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README.md

@@ -1,3 +1,1420 @@
----
-license: apache-2.0
----
+---
+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: 0.5023809523809524
+      name: Cosine Precision@20
+    - type: cosine_precision@50
+      value: 0.30800000000000005
+      name: Cosine Precision@50
+    - type: cosine_precision@100
+      value: 0.18628571428571428
+      name: Cosine Precision@100
+    - type: cosine_precision@150
+      value: 0.1321904761904762
+      name: Cosine Precision@150
+    - type: cosine_precision@200
+      value: 0.10295238095238096
+      name: Cosine Precision@200
+    - type: cosine_recall@1
+      value: 0.0680237860830842
+      name: Cosine Recall@1
+    - type: cosine_recall@20
+      value: 0.5384852963395483
+      name: Cosine Recall@20
+    - type: cosine_recall@50
+      value: 0.7260449077992874
+      name: Cosine Recall@50
+    - type: cosine_recall@100
+      value: 0.8328530702930984
+      name: Cosine Recall@100
+    - type: cosine_recall@150
+      value: 0.8745262490032277
+      name: Cosine Recall@150
+    - type: cosine_recall@200
+      value: 0.9056960100263424
+      name: Cosine Recall@200
+    - type: cosine_ndcg@1
+      value: 0.6571428571428571
+      name: Cosine Ndcg@1
+    - type: cosine_ndcg@20
+      value: 0.6845256340390302
+      name: Cosine Ndcg@20
+    - type: cosine_ndcg@50
+      value: 0.7040452093638513
+      name: Cosine Ndcg@50
+    - type: cosine_ndcg@100
+      value: 0.758935932285001
+      name: Cosine Ndcg@100
+    - type: cosine_ndcg@150
+      value: 0.7774414598948007
+      name: Cosine Ndcg@150
+    - type: cosine_ndcg@200
+      value: 0.7892946240668293
+      name: Cosine Ndcg@200
+    - type: cosine_mrr@1
+      value: 0.6571428571428571
+      name: Cosine Mrr@1
+    - type: cosine_mrr@20
+      value: 0.8103174603174604
+      name: Cosine Mrr@20
+    - type: cosine_mrr@50
+      value: 0.8103174603174604
+      name: Cosine Mrr@50
+    - type: cosine_mrr@100
+      value: 0.8103174603174604
+      name: Cosine Mrr@100
+    - type: cosine_mrr@150
+      value: 0.8103174603174604
+      name: Cosine Mrr@150
+    - type: cosine_mrr@200
+      value: 0.8103174603174604
+      name: Cosine Mrr@200
+    - type: cosine_map@1
+      value: 0.6571428571428571
+      name: Cosine Map@1
+    - type: cosine_map@20
+      value: 0.5418235787800474
+      name: Cosine Map@20
+    - type: cosine_map@50
+      value: 0.5327215779103721
+      name: Cosine Map@50
+    - type: cosine_map@100
+      value: 0.565706253334091
+      name: Cosine Map@100
+    - type: cosine_map@150
+      value: 0.5733951147399983
+      name: Cosine Map@150
+    - type: cosine_map@200
+      value: 0.5771587776237981
+      name: Cosine Map@200
+    - type: cosine_map@500
+      value: 0.5813892452974444
+      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: 0.4897297297297297
+      name: Cosine Precision@20
+    - type: cosine_precision@50
+      value: 0.31794594594594594
+      name: Cosine Precision@50
+    - type: cosine_precision@100
+      value: 0.19864864864864865
+      name: Cosine Precision@100
+    - type: cosine_precision@150
+      value: 0.14688288288288287
+      name: Cosine Precision@150
+    - type: cosine_precision@200
+      value: 0.11789189189189188
+      name: Cosine Precision@200
+    - type: cosine_recall@1
+      value: 0.003111544931768446
+      name: Cosine Recall@1
+    - type: cosine_recall@20
+      value: 0.32208664960961075
+      name: Cosine Recall@20
+    - type: cosine_recall@50
+      value: 0.46383117404893587
+      name: Cosine Recall@50
+    - type: cosine_recall@100
+      value: 0.5437537828683688
+      name: Cosine Recall@100
+    - type: cosine_recall@150
+      value: 0.5824968655076911
+      name: Cosine Recall@150
+    - type: cosine_recall@200
+      value: 0.6146962508233631
+      name: Cosine Recall@200
+    - type: cosine_ndcg@1
+      value: 0.12432432432432433
+      name: Cosine Ndcg@1
+    - type: cosine_ndcg@20
+      value: 0.5384577730264963
+      name: Cosine Ndcg@20
+    - type: cosine_ndcg@50
+      value: 0.5012455261232941
+      name: Cosine Ndcg@50
+    - type: cosine_ndcg@100
+      value: 0.5147486871284331
+      name: Cosine Ndcg@100
+    - type: cosine_ndcg@150
+      value: 0.5348194013794069
+      name: Cosine Ndcg@150
+    - type: cosine_ndcg@200
+      value: 0.5505397598095297
+      name: Cosine Ndcg@200
+    - type: cosine_mrr@1
+      value: 0.12432432432432433
+      name: Cosine Mrr@1
+    - type: cosine_mrr@20
+      value: 0.5515015015015016
+      name: Cosine Mrr@20
+    - type: cosine_mrr@50
+      value: 0.5515015015015016
+      name: Cosine Mrr@50
+    - type: cosine_mrr@100
+      value: 0.5515015015015016
+      name: Cosine Mrr@100
+    - type: cosine_mrr@150
+      value: 0.5515015015015016
+      name: Cosine Mrr@150
+    - type: cosine_mrr@200
+      value: 0.5515015015015016
+      name: Cosine Mrr@200
+    - type: cosine_map@1
+      value: 0.12432432432432433
+      name: Cosine Map@1
+    - type: cosine_map@20
+      value: 0.40280623036556984
+      name: Cosine Map@20
+    - type: cosine_map@50
+      value: 0.3421710529569103
+      name: Cosine Map@50
+    - type: cosine_map@100
+      value: 0.33947884152876345
+      name: Cosine Map@100
+    - type: cosine_map@150
+      value: 0.34777364049184706
+      name: Cosine Map@150
+    - type: cosine_map@200
+      value: 0.35339765423089375
+      name: Cosine Map@200
+    - type: cosine_map@500
+      value: 0.3631043007370563
+      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: 0.9655172413793104
+      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: 0.4246305418719211
+      name: Cosine Precision@20
+    - type: cosine_precision@50
+      value: 0.2813793103448276
+      name: Cosine Precision@50
+    - type: cosine_precision@100
+      value: 0.1800985221674877
+      name: Cosine Precision@100
+    - type: cosine_precision@150
+      value: 0.1362233169129721
+      name: Cosine Precision@150
+    - type: cosine_precision@200
+      value: 0.11054187192118226
+      name: Cosine Precision@200
+    - type: cosine_recall@1
+      value: 0.01108543831680986
+      name: Cosine Recall@1
+    - type: cosine_recall@20
+      value: 0.26139377973111655
+      name: Cosine Recall@20
+    - type: cosine_recall@50
+      value: 0.3835171819041212
+      name: Cosine Recall@50
+    - type: cosine_recall@100
+      value: 0.4676892706124872
+      name: Cosine Recall@100
+    - type: cosine_recall@150
+      value: 0.5183014504752351
+      name: Cosine Recall@150
+    - type: cosine_recall@200
+      value: 0.551717511250073
+      name: Cosine Recall@200
+    - type: cosine_ndcg@1
+      value: 0.2955665024630542
+      name: Cosine Ndcg@1
+    - type: cosine_ndcg@20
+      value: 0.4600580109269636
+      name: Cosine Ndcg@20
+    - type: cosine_ndcg@50
+      value: 0.4229190542750304
+      name: Cosine Ndcg@50
