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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:124788
- loss:GISTEmbedLoss
base_model: BAAI/bge-small-en-v1.5
widget:
- source_sentence: 其他机械、设备和有形货物租赁服务代表
sentences:
- 其他机械和设备租赁服务工作人员
- 电子和电信设备及零部件物流经理
- 工业主厨
- source_sentence: 公交车司机
sentences:
- 表演灯光设计师
- 乙烯基地板安装工
- 国际巴士司机
- source_sentence: online communication manager
sentences:
- trades union official
- social media manager
- budget manager
- source_sentence: Projektmanagerin
sentences:
- Projektmanager/Projektmanagerin
- Category-Manager
- Infanterist
- source_sentence: Volksvertreter
sentences:
- Parlamentarier
- Oberbürgermeister
- Konsul
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 based on BAAI/bge-small-en-v1.5
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: full en
type: full_en
metrics:
- type: cosine_accuracy@1
value: 0.6571428571428571
name: Cosine Accuracy@1
- type: cosine_accuracy@20
value: 0.9904761904761905
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: 0.6571428571428571
name: Cosine Precision@1
- type: cosine_precision@20
value: 0.5023809523809524
name: Cosine Precision@20
- type: cosine_precision@50
value: 0.30800000000000005
name: Cosine Precision@50
- type: cosine_precision@100
value: 0.18628571428571428
name: Cosine Precision@100
- type: cosine_precision@150
value: 0.1321904761904762
name: Cosine Precision@150
- type: cosine_precision@200
value: 0.10295238095238096
name: Cosine Precision@200
- type: cosine_recall@1
value: 0.0680237860830842
name: Cosine Recall@1
- type: cosine_recall@20
value: 0.5384852963395483
name: Cosine Recall@20
- type: cosine_recall@50
value: 0.7260449077992874
name: Cosine Recall@50
- type: cosine_recall@100
value: 0.8328530702930984
name: Cosine Recall@100
- type: cosine_recall@150
value: 0.8745262490032277
name: Cosine Recall@150
- type: cosine_recall@200
value: 0.9056960100263424
name: Cosine Recall@200
- type: cosine_ndcg@1
value: 0.6571428571428571
name: Cosine Ndcg@1
- type: cosine_ndcg@20
value: 0.6845256340390302
name: Cosine Ndcg@20
- type: cosine_ndcg@50
value: 0.7040452093638513
name: Cosine Ndcg@50
- type: cosine_ndcg@100
value: 0.758935932285001
name: Cosine Ndcg@100
- type: cosine_ndcg@150
value: 0.7774414598948007
name: Cosine Ndcg@150
- type: cosine_ndcg@200
value: 0.7892946240668293
name: Cosine Ndcg@200
- type: cosine_mrr@1
value: 0.6571428571428571
name: Cosine Mrr@1
- type: cosine_mrr@20
value: 0.8103174603174604
name: Cosine Mrr@20
- type: cosine_mrr@50
value: 0.8103174603174604
name: Cosine Mrr@50
- type: cosine_mrr@100
value: 0.8103174603174604
name: Cosine Mrr@100
- type: cosine_mrr@150
value: 0.8103174603174604
name: Cosine Mrr@150
- type: cosine_mrr@200
value: 0.8103174603174604
name: Cosine Mrr@200
- type: cosine_map@1
value: 0.6571428571428571
name: Cosine Map@1
- type: cosine_map@20
value: 0.5418235787800474
name: Cosine Map@20
- type: cosine_map@50
value: 0.5327215779103721
name: Cosine Map@50
- type: cosine_map@100
value: 0.565706253334091
name: Cosine Map@100
- type: cosine_map@150
value: 0.5733951147399983
name: Cosine Map@150
- type: cosine_map@200
value: 0.5771587776237981
name: Cosine Map@200
- type: cosine_map@500
value: 0.5813892452974444
name: Cosine Map@500
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: full es
type: full_es
metrics:
- type: cosine_accuracy@1
value: 0.12432432432432433
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: 0.12432432432432433
name: Cosine Precision@1
- type: cosine_precision@20
value: 0.4897297297297297
name: Cosine Precision@20
- type: cosine_precision@50
value: 0.31794594594594594
name: Cosine Precision@50
- type: cosine_precision@100
value: 0.19864864864864865
