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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:124788
- loss:GISTEmbedLoss
base_model: BAAI/bge-small-en-v1.5
widget:
- source_sentence: 其他机械、设备和有形货物租赁服务代表
sentences:
- 其他机械和设备租赁服务工作人员
- 电子和电信设备及零部件物流经理
- 工业主厨
- source_sentence: 公交车司机
sentences:
- 表演灯光设计师
- 乙烯基地板安装工
- 国际巴士司机
- source_sentence: online communication manager
sentences:
- trades union official
- social media manager
- budget manager
- source_sentence: Projektmanagerin
sentences:
- Projektmanager/Projektmanagerin
- Category-Manager
- Infanterist
- source_sentence: Volksvertreter
sentences:
- Parlamentarier
- Oberbürgermeister
- Konsul
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@20
- cosine_accuracy@50
- cosine_accuracy@100
- cosine_accuracy@150
- cosine_accuracy@200
- cosine_precision@1
- cosine_precision@20
- cosine_precision@50
- cosine_precision@100
- cosine_precision@150
- cosine_precision@200
- cosine_recall@1
- cosine_recall@20
- cosine_recall@50
- cosine_recall@100
- cosine_recall@150
- cosine_recall@200
- cosine_ndcg@1
- cosine_ndcg@20
- cosine_ndcg@50
- cosine_ndcg@100
- cosine_ndcg@150
- cosine_ndcg@200
- cosine_mrr@1
- cosine_mrr@20
- cosine_mrr@50
- cosine_mrr@100
- cosine_mrr@150
- cosine_mrr@200
- cosine_map@1
- cosine_map@20
- cosine_map@50
- cosine_map@100
- cosine_map@150
- cosine_map@200
- cosine_map@500
model-index:
- name: SentenceTransformer based on BAAI/bge-small-en-v1.5
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: full en
type: full_en
metrics:
- type: cosine_accuracy@1
value: 0.6571428571428571
name: Cosine Accuracy@1
- type: cosine_accuracy@20
value: 0.9904761904761905
name: Cosine Accuracy@20
- type: cosine_accuracy@50
value: 0.9904761904761905
name: Cosine Accuracy@50
- type: cosine_accuracy@100
value: 0.9904761904761905
name: Cosine Accuracy@100
- type: cosine_accuracy@150
value: 0.9904761904761905
name: Cosine Accuracy@150
- type: cosine_accuracy@200
value: 0.9904761904761905
name: Cosine Accuracy@200
- type: cosine_precision@1
value: 0.6571428571428571
name: Cosine Precision@1
- type: cosine_precision@20
value: 0.5076190476190475
name: Cosine Precision@20
- type: cosine_precision@50
value: 0.3089523809523809
name: Cosine Precision@50
- type: cosine_precision@100
value: 0.1872380952380952
name: Cosine Precision@100
- type: cosine_precision@150
value: 0.1321904761904762
name: Cosine Precision@150
- type: cosine_precision@200
value: 0.1027142857142857
name: Cosine Precision@200
- type: cosine_recall@1
value: 0.0680237860830842
name: Cosine Recall@1
- type: cosine_recall@20
value: 0.5459242543214992
name: Cosine Recall@20
- type: cosine_recall@50
value: 0.728483344815942
name: Cosine Recall@50
- type: cosine_recall@100
value: 0.8382149119179341
name: Cosine Recall@100
- type: cosine_recall@150
value: 0.8762032488748317
name: Cosine Recall@150
- type: cosine_recall@200
value: 0.9059964336434017
name: Cosine Recall@200
- type: cosine_ndcg@1
value: 0.6571428571428571
name: Cosine Ndcg@1
- type: cosine_ndcg@20
value: 0.6895375515490911
name: Cosine Ndcg@20
- type: cosine_ndcg@50
value: 0.7060633068166344
name: Cosine Ndcg@50
- type: cosine_ndcg@100
value: 0.7619501692018719
name: Cosine Ndcg@100
- type: cosine_ndcg@150
value: 0.778798440383198
name: Cosine Ndcg@150
- type: cosine_ndcg@200
value: 0.7899830993214225
name: Cosine Ndcg@200
- type: cosine_mrr@1
value: 0.6571428571428571
name: Cosine Mrr@1
- type: cosine_mrr@20
value: 0.8098412698412698
name: Cosine Mrr@20
- type: cosine_mrr@50
value: 0.8098412698412698
name: Cosine Mrr@50
- type: cosine_mrr@100
value: 0.8098412698412698
name: Cosine Mrr@100
- type: cosine_mrr@150
value: 0.8098412698412698
name: Cosine Mrr@150
- type: cosine_mrr@200
value: 0.8098412698412698
name: Cosine Mrr@200
- type: cosine_map@1
value: 0.6571428571428571
name: Cosine Map@1
- type: cosine_map@20
value: 0.5464916843297755
name: Cosine Map@20
- type: cosine_map@50
value: 0.5351890636433139
name: Cosine Map@50