+    - type: cosine_ndcg@100
+      value: 0.4370543021366767
+      name: Cosine Ndcg@100
+    - type: cosine_ndcg@150
+      value: 0.46289045418097646
+      name: Cosine Ndcg@150
+    - type: cosine_ndcg@200
+      value: 0.4796711024513544
+      name: Cosine Ndcg@200
+    - type: cosine_mrr@1
+      value: 0.2955665024630542
+      name: Cosine Mrr@1
+    - type: cosine_mrr@20
+      value: 0.48958320005117995
+      name: Cosine Mrr@20
+    - type: cosine_mrr@50
+      value: 0.49093477998292195
+      name: Cosine Mrr@50
+    - type: cosine_mrr@100
+      value: 0.4910841931964832
+      name: Cosine Mrr@100
+    - type: cosine_mrr@150
+      value: 0.4911623560854821
+      name: Cosine Mrr@150
+    - type: cosine_mrr@200
+      value: 0.4911623560854821
+      name: Cosine Mrr@200
+    - type: cosine_map@1
+      value: 0.2955665024630542
+      name: Cosine Map@1
+    - type: cosine_map@20
+      value: 0.32364842421740225
+      name: Cosine Map@20
+    - type: cosine_map@50
+      value: 0.2643813390551392
+      name: Cosine Map@50
+    - type: cosine_map@100
+      value: 0.2576413544507463
+      name: Cosine Map@100
+    - type: cosine_map@150
+      value: 0.2669126239698539
+      name: Cosine Map@150
+    - type: cosine_map@200
+      value: 0.27215799504041416
+      name: Cosine Map@200
+    - type: cosine_map@500
+      value: 0.28329484592874316
+      name: Cosine Map@500
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: full zh
+      type: full_zh
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.34951456310679613
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@20
+      value: 0.7378640776699029
+      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: 0.9029126213592233
+      name: Cosine Accuracy@150
+    - type: cosine_accuracy@200
+      value: 0.941747572815534
+      name: Cosine Accuracy@200
+    - type: cosine_precision@1
+      value: 0.34951456310679613
+      name: Cosine Precision@1
+    - type: cosine_precision@20
+      value: 0.17330097087378643
+      name: Cosine Precision@20
+    - type: cosine_precision@50
+      value: 0.09436893203883494
+      name: Cosine Precision@50
+    - type: cosine_precision@100
+      value: 0.05893203883495146
+      name: Cosine Precision@100
+    - type: cosine_precision@150
+      value: 0.0458252427184466
+      name: Cosine Precision@150
+    - type: cosine_precision@200
+      value: 0.03854368932038834
+      name: Cosine Precision@200
+    - type: cosine_recall@1
+      value: 0.02726635297033844
+      name: Cosine Recall@1
+    - type: cosine_recall@20
+      value: 0.17661061398990294
+      name: Cosine Recall@20
+    - type: cosine_recall@50
+      value: 0.2392861843604663
+      name: Cosine Recall@50
+    - type: cosine_recall@100
+      value: 0.2862639658547104
+      name: Cosine Recall@100
+    - type: cosine_recall@150
+      value: 0.3286954340443375
+      name: Cosine Recall@150
+    - type: cosine_recall@200
+      value: 0.3630829587412431
+      name: Cosine Recall@200
+    - type: cosine_ndcg@1
+      value: 0.34951456310679613
+      name: Cosine Ndcg@1
+    - type: cosine_ndcg@20
+      value: 0.24683538489164747
+      name: Cosine Ndcg@20
+    - type: cosine_ndcg@50
+      value: 0.23936442282824424
+      name: Cosine Ndcg@50
+    - type: cosine_ndcg@100
+      value: 0.2618891246293786
+      name: Cosine Ndcg@100
+    - type: cosine_ndcg@150
+      value: 0.27867525817923894
+      name: Cosine Ndcg@150
+    - type: cosine_ndcg@200
+      value: 0.29190260238165355
+      name: Cosine Ndcg@200
+    - type: cosine_mrr@1
+      value: 0.34951456310679613
+      name: Cosine Mrr@1
+    - type: cosine_mrr@20
+      value: 0.44845699819699636
+      name: Cosine Mrr@20
+    - type: cosine_mrr@50
+      value: 0.4514515915598798
+      name: Cosine Mrr@50
+    - type: cosine_mrr@100
+      value: 0.451864194979824
+      name: Cosine Mrr@100
+    - type: cosine_mrr@150
+      value: 0.4522894025156287
+      name: Cosine Mrr@150
+    - type: cosine_mrr@200
+      value: 0.45250948321580986
+      name: Cosine Mrr@200
+    - type: cosine_map@1
+      value: 0.34951456310679613
+      name: Cosine Map@1
+    - type: cosine_map@20
+      value: 0.1470309927546457
+      name: Cosine Map@20
+    - type: cosine_map@50
+      value: 0.12671489844037503
+      name: Cosine Map@50
+    - type: cosine_map@100
+      value: 0.13257859039926595
+      name: Cosine Map@100
+    - type: cosine_map@150
+      value: 0.13523273342027425
+      name: Cosine Map@150
+    - type: cosine_map@200
+      value: 0.13679857663871084
+      name: Cosine Map@200
+    - type: cosine_map@500
+      value: 0.14069476480399515
+      name: Cosine Map@500
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: mix es
+      type: mix_es
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.41133645345813835
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@20
+      value: 0.7613104524180967
+      name: Cosine Accuracy@20
+    - type: cosine_accuracy@50
+      value: 0.8523140925637025
+      name: Cosine Accuracy@50
+    - type: cosine_accuracy@100
+      value: 0.9121164846593863
+      name: Cosine Accuracy@100
+    - type: cosine_accuracy@150
+      value: 0.9417576703068122
+      name: Cosine Accuracy@150
+    - type: cosine_accuracy@200
+      value: 0.9547581903276131
+      name: Cosine Accuracy@200
+    - type: cosine_precision@1
+      value: 0.41133645345813835
+      name: Cosine Precision@1
+    - type: cosine_precision@20
+      value: 0.08920956838273532
+      name: Cosine Precision@20
+    - type: cosine_precision@50
+      value: 0.04175767030681228
+      name: Cosine Precision@50
+    - type: cosine_precision@100
+      value: 0.02291731669266771
+      name: Cosine Precision@100
+    - type: cosine_precision@150
+      value: 0.015905702894782457
+      name: Cosine Precision@150
+    - type: cosine_precision@200
+      value: 0.012243889755590227
+      name: Cosine Precision@200
+    - type: cosine_recall@1
+      value: 0.15653988064284477
+      name: Cosine Recall@1
+    - type: cosine_recall@20
+      value: 0.6593678032835598
+      name: Cosine Recall@20
+    - type: cosine_recall@50
+      value: 0.7704838669737266
+      name: Cosine Recall@50
+    - type: cosine_recall@100
+      value: 0.847169601069757
+      name: Cosine Recall@100
+    - type: cosine_recall@150
+      value: 0.8825483495530297
+      name: Cosine Recall@150
+    - type: cosine_recall@200
+      value: 0.9050999182824455
+      name: Cosine Recall@200
+    - type: cosine_ndcg@1
+      value: 0.41133645345813835
+      name: Cosine Ndcg@1
+    - type: cosine_ndcg@20
+      value: 0.5116672519515115
+      name: Cosine Ndcg@20
+    - type: cosine_ndcg@50
+      value: 0.542000920569141
+      name: Cosine Ndcg@50
+    - type: cosine_ndcg@100
+      value: 0.558759964344595
+      name: Cosine Ndcg@100
+    - type: cosine_ndcg@150
+      value: 0.5655977162199296
