---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:124788
- loss:GISTEmbedLoss
base_model: BAAI/bge-m3
widget:
- source_sentence: 其他机械、设备和有形货物租赁服务代表
  sentences:
  - 其他机械和设备租赁服务工作人员
  - 电子和电信设备及零部件物流经理
  - 工业主厨
- source_sentence: 公交车司机
  sentences:
  - 表演灯光设计师
  - 乙烯基地板安装工
  - 国际巴士司机
- source_sentence: online communication manager
  sentences:
  - trades union official
  - social media manager
  - budget manager
- source_sentence: Projektmanagerin
  sentences:
  - Projektmanager/Projektmanagerin
  - Category-Manager
  - Infanterist
- source_sentence: Volksvertreter
  sentences:
  - Parlamentarier
  - Oberbürgermeister
  - Konsul
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@20
- cosine_accuracy@50
- cosine_accuracy@100
- cosine_accuracy@150
- cosine_accuracy@200
- cosine_precision@1
- cosine_precision@20
- cosine_precision@50
- cosine_precision@100
- cosine_precision@150
- cosine_precision@200
- cosine_recall@1
- cosine_recall@20
- cosine_recall@50
- cosine_recall@100
- cosine_recall@150
- cosine_recall@200
- cosine_ndcg@1
- cosine_ndcg@20
- cosine_ndcg@50
- cosine_ndcg@100
- cosine_ndcg@150
- cosine_ndcg@200
- cosine_mrr@1
- cosine_mrr@20
- cosine_mrr@50
- cosine_mrr@100
- cosine_mrr@150
- cosine_mrr@200
- cosine_map@1
- cosine_map@20
- cosine_map@50
- cosine_map@100
- cosine_map@150
- cosine_map@200
- cosine_map@500
model-index:
- name: SentenceTransformer based on BAAI/bge-m3
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: full en
      type: full_en
    metrics:
    - type: cosine_accuracy@1
      value: 0.6476190476190476
      name: Cosine Accuracy@1
    - type: cosine_accuracy@20
      value: 0.9904761904761905
      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: 0.6476190476190476
      name: Cosine Precision@1
    - type: cosine_precision@20
      value: 0.5061904761904762
      name: Cosine Precision@20
    - type: cosine_precision@50
      value: 0.30647619047619057
      name: Cosine Precision@50
    - type: cosine_precision@100
      value: 0.1858095238095238
      name: Cosine Precision@100
    - type: cosine_precision@150
      value: 0.13250793650793652
      name: Cosine Precision@150
    - type: cosine_precision@200
      value: 0.10247619047619047
      name: Cosine Precision@200
    - type: cosine_recall@1
      value: 0.06690172806447445
      name: Cosine Recall@1
    - type: cosine_recall@20
      value: 0.5391510592522911
      name: Cosine Recall@20
    - type: cosine_recall@50
      value: 0.7199711948587544
      name: Cosine Recall@50
    - type: cosine_recall@100
      value: 0.8253770621157605
      name: Cosine Recall@100
    - type: cosine_recall@150
      value: 0.8719997123512196
      name: Cosine Recall@150
    - type: cosine_recall@200
      value: 0.9006382758109558
      name: Cosine Recall@200
    - type: cosine_ndcg@1
      value: 0.6476190476190476
      name: Cosine Ndcg@1
    - type: cosine_ndcg@20
      value: 0.6822066814233797
      name: Cosine Ndcg@20
    - type: cosine_ndcg@50
      value: 0.6975329548006446
      name: Cosine Ndcg@50
    - type: cosine_ndcg@100
      value: 0.7519637922809941
      name: Cosine Ndcg@100
    - type: cosine_ndcg@150
      value: 0.7724946802449859
      name: Cosine Ndcg@150
    - type: cosine_ndcg@200
      value: 0.7827357067553371
      name: Cosine Ndcg@200
    - type: cosine_mrr@1
      value: 0.6476190476190476
      name: Cosine Mrr@1
    - type: cosine_mrr@20
      value: 0.7999999999999998
      name: Cosine Mrr@20
    - type: cosine_mrr@50
      value: 0.7999999999999998
      name: Cosine Mrr@50
    - type: cosine_mrr@100
      value: 0.7999999999999998
      name: Cosine Mrr@100
    - type: cosine_mrr@150
      value: 0.7999999999999998
      name: Cosine Mrr@150
    - type: cosine_mrr@200
      value: 0.7999999999999998
      name: Cosine Mrr@200
    - type: cosine_map@1
      value: 0.6476190476190476
      name: Cosine Map@1
    - type: cosine_map@20
      value: 0.5391784054866918
      name: Cosine Map@20
    - type: cosine_map@50
      value: 0.5258287715484311
      name: Cosine Map@50
    - type: cosine_map@100
      value: 0.5580109313638075
      name: Cosine Map@100
    - type: cosine_map@150
      value: 0.5665715227835532
      name: Cosine Map@150
    - type: cosine_map@200
      value: 0.569529009182472
      name: Cosine Map@200
