---
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: 0.6571428571428571
      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: 0.5171428571428571
      name: Cosine Precision@20
    - type: cosine_precision@50
      value: 0.316
      name: Cosine Precision@50
    - type: cosine_precision@100
      value: 0.18895238095238095
      name: Cosine Precision@100
    - type: cosine_precision@150
      value: 0.13384126984126984
      name: Cosine Precision@150
    - type: cosine_precision@200
      value: 0.10433333333333335
      name: Cosine Precision@200
    - type: cosine_recall@1
      value: 0.0678253733846715
      name: Cosine Recall@1
    - type: cosine_recall@20
      value: 0.5470006025464504
      name: Cosine Recall@20
    - type: cosine_recall@50
      value: 0.7399645316315758
      name: Cosine Recall@50
    - type: cosine_recall@100
      value: 0.8452891149669638
      name: Cosine Recall@100
    - type: cosine_recall@150
      value: 0.8838497168796887
      name: Cosine Recall@150
    - type: cosine_recall@200
      value: 0.9109269128757174
      name: Cosine Recall@200
    - type: cosine_ndcg@1
      value: 0.6571428571428571
      name: Cosine Ndcg@1
    - type: cosine_ndcg@20
      value: 0.6953571805621692
      name: Cosine Ndcg@20
    - type: cosine_ndcg@50
      value: 0.7150421121165462
      name: Cosine Ndcg@50
    - type: cosine_ndcg@100
      value: 0.7679394555495317
      name: Cosine Ndcg@100
    - type: cosine_ndcg@150
      value: 0.7856911059911225
      name: Cosine Ndcg@150
    - type: cosine_ndcg@200
      value: 0.7969632777290026
      name: Cosine Ndcg@200
    - type: cosine_mrr@1
      value: 0.6571428571428571
      name: Cosine Mrr@1
    - type: cosine_mrr@20
      value: 0.8138095238095239
      name: Cosine Mrr@20
    - type: cosine_mrr@50
      value: 0.8138095238095239
      name: Cosine Mrr@50
    - type: cosine_mrr@100
      value: 0.8138095238095239
      name: Cosine Mrr@100
    - type: cosine_mrr@150
      value: 0.8138095238095239
      name: Cosine Mrr@150
    - type: cosine_mrr@200
      value: 0.8138095238095239
      name: Cosine Mrr@200
    - type: cosine_map@1
      value: 0.6571428571428571
      name: Cosine Map@1
    - type: cosine_map@20
      value: 0.5578605627627369
      name: Cosine Map@20
    - type: cosine_map@50
      value: 0.5471407389299809
      name: Cosine Map@50
    - type: cosine_map@100
      value: 0.5795933384755297
      name: Cosine Map@100
    - type: cosine_map@150
      value: 0.5874505508842796
      name: Cosine Map@150
    - type: cosine_map@200
      value: 0.5912226659397186
      name: Cosine Map@200
    - type: cosine_map@500
      value: 0.5952587557760031
      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: 0.5718918918918919
      name: Cosine Precision@20
    - type: cosine_precision@50
      value: 0.3885405405405405
      name: Cosine Precision@50
    - type: cosine_precision@100
      value: 0.25172972972972973
      name: Cosine Precision@100
    - type: cosine_precision@150
      value: 0.1904864864864865
      name: Cosine Precision@150
    - type: cosine_precision@200
      value: 0.1521891891891892
      name: Cosine Precision@200
    - type: cosine_recall@1
      value: 0.0036619075252531876
      name: Cosine Recall@1
    - type: cosine_recall@20
      value: 0.3842245968041533
      name: Cosine Recall@20
    - type: cosine_recall@50
      value: 0.5640822196868902
      name: Cosine Recall@50
    - type: cosine_recall@100
      value: 0.6741986120580108
      name: Cosine Recall@100
    - type: cosine_recall@150
      value: 0.7463851968088967
      name: Cosine Recall@150
    - type: cosine_recall@200
      value: 0.7825399601398452
      name: Cosine Recall@200
    - type: cosine_ndcg@1
      value: 0.12432432432432433
      name: Cosine Ndcg@1
    - type: cosine_ndcg@20
      value: 0.6139182209948354
      name: Cosine Ndcg@20
    - type: cosine_ndcg@50
      value: 0.5873893466818746
      name: Cosine Ndcg@50
    - type: cosine_ndcg@100
      value: 0.6144038475288277
