuracy@1
value: 0.2955665024630542
name: Cosine Accuracy@1
- type: cosine_accuracy@20
value: 0.9704433497536946
name: Cosine Accuracy@20
- type: cosine_accuracy@50
value: 0.9753694581280788
name: Cosine Accuracy@50
- type: cosine_accuracy@100
value: 0.9901477832512315
name: Cosine Accuracy@100
- type: cosine_accuracy@150
value: 0.9901477832512315
name: Cosine Accuracy@150
- type: cosine_accuracy@200
value: 0.9901477832512315
name: Cosine Accuracy@200
- type: cosine_precision@1
value: 0.2955665024630542
name: Cosine Precision@1
- type: cosine_precision@20
value: 0.42906403940886706
name: Cosine Precision@20
- type: cosine_precision@50
value: 0.29802955665024633
name: Cosine Precision@50
- type: cosine_precision@100
value: 0.19433497536945815
name: Cosine Precision@100
- type: cosine_precision@150
value: 0.14824302134646963
name: Cosine Precision@150
- type: cosine_precision@200
value: 0.1197783251231527
name: Cosine Precision@200
- type: cosine_recall@1
value: 0.01108543831680986
name: Cosine Recall@1
- type: cosine_recall@20
value: 0.26675038089672504
name: Cosine Recall@20
- type: cosine_recall@50
value: 0.40921566733257536
name: Cosine Recall@50
- type: cosine_recall@100
value: 0.5097664540706716
name: Cosine Recall@100
- type: cosine_recall@150
value: 0.5728593162394238
name: Cosine Recall@150
- type: cosine_recall@200
value: 0.6120176690658915
name: Cosine Recall@200
- type: cosine_ndcg@1
value: 0.2955665024630542
name: Cosine Ndcg@1
- type: cosine_ndcg@20
value: 0.46962753993631184
name: Cosine Ndcg@20
- type: cosine_ndcg@50
value: 0.444898497416845
name: Cosine Ndcg@50
- type: cosine_ndcg@100
value: 0.466960324034805
name: Cosine Ndcg@100
- type: cosine_ndcg@150
value: 0.49816218513136795
name: Cosine Ndcg@150
- type: cosine_ndcg@200
value: 0.5165485300965951
name: Cosine Ndcg@200
- type: cosine_mrr@1
value: 0.2955665024630542
name: Cosine Mrr@1
- type: cosine_mrr@20
value: 0.5046767633988724
name: Cosine Mrr@20
- type: cosine_mrr@50
value: 0.50477528556636
name: Cosine Mrr@50
- type: cosine_mrr@100
value: 0.5049589761635289
name: Cosine Mrr@100
- type: cosine_mrr@150
value: 0.5049589761635289
name: Cosine Mrr@150
- type: cosine_mrr@200
value: 0.5049589761635289
name: Cosine Mrr@200
- type: cosine_map@1
value: 0.2955665024630542
name: Cosine Map@1
- type: cosine_map@20
value: 0.33658821160388247
name: Cosine Map@20
- type: cosine_map@50
value: 0.2853400586620685
name: Cosine Map@50
- type: cosine_map@100
value: 0.2817732307206079
name: Cosine Map@100
- type: cosine_map@150
value: 0.2931317333364438
name: Cosine Map@150
- type: cosine_map@200
value: 0.2988160532231927
name: Cosine Map@200
- type: cosine_map@500
value: 0.31093362375086947
name: Cosine Map@500
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: full zh
type: full_zh
metrics:
- type: cosine_accuracy@1
value: 0.6601941747572816
name: Cosine Accuracy@1
- type: cosine_accuracy@20
value: 0.970873786407767
name: Cosine Accuracy@20
- type: cosine_accuracy@50
value: 0.9902912621359223
name: Cosine Accuracy@50
- type: cosine_accuracy@100
value: 0.9902912621359223
name: Cosine Accuracy@100
- type: cosine_accuracy@150
value: 0.9902912621359223
name: Cosine Accuracy@150
- type: cosine_accuracy@200