- type: cosine_map@100
value: 0.5685440196941911
name: Cosine Map@100
- type: cosine_map@150
value: 0.5756567539581475
name: Cosine Map@150
- type: cosine_map@200
value: 0.5791635361565666
name: Cosine Map@200
- type: cosine_map@500
value: 0.5835322146366259
name: Cosine Map@500
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: full es
type: full_es
metrics:
- type: cosine_accuracy@1
value: 0.12432432432432433
name: Cosine Accuracy@1
- type: cosine_accuracy@20
value: 1.0
name: Cosine Accuracy@20
- type: cosine_accuracy@50
value: 1.0
name: Cosine Accuracy@50
- type: cosine_accuracy@100
value: 1.0
name: Cosine Accuracy@100
- type: cosine_accuracy@150
value: 1.0
name: Cosine Accuracy@150
- type: cosine_accuracy@200
value: 1.0
name: Cosine Accuracy@200
- type: cosine_precision@1
value: 0.12432432432432433
name: Cosine Precision@1
- type: cosine_precision@20
value: 0.4924324324324324
name: Cosine Precision@20
- type: cosine_precision@50
value: 0.31686486486486487
name: Cosine Precision@50
- type: cosine_precision@100
value: 0.19843243243243244
name: Cosine Precision@100
- type: cosine_precision@150
value: 0.14702702702702705
name: Cosine Precision@150
- type: cosine_precision@200
value: 0.11762162162162161
name: Cosine Precision@200
- type: cosine_recall@1
value: 0.003111544931768446
name: Cosine Recall@1
- type: cosine_recall@20
value: 0.3235933309332048
name: Cosine Recall@20
- type: cosine_recall@50
value: 0.4622883553307717
name: Cosine Recall@50
- type: cosine_recall@100
value: 0.5424114301447981
name: Cosine Recall@100
- type: cosine_recall@150
value: 0.5822792579944903
name: Cosine Recall@150
- type: cosine_recall@200
value: 0.612586126212026
name: Cosine Recall@200
- type: cosine_ndcg@1
value: 0.12432432432432433
name: Cosine Ndcg@1
- type: cosine_ndcg@20
value: 0.5406828319866788
name: Cosine Ndcg@20
- type: cosine_ndcg@50
value: 0.500776817925352
name: Cosine Ndcg@50
- type: cosine_ndcg@100
value: 0.5143442473922782
name: Cosine Ndcg@100
- type: cosine_ndcg@150
value: 0.5349751306205418
name: Cosine Ndcg@150
- type: cosine_ndcg@200
value: 0.5498255219419508
name: Cosine Ndcg@200
- type: cosine_mrr@1
value: 0.12432432432432433
name: Cosine Mrr@1
- type: cosine_mrr@20
value: 0.5516816816816817
name: Cosine Mrr@20
- type: cosine_mrr@50
value: 0.5516816816816817
name: Cosine Mrr@50
- type: cosine_mrr@100
value: 0.5516816816816817
name: Cosine Mrr@100
- type: cosine_mrr@150
value: 0.5516816816816817
name: Cosine Mrr@150
- type: cosine_mrr@200
value: 0.5516816816816817
name: Cosine Mrr@200
- type: cosine_map@1
value: 0.12432432432432433
name: Cosine Map@1
- type: cosine_map@20
value: 0.4061591888137979
name: Cosine Map@20
- type: cosine_map@50
value: 0.3426196432849601
name: Cosine Map@50
- type: cosine_map@100
value: 0.3398108870028267
name: Cosine Map@100
- type: cosine_map@150
value: 0.3482007813358776
name: Cosine Map@150
- type: cosine_map@200
value: 0.3534583367060008
name: Cosine Map@200
- type: cosine_map@500
value: 0.36353547903357536
name: Cosine Map@500
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: full de
type: full_de
metrics:
- type: cosine_accuracy@1
value: 0.2955665024630542
name: Cosine Accuracy@1
- type: cosine_accuracy@20
value: 0.9211822660098522
name: Cosine Accuracy@20
- type: cosine_accuracy@50
value: 0.9605911330049262
name: Cosine Accuracy@50
- type: cosine_accuracy@100
value: 0.9753694581280788
name: Cosine Accuracy@100
- type: cosine_accuracy@150
value: 0.9852216748768473
name: Cosine Accuracy@150
- type: cosine_accuracy@200
value: 0.9852216748768473
name: Cosine Accuracy@200
- type: cosine_precision@1
value: 0.2955665024630542
name: Cosine Precision@1
- type: cosine_precision@20
value: 0.4226600985221674
name: Cosine Precision@20
- type: cosine_precision@50
value: 0.2775369458128079
name: Cosine Precision@50
- type: cosine_precision@100
value: 0.1787192118226601
name: Cosine Precision@100
- type: cosine_precision@150
value: 0.1349753694581281
name: Cosine Precision@150