      name: Cosine Ndcg@100
    - type: cosine_ndcg@150
      value: 0.6498632077214272
      name: Cosine Ndcg@150
    - type: cosine_ndcg@200
      value: 0.6680602466150343
      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: 0.47988875190050484
      name: Cosine Map@20
    - type: cosine_map@50
      value: 0.4249833337950364
      name: Cosine Map@50
    - type: cosine_map@100
      value: 0.430155652024808
      name: Cosine Map@100
    - type: cosine_map@150
      value: 0.4458862132745998
      name: Cosine Map@150
    - type: cosine_map@200
      value: 0.45334655744992447
      name: Cosine Map@200
    - type: cosine_map@500
      value: 0.4656066165331343
      name: Cosine Map@500
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: full de
      type: full_de
    metrics:
    - type: cosine_accuracy@1
      value: 0.2955665024630542
      name: Cosine Accuracy@1
    - type: cosine_accuracy@20
      value: 0.9704433497536946
      name: Cosine Accuracy@20
    - type: cosine_accuracy@50
      value: 0.9852216748768473
      name: Cosine Accuracy@50
    - type: cosine_accuracy@100
      value: 0.9852216748768473
      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: 0.5083743842364532
      name: Cosine Precision@20
    - type: cosine_precision@50
      value: 0.3654187192118227
      name: Cosine Precision@50
    - type: cosine_precision@100
      value: 0.24133004926108376
      name: Cosine Precision@100
    - type: cosine_precision@150
      value: 0.18036124794745487
      name: Cosine Precision@150
    - type: cosine_precision@200
      value: 0.14467980295566504
      name: Cosine Precision@200
    - type: cosine_recall@1
      value: 0.01108543831680986
      name: Cosine Recall@1
    - type: cosine_recall@20
      value: 0.3221185941380065
      name: Cosine Recall@20
    - type: cosine_recall@50
      value: 0.5024502430161547
      name: Cosine Recall@50
    - type: cosine_recall@100
      value: 0.6247617904371989
      name: Cosine Recall@100
    - type: cosine_recall@150
      value: 0.6829583450315939
      name: Cosine Recall@150
    - type: cosine_recall@200
      value: 0.7216293640715983
      name: Cosine Recall@200
    - type: cosine_ndcg@1
      value: 0.2955665024630542
      name: Cosine Ndcg@1
    - type: cosine_ndcg@20
      value: 0.5393376062142305
      name: Cosine Ndcg@20
    - type: cosine_ndcg@50
      value: 0.5267125529267169
      name: Cosine Ndcg@50
    - type: cosine_ndcg@100
      value: 0.55793511917882
      name: Cosine Ndcg@100
    - type: cosine_ndcg@150
      value: 0.5879547828450983
      name: Cosine Ndcg@150
    - type: cosine_ndcg@200
      value: 0.6071252185389439
      name: Cosine Ndcg@200
    - type: cosine_mrr@1
      value: 0.2955665024630542
      name: Cosine Mrr@1
    - type: cosine_mrr@20
      value: 0.5104381157401634
      name: Cosine Mrr@20
    - type: cosine_mrr@50
      value: 0.5109752961295605
      name: Cosine Mrr@50
    - type: cosine_mrr@100
      value: 0.5109752961295605
      name: Cosine Mrr@100
    - type: cosine_mrr@150
      value: 0.5110222114474118
      name: Cosine Mrr@150
    - type: cosine_mrr@200
      value: 0.5110222114474118
      name: Cosine Mrr@200
    - type: cosine_map@1
      value: 0.2955665024630542
      name: Cosine Map@1
    - type: cosine_map@20
      value: 0.40097257642946377
      name: Cosine Map@20
    - type: cosine_map@50
      value: 0.35882787401455
      name: Cosine Map@50
    - type: cosine_map@100
      value: 0.3633182590941781
      name: Cosine Map@100
    - type: cosine_map@150
      value: 0.3776727961080201
      name: Cosine Map@150
    - type: cosine_map@200
      value: 0.3848401555555339
      name: Cosine Map@200
    - type: cosine_map@500
      value: 0.3978065874082948
      name: Cosine Map@500
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: full zh
      type: full_zh
    metrics:
    - type: cosine_accuracy@1
      value: 0.6601941747572816
      name: Cosine Accuracy@1
    - type: cosine_accuracy@20
      value: 0.9805825242718447