name: Cosine Precision@100
- type: cosine_precision@150
value: 0.14688288288288287
name: Cosine Precision@150
- type: cosine_precision@200
value: 0.11789189189189188
name: Cosine Precision@200
- type: cosine_recall@1
value: 0.003111544931768446
name: Cosine Recall@1
- type: cosine_recall@20
value: 0.32208664960961075
name: Cosine Recall@20
- type: cosine_recall@50
value: 0.46383117404893587
name: Cosine Recall@50
- type: cosine_recall@100
value: 0.5437537828683688
name: Cosine Recall@100
- type: cosine_recall@150
value: 0.5824968655076911
name: Cosine Recall@150
- type: cosine_recall@200
value: 0.6146962508233631
name: Cosine Recall@200
- type: cosine_ndcg@1
value: 0.12432432432432433
name: Cosine Ndcg@1
- type: cosine_ndcg@20
value: 0.5384577730264963
name: Cosine Ndcg@20
- type: cosine_ndcg@50
value: 0.5012455261232941
name: Cosine Ndcg@50
- type: cosine_ndcg@100
value: 0.5147486871284331
name: Cosine Ndcg@100
- type: cosine_ndcg@150
value: 0.5348194013794069
name: Cosine Ndcg@150
- type: cosine_ndcg@200
value: 0.5505397598095297
name: Cosine Ndcg@200
- type: cosine_mrr@1
value: 0.12432432432432433
name: Cosine Mrr@1
- type: cosine_mrr@20
value: 0.5515015015015016
name: Cosine Mrr@20
- type: cosine_mrr@50
value: 0.5515015015015016
name: Cosine Mrr@50
- type: cosine_mrr@100
value: 0.5515015015015016
name: Cosine Mrr@100
- type: cosine_mrr@150
value: 0.5515015015015016
name: Cosine Mrr@150
- type: cosine_mrr@200
value: 0.5515015015015016
name: Cosine Mrr@200
- type: cosine_map@1
value: 0.12432432432432433
name: Cosine Map@1
- type: cosine_map@20
value: 0.40280623036556984
name: Cosine Map@20
- type: cosine_map@50
value: 0.3421710529569103
name: Cosine Map@50
- type: cosine_map@100
value: 0.33947884152876345
name: Cosine Map@100
- type: cosine_map@150
value: 0.34777364049184706
name: Cosine Map@150
- type: cosine_map@200
value: 0.35339765423089375
name: Cosine Map@200
- type: cosine_map@500
value: 0.3631043007370563
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: 0.9211822660098522
name: Cosine Accuracy@20
- type: cosine_accuracy@50
value: 0.9655172413793104
name: Cosine Accuracy@50
- type: cosine_accuracy@100
value: 0.9753694581280788
name: Cosine Accuracy@100
- type: cosine_accuracy@150
value: 0.9852216748768473
name: Cosine Accuracy@150
- type: cosine_accuracy@200
value: 0.9852216748768473
name: Cosine Accuracy@200
- type: cosine_precision@1
value: 0.2955665024630542
name: Cosine Precision@1
- type: cosine_precision@20
value: 0.4246305418719211
name: Cosine Precision@20
- type: cosine_precision@50
value: 0.2813793103448276
name: Cosine Precision@50
- type: cosine_precision@100
value: 0.1800985221674877
name: Cosine Precision@100
- type: cosine_precision@150
value: 0.1362233169129721
name: Cosine Precision@150
- type: cosine_precision@200
value: 0.11054187192118226
name: Cosine Precision@200
- type: cosine_recall@1
value: 0.01108543831680986
name: Cosine Recall@1
- type: cosine_recall@20
value: 0.26139377973111655
name: Cosine Recall@20
- type: cosine_recall@50
value: 0.3835171819041212
name: Cosine Recall@50
- type: cosine_recall@100
value: 0.4676892706124872
name: Cosine Recall@100
- type: cosine_recall@150
value: 0.5183014504752351
name: Cosine Recall@150
- type: cosine_recall@200
value: 0.551717511250073
name: Cosine Recall@200
- type: cosine_ndcg@1
value: 0.2955665024630542
name: Cosine Ndcg@1
- type: cosine_ndcg@20
value: 0.4600580109269636
name: Cosine Ndcg@20
- type: cosine_ndcg@50
value: 0.4229190542750304
name: Cosine Ndcg@50
- type: cosine_ndcg@100
value: 0.4370543021366767
name: Cosine Ndcg@100
- type: cosine_ndcg@150
value: 0.46289045418097646
name: Cosine Ndcg@150
- type: cosine_ndcg@200
value: 0.4796711024513544
