---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:124788
- loss:GISTEmbedLoss
base_model: Alibaba-NLP/gte-multilingual-base
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 Alibaba-NLP/gte-multilingual-base
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: full en
      type: full_en
    metrics:
    - type: cosine_accuracy@1
      value: 0.6666666666666666
      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: 0.6666666666666666
      name: Cosine Precision@1
    - type: cosine_precision@20
      value: 0.5147619047619048
      name: Cosine Precision@20
    - type: cosine_precision@50
      value: 0.31999999999999995
      name: Cosine Precision@50
    - type: cosine_precision@100
      value: 0.19047619047619047
      name: Cosine Precision@100
    - type: cosine_precision@150
      value: 0.1361904761904762
      name: Cosine Precision@150
    - type: cosine_precision@200
      value: 0.10542857142857143
      name: Cosine Precision@200
    - type: cosine_recall@1
      value: 0.06854687410617222
      name: Cosine Recall@1
    - type: cosine_recall@20
      value: 0.5491240579458434
      name: Cosine Recall@20
    - type: cosine_recall@50
      value: 0.7553654907661455
      name: Cosine Recall@50
    - type: cosine_recall@100
      value: 0.8503209224897438
      name: Cosine Recall@100
    - type: cosine_recall@150
      value: 0.8994749092946579
      name: Cosine Recall@150
    - type: cosine_recall@200
      value: 0.9207884118691805
      name: Cosine Recall@200
    - type: cosine_ndcg@1
      value: 0.6666666666666666
      name: Cosine Ndcg@1
    - type: cosine_ndcg@20
      value: 0.6952098522285352
      name: Cosine Ndcg@20
    - type: cosine_ndcg@50
      value: 0.7229572913271685
      name: Cosine Ndcg@50
    - type: cosine_ndcg@100
      value: 0.7732532874348539
      name: Cosine Ndcg@100
    - type: cosine_ndcg@150
      value: 0.7947334799125039
      name: Cosine Ndcg@150
    - type: cosine_ndcg@200
      value: 0.8038564389556094
      name: Cosine Ndcg@200
    - type: cosine_mrr@1
      value: 0.6666666666666666
      name: Cosine Mrr@1
    - type: cosine_mrr@20
      value: 0.8182539682539683
      name: Cosine Mrr@20
    - type: cosine_mrr@50
      value: 0.8182539682539683
      name: Cosine Mrr@50
    - type: cosine_mrr@100
      value: 0.8182539682539683
      name: Cosine Mrr@100
    - type: cosine_mrr@150
      value: 0.8182539682539683
      name: Cosine Mrr@150
    - type: cosine_mrr@200
      value: 0.8182539682539683
      name: Cosine Mrr@200
    - type: cosine_map@1
      value: 0.6666666666666666
      name: Cosine Map@1
    - type: cosine_map@20
      value: 0.5566401101002375
      name: Cosine Map@20
    - type: cosine_map@50
      value: 0.55344017265156
      name: Cosine Map@50
    - type: cosine_map@100
      value: 0.5852249415484134
      name: Cosine Map@100
    - type: cosine_map@150
      value: 0.5943042662925763
      name: Cosine Map@150
    - type: cosine_map@200
      value: 0.5975837437975446
      name: Cosine Map@200
    - type: cosine_map@500
      value: 0.6015742986218369
      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: 0.575945945945946
      name: Cosine Precision@20
    - type: cosine_precision@50
      value: 0.3923243243243244
      name: Cosine Precision@50
    - type: cosine_precision@100
      value: 0.2565945945945946
      name: Cosine Precision@100
    - type: cosine_precision@150
      value: 0.19282882882882882
      name: Cosine Precision@150
    - type: cosine_precision@200
      value: 0.1527837837837838
      name: Cosine Precision@200
    - type: cosine_recall@1
      value: 0.0036138931714884822
      name: Cosine Recall@1
    - type: cosine_recall@20
      value: 0.3852888120551914
      name: Cosine Recall@20