+      name: Cosine Ndcg@150
+    - type: cosine_ndcg@200
+      value: 0.5697289878952349
+      name: Cosine Ndcg@200
+    - type: cosine_mrr@1
+      value: 0.41133645345813835
+      name: Cosine Mrr@1
+    - type: cosine_mrr@20
+      value: 0.4978677179556957
+      name: Cosine Mrr@20
+    - type: cosine_mrr@50
+      value: 0.5009543893008301
+      name: Cosine Mrr@50
+    - type: cosine_mrr@100
+      value: 0.5018183607581652
+      name: Cosine Mrr@100
+    - type: cosine_mrr@150
+      value: 0.5020589846475842
+      name: Cosine Mrr@150
+    - type: cosine_mrr@200
+      value: 0.5021321446410069
+      name: Cosine Mrr@200
+    - type: cosine_map@1
+      value: 0.41133645345813835
+      name: Cosine Map@1
+    - type: cosine_map@20
+      value: 0.4263681424556441
+      name: Cosine Map@20
+    - type: cosine_map@50
+      value: 0.4338209025376249
+      name: Cosine Map@50
+    - type: cosine_map@100
+      value: 0.4359939776007631
+      name: Cosine Map@100
+    - type: cosine_map@150
+      value: 0.43656970643226983
+      name: Cosine Map@150
+    - type: cosine_map@200
+      value: 0.4368426702726571
+      name: Cosine Map@200
+    - type: cosine_map@500
+      value: 0.43729529920887905
+      name: Cosine Map@500
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: mix de
+      type: mix_de
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.29433177327093085
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@20
+      value: 0.6500260010400416
+      name: Cosine Accuracy@20
+    - type: cosine_accuracy@50
+      value: 0.7607904316172647
+      name: Cosine Accuracy@50
+    - type: cosine_accuracy@100
+      value: 0.8507540301612064
+      name: Cosine Accuracy@100
+    - type: cosine_accuracy@150
+      value: 0.889755590223609
+      name: Cosine Accuracy@150
+    - type: cosine_accuracy@200
+      value: 0.9204368174726989
+      name: Cosine Accuracy@200
+    - type: cosine_precision@1
+      value: 0.29433177327093085
+      name: Cosine Precision@1
+    - type: cosine_precision@20
+      value: 0.07308892355694228
+      name: Cosine Precision@20
+    - type: cosine_precision@50
+      value: 0.036141445657826315
+      name: Cosine Precision@50
+    - type: cosine_precision@100
+      value: 0.020634425377015084
+      name: Cosine Precision@100
+    - type: cosine_precision@150
+      value: 0.014681920610157736
+      name: Cosine Precision@150
+    - type: cosine_precision@200
+      value: 0.011552262090483621
+      name: Cosine Precision@200
+    - type: cosine_recall@1
+      value: 0.1109031027907783
+      name: Cosine Recall@1
+    - type: cosine_recall@20
+      value: 0.534356040908303
+      name: Cosine Recall@20
+    - type: cosine_recall@50
+      value: 0.6584676720402148
+      name: Cosine Recall@50
+    - type: cosine_recall@100
+      value: 0.752470098803952
+      name: Cosine Recall@100
+    - type: cosine_recall@150
+      value: 0.8025567689374241
+      name: Cosine Recall@150
+    - type: cosine_recall@200
+      value: 0.8417663373201595
+      name: Cosine Recall@200
+    - type: cosine_ndcg@1
+      value: 0.29433177327093085
+      name: Cosine Ndcg@1
+    - type: cosine_ndcg@20
+      value: 0.3919428679123834
+      name: Cosine Ndcg@20
+    - type: cosine_ndcg@50
+      value: 0.425599899100406
+      name: Cosine Ndcg@50
+    - type: cosine_ndcg@100
+      value: 0.4462421162922913
+      name: Cosine Ndcg@100
+    - type: cosine_ndcg@150
+      value: 0.45606402272845137
+      name: Cosine Ndcg@150
+    - type: cosine_ndcg@200
+      value: 0.4632312746623382
+      name: Cosine Ndcg@200
+    - type: cosine_mrr@1
+      value: 0.29433177327093085
+      name: Cosine Mrr@1
+    - type: cosine_mrr@20
+      value: 0.37785395494554963
+      name: Cosine Mrr@20
+    - type: cosine_mrr@50
+      value: 0.38148321196953044
+      name: Cosine Mrr@50
+    - type: cosine_mrr@100
+      value: 0.38274724688611994
+      name: Cosine Mrr@100
+    - type: cosine_mrr@150
+      value: 0.3830666241433367
+      name: Cosine Mrr@150
+    - type: cosine_mrr@200
+      value: 0.3832429794087988
+      name: Cosine Mrr@200
+    - type: cosine_map@1
+      value: 0.29433177327093085
+      name: Cosine Map@1
+    - type: cosine_map@20
+      value: 0.3096720133634083
+      name: Cosine Map@20
+    - type: cosine_map@50
+      value: 0.31740714963039135
+      name: Cosine Map@50
+    - type: cosine_map@100
+      value: 0.31992557448195186
+      name: Cosine Map@100
+    - type: cosine_map@150
+      value: 0.3207379270967634
+      name: Cosine Map@150
+    - type: cosine_map@200
+      value: 0.3211962807999124
+      name: Cosine Map@200
+    - type: cosine_map@500
+      value: 0.3219246841517722
+      name: Cosine Map@500
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: mix zh
+      type: mix_zh
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.09707724425887265
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@20
+      value: 0.3585594989561587
+      name: Cosine Accuracy@20
+    - type: cosine_accuracy@50
+      value: 0.4900835073068894
+      name: Cosine Accuracy@50
+    - type: cosine_accuracy@100
+      value: 0.6002087682672234
+      name: Cosine Accuracy@100
+    - type: cosine_accuracy@150
+      value: 0.6612734864300627
+      name: Cosine Accuracy@150
+    - type: cosine_accuracy@200
+      value: 0.7061586638830898
+      name: Cosine Accuracy@200
+    - type: cosine_precision@1
+      value: 0.09707724425887265
+      name: Cosine Precision@1
+    - type: cosine_precision@20
+      value: 0.03144572025052192
+      name: Cosine Precision@20
+    - type: cosine_precision@50
+      value: 0.018486430062630482
+      name: Cosine Precision@50
+    - type: cosine_precision@100
+      value: 0.011612734864300627
+      name: Cosine Precision@100
+    - type: cosine_precision@150
+      value: 0.008688239387613084
+      name: Cosine Precision@150
+    - type: cosine_precision@200
+      value: 0.007132045929018789
+      name: Cosine Precision@200
+    - type: cosine_recall@1
+      value: 0.032868575405109846
+      name: Cosine Recall@1
+    - type: cosine_recall@20
+      value: 0.20912118500845014
+      name: Cosine Recall@20
+    - type: cosine_recall@50
+      value: 0.305353414852371
+      name: Cosine Recall@50
+    - type: cosine_recall@100
+      value: 0.3834696126188819
+      name: Cosine Recall@100
+    - type: cosine_recall@150
+      value: 0.43087740663419155
+      name: Cosine Recall@150
+    - type: cosine_recall@200
+      value: 0.4714567385757365
+      name: Cosine Recall@200
+    - type: cosine_ndcg@1
+      value: 0.09707724425887265
+      name: Cosine Ndcg@1
+    - type: cosine_ndcg@20
+      value: 0.13847583254619214
+      name: Cosine Ndcg@20
+    - type: cosine_ndcg@50
+      value: 0.16556220177827802
+      name: Cosine Ndcg@50
+    - type: cosine_ndcg@100
+      value: 0.1834871578549362
+      name: Cosine Ndcg@100
