checkpoint-4000/README.md

---
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: 0.6476190476190476
      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: 0.5133333333333332
      name: Cosine Precision@20
    - type: cosine_precision@50
      value: 0.3165714285714285
      name: Cosine Precision@50
    - type: cosine_precision@100
      value: 0.18857142857142858
      name: Cosine Precision@100
    - type: cosine_precision@150
      value: 0.13396825396825396
      name: Cosine Precision@150
    - type: cosine_precision@200
      value: 0.10433333333333335
      name: Cosine Precision@200
    - type: cosine_recall@1
      value: 0.06742481608756247
      name: Cosine Recall@1
    - type: cosine_recall@20
      value: 0.5411228142559339
      name: Cosine Recall@20
    - type: cosine_recall@50
      value: 0.7397482609380314
      name: Cosine Recall@50
    - type: cosine_recall@100
      value: 0.8429667985290079
      name: Cosine Recall@100
    - type: cosine_recall@150
      value: 0.8856357375498775
      name: Cosine Recall@150
    - type: cosine_recall@200
      value: 0.9091330295382077
      name: Cosine Recall@200
    - type: cosine_ndcg@1
      value: 0.6476190476190476
      name: Cosine Ndcg@1
    - type: cosine_ndcg@20
      value: 0.6917131025478591
      name: Cosine Ndcg@20
    - type: cosine_ndcg@50
      value: 0.71478335831634
      name: Cosine Ndcg@50
    - type: cosine_ndcg@100
      value: 0.7666819432677721
      name: Cosine Ndcg@100
    - type: cosine_ndcg@150
      value: 0.7855970749692088
      name: Cosine Ndcg@150
    - type: cosine_ndcg@200
      value: 0.7960468614602451
      name: Cosine Ndcg@200
    - type: cosine_mrr@1
      value: 0.6476190476190476
      name: Cosine Mrr@1
    - type: cosine_mrr@20
      value: 0.8090476190476191
      name: Cosine Mrr@20
    - type: cosine_mrr@50
      value: 0.8090476190476191
      name: Cosine Mrr@50
    - type: cosine_mrr@100
      value: 0.8090476190476191
      name: Cosine Mrr@100
    - type: cosine_mrr@150
      value: 0.8090476190476191
      name: Cosine Mrr@150
    - type: cosine_mrr@200
      value: 0.8090476190476191
      name: Cosine Mrr@200
    - type: cosine_map@1
      value: 0.6476190476190476
      name: Cosine Map@1
    - type: cosine_map@20
      value: 0.5561135670751935
      name: Cosine Map@20
    - type: cosine_map@50
      value: 0.5477711353289022
      name: Cosine Map@50
    - type: cosine_map@100
      value: 0.5791852239372863
      name: Cosine Map@100
    - type: cosine_map@150
      value: 0.5872469517518495
      name: Cosine Map@150
    - type: cosine_map@200
      value: 0.5908784036739082
      name: Cosine Map@200
    - type: cosine_map@500
      value: 0.5948564356607342
      name: Cosine Map@500
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: full es
      type: full_es
    metrics:
    - type: cosine_accuracy@1
      value: 0.12972972972972974
      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: 0.12972972972972974
      name: Cosine Precision@1
    - type: cosine_precision@20
      value: 0.5705405405405405
      name: Cosine Precision@20
    - type: cosine_precision@50
      value: 0.38962162162162167
      name: Cosine Precision@50
    - type: cosine_precision@100
      value: 0.25140540540540546
      name: Cosine Precision@100
    - type: cosine_precision@150
      value: 0.19012612612612612
      name: Cosine Precision@150
    - type: cosine_precision@200
      value: 0.15154054054054056
      name: Cosine Precision@200
    - type: cosine_recall@1