    - type: cosine_recall@50
      value: 0.5659574514538841
      name: Cosine Recall@50
    - type: cosine_recall@100
      value: 0.6898678629281393
      name: Cosine Recall@100
    - type: cosine_recall@150
      value: 0.7540209165372845
      name: Cosine Recall@150
    - type: cosine_recall@200
      value: 0.7858170054407897
      name: Cosine Recall@200
    - type: cosine_ndcg@1
      value: 0.12432432432432433
      name: Cosine Ndcg@1
    - type: cosine_ndcg@20
      value: 0.6168674053047035
      name: Cosine Ndcg@20
    - type: cosine_ndcg@50
      value: 0.5913690595071309
      name: Cosine Ndcg@50
    - type: cosine_ndcg@100
      value: 0.62350509928888
      name: Cosine Ndcg@100
    - type: cosine_ndcg@150
      value: 0.6556716735369459
      name: Cosine Ndcg@150
    - type: cosine_ndcg@200
      value: 0.6716557949894583
      name: Cosine Ndcg@200
    - type: cosine_mrr@1
      value: 0.12432432432432433
      name: Cosine Mrr@1
    - type: cosine_mrr@20
      value: 0.5581081081081081
      name: Cosine Mrr@20
    - type: cosine_mrr@50
      value: 0.5581081081081081
      name: Cosine Mrr@50
    - type: cosine_mrr@100
      value: 0.5581081081081081
      name: Cosine Mrr@100
    - type: cosine_mrr@150
      value: 0.5581081081081081
      name: Cosine Mrr@150
    - type: cosine_mrr@200
      value: 0.5581081081081081
      name: Cosine Mrr@200
    - type: cosine_map@1
      value: 0.12432432432432433
      name: Cosine Map@1
    - type: cosine_map@20
      value: 0.48407152706202555
      name: Cosine Map@20
    - type: cosine_map@50
      value: 0.43043374125481026
      name: Cosine Map@50
    - type: cosine_map@100
      value: 0.43735327570764515
      name: Cosine Map@100
    - type: cosine_map@150
      value: 0.45269435912524697
      name: Cosine Map@150
    - type: cosine_map@200
      value: 0.45930097680668164
      name: Cosine Map@200
    - type: cosine_map@500
      value: 0.47204219228541466
      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: 0.9753694581280788
      name: Cosine Accuracy@20
    - type: cosine_accuracy@50
      value: 0.9852216748768473
      name: Cosine Accuracy@50
    - type: cosine_accuracy@100
      value: 0.9901477832512315
      name: Cosine Accuracy@100
    - type: cosine_accuracy@150
      value: 0.9901477832512315
      name: Cosine Accuracy@150
    - type: cosine_accuracy@200
      value: 0.9901477832512315
      name: Cosine Accuracy@200
    - type: cosine_precision@1
      value: 0.2955665024630542
      name: Cosine Precision@1
    - type: cosine_precision@20
      value: 0.5103448275862069
      name: Cosine Precision@20
    - type: cosine_precision@50
      value: 0.36935960591133016
      name: Cosine Precision@50
    - type: cosine_precision@100
      value: 0.23965517241379314
      name: Cosine Precision@100
    - type: cosine_precision@150
      value: 0.1807881773399015
      name: Cosine Precision@150
    - type: cosine_precision@200
      value: 0.1461576354679803
      name: Cosine Precision@200
    - type: cosine_recall@1
      value: 0.01108543831680986
      name: Cosine Recall@1
    - type: cosine_recall@20
      value: 0.3207974783481294
      name: Cosine Recall@20
    - type: cosine_recall@50
      value: 0.5042046446720455
      name: Cosine Recall@50
    - type: cosine_recall@100
      value: 0.6172666777909689
      name: Cosine Recall@100
    - type: cosine_recall@150
      value: 0.6848138831682932
      name: Cosine Recall@150
    - type: cosine_recall@200
      value: 0.7253195006357535
      name: Cosine Recall@200
    - type: cosine_ndcg@1
      value: 0.2955665024630542
      name: Cosine Ndcg@1
    - type: cosine_ndcg@20
      value: 0.537849085734973
      name: Cosine Ndcg@20
    - type: cosine_ndcg@50