- type: cosine_precision@200
value: 0.10960591133004927
name: Cosine Precision@200
- type: cosine_recall@1
value: 0.01108543831680986
name: Cosine Recall@1
- type: cosine_recall@20
value: 0.25787568646307335
name: Cosine Recall@20
- type: cosine_recall@50
value: 0.378544115518205
name: Cosine Recall@50
- type: cosine_recall@100
value: 0.4646991741198787
name: Cosine Recall@100
- type: cosine_recall@150
value: 0.514077820298434
name: Cosine Recall@150
- type: cosine_recall@200
value: 0.5479242719935129
name: Cosine Recall@200
- type: cosine_ndcg@1
value: 0.2955665024630542
name: Cosine Ndcg@1
- type: cosine_ndcg@20
value: 0.4571806408684656
name: Cosine Ndcg@20
- type: cosine_ndcg@50
value: 0.4186161244795668
name: Cosine Ndcg@50
- type: cosine_ndcg@100
value: 0.43413691996468995
name: Cosine Ndcg@100
- type: cosine_ndcg@150
value: 0.45936827865079527
name: Cosine Ndcg@150
- type: cosine_ndcg@200
value: 0.4762742892652946
name: Cosine Ndcg@200
- type: cosine_mrr@1
value: 0.2955665024630542
name: Cosine Mrr@1
- type: cosine_mrr@20
value: 0.488501497777794
name: Cosine Mrr@20
- type: cosine_mrr@50
value: 0.48978270334574775
name: Cosine Mrr@50
- type: cosine_mrr@100
value: 0.4900376562912742
name: Cosine Mrr@100
- type: cosine_mrr@150
value: 0.4901135143922775
name: Cosine Mrr@150
- type: cosine_mrr@200
value: 0.4901135143922775
name: Cosine Mrr@200
- type: cosine_map@1
value: 0.2955665024630542
name: Cosine Map@1
- type: cosine_map@20
value: 0.3211048669539684
name: Cosine Map@20
- type: cosine_map@50
value: 0.261888445835493
name: Cosine Map@50
- type: cosine_map@100
value: 0.2558901722323677
name: Cosine Map@100
- type: cosine_map@150
value: 0.2649913870834412
name: Cosine Map@150
- type: cosine_map@200
value: 0.27010541031599244
name: Cosine Map@200
- type: cosine_map@500
value: 0.28106938786931224
name: Cosine Map@500
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: full zh
type: full_zh
metrics:
- type: cosine_accuracy@1
value: 0.30097087378640774
name: Cosine Accuracy@1
- type: cosine_accuracy@20
value: 0.7087378640776699
name: Cosine Accuracy@20
- type: cosine_accuracy@50
value: 0.8252427184466019
name: Cosine Accuracy@50
- type: cosine_accuracy@100
value: 0.8543689320388349
name: Cosine Accuracy@100
- type: cosine_accuracy@150
value: 0.912621359223301
name: Cosine Accuracy@150
- type: cosine_accuracy@200
value: 0.941747572815534
name: Cosine Accuracy@200
- type: cosine_precision@1
value: 0.30097087378640774
name: Cosine Precision@1
- type: cosine_precision@20
value: 0.16844660194174763
name: Cosine Precision@20
- type: cosine_precision@50
value: 0.09436893203883494
name: Cosine Precision@50
- type: cosine_precision@100
value: 0.05844660194174757
name: Cosine Precision@100
- type: cosine_precision@150
value: 0.04601941747572815
name: Cosine Precision@150
- type: cosine_precision@200
value: 0.038203883495145634
name: Cosine Precision@200
- type: cosine_recall@1
value: 0.024446152054452382
name: Cosine Recall@1
- type: cosine_recall@20
value: 0.17513112391433697
name: Cosine Recall@20
- type: cosine_recall@50
value: 0.23948897590045773
name: Cosine Recall@50
- type: cosine_recall@100
value: 0.2859906000645493
name: Cosine Recall@100
- type: cosine_recall@150
value: 0.32910264724851107
name: Cosine Recall@150
- type: cosine_recall@200
value: 0.36304017348331746
name: Cosine Recall@200
- type: cosine_ndcg@1
value: 0.30097087378640774
name: Cosine Ndcg@1
- type: cosine_ndcg@20
value: 0.23848199480652515
name: Cosine Ndcg@20
- type: cosine_ndcg@50
value: 0.23417872356945213
name: Cosine Ndcg@50
- type: cosine_ndcg@100
value: 0.2558557487315817
name: Cosine Ndcg@100
- type: cosine_ndcg@150
value: 0.27344459654855646
name: Cosine Ndcg@150
- type: cosine_ndcg@200
value: 0.28574499658549296
name: Cosine Ndcg@200
- type: cosine_mrr@1
value: 0.30097087378640774
name: Cosine Mrr@1
- type: cosine_mrr@20
value: 0.4211010881954553
name: Cosine Mrr@20
- type: cosine_mrr@50
value: 0.4249525777196882
name: Cosine Mrr@50