value: 0.9902912621359223
name: Cosine Accuracy@200
- type: cosine_precision@1
value: 0.6601941747572816
name: Cosine Precision@1
- type: cosine_precision@20
value: 0.44805825242718444
name: Cosine Precision@20
- type: cosine_precision@50
value: 0.27126213592233006
name: Cosine Precision@50
- type: cosine_precision@100
value: 0.16650485436893206
name: Cosine Precision@100
- type: cosine_precision@150
value: 0.1211003236245955
name: Cosine Precision@150
- type: cosine_precision@200
value: 0.09529126213592234
name: Cosine Precision@200
- type: cosine_recall@1
value: 0.06611246215014785
name: Cosine Recall@1
- type: cosine_recall@20
value: 0.48409390608352504
name: Cosine Recall@20
- type: cosine_recall@50
value: 0.6568473638827299
name: Cosine Recall@50
- type: cosine_recall@100
value: 0.7685416895166794
name: Cosine Recall@100
- type: cosine_recall@150
value: 0.8277686060133904
name: Cosine Recall@150
- type: cosine_recall@200
value: 0.8616979590623105
name: Cosine Recall@200
- type: cosine_ndcg@1
value: 0.6601941747572816
name: Cosine Ndcg@1
- type: cosine_ndcg@20
value: 0.6231250904534316
name: Cosine Ndcg@20
- type: cosine_ndcg@50
value: 0.6383496204608501
name: Cosine Ndcg@50
- type: cosine_ndcg@100
value: 0.6917257705456975
name: Cosine Ndcg@100
- type: cosine_ndcg@150
value: 0.7167434657424917
name: Cosine Ndcg@150
- type: cosine_ndcg@200
value: 0.7303448958665071
name: Cosine Ndcg@200
- type: cosine_mrr@1
value: 0.6601941747572816
name: Cosine Mrr@1
- type: cosine_mrr@20
value: 0.8015776699029126
name: Cosine Mrr@20
- type: cosine_mrr@50
value: 0.8020876238109248
name: Cosine Mrr@50
- type: cosine_mrr@100
value: 0.8020876238109248
name: Cosine Mrr@100
- type: cosine_mrr@150
value: 0.8020876238109248
name: Cosine Mrr@150
- type: cosine_mrr@200
value: 0.8020876238109248
name: Cosine Mrr@200
- type: cosine_map@1
value: 0.6601941747572816
name: Cosine Map@1
- type: cosine_map@20
value: 0.4750205237443607
name: Cosine Map@20
- type: cosine_map@50
value: 0.45785161483741715
name: Cosine Map@50
- type: cosine_map@100
value: 0.4848085275553208
name: Cosine Map@100
- type: cosine_map@150
value: 0.4937216396074153
name: Cosine Map@150
- type: cosine_map@200
value: 0.49777622471594557
name: Cosine Map@200
- type: cosine_map@500
value: 0.5039795405740248
name: Cosine Map@500
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: mix es
type: mix_es
metrics:
- type: cosine_accuracy@1
value: 0.6297451898075923
name: Cosine Accuracy@1
- type: cosine_accuracy@20
value: 0.9105564222568903
name: Cosine Accuracy@20
- type: cosine_accuracy@50
value: 0.9495579823192928
name: Cosine Accuracy@50
- type: cosine_accuracy@100
value: 0.9729589183567343
name: Cosine Accuracy@100
- type: cosine_accuracy@150
value: 0.983359334373375
name: Cosine Accuracy@150
- type: cosine_accuracy@200
value: 0.9901196047841914
name: Cosine Accuracy@200
- type: cosine_precision@1
value: 0.6297451898075923
name: Cosine Precision@1
- type: cosine_precision@20
value: 0.11167446697867915
name: Cosine Precision@20
- type: cosine_precision@50
value: 0.04850754030161208
name: Cosine Precision@50
- type: cosine_precision@100
value: 0.02535101404056163
name: Cosine Precision@100