      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: 0.6601941747572816
      name: Cosine Precision@1
    - type: cosine_precision@20
      value: 0.4781553398058253
      name: Cosine Precision@20
    - type: cosine_precision@50
      value: 0.28951456310679613
      name: Cosine Precision@50
    - type: cosine_precision@100
      value: 0.17572815533980585
      name: Cosine Precision@100
    - type: cosine_precision@150
      value: 0.12595469255663433
      name: Cosine Precision@150
    - type: cosine_precision@200
      value: 0.09815533980582528
      name: Cosine Precision@200
    - type: cosine_recall@1
      value: 0.06151358631979527
      name: Cosine Recall@1
    - type: cosine_recall@20
      value: 0.5107966412908705
      name: Cosine Recall@20
    - type: cosine_recall@50
      value: 0.6922746152164951
      name: Cosine Recall@50
    - type: cosine_recall@100
      value: 0.8004152884148357
      name: Cosine Recall@100
    - type: cosine_recall@150
      value: 0.8465065661615649
      name: Cosine Recall@150
    - type: cosine_recall@200
      value: 0.8770990926698364
      name: Cosine Recall@200
    - type: cosine_ndcg@1
      value: 0.6601941747572816
      name: Cosine Ndcg@1
    - type: cosine_ndcg@20
      value: 0.6539867858378715
      name: Cosine Ndcg@20
    - type: cosine_ndcg@50
      value: 0.6707332209240133
      name: Cosine Ndcg@50
    - type: cosine_ndcg@100
      value: 0.72342020484322
      name: Cosine Ndcg@100
    - type: cosine_ndcg@150
      value: 0.7437750875502527
      name: Cosine Ndcg@150
    - type: cosine_ndcg@200
      value: 0.7553648453187212
      name: Cosine Ndcg@200
    - type: cosine_mrr@1
      value: 0.6601941747572816
      name: Cosine Mrr@1
    - type: cosine_mrr@20
      value: 0.8037216828478965
      name: Cosine Mrr@20
    - type: cosine_mrr@50
      value: 0.8040950958426687
      name: Cosine Mrr@50
    - type: cosine_mrr@100
      value: 0.8040950958426687
      name: Cosine Mrr@100
    - type: cosine_mrr@150
      value: 0.8040950958426687
      name: Cosine Mrr@150
    - type: cosine_mrr@200
      value: 0.8040950958426687
      name: Cosine Mrr@200
    - type: cosine_map@1
      value: 0.6601941747572816
      name: Cosine Map@1
    - type: cosine_map@20
      value: 0.5087334164702914
      name: Cosine Map@20
    - type: cosine_map@50
      value: 0.49260246320797585
      name: Cosine Map@50
    - type: cosine_map@100
      value: 0.5217412166882693
      name: Cosine Map@100
    - type: cosine_map@150
      value: 0.529859818130126
      name: Cosine Map@150
    - type: cosine_map@200
      value: 0.533378795921413
      name: Cosine Map@200
    - type: cosine_map@500
      value: 0.5386011712914499
      name: Cosine Map@500
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: mix es
      type: mix_es
    metrics:
    - type: cosine_accuracy@1
      value: 0.7280291211648466
      name: Cosine Accuracy@1
    - type: cosine_accuracy@20
      value: 0.9599583983359334
      name: Cosine Accuracy@20
    - type: cosine_accuracy@50
      value: 0.9791991679667187
      name: Cosine Accuracy@50
    - type: cosine_accuracy@100
      value: 0.9942797711908476
      name: Cosine Accuracy@100
    - type: cosine_accuracy@150
      value: 0.9958398335933437
      name: Cosine Accuracy@150
    - type: cosine_accuracy@200
      value: 0.9973998959958398
      name: Cosine Accuracy@200
    - type: cosine_precision@1
      value: 0.7280291211648466
      name: Cosine Precision@1
    - type: cosine_precision@20
      value: 0.12433697347893914
      name: Cosine Precision@20
    - type: cosine_precision@50
      value: 0.05145085803432139
      name: Cosine Precision@50
    - type: cosine_precision@100
      value: 0.02625065002600105
      name: Cosine Precision@100
    - type: cosine_precision@150
      value: 0.017621771537528162
      name: Cosine Precision@150
    - type: cosine_precision@200
      value: 0.013283931357254294
      name: Cosine Precision@200
    - type: cosine_recall@1
      value: 0.28133620582918556