+    - type: cosine_ndcg@150
+      value: 0.1930615498205831
+      name: Cosine Ndcg@150
+    - type: cosine_ndcg@200
+      value: 0.20074882110420836
+      name: Cosine Ndcg@200
+    - type: cosine_mrr@1
+      value: 0.09707724425887265
+      name: Cosine Mrr@1
+    - type: cosine_mrr@20
+      value: 0.15220960831749397
+      name: Cosine Mrr@20
+    - type: cosine_mrr@50
+      value: 0.15642354470896513
+      name: Cosine Mrr@50
+    - type: cosine_mrr@100
+      value: 0.1580041495008456
+      name: Cosine Mrr@100
+    - type: cosine_mrr@150
+      value: 0.15850022553236756
+      name: Cosine Mrr@150
+    - type: cosine_mrr@200
+      value: 0.1587557913720219
+      name: Cosine Mrr@200
+    - type: cosine_map@1
+      value: 0.09707724425887265
+      name: Cosine Map@1
+    - type: cosine_map@20
+      value: 0.08751052569766739
+      name: Cosine Map@20
+    - type: cosine_map@50
+      value: 0.09304075210745723
+      name: Cosine Map@50
+    - type: cosine_map@100
+      value: 0.09500635866296525
+      name: Cosine Map@100
+    - type: cosine_map@150
+      value: 0.09570276054684158
+      name: Cosine Map@150
+    - type: cosine_map@200
+      value: 0.09614394028730197
+      name: Cosine Map@200
+    - type: cosine_map@500
+      value: 0.09706713378133278
+      name: Cosine Map@500
+---
+
+# Job - Job matching BAAI/bge-small-en-v1.5
+
+Top performing model on [TalentCLEF 2025](https://talentclef.github.io/talentclef/) Task A.  Use it for multilingual job title matching
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [BAAI/bge-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     | 0.3495     | 0.4113     | 0.2943     | 0.0971     |
+| cosine_accuracy@20   | 0.9905     | 1.0        | 0.9212     | 0.7379     | 0.7613     | 0.65       | 0.3586     |
+| cosine_accuracy@50   | 0.9905     | 1.0        | 0.9655     | 0.8252     | 0.8523     | 0.7608     | 0.4901     |
+| cosine_accuracy@100  | 0.9905     | 1.0        | 0.9754     | 0.8544     | 0.9121     | 0.8508     | 0.6002     |
+| cosine_accuracy@150  | 0.9905     | 1.0        | 0.9852     | 0.9029     | 0.9418     | 0.8898     | 0.6613     |
+| cosine_accuracy@200  | 0.9905     | 1.0        | 0.9852     | 0.9417     | 0.9548     | 0.9204     | 0.7062     |
+| cosine_precision@1   | 0.6571     | 0.1243     | 0.2956     | 0.3495     | 0.4113     | 0.2943     | 0.0971     |
+| cosine_precision@20  | 0.5024     | 0.4897     | 0.4246     | 0.1733     | 0.0892     | 0.0731     | 0.0314     |
+| cosine_precision@50  | 0.308      | 0.3179     | 0.2814     | 0.0944     | 0.0418     | 0.0361     | 0.0185     |
+| cosine_precision@100 | 0.1863     | 0.1986     | 0.1801     | 0.0589     | 0.0229     | 0.0206     | 0.0116     |
+| cosine_precision@150 | 0.1322     | 0.1469     | 0.1362     | 0.0458     | 0.0159     | 0.0147     | 0.0087     |
+| cosine_precision@200 | 0.103      | 0.1179     | 0.1105     | 0.0385     | 0.0122     | 0.0116     | 0.0071     |
+| cosine_recall@1      | 0.068      | 0.0031     | 0.0111     | 0.0273     | 0.1565     | 0.1109     | 0.0329     |
+| cosine_recall@20     | 0.5385     | 0.3221     | 0.2614     | 0.1766     | 0.6594     | 0.5344     | 0.2091     |
+| cosine_recall@50     | 0.726      | 0.4638     | 0.3835     | 0.2393     | 0.7705     | 0.6585     | 0.3054     |
+| cosine_recall@100    | 0.8329     | 0.5438     | 0.4677     | 0.2863     | 0.8472     | 0.7525     | 0.3835     |
+| cosine_recall@150    | 0.8745     | 0.5825     | 0.5183     | 0.3287     | 0.8825     | 0.8026     | 0.4309     |
+| cosine_recall@200    | 0.9057     | 0.6147     | 0.5517     | 0.3631     | 0.9051     | 0.8418     | 0.4715     |
+| cosine_ndcg@1        | 0.6571     | 0.1243     | 0.2956     | 0.3495     | 0.4113     | 0.2943     | 0.0971     |
+| cosine_ndcg@20       | 0.6845     | 0.5385     | 0.4601     | 0.2468     | 0.5117     | 0.3919     | 0.1385     |
+| cosine_ndcg@50       | 0.704      | 0.5012     | 0.4229     | 0.2394     | 0.542      | 0.4256     | 0.1656     |
+| cosine_ndcg@100      | 0.7589     | 0.5147     | 0.4371     | 0.2619     | 0.5588     | 0.4462     | 0.1835     |
+| cosine_ndcg@150      | 0.7774     | 0.5348     | 0.4629     | 0.2787     | 0.5656     | 0.4561     | 0.1931     |
+| **cosine_ndcg@200**  | **0.7893** | **0.5505** | **0.4797** | **0.2919** | **0.5697** | **0.4632** | **0.2007** |
+| cosine_mrr@1         | 0.6571     | 0.1243     | 0.2956     | 0.3495     | 0.4113     | 0.2943     | 0.0971     |
+| cosine_mrr@20        | 0.8103     | 0.5515     | 0.4896     | 0.4485     | 0.4979     | 0.3779     | 0.1522     |
+| cosine_mrr@50        | 0.8103     | 0.5515     | 0.4909     | 0.4515     | 0.501      | 0.3815     | 0.1564     |
+| cosine_mrr@100       | 0.8103     | 0.5515     | 0.4911     | 0.4519     | 0.5018     | 0.3827     | 0.158      |
+| cosine_mrr@150       | 0.8103     | 0.5515     | 0.4912     | 0.4523     | 0.5021     | 0.3831     | 0.1585     |
+| cosine_mrr@200       | 0.8103     | 0.5515     | 0.4912     | 0.4525     | 0.5021     | 0.3832     | 0.1588     |
+| cosine_map@1         | 0.6571     | 0.1243     | 0.2956     | 0.3495     | 0.4113     | 0.2943     | 0.0971     |
+| cosine_map@20        | 0.5418     | 0.4028     | 0.3236     | 0.147      | 0.4264     | 0.3097     | 0.0875     |
+| cosine_map@50        | 0.5327     | 0.3422     | 0.2644     | 0.1267     | 0.4338     | 0.3174     | 0.093      |
+| cosine_map@100       | 0.5657     | 0.3395     | 0.2576     | 0.1326     | 0.436      | 0.3199     | 0.095      |
+| cosine_map@150       | 0.5734     | 0.3478     | 0.2669     | 0.1352     | 0.4366     | 0.3207     | 0.0957     |
+| cosine_map@200       | 0.5772     | 0.3534     | 0.2722     | 0.1368     | 0.4368     | 0.3212     | 0.0961     |
+| cosine_map@500       | 0.5814     | 0.3631     | 0.2833     | 0.1407     | 0.4373     | 0.3219     | 0.0971     |
+
+<!--
+## 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                 |
+| 3.9156 | 1900 | 2.4983        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 4.1235 | 2000 | 2.5423        | 0.7894                  | 0.5499                  | 0.4786                  | 0.2870                  | 0.5644                 | 0.4576                 | 0.1974                 |
+| 4.3292 | 2100 | 2.5674        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 4.5350 | 2200 | 2.6237        | 0.7899                  | 0.5502                  | 0.4802                  | 0.2843                  | 0.5674                 | 0.4607                 | 0.1993                 |
+| 4.7407 | 2300 | 2.3776        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 4.9465 | 2400 | 2.1116        | 0.7893                  | 0.5505                  | 0.4797                  | 0.2919                  | 0.5697                 | 0.4632                 | 0.2007                 |
+
+
+### 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}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