- type: cosine_precision@150
value: 0.0172300225342347
name: Cosine Precision@150
- type: cosine_precision@200
value: 0.0130811232449298
name: Cosine Precision@200
- type: cosine_recall@1
value: 0.24340068840848872
name: Cosine Recall@1
- type: cosine_recall@20
value: 0.8288215338137336
name: Cosine Recall@20
- type: cosine_recall@50
value: 0.8986566129311838
name: Cosine Recall@50
- type: cosine_recall@100
value: 0.9398509273704282
name: Cosine Recall@100
- type: cosine_recall@150
value: 0.9576876408389668
name: Cosine Recall@150
- type: cosine_recall@200
value: 0.9695267810712429
name: Cosine Recall@200
- type: cosine_ndcg@1
value: 0.6297451898075923
name: Cosine Ndcg@1
- type: cosine_ndcg@20
value: 0.7010427232190379
name: Cosine Ndcg@20
- type: cosine_ndcg@50
value: 0.7200844211181043
name: Cosine Ndcg@50
- type: cosine_ndcg@100
value: 0.7290848607488584
name: Cosine Ndcg@100
- type: cosine_ndcg@150
value: 0.7325985285606116
name: Cosine Ndcg@150
- type: cosine_ndcg@200
value: 0.7347463892077523
name: Cosine Ndcg@200
- type: cosine_mrr@1
value: 0.6297451898075923
name: Cosine Mrr@1
- type: cosine_mrr@20
value: 0.7036709577939534
name: Cosine Mrr@20
- type: cosine_mrr@50
value: 0.7049808414398148
name: Cosine Mrr@50
- type: cosine_mrr@100
value: 0.7053260954286938
name: Cosine Mrr@100
- type: cosine_mrr@150
value: 0.7054145837924506
name: Cosine Mrr@150
- type: cosine_mrr@200
value: 0.7054541569954363
name: Cosine Mrr@200
- type: cosine_map@1
value: 0.6297451898075923
name: Cosine Map@1
- type: cosine_map@20
value: 0.6194189058349782
name: Cosine Map@20
- type: cosine_map@50
value: 0.6244340507841626
name: Cosine Map@50
- type: cosine_map@100
value: 0.6256943736433496
name: Cosine Map@100
- type: cosine_map@150
value: 0.6260195205413376
name: Cosine Map@150
- type: cosine_map@200
value: 0.6261650797332174
name: Cosine Map@200
- type: cosine_map@500
value: 0.6263452093477304
name: Cosine Map@500
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: mix de
type: mix_de
metrics:
- type: cosine_accuracy@1
value: 0.5564222568902756
name: Cosine Accuracy@1
- type: cosine_accuracy@20
value: 0.8866354654186167
name: Cosine Accuracy@20
- type: cosine_accuracy@50
value: 0.9381175247009881
name: Cosine Accuracy@50
- type: cosine_accuracy@100
value: 0.9594383775351014
name: Cosine Accuracy@100
- type: cosine_accuracy@150
value: 0.9708788351534061
name: Cosine Accuracy@150
- type: cosine_accuracy@200
value: 0.9776391055642226
name: Cosine Accuracy@200
- type: cosine_precision@1
value: 0.5564222568902756
name: Cosine Precision@1
- type: cosine_precision@20
value: 0.109464378575143
name: Cosine Precision@20
- type: cosine_precision@50
value: 0.048060322412896525
name: Cosine Precision@50
- type: cosine_precision@100
value: 0.025273010920436823
name: Cosine Precision@100
- type: cosine_precision@150
value: 0.017313225862367825
name: Cosine Precision@150
- type: cosine_precision@200
value: 0.013143525741029644
name: Cosine Precision@200
- type: cosine_recall@1
value: 0.20931703934824059
name: Cosine Recall@1
- type: cosine_recall@20
value: 0.7988992893049055
name: Cosine Recall@20
- type: cosine_recall@50
value: 0.8741029641185647
name: Cosine Recall@50
- type: cosine_recall@100
