Add files using upload-large-folder tool

.gitattributes

@@ -33,3 +33,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+checkpoint-1600/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+checkpoint-1000/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+checkpoint-1200/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+checkpoint-1400/tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,3 +1,1290 @@
----
-license: apache-2.0
----
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:86648
+- loss:MSELoss
+widget:
+- source_sentence: Familienberaterin
+  sentences:
+  - electric power station operator
+  - venue booker & promoter
+  - betrieblicher Aus- und Weiterbildner/betriebliche Aus- und Weiterbildnerin
+- source_sentence: high school RS teacher
+  sentences:
+  - infantryman
+  - Schnellbedienungsrestaurantteamleiter
+  - drill setup operator
+- source_sentence: lighting designer
+  sentences:
+  - software support manager
+  - 直升机维护协调员
+  - bus maintenance supervisor
+- source_sentence: 机场消防员
+  sentences:
+  - Flake操作员
+  - técnico en gestión de residuos peligrosos/técnica en gestión de residuos peligrosos
+  - 专门学校老师
+- source_sentence: Entwicklerin für mobile Anwendungen
+  sentences:
+  - fashion design expert
+  - Mergers-and-Acquisitions-Analyst/Mergers-and-Acquisitions-Analystin
+  - commercial bid manager
+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
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: full en
+      type: full_en
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.6476190476190476
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@20
+      value: 0.9714285714285714
+      name: Cosine Accuracy@20
+    - type: cosine_accuracy@50
+      value: 0.9904761904761905
+      name: Cosine Accuracy@50
+    - type: cosine_accuracy@100
+      value: 0.9904761904761905
+      name: Cosine Accuracy@100
+    - type: cosine_accuracy@150
+      value: 0.9904761904761905
+      name: Cosine Accuracy@150
+    - type: cosine_accuracy@200
+      value: 0.9904761904761905
+      name: Cosine Accuracy@200
+    - type: cosine_precision@1
+      value: 0.6476190476190476
+      name: Cosine Precision@1
+    - type: cosine_precision@20
+      value: 0.47952380952380946
+      name: Cosine Precision@20
+    - type: cosine_precision@50
+      value: 0.28838095238095235
+      name: Cosine Precision@50
+    - type: cosine_precision@100
+      value: 0.17304761904761906
+      name: Cosine Precision@100
+    - type: cosine_precision@150
+      value: 0.12444444444444444
+      name: Cosine Precision@150
+    - type: cosine_precision@200
+      value: 0.09857142857142859
+      name: Cosine Precision@200
+    - type: cosine_recall@1
+      value: 0.06609801577496094
+      name: Cosine Recall@1
+    - type: cosine_recall@20
+      value: 0.5122224752770898
+      name: Cosine Recall@20
+    - type: cosine_recall@50
+      value: 0.6835205863376973
+      name: Cosine Recall@50
+    - type: cosine_recall@100
+      value: 0.7899550177449521
+      name: Cosine Recall@100
+    - type: cosine_recall@150
+      value: 0.8399901051245952
+      name: Cosine Recall@150
+    - type: cosine_recall@200
+      value: 0.875868212220809
+      name: Cosine Recall@200
+    - type: cosine_ndcg@1
+      value: 0.6476190476190476
+      name: Cosine Ndcg@1
+    - type: cosine_ndcg@20
+      value: 0.6467537144833913
+      name: Cosine Ndcg@20
+    - type: cosine_ndcg@50
+      value: 0.6579566361404572
+      name: Cosine Ndcg@50
+    - type: cosine_ndcg@100
+      value: 0.7095129047395976
+      name: Cosine Ndcg@100
+    - type: cosine_ndcg@150