      value: 0.0037413987812150314
      name: Cosine Recall@1
    - type: cosine_recall@20
      value: 0.38432915927625627
      name: Cosine Recall@20
    - type: cosine_recall@50
      value: 0.5663097940153319
      name: Cosine Recall@50
    - type: cosine_recall@100
      value: 0.6710180189388714
      name: Cosine Recall@100
    - type: cosine_recall@150
      value: 0.7443549924512646
      name: Cosine Recall@150
    - type: cosine_recall@200
      value: 0.7804985217049148
      name: Cosine Recall@200
    - type: cosine_ndcg@1
      value: 0.12972972972972974
      name: Cosine Ndcg@1
    - type: cosine_ndcg@20
      value: 0.6133809590566169
      name: Cosine Ndcg@20
    - type: cosine_ndcg@50
      value: 0.5888378318443163
      name: Cosine Ndcg@50
    - type: cosine_ndcg@100
      value: 0.613553130716134
      name: Cosine Ndcg@100
    - type: cosine_ndcg@150
      value: 0.6492700673561147
      name: Cosine Ndcg@150
    - type: cosine_ndcg@200
      value: 0.6672020616803231
      name: Cosine Ndcg@200
    - type: cosine_mrr@1
      value: 0.12972972972972974
      name: Cosine Mrr@1
    - type: cosine_mrr@20
      value: 0.5608108108108109
      name: Cosine Mrr@20
    - type: cosine_mrr@50
      value: 0.5608108108108109
      name: Cosine Mrr@50
    - type: cosine_mrr@100
      value: 0.5608108108108109
      name: Cosine Mrr@100
    - type: cosine_mrr@150
      value: 0.5608108108108109
      name: Cosine Mrr@150
    - type: cosine_mrr@200
      value: 0.5608108108108109
      name: Cosine Mrr@200
    - type: cosine_map@1
      value: 0.12972972972972974
      name: Cosine Map@1
    - type: cosine_map@20
      value: 0.47928087268629077
      name: Cosine Map@20
    - type: cosine_map@50
      value: 0.4265150109477007
      name: Cosine Map@50
    - type: cosine_map@100
      value: 0.4308614258675324
      name: Cosine Map@100
    - type: cosine_map@150
      value: 0.446315567522346
      name: Cosine Map@150
    - type: cosine_map@200
      value: 0.45361884446786194
      name: Cosine Map@200
    - type: cosine_map@500
      value: 0.46587892353181215
      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: 0.5120689655172413
      name: Cosine Precision@20
    - type: cosine_precision@50
      value: 0.3664039408866995
      name: Cosine Precision@50
    - type: cosine_precision@100
      value: 0.2411330049261084
      name: Cosine Precision@100
    - type: cosine_precision@150
      value: 0.180623973727422
      name: Cosine Precision@150
    - type: cosine_precision@200
      value: 0.1453448275862069
      name: Cosine Precision@200
    - type: cosine_recall@1
      value: 0.01108543831680986
      name: Cosine Recall@1
    - type: cosine_recall@20
      value: 0.3229666331805043
      name: Cosine Recall@20
    - type: cosine_recall@50
      value: 0.5039915991834915
      name: Cosine Recall@50
    - type: cosine_recall@100
      value: 0.6239950018657238
      name: Cosine Recall@100
    - type: cosine_recall@150
      value: 0.6837127628220585
      name: Cosine Recall@150
    - type: cosine_recall@200
      value: 0.724182886190782
      name: Cosine Recall@200
    - type: cosine_ndcg@1
      value: 0.2955665024630542
      name: Cosine Ndcg@1
    - type: cosine_ndcg@20
      value: 0.5416271120841382
      name: Cosine Ndcg@20
    - type: cosine_ndcg@50
      value: 0.5273905187096658
      name: Cosine Ndcg@50
    - type: cosine_ndcg@100
      value: 0.5573943264798527
      name: Cosine Ndcg@100
    - type: cosine_ndcg@150