      value: 0.5288037060639387
      name: Cosine Ndcg@50
    - type: cosine_ndcg@100
      value: 0.5551941695921919
      name: Cosine Ndcg@100
    - type: cosine_ndcg@150
      value: 0.5887611959940118
      name: Cosine Ndcg@150
    - type: cosine_ndcg@200
      value: 0.6092219717029682
      name: Cosine Ndcg@200
    - type: cosine_mrr@1
      value: 0.2955665024630542
      name: Cosine Mrr@1
    - type: cosine_mrr@20
      value: 0.5164773875147672
      name: Cosine Mrr@20
    - type: cosine_mrr@50
      value: 0.5167647438366063
      name: Cosine Mrr@50
    - type: cosine_mrr@100
      value: 0.5168213657719442
      name: Cosine Mrr@100
    - type: cosine_mrr@150
      value: 0.5168213657719442
      name: Cosine Mrr@150
    - type: cosine_mrr@200
      value: 0.5168213657719442
      name: Cosine Mrr@200
    - type: cosine_map@1
      value: 0.2955665024630542
      name: Cosine Map@1
    - type: cosine_map@20
      value: 0.398398563122481
      name: Cosine Map@20
    - type: cosine_map@50
      value: 0.36032758502543594
      name: Cosine Map@50
    - type: cosine_map@100
      value: 0.3632259128424842
      name: Cosine Map@100
    - type: cosine_map@150
      value: 0.37822275477623696
      name: Cosine Map@150
    - type: cosine_map@200
      value: 0.3863148456840816
      name: Cosine Map@200
    - type: cosine_map@500
      value: 0.399227009561676
      name: Cosine Map@500
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: full zh
      type: full_zh
    metrics:
    - type: cosine_accuracy@1
      value: 0.6796116504854369
      name: Cosine Accuracy@1
    - type: cosine_accuracy@20
      value: 0.9805825242718447
      name: Cosine Accuracy@20
    - type: cosine_accuracy@50
      value: 0.9902912621359223
      name: Cosine Accuracy@50
    - type: cosine_accuracy@100
      value: 0.9902912621359223
      name: Cosine Accuracy@100
    - type: cosine_accuracy@150
      value: 0.9902912621359223
      name: Cosine Accuracy@150
    - type: cosine_accuracy@200
      value: 0.9902912621359223
      name: Cosine Accuracy@200
    - type: cosine_precision@1
      value: 0.6796116504854369
      name: Cosine Precision@1
    - type: cosine_precision@20
      value: 0.488349514563107
      name: Cosine Precision@20
    - type: cosine_precision@50
      value: 0.29631067961165053
      name: Cosine Precision@50
    - type: cosine_precision@100
      value: 0.17883495145631062
      name: Cosine Precision@100
    - type: cosine_precision@150
      value: 0.12776699029126212
      name: Cosine Precision@150
    - type: cosine_precision@200
      value: 0.09990291262135924
      name: Cosine Precision@200
    - type: cosine_recall@1
      value: 0.06931865009287731
      name: Cosine Recall@1
    - type: cosine_recall@20
      value: 0.5250914458143515
      name: Cosine Recall@20
    - type: cosine_recall@50
      value: 0.7082715439925011
      name: Cosine Recall@50
    - type: cosine_recall@100
      value: 0.8169166539243944
      name: Cosine Recall@100
    - type: cosine_recall@150
      value: 0.8613232254521018
      name: Cosine Recall@150
    - type: cosine_recall@200
      value: 0.8898175710074696
      name: Cosine Recall@200
    - type: cosine_ndcg@1
      value: 0.6796116504854369
      name: Cosine Ndcg@1
    - type: cosine_ndcg@20
      value: 0.6680745295820606
      name: Cosine Ndcg@20
    - type: cosine_ndcg@50
      value: 0.6856578240865067
      name: Cosine Ndcg@50
    - type: cosine_ndcg@100
      value: 0.7378907298421352
      name: Cosine Ndcg@100
    - type: cosine_ndcg@150
      value: 0.7576651805692517
      name: Cosine Ndcg@150
    - type: cosine_ndcg@200
      value: 0.7696718049970358
      name: Cosine Ndcg@200
    - type: cosine_mrr@1
      value: 0.6796116504854369
      name: Cosine Mrr@1