    - type: cosine_map@500
      value: 0.5743595458034346
      name: Cosine Map@500
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: full es
      type: full_es
    metrics:
    - type: cosine_accuracy@1
      value: 0.11351351351351352
      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: 0.11351351351351352
      name: Cosine Precision@1
    - type: cosine_precision@20
      value: 0.5667567567567567
      name: Cosine Precision@20
    - type: cosine_precision@50
      value: 0.3902702702702703
      name: Cosine Precision@50
    - type: cosine_precision@100
      value: 0.25254054054054054
      name: Cosine Precision@100
    - type: cosine_precision@150
      value: 0.19005405405405407
      name: Cosine Precision@150
    - type: cosine_precision@200
      value: 0.1507837837837838
      name: Cosine Precision@200
    - type: cosine_recall@1
      value: 0.0035155918996302815
      name: Cosine Recall@1
    - type: cosine_recall@20
      value: 0.37958552840441906
      name: Cosine Recall@20
    - type: cosine_recall@50
      value: 0.5635730197468752
      name: Cosine Recall@50
    - type: cosine_recall@100
      value: 0.672698242387141
      name: Cosine Recall@100
    - type: cosine_recall@150
      value: 0.7360036980055802
      name: Cosine Recall@150
    - type: cosine_recall@200
      value: 0.7697561816436992
      name: Cosine Recall@200
    - type: cosine_ndcg@1
      value: 0.11351351351351352
      name: Cosine Ndcg@1
    - type: cosine_ndcg@20
      value: 0.6136401766234348
      name: Cosine Ndcg@20
    - type: cosine_ndcg@50
      value: 0.5908459924766464
      name: Cosine Ndcg@50
    - type: cosine_ndcg@100
      value: 0.6168063266629416
      name: Cosine Ndcg@100
    - type: cosine_ndcg@150
      value: 0.6488575731321932
      name: Cosine Ndcg@150
    - type: cosine_ndcg@200
      value: 0.665316090087272
      name: Cosine Ndcg@200
    - type: cosine_mrr@1
      value: 0.11351351351351352
      name: Cosine Mrr@1
    - type: cosine_mrr@20
      value: 0.5536036036036036
      name: Cosine Mrr@20
    - type: cosine_mrr@50
      value: 0.5536036036036036
      name: Cosine Mrr@50
    - type: cosine_mrr@100
      value: 0.5536036036036036
      name: Cosine Mrr@100
    - type: cosine_mrr@150
      value: 0.5536036036036036
      name: Cosine Mrr@150
    - type: cosine_mrr@200
      value: 0.5536036036036036
      name: Cosine Mrr@200
    - type: cosine_map@1
      value: 0.11351351351351352
      name: Cosine Map@1
    - type: cosine_map@20
      value: 0.48095830339282386
      name: Cosine Map@20
    - type: cosine_map@50
      value: 0.43038606337879926
      name: Cosine Map@50
    - type: cosine_map@100
      value: 0.4335284717646407
      name: Cosine Map@100
    - type: cosine_map@150
      value: 0.44851036812148526
      name: Cosine Map@150
    - type: cosine_map@200
      value: 0.4550924585301385
      name: Cosine Map@200
    - type: cosine_map@500
      value: 0.4677023132311536
      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: 0.9852216748768473
      name: Cosine Accuracy@20
    - type: cosine_accuracy@50
      value: 0.9901477832512315
      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: 0.5403940886699506
      name: Cosine Precision@20
    - type: cosine_precision@50
      value: 0.38275862068965516
      name: Cosine Precision@50
    - type: cosine_precision@100
      value: 0.2503448275862069
      name: Cosine Precision@100
    - type: cosine_precision@150
      value: 0.187816091954023
      name: Cosine Precision@150
    - type: cosine_precision@200
      value: 0.15027093596059116
      name: Cosine Precision@200
    - type: cosine_recall@1
      value: 0.01108543831680986
      name: Cosine Recall@1
    - type: cosine_recall@20
      value: 0.3432684453555553
      name: Cosine Recall@20
    - type: cosine_recall@50
      value: 0.5339871522541048
      name: Cosine Recall@50
    - type: cosine_recall@100
      value: 0.6498636280219438
      name: Cosine Recall@100
    - type: cosine_recall@150
      value: 0.7100921836539074
      name: Cosine Recall@150
    - type: cosine_recall@200
      value: 0.7513351913056898
      name: Cosine Recall@200
    - type: cosine_ndcg@1
      value: 0.2955665024630542
      name: Cosine Ndcg@1
    - type: cosine_ndcg@20