      value: 0.5882759422186796
      name: Cosine Ndcg@150
    - type: cosine_ndcg@200
      value: 0.6082376029646045
      name: Cosine Ndcg@200
    - type: cosine_mrr@1
      value: 0.2955665024630542
      name: Cosine Mrr@1
    - type: cosine_mrr@20
      value: 0.510702296647636
      name: Cosine Mrr@20
    - type: cosine_mrr@50
      value: 0.5111935025343795
      name: Cosine Mrr@50
    - type: cosine_mrr@100
      value: 0.5111935025343795
      name: Cosine Mrr@100
    - type: cosine_mrr@150
      value: 0.5112378818891037
      name: Cosine Mrr@150
    - type: cosine_mrr@200
      value: 0.5112378818891037
      name: Cosine Mrr@200
    - type: cosine_map@1
      value: 0.2955665024630542
      name: Cosine Map@1
    - type: cosine_map@20
      value: 0.4032624181455029
      name: Cosine Map@20
    - type: cosine_map@50
      value: 0.35929856113701575
      name: Cosine Map@50
    - type: cosine_map@100
      value: 0.3633301227599498
      name: Cosine Map@100
    - type: cosine_map@150
      value: 0.3779770424201306
      name: Cosine Map@150
    - type: cosine_map@200
      value: 0.38546911827821406
      name: Cosine Map@200
    - type: cosine_map@500
      value: 0.3983960288142158
      name: Cosine Map@500
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: full zh
      type: full_zh
    metrics:
    - type: cosine_accuracy@1
      value: 0.6504854368932039
      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: 0.6504854368932039
      name: Cosine Precision@1
    - type: cosine_precision@20
      value: 0.47815533980582525
      name: Cosine Precision@20
    - type: cosine_precision@50
      value: 0.28699029126213593
      name: Cosine Precision@50
    - type: cosine_precision@100
      value: 0.17563106796116504
      name: Cosine Precision@100
    - type: cosine_precision@150
      value: 0.12543689320388354
      name: Cosine Precision@150
    - type: cosine_precision@200
      value: 0.09786407766990295
      name: Cosine Precision@200
    - type: cosine_recall@1
      value: 0.06122803520614593
      name: Cosine Recall@1
    - type: cosine_recall@20
      value: 0.512665335199255
      name: Cosine Recall@20
    - type: cosine_recall@50
      value: 0.6880766978766553
      name: Cosine Recall@50
    - type: cosine_recall@100
      value: 0.8002784995071653
      name: Cosine Recall@100
    - type: cosine_recall@150
      value: 0.8453144636093844
      name: Cosine Recall@150
    - type: cosine_recall@200
      value: 0.8773140543871931
      name: Cosine Recall@200
    - type: cosine_ndcg@1
      value: 0.6504854368932039
      name: Cosine Ndcg@1
    - type: cosine_ndcg@20
      value: 0.6531212612064398
      name: Cosine Ndcg@20
    - type: cosine_ndcg@50
      value: 0.6669362863744952
      name: Cosine Ndcg@50
    - type: cosine_ndcg@100
      value: 0.7218911998936125
      name: Cosine Ndcg@100
    - type: cosine_ndcg@150
      value: 0.7415597018345085
      name: Cosine Ndcg@150
    - type: cosine_ndcg@200
      value: 0.7535751066625261
      name: Cosine Ndcg@200
    - type: cosine_mrr@1
      value: 0.6504854368932039
      name: Cosine Mrr@1
    - type: cosine_mrr@20
      value: 0.7993527508090615
      name: Cosine Mrr@20
    - type: cosine_mrr@50
      value: 0.7997572815533981
      name: Cosine Mrr@50
    - type: cosine_mrr@100
      value: 0.7997572815533981
      name: Cosine Mrr@100
    - type: cosine_mrr@150
      value: 0.7997572815533981
      name: Cosine Mrr@150
    - type: cosine_mrr@200
      value: 0.7997572815533981