name: Cosine Ndcg@200
- type: cosine_mrr@1
value: 0.2955665024630542
name: Cosine Mrr@1
- type: cosine_mrr@20
value: 0.48958320005117995
name: Cosine Mrr@20
- type: cosine_mrr@50
value: 0.49093477998292195
name: Cosine Mrr@50
- type: cosine_mrr@100
value: 0.4910841931964832
name: Cosine Mrr@100
- type: cosine_mrr@150
value: 0.4911623560854821
name: Cosine Mrr@150
- type: cosine_mrr@200
value: 0.4911623560854821
name: Cosine Mrr@200
- type: cosine_map@1
value: 0.2955665024630542
name: Cosine Map@1
- type: cosine_map@20
value: 0.32364842421740225
name: Cosine Map@20
- type: cosine_map@50
value: 0.2643813390551392
name: Cosine Map@50
- type: cosine_map@100
value: 0.2576413544507463
name: Cosine Map@100
- type: cosine_map@150
value: 0.2669126239698539
name: Cosine Map@150
- type: cosine_map@200
value: 0.27215799504041416
name: Cosine Map@200
- type: cosine_map@500
value: 0.28329484592874316
name: Cosine Map@500
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: full zh
type: full_zh
metrics:
- type: cosine_accuracy@1
value: 0.34951456310679613
name: Cosine Accuracy@1
- type: cosine_accuracy@20
value: 0.7378640776699029
name: Cosine Accuracy@20
- type: cosine_accuracy@50
value: 0.8252427184466019
name: Cosine Accuracy@50
- type: cosine_accuracy@100
value: 0.8543689320388349
name: Cosine Accuracy@100
- type: cosine_accuracy@150
value: 0.9029126213592233
name: Cosine Accuracy@150
- type: cosine_accuracy@200
value: 0.941747572815534
name: Cosine Accuracy@200
- type: cosine_precision@1
value: 0.34951456310679613
name: Cosine Precision@1
- type: cosine_precision@20
value: 0.17330097087378643
name: Cosine Precision@20
- type: cosine_precision@50
value: 0.09436893203883494
name: Cosine Precision@50
- type: cosine_precision@100
value: 0.05893203883495146
name: Cosine Precision@100
- type: cosine_precision@150
value: 0.0458252427184466
name: Cosine Precision@150
- type: cosine_precision@200
value: 0.03854368932038834
name: Cosine Precision@200
- type: cosine_recall@1
value: 0.02726635297033844
name: Cosine Recall@1
- type: cosine_recall@20
value: 0.17661061398990294
name: Cosine Recall@20
- type: cosine_recall@50
value: 0.2392861843604663
name: Cosine Recall@50
- type: cosine_recall@100
value: 0.2862639658547104
name: Cosine Recall@100
- type: cosine_recall@150
value: 0.3286954340443375
name: Cosine Recall@150
- type: cosine_recall@200
value: 0.3630829587412431
name: Cosine Recall@200
- type: cosine_ndcg@1
value: 0.34951456310679613
name: Cosine Ndcg@1
- type: cosine_ndcg@20
value: 0.24683538489164747
name: Cosine Ndcg@20
- type: cosine_ndcg@50
value: 0.23936442282824424
name: Cosine Ndcg@50
- type: cosine_ndcg@100
value: 0.2618891246293786
name: Cosine Ndcg@100
- type: cosine_ndcg@150
value: 0.27867525817923894
name: Cosine Ndcg@150
- type: cosine_ndcg@200
value: 0.29190260238165355
name: Cosine Ndcg@200
- type: cosine_mrr@1
value: 0.34951456310679613
name: Cosine Mrr@1
- type: cosine_mrr@20
value: 0.44845699819699636
name: Cosine Mrr@20
- type: cosine_mrr@50
value: 0.4514515915598798
name: Cosine Mrr@50
- type: cosine_mrr@100
value: 0.451864194979824
name: Cosine Mrr@100
- type: cosine_mrr@150
value: 0.4522894025156287
name: Cosine Mrr@150
- type: cosine_mrr@200
value: 0.45250948321580986
name: Cosine Mrr@200
- type: cosine_map@1
value: 0.34951456310679613
name: Cosine Map@1
- type: cosine_map@20
value: 0.1470309927546457
name: Cosine Map@20
- type: cosine_map@50
value: 0.12671489844037503
name: Cosine Map@50
- type: cosine_map@100
value: 0.13257859039926595
name: Cosine Map@100
- type: cosine_map@150
value: 0.13523273342027425
name: Cosine Map@150
- type: cosine_map@200
value: 0.13679857663871084
name: Cosine Map@200
- type: cosine_map@500