      value: 0.5647628262992046
      name: Cosine Ndcg@20
    - type: cosine_ndcg@50
      value: 0.5522057083055792
      name: Cosine Ndcg@50
    - type: cosine_ndcg@100
      value: 0.5796033728499559
      name: Cosine Ndcg@100
    - type: cosine_ndcg@150
      value: 0.6111851705889818
      name: Cosine Ndcg@150
    - type: cosine_ndcg@200
      value: 0.6309313367878393
      name: Cosine Ndcg@200
    - type: cosine_mrr@1
      value: 0.2955665024630542
      name: Cosine Mrr@1
    - type: cosine_mrr@20
      value: 0.5164425017655958
      name: Cosine Mrr@20
    - type: cosine_mrr@50
      value: 0.516559790060224
      name: Cosine Mrr@50
    - type: cosine_mrr@100
      value: 0.516559790060224
      name: Cosine Mrr@100
    - type: cosine_mrr@150
      value: 0.516559790060224
      name: Cosine Mrr@150
    - type: cosine_mrr@200
      value: 0.516559790060224
      name: Cosine Mrr@200
    - type: cosine_map@1
      value: 0.2955665024630542
      name: Cosine Map@1
    - type: cosine_map@20
      value: 0.4221760589983628
      name: Cosine Map@20
    - type: cosine_map@50
      value: 0.37913413777890953
      name: Cosine Map@50
    - type: cosine_map@100
      value: 0.3829298798486122
      name: Cosine Map@100
    - type: cosine_map@150
      value: 0.39811624371681004
      name: Cosine Map@150
    - type: cosine_map@200
      value: 0.40559711033541546
      name: Cosine Map@200
    - type: cosine_map@500
      value: 0.4188841643667456
      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: 0.9902912621359223
      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: 0.470873786407767
      name: Cosine Precision@20
    - type: cosine_precision@50
      value: 0.28038834951456315
      name: Cosine Precision@50
    - type: cosine_precision@100
      value: 0.17320388349514557
      name: Cosine Precision@100
    - type: cosine_precision@150
      value: 0.12394822006472495
      name: Cosine Precision@150
    - type: cosine_precision@200
      value: 0.09766990291262137
      name: Cosine Precision@200
    - type: cosine_recall@1
      value: 0.06427555485009323
      name: Cosine Recall@1
    - type: cosine_recall@20
      value: 0.5119331913488326
      name: Cosine Recall@20
    - type: cosine_recall@50
      value: 0.6726577129232287
      name: Cosine Recall@50
    - type: cosine_recall@100
      value: 0.788021792964523
      name: Cosine Recall@100
    - type: cosine_recall@150
      value: 0.8328962977521837
      name: Cosine Recall@150
    - type: cosine_recall@200
      value: 0.8687397875786594
      name: Cosine Recall@200
    - type: cosine_ndcg@1
      value: 0.6796116504854369
      name: Cosine Ndcg@1
    - type: cosine_ndcg@20
      value: 0.6515292076635256
      name: Cosine Ndcg@20
    - type: cosine_ndcg@50
      value: 0.6598571989751485
      name: Cosine Ndcg@50
    - type: cosine_ndcg@100
      value: 0.7157338182976709
      name: Cosine Ndcg@100
    - type: cosine_ndcg@150
      value: 0.7357126940189814
      name: Cosine Ndcg@150
    - type: cosine_ndcg@200
      value: 0.7500853808896866
      name: Cosine Ndcg@200
    - type: cosine_mrr@1
      value: 0.6796116504854369
      name: Cosine Mrr@1
    - type: cosine_mrr@20
      value: 0.8216828478964402
      name: Cosine Mrr@20
    - type: cosine_mrr@50
      value: 0.8216828478964402
      name: Cosine Mrr@50
    - type: cosine_mrr@100
      value: 0.8216828478964402
      name: Cosine Mrr@100
    - type: cosine_mrr@150
      value: 0.8216828478964402
      name: Cosine Mrr@150
    - type: cosine_mrr@200
      value: 0.8216828478964402
      name: Cosine Mrr@200
    - type: cosine_map@1
      value: 0.6796116504854369
      name: Cosine Map@1
    - type: cosine_map@20
      value: 0.5012149610968577
      name: Cosine Map@20
    - type: cosine_map@50
      value: 0.48128476255481567
      name: Cosine Map@50
    - type: cosine_map@100
      value: 0.5105374388587102
      name: Cosine Map@100
    - type: cosine_map@150
      value: 0.518381647971727
      name: Cosine Map@150
    - type: cosine_map@200
      value: 0.5228375783347256
      name: Cosine Map@200
    - type: cosine_map@500
      value: 0.52765377953199
      name: Cosine Map@500
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: mix es
      type: mix_es
    metrics:
    - type: cosine_accuracy@1