      name: Cosine Mrr@200
    - type: cosine_map@1
      value: 0.6504854368932039
      name: Cosine Map@1
    - type: cosine_map@20
      value: 0.5072300500933464
      name: Cosine Map@20
    - type: cosine_map@50
      value: 0.4897274345176646
      name: Cosine Map@50
    - type: cosine_map@100
      value: 0.5196798622563865
      name: Cosine Map@100
    - type: cosine_map@150
      value: 0.5276837053538445
      name: Cosine Map@150
    - type: cosine_map@200
      value: 0.5311205359244624
      name: Cosine Map@200
    - type: cosine_map@500
      value: 0.5365056842045905
      name: Cosine Map@500
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: mix es
      type: mix_es
    metrics:
    - type: cosine_accuracy@1
      value: 0.7243889755590224
      name: Cosine Accuracy@1
    - type: cosine_accuracy@20
      value: 0.9609984399375975
      name: Cosine Accuracy@20
    - type: cosine_accuracy@50
      value: 0.9797191887675507
      name: Cosine Accuracy@50
    - type: cosine_accuracy@100
      value: 0.9937597503900156
      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: 0.7243889755590224
      name: Cosine Precision@1
    - type: cosine_precision@20
      value: 0.12428497139885596
      name: Cosine Precision@20
    - type: cosine_precision@50
      value: 0.05134685387415497
      name: Cosine Precision@50
    - type: cosine_precision@100
      value: 0.026214248569942804
      name: Cosine Precision@100
    - type: cosine_precision@150
      value: 0.017597503900156002
      name: Cosine Precision@150
    - type: cosine_precision@200
      value: 0.013281331253250133
      name: Cosine Precision@200
    - type: cosine_recall@1
      value: 0.2802961642275215
      name: Cosine Recall@1
    - type: cosine_recall@20
      value: 0.9183394002426764
      name: Cosine Recall@20
    - type: cosine_recall@50
      value: 0.9482665973305597
      name: Cosine Recall@50
    - type: cosine_recall@100
      value: 0.9692234356040907
      name: Cosine Recall@100
    - type: cosine_recall@150
      value: 0.9756023574276305
      name: Cosine Recall@150
    - type: cosine_recall@200
      value: 0.9821892875715027
      name: Cosine Recall@200
    - type: cosine_ndcg@1
      value: 0.7243889755590224
      name: Cosine Ndcg@1
    - type: cosine_ndcg@20
      value: 0.8023352815755668
      name: Cosine Ndcg@20
    - type: cosine_ndcg@50
      value: 0.8104895152869938
      name: Cosine Ndcg@50
    - type: cosine_ndcg@100
      value: 0.8150081000806421
      name: Cosine Ndcg@100
    - type: cosine_ndcg@150
      value: 0.8162651648802736
      name: Cosine Ndcg@150
    - type: cosine_ndcg@200
      value: 0.8174362445077372
      name: Cosine Ndcg@200
    - type: cosine_mrr@1
      value: 0.7243889755590224
      name: Cosine Mrr@1
    - type: cosine_mrr@20
      value: 0.7938466413093047
      name: Cosine Mrr@20
    - type: cosine_mrr@50
      value: 0.7944053350960067
      name: Cosine Mrr@50
    - type: cosine_mrr@100
      value: 0.794613049565821
      name: Cosine Mrr@100
    - type: cosine_mrr@150
      value: 0.7946306448507517
      name: Cosine Mrr@150
    - type: cosine_mrr@200
      value: 0.7946402095756717
      name: Cosine Mrr@200
    - type: cosine_map@1
      value: 0.7243889755590224
      name: Cosine Map@1
    - type: cosine_map@20
      value: 0.7324440771234734
      name: Cosine Map@20
    - type: cosine_map@50
      value: 0.734716178743038
      name: Cosine Map@50
    - type: cosine_map@100
      value: 0.7353155432601859