value: 0.14069476480399515
name: Cosine Map@500
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: mix es
type: mix_es
metrics:
- type: cosine_accuracy@1
value: 0.41133645345813835
name: Cosine Accuracy@1
- type: cosine_accuracy@20
value: 0.7613104524180967
name: Cosine Accuracy@20
- type: cosine_accuracy@50
value: 0.8523140925637025
name: Cosine Accuracy@50
- type: cosine_accuracy@100
value: 0.9121164846593863
name: Cosine Accuracy@100
- type: cosine_accuracy@150
value: 0.9417576703068122
name: Cosine Accuracy@150
- type: cosine_accuracy@200
value: 0.9547581903276131
name: Cosine Accuracy@200
- type: cosine_precision@1
value: 0.41133645345813835
name: Cosine Precision@1
- type: cosine_precision@20
value: 0.08920956838273532
name: Cosine Precision@20
- type: cosine_precision@50
value: 0.04175767030681228
name: Cosine Precision@50
- type: cosine_precision@100
value: 0.02291731669266771
name: Cosine Precision@100
- type: cosine_precision@150
value: 0.015905702894782457
name: Cosine Precision@150
- type: cosine_precision@200
value: 0.012243889755590227
name: Cosine Precision@200
- type: cosine_recall@1
value: 0.15653988064284477
name: Cosine Recall@1
- type: cosine_recall@20
value: 0.6593678032835598
name: Cosine Recall@20
- type: cosine_recall@50
value: 0.7704838669737266
name: Cosine Recall@50
- type: cosine_recall@100
value: 0.847169601069757
name: Cosine Recall@100
- type: cosine_recall@150
value: 0.8825483495530297
name: Cosine Recall@150
- type: cosine_recall@200
value: 0.9050999182824455
name: Cosine Recall@200
- type: cosine_ndcg@1
value: 0.41133645345813835
name: Cosine Ndcg@1
- type: cosine_ndcg@20
value: 0.5116672519515115
name: Cosine Ndcg@20
- type: cosine_ndcg@50
value: 0.542000920569141
name: Cosine Ndcg@50
- type: cosine_ndcg@100
value: 0.558759964344595
name: Cosine Ndcg@100
- type: cosine_ndcg@150
value: 0.5655977162199296
name: Cosine Ndcg@150
- type: cosine_ndcg@200
value: 0.5697289878952349
name: Cosine Ndcg@200
- type: cosine_mrr@1
value: 0.41133645345813835
name: Cosine Mrr@1
- type: cosine_mrr@20
value: 0.4978677179556957
name: Cosine Mrr@20
- type: cosine_mrr@50
value: 0.5009543893008301
name: Cosine Mrr@50
- type: cosine_mrr@100
value: 0.5018183607581652
name: Cosine Mrr@100
- type: cosine_mrr@150
value: 0.5020589846475842
name: Cosine Mrr@150
- type: cosine_mrr@200
value: 0.5021321446410069
name: Cosine Mrr@200
- type: cosine_map@1
value: 0.41133645345813835
name: Cosine Map@1
- type: cosine_map@20
value: 0.4263681424556441
name: Cosine Map@20
- type: cosine_map@50
value: 0.4338209025376249
name: Cosine Map@50
- type: cosine_map@100
value: 0.4359939776007631
name: Cosine Map@100
- type: cosine_map@150
value: 0.43656970643226983
name: Cosine Map@150
- type: cosine_map@200
value: 0.4368426702726571
name: Cosine Map@200
- type: cosine_map@500
value: 0.43729529920887905
name: Cosine Map@500
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: mix de
type: mix_de
metrics:
- type: cosine_accuracy@1
value: 0.29433177327093085
name: Cosine Accuracy@1
- type: cosine_accuracy@20
value: 0.6500260010400416
name: Cosine Accuracy@20
- type: cosine_accuracy@50
value: 0.7607904316172647
name: Cosine Accuracy@50
- type: cosine_accuracy@100
value: 0.8507540301612064
name: Cosine Accuracy@100
- type: cosine_accuracy@150
value: 0.889755590223609
name: Cosine Accuracy@150
- type: cosine_accuracy@200
value: 0.9204368174726989
name: Cosine Accuracy@200
- type: cosine_precision@1
value: 0.29433177327093085
name: Cosine Precision@1
- type: cosine_precision@20
value: 0.07308892355694228
name: Cosine Precision@20
- type: cosine_precision@50
value: 0.036141445657826315
name: Cosine Precision@50
- type: cosine_precision@100