- type: cosine_mrr@100
value: 0.42539460155740233
name: Cosine Mrr@100
- type: cosine_mrr@150
value: 0.42587488715939736
name: Cosine Mrr@150
- type: cosine_mrr@200
value: 0.4260266850474542
name: Cosine Mrr@200
- type: cosine_map@1
value: 0.30097087378640774
name: Cosine Map@1
- type: cosine_map@20
value: 0.14164601531439067
name: Cosine Map@20
- type: cosine_map@50
value: 0.12333195286802508
name: Cosine Map@50
- type: cosine_map@100
value: 0.12884550949445583
name: Cosine Map@100
- type: cosine_map@150
value: 0.13149151347084506
name: Cosine Map@150
- type: cosine_map@200
value: 0.1329204280861929
name: Cosine Map@200
- type: cosine_map@500
value: 0.13684460640814028
name: Cosine Map@500
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: mix es
type: mix_es
metrics:
- type: cosine_accuracy@1
value: 0.40717628705148207
name: Cosine Accuracy@1
- type: cosine_accuracy@20
value: 0.7581903276131046
name: Cosine Accuracy@20
- type: cosine_accuracy@50
value: 0.8455538221528861
name: Cosine Accuracy@50
- type: cosine_accuracy@100
value: 0.9048361934477379
name: Cosine Accuracy@100
- type: cosine_accuracy@150
value: 0.9370774830993239
name: Cosine Accuracy@150
- type: cosine_accuracy@200
value: 0.9547581903276131
name: Cosine Accuracy@200
- type: cosine_precision@1
value: 0.40717628705148207
name: Cosine Precision@1
- type: cosine_precision@20
value: 0.08819552782111284
name: Cosine Precision@20
- type: cosine_precision@50
value: 0.04139365574622985
name: Cosine Precision@50
- type: cosine_precision@100
value: 0.022704108164326574
name: Cosine Precision@100
- type: cosine_precision@150
value: 0.015822499566649332
name: Cosine Precision@150
- type: cosine_precision@200
value: 0.012189287571502862
name: Cosine Precision@200
- type: cosine_recall@1
value: 0.1547198078399326
name: Cosine Recall@1
- type: cosine_recall@20
value: 0.652070178045217
name: Cosine Recall@20
- type: cosine_recall@50
value: 0.7639576059232845
name: Cosine Recall@50
- type: cosine_recall@100
value: 0.8390052744966942
name: Cosine Recall@100
- type: cosine_recall@150
value: 0.877764158185375
name: Cosine Recall@150
- type: cosine_recall@200
value: 0.9016677809969541
name: Cosine Recall@200
- type: cosine_ndcg@1
value: 0.40717628705148207
name: Cosine Ndcg@1
- type: cosine_ndcg@20
value: 0.5052212563002627
name: Cosine Ndcg@20
- type: cosine_ndcg@50
value: 0.5356805472279078
name: Cosine Ndcg@50
- type: cosine_ndcg@100
value: 0.5521009256440798
name: Cosine Ndcg@100
- type: cosine_ndcg@150
value: 0.5595924746165437
name: Cosine Ndcg@150
- type: cosine_ndcg@200
value: 0.5639157869620031
name: Cosine Ndcg@200
- type: cosine_mrr@1
value: 0.40717628705148207
name: Cosine Mrr@1
- type: cosine_mrr@20
value: 0.49319826751048873
name: Cosine Mrr@20
- type: cosine_mrr@50
value: 0.49612329710657893
name: Cosine Mrr@50
- type: cosine_mrr@100
value: 0.4969826860284758
name: Cosine Mrr@100
- type: cosine_mrr@150
value: 0.49724671253026886
name: Cosine Mrr@150
- type: cosine_mrr@200
value: 0.49734552981578295
name: Cosine Mrr@200
- type: cosine_map@1
value: 0.40717628705148207
name: Cosine Map@1
- type: cosine_map@20
value: 0.419614969499609
name: Cosine Map@20
- type: cosine_map@50
value: 0.427088485280225
name: Cosine Map@50
- type: cosine_map@100
value: 0.4292014145714
name: Cosine Map@100
- type: cosine_map@150
value: 0.4298202694277485
name: Cosine Map@150
- type: cosine_map@200
value: 0.4300980044671579
name: Cosine Map@200
- type: cosine_map@500
value: 0.43055010151569684
name: Cosine Map@500
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: mix de
type: mix_de
metrics:
- type: cosine_accuracy@1
value: 0.29017160686427457
name: Cosine Accuracy@1
- type: cosine_accuracy@20
value: 0.6484659386375455
name: Cosine Accuracy@20
- type: cosine_accuracy@50
value: 0.7540301612064483
name: Cosine Accuracy@50
- type: cosine_accuracy@100
value: 0.8419136765470618
name: Cosine Accuracy@100
- type: cosine_accuracy@150
value: 0.8788351534061363