      name: Cosine Recall@1
    - type: cosine_recall@20
      value: 0.9183394002426764
      name: Cosine Recall@20
    - type: cosine_recall@50
      value: 0.9499306638932224
      name: Cosine Recall@50
    - type: cosine_recall@100
      value: 0.9700901369388107
      name: Cosine Recall@100
    - type: cosine_recall@150
      value: 0.9767724042295025
      name: Cosine Recall@150
    - type: cosine_recall@200
      value: 0.9818166059975733
      name: Cosine Recall@200
    - type: cosine_ndcg@1
      value: 0.7280291211648466
      name: Cosine Ndcg@1
    - type: cosine_ndcg@20
      value: 0.8043549768911603
      name: Cosine Ndcg@20
    - type: cosine_ndcg@50
      value: 0.81295852465432
      name: Cosine Ndcg@50
    - type: cosine_ndcg@100
      value: 0.817339429558165
      name: Cosine Ndcg@100
    - type: cosine_ndcg@150
      value: 0.8186380742931886
      name: Cosine Ndcg@150
    - type: cosine_ndcg@200
      value: 0.8195485984235017
      name: Cosine Ndcg@200
    - type: cosine_mrr@1
      value: 0.7280291211648466
      name: Cosine Mrr@1
    - type: cosine_mrr@20
      value: 0.7968549154271433
      name: Cosine Mrr@20
    - type: cosine_mrr@50
      value: 0.7974653825839162
      name: Cosine Mrr@50
    - type: cosine_mrr@100
      value: 0.7976914864910069
      name: Cosine Mrr@100
    - type: cosine_mrr@150
      value: 0.7977044635908871
      name: Cosine Mrr@150
    - type: cosine_mrr@200
      value: 0.7977139196654446
      name: Cosine Mrr@200
    - type: cosine_map@1
      value: 0.7280291211648466
      name: Cosine Map@1
    - type: cosine_map@20
      value: 0.7350836192117531
      name: Cosine Map@20
    - type: cosine_map@50
      value: 0.7374205090112232
      name: Cosine Map@50
    - type: cosine_map@100
      value: 0.737988888492803
      name: Cosine Map@100
    - type: cosine_map@150
      value: 0.7381133157945164
      name: Cosine Map@150
    - type: cosine_map@200
      value: 0.7381788581828236
      name: Cosine Map@200
    - type: cosine_map@500
      value: 0.7382854440643231
      name: Cosine Map@500
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: mix de
      type: mix_de
    metrics:
    - type: cosine_accuracy@1
      value: 0.6703068122724909
      name: Cosine Accuracy@1
    - type: cosine_accuracy@20
      value: 0.9505980239209568
      name: Cosine Accuracy@20
    - type: cosine_accuracy@50
      value: 0.9776391055642226
      name: Cosine Accuracy@50
    - type: cosine_accuracy@100
      value: 0.9864794591783671
      name: Cosine Accuracy@100
    - type: cosine_accuracy@150
      value: 0.9932397295891836
      name: Cosine Accuracy@150
    - type: cosine_accuracy@200
      value: 0.9947997919916797
      name: Cosine Accuracy@200
    - type: cosine_precision@1
      value: 0.6703068122724909
      name: Cosine Precision@1
    - type: cosine_precision@20
      value: 0.1251690067602704
      name: Cosine Precision@20
    - type: cosine_precision@50
      value: 0.052282891315652634
      name: Cosine Precision@50
    - type: cosine_precision@100
      value: 0.026729069162766517
      name: Cosine Precision@100
    - type: cosine_precision@150
      value: 0.01799965331946611
      name: Cosine Precision@150
    - type: cosine_precision@200
      value: 0.013541341653666149
      name: Cosine Precision@200
    - type: cosine_recall@1
      value: 0.25235742763043856
      name: Cosine Recall@1
    - type: cosine_recall@20
      value: 0.9095857167620037
      name: Cosine Recall@20
    - type: cosine_recall@50
      value: 0.9482405962905183
      name: Cosine Recall@50
    - type: cosine_recall@100
      value: 0.96845207141619
      name: Cosine Recall@100
    - type: cosine_recall@150
      value: 0.9781591263650546
      name: Cosine Recall@150
    - type: cosine_recall@200
      value: 0.9810192407696308
      name: Cosine Recall@200
    - type: cosine_ndcg@1
      value: 0.6703068122724909
      name: Cosine Ndcg@1
    - type: cosine_ndcg@20
      value: 0.7735712514376322
      name: Cosine Ndcg@20
    - type: cosine_ndcg@50