+      value: 0.7310060454392588
+      name: Cosine Ndcg@150
+    - type: cosine_ndcg@200
+      value: 0.746053293561821
+      name: Cosine Ndcg@200
+    - type: cosine_mrr@1
+      value: 0.6476190476190476
+      name: Cosine Mrr@1
+    - type: cosine_mrr@20
+      value: 0.7901817137111254
+      name: Cosine Mrr@20
+    - type: cosine_mrr@50
+      value: 0.7909547501984476
+      name: Cosine Mrr@50
+    - type: cosine_mrr@100
+      value: 0.7909547501984476
+      name: Cosine Mrr@100
+    - type: cosine_mrr@150
+      value: 0.7909547501984476
+      name: Cosine Mrr@150
+    - type: cosine_mrr@200
+      value: 0.7909547501984476
+      name: Cosine Mrr@200
+    - type: cosine_map@1
+      value: 0.6476190476190476
+      name: Cosine Map@1
+    - type: cosine_map@20
+      value: 0.5025649155749793
+      name: Cosine Map@20
+    - type: cosine_map@50
+      value: 0.48398477448194993
+      name: Cosine Map@50
+    - type: cosine_map@100
+      value: 0.5117703759309522
+      name: Cosine Map@100
+    - type: cosine_map@150
+      value: 0.520199435224254
+      name: Cosine Map@150
+    - type: cosine_map@200
+      value: 0.5249113393002316
+      name: Cosine Map@200
+    - type: cosine_map@500
+      value: 0.5304170344184883
+      name: Cosine Map@500
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: full es
+      type: full_es
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.11891891891891893
+      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: 0.11891891891891893
+      name: Cosine Precision@1
+    - type: cosine_precision@20
+      value: 0.5267567567567567
+      name: Cosine Precision@20
+    - type: cosine_precision@50
+      value: 0.3437837837837838
+      name: Cosine Precision@50
+    - type: cosine_precision@100
+      value: 0.21897297297297297
+      name: Cosine Precision@100
+    - type: cosine_precision@150
+      value: 0.1658018018018018
+      name: Cosine Precision@150
+    - type: cosine_precision@200
+      value: 0.1332972972972973
+      name: Cosine Precision@200
+    - type: cosine_recall@1
+      value: 0.0035840147528632613
+      name: Cosine Recall@1
+    - type: cosine_recall@20
+      value: 0.35407760203362965
+      name: Cosine Recall@20
+    - type: cosine_recall@50
+      value: 0.5097999383006715
+      name: Cosine Recall@50
+    - type: cosine_recall@100
+      value: 0.6076073817878247
+      name: Cosine Recall@100
+    - type: cosine_recall@150
+      value: 0.6705429838138021
+      name: Cosine Recall@150
+    - type: cosine_recall@200
+      value: 0.7125464731776301
+      name: Cosine Recall@200
+    - type: cosine_ndcg@1
+      value: 0.11891891891891893
+      name: Cosine Ndcg@1
+    - type: cosine_ndcg@20
+      value: 0.5708144272431339
+      name: Cosine Ndcg@20
+    - type: cosine_ndcg@50
+      value: 0.535516963498245
+      name: Cosine Ndcg@50
+    - type: cosine_ndcg@100
+      value: 0.558980163264909
+      name: Cosine Ndcg@100
+    - type: cosine_ndcg@150
+      value: 0.5900024611410689
+      name: Cosine Ndcg@150
+    - type: cosine_ndcg@200
+      value: 0.609478782549869
+      name: Cosine Ndcg@200
+    - type: cosine_mrr@1
+      value: 0.11891891891891893
+      name: Cosine Mrr@1
+    - type: cosine_mrr@20
+      value: 0.5531531531531532
+      name: Cosine Mrr@20
+    - type: cosine_mrr@50
+      value: 0.5531531531531532
+      name: Cosine Mrr@50
+    - type: cosine_mrr@100
+      value: 0.5531531531531532
+      name: Cosine Mrr@100
+    - type: cosine_mrr@150
+      value: 0.5531531531531532
+      name: Cosine Mrr@150
+    - type: cosine_mrr@200