value: 0.9173426937077482
name: Cosine Recall@100
- type: cosine_recall@150
value: 0.9424076963078523
name: Cosine Recall@150
- type: cosine_recall@200
value: 0.953631478592477
name: Cosine Recall@200
- type: cosine_ndcg@1
value: 0.5564222568902756
name: Cosine Ndcg@1
- type: cosine_ndcg@20
value: 0.6541310877479573
name: Cosine Ndcg@20
- type: cosine_ndcg@50
value: 0.674790854916742
name: Cosine Ndcg@50
- type: cosine_ndcg@100
value: 0.6844997445798996
name: Cosine Ndcg@100
- type: cosine_ndcg@150
value: 0.6894214573457343
name: Cosine Ndcg@150
- type: cosine_ndcg@200
value: 0.6914881284159038
name: Cosine Ndcg@200
- type: cosine_mrr@1
value: 0.5564222568902756
name: Cosine Mrr@1
- type: cosine_mrr@20
value: 0.6476945170199107
name: Cosine Mrr@20
- type: cosine_mrr@50
value: 0.6493649946597936
name: Cosine Mrr@50
- type: cosine_mrr@100
value: 0.6496801333421218
name: Cosine Mrr@100
- type: cosine_mrr@150
value: 0.6497778366579644
name: Cosine Mrr@150
- type: cosine_mrr@200
value: 0.6498156890114056
name: Cosine Mrr@200
- type: cosine_map@1
value: 0.5564222568902756
name: Cosine Map@1
- type: cosine_map@20
value: 0.5648326970643027
name: Cosine Map@20
- type: cosine_map@50
value: 0.57003456255067
name: Cosine Map@50
- type: cosine_map@100
value: 0.5714370828517599
name: Cosine Map@100
- type: cosine_map@150
value: 0.5719002990233493
name: Cosine Map@150
- type: cosine_map@200
value: 0.5720497397197026
name: Cosine Map@200
- type: cosine_map@500
value: 0.5723109788233504
name: Cosine Map@500
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: mix zh
type: mix_zh
metrics:
- type: cosine_accuracy@1
value: 0.6085594989561587
name: Cosine Accuracy@1
- type: cosine_accuracy@20
value: 0.9592901878914405
name: Cosine Accuracy@20
- type: cosine_accuracy@50
value: 0.9791231732776617
name: Cosine Accuracy@50
- type: cosine_accuracy@100
value: 0.9874739039665971
name: Cosine Accuracy@100
- type: cosine_accuracy@150
value: 0.9911273486430062
name: Cosine Accuracy@150
- type: cosine_accuracy@200
value: 0.9937369519832986
name: Cosine Accuracy@200
- type: cosine_precision@1
value: 0.6085594989561587
name: Cosine Precision@1
- type: cosine_precision@20
value: 0.12656576200417535
name: Cosine Precision@20
- type: cosine_precision@50
value: 0.05518789144050106
name: Cosine Precision@50
- type: cosine_precision@100
value: 0.028747390396659713
name: Cosine Precision@100
- type: cosine_precision@150
value: 0.019425887265135697
name: Cosine Precision@150
- type: cosine_precision@200
value: 0.014705114822546978
name: Cosine Precision@200
- type: cosine_recall@1
value: 0.2043804056069192
name: Cosine Recall@1
- type: cosine_recall@20
value: 0.8346468336812805
name: Cosine Recall@20
- type: cosine_recall@50
value: 0.9095772442588727
name: Cosine Recall@50
- type: cosine_recall@100
value: 0.9475643702157271
name: Cosine Recall@100
- type: cosine_recall@150
value: 0.9609168406402228
name: Cosine Recall@150
- type: cosine_recall@200
value: 0.9697807933194154
name: Cosine Recall@200
- type: cosine_ndcg@1
value: 0.6085594989561587
name: Cosine Ndcg@1
- type: cosine_ndcg@20
value: 0.6853247290079303
name: Cosine Ndcg@20
- type: cosine_ndcg@50
value: 0.7066940880968873