    - type: cosine_mrr@20
      value: 0.8158576051779936
      name: Cosine Mrr@20
    - type: cosine_mrr@50
      value: 0.816279724215562
      name: Cosine Mrr@50
    - type: cosine_mrr@100
      value: 0.816279724215562
      name: Cosine Mrr@100
    - type: cosine_mrr@150
      value: 0.816279724215562
      name: Cosine Mrr@150
    - type: cosine_mrr@200
      value: 0.816279724215562
      name: Cosine Mrr@200
    - type: cosine_map@1
      value: 0.6796116504854369
      name: Cosine Map@1
    - type: cosine_map@20
      value: 0.522177160195635
      name: Cosine Map@20
    - type: cosine_map@50
      value: 0.5082601209392789
      name: Cosine Map@50
    - type: cosine_map@100
      value: 0.5371705298206915
      name: Cosine Map@100
    - type: cosine_map@150
      value: 0.5454012672534121
      name: Cosine Map@150
    - type: cosine_map@200
      value: 0.5494570875591636
      name: Cosine Map@200
    - type: cosine_map@500
      value: 0.5542116087189223
      name: Cosine Map@500
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: mix es
      type: mix_es
    metrics:
    - type: cosine_accuracy@1
      value: 0.7087883515340614
      name: Cosine Accuracy@1
    - type: cosine_accuracy@20
      value: 0.9552782111284451
      name: Cosine Accuracy@20
    - type: cosine_accuracy@50
      value: 0.9802392095683827
      name: Cosine Accuracy@50
    - type: cosine_accuracy@100
      value: 0.9901196047841914
      name: Cosine Accuracy@100
    - type: cosine_accuracy@150
      value: 0.9937597503900156
      name: Cosine Accuracy@150
    - type: cosine_accuracy@200
      value: 0.9958398335933437
      name: Cosine Accuracy@200
    - type: cosine_precision@1
      value: 0.7087883515340614
      name: Cosine Precision@1
    - type: cosine_precision@20
      value: 0.12158086323452937
      name: Cosine Precision@20
    - type: cosine_precision@50
      value: 0.05122204888195529
      name: Cosine Precision@50
    - type: cosine_precision@100
      value: 0.026125845033801356
      name: Cosine Precision@100
    - type: cosine_precision@150
      value: 0.017548968625411682
      name: Cosine Precision@150
    - type: cosine_precision@200
      value: 0.013239729589183572
      name: Cosine Precision@200
    - type: cosine_recall@1
      value: 0.2737959042171211
      name: Cosine Recall@1
    - type: cosine_recall@20
      value: 0.8990032934650719
      name: Cosine Recall@20
    - type: cosine_recall@50
      value: 0.9459438377535101
      name: Cosine Recall@50
    - type: cosine_recall@100
      value: 0.9650979372508233
      name: Cosine Recall@100
    - type: cosine_recall@150
      value: 0.9731582596637198
      name: Cosine Recall@150
    - type: cosine_recall@200
      value: 0.979086496793205
      name: Cosine Recall@200
    - type: cosine_ndcg@1
      value: 0.7087883515340614
      name: Cosine Ndcg@1
    - type: cosine_ndcg@20
      value: 0.7814741332820433
      name: Cosine Ndcg@20
    - type: cosine_ndcg@50
      value: 0.7944033394497885
      name: Cosine Ndcg@50
    - type: cosine_ndcg@100
      value: 0.7986024294603647
      name: Cosine Ndcg@100
    - type: cosine_ndcg@150
      value: 0.8001222520801115
      name: Cosine Ndcg@150
    - type: cosine_ndcg@200
      value: 0.801183843730514
      name: Cosine Ndcg@200
    - type: cosine_mrr@1
      value: 0.7087883515340614
      name: Cosine Mrr@1
    - type: cosine_mrr@20
      value: 0.7804158804359833
      name: Cosine Mrr@20
    - type: cosine_mrr@50
      value: 0.7812547046826683
      name: Cosine Mrr@50
    - type: cosine_mrr@100
      value: 0.7813961782842836
      name: Cosine Mrr@100
    - type: cosine_mrr@150
      value: 0.7814280971923943
      name: Cosine Mrr@150
    - type: cosine_mrr@200