value: 0.020634425377015084
name: Cosine Precision@100
- type: cosine_precision@150
value: 0.014681920610157736
name: Cosine Precision@150
- type: cosine_precision@200
value: 0.011552262090483621
name: Cosine Precision@200
- type: cosine_recall@1
value: 0.1109031027907783
name: Cosine Recall@1
- type: cosine_recall@20
value: 0.534356040908303
name: Cosine Recall@20
- type: cosine_recall@50
value: 0.6584676720402148
name: Cosine Recall@50
- type: cosine_recall@100
value: 0.752470098803952
name: Cosine Recall@100
- type: cosine_recall@150
value: 0.8025567689374241
name: Cosine Recall@150
- type: cosine_recall@200
value: 0.8417663373201595
name: Cosine Recall@200
- type: cosine_ndcg@1
value: 0.29433177327093085
name: Cosine Ndcg@1
- type: cosine_ndcg@20
value: 0.3919428679123834
name: Cosine Ndcg@20
- type: cosine_ndcg@50
value: 0.425599899100406
name: Cosine Ndcg@50
- type: cosine_ndcg@100
value: 0.4462421162922913
name: Cosine Ndcg@100
- type: cosine_ndcg@150
value: 0.45606402272845137
name: Cosine Ndcg@150
- type: cosine_ndcg@200
value: 0.4632312746623382
name: Cosine Ndcg@200
- type: cosine_mrr@1
value: 0.29433177327093085
name: Cosine Mrr@1
- type: cosine_mrr@20
value: 0.37785395494554963
name: Cosine Mrr@20
- type: cosine_mrr@50
value: 0.38148321196953044
name: Cosine Mrr@50
- type: cosine_mrr@100
value: 0.38274724688611994
name: Cosine Mrr@100
- type: cosine_mrr@150
value: 0.3830666241433367
name: Cosine Mrr@150
- type: cosine_mrr@200
value: 0.3832429794087988
name: Cosine Mrr@200
- type: cosine_map@1
value: 0.29433177327093085
name: Cosine Map@1
- type: cosine_map@20
value: 0.3096720133634083
name: Cosine Map@20
- type: cosine_map@50
value: 0.31740714963039135
name: Cosine Map@50
- type: cosine_map@100
value: 0.31992557448195186
name: Cosine Map@100
- type: cosine_map@150
value: 0.3207379270967634
name: Cosine Map@150
- type: cosine_map@200
value: 0.3211962807999124
name: Cosine Map@200
- type: cosine_map@500
value: 0.3219246841517722
name: Cosine Map@500
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: mix zh
type: mix_zh
metrics:
- type: cosine_accuracy@1
value: 0.09707724425887265
name: Cosine Accuracy@1
- type: cosine_accuracy@20
value: 0.3585594989561587
name: Cosine Accuracy@20
- type: cosine_accuracy@50
value: 0.4900835073068894
name: Cosine Accuracy@50
- type: cosine_accuracy@100
value: 0.6002087682672234
name: Cosine Accuracy@100
- type: cosine_accuracy@150
value: 0.6612734864300627
name: Cosine Accuracy@150
- type: cosine_accuracy@200
value: 0.7061586638830898
name: Cosine Accuracy@200
- type: cosine_precision@1
value: 0.09707724425887265
name: Cosine Precision@1
- type: cosine_precision@20
value: 0.03144572025052192
name: Cosine Precision@20
- type: cosine_precision@50
value: 0.018486430062630482
name: Cosine Precision@50
- type: cosine_precision@100
value: 0.011612734864300627
name: Cosine Precision@100
- type: cosine_precision@150
value: 0.008688239387613084
name: Cosine Precision@150
- type: cosine_precision@200
value: 0.007132045929018789
name: Cosine Precision@200
- type: cosine_recall@1
value: 0.032868575405109846
name: Cosine Recall@1
- type: cosine_recall@20
value: 0.20912118500845014
name: Cosine Recall@20
- type: cosine_recall@50
value: 0.305353414852371
name: Cosine Recall@50
- type: cosine_recall@100
value: 0.3834696126188819
name: Cosine Recall@100
- type: cosine_recall@150
value: 0.43087740663419155
name: Cosine Recall@150
- type: cosine_recall@200
value: 0.4714567385757365
name: Cosine Recall@200
- type: cosine_ndcg@1
value: 0.09707724425887265
name: Cosine Ndcg@1
- type: cosine_ndcg@20
value: 0.13847583254619214
name: Cosine Ndcg@20
- type: cosine_ndcg@50
value: 0.16556220177827802
name: Cosine Ndcg@50