name: Cosine Ndcg@50
- type: cosine_ndcg@100
value: 0.715400790265437
name: Cosine Ndcg@100
- type: cosine_ndcg@150
value: 0.7180808450243259
name: Cosine Ndcg@150
- type: cosine_ndcg@200
value: 0.7197629642909036
name: Cosine Ndcg@200
- type: cosine_mrr@1
value: 0.6085594989561587
name: Cosine Mrr@1
- type: cosine_mrr@20
value: 0.7236528792595264
name: Cosine Mrr@20
- type: cosine_mrr@50
value: 0.7243308740364213
name: Cosine Mrr@50
- type: cosine_mrr@100
value: 0.7244524590415827
name: Cosine Mrr@100
- type: cosine_mrr@150
value: 0.7244814620971008
name: Cosine Mrr@150
- type: cosine_mrr@200
value: 0.7244960285685315
name: Cosine Mrr@200
- type: cosine_map@1
value: 0.6085594989561587
name: Cosine Map@1
- type: cosine_map@20
value: 0.5652211952239553
name: Cosine Map@20
- type: cosine_map@50
value: 0.5716374350069462
name: Cosine Map@50
- type: cosine_map@100
value: 0.5730756815932735
name: Cosine Map@100
- type: cosine_map@150
value: 0.5733543252173214
name: Cosine Map@150
- type: cosine_map@200
value: 0.5734860037813889
name: Cosine Map@200
- type: cosine_map@500
value: 0.5736416699680624
name: Cosine Map@500
---
# Job - Job matching Alibaba-NLP/gte-multilingual-base pruned
Top performing model on [TalentCLEF 2025](https://talentclef.github.io/talentclef/) Task A. Use it for multilingual job title matching
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **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': 512, 'do_lower_case': False}) with Transformer model: NewModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## 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("pj-mathematician/JobGTE-multilingual-base-pruned")
# Run inference
sentences = [
'Entwicklerin für mobile Anwendungen',
'Mergers-and-Acquisitions-Analyst/Mergers-and-Acquisitions-Analystin',
'fashion design expert',
]
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
#### Information Retrieval
* Datasets: `full_en`, `full_es`, `full_de`, `full_zh`, `mix_es`, `mix_de` and `mix_zh`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | full_en | full_es | full_de | full_zh | mix_es | mix_de | mix_zh |
|:---------------------|:-----------|:-----------|:-----------|:-----------|:-----------|:-----------|:-----------|
| cosine_accuracy@1 | 0.6476 | 0.1189 | 0.2956 | 0.6602 | 0.6297 | 0.5564 | 0.6086 |
| cosine_accuracy@20 | 0.9714 | 1.0 | 0.9704 | 0.9709 | 0.9106 | 0.8866 | 0.9593 |
| cosine_accuracy@50 | 0.9905 | 1.0 | 0.9754 | 0.9903 | 0.9496 | 0.9381 | 0.9791 |
| cosine_accuracy@100 | 0.9905 | 1.0 | 0.9901 | 0.9903 | 0.973 | 0.9594 | 0.9875 |
| cosine_accuracy@150 | 0.9905 | 1.0 | 0.9901 | 0.9903 | 0.9834 | 0.9709 | 0.9911 |
| cosine_accuracy@200 | 0.9905 | 1.0 | 0.9901 | 0.9903 | 0.9901 | 0.9776 | 0.9937 |
| cosine_precision@1 | 0.6476 | 0.1189 | 0.2956 | 0.6602 | 0.6297 | 0.5564 | 0.6086 |
| cosine_precision@20 | 0.4795 | 0.5268 | 0.4291 | 0.4481 | 0.1117 | 0.1095 | 0.1266 |
| cosine_precision@50 | 0.2884 | 0.3438 | 0.298 | 0.2713 | 0.0485 | 0.0481 | 0.0552 |
| cosine_precision@100 | 0.173 | 0.219 | 0.1943 | 0.1665 | 0.0254 | 0.0253 | 0.0287 |
| cosine_precision@150 | 0.1244 | 0.1658 | 0.1482 | 0.1211 | 0.0172 | 0.0173 | 0.0194 |