      value: 0.7814392363829243
      name: Cosine Mrr@200
    - type: cosine_map@1
      value: 0.7087883515340614
      name: Cosine Map@1
    - type: cosine_map@20
      value: 0.7070596364024803
      name: Cosine Map@20
    - type: cosine_map@50
      value: 0.7106867578203881
      name: Cosine Map@50
    - type: cosine_map@100
      value: 0.7112928928384499
      name: Cosine Map@100
    - type: cosine_map@150
      value: 0.7114314004578745
      name: Cosine Map@150
    - type: cosine_map@200
      value: 0.711504950521157
      name: Cosine Map@200
    - type: cosine_map@500
      value: 0.7116431478000537
      name: Cosine Map@500
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: mix de
      type: mix_de
    metrics:
    - type: cosine_accuracy@1
      value: 0.6484659386375455
      name: Cosine Accuracy@1
    - type: cosine_accuracy@20
      value: 0.9323972958918356
      name: Cosine Accuracy@20
    - type: cosine_accuracy@50
      value: 0.968278731149246
      name: Cosine Accuracy@50
    - type: cosine_accuracy@100
      value: 0.984919396775871
      name: Cosine Accuracy@100
    - type: cosine_accuracy@150
      value: 0.9885595423816953
      name: Cosine Accuracy@150
    - type: cosine_accuracy@200
      value: 0.9937597503900156
      name: Cosine Accuracy@200
    - type: cosine_precision@1
      value: 0.6484659386375455
      name: Cosine Precision@1
    - type: cosine_precision@20
      value: 0.12093083723348932
      name: Cosine Precision@20
    - type: cosine_precision@50
      value: 0.05140925637025482
      name: Cosine Precision@50
    - type: cosine_precision@100
      value: 0.02647425897035882
      name: Cosine Precision@100
    - type: cosine_precision@150
      value: 0.017892182353960822
      name: Cosine Precision@150
    - type: cosine_precision@200
      value: 0.013530941237649509
      name: Cosine Precision@200
    - type: cosine_recall@1
      value: 0.2435517420696828
      name: Cosine Recall@1
    - type: cosine_recall@20
      value: 0.87873114924597
      name: Cosine Recall@20
    - type: cosine_recall@50
      value: 0.9319899462645173
      name: Cosine Recall@50
    - type: cosine_recall@100
      value: 0.9596117178020455
      name: Cosine Recall@100
    - type: cosine_recall@150
      value: 0.9718322066215982
      name: Cosine Recall@150
    - type: cosine_recall@200
      value: 0.9799791991679667
      name: Cosine Recall@200
    - type: cosine_ndcg@1
      value: 0.6484659386375455
      name: Cosine Ndcg@1
    - type: cosine_ndcg@20
      value: 0.7448150588358
      name: Cosine Ndcg@20
    - type: cosine_ndcg@50
      value: 0.7595232400510039
      name: Cosine Ndcg@50
    - type: cosine_ndcg@100
      value: 0.7656851368194345
      name: Cosine Ndcg@100
    - type: cosine_ndcg@150
      value: 0.7681576326024331
      name: Cosine Ndcg@150
    - type: cosine_ndcg@200
      value: 0.7696474672652458
      name: Cosine Ndcg@200
    - type: cosine_mrr@1
      value: 0.6484659386375455
      name: Cosine Mrr@1
    - type: cosine_mrr@20
      value: 0.7323691045739125
      name: Cosine Mrr@20
    - type: cosine_mrr@50
      value: 0.733538875120878
      name: Cosine Mrr@50
    - type: cosine_mrr@100
      value: 0.733776247038599
      name: Cosine Mrr@100
    - type: cosine_mrr@150
      value: 0.7338087409764548
      name: Cosine Mrr@150
    - type: cosine_mrr@200
      value: 0.7338398642058079
      name: Cosine Mrr@200
    - type: cosine_map@1
      value: 0.6484659386375455
      name: Cosine Map@1
    - type: cosine_map@20
      value: 0.6646138211839377
      name: Cosine Map@20
    - type: cosine_map@50
      value: 0.6683657128313888
      name: Cosine Map@50
    - type: cosine_map@100
      value: 0.6692634410264182
      name: Cosine Map@100