- type: cosine_ndcg@100
value: 0.1834871578549362
name: Cosine Ndcg@100
- type: cosine_ndcg@150
value: 0.1930615498205831
name: Cosine Ndcg@150
- type: cosine_ndcg@200
value: 0.20074882110420836
name: Cosine Ndcg@200
- type: cosine_mrr@1
value: 0.09707724425887265
name: Cosine Mrr@1
- type: cosine_mrr@20
value: 0.15220960831749397
name: Cosine Mrr@20
- type: cosine_mrr@50
value: 0.15642354470896513
name: Cosine Mrr@50
- type: cosine_mrr@100
value: 0.1580041495008456
name: Cosine Mrr@100
- type: cosine_mrr@150
value: 0.15850022553236756
name: Cosine Mrr@150
- type: cosine_mrr@200
value: 0.1587557913720219
name: Cosine Mrr@200
- type: cosine_map@1
value: 0.09707724425887265
name: Cosine Map@1
- type: cosine_map@20
value: 0.08751052569766739
name: Cosine Map@20
- type: cosine_map@50
value: 0.09304075210745723
name: Cosine Map@50
- type: cosine_map@100
value: 0.09500635866296525
name: Cosine Map@100
- type: cosine_map@150
value: 0.09570276054684158
name: Cosine Map@150
- type: cosine_map@200
value: 0.09614394028730197
name: Cosine Map@200
- type: cosine_map@500
value: 0.09706713378133278
name: Cosine Map@500
---
# Job - Job matching BAAI/bge-small-en-v1.5
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:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) <!-- at revision 5c38ec7c405ec4b44b94cc5a9bb96e735b38267a -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Datasets:**
- full_en
- full_de
- full_es
- full_zh
- mix
<!-- - **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': True}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, '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("sentence_transformers_model_id")
# Run inference
sentences = [
'Volksvertreter',
'Parlamentarier',
'Oberbürgermeister',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# 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 | 0.6571 | 0.1243 | 0.2956 | 0.3495 | 0.4113 | 0.2943 | 0.0971 |
| cosine_accuracy@20 | 0.9905 | 1.0 | 0.9212 | 0.7379 | 0.7613 | 0.65 | 0.3586 |
| cosine_accuracy@50 | 0.9905 | 1.0 | 0.9655 | 0.8252 | 0.8523 | 0.7608 | 0.4901 |
| cosine_accuracy@100 | 0.9905 | 1.0 | 0.9754 | 0.8544 | 0.9121 | 0.8508 | 0.6002 |
| cosine_accuracy@150 | 0.9905 | 1.0 | 0.9852 | 0.9029 | 0.9418 | 0.8898 | 0.6613 |
| cosine_accuracy@200 | 0.9905 | 1.0 | 0.9852 | 0.9417 | 0.9548 | 0.9204 | 0.7062 |
| cosine_precision@1 | 0.6571 | 0.1243 | 0.2956 | 0.3495 | 0.4113 | 0.2943 | 0.0971 |
| cosine_precision@20 | 0.5024 | 0.4897 | 0.4246 | 0.1733 | 0.0892 | 0.0731 | 0.0314 |
| cosine_precision@50 | 0.308 | 0.3179 | 0.2814 | 0.0944 | 0.0418 | 0.0361 | 0.0185 |
| cosine_precision@100 | 0.1863 | 0.1986 | 0.1801 | 0.0589 | 0.0229 | 0.0206 | 0.0116 |
| cosine_precision@150 | 0.1322 | 0.1469 | 0.1362 | 0.0458 | 0.0159 | 0.0147 | 0.0087 |
| cosine_precision@200 | 0.103 | 0.1179 | 0.1105 | 0.0385 | 0.0122 | 0.0116 | 0.0071 |
| cosine_recall@1 | 0.068 | 0.0031 | 0.0111 | 0.0273 | 0.1565 | 0.1109 | 0.0329 |
| cosine_recall@20 | 0.5385 | 0.3221 | 0.2614 | 0.1766 | 0.6594 | 0.5344 | 0.2091 |
| cosine_recall@50 | 0.726 | 0.4638 | 0.3835 | 0.2393 | 0.7705 | 0.6585 | 0.3054 |
| cosine_recall@100 | 0.8329 | 0.5438 | 0.4677 | 0.2863 | 0.8472 | 0.7525 | 0.3835 |
| cosine_recall@150 | 0.8745 | 0.5825 | 0.5183 | 0.3287 | 0.8825 | 0.8026 | 0.4309 |
| cosine_recall@200 | 0.9057 | 0.6147 | 0.5517 | 0.3631 | 0.9051 | 0.8418 | 0.4715 |
| cosine_ndcg@1 | 0.6571 | 0.1243 | 0.2956 | 0.3495 | 0.4113 | 0.2943 | 0.0971 |
| cosine_ndcg@20 | 0.6845 | 0.5385 | 0.4601 | 0.2468 | 0.5117 | 0.3919 | 0.1385 |
| cosine_ndcg@50 | 0.704 | 0.5012 | 0.4229 | 0.2394 | 0.542 | 0.4256 | 0.1656 |
| cosine_ndcg@100 | 0.7589 | 0.5147 | 0.4371 | 0.2619 | 0.5588 | 0.4462 | 0.1835 |