| cosine_precision@200 | 0.0986 | 0.1333 | 0.1198 | 0.0953 | 0.0131 | 0.0131 | 0.0147 |
| cosine_recall@1 | 0.0661 | 0.0036 | 0.0111 | 0.0661 | 0.2434 | 0.2093 | 0.2044 |
| cosine_recall@20 | 0.5122 | 0.3541 | 0.2668 | 0.4841 | 0.8288 | 0.7989 | 0.8346 |
| cosine_recall@50 | 0.6835 | 0.5098 | 0.4092 | 0.6568 | 0.8987 | 0.8741 | 0.9096 |
| cosine_recall@100 | 0.79 | 0.6076 | 0.5098 | 0.7685 | 0.9399 | 0.9173 | 0.9476 |
| cosine_recall@150 | 0.84 | 0.6705 | 0.5729 | 0.8278 | 0.9577 | 0.9424 | 0.9609 |
| cosine_recall@200 | 0.8759 | 0.7125 | 0.612 | 0.8617 | 0.9695 | 0.9536 | 0.9698 |
| cosine_ndcg@1 | 0.6476 | 0.1189 | 0.2956 | 0.6602 | 0.6297 | 0.5564 | 0.6086 |
| cosine_ndcg@20 | 0.6468 | 0.5708 | 0.4696 | 0.6231 | 0.701 | 0.6541 | 0.6853 |
| cosine_ndcg@50 | 0.658 | 0.5355 | 0.4449 | 0.6383 | 0.7201 | 0.6748 | 0.7067 |
| cosine_ndcg@100 | 0.7095 | 0.559 | 0.467 | 0.6917 | 0.7291 | 0.6845 | 0.7154 |
| cosine_ndcg@150 | 0.731 | 0.59 | 0.4982 | 0.7167 | 0.7326 | 0.6894 | 0.7181 |
| **cosine_ndcg@200** | **0.7461** | **0.6095** | **0.5165** | **0.7303** | **0.7347** | **0.6915** | **0.7198** |
| cosine_mrr@1 | 0.6476 | 0.1189 | 0.2956 | 0.6602 | 0.6297 | 0.5564 | 0.6086 |
| cosine_mrr@20 | 0.7902 | 0.5532 | 0.5047 | 0.8016 | 0.7037 | 0.6477 | 0.7237 |
| cosine_mrr@50 | 0.791 | 0.5532 | 0.5048 | 0.8021 | 0.705 | 0.6494 | 0.7243 |
| cosine_mrr@100 | 0.791 | 0.5532 | 0.505 | 0.8021 | 0.7053 | 0.6497 | 0.7245 |
| cosine_mrr@150 | 0.791 | 0.5532 | 0.505 | 0.8021 | 0.7054 | 0.6498 | 0.7245 |
| cosine_mrr@200 | 0.791 | 0.5532 | 0.505 | 0.8021 | 0.7055 | 0.6498 | 0.7245 |
| cosine_map@1 | 0.6476 | 0.1189 | 0.2956 | 0.6602 | 0.6297 | 0.5564 | 0.6086 |
| cosine_map@20 | 0.5026 | 0.4379 | 0.3366 | 0.475 | 0.6194 | 0.5648 | 0.5652 |
| cosine_map@50 | 0.484 | 0.3739 | 0.2853 | 0.4579 | 0.6244 | 0.57 | 0.5716 |
| cosine_map@100 | 0.5118 | 0.3763 | 0.2818 | 0.4848 | 0.6257 | 0.5714 | 0.5731 |
| cosine_map@150 | 0.5202 | 0.3892 | 0.2931 | 0.4937 | 0.626 | 0.5719 | 0.5734 |
| cosine_map@200 | 0.5249 | 0.3958 | 0.2988 | 0.4978 | 0.6262 | 0.572 | 0.5735 |
| cosine_map@500 | 0.5304 | 0.4063 | 0.3109 | 0.504 | 0.6263 | 0.5723 | 0.5736 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 86,648 training samples
* Columns: <code>sentence</code> and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence | label |
|:--------|:---------------------------------------------------------------------------------|:-------------------------------------|
| type | string | list |
| details | <ul><li>min: 2 tokens</li><li>mean: 8.25 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>size: 768 elements</li></ul> |
* Samples:
| sentence | label |
|:-----------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------|
| <code></code> | <code>[-0.07171934843063354, 0.03595816716551781, -0.029780959710478783, 0.006593302357941866, 0.040611181408166885, ...]</code> |
| <code>airport environment officer</code> | <code>[-0.022075481712818146, 0.02999737113714218, -0.02189866080880165, 0.016531817615032196, 0.012234307825565338, ...]</code> |
| <code>Flake操作员</code> | <code>[-0.04815564677119255, 0.023524893447756767, -0.01583661139011383, 0.042527906596660614, 0.03815540298819542, ...]</code> |
* Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `gradient_accumulation_steps`: 2
- `learning_rate`: 0.0001
- `num_train_epochs`: 5
- `warmup_ratio`: 0.05
- `log_on_each_node`: False
- `fp16`: True
- `dataloader_num_workers`: 4
- `ddp_find_unused_parameters`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 2
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 0.0001
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 5
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.05
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: False
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: 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`: True
- `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`: True
- `dataloader_num_workers`: 4
- `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}
- `tp_size`: 0
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, '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`: True
- `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`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `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
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | full_en_cosine_ndcg@200 | full_es_cosine_ndcg@200 | full_de_cosine_ndcg@200 | full_zh_cosine_ndcg@200 | mix_es_cosine_ndcg@200 | mix_de_cosine_ndcg@200 | mix_zh_cosine_ndcg@200 |
|:------:|:----:|:-------------:|:-----------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|
| -1 | -1 | - | 0.5348 | 0.4311 | 0.3678 | 0.5333 | 0.2580 | 0.1924 | 0.2871 |
| 0.0030 | 1 | 0.0017 | - | - | - | - | - | - | - |
| 0.2959 | 100 | 0.001 | - | - | - | - | - | - | - |
| 0.5917 | 200 | 0.0005 | 0.6702 | 0.5287 | 0.4566 | 0.6809 | 0.5864 | 0.5302 | 0.4739 |
| 0.8876 | 300 | 0.0004 | - | - | - | - | - | - | - |
| 1.1834 | 400 | 0.0004 | 0.7057 | 0.5643 | 0.4790 | 0.7033 | 0.6604 | 0.6055 | 0.6003 |
| 1.4793 | 500 | 0.0004 | - | - | - | - | - | - | - |
| 1.7751 | 600 | 0.0003 | 0.7184 | 0.5783 | 0.4910 | 0.7127 | 0.6927 | 0.6416 | 0.6485 |
| 2.0710 | 700 | 0.0003 | - | - | - | - | - | - | - |
| 2.3669 | 800 | 0.0003 | 0.7307 | 0.5938 | 0.5023 | 0.7233 | 0.7125 | 0.6639 | 0.6847 |
| 2.6627 | 900 | 0.0003 | - | - | - | - | - | - | - |
| 2.9586 | 1000 | 0.0003 | 0.7371 | 0.6002 | 0.5085 | 0.7228 | 0.7222 | 0.6761 | 0.6998 |
| 3.2544 | 1100 | 0.0003 | - | - | - | - | - | - | - |
| 3.5503 | 1200 | 0.0003 | 0.7402 | 0.6059 | 0.5109 | 0.7279 | 0.7285 | 0.6841 | 0.7120 |
| 3.8462 | 1300 | 0.0003 | - | - | - | - | - | - | - |
| 4.1420 | 1400 | 0.0003 | 0.7449 | 0.6083 | 0.5154 | 0.7294 | 0.7333 | 0.6894 | 0.7176 |
| 4.4379 | 1500 | 0.0003 | - | - | - | - | - | - | - |
| 4.7337 | 1600 | 0.0003 | 0.7461 | 0.6095 | 0.5165 | 0.7303 | 0.7347 | 0.6915 | 0.7198 |
### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 4.1.0
- Transformers: 4.51.3
- PyTorch: 2.6.0+cu124
- Accelerate: 1.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## 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",
}
```
#### MSELoss
```bibtex
@inproceedings{reimers-2020-multilingual-sentence-bert,
title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2020",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/2004.09813",
}
```
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