      name: Cosine Map@100
    - type: cosine_map@150
      value: 0.735429453970343
      name: Cosine Map@150
    - type: cosine_map@200
      value: 0.7355154445871764
      name: Cosine Map@200
    - type: cosine_map@500
      value: 0.7356208832908805
      name: Cosine Map@500
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: mix de
      type: mix_de
    metrics:
    - type: cosine_accuracy@1
      value: 0.6697867914716589
      name: Cosine Accuracy@1
    - type: cosine_accuracy@20
      value: 0.9505980239209568
      name: Cosine Accuracy@20
    - type: cosine_accuracy@50
      value: 0.9771190847633905
      name: Cosine Accuracy@50
    - type: cosine_accuracy@100
      value: 0.9859594383775351
      name: Cosine Accuracy@100
    - type: cosine_accuracy@150
      value: 0.9921996879875195
      name: Cosine Accuracy@150
    - type: cosine_accuracy@200
      value: 0.9942797711908476
      name: Cosine Accuracy@200
    - type: cosine_precision@1
      value: 0.6697867914716589
      name: Cosine Precision@1
    - type: cosine_precision@20
      value: 0.12470098803952159
      name: Cosine Precision@20
    - type: cosine_precision@50
      value: 0.05225169006760271
      name: Cosine Precision@50
    - type: cosine_precision@100
      value: 0.026708268330733236
      name: Cosine Precision@100
    - type: cosine_precision@150
      value: 0.01798231929277171
      name: Cosine Precision@150
    - type: cosine_precision@200
      value: 0.01353874154966199
      name: Cosine Precision@200
    - type: cosine_recall@1
      value: 0.2517940717628705
      name: Cosine Recall@1
    - type: cosine_recall@20
      value: 0.9059022360894435
      name: Cosine Recall@20
    - type: cosine_recall@50
      value: 0.9474345640492287
      name: Cosine Recall@50
    - type: cosine_recall@100
      value: 0.967932050615358
      name: Cosine Recall@100
    - type: cosine_recall@150
      value: 0.9771190847633905
      name: Cosine Recall@150
    - type: cosine_recall@200
      value: 0.9807592303692148
      name: Cosine Recall@200
    - type: cosine_ndcg@1
      value: 0.6697867914716589
      name: Cosine Ndcg@1
    - type: cosine_ndcg@20
      value: 0.770344092734726
      name: Cosine Ndcg@20
    - type: cosine_ndcg@50
      value: 0.7819450345813985
      name: Cosine Ndcg@50
    - type: cosine_ndcg@100
      value: 0.7865455025019679
      name: Cosine Ndcg@100
    - type: cosine_ndcg@150
      value: 0.7883807621544129
      name: Cosine Ndcg@150
    - type: cosine_ndcg@200
      value: 0.7890604802329748
      name: Cosine Ndcg@200
    - type: cosine_mrr@1
      value: 0.6697867914716589
      name: Cosine Mrr@1
    - type: cosine_mrr@20
      value: 0.7504302722692131
      name: Cosine Mrr@20
    - type: cosine_mrr@50
      value: 0.7513280223222801
      name: Cosine Mrr@50
    - type: cosine_mrr@100
      value: 0.7514573016845009
      name: Cosine Mrr@100
    - type: cosine_mrr@150
      value: 0.7515108675350354
      name: Cosine Mrr@150
    - type: cosine_mrr@200
      value: 0.7515238522218625
      name: Cosine Mrr@200
    - type: cosine_map@1
      value: 0.6697867914716589
      name: Cosine Map@1
    - type: cosine_map@20
      value: 0.6929705838065172
      name: Cosine Map@20
    - type: cosine_map@50
      value: 0.696080766802269
      name: Cosine Map@50
    - type: cosine_map@100
      value: 0.6967651580129317
      name: Cosine Map@100
    - type: cosine_map@150
      value: 0.6969258122016383
      name: Cosine Map@150
    - type: cosine_map@200