    - type: cosine_map@150
      value: 0.669518875077899
      name: Cosine Map@150
    - type: cosine_map@200
      value: 0.6696171599377958
      name: Cosine Map@200
    - type: cosine_map@500
      value: 0.6697127210085475
      name: Cosine Map@500
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: mix zh
      type: mix_zh
    metrics:
    - type: cosine_accuracy@1
      value: 0.7667014613778705
      name: Cosine Accuracy@1
    - type: cosine_accuracy@20
      value: 0.9843423799582464
      name: Cosine Accuracy@20
    - type: cosine_accuracy@50
      value: 0.9932150313152401
      name: Cosine Accuracy@50
    - type: cosine_accuracy@100
      value: 0.9958246346555324
      name: Cosine Accuracy@100
    - type: cosine_accuracy@150
      value: 0.9973903966597077
      name: Cosine Accuracy@150
    - type: cosine_accuracy@200
      value: 0.9979123173277662
      name: Cosine Accuracy@200
    - type: cosine_precision@1
      value: 0.7667014613778705
      name: Cosine Precision@1
    - type: cosine_precision@20
      value: 0.13870041753653445
      name: Cosine Precision@20
    - type: cosine_precision@50
      value: 0.05810020876826725
      name: Cosine Precision@50
    - type: cosine_precision@100
      value: 0.029598121085595
      name: Cosine Precision@100
    - type: cosine_precision@150
      value: 0.01986778009742519
      name: Cosine Precision@150
    - type: cosine_precision@200
      value: 0.014945198329853866
      name: Cosine Precision@200
    - type: cosine_recall@1
      value: 0.25692041952480366
      name: Cosine Recall@1
    - type: cosine_recall@20
      value: 0.9156576200417536
      name: Cosine Recall@20
    - type: cosine_recall@50
      value: 0.9582637439109255
      name: Cosine Recall@50
    - type: cosine_recall@100
      value: 0.9765483646485734
      name: Cosine Recall@100
    - type: cosine_recall@150
      value: 0.9833768267223383
      name: Cosine Recall@150
    - type: cosine_recall@200
      value: 0.986464857341684
      name: Cosine Recall@200
    - type: cosine_ndcg@1
      value: 0.7667014613778705
      name: Cosine Ndcg@1
    - type: cosine_ndcg@20
      value: 0.8002168358295473
      name: Cosine Ndcg@20
    - type: cosine_ndcg@50
      value: 0.8125113081884888
      name: Cosine Ndcg@50
    - type: cosine_ndcg@100
      value: 0.8167350090334409
      name: Cosine Ndcg@100
    - type: cosine_ndcg@150
      value: 0.8181122471507385
      name: Cosine Ndcg@150
    - type: cosine_ndcg@200
      value: 0.8186874070081017
      name: Cosine Ndcg@200
    - type: cosine_mrr@1
      value: 0.7667014613778705
      name: Cosine Mrr@1
    - type: cosine_mrr@20
      value: 0.8421752732824312
      name: Cosine Mrr@20
    - type: cosine_mrr@50
      value: 0.8424954415974232
      name: Cosine Mrr@50
    - type: cosine_mrr@100
      value: 0.8425358910333786
      name: Cosine Mrr@100
    - type: cosine_mrr@150
      value: 0.8425483391786986
      name: Cosine Mrr@150
    - type: cosine_mrr@200
      value: 0.8425515411459873
      name: Cosine Mrr@200
    - type: cosine_map@1
      value: 0.7667014613778705
      name: Cosine Map@1
    - type: cosine_map@20
      value: 0.7007206423896271
      name: Cosine Map@20
    - type: cosine_map@50
      value: 0.7046277360194696
      name: Cosine Map@50
    - type: cosine_map@100
      value: 0.7053668771050886
      name: Cosine Map@100
    - type: cosine_map@150
      value: 0.7055166914145262
      name: Cosine Map@150
    - type: cosine_map@200
      value: 0.7055658329670217
      name: Cosine Map@200
    - type: cosine_map@500
      value: 0.7056512281794008
      name: Cosine Map@500
---