      value: 0.7843644592705362
      name: Cosine Ndcg@50
    - type: cosine_ndcg@100
      value: 0.7889444470773866
      name: Cosine Ndcg@100
    - type: cosine_ndcg@150
      value: 0.7908660087982327
      name: Cosine Ndcg@150
    - type: cosine_ndcg@200
      value: 0.791403470160319
      name: Cosine Ndcg@200
    - type: cosine_mrr@1
      value: 0.6703068122724909
      name: Cosine Mrr@1
    - type: cosine_mrr@20
      value: 0.7520307321055828
      name: Cosine Mrr@20
    - type: cosine_mrr@50
      value: 0.7529374175534339
      name: Cosine Mrr@50
    - type: cosine_mrr@100
      value: 0.7530616872072472
      name: Cosine Mrr@100
    - type: cosine_mrr@150
      value: 0.7531202644382351
      name: Cosine Mrr@150
    - type: cosine_mrr@200
      value: 0.7531293951311296
      name: Cosine Mrr@200
    - type: cosine_map@1
      value: 0.6703068122724909
      name: Cosine Map@1
    - type: cosine_map@20
      value: 0.6967639778693541
      name: Cosine Map@20
    - type: cosine_map@50
      value: 0.699575457224443
      name: Cosine Map@50
    - type: cosine_map@100
      value: 0.70027844357658
      name: Cosine Map@100
    - type: cosine_map@150
      value: 0.7004487000056766
      name: Cosine Map@150
    - type: cosine_map@200
      value: 0.7004863395843564
      name: Cosine Map@200
    - type: cosine_map@500
      value: 0.7005835771389989
      name: Cosine Map@500
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: mix zh
      type: mix_zh
    metrics:
    - type: cosine_accuracy@1
      value: 0.19084763390535622
      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: 0.19084763390535622
      name: Cosine Precision@1
    - type: cosine_precision@20
      value: 0.15439417576703063
      name: Cosine Precision@20
    - type: cosine_precision@50
      value: 0.0617576703068123
      name: Cosine Precision@50
    - type: cosine_precision@100
      value: 0.03087883515340615
      name: Cosine Precision@100
    - type: cosine_precision@150
      value: 0.020585890102270757
      name: Cosine Precision@150
    - type: cosine_precision@200
      value: 0.015439417576703075
      name: Cosine Precision@200
    - type: cosine_recall@1
      value: 0.06137978852487433
      name: Cosine Recall@1
    - type: cosine_recall@20
      value: 1.0
      name: Cosine Recall@20
    - type: cosine_recall@50
      value: 1.0
      name: Cosine Recall@50
    - type: cosine_recall@100
      value: 1.0
      name: Cosine Recall@100
    - type: cosine_recall@150
      value: 1.0
      name: Cosine Recall@150
    - type: cosine_recall@200
      value: 1.0
      name: Cosine Recall@200
    - type: cosine_ndcg@1
      value: 0.19084763390535622
      name: Cosine Ndcg@1
    - type: cosine_ndcg@20
      value: 0.5474303590499686
      name: Cosine Ndcg@20
    - type: cosine_ndcg@50
      value: 0.5474303590499686
      name: Cosine Ndcg@50
    - type: cosine_ndcg@100
      value: 0.5474303590499686
      name: Cosine Ndcg@100
    - type: cosine_ndcg@150
      value: 0.5474303590499686
      name: Cosine Ndcg@150
    - type: cosine_ndcg@200
      value: 0.5474303590499686
      name: Cosine Ndcg@200
    - type: cosine_mrr@1
      value: 0.19084763390535622
      name: Cosine Mrr@1
    - type: cosine_mrr@20
      value: 0.4093433087972877
      name: Cosine Mrr@20
    - type: cosine_mrr@50
      value: 0.4093433087972877
      name: Cosine Mrr@50
    - type: cosine_mrr@100
      value: 0.4093433087972877
      name: Cosine Mrr@100
    - type: cosine_mrr@150
      value: 0.4093433087972877
      name: Cosine Mrr@150
    - type: cosine_mrr@200
      value: 0.4093433087972877
      name: Cosine Mrr@200
    - type: cosine_map@1
      value: 0.19084763390535622
      name: Cosine Map@1
    - type: cosine_map@20
      value: 0.32981711891302556
      name: Cosine Map@20
    - type: cosine_map@50
      value: 0.32981711891302556
      name: Cosine Map@50
    - type: cosine_map@100
      value: 0.32981711891302556
      name: Cosine Map@100
    - type: cosine_map@150
      value: 0.32981711891302556