name: Cosine Accuracy@150
- type: cosine_accuracy@200
value: 0.9089963598543942
name: Cosine Accuracy@200
- type: cosine_precision@1
value: 0.29017160686427457
name: Cosine Precision@1
- type: cosine_precision@20
value: 0.07251690067602704
name: Cosine Precision@20
- type: cosine_precision@50
value: 0.0355486219448778
name: Cosine Precision@50
- type: cosine_precision@100
value: 0.02041601664066563
name: Cosine Precision@100
- type: cosine_precision@150
value: 0.014505113537874847
name: Cosine Precision@150
- type: cosine_precision@200
value: 0.01137545501820073
name: Cosine Precision@200
- type: cosine_recall@1
value: 0.10890968972092217
name: Cosine Recall@1
- type: cosine_recall@20
value: 0.5299011960478419
name: Cosine Recall@20
- type: cosine_recall@50
value: 0.647191887675507
name: Cosine Recall@50
- type: cosine_recall@100
value: 0.7442624371641533
name: Cosine Recall@100
- type: cosine_recall@150
value: 0.7928583810019068
name: Cosine Recall@150
- type: cosine_recall@200
value: 0.8289478245796498
name: Cosine Recall@200
- type: cosine_ndcg@1
value: 0.29017160686427457
name: Cosine Ndcg@1
- type: cosine_ndcg@20
value: 0.38598145754556046
name: Cosine Ndcg@20
- type: cosine_ndcg@50
value: 0.41773491829410075
name: Cosine Ndcg@50
- type: cosine_ndcg@100
value: 0.43906545567486005
name: Cosine Ndcg@100
- type: cosine_ndcg@150
value: 0.4485955578737219
name: Cosine Ndcg@150
- type: cosine_ndcg@200
value: 0.45520732213321263
name: Cosine Ndcg@200
- type: cosine_mrr@1
value: 0.29017160686427457
name: Cosine Mrr@1
- type: cosine_mrr@20
value: 0.37232551415227233
name: Cosine Mrr@20
- type: cosine_mrr@50
value: 0.375685507642469
name: Cosine Mrr@50
- type: cosine_mrr@100
value: 0.3769348294784883
name: Cosine Mrr@100
- type: cosine_mrr@150
value: 0.377239930826995
name: Cosine Mrr@150
- type: cosine_mrr@200
value: 0.3774183771765249
name: Cosine Mrr@200
- type: cosine_map@1
value: 0.29017160686427457
name: Cosine Map@1
- type: cosine_map@20
value: 0.30311022602590254
name: Cosine Map@20
- type: cosine_map@50
value: 0.31036427264538485
name: Cosine Map@50
- type: cosine_map@100
value: 0.31304585670015317
name: Cosine Map@100
- type: cosine_map@150
value: 0.3138396622777036
name: Cosine Map@150
- type: cosine_map@200
value: 0.31426372512191
name: Cosine Map@200
- type: cosine_map@500
value: 0.3150399864057635
name: Cosine Map@500
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: mix zh
type: mix_zh
metrics:
- type: cosine_accuracy@1
value: 0.09394572025052192
name: Cosine Accuracy@1
- type: cosine_accuracy@20
value: 0.35281837160751567
name: Cosine Accuracy@20
- type: cosine_accuracy@50
value: 0.48329853862212946
name: Cosine Accuracy@50
- type: cosine_accuracy@100
value: 0.5918580375782881
name: Cosine Accuracy@100
- type: cosine_accuracy@150
value: 0.6649269311064718
name: Cosine Accuracy@150
- type: cosine_accuracy@200
value: 0.7004175365344467
name: Cosine Accuracy@200
- type: cosine_precision@1
value: 0.09394572025052192
name: Cosine Precision@1
- type: cosine_precision@20
value: 0.030897703549060546
name: Cosine Precision@20
- type: cosine_precision@50
value: 0.018204592901878917
name: Cosine Precision@50
- type: cosine_precision@100
value: 0.011362212943632568
name: Cosine Precision@100
- type: cosine_precision@150
value: 0.008639526791927627
name: Cosine Precision@150
- type: cosine_precision@200
value: 0.007019832985386221
name: Cosine Precision@200
- type: cosine_recall@1
value: 0.03185455810716771
name: Cosine Recall@1
- type: cosine_recall@20
value: 0.20592877025549258
name: Cosine Recall@20
- type: cosine_recall@50
value: 0.30069837956059253
name: Cosine Recall@50
- type: cosine_recall@100
value: 0.3754792557245584
name: Cosine Recall@100
- type: cosine_recall@150
value: 0.4282591046160983
name: Cosine Recall@150
- type: cosine_recall@200
value: 0.46372361401067036
name: Cosine Recall@200
- type: cosine_ndcg@1
value: 0.09394572025052192
name: Cosine Ndcg@1
- type: cosine_ndcg@20
value: 0.13433471892252347