      value: 0.6969715581100935
      name: Cosine Map@200
    - type: cosine_map@500
      value: 0.6970655432634698
      name: Cosine Map@500
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: mix zh
      type: mix_zh
    metrics:
    - type: cosine_accuracy@1
      value: 0.19760790431617264
      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: 0.19760790431617264
      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: 0.06371492954956293
      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: 0.19760790431617264
      name: Cosine Ndcg@1
    - type: cosine_ndcg@20
      value: 0.5478938300274205
      name: Cosine Ndcg@20
    - type: cosine_ndcg@50
      value: 0.5478938300274205
      name: Cosine Ndcg@50
    - type: cosine_ndcg@100
      value: 0.5478938300274205
      name: Cosine Ndcg@100
    - type: cosine_ndcg@150
      value: 0.5478938300274205
      name: Cosine Ndcg@150
    - type: cosine_ndcg@200
      value: 0.5478938300274205
      name: Cosine Ndcg@200
    - type: cosine_mrr@1
      value: 0.19760790431617264
      name: Cosine Mrr@1
    - type: cosine_mrr@20
      value: 0.4124442798779788
      name: Cosine Mrr@20
    - type: cosine_mrr@50
      value: 0.4124442798779788
      name: Cosine Mrr@50
    - type: cosine_mrr@100
      value: 0.4124442798779788
      name: Cosine Mrr@100
    - type: cosine_mrr@150
      value: 0.4124442798779788
      name: Cosine Mrr@150
    - type: cosine_mrr@200
      value: 0.4124442798779788
      name: Cosine Mrr@200
    - type: cosine_map@1
      value: 0.19760790431617264
      name: Cosine Map@1
    - type: cosine_map@20
      value: 0.32993583709540925
      name: Cosine Map@20
    - type: cosine_map@50
      value: 0.32993583709540925
      name: Cosine Map@50
    - type: cosine_map@100
      value: 0.32993583709540925
      name: Cosine Map@100
    - type: cosine_map@150
      value: 0.32993583709540925
      name: Cosine Map@150
    - type: cosine_map@200
      value: 0.32993583709540925
      name: Cosine Map@200
    - type: cosine_map@500
      value: 0.32993583709540925
      name: Cosine Map@500
---

# SentenceTransformer based on Alibaba-NLP/gte-multilingual-base

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base) on the full_en, full_de, full_es, full_zh and mix datasets. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

## 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("sentence_transformers_model_id")
# 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

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## 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    | 0.1297     | 0.2956     | 0.6505     | 0.7244     | 0.6698     | 0.1976     |
| cosine_accuracy@20   | 0.9905    | 1.0        | 0.9704     | 0.9806     | 0.961      | 0.9506     | 1.0        |
| cosine_accuracy@50   | 0.9905    | 1.0        | 0.9852     | 0.9903     | 0.9797     | 0.9771     | 1.0        |
| cosine_accuracy@100  | 0.9905    | 1.0        | 0.9852     | 0.9903     | 0.9938     | 0.986      | 1.0        |
| cosine_accuracy@150  | 0.9905    | 1.0        | 0.9901     | 0.9903     | 0.9958     | 0.9922     | 1.0        |
| cosine_accuracy@200  | 0.9905    | 1.0        | 0.9901     | 0.9903     | 0.9974     | 0.9943     | 1.0        |
| cosine_precision@1   | 0.6476    | 0.1297     | 0.2956     | 0.6505     | 0.7244     | 0.6698     | 0.1976     |
| cosine_precision@20  | 0.5133    | 0.5705     | 0.5121     | 0.4782     | 0.1243     | 0.1247     | 0.1544     |
| cosine_precision@50  | 0.3166    | 0.3896     | 0.3664     | 0.287      | 0.0513     | 0.0523     | 0.0618     |