      value: 0.7394695787831513
      name: Cosine Accuracy@1
    - type: cosine_accuracy@20
      value: 0.9635985439417577
      name: Cosine Accuracy@20
    - type: cosine_accuracy@50
      value: 0.982839313572543
      name: Cosine Accuracy@50
    - type: cosine_accuracy@100
      value: 0.9927197087883516
      name: Cosine Accuracy@100
    - type: cosine_accuracy@150
      value: 0.9947997919916797
      name: Cosine Accuracy@150
    - type: cosine_accuracy@200
      value: 0.9963598543941757
      name: Cosine Accuracy@200
    - type: cosine_precision@1
      value: 0.7394695787831513
      name: Cosine Precision@1
    - type: cosine_precision@20
      value: 0.12488299531981278
      name: Cosine Precision@20
    - type: cosine_precision@50
      value: 0.05174206968278733
      name: Cosine Precision@50
    - type: cosine_precision@100
      value: 0.02629225169006761
      name: Cosine Precision@100
    - type: cosine_precision@150
      value: 0.017635638758883684
      name: Cosine Precision@150
    - type: cosine_precision@200
      value: 0.013281331253250133
      name: Cosine Precision@200
    - type: cosine_recall@1
      value: 0.28537503404898107
      name: Cosine Recall@1
    - type: cosine_recall@20
      value: 0.9225949037961519
      name: Cosine Recall@20
    - type: cosine_recall@50
      value: 0.9548015253943491
      name: Cosine Recall@50
    - type: cosine_recall@100
      value: 0.970532154619518
      name: Cosine Recall@100
    - type: cosine_recall@150
      value: 0.9766337320159473
      name: Cosine Recall@150
    - type: cosine_recall@200
      value: 0.9810747096550528
      name: Cosine Recall@200
    - type: cosine_ndcg@1
      value: 0.7394695787831513
      name: Cosine Ndcg@1
    - type: cosine_ndcg@20
      value: 0.8119072371250002
      name: Cosine Ndcg@20
    - type: cosine_ndcg@50
      value: 0.8208055075822587
      name: Cosine Ndcg@50
    - type: cosine_ndcg@100
      value: 0.8242798548838444
      name: Cosine Ndcg@100
    - type: cosine_ndcg@150
      value: 0.8254601712767063
      name: Cosine Ndcg@150
    - type: cosine_ndcg@200
      value: 0.826231823086538
      name: Cosine Ndcg@200
    - type: cosine_mrr@1
      value: 0.7394695787831513
      name: Cosine Mrr@1
    - type: cosine_mrr@20
      value: 0.8059183822863336
      name: Cosine Mrr@20
    - type: cosine_mrr@50
      value: 0.8065662458714291
      name: Cosine Mrr@50
    - type: cosine_mrr@100
      value: 0.8067209669800003
      name: Cosine Mrr@100
    - type: cosine_mrr@150
      value: 0.8067371899834064
      name: Cosine Mrr@150
    - type: cosine_mrr@200
      value: 0.8067455244059942
      name: Cosine Mrr@200
    - type: cosine_map@1
      value: 0.7394695787831513
      name: Cosine Map@1
    - type: cosine_map@20
      value: 0.7439811728319751
      name: Cosine Map@20
    - type: cosine_map@50
      value: 0.7464542457655368
      name: Cosine Map@50
    - type: cosine_map@100
      value: 0.7469341154545359
      name: Cosine Map@100
    - type: cosine_map@150
      value: 0.7470471963812441
      name: Cosine Map@150
    - type: cosine_map@200
      value: 0.7471010455519603
      name: Cosine Map@200
    - type: cosine_map@500
      value: 0.7471920688836787
      name: Cosine Map@500
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: mix de
      type: mix_de
    metrics:
    - type: cosine_accuracy@1
      value: 0.6926677067082684
      name: Cosine Accuracy@1
    - type: cosine_accuracy@20
      value: 0.9641185647425897
      name: Cosine Accuracy@20
    - type: cosine_accuracy@50
      value: 0.983879355174207
      name: Cosine Accuracy@50
    - type: cosine_accuracy@100
      value: 0.9921996879875195
      name: Cosine Accuracy@100
    - type: cosine_accuracy@150
      value: 0.9932397295891836
      name: Cosine Accuracy@150
    - type: cosine_accuracy@200
      value: 0.9942797711908476
      name: Cosine Accuracy@200
    - type: cosine_precision@1
      value: 0.6926677067082684
      name: Cosine Precision@1
    - type: cosine_precision@20
      value: 0.12797711908476336
      name: Cosine Precision@20
    - type: cosine_precision@50
      value: 0.053281331253250144
      name: Cosine Precision@50
    - type: cosine_precision@100
      value: 0.027051482059282376
      name: Cosine Precision@100