+      value: 0.5531531531531532
+      name: Cosine Mrr@200
+    - type: cosine_map@1
+      value: 0.11891891891891893
+      name: Cosine Map@1
+    - type: cosine_map@20
+      value: 0.4379349002801489
+      name: Cosine Map@20
+    - type: cosine_map@50
+      value: 0.3739269627118989
+      name: Cosine Map@50
+    - type: cosine_map@100
+      value: 0.37629843599877466
+      name: Cosine Map@100
+    - type: cosine_map@150
+      value: 0.3891828650842837
+      name: Cosine Map@150
+    - type: cosine_map@200
+      value: 0.39584338663408436
+      name: Cosine Map@200
+    - type: cosine_map@500
+      value: 0.4062909401616274
+      name: Cosine Map@500
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: full de
+      type: full_de
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.2955665024630542
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@20
+      value: 0.9704433497536946
+      name: Cosine Accuracy@20
+    - type: cosine_accuracy@50
+      value: 0.9753694581280788
+      name: Cosine Accuracy@50
+    - type: cosine_accuracy@100
+      value: 0.9901477832512315
+      name: Cosine Accuracy@100
+    - type: cosine_accuracy@150
+      value: 0.9901477832512315
+      name: Cosine Accuracy@150
+    - type: cosine_accuracy@200
+      value: 0.9901477832512315
+      name: Cosine Accuracy@200
+    - type: cosine_precision@1
+      value: 0.2955665024630542
+      name: Cosine Precision@1
+    - type: cosine_precision@20
+      value: 0.42906403940886706
+      name: Cosine Precision@20
+    - type: cosine_precision@50
+      value: 0.29802955665024633
+      name: Cosine Precision@50
+    - type: cosine_precision@100
+      value: 0.19433497536945815
+      name: Cosine Precision@100
+    - type: cosine_precision@150
+      value: 0.14824302134646963
+      name: Cosine Precision@150
+    - type: cosine_precision@200
+      value: 0.1197783251231527
+      name: Cosine Precision@200
+    - type: cosine_recall@1
+      value: 0.01108543831680986
+      name: Cosine Recall@1
+    - type: cosine_recall@20
+      value: 0.26675038089672504
+      name: Cosine Recall@20
+    - type: cosine_recall@50
+      value: 0.40921566733257536
+      name: Cosine Recall@50
+    - type: cosine_recall@100
+      value: 0.5097664540706716
+      name: Cosine Recall@100
+    - type: cosine_recall@150
+      value: 0.5728593162394238
+      name: Cosine Recall@150
+    - type: cosine_recall@200
+      value: 0.6120176690658915
+      name: Cosine Recall@200
+    - type: cosine_ndcg@1
+      value: 0.2955665024630542
+      name: Cosine Ndcg@1
+    - type: cosine_ndcg@20
+      value: 0.46962753993631184
+      name: Cosine Ndcg@20
+    - type: cosine_ndcg@50
+      value: 0.444898497416845
+      name: Cosine Ndcg@50
+    - type: cosine_ndcg@100
+      value: 0.466960324034805
+      name: Cosine Ndcg@100
+    - type: cosine_ndcg@150
+      value: 0.49816218513136795
+      name: Cosine Ndcg@150
+    - type: cosine_ndcg@200
+      value: 0.5165485300965951
+      name: Cosine Ndcg@200
+    - type: cosine_mrr@1
+      value: 0.2955665024630542
+      name: Cosine Mrr@1
+    - type: cosine_mrr@20
+      value: 0.5046767633988724
+      name: Cosine Mrr@20
+    - type: cosine_mrr@50
+      value: 0.50477528556636
+      name: Cosine Mrr@50
+    - type: cosine_mrr@100
+      value: 0.5049589761635289
+      name: Cosine Mrr@100
+    - type: cosine_mrr@150
+      value: 0.5049589761635289
+      name: Cosine Mrr@150
+    - type: cosine_mrr@200
+      value: 0.5049589761635289
+      name: Cosine Mrr@200
+    - type: cosine_map@1
+      value: 0.2955665024630542