| cosine_ndcg@150 | 0.7774 | 0.5348 | 0.4629 | 0.2787 | 0.5656 | 0.4561 | 0.1931 |
| **cosine_ndcg@200** | **0.7893** | **0.5505** | **0.4797** | **0.2919** | **0.5697** | **0.4632** | **0.2007** |
| cosine_mrr@1 | 0.6571 | 0.1243 | 0.2956 | 0.3495 | 0.4113 | 0.2943 | 0.0971 |
| cosine_mrr@20 | 0.8103 | 0.5515 | 0.4896 | 0.4485 | 0.4979 | 0.3779 | 0.1522 |
| cosine_mrr@50 | 0.8103 | 0.5515 | 0.4909 | 0.4515 | 0.501 | 0.3815 | 0.1564 |
| cosine_mrr@100 | 0.8103 | 0.5515 | 0.4911 | 0.4519 | 0.5018 | 0.3827 | 0.158 |
| cosine_mrr@150 | 0.8103 | 0.5515 | 0.4912 | 0.4523 | 0.5021 | 0.3831 | 0.1585 |
| cosine_mrr@200 | 0.8103 | 0.5515 | 0.4912 | 0.4525 | 0.5021 | 0.3832 | 0.1588 |
| cosine_map@1 | 0.6571 | 0.1243 | 0.2956 | 0.3495 | 0.4113 | 0.2943 | 0.0971 |
| cosine_map@20 | 0.5418 | 0.4028 | 0.3236 | 0.147 | 0.4264 | 0.3097 | 0.0875 |
| cosine_map@50 | 0.5327 | 0.3422 | 0.2644 | 0.1267 | 0.4338 | 0.3174 | 0.093 |
| cosine_map@100 | 0.5657 | 0.3395 | 0.2576 | 0.1326 | 0.436 | 0.3199 | 0.095 |
| cosine_map@150 | 0.5734 | 0.3478 | 0.2669 | 0.1352 | 0.4366 | 0.3207 | 0.0957 |
| cosine_map@200 | 0.5772 | 0.3534 | 0.2722 | 0.1368 | 0.4368 | 0.3212 | 0.0961 |
| cosine_map@500 | 0.5814 | 0.3631 | 0.2833 | 0.1407 | 0.4373 | 0.3219 | 0.0971 |
<!--
## 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 Datasets
<details><summary>full_en</summary>
#### full_en
* Dataset: full_en
* Size: 28,880 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:--------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 3 tokens</li><li>mean: 5.0 tokens</li><li>max: 10 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.01 tokens</li><li>max: 13 tokens</li></ul> |
* Samples:
| anchor | positive |
|:-----------------------------------------|:-----------------------------------------|
| <code>air commodore</code> | <code>flight lieutenant</code> |
| <code>command and control officer</code> | <code>flight officer</code> |
| <code>air commodore</code> | <code>command and control officer</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
```json
{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
```
</details>
<details><summary>full_de</summary>
#### full_de
* Dataset: full_de
* Size: 23,023 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 3 tokens</li><li>mean: 11.05 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 11.43 tokens</li><li>max: 45 tokens</li></ul> |
* Samples:
| anchor | positive |
|:----------------------------------|:-----------------------------------------------------|
| <code>Staffelkommandantin</code> | <code>Kommodore</code> |
| <code>Luftwaffenoffizierin</code> | <code>Luftwaffenoffizier/Luftwaffenoffizierin</code> |
| <code>Staffelkommandantin</code> | <code>Luftwaffenoffizierin</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
```json
{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
```
</details>
<details><summary>full_es</summary>
#### full_es
* Dataset: full_es
* Size: 20,724 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 3 tokens</li><li>mean: 12.95 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 12.57 tokens</li><li>max: 50 tokens</li></ul> |
* Samples:
| anchor | positive |
|:------------------------------------|:-------------------------------------------|
| <code>jefe de escuadrón</code> | <code>instructor</code> |
| <code>comandante de aeronave</code> | <code>instructor de simulador</code> |
| <code>instructor</code> | <code>oficial del Ejército del Aire</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
```json
{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
```
</details>
<details><summary>full_zh</summary>
#### full_zh
* Dataset: full_zh