checkpoint-1600/README.md

@@ -0,0 +1,1412 @@
+---
+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: 0.5019047619047619
+      name: Cosine Precision@20
+    - type: cosine_precision@50
+      value: 0.3087619047619048
+      name: Cosine Precision@50
+    - type: cosine_precision@100
+      value: 0.18676190476190477
+      name: Cosine Precision@100
+    - type: cosine_precision@150
+      value: 0.13282539682539682
+      name: Cosine Precision@150
+    - type: cosine_precision@200
+      value: 0.10247619047619048
+      name: Cosine Precision@200
+    - type: cosine_recall@1
+      value: 0.0680237860830842
+      name: Cosine Recall@1
+    - type: cosine_recall@20
+      value: 0.5375018845788361
+      name: Cosine Recall@20
+    - type: cosine_recall@50
+      value: 0.7285392134841354
+      name: Cosine Recall@50
+    - type: cosine_recall@100
+      value: 0.8322341922506664
+      name: Cosine Recall@100
+    - type: cosine_recall@150
+      value: 0.8801590338170654
+      name: Cosine Recall@150
+    - type: cosine_recall@200
+      value: 0.902805836843439
+      name: Cosine Recall@200
+    - type: cosine_ndcg@1
+      value: 0.6571428571428571
+      name: Cosine Ndcg@1
+    - type: cosine_ndcg@20
+      value: 0.6842763252168604
+      name: Cosine Ndcg@20
+    - type: cosine_ndcg@50
+      value: 0.7050658917677234
+      name: Cosine Ndcg@50
+    - type: cosine_ndcg@100
+      value: 0.7592503116780603
+      name: Cosine Ndcg@100
+    - type: cosine_ndcg@150
+      value: 0.7793217469818706
+      name: Cosine Ndcg@150
+    - type: cosine_ndcg@200
+      value: 0.7881724081346186
+      name: Cosine Ndcg@200
+    - type: cosine_mrr@1
+      value: 0.6571428571428571
+      name: Cosine Mrr@1
+    - type: cosine_mrr@20
+      value: 0.8095238095238095
+      name: Cosine Mrr@20
+    - type: cosine_mrr@50
+      value: 0.8095238095238095
+      name: Cosine Mrr@50
+    - type: cosine_mrr@100
+      value: 0.8095238095238095
+      name: Cosine Mrr@100
+    - type: cosine_mrr@150
+      value: 0.8095238095238095
+      name: Cosine Mrr@150
+    - type: cosine_mrr@200
+      value: 0.8095238095238095
+      name: Cosine Mrr@200
+    - type: cosine_map@1
+      value: 0.6571428571428571
+      name: Cosine Map@1
+    - type: cosine_map@20
+      value: 0.5422881048847362
+      name: Cosine Map@20
+    - type: cosine_map@50
+      value: 0.5339554214943681
+      name: Cosine Map@50
+    - type: cosine_map@100
+      value: 0.5668962932951893
+      name: Cosine Map@100
+    - type: cosine_map@150
+      value: 0.5749918744199348
+      name: Cosine Map@150
+    - type: cosine_map@200
+      value: 0.5778159227565198
+      name: Cosine Map@200
+    - type: cosine_map@500
+      value: 0.5826470723639171
+      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: 0.48837837837837833
+      name: Cosine Precision@20
+    - type: cosine_precision@50
+      value: 0.31416216216216214
+      name: Cosine Precision@50
+    - type: cosine_precision@100
+      value: 0.19681081081081084
+      name: Cosine Precision@100
+    - type: cosine_precision@150
+      value: 0.1451891891891892
+      name: Cosine Precision@150
+    - type: cosine_precision@200
+      value: 0.11681081081081081
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+  - task:
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+      name: Information Retrieval
+    dataset:
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+      type: mix_es
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+  - task:
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+      name: Information Retrieval
+    dataset:
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+      type: mix_de
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+    - type: cosine_map@500
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+      name: Cosine Map@500
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: mix zh
+      type: mix_zh
+    metrics:
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+      name: Cosine Ndcg@50
+    - type: cosine_ndcg@100
+      value: 0.180685476350191
+      name: Cosine Ndcg@100
+    - type: cosine_ndcg@150
+      value: 0.1913175060746284
+      name: Cosine Ndcg@150
+    - type: cosine_ndcg@200
+      value: 0.19756360996316394
+      name: Cosine Ndcg@200
+    - type: cosine_mrr@1
+      value: 0.09394572025052192
+      name: Cosine Mrr@1
+    - type: cosine_mrr@20
+      value: 0.1486268139347834
+      name: Cosine Mrr@20
+    - type: cosine_mrr@50
+      value: 0.15284573002617868
+      name: Cosine Mrr@50
+    - type: cosine_mrr@100
+      value: 0.15439034982296526
+      name: Cosine Mrr@100
+    - type: cosine_mrr@150
+      value: 0.1549448592625029
+      name: Cosine Mrr@150
+    - type: cosine_mrr@200
+      value: 0.15516529379486205
+      name: Cosine Mrr@200
+    - type: cosine_map@1
+      value: 0.09394572025052192
+      name: Cosine Map@1
+    - type: cosine_map@20
+      value: 0.08491818175758956
+      name: Cosine Map@20
+    - type: cosine_map@50
+      value: 0.09052948294713963
+      name: Cosine Map@50
+    - type: cosine_map@100
+      value: 0.09248066772963609
+      name: Cosine Map@100
+    - type: cosine_map@150
+      value: 0.09326228290776101
+      name: Cosine Map@150
+    - type: cosine_map@200
+      value: 0.09361529404962608
+      name: Cosine Map@200
+    - type: cosine_map@500
+      value: 0.09455859576932324
+      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     | 0.1297     | 0.2956     | 0.3204     | 0.3942     | 0.2777     | 0.0939     |
+| cosine_accuracy@20   | 0.9905     | 1.0        | 0.9261     | 0.7282     | 0.7499     | 0.6349     | 0.3539     |
+| cosine_accuracy@50   | 0.9905     | 1.0        | 0.9557     | 0.8155     | 0.8388     | 0.74       | 0.487      |
+| cosine_accuracy@100  | 0.9905     | 1.0        | 0.9803     | 0.8641     | 0.9012     | 0.8274     | 0.5955     |
+| cosine_accuracy@150  | 0.9905     | 1.0        | 0.9852     | 0.9126     | 0.9314     | 0.8747     | 0.6628     |
+| cosine_accuracy@200  | 0.9905     | 1.0        | 0.9852     | 0.9417     | 0.9454     | 0.9054     | 0.7009     |
+| cosine_precision@1   | 0.6571     | 0.1297     | 0.2956     | 0.3204     | 0.3942     | 0.2777     | 0.0939     |
+| cosine_precision@20  | 0.5019     | 0.4884     | 0.4264     | 0.1723     | 0.0873     | 0.0709     | 0.0308     |
+| cosine_precision@50  | 0.3088     | 0.3142     | 0.277      | 0.0938     | 0.0408     | 0.0347     | 0.0184     |
+| cosine_precision@100 | 0.1868     | 0.1968     | 0.179      | 0.0575     | 0.0224     | 0.02       | 0.0116     |
+| cosine_precision@150 | 0.1328     | 0.1452     | 0.1349     | 0.0454     | 0.0157     | 0.0143     | 0.0088     |
+| cosine_precision@200 | 0.1025     | 0.1168     | 0.109      | 0.0383     | 0.0121     | 0.0113     | 0.0071     |
+| cosine_recall@1      | 0.068      | 0.0032     | 0.0111     | 0.0247     | 0.1498     | 0.1046     | 0.0322     |
+| cosine_recall@20     | 0.5375     | 0.3208     | 0.2601     | 0.1828     | 0.6446     | 0.519      | 0.2054     |
+| cosine_recall@50     | 0.7285     | 0.4568     | 0.3747     | 0.2361     | 0.7537     | 0.6331     | 0.3033     |
+| cosine_recall@100    | 0.8322     | 0.5382     | 0.4638     | 0.2817     | 0.8297     | 0.7301     | 0.3818     |
+| cosine_recall@150    | 0.8802     | 0.5774     | 0.5124     | 0.3245     | 0.8702     | 0.7835     | 0.4344     |
+| cosine_recall@200    | 0.9028     | 0.609      | 0.5445     | 0.358      | 0.8944     | 0.821      | 0.4673     |
+| cosine_ndcg@1        | 0.6571     | 0.1297     | 0.2956     | 0.3204     | 0.3942     | 0.2777     | 0.0939     |
+| cosine_ndcg@20       | 0.6843     | 0.5381     | 0.4597     | 0.2449     | 0.4956     | 0.3757     | 0.1353     |
+| cosine_ndcg@50       | 0.7051     | 0.4974     | 0.4174     | 0.2354     | 0.5252     | 0.4066     | 0.1627     |
+| cosine_ndcg@100      | 0.7593     | 0.5114     | 0.434      | 0.2562     | 0.5419     | 0.4281     | 0.1807     |
+| cosine_ndcg@150      | 0.7793     | 0.5312     | 0.4587     | 0.2738     | 0.5498     | 0.4385     | 0.1913     |
+| **cosine_ndcg@200**  | **0.7882** | **0.5471** | **0.4748** | **0.2868** | **0.5542** | **0.4454** | **0.1976** |
+| cosine_mrr@1         | 0.6571     | 0.1297     | 0.2956     | 0.3204     | 0.3942     | 0.2777     | 0.0939     |
+| cosine_mrr@20        | 0.8095     | 0.5547     | 0.4893     | 0.4399     | 0.4816     | 0.3598     | 0.1486     |
+| cosine_mrr@50        | 0.8095     | 0.5547     | 0.4903     | 0.4428     | 0.4846     | 0.3631     | 0.1528     |
+| cosine_mrr@100       | 0.8095     | 0.5547     | 0.4907     | 0.4434     | 0.4855     | 0.3643     | 0.1544     |
+| cosine_mrr@150       | 0.8095     | 0.5547     | 0.4907     | 0.4439     | 0.4857     | 0.3647     | 0.1549     |
+| cosine_mrr@200       | 0.8095     | 0.5547     | 0.4907     | 0.444      | 0.4858     | 0.3649     | 0.1552     |
+| cosine_map@1         | 0.6571     | 0.1297     | 0.2956     | 0.3204     | 0.3942     | 0.2777     | 0.0939     |
+| cosine_map@20        | 0.5423     | 0.4043     | 0.3233     | 0.1448     | 0.4099     | 0.2941     | 0.0849     |
+| cosine_map@50        | 0.534      | 0.3394     | 0.261      | 0.1244     | 0.417      | 0.3011     | 0.0905     |
+| cosine_map@100       | 0.5669     | 0.3369     | 0.2554     | 0.1294     | 0.4192     | 0.3039     | 0.0925     |
+| cosine_map@150       | 0.575      | 0.3445     | 0.2643     | 0.132      | 0.4199     | 0.3047     | 0.0933     |
+| cosine_map@200       | 0.5778     | 0.3502     | 0.2692     | 0.1336     | 0.4202     | 0.3051     | 0.0936     |
+| cosine_map@500       | 0.5826     | 0.3601     | 0.2803     | 0.1374     | 0.4207     | 0.306      | 0.0946     |
+
+<!--
+## 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                 |
+
+
+### 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}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

checkpoint-1600/config.json

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+  "position_embedding_type": "absolute",
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+  "transformers_version": "4.51.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

checkpoint-1600/modules.json

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+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
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+  {
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+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

checkpoint-1600/tokenizer_config.json

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+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

checkpoint-1800/1_Pooling/config.json

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+{
+  "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
+}

checkpoint-1800/config.json

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+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
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+  "id2label": {
+    "0": "LABEL_0"
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+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.51.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

checkpoint-1800/modules.json

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+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
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+    "type": "sentence_transformers.models.Pooling"
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+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

checkpoint-1800/rng_state.pth

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checkpoint-1800/scheduler.pt