+      name: Cosine Map@1
+    - type: cosine_map@20
+      value: 0.33658821160388247
+      name: Cosine Map@20
+    - type: cosine_map@50
+      value: 0.2853400586620685
+      name: Cosine Map@50
+    - type: cosine_map@100
+      value: 0.2817732307206079
+      name: Cosine Map@100
+    - type: cosine_map@150
+      value: 0.2931317333364438
+      name: Cosine Map@150
+    - type: cosine_map@200
+      value: 0.2988160532231927
+      name: Cosine Map@200
+    - type: cosine_map@500
+      value: 0.31093362375086947
+      name: Cosine Map@500
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: full zh
+      type: full_zh
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.6601941747572816
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@20
+      value: 0.970873786407767
+      name: Cosine Accuracy@20
+    - type: cosine_accuracy@50
+      value: 0.9902912621359223
+      name: Cosine Accuracy@50
+    - type: cosine_accuracy@100
+      value: 0.9902912621359223
+      name: Cosine Accuracy@100
+    - type: cosine_accuracy@150
+      value: 0.9902912621359223
+      name: Cosine Accuracy@150
+    - type: cosine_accuracy@200
+      value: 0.9902912621359223
+      name: Cosine Accuracy@200
+    - type: cosine_precision@1
+      value: 0.6601941747572816
+      name: Cosine Precision@1
+    - type: cosine_precision@20
+      value: 0.44805825242718444
+      name: Cosine Precision@20
+    - type: cosine_precision@50
+      value: 0.27126213592233006
+      name: Cosine Precision@50
+    - type: cosine_precision@100
+      value: 0.16650485436893206
+      name: Cosine Precision@100
+    - type: cosine_precision@150
+      value: 0.1211003236245955
+      name: Cosine Precision@150
+    - type: cosine_precision@200
+      value: 0.09529126213592234
+      name: Cosine Precision@200
+    - type: cosine_recall@1
+      value: 0.06611246215014785
+      name: Cosine Recall@1
+    - type: cosine_recall@20
+      value: 0.48409390608352504
+      name: Cosine Recall@20
+    - type: cosine_recall@50
+      value: 0.6568473638827299
+      name: Cosine Recall@50
+    - type: cosine_recall@100
+      value: 0.7685416895166794
+      name: Cosine Recall@100
+    - type: cosine_recall@150
+      value: 0.8277686060133904
+      name: Cosine Recall@150
+    - type: cosine_recall@200
+      value: 0.8616979590623105
+      name: Cosine Recall@200
+    - type: cosine_ndcg@1
+      value: 0.6601941747572816
+      name: Cosine Ndcg@1
+    - type: cosine_ndcg@20
+      value: 0.6231250904534316
+      name: Cosine Ndcg@20
+    - type: cosine_ndcg@50
+      value: 0.6383496204608501
+      name: Cosine Ndcg@50
+    - type: cosine_ndcg@100
+      value: 0.6917257705456975
+      name: Cosine Ndcg@100
+    - type: cosine_ndcg@150
+      value: 0.7167434657424917
+      name: Cosine Ndcg@150
+    - type: cosine_ndcg@200
+      value: 0.7303448958665071
+      name: Cosine Ndcg@200
+    - type: cosine_mrr@1
+      value: 0.6601941747572816
+      name: Cosine Mrr@1
+    - type: cosine_mrr@20
+      value: 0.8015776699029126
+      name: Cosine Mrr@20
+    - type: cosine_mrr@50
+      value: 0.8020876238109248
+      name: Cosine Mrr@50
+    - type: cosine_mrr@100
+      value: 0.8020876238109248
+      name: Cosine Mrr@100
+    - type: cosine_mrr@150
+      value: 0.8020876238109248
+      name: Cosine Mrr@150
+    - type: cosine_mrr@200
+      value: 0.8020876238109248
+      name: Cosine Mrr@200
+    - type: cosine_map@1
+      value: 0.6601941747572816
+      name: Cosine Map@1
+    - type: cosine_map@20
+      value: 0.4750205237443607
+      name: Cosine Map@20
