---
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

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### 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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