    - type: cosine_precision@150
      value: 0.018110591090310275
      name: Cosine Precision@150
    - type: cosine_precision@200
      value: 0.013619344773790953
      name: Cosine Precision@200
    - type: cosine_recall@1
      value: 0.2603830819899463
      name: Cosine Recall@1
    - type: cosine_recall@20
      value: 0.928479805858901
      name: Cosine Recall@20
    - type: cosine_recall@50
      value: 0.9650286011440458
      name: Cosine Recall@50
    - type: cosine_recall@100
      value: 0.9796325186340786
      name: Cosine Recall@100
    - type: cosine_recall@150
      value: 0.9837060149072628
      name: Cosine Recall@150
    - type: cosine_recall@200
      value: 0.9862194487779511
      name: Cosine Recall@200
    - type: cosine_ndcg@1
      value: 0.6926677067082684
      name: Cosine Ndcg@1
    - type: cosine_ndcg@20
      value: 0.7967328692326251
      name: Cosine Ndcg@20
    - type: cosine_ndcg@50
      value: 0.8068705787791701
      name: Cosine Ndcg@50
    - type: cosine_ndcg@100
      value: 0.810158579950017
      name: Cosine Ndcg@100
    - type: cosine_ndcg@150
      value: 0.8109641919896999
      name: Cosine Ndcg@150
    - type: cosine_ndcg@200
      value: 0.8114360342473703
      name: Cosine Ndcg@200
    - type: cosine_mrr@1
      value: 0.6926677067082684
      name: Cosine Mrr@1
    - type: cosine_mrr@20
      value: 0.7766838069642311
      name: Cosine Mrr@20
    - type: cosine_mrr@50
      value: 0.7773792960985305
      name: Cosine Mrr@50
    - type: cosine_mrr@100
      value: 0.7775026273925645
      name: Cosine Mrr@100
    - type: cosine_mrr@150
      value: 0.7775124036000293
      name: Cosine Mrr@150
    - type: cosine_mrr@200
      value: 0.7775182983569378
      name: Cosine Mrr@200
    - type: cosine_map@1
      value: 0.6926677067082684
      name: Cosine Map@1
    - type: cosine_map@20
      value: 0.7210301157895639
      name: Cosine Map@20
    - type: cosine_map@50
      value: 0.7237555751939095
      name: Cosine Map@50
    - type: cosine_map@100
      value: 0.7242426468613273
      name: Cosine Map@100
    - type: cosine_map@150
      value: 0.7243265313145111
      name: Cosine Map@150
    - type: cosine_map@200
      value: 0.7243628241480395
      name: Cosine Map@200
    - type: cosine_map@500
      value: 0.7244144669299598
      name: Cosine Map@500
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: mix zh
      type: mix_zh
    metrics:
    - type: cosine_accuracy@1
      value: 0.17888715548621945
      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: 0.17888715548621945
      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: 0.05768764083896689
      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: 0.17888715548621945
      name: Cosine Ndcg@1
    - type: cosine_ndcg@20
      value: 0.5443156532634228
      name: Cosine Ndcg@20
    - type: cosine_ndcg@50
      value: 0.5443156532634228
      name: Cosine Ndcg@50
    - type: cosine_ndcg@100
      value: 0.5443156532634228
      name: Cosine Ndcg@100
    - type: cosine_ndcg@150
      value: 0.5443156532634228
      name: Cosine Ndcg@150
    - type: cosine_ndcg@200
      value: 0.5443156532634228
      name: Cosine Ndcg@200
    - type: cosine_mrr@1
      value: 0.17888715548621945
      name: Cosine Mrr@1
    - type: cosine_mrr@20
      value: 0.4002437442375043
      name: Cosine Mrr@20
    - type: cosine_mrr@50
      value: 0.4002437442375043
      name: Cosine Mrr@50
    - type: cosine_mrr@100
      value: 0.4002437442375043
      name: Cosine Mrr@100
    - type: cosine_mrr@150
      value: 0.4002437442375043
      name: Cosine Mrr@150
    - type: cosine_mrr@200
      value: 0.4002437442375043
      name: Cosine Mrr@200
    - type: cosine_map@1
      value: 0.17888715548621945
      name: Cosine Map@1
    - type: cosine_map@20
      value: 0.32718437256695937
      name: Cosine Map@20
    - type: cosine_map@50
      value: 0.32718437256695937
      name: Cosine Map@50
    - type: cosine_map@100
      value: 0.32718437256695937
      name: Cosine Map@100
    - type: cosine_map@150
      value: 0.32718437256695937
      name: Cosine Map@150
    - type: cosine_map@200
      value: 0.32718437256695937
      name: Cosine Map@200
    - type: cosine_map@500
      value: 0.32718437256695937
      name: Cosine Map@500
---