      name: Cosine Map@150
    - type: cosine_map@200
      value: 0.32981711891302556
      name: Cosine Map@200
    - type: cosine_map@500
      value: 0.32981711891302556
      name: Cosine Map@500
---

# Job - Job matching Alibaba-NLP/gte-multilingual-base (v1)

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-v1")
# 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    | 0.6571    | 0.1243     | 0.2956     | 0.6602     | 0.728      | 0.6703     | 0.1908     |
| cosine_accuracy@20   | 0.9905    | 1.0        | 0.9704     | 0.9806     | 0.96       | 0.9506     | 1.0        |
| cosine_accuracy@50   | 0.9905    | 1.0        | 0.9852     | 0.9903     | 0.9792     | 0.9776     | 1.0        |
| cosine_accuracy@100  | 0.9905    | 1.0        | 0.9852     | 0.9903     | 0.9943     | 0.9865     | 1.0        |
| cosine_accuracy@150  | 0.9905    | 1.0        | 0.9901     | 0.9903     | 0.9958     | 0.9932     | 1.0        |
| cosine_accuracy@200  | 0.9905    | 1.0        | 0.9901     | 0.9903     | 0.9974     | 0.9948     | 1.0        |
| cosine_precision@1   | 0.6571    | 0.1243     | 0.2956     | 0.6602     | 0.728      | 0.6703     | 0.1908     |
| cosine_precision@20  | 0.5171    | 0.5719     | 0.5084     | 0.4782     | 0.1243     | 0.1252     | 0.1544     |
| cosine_precision@50  | 0.316     | 0.3885     | 0.3654     | 0.2895     | 0.0515     | 0.0523     | 0.0618     |
| cosine_precision@100 | 0.189     | 0.2517     | 0.2413     | 0.1757     | 0.0263     | 0.0267     | 0.0309     |
| cosine_precision@150 | 0.1338    | 0.1905     | 0.1804     | 0.126      | 0.0176     | 0.018      | 0.0206     |
| cosine_precision@200 | 0.1043    | 0.1522     | 0.1447     | 0.0982     | 0.0133     | 0.0135     | 0.0154     |
| cosine_recall@1      | 0.0678    | 0.0037     | 0.0111     | 0.0615     | 0.2813     | 0.2524     | 0.0614     |
| cosine_recall@20     | 0.547     | 0.3842     | 0.3221     | 0.5108     | 0.9183     | 0.9096     | 1.0        |
| cosine_recall@50     | 0.74      | 0.5641     | 0.5025     | 0.6923     | 0.9499     | 0.9482     | 1.0        |
| cosine_recall@100    | 0.8453    | 0.6742     | 0.6248     | 0.8004     | 0.9701     | 0.9685     | 1.0        |
| cosine_recall@150    | 0.8838    | 0.7464     | 0.683      | 0.8465     | 0.9768     | 0.9782     | 1.0        |
| cosine_recall@200    | 0.9109    | 0.7825     | 0.7216     | 0.8771     | 0.9818     | 0.981      | 1.0        |
| cosine_ndcg@1        | 0.6571    | 0.1243     | 0.2956     | 0.6602     | 0.728      | 0.6703     | 0.1908     |
| cosine_ndcg@20       | 0.6954    | 0.6139     | 0.5393     | 0.654      | 0.8044     | 0.7736     | 0.5474     |
| cosine_ndcg@50       | 0.715     | 0.5874     | 0.5267     | 0.6707     | 0.813      | 0.7844     | 0.5474     |
| cosine_ndcg@100      | 0.7679    | 0.6144     | 0.5579     | 0.7234     | 0.8173     | 0.7889     | 0.5474     |
| cosine_ndcg@150      | 0.7857    | 0.6499     | 0.588      | 0.7438     | 0.8186     | 0.7909     | 0.5474     |
| **cosine_ndcg@200**  | **0.797** | **0.6681** | **0.6071** | **0.7554** | **0.8195** | **0.7914** | **0.5474** |
| cosine_mrr@1         | 0.6571    | 0.1243     | 0.2956     | 0.6602     | 0.728      | 0.6703     | 0.1908     |
| cosine_mrr@20        | 0.8138    | 0.5581     | 0.5104     | 0.8037     | 0.7969     | 0.752      | 0.4093     |
| cosine_mrr@50        | 0.8138    | 0.5581     | 0.511      | 0.8041     | 0.7975     | 0.7529     | 0.4093     |
| cosine_mrr@100       | 0.8138    | 0.5581     | 0.511      | 0.8041     | 0.7977     | 0.7531     | 0.4093     |
| cosine_mrr@150       | 0.8138    | 0.5581     | 0.511      | 0.8041     | 0.7977     | 0.7531     | 0.4093     |
| cosine_mrr@200       | 0.8138    | 0.5581     | 0.511      | 0.8041     | 0.7977     | 0.7531     | 0.4093     |
| cosine_map@1         | 0.6571    | 0.1243     | 0.2956     | 0.6602     | 0.728      | 0.6703     | 0.1908     |
| cosine_map@20        | 0.5579    | 0.4799     | 0.401      | 0.5087     | 0.7351     | 0.6968     | 0.3298     |