name: Cosine Ndcg@20
- type: cosine_ndcg@50
value: 0.16091824243484512
name: Cosine Ndcg@50
- type: cosine_ndcg@100
value: 0.1780017996510726
name: Cosine Ndcg@100
- type: cosine_ndcg@150
value: 0.1886875211403746
name: Cosine Ndcg@150
- type: cosine_ndcg@200
value: 0.19541417908856412
name: Cosine Ndcg@200
- type: cosine_mrr@1
value: 0.09394572025052192
name: Cosine Mrr@1
- type: cosine_mrr@20
value: 0.14710513443845905
name: Cosine Mrr@20
- type: cosine_mrr@50
value: 0.15122849766144658
name: Cosine Mrr@50
- type: cosine_mrr@100
value: 0.15275090014884107
name: Cosine Mrr@100
- type: cosine_mrr@150
value: 0.1533445728241347
name: Cosine Mrr@150
- type: cosine_mrr@200
value: 0.1535456563541225
name: Cosine Mrr@200
- type: cosine_map@1
value: 0.09394572025052192
name: Cosine Map@1
- type: cosine_map@20
value: 0.083759101073897
name: Cosine Map@20
- type: cosine_map@50
value: 0.08908800548950695
name: Cosine Map@50
- type: cosine_map@100
value: 0.09092612397080438
name: Cosine Map@100
- type: cosine_map@150
value: 0.09168814149038751
name: Cosine Map@150
- type: cosine_map@200
value: 0.09208168156532727
name: Cosine Map@200
- type: cosine_map@500
value: 0.09301554391402207
name: Cosine Map@500
---
# SentenceTransformer based on BAAI/bge-small-en-v1.5
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) on the full_en, full_de, full_es, full_zh and mix datasets. It maps sentences & paragraphs to a 384-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:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) <!-- at revision 5c38ec7c405ec4b44b94cc5a9bb96e735b38267a -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Datasets:**
- full_en
- full_de
- full_es
- full_zh
- mix
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'Volksvertreter',
'Parlamentarier',
'Oberbürgermeister',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Information Retrieval
* Datasets: `full_en`, `full_es`, `full_de`, `full_zh`, `mix_es`, `mix_de` and `mix_zh`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | full_en | full_es | full_de | full_zh | mix_es | mix_de | mix_zh |
|:---------------------|:---------|:-----------|:-----------|:-----------|:-----------|:-----------|:-----------|
| cosine_accuracy@1 | 0.6571 | 0.1243 | 0.2956 | 0.301 | 0.4072 | 0.2902 | 0.0939 |
| cosine_accuracy@20 | 0.9905 | 1.0 | 0.9212 | 0.7087 | 0.7582 | 0.6485 | 0.3528 |
| cosine_accuracy@50 | 0.9905 | 1.0 | 0.9606 | 0.8252 | 0.8456 | 0.754 | 0.4833 |
| cosine_accuracy@100 | 0.9905 | 1.0 | 0.9754 | 0.8544 | 0.9048 | 0.8419 | 0.5919 |
| cosine_accuracy@150 | 0.9905 | 1.0 | 0.9852 | 0.9126 | 0.9371 | 0.8788 | 0.6649 |
| cosine_accuracy@200 | 0.9905 | 1.0 | 0.9852 | 0.9417 | 0.9548 | 0.909 | 0.7004 |
| cosine_precision@1 | 0.6571 | 0.1243 | 0.2956 | 0.301 | 0.4072 | 0.2902 | 0.0939 |
| cosine_precision@20 | 0.5076 | 0.4924 | 0.4227 | 0.1684 | 0.0882 | 0.0725 | 0.0309 |
| cosine_precision@50 | 0.309 | 0.3169 | 0.2775 | 0.0944 | 0.0414 | 0.0355 | 0.0182 |
| cosine_precision@100 | 0.1872 | 0.1984 | 0.1787 | 0.0584 | 0.0227 | 0.0204 | 0.0114 |
| cosine_precision@150 | 0.1322 | 0.147 | 0.135 | 0.046 | 0.0158 | 0.0145 | 0.0086 |
| cosine_precision@200 | 0.1027 | 0.1176 | 0.1096 | 0.0382 | 0.0122 | 0.0114 | 0.007 |
| cosine_recall@1 | 0.068 | 0.0031 | 0.0111 | 0.0244 | 0.1547 | 0.1089 | 0.0319 |
| cosine_recall@20 | 0.5459 | 0.3236 | 0.2579 | 0.1751 | 0.6521 | 0.5299 | 0.2059 |
| cosine_recall@50 | 0.7285 | 0.4623 | 0.3785 | 0.2395 | 0.764 | 0.6472 | 0.3007 |
| cosine_recall@100 | 0.8382 | 0.5424 | 0.4647 | 0.286 | 0.839 | 0.7443 | 0.3755 |
| cosine_recall@150 | 0.8762 | 0.5823 | 0.5141 | 0.3291 | 0.8778 | 0.7929 | 0.4283 |
| cosine_recall@200 | 0.906 | 0.6126 | 0.5479 | 0.363 | 0.9017 | 0.8289 | 0.4637 |
| cosine_ndcg@1 | 0.6571 | 0.1243 | 0.2956 | 0.301 | 0.4072 | 0.2902 | 0.0939 |
| cosine_ndcg@20 | 0.6895 | 0.5407 | 0.4572 | 0.2385 | 0.5052 | 0.386 | 0.1343 |
| cosine_ndcg@50 | 0.7061 | 0.5008 | 0.4186 | 0.2342 | 0.5357 | 0.4177 | 0.1609 |