# Job - Job matching finetuned BAAI/bge-m3

Top performing model on [TalentCLEF 2025](https://talentclef.github.io/talentclef/) Task A.  Use it for multilingual job title matching

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) <!-- at revision 5617a9f61b028005a4858fdac845db406aefb181 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 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: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 1024, '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/JobBGE-m3")
# Run inference
sentences = [
    'Volksvertreter',
    'Parlamentarier',
    'Oberbürgermeister',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

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

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

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

</details>
-->

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

You can finetune this model on your own dataset.

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

</details>
-->

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

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

## Evaluation

### Metrics

#### Information Retrieval

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

| Metric               | full_en    | full_es    | full_de    | full_zh    | mix_es     | mix_de     | mix_zh     |
|:---------------------|:-----------|:-----------|:-----------|:-----------|:-----------|:-----------|:-----------|
| cosine_accuracy@1    | 0.6476     | 0.1135     | 0.2956     | 0.6796     | 0.7395     | 0.6927     | 0.1789     |
| cosine_accuracy@20   | 0.9905     | 1.0        | 0.9852     | 0.9903     | 0.9636     | 0.9641     | 1.0        |
| cosine_accuracy@50   | 0.9905     | 1.0        | 0.9901     | 0.9903     | 0.9828     | 0.9839     | 1.0        |
| cosine_accuracy@100  | 0.9905     | 1.0        | 0.9901     | 0.9903     | 0.9927     | 0.9922     | 1.0        |
| cosine_accuracy@150  | 0.9905     | 1.0        | 0.9901     | 0.9903     | 0.9948     | 0.9932     | 1.0        |
| cosine_accuracy@200  | 0.9905     | 1.0        | 0.9901     | 0.9903     | 0.9964     | 0.9943     | 1.0        |
| cosine_precision@1   | 0.6476     | 0.1135     | 0.2956     | 0.6796     | 0.7395     | 0.6927     | 0.1789     |
| cosine_precision@20  | 0.5062     | 0.5668     | 0.5404     | 0.4709     | 0.1249     | 0.128      | 0.1544     |
| cosine_precision@50  | 0.3065     | 0.3903     | 0.3828     | 0.2804     | 0.0517     | 0.0533     | 0.0618     |
| cosine_precision@100 | 0.1858     | 0.2525     | 0.2503     | 0.1732     | 0.0263     | 0.0271     | 0.0309     |
| cosine_precision@150 | 0.1325     | 0.1901     | 0.1878     | 0.1239     | 0.0176     | 0.0181     | 0.0206     |
| cosine_precision@200 | 0.1025     | 0.1508     | 0.1503     | 0.0977     | 0.0133     | 0.0136     | 0.0154     |