| cosine_precision@100 | 0.1886    | 0.2514     | 0.2411     | 0.1756     | 0.0262     | 0.0267     | 0.0309     |
| cosine_precision@150 | 0.134     | 0.1901     | 0.1806     | 0.1254     | 0.0176     | 0.018      | 0.0206     |
| cosine_precision@200 | 0.1043    | 0.1515     | 0.1453     | 0.0979     | 0.0133     | 0.0135     | 0.0154     |
| cosine_recall@1      | 0.0674    | 0.0037     | 0.0111     | 0.0612     | 0.2803     | 0.2518     | 0.0637     |
| cosine_recall@20     | 0.5411    | 0.3843     | 0.323      | 0.5127     | 0.9183     | 0.9059     | 1.0        |
| cosine_recall@50     | 0.7397    | 0.5663     | 0.504      | 0.6881     | 0.9483     | 0.9474     | 1.0        |
| cosine_recall@100    | 0.843     | 0.671      | 0.624      | 0.8003     | 0.9692     | 0.9679     | 1.0        |
| cosine_recall@150    | 0.8856    | 0.7444     | 0.6837     | 0.8453     | 0.9756     | 0.9771     | 1.0        |
| cosine_recall@200    | 0.9091    | 0.7805     | 0.7242     | 0.8773     | 0.9822     | 0.9808     | 1.0        |
| cosine_ndcg@1        | 0.6476    | 0.1297     | 0.2956     | 0.6505     | 0.7244     | 0.6698     | 0.1976     |
| cosine_ndcg@20       | 0.6917    | 0.6134     | 0.5416     | 0.6531     | 0.8023     | 0.7703     | 0.5479     |
| cosine_ndcg@50       | 0.7148    | 0.5888     | 0.5274     | 0.6669     | 0.8105     | 0.7819     | 0.5479     |
| cosine_ndcg@100      | 0.7667    | 0.6136     | 0.5574     | 0.7219     | 0.815      | 0.7865     | 0.5479     |
| cosine_ndcg@150      | 0.7856    | 0.6493     | 0.5883     | 0.7416     | 0.8163     | 0.7884     | 0.5479     |
| **cosine_ndcg@200**  | **0.796** | **0.6672** | **0.6082** | **0.7536** | **0.8174** | **0.7891** | **0.5479** |
| cosine_mrr@1         | 0.6476    | 0.1297     | 0.2956     | 0.6505     | 0.7244     | 0.6698     | 0.1976     |
| cosine_mrr@20        | 0.809     | 0.5608     | 0.5107     | 0.7994     | 0.7938     | 0.7504     | 0.4124     |
| cosine_mrr@50        | 0.809     | 0.5608     | 0.5112     | 0.7998     | 0.7944     | 0.7513     | 0.4124     |
| cosine_mrr@100       | 0.809     | 0.5608     | 0.5112     | 0.7998     | 0.7946     | 0.7515     | 0.4124     |
| cosine_mrr@150       | 0.809     | 0.5608     | 0.5112     | 0.7998     | 0.7946     | 0.7515     | 0.4124     |
| cosine_mrr@200       | 0.809     | 0.5608     | 0.5112     | 0.7998     | 0.7946     | 0.7515     | 0.4124     |
| cosine_map@1         | 0.6476    | 0.1297     | 0.2956     | 0.6505     | 0.7244     | 0.6698     | 0.1976     |
| cosine_map@20        | 0.5561    | 0.4793     | 0.4033     | 0.5072     | 0.7324     | 0.693      | 0.3299     |
| cosine_map@50        | 0.5478    | 0.4265     | 0.3593     | 0.4897     | 0.7347     | 0.6961     | 0.3299     |
| cosine_map@100       | 0.5792    | 0.4309     | 0.3633     | 0.5197     | 0.7353     | 0.6968     | 0.3299     |
| cosine_map@150       | 0.5872    | 0.4463     | 0.378      | 0.5277     | 0.7354     | 0.6969     | 0.3299     |
| cosine_map@200       | 0.5909    | 0.4536     | 0.3855     | 0.5311     | 0.7355     | 0.697      | 0.3299     |
| cosine_map@500       | 0.5949    | 0.4659     | 0.3984     | 0.5365     | 0.7356     | 0.6971     | 0.3299     |

<!--
## Bias, Risks and Limitations

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### Recommendations

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


### 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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