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checkpoint-1800/tokenizer_config.json

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checkpoint-1800/training_args.bin

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checkpoint-2000/1_Pooling/config.json

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+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "pooling_mode_max_tokens": false,
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+}

checkpoint-2000/README.md

@@ -0,0 +1,1416 @@
+---
+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: 0.5057142857142858
+      name: Cosine Precision@20
+    - type: cosine_precision@50
+      value: 0.3085714285714286
+      name: Cosine Precision@50
+    - type: cosine_precision@100
+      value: 0.18685714285714286
+      name: Cosine Precision@100
+    - type: cosine_precision@150
+      value: 0.13263492063492063
+      name: Cosine Precision@150
+    - type: cosine_precision@200
+      value: 0.10261904761904762
+      name: Cosine Precision@200
+    - type: cosine_recall@1
+      value: 0.0680237860830842
+      name: Cosine Recall@1
+    - type: cosine_recall@20
+      value: 0.539814746481506
+      name: Cosine Recall@20
+    - type: cosine_recall@50
+      value: 0.7281788406466259
+      name: Cosine Recall@50
+    - type: cosine_recall@100
+      value: 0.8369695734692713
+      name: Cosine Recall@100
+    - type: cosine_recall@150
+      value: 0.8797734908498225
+      name: Cosine Recall@150
+    - type: cosine_recall@200
+      value: 0.9040821090543185
+      name: Cosine Recall@200
+    - type: cosine_ndcg@1
+      value: 0.6571428571428571
+      name: Cosine Ndcg@1
+    - type: cosine_ndcg@20
+      value: 0.6871892352981543
+      name: Cosine Ndcg@20
+    - type: cosine_ndcg@50
+      value: 0.7057435134474674
+      name: Cosine Ndcg@50
+    - type: cosine_ndcg@100
+      value: 0.7611594394123498
+      name: Cosine Ndcg@100
+    - type: cosine_ndcg@150
+      value: 0.7798336860589586
+      name: Cosine Ndcg@150
+    - type: cosine_ndcg@200
+      value: 0.7894131768304745
+      name: Cosine Ndcg@200
+    - type: cosine_mrr@1
+      value: 0.6571428571428571
+      name: Cosine Mrr@1
+    - type: cosine_mrr@20
+      value: 0.8095238095238095
+      name: Cosine Mrr@20
+    - type: cosine_mrr@50
+      value: 0.8095238095238095
+      name: Cosine Mrr@50
+    - type: cosine_mrr@100
+      value: 0.8095238095238095
+      name: Cosine Mrr@100
+    - type: cosine_mrr@150
+      value: 0.8095238095238095
+      name: Cosine Mrr@150
+    - type: cosine_mrr@200
+      value: 0.8095238095238095
+      name: Cosine Mrr@200
+    - type: cosine_map@1
+      value: 0.6571428571428571
+      name: Cosine Map@1
+    - type: cosine_map@20
+      value: 0.5458017299619246
+      name: Cosine Map@20
+    - type: cosine_map@50
+      value: 0.5350568967293148
+      name: Cosine Map@50
+    - type: cosine_map@100
+      value: 0.5681338314009312
+      name: Cosine Map@100
+    - type: cosine_map@150
+      value: 0.5758337072896192
+      name: Cosine Map@150
+    - type: cosine_map@200
+      value: 0.5788774324789392
+      name: Cosine Map@200
+    - type: cosine_map@500
+      value: 0.5832951333498196
+      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: 0.49054054054054047
+      name: Cosine Precision@20
+    - type: cosine_precision@50
+      value: 0.3163243243243243
+      name: Cosine Precision@50
+    - type: cosine_precision@100
+      value: 0.19794594594594597
+      name: Cosine Precision@100
+    - type: cosine_precision@150
+      value: 0.1476036036036036
+      name: Cosine Precision@150
+    - type: cosine_precision@200
+      value: 0.11764864864864866
+      name: Cosine Precision@200
+    - type: cosine_recall@1
+      value: 0.003111544931768446
+      name: Cosine Recall@1
+    - type: cosine_recall@20
+      value: 0.3226281360780687
+      name: Cosine Recall@20
+    - type: cosine_recall@50
+      value: 0.460233186838451
+      name: Cosine Recall@50
+    - type: cosine_recall@100
+      value: 0.541868009988165
+      name: Cosine Recall@100
+    - type: cosine_recall@150
+      value: 0.5852603494024129
+      name: Cosine Recall@150
+    - type: cosine_recall@200
+      value: 0.6129186722388266
+      name: Cosine Recall@200
+    - type: cosine_ndcg@1
+      value: 0.12432432432432433
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+      type: mix_zh
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+      value: 0.15489924735195337
+      name: Cosine Mrr@100
+    - type: cosine_mrr@150
+      value: 0.15543781318663716
+      name: Cosine Mrr@150
+    - type: cosine_mrr@200
+      value: 0.15567492413016254
+      name: Cosine Mrr@200
+    - type: cosine_map@1
+      value: 0.09551148225469729
+      name: Cosine Map@1
+    - type: cosine_map@20
+      value: 0.08472355170504958
+      name: Cosine Map@20
+    - type: cosine_map@50
+      value: 0.09038650929811239
+      name: Cosine Map@50
+    - type: cosine_map@100
+      value: 0.0922515804329945
+      name: Cosine Map@100
+    - type: cosine_map@150
+      value: 0.09296251507722719
+      name: Cosine Map@150
+    - type: cosine_map@200
+      value: 0.09340391507908284
+      name: Cosine Map@200
+    - type: cosine_map@500
+      value: 0.09434895514443004
+      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     | 0.4056     | 0.2897     | 0.0955     |
+| cosine_accuracy@20   | 0.9905     | 1.0        | 0.9212     | 0.699     | 0.7556     | 0.6453     | 0.3481     |
+| cosine_accuracy@50   | 0.9905     | 1.0        | 0.9655     | 0.8447    | 0.8466     | 0.7514     | 0.4932     |
+| cosine_accuracy@100  | 0.9905     | 1.0        | 0.9754     | 0.8835    | 0.9054     | 0.8424     | 0.5971     |
+| cosine_accuracy@150  | 0.9905     | 1.0        | 0.9852     | 0.9417    | 0.9392     | 0.884      | 0.6623     |
+| cosine_accuracy@200  | 0.9905     | 1.0        | 0.9852     | 0.9417    | 0.9522     | 0.9132     | 0.7035     |
+| cosine_precision@1   | 0.6571     | 0.1243     | 0.2956     | 0.301     | 0.4056     | 0.2897     | 0.0955     |
+| cosine_precision@20  | 0.5057     | 0.4905     | 0.4273     | 0.165     | 0.088      | 0.0723     | 0.0305     |
+| cosine_precision@50  | 0.3086     | 0.3163     | 0.2807     | 0.0932    | 0.0415     | 0.0356     | 0.0184     |
+| cosine_precision@100 | 0.1869     | 0.1979     | 0.1797     | 0.0583    | 0.0227     | 0.0205     | 0.0114     |
+| cosine_precision@150 | 0.1326     | 0.1476     | 0.1354     | 0.046     | 0.0158     | 0.0145     | 0.0086     |
+| cosine_precision@200 | 0.1026     | 0.1176     | 0.1102     | 0.0388    | 0.0122     | 0.0114     | 0.0071     |
+| cosine_recall@1      | 0.068      | 0.0031     | 0.0111     | 0.0239    | 0.1542     | 0.1087     | 0.0322     |
+| cosine_recall@20     | 0.5398     | 0.3226     | 0.2624     | 0.1766    | 0.6504     | 0.5285     | 0.2032     |
+| cosine_recall@50     | 0.7282     | 0.4602     | 0.3817     | 0.2365    | 0.7645     | 0.6477     | 0.304      |
+| cosine_recall@100    | 0.837      | 0.5419     | 0.4666     | 0.2843    | 0.8407     | 0.7456     | 0.3782     |
+| cosine_recall@150    | 0.8798     | 0.5853     | 0.5156     | 0.3277    | 0.8773     | 0.7948     | 0.4274     |
+| cosine_recall@200    | 0.9041     | 0.6129     | 0.5501     | 0.3597    | 0.9011     | 0.8334     | 0.4676     |
+| cosine_ndcg@1        | 0.6571     | 0.1243     | 0.2956     | 0.301     | 0.4056     | 0.2897     | 0.0955     |
+| cosine_ndcg@20       | 0.6872     | 0.5392     | 0.4613     | 0.2386    | 0.5053     | 0.3869     | 0.1345     |
+| cosine_ndcg@50       | 0.7057     | 0.4997     | 0.4218     | 0.2339    | 0.5364     | 0.4193     | 0.1628     |
+| cosine_ndcg@100      | 0.7612     | 0.5138     | 0.4363     | 0.2561    | 0.5529     | 0.4409     | 0.1798     |
+| cosine_ndcg@150      | 0.7798     | 0.5361     | 0.4612     | 0.274     | 0.56       | 0.4505     | 0.1898     |
+| **cosine_ndcg@200**  | **0.7894** | **0.5499** | **0.4786** | **0.287** | **0.5644** | **0.4576** | **0.1974** |
+| cosine_mrr@1         | 0.6571     | 0.1243     | 0.2956     | 0.301     | 0.4056     | 0.2897     | 0.0955     |
+| cosine_mrr@20        | 0.8095     | 0.5499     | 0.4902     | 0.419     | 0.4919     | 0.3732     | 0.1488     |
+| cosine_mrr@50        | 0.8095     | 0.5499     | 0.4916     | 0.424     | 0.495      | 0.3767     | 0.1534     |
+| cosine_mrr@100       | 0.8095     | 0.5499     | 0.4917     | 0.4245    | 0.4958     | 0.378      | 0.1549     |
+| cosine_mrr@150       | 0.8095     | 0.5499     | 0.4918     | 0.425     | 0.4961     | 0.3783     | 0.1554     |
+| cosine_mrr@200       | 0.8095     | 0.5499     | 0.4918     | 0.425     | 0.4962     | 0.3785     | 0.1557     |
+| cosine_map@1         | 0.6571     | 0.1243     | 0.2956     | 0.301     | 0.4056     | 0.2897     | 0.0955     |
+| cosine_map@20        | 0.5458     | 0.4038     | 0.324      | 0.1429    | 0.4211     | 0.3048     | 0.0847     |
+| cosine_map@50        | 0.5351     | 0.3418     | 0.2639     | 0.1244    | 0.4288     | 0.3123     | 0.0904     |
+| cosine_map@100       | 0.5681     | 0.3392     | 0.2572     | 0.1298    | 0.4309     | 0.315      | 0.0923     |
+| cosine_map@150       | 0.5758     | 0.3484     | 0.2662     | 0.1324    | 0.4315     | 0.3158     | 0.093      |
+| cosine_map@200       | 0.5789     | 0.3535     | 0.2716     | 0.134     | 0.4318     | 0.3162     | 0.0934     |
+| cosine_map@500       | 0.5833     | 0.3632     | 0.2826     | 0.1379    | 0.4323     | 0.317      | 0.0943     |
+
+<!--
+## 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                 |
+| 3.9156 | 1900 | 2.4983        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 4.1235 | 2000 | 2.5423        | 0.7894                  | 0.5499                  | 0.4786                  | 0.2870                  | 0.5644                 | 0.4576                 | 0.1974                 |
+
+
+### 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}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