| cosine_ndcg@100 | 0.762 | 0.5143 | 0.4341 | 0.2559 | 0.5521 | 0.4391 | 0.178 |
| cosine_ndcg@150 | 0.7788 | 0.535 | 0.4594 | 0.2734 | 0.5596 | 0.4486 | 0.1887 |
| **cosine_ndcg@200** | **0.79** | **0.5498** | **0.4763** | **0.2857** | **0.5639** | **0.4552** | **0.1954** |
| cosine_mrr@1 | 0.6571 | 0.1243 | 0.2956 | 0.301 | 0.4072 | 0.2902 | 0.0939 |
| cosine_mrr@20 | 0.8098 | 0.5517 | 0.4885 | 0.4211 | 0.4932 | 0.3723 | 0.1471 |
| cosine_mrr@50 | 0.8098 | 0.5517 | 0.4898 | 0.425 | 0.4961 | 0.3757 | 0.1512 |
| cosine_mrr@100 | 0.8098 | 0.5517 | 0.49 | 0.4254 | 0.497 | 0.3769 | 0.1528 |
| cosine_mrr@150 | 0.8098 | 0.5517 | 0.4901 | 0.4259 | 0.4972 | 0.3772 | 0.1533 |
| cosine_mrr@200 | 0.8098 | 0.5517 | 0.4901 | 0.426 | 0.4973 | 0.3774 | 0.1535 |
| cosine_map@1 | 0.6571 | 0.1243 | 0.2956 | 0.301 | 0.4072 | 0.2902 | 0.0939 |
| cosine_map@20 | 0.5465 | 0.4062 | 0.3211 | 0.1416 | 0.4196 | 0.3031 | 0.0838 |
| cosine_map@50 | 0.5352 | 0.3426 | 0.2619 | 0.1233 | 0.4271 | 0.3104 | 0.0891 |
| cosine_map@100 | 0.5685 | 0.3398 | 0.2559 | 0.1288 | 0.4292 | 0.313 | 0.0909 |
| cosine_map@150 | 0.5757 | 0.3482 | 0.265 | 0.1315 | 0.4298 | 0.3138 | 0.0917 |
| cosine_map@200 | 0.5792 | 0.3535 | 0.2701 | 0.1329 | 0.4301 | 0.3143 | 0.0921 |
| cosine_map@500 | 0.5835 | 0.3635 | 0.2811 | 0.1368 | 0.4306 | 0.315 | 0.093 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Datasets
<details><summary>full_en</summary>
#### full_en
* Dataset: full_en
* Size: 28,880 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:--------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 3 tokens</li><li>mean: 5.0 tokens</li><li>max: 10 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.01 tokens</li><li>max: 13 tokens</li></ul> |
* Samples:
| anchor | positive |
|:-----------------------------------------|:-----------------------------------------|
| <code>air commodore</code> | <code>flight lieutenant</code> |
| <code>command and control officer</code> | <code>flight officer</code> |
| <code>air commodore</code> | <code>command and control officer</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
```json
{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
```
</details>
<details><summary>full_de</summary>
#### full_de
* Dataset: full_de
* Size: 23,023 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 3 tokens</li><li>mean: 11.05 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 11.43 tokens</li><li>max: 45 tokens</li></ul> |
* Samples:
| anchor | positive |
|:----------------------------------|:-----------------------------------------------------|
| <code>Staffelkommandantin</code> | <code>Kommodore</code> |
| <code>Luftwaffenoffizierin</code> | <code>Luftwaffenoffizier/Luftwaffenoffizierin</code> |
| <code>Staffelkommandantin</code> | <code>Luftwaffenoffizierin</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
```json
{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
```
</details>
<details><summary>full_es</summary>
#### full_es
* Dataset: full_es
* Size: 20,724 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 3 tokens</li><li>mean: 12.95 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 12.57 tokens</li><li>max: 50 tokens</li></ul> |
* Samples:
| anchor | positive |
|:------------------------------------|:-------------------------------------------|
| <code>jefe de escuadrón</code> | <code>instructor</code> |
| <code>comandante de aeronave</code> | <code>instructor de simulador</code> |
| <code>instructor</code> | <code>oficial del Ejército del Aire</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
```json
{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
```
</details>
<details><summary>full_zh</summary>
#### full_zh
* Dataset: full_zh
* Size: 30,401 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 4 tokens</li><li>mean: 8.36 tokens</li><li>max: 20 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 8.95 tokens</li><li>max: 27 tokens</li></ul> |
* Samples:
| anchor | positive |