| cosine_recall@1      | 0.0669     | 0.0035     | 0.0111     | 0.0643     | 0.2854     | 0.2604     | 0.0577     |
| cosine_recall@20     | 0.5392     | 0.3796     | 0.3433     | 0.5119     | 0.9226     | 0.9285     | 1.0        |
| cosine_recall@50     | 0.72       | 0.5636     | 0.534      | 0.6727     | 0.9548     | 0.965      | 1.0        |
| cosine_recall@100    | 0.8254     | 0.6727     | 0.6499     | 0.788      | 0.9705     | 0.9796     | 1.0        |
| cosine_recall@150    | 0.872      | 0.736      | 0.7101     | 0.8329     | 0.9766     | 0.9837     | 1.0        |
| cosine_recall@200    | 0.9006     | 0.7698     | 0.7513     | 0.8687     | 0.9811     | 0.9862     | 1.0        |
| cosine_ndcg@1        | 0.6476     | 0.1135     | 0.2956     | 0.6796     | 0.7395     | 0.6927     | 0.1789     |
| cosine_ndcg@20       | 0.6822     | 0.6136     | 0.5648     | 0.6515     | 0.8119     | 0.7967     | 0.5443     |
| cosine_ndcg@50       | 0.6975     | 0.5908     | 0.5522     | 0.6599     | 0.8208     | 0.8069     | 0.5443     |
| cosine_ndcg@100      | 0.752      | 0.6168     | 0.5796     | 0.7157     | 0.8243     | 0.8102     | 0.5443     |
| cosine_ndcg@150      | 0.7725     | 0.6489     | 0.6112     | 0.7357     | 0.8255     | 0.811      | 0.5443     |
| **cosine_ndcg@200**  | **0.7827** | **0.6653** | **0.6309** | **0.7501** | **0.8262** | **0.8114** | **0.5443** |
| cosine_mrr@1         | 0.6476     | 0.1135     | 0.2956     | 0.6796     | 0.7395     | 0.6927     | 0.1789     |
| cosine_mrr@20        | 0.8        | 0.5536     | 0.5164     | 0.8217     | 0.8059     | 0.7767     | 0.4002     |
| cosine_mrr@50        | 0.8        | 0.5536     | 0.5166     | 0.8217     | 0.8066     | 0.7774     | 0.4002     |
| cosine_mrr@100       | 0.8        | 0.5536     | 0.5166     | 0.8217     | 0.8067     | 0.7775     | 0.4002     |
| cosine_mrr@150       | 0.8        | 0.5536     | 0.5166     | 0.8217     | 0.8067     | 0.7775     | 0.4002     |
| cosine_mrr@200       | 0.8        | 0.5536     | 0.5166     | 0.8217     | 0.8067     | 0.7775     | 0.4002     |
| cosine_map@1         | 0.6476     | 0.1135     | 0.2956     | 0.6796     | 0.7395     | 0.6927     | 0.1789     |
| cosine_map@20        | 0.5392     | 0.481      | 0.4222     | 0.5012     | 0.744      | 0.721      | 0.3272     |
| cosine_map@50        | 0.5258     | 0.4304     | 0.3791     | 0.4813     | 0.7465     | 0.7238     | 0.3272     |
| cosine_map@100       | 0.558      | 0.4335     | 0.3829     | 0.5105     | 0.7469     | 0.7242     | 0.3272     |
| cosine_map@150       | 0.5666     | 0.4485     | 0.3981     | 0.5184     | 0.747      | 0.7243     | 0.3272     |
| cosine_map@200       | 0.5695     | 0.4551     | 0.4056     | 0.5228     | 0.7471     | 0.7244     | 0.3272     |
| cosine_map@500       | 0.5744     | 0.4677     | 0.4189     | 0.5277     | 0.7472     | 0.7244     | 0.3272     |