checkpoint-2000/config.json

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+  "position_embedding_type": "absolute",
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+  "use_cache": true,
+  "vocab_size": 30522
+}

checkpoint-2000/config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "4.1.0",
+    "transformers": "4.51.3",
+    "pytorch": "2.6.0+cu124"
+  },
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+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

checkpoint-2000/modules.json

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+    "type": "sentence_transformers.models.Pooling"
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+    "path": "2_Normalize",
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+  }
+]

checkpoint-2000/rng_state.pth

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checkpoint-2000/sentence_bert_config.json

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checkpoint-2000/special_tokens_map.json

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checkpoint-2000/tokenizer_config.json

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+  "unk_token": "[UNK]"
+}

checkpoint-2000/training_args.bin

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checkpoint-2200/tokenizer_config.json

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+}

checkpoint-2400/1_Pooling/config.json

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+  "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
+}

checkpoint-2400/README.md

@@ -0,0 +1,1420 @@
+---
+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: 0.5023809523809524
+      name: Cosine Precision@20
+    - type: cosine_precision@50
+      value: 0.30800000000000005
+      name: Cosine Precision@50
+    - type: cosine_precision@100
+      value: 0.18628571428571428
+      name: Cosine Precision@100
+    - type: cosine_precision@150
+      value: 0.1321904761904762
+      name: Cosine Precision@150
+    - type: cosine_precision@200
+      value: 0.10295238095238096
+      name: Cosine Precision@200
+    - type: cosine_recall@1
+      value: 0.0680237860830842
+      name: Cosine Recall@1
+    - type: cosine_recall@20
+      value: 0.5384852963395483
+      name: Cosine Recall@20
+    - type: cosine_recall@50
+      value: 0.7260449077992874
+      name: Cosine Recall@50
+    - type: cosine_recall@100
+      value: 0.8328530702930984
+      name: Cosine Recall@100
+    - type: cosine_recall@150
+      value: 0.8745262490032277
+      name: Cosine Recall@150
+    - type: cosine_recall@200
+      value: 0.9056960100263424
+      name: Cosine Recall@200
+    - type: cosine_ndcg@1
+      value: 0.6571428571428571
+      name: Cosine Ndcg@1
+    - type: cosine_ndcg@20
+      value: 0.6845256340390302
+      name: Cosine Ndcg@20
+    - type: cosine_ndcg@50
+      value: 0.7040452093638513
+      name: Cosine Ndcg@50
+    - type: cosine_ndcg@100
+      value: 0.758935932285001
+      name: Cosine Ndcg@100
+    - type: cosine_ndcg@150
+      value: 0.7774414598948007
+      name: Cosine Ndcg@150
+    - type: cosine_ndcg@200
+      value: 0.7892946240668293
+      name: Cosine Ndcg@200
+    - type: cosine_mrr@1
+      value: 0.6571428571428571
+      name: Cosine Mrr@1
+    - type: cosine_mrr@20
+      value: 0.8103174603174604
+      name: Cosine Mrr@20
+    - type: cosine_mrr@50
+      value: 0.8103174603174604
+      name: Cosine Mrr@50
+    - type: cosine_mrr@100
+      value: 0.8103174603174604
+      name: Cosine Mrr@100
+    - type: cosine_mrr@150
+      value: 0.8103174603174604
+      name: Cosine Mrr@150
+    - type: cosine_mrr@200
+      value: 0.8103174603174604
+      name: Cosine Mrr@200
+    - type: cosine_map@1
+      value: 0.6571428571428571
+      name: Cosine Map@1
+    - type: cosine_map@20
+      value: 0.5418235787800474
+      name: Cosine Map@20
+    - type: cosine_map@50
+      value: 0.5327215779103721
+      name: Cosine Map@50
+    - type: cosine_map@100
+      value: 0.565706253334091
+      name: Cosine Map@100
+    - type: cosine_map@150
+      value: 0.5733951147399983
+      name: Cosine Map@150
+    - type: cosine_map@200
+      value: 0.5771587776237981
+      name: Cosine Map@200
+    - type: cosine_map@500
+      value: 0.5813892452974444
+      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: 0.4897297297297297
+      name: Cosine Precision@20
+    - type: cosine_precision@50
+      value: 0.31794594594594594
+      name: Cosine Precision@50
+    - type: cosine_precision@100
+      value: 0.19864864864864865
+      name: Cosine Precision@100
+    - type: cosine_precision@150
+      value: 0.14688288288288287
+      name: Cosine Precision@150
+    - type: cosine_precision@200
+      value: 0.11789189189189188
+      name: Cosine Precision@200
+    - type: cosine_recall@1
+      value: 0.003111544931768446
+      name: Cosine Recall@1
+    - type: cosine_recall@20
+      value: 0.32208664960961075
+      name: Cosine Recall@20
+    - type: cosine_recall@50
+      value: 0.46383117404893587
+      name: Cosine Recall@50
+    - type: cosine_recall@100
+      value: 0.5437537828683688
+      name: Cosine Recall@100
+    - type: cosine_recall@150
+      value: 0.5824968655076911
+      name: Cosine Recall@150
+    - type: cosine_recall@200
+      value: 0.6146962508233631
+      name: Cosine Recall@200
+    - type: cosine_ndcg@1
+      value: 0.12432432432432433
+      name: Cosine Ndcg@1
+    - type: cosine_ndcg@20
+      value: 0.5384577730264963
+      name: Cosine Ndcg@20
+    - type: cosine_ndcg@50
+      value: 0.5012455261232941
+      name: Cosine Ndcg@50
+    - type: cosine_ndcg@100
+      value: 0.5147486871284331
+      name: Cosine Ndcg@100
+    - type: cosine_ndcg@150
+      value: 0.5348194013794069
+      name: Cosine Ndcg@150
+    - type: cosine_ndcg@200
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+      name: Cosine Map@500
+  - task:
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+      name: Information Retrieval
+    dataset:
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+      type: full_de
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+      name: Cosine Map@500
+  - task:
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+      name: Information Retrieval
+    dataset:
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+      type: full_zh
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+      name: Cosine Map@500
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: mix es
+      type: mix_es
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+    - type: cosine_map@500
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+      name: Cosine Map@500
+  - task:
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+      name: Information Retrieval
+    dataset:
+      name: mix de
+      type: mix_de
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+    - type: cosine_map@500
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+      name: Cosine Map@500
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: mix zh
+      type: mix_zh