|:------------------|:---------------------|
| <code>技术总监</code> | <code>技术和运营总监</code> |
| <code>技术总监</code> | <code>技术主管</code> |
| <code>技术总监</code> | <code>技术艺术总监</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
```json
{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
```
</details>
<details><summary>mix</summary>
#### mix
* Dataset: mix
* Size: 21,760 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 2 tokens</li><li>mean: 5.65 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 10.08 tokens</li><li>max: 30 tokens</li></ul> |
* Samples:
| anchor | positive |
|:------------------------------------------|:----------------------------------------------------------------|
| <code>technical manager</code> | <code>Technischer Direktor für Bühne, Film und Fernsehen</code> |
| <code>head of technical</code> | <code>directora técnica</code> |
| <code>head of technical department</code> | <code>技术艺术总监</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
```json
{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
```
</details>
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `gradient_accumulation_steps`: 2
- `num_train_epochs`: 5
- `warmup_ratio`: 0.05
- `log_on_each_node`: False
- `fp16`: True
- `dataloader_num_workers`: 4
- `ddp_find_unused_parameters`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 2
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 5
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.05
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: False
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: True
- `dataloader_num_workers`: 4
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `tp_size`: 0
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: True
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | full_en_cosine_ndcg@200 | full_es_cosine_ndcg@200 | full_de_cosine_ndcg@200 | full_zh_cosine_ndcg@200 | mix_es_cosine_ndcg@200 | mix_de_cosine_ndcg@200 | mix_zh_cosine_ndcg@200 |
|:------:|:----:|:-------------:|:-----------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|
| -1 | -1 | - | 0.7322 | 0.4690 | 0.3853 | 0.2723 | 0.3209 | 0.2244 | 0.0919 |
| 0.0021 | 1 | 23.8878 | - | - | - | - | - | - | - |
| 0.2058 | 100 | 7.2098 | - | - | - | - | - | - | - |
| 0.4115 | 200 | 4.2635 | 0.7800 | 0.5132 | 0.4268 | 0.2798 | 0.4372 | 0.2996 | 0.1447 |
| 0.6173 | 300 | 4.1931 | - | - | - | - | - | - | - |
| 0.8230 | 400 | 3.73 | 0.7863 | 0.5274 | 0.4451 | 0.2805 | 0.4762 | 0.3455 | 0.1648 |
| 1.0309 | 500 | 3.3569 | - | - | - | - | - | - | - |
| 1.2366 | 600 | 3.6464 | 0.7868 | 0.5372 | 0.4540 | 0.2813 | 0.5063 | 0.3794 | 0.1755 |
| 1.4424 | 700 | 3.0772 | - | - | - | - | - | - | - |
| 1.6481 | 800 | 3.114 | 0.7906 | 0.5391 | 0.4576 | 0.2832 | 0.5221 | 0.4047 | 0.1779 |
| 1.8539 | 900 | 2.9246 | - | - | - | - | - | - | - |
| 2.0617 | 1000 | 2.7479 | 0.7873 | 0.5423 | 0.4631 | 0.2871 | 0.5323 | 0.4143 | 0.1843 |
| 2.2675 | 1100 | 3.049 | - | - | - | - | - | - | - |
| 2.4733 | 1200 | 2.6137 | 0.7878 | 0.5418 | 0.4685 | 0.2870 | 0.5470 | 0.4339 | 0.1932 |
| 2.6790 | 1300 | 2.8607 | - | - | - | - | - | - | - |
| 2.8848 | 1400 | 2.7071 | 0.7889 | 0.5465 | 0.4714 | 0.2891 | 0.5504 | 0.4362 | 0.1944 |
| 3.0926 | 1500 | 2.7012 | - | - | - | - | - | - | - |
| 3.2984 | 1600 | 2.7423 | 0.7882 | 0.5471 | 0.4748 | 0.2868 | 0.5542 | 0.4454 | 0.1976 |
| 3.5041 | 1700 | 2.5316 | - | - | - | - | - | - | - |
| 3.7099 | 1800 | 2.6344 | 0.7900 | 0.5498 | 0.4763 | 0.2857 | 0.5639 | 0.4552 | 0.1954 |
### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 4.1.0
- Transformers: 4.51.3
- PyTorch: 2.6.0+cu124
- Accelerate: 1.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### GISTEmbedLoss
```bibtex
@misc{solatorio2024gistembed,
title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning},
author={Aivin V. Solatorio},
year={2024},
eprint={2402.16829},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
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