| cosine_map@50        | 0.5471    | 0.425      | 0.3588     | 0.4926     | 0.7374     | 0.6996     | 0.3298     |
| cosine_map@100       | 0.5796    | 0.4302     | 0.3633     | 0.5217     | 0.738      | 0.7003     | 0.3298     |
| cosine_map@150       | 0.5875    | 0.4459     | 0.3777     | 0.5299     | 0.7381     | 0.7004     | 0.3298     |
| cosine_map@200       | 0.5912    | 0.4533     | 0.3848     | 0.5334     | 0.7382     | 0.7005     | 0.3298     |
| cosine_map@500       | 0.5953    | 0.4656     | 0.3978     | 0.5386     | 0.7383     | 0.7006     | 0.3298     |

<!--
## 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`: 64
- `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`: 64
- `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                 | 0.5531                 |
| 0.0010 | 1    | 3.4866        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
| 0.1027 | 100  | 2.5431        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
| 0.2053 | 200  | 1.4536        | 0.7993                  | 0.6633                  | 0.5974                  | 0.7642                  | 0.7567                 | 0.7011                 | 0.5498                 |
| 0.3080 | 300  | 1.1018        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
| 0.4107 | 400  | 0.9184        | 0.7925                  | 0.6586                  | 0.6058                  | 0.7587                  | 0.7749                 | 0.7278                 | 0.5486                 |
| 0.5133 | 500  | 0.8973        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
| 0.6160 | 600  | 0.7309        | 0.7951                  | 0.6671                  | 0.6096                  | 0.7708                  | 0.7793                 | 0.7339                 | 0.5525                 |
| 0.7187 | 700  | 0.7297        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
| 0.8214 | 800  | 0.7281        | 0.7929                  | 0.6711                  | 0.6088                  | 0.7645                  | 0.7899                 | 0.7444                 | 0.5479                 |
| 0.9240 | 900  | 0.6607        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
| 1.0267 | 1000 | 0.6075        | 0.7915                  | 0.6659                  | 0.6088                  | 0.7665                  | 0.7968                 | 0.7588                 | 0.5482                 |
| 1.1294 | 1100 | 0.4553        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
| 1.2320 | 1200 | 0.4775        | 0.7979                  | 0.6696                  | 0.6033                  | 0.7669                  | 0.7959                 | 0.7624                 | 0.5484                 |
| 1.3347 | 1300 | 0.4838        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
| 1.4374 | 1400 | 0.4912        | 0.7973                  | 0.6757                  | 0.6112                  | 0.7656                  | 0.7978                 | 0.7650                 | 0.5487                 |
| 1.5400 | 1500 | 0.4732        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
| 1.6427 | 1600 | 0.5269        | 0.8031                  | 0.6723                  | 0.6108                  | 0.7654                  | 0.8008                 | 0.7660                 | 0.5492                 |
| 1.7454 | 1700 | 0.4822        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
| 1.8480 | 1800 | 0.5072        | 0.7962                  | 0.6668                  | 0.6051                  | 0.7592                  | 0.8001                 | 0.7714                 | 0.5486                 |
| 1.9507 | 1900 | 0.4709        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
| 2.0544 | 2000 | 0.3772        | 0.7940                  | 0.6647                  | 0.6037                  | 0.7579                  | 0.8064                 | 0.7732                 | 0.5479                 |
| 2.1571 | 2100 | 0.3982        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
| 2.2598 | 2200 | 0.3073        | 0.7969                  | 0.6652                  | 0.6005                  | 0.7625                  | 0.8054                 | 0.7734                 | 0.5493                 |