+    metrics:
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+      value: 0.09304075210745723
+      name: Cosine Map@50
+    - type: cosine_map@100
+      value: 0.09500635866296525
+      name: Cosine Map@100
+    - type: cosine_map@150
+      value: 0.09570276054684158
+      name: Cosine Map@150
+    - type: cosine_map@200
+      value: 0.09614394028730197
+      name: Cosine Map@200
+    - type: cosine_map@500
+      value: 0.09706713378133278
+      name: Cosine Map@500
+---
+
+# Job - Job matching BAAI/bge-small-en-v1.5
+
+Top performing model on [TalentCLEF 2025](https://talentclef.github.io/talentclef/) Task A.  Use it for multilingual job title matching
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [BAAI/bge-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     | 0.3495     | 0.4113     | 0.2943     | 0.0971     |
+| cosine_accuracy@20   | 0.9905     | 1.0        | 0.9212     | 0.7379     | 0.7613     | 0.65       | 0.3586     |
+| cosine_accuracy@50   | 0.9905     | 1.0        | 0.9655     | 0.8252     | 0.8523     | 0.7608     | 0.4901     |
+| cosine_accuracy@100  | 0.9905     | 1.0        | 0.9754     | 0.8544     | 0.9121     | 0.8508     | 0.6002     |
+| cosine_accuracy@150  | 0.9905     | 1.0        | 0.9852     | 0.9029     | 0.9418     | 0.8898     | 0.6613     |
+| cosine_accuracy@200  | 0.9905     | 1.0        | 0.9852     | 0.9417     | 0.9548     | 0.9204     | 0.7062     |
+| cosine_precision@1   | 0.6571     | 0.1243     | 0.2956     | 0.3495     | 0.4113     | 0.2943     | 0.0971     |
+| cosine_precision@20  | 0.5024     | 0.4897     | 0.4246     | 0.1733     | 0.0892     | 0.0731     | 0.0314     |
+| cosine_precision@50  | 0.308      | 0.3179     | 0.2814     | 0.0944     | 0.0418     | 0.0361     | 0.0185     |
+| cosine_precision@100 | 0.1863     | 0.1986     | 0.1801     | 0.0589     | 0.0229     | 0.0206     | 0.0116     |
+| cosine_precision@150 | 0.1322     | 0.1469     | 0.1362     | 0.0458     | 0.0159     | 0.0147     | 0.0087     |
+| cosine_precision@200 | 0.103      | 0.1179     | 0.1105     | 0.0385     | 0.0122     | 0.0116     | 0.0071     |
+| cosine_recall@1      | 0.068      | 0.0031     | 0.0111     | 0.0273     | 0.1565     | 0.1109     | 0.0329     |
+| cosine_recall@20     | 0.5385     | 0.3221     | 0.2614     | 0.1766     | 0.6594     | 0.5344     | 0.2091     |
+| cosine_recall@50     | 0.726      | 0.4638     | 0.3835     | 0.2393     | 0.7705     | 0.6585     | 0.3054     |
+| cosine_recall@100    | 0.8329     | 0.5438     | 0.4677     | 0.2863     | 0.8472     | 0.7525     | 0.3835     |
+| cosine_recall@150    | 0.8745     | 0.5825     | 0.5183     | 0.3287     | 0.8825     | 0.8026     | 0.4309     |
+| cosine_recall@200    | 0.9057     | 0.6147     | 0.5517     | 0.3631     | 0.9051     | 0.8418     | 0.4715     |
+| cosine_ndcg@1        | 0.6571     | 0.1243     | 0.2956     | 0.3495     | 0.4113     | 0.2943     | 0.0971     |
+| cosine_ndcg@20       | 0.6845     | 0.5385     | 0.4601     | 0.2468     | 0.5117     | 0.3919     | 0.1385     |
+| cosine_ndcg@50       | 0.704      | 0.5012     | 0.4229     | 0.2394     | 0.542      | 0.4256     | 0.1656     |
+| cosine_ndcg@100      | 0.7589     | 0.5147     | 0.4371     | 0.2619     | 0.5588     | 0.4462     | 0.1835     |
+| cosine_ndcg@150      | 0.7774     | 0.5348     | 0.4629     | 0.2787     | 0.5656     | 0.4561     | 0.1931     |
+| **cosine_ndcg@200**  | **0.7893** | **0.5505** | **0.4797** | **0.2919** | **0.5697** | **0.4632** | **0.2007** |
+| cosine_mrr@1         | 0.6571     | 0.1243     | 0.2956     | 0.3495     | 0.4113     | 0.2943     | 0.0971     |
+| cosine_mrr@20        | 0.8103     | 0.5515     | 0.4896     | 0.4485     | 0.4979     | 0.3779     | 0.1522     |
+| cosine_mrr@50        | 0.8103     | 0.5515     | 0.4909     | 0.4515     | 0.501      | 0.3815     | 0.1564     |
+| cosine_mrr@100       | 0.8103     | 0.5515     | 0.4911     | 0.4519     | 0.5018     | 0.3827     | 0.158      |
+| cosine_mrr@150       | 0.8103     | 0.5515     | 0.4912     | 0.4523     | 0.5021     | 0.3831     | 0.1585     |
+| cosine_mrr@200       | 0.8103     | 0.5515     | 0.4912     | 0.4525     | 0.5021     | 0.3832     | 0.1588     |
+| cosine_map@1         | 0.6571     | 0.1243     | 0.2956     | 0.3495     | 0.4113     | 0.2943     | 0.0971     |
+| cosine_map@20        | 0.5418     | 0.4028     | 0.3236     | 0.147      | 0.4264     | 0.3097     | 0.0875     |
+| cosine_map@50        | 0.5327     | 0.3422     | 0.2644     | 0.1267     | 0.4338     | 0.3174     | 0.093      |
+| cosine_map@100       | 0.5657     | 0.3395     | 0.2576     | 0.1326     | 0.436      | 0.3199     | 0.095      |
+| cosine_map@150       | 0.5734     | 0.3478     | 0.2669     | 0.1352     | 0.4366     | 0.3207     | 0.0957     |
+| cosine_map@200       | 0.5772     | 0.3534     | 0.2722     | 0.1368     | 0.4368     | 0.3212     | 0.0961     |
+| cosine_map@500       | 0.5814     | 0.3631     | 0.2833     | 0.1407     | 0.4373     | 0.3219     | 0.0971     |
+
+<!--
+## 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                 |
+| 3.9156 | 1900 | 2.4983        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 4.1235 | 2000 | 2.5423        | 0.7894                  | 0.5499                  | 0.4786                  | 0.2870                  | 0.5644                 | 0.4576                 | 0.1974                 |
+| 4.3292 | 2100 | 2.5674        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 4.5350 | 2200 | 2.6237        | 0.7899                  | 0.5502                  | 0.4802                  | 0.2843                  | 0.5674                 | 0.4607                 | 0.1993                 |
+| 4.7407 | 2300 | 2.3776        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 4.9465 | 2400 | 2.1116        | 0.7893                  | 0.5505                  | 0.4797                  | 0.2919                  | 0.5697                 | 0.4632                 | 0.2007                 |
+
+
+### 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}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

checkpoint-2400/config.json

@@ -0,0 +1,30 @@
+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.51.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

checkpoint-2400/config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "4.1.0",
+    "transformers": "4.51.3",
+    "pytorch": "2.6.0+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

checkpoint-2400/modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

checkpoint-2400/sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": true
+}

checkpoint-2400/special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

checkpoint-2400/tokenizer_config.json

@@ -0,0 +1,58 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

eval/Information-Retrieval_evaluation_full_de_results.csv

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eval/Information-Retrieval_evaluation_mix_de_results.csv

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