* Size: 30,401 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 4 tokens</li><li>mean: 8.36 tokens</li><li>max: 20 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 8.95 tokens</li><li>max: 27 tokens</li></ul> |
* Samples:
| anchor | positive |
|:------------------|:---------------------|
| <code>技术总监</code> | <code>技术和运营总监</code> |
| <code>技术总监</code> | <code>技术主管</code> |
| <code>技术总监</code> | <code>技术艺术总监</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
```json
{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
```
</details>
<details><summary>mix</summary>
#### mix
* Dataset: mix
* Size: 21,760 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 2 tokens</li><li>mean: 5.65 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 10.08 tokens</li><li>max: 30 tokens</li></ul> |
* Samples:
| anchor | positive |
|:------------------------------------------|:----------------------------------------------------------------|
| <code>technical manager</code> | <code>Technischer Direktor für Bühne, Film und Fernsehen</code> |
| <code>head of technical</code> | <code>directora técnica</code> |
| <code>head of technical department</code> | <code>技术艺术总监</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
```json
{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
```
</details>
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `gradient_accumulation_steps`: 2
- `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`: 5e-05
- `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 | - | 0.7322 | 0.4690 | 0.3853 | 0.2723 | 0.3209 | 0.2244 | 0.0919 |
| 0.0021 | 1 | 23.8878 | - | - | - | - | - | - | - |
| 0.2058 | 100 | 7.2098 | - | - | - | - | - | - | - |
| 0.4115 | 200 | 4.2635 | 0.7800 | 0.5132 | 0.4268 | 0.2798 | 0.4372 | 0.2996 | 0.1447 |
| 0.6173 | 300 | 4.1931 | - | - | - | - | - | - | - |
| 0.8230 | 400 | 3.73 | 0.7863 | 0.5274 | 0.4451 | 0.2805 | 0.4762 | 0.3455 | 0.1648 |
| 1.0309 | 500 | 3.3569 | - | - | - | - | - | - | - |
| 1.2366 | 600 | 3.6464 | 0.7868 | 0.5372 | 0.4540 | 0.2813 | 0.5063 | 0.3794 | 0.1755 |
| 1.4424 | 700 | 3.0772 | - | - | - | - | - | - | - |
| 1.6481 | 800 | 3.114 | 0.7906 | 0.5391 | 0.4576 | 0.2832 | 0.5221 | 0.4047 | 0.1779 |
| 1.8539 | 900 | 2.9246 | - | - | - | - | - | - | - |
| 2.0617 | 1000 | 2.7479 | 0.7873 | 0.5423 | 0.4631 | 0.2871 | 0.5323 | 0.4143 | 0.1843 |
| 2.2675 | 1100 | 3.049 | - | - | - | - | - | - | - |
| 2.4733 | 1200 | 2.6137 | 0.7878 | 0.5418 | 0.4685 | 0.2870 | 0.5470 | 0.4339 | 0.1932 |
| 2.6790 | 1300 | 2.8607 | - | - | - | - | - | - | - |
| 2.8848 | 1400 | 2.7071 | 0.7889 | 0.5465 | 0.4714 | 0.2891 | 0.5504 | 0.4362 | 0.1944 |
| 3.0926 | 1500 | 2.7012 | - | - | - | - | - | - | - |
| 3.2984 | 1600 | 2.7423 | 0.7882 | 0.5471 | 0.4748 | 0.2868 | 0.5542 | 0.4454 | 0.1976 |
| 3.5041 | 1700 | 2.5316 | - | - | - | - | - | - | - |
| 3.7099 | 1800 | 2.6344 | 0.7900 | 0.5498 | 0.4763 | 0.2857 | 0.5639 | 0.4552 | 0.1954 |
| 3.9156 | 1900 | 2.4983 | - | - | - | - | - | - | - |
| 4.1235 | 2000 | 2.5423 | 0.7894 | 0.5499 | 0.4786 | 0.2870 | 0.5644 | 0.4576 | 0.1974 |
| 4.3292 | 2100 | 2.5674 | - | - | - | - | - | - | - |
| 4.5350 | 2200 | 2.6237 | 0.7899 | 0.5502 | 0.4802 | 0.2843 | 0.5674 | 0.4607 | 0.1993 |
| 4.7407 | 2300 | 2.3776 | - | - | - | - | - | - | - |
| 4.9465 | 2400 | 2.1116 | 0.7893 | 0.5505 | 0.4797 | 0.2919 | 0.5697 | 0.4632 | 0.2007 |
### 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",
}
```
#### GISTEmbedLoss
```bibtex
@misc{solatorio2024gistembed,
title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning},
author={Aivin V. Solatorio},
year={2024},
eprint={2402.16829},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
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