+    - type: cosine_map@50
+      value: 0.45785161483741715
+      name: Cosine Map@50
+    - type: cosine_map@100
+      value: 0.4848085275553208
+      name: Cosine Map@100
+    - type: cosine_map@150
+      value: 0.4937216396074153
+      name: Cosine Map@150
+    - type: cosine_map@200
+      value: 0.49777622471594557
+      name: Cosine Map@200
+    - type: cosine_map@500
+      value: 0.5039795405740248
+      name: Cosine Map@500
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: mix es
+      type: mix_es
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.6297451898075923
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@20
+      value: 0.9105564222568903
+      name: Cosine Accuracy@20
+    - type: cosine_accuracy@50
+      value: 0.9495579823192928
+      name: Cosine Accuracy@50
+    - type: cosine_accuracy@100
+      value: 0.9729589183567343
+      name: Cosine Accuracy@100
+    - type: cosine_accuracy@150
+      value: 0.983359334373375
+      name: Cosine Accuracy@150
+    - type: cosine_accuracy@200
+      value: 0.9901196047841914
+      name: Cosine Accuracy@200
+    - type: cosine_precision@1
+      value: 0.6297451898075923
+      name: Cosine Precision@1
+    - type: cosine_precision@20
+      value: 0.11167446697867915
+      name: Cosine Precision@20
+    - type: cosine_precision@50
+      value: 0.04850754030161208
+      name: Cosine Precision@50
+    - type: cosine_precision@100
+      value: 0.02535101404056163
+      name: Cosine Precision@100
+    - type: cosine_precision@150
+      value: 0.0172300225342347
+      name: Cosine Precision@150
+    - type: cosine_precision@200
+      value: 0.0130811232449298
+      name: Cosine Precision@200
+    - type: cosine_recall@1
+      value: 0.24340068840848872
+      name: Cosine Recall@1
+    - type: cosine_recall@20
+      value: 0.8288215338137336
+      name: Cosine Recall@20
+    - type: cosine_recall@50
+      value: 0.8986566129311838
+      name: Cosine Recall@50
+    - type: cosine_recall@100
+      value: 0.9398509273704282
+      name: Cosine Recall@100
+    - type: cosine_recall@150
+      value: 0.9576876408389668
+      name: Cosine Recall@150
+    - type: cosine_recall@200
+      value: 0.9695267810712429
+      name: Cosine Recall@200
+    - type: cosine_ndcg@1
+      value: 0.6297451898075923
+      name: Cosine Ndcg@1
+    - type: cosine_ndcg@20
+      value: 0.7010427232190379
+      name: Cosine Ndcg@20
+    - type: cosine_ndcg@50
+      value: 0.7200844211181043
+      name: Cosine Ndcg@50
+    - type: cosine_ndcg@100
+      value: 0.7290848607488584
+      name: Cosine Ndcg@100
+    - type: cosine_ndcg@150
+      value: 0.7325985285606116
+      name: Cosine Ndcg@150
+    - type: cosine_ndcg@200
+      value: 0.7347463892077523
+      name: Cosine Ndcg@200
+    - type: cosine_mrr@1
+      value: 0.6297451898075923
+      name: Cosine Mrr@1
+    - type: cosine_mrr@20
+      value: 0.7036709577939534
+      name: Cosine Mrr@20
+    - type: cosine_mrr@50
+      value: 0.7049808414398148
+      name: Cosine Mrr@50
+    - type: cosine_mrr@100
+      value: 0.7053260954286938
+      name: Cosine Mrr@100
+    - type: cosine_mrr@150
+      value: 0.7054145837924506
+      name: Cosine Mrr@150
+    - type: cosine_mrr@200
+      value: 0.7054541569954363
+      name: Cosine Mrr@200
+    - type: cosine_map@1
+      value: 0.6297451898075923
+      name: Cosine Map@1
+    - type: cosine_map@20
+      value: 0.6194189058349782
+      name: Cosine Map@20
+    - type: cosine_map@50
+      value: 0.6244340507841626
+      name: Cosine Map@50
+    - type: cosine_map@100