# Job - Job matching Alibaba-NLP/gte-multilingual-base (v2)

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:** [Alibaba-NLP/gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base) <!-- at revision 9fdd4ee8bba0e2808a34e0e739576f6740d2b225 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 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': False}) with Transformer model: NewModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)
```

## Usage

### Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

```bash
pip install -U sentence-transformers
```

Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("pj-mathematician/JobGTE-multilingual-base-v2")
# Run inference
sentences = [
    'Volksvertreter',
    'Parlamentarier',
    'Oberbürgermeister',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

<!--
### Direct Usage (Transformers)

<details><summary>Click to see the direct usage in Transformers</summary>

</details>
-->

<!--
### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

</details>
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

## Evaluation

### Metrics

#### Information Retrieval

* Datasets: `full_en`, `full_es`, `full_de`, `full_zh`, `mix_es`, `mix_de` and `mix_zh`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric               | full_en    | full_es    | full_de    | full_zh    | mix_es     | mix_de     | mix_zh     |
|:---------------------|:-----------|:-----------|:-----------|:-----------|:-----------|:-----------|:-----------|
| cosine_accuracy@1    | 0.6667     | 0.1243     | 0.2956     | 0.6796     | 0.7088     | 0.6485     | 0.7667     |
| cosine_accuracy@20   | 0.9905     | 1.0        | 0.9754     | 0.9806     | 0.9553     | 0.9324     | 0.9843     |
| cosine_accuracy@50   | 0.9905     | 1.0        | 0.9852     | 0.9903     | 0.9802     | 0.9683     | 0.9932     |
| cosine_accuracy@100  | 0.9905     | 1.0        | 0.9901     | 0.9903     | 0.9901     | 0.9849     | 0.9958     |
| cosine_accuracy@150  | 0.9905     | 1.0        | 0.9901     | 0.9903     | 0.9938     | 0.9886     | 0.9974     |
| cosine_accuracy@200  | 0.9905     | 1.0        | 0.9901     | 0.9903     | 0.9958     | 0.9938     | 0.9979     |
| cosine_precision@1   | 0.6667     | 0.1243     | 0.2956     | 0.6796     | 0.7088     | 0.6485     | 0.7667     |
| cosine_precision@20  | 0.5148     | 0.5759     | 0.5103     | 0.4883     | 0.1216     | 0.1209     | 0.1387     |
| cosine_precision@50  | 0.32       | 0.3923     | 0.3694     | 0.2963     | 0.0512     | 0.0514     | 0.0581     |
| cosine_precision@100 | 0.1905     | 0.2566     | 0.2397     | 0.1788     | 0.0261     | 0.0265     | 0.0296     |
| cosine_precision@150 | 0.1362     | 0.1928     | 0.1808     | 0.1278     | 0.0175     | 0.0179     | 0.0199     |
| cosine_precision@200 | 0.1054     | 0.1528     | 0.1462     | 0.0999     | 0.0132     | 0.0135     | 0.0149     |
| cosine_recall@1      | 0.0685     | 0.0036     | 0.0111     | 0.0693     | 0.2738     | 0.2436     | 0.2569     |
| cosine_recall@20     | 0.5491     | 0.3853     | 0.3208     | 0.5251     | 0.899      | 0.8787     | 0.9157     |
| cosine_recall@50     | 0.7554     | 0.566      | 0.5042     | 0.7083     | 0.9459     | 0.932      | 0.9583     |
| cosine_recall@100    | 0.8503     | 0.6899     | 0.6173     | 0.8169     | 0.9651     | 0.9596     | 0.9765     |
| cosine_recall@150    | 0.8995     | 0.754      | 0.6848     | 0.8613     | 0.9732     | 0.9718     | 0.9834     |
| cosine_recall@200    | 0.9208     | 0.7858     | 0.7253     | 0.8898     | 0.9791     | 0.98       | 0.9865     |
| cosine_ndcg@1        | 0.6667     | 0.1243     | 0.2956     | 0.6796     | 0.7088     | 0.6485     | 0.7667     |
| cosine_ndcg@20       | 0.6952     | 0.6169     | 0.5378     | 0.6681     | 0.7815     | 0.7448     | 0.8002     |
| cosine_ndcg@50       | 0.723      | 0.5914     | 0.5288     | 0.6857     | 0.7944     | 0.7595     | 0.8125     |