<!--
## 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.*
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### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Datasets
<details><summary>full_en</summary>

#### full_en

* Dataset: full_en
* Size: 28,880 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                           | positive                                                                         |
  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
  | type    | string                                                                           | string                                                                           |
  | details | <ul><li>min: 3 tokens</li><li>mean: 5.68 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   | -             | 0.6856                  | 0.5207                  | 0.4655                  | 0.6713                  | 0.6224                 | 0.5604                 | 0.5548                 |
| 0.0010 | 1    | 5.3354        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
| 0.1027 | 100  | 2.665         | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
| 0.2053 | 200  | 1.3375        | 0.7691                  | 0.6530                  | 0.6298                  | 0.7517                  | 0.7513                 | 0.7393                 | 0.5490                 |
| 0.3080 | 300  | 1.1101        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
| 0.4107 | 400  | 0.9453        | 0.7802                  | 0.6643                  | 0.6246                  | 0.7531                  | 0.7610                 | 0.7441                 | 0.5493                 |
| 0.5133 | 500  | 0.9202        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
| 0.6160 | 600  | 0.7887        | 0.7741                  | 0.6549                  | 0.6171                  | 0.7542                  | 0.7672                 | 0.7540                 | 0.5482                 |
| 0.7187 | 700  | 0.7604        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
| 0.8214 | 800  | 0.7219        | 0.7846                  | 0.6674                  | 0.6244                  | 0.7648                  | 0.7741                 | 0.7592                 | 0.5497                 |
| 0.9240 | 900  | 0.6965        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
| 1.0267 | 1000 | 0.6253        | 0.7646                  | 0.6391                  | 0.6122                  | 0.7503                  | 0.7825                 | 0.7704                 | 0.5463                 |
| 1.1294 | 1100 | 0.4737        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
| 1.2320 | 1200 | 0.5055        | 0.7758                  | 0.6582                  | 0.6178                  | 0.7514                  | 0.7857                 | 0.7764                 | 0.5501                 |
| 1.3347 | 1300 | 0.5042        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
| 1.4374 | 1400 | 0.5073        | 0.7613                  | 0.6578                  | 0.6178                  | 0.7505                  | 0.7829                 | 0.7762                 | 0.5452                 |
| 1.5400 | 1500 | 0.4975        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
| 1.6427 | 1600 | 0.5242        | 0.7736                  | 0.6673                  | 0.6279                  | 0.7555                  | 0.7940                 | 0.7859                 | 0.5477                 |
| 1.7454 | 1700 | 0.4713        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
| 1.8480 | 1800 | 0.4814        | 0.7845                  | 0.6733                  | 0.6285                  | 0.7642                  | 0.7992                 | 0.7904                 | 0.5449                 |
| 1.9507 | 1900 | 0.4526        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
| 2.0544 | 2000 | 0.36          | 0.7790                  | 0.6639                  | 0.6252                  | 0.7500                  | 0.8032                 | 0.7888                 | 0.5499                 |
| 2.1571 | 2100 | 0.3744        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
| 2.2598 | 2200 | 0.3031        | 0.7787                  | 0.6614                  | 0.6190                  | 0.7537                  | 0.7993                 | 0.7811                 | 0.5476                 |
| 2.3624 | 2300 | 0.3638        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
| 2.4651 | 2400 | 0.358         | 0.7798                  | 0.6615                  | 0.6258                  | 0.7497                  | 0.8018                 | 0.7828                 | 0.5481                 |
| 2.5678 | 2500 | 0.3247        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
| 2.6704 | 2600 | 0.3247        | 0.7854                  | 0.6663                  | 0.6248                  | 0.7560                  | 0.8081                 | 0.7835                 | 0.5452                 |
| 2.7731 | 2700 | 0.3263        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
| 2.8758 | 2800 | 0.3212        | 0.7761                  | 0.6681                  | 0.6250                  | 0.7517                  | 0.8121                 | 0.7927                 | 0.5458                 |
| 2.9784 | 2900 | 0.3291        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
| 3.0821 | 3000 | 0.2816        | 0.7727                  | 0.6604                  | 0.6163                  | 0.7370                  | 0.8163                 | 0.7985                 | 0.5473                 |
| 3.1848 | 3100 | 0.2698        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
| 3.2875 | 3200 | 0.2657        | 0.7757                  | 0.6615                  | 0.6247                  | 0.7417                  | 0.8117                 | 0.8004                 | 0.5436                 |
| 3.3901 | 3300 | 0.2724        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
| 3.4928 | 3400 | 0.2584        | 0.7850                  | 0.6583                  | 0.6320                  | 0.7458                  | 0.8120                 | 0.7980                 | 0.5454                 |
| 3.5955 | 3500 | 0.2573        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
| 3.6982 | 3600 | 0.2744        | 0.7796                  | 0.6552                  | 0.6237                  | 0.7409                  | 0.8193                 | 0.8018                 | 0.5466                 |
| 3.8008 | 3700 | 0.3054        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
| 3.9035 | 3800 | 0.2727        | 0.7825                  | 0.6642                  | 0.6293                  | 0.7504                  | 0.8213                 | 0.8058                 | 0.5463                 |
| 4.0062 | 3900 | 0.2353        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
| 4.1088 | 4000 | 0.2353        | 0.7747                  | 0.6628                  | 0.6263                  | 0.7384                  | 0.8239                 | 0.8065                 | 0.5447                 |
| 4.2115 | 4100 | 0.2385        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
| 4.3142 | 4200 | 0.231         | 0.7811                  | 0.6608                  | 0.6254                  | 0.7463                  | 0.8226                 | 0.8051                 | 0.5442                 |
| 4.4168 | 4300 | 0.2115        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
| 4.5195 | 4400 | 0.2151        | 0.7815                  | 0.6634                  | 0.6301                  | 0.7489                  | 0.8251                 | 0.8101                 | 0.5450                 |
| 4.6222 | 4500 | 0.2496        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
| 4.7248 | 4600 | 0.2146        | 0.7814                  | 0.6654                  | 0.6294                  | 0.7523                  | 0.8258                 | 0.8104                 | 0.5436                 |
| 4.8275 | 4700 | 0.2535        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
| 4.9302 | 4800 | 0.2058        | 0.7827                  | 0.6653                  | 0.6309                  | 0.7501                  | 0.8262                 | 0.8114                 | 0.5443                 |


### 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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