| 2.3624 | 2300 | 0.383         | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
| 2.4651 | 2400 | 0.3687        | 0.7925                  | 0.6690                  | 0.5987                  | 0.7583                  | 0.8081                 | 0.7735                 | 0.5477                 |
| 2.5678 | 2500 | 0.3472        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
| 2.6704 | 2600 | 0.3557        | 0.7956                  | 0.6758                  | 0.6019                  | 0.7659                  | 0.8082                 | 0.7767                 | 0.5491                 |
| 2.7731 | 2700 | 0.3527        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
| 2.8758 | 2800 | 0.3446        | 0.7945                  | 0.6719                  | 0.6020                  | 0.7616                  | 0.8124                 | 0.7818                 | 0.5496                 |
| 2.9784 | 2900 | 0.3566        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
| 3.0821 | 3000 | 0.3252        | 0.7948                  | 0.6682                  | 0.6025                  | 0.7617                  | 0.8152                 | 0.7848                 | 0.5516                 |
| 3.1848 | 3100 | 0.2968        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
| 3.2875 | 3200 | 0.2962        | 0.7953                  | 0.6717                  | 0.6086                  | 0.7613                  | 0.8110                 | 0.7824                 | 0.5482                 |
| 3.3901 | 3300 | 0.3084        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
| 3.4928 | 3400 | 0.2909        | 0.7940                  | 0.6634                  | 0.6023                  | 0.7615                  | 0.8138                 | 0.7822                 | 0.5457                 |
| 3.5955 | 3500 | 0.2964        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
| 3.6982 | 3600 | 0.3193        | 0.7960                  | 0.6635                  | 0.6070                  | 0.7534                  | 0.8164                 | 0.7844                 | 0.5467                 |
| 3.8008 | 3700 | 0.3514        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
| 3.9035 | 3800 | 0.3147        | 0.7973                  | 0.6696                  | 0.6125                  | 0.7616                  | 0.8176                 | 0.7885                 | 0.5469                 |
| 4.0062 | 3900 | 0.2738        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
| 4.1088 | 4000 | 0.2842        | 0.7960                  | 0.6672                  | 0.6082                  | 0.7536                  | 0.8174                 | 0.7891                 | 0.5479                 |
| 4.2115 | 4100 | 0.2739        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
| 4.3142 | 4200 | 0.2704        | 0.7979                  | 0.6681                  | 0.6111                  | 0.7540                  | 0.8180                 | 0.7891                 | 0.5476                 |
| 4.4168 | 4300 | 0.2529        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
| 4.5195 | 4400 | 0.272         | 0.7968                  | 0.6685                  | 0.6087                  | 0.7564                  | 0.8185                 | 0.7901                 | 0.5476                 |
| 4.6222 | 4500 | 0.3           | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
| 4.7248 | 4600 | 0.2598        | 0.7972                  | 0.6675                  | 0.6072                  | 0.7556                  | 0.8190                 | 0.7909                 | 0.5478                 |
| 4.8275 | 4700 | 0.3101        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
| 4.9302 | 4800 | 0.2524        | 0.7970                  | 0.6681                  | 0.6071                  | 0.7554                  | 0.8195                 | 0.7914                 | 0.5474                 |


### 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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