| cosine_ndcg@100      | 0.7733     | 0.6235     | 0.5552     | 0.7379     | 0.7986     | 0.7657     | 0.8167     |
| cosine_ndcg@150      | 0.7947     | 0.6557     | 0.5888     | 0.7577     | 0.8001     | 0.7682     | 0.8181     |
| **cosine_ndcg@200**  | **0.8039** | **0.6717** | **0.6092** | **0.7697** | **0.8012** | **0.7696** | **0.8187** |
| cosine_mrr@1         | 0.6667     | 0.1243     | 0.2956     | 0.6796     | 0.7088     | 0.6485     | 0.7667     |
| cosine_mrr@20        | 0.8183     | 0.5581     | 0.5165     | 0.8159     | 0.7804     | 0.7324     | 0.8422     |
| cosine_mrr@50        | 0.8183     | 0.5581     | 0.5168     | 0.8163     | 0.7813     | 0.7335     | 0.8425     |
| cosine_mrr@100       | 0.8183     | 0.5581     | 0.5168     | 0.8163     | 0.7814     | 0.7338     | 0.8425     |
| cosine_mrr@150       | 0.8183     | 0.5581     | 0.5168     | 0.8163     | 0.7814     | 0.7338     | 0.8425     |
| cosine_mrr@200       | 0.8183     | 0.5581     | 0.5168     | 0.8163     | 0.7814     | 0.7338     | 0.8426     |
| cosine_map@1         | 0.6667     | 0.1243     | 0.2956     | 0.6796     | 0.7088     | 0.6485     | 0.7667     |
| cosine_map@20        | 0.5566     | 0.4841     | 0.3984     | 0.5222     | 0.7071     | 0.6646     | 0.7007     |
| cosine_map@50        | 0.5534     | 0.4304     | 0.3603     | 0.5083     | 0.7107     | 0.6684     | 0.7046     |
| cosine_map@100       | 0.5852     | 0.4374     | 0.3632     | 0.5372     | 0.7113     | 0.6693     | 0.7054     |
| cosine_map@150       | 0.5943     | 0.4527     | 0.3782     | 0.5454     | 0.7114     | 0.6695     | 0.7055     |
| cosine_map@200       | 0.5976     | 0.4593     | 0.3863     | 0.5495     | 0.7115     | 0.6696     | 0.7056     |
| cosine_map@500       | 0.6016     | 0.472      | 0.3992     | 0.5542     | 0.7116     | 0.6697     | 0.7057     |

<!--
## 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: 5.68 tokens</li><li>max: 11 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.76 tokens</li><li>max: 12 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: 7.99 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 8.19 tokens</li><li>max: 30 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: 9.13 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 8.84 tokens</li><li>max: 32 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: 5 tokens</li><li>mean: 7.15 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 7.46 tokens</li><li>max: 21 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: 6.71 tokens</li><li>max: 19 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 7.69 tokens</li><li>max: 19 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   | -             | 0.7447                  | 0.6125                  | 0.5378                  | 0.7240                  | 0.7029                 | 0.6345                 | 0.7437                 |
| 0.0082 | 1    | 4.3088        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
| 0.8230 | 100  | 1.9026        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
| 1.6502 | 200  | 0.9336        | 0.8024                  | 0.6703                  | 0.6109                  | 0.7695                  | 0.7914                 | 0.7594                 | 0.8136                 |
| 2.4774 | 300  | 0.161         | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
| 3.3045 | 400  | 0.1398        | 0.8039                  | 0.6717                  | 0.6092                  | 0.7697                  | 0.8012                 | 0.7696                 | 0.8187                 |


### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 4.1.0
- Transformers: 4.51.2
- 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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