+      value: 0.6256943736433496
+      name: Cosine Map@100
+    - type: cosine_map@150
+      value: 0.6260195205413376
+      name: Cosine Map@150
+    - type: cosine_map@200
+      value: 0.6261650797332174
+      name: Cosine Map@200
+    - type: cosine_map@500
+      value: 0.6263452093477304
+      name: Cosine Map@500
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: mix de
+      type: mix_de
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.5564222568902756
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@20
+      value: 0.8866354654186167
+      name: Cosine Accuracy@20
+    - type: cosine_accuracy@50
+      value: 0.9381175247009881
+      name: Cosine Accuracy@50
+    - type: cosine_accuracy@100
+      value: 0.9594383775351014
+      name: Cosine Accuracy@100
+    - type: cosine_accuracy@150
+      value: 0.9708788351534061
+      name: Cosine Accuracy@150
+    - type: cosine_accuracy@200
+      value: 0.9776391055642226
+      name: Cosine Accuracy@200
+    - type: cosine_precision@1
+      value: 0.5564222568902756
+      name: Cosine Precision@1
+    - type: cosine_precision@20
+      value: 0.109464378575143
+      name: Cosine Precision@20
+    - type: cosine_precision@50
+      value: 0.048060322412896525
+      name: Cosine Precision@50
+    - type: cosine_precision@100
+      value: 0.025273010920436823
+      name: Cosine Precision@100
+    - type: cosine_precision@150
+      value: 0.017313225862367825
+      name: Cosine Precision@150
+    - type: cosine_precision@200
+      value: 0.013143525741029644
+      name: Cosine Precision@200
+    - type: cosine_recall@1
+      value: 0.20931703934824059
+      name: Cosine Recall@1
+    - type: cosine_recall@20
+      value: 0.7988992893049055
+      name: Cosine Recall@20
+    - type: cosine_recall@50
+      value: 0.8741029641185647
+      name: Cosine Recall@50
+    - type: cosine_recall@100
+      value: 0.9173426937077482
+      name: Cosine Recall@100
+    - type: cosine_recall@150
+      value: 0.9424076963078523
+      name: Cosine Recall@150
+    - type: cosine_recall@200
+      value: 0.953631478592477
+      name: Cosine Recall@200
+    - type: cosine_ndcg@1
+      value: 0.5564222568902756
+      name: Cosine Ndcg@1
+    - type: cosine_ndcg@20
+      value: 0.6541310877479573
+      name: Cosine Ndcg@20
+    - type: cosine_ndcg@50
+      value: 0.674790854916742
+      name: Cosine Ndcg@50
+    - type: cosine_ndcg@100
+      value: 0.6844997445798996
+      name: Cosine Ndcg@100
+    - type: cosine_ndcg@150
+      value: 0.6894214573457343
+      name: Cosine Ndcg@150
+    - type: cosine_ndcg@200
+      value: 0.6914881284159038
+      name: Cosine Ndcg@200
+    - type: cosine_mrr@1
+      value: 0.5564222568902756
+      name: Cosine Mrr@1
+    - type: cosine_mrr@20
+      value: 0.6476945170199107
+      name: Cosine Mrr@20
+    - type: cosine_mrr@50
+      value: 0.6493649946597936
+      name: Cosine Mrr@50
+    - type: cosine_mrr@100
+      value: 0.6496801333421218
+      name: Cosine Mrr@100
+    - type: cosine_mrr@150
+      value: 0.6497778366579644
+      name: Cosine Mrr@150
+    - type: cosine_mrr@200
+      value: 0.6498156890114056
+      name: Cosine Mrr@200
+    - type: cosine_map@1
+      value: 0.5564222568902756
+      name: Cosine Map@1
+    - type: cosine_map@20
+      value: 0.5648326970643027
+      name: Cosine Map@20
+    - type: cosine_map@50
+      value: 0.57003456255067
+      name: Cosine Map@50
+    - type: cosine_map@100
+      value: 0.5714370828517599
+      name: Cosine Map@100
+    - type: cosine_map@150
+      value: 0.5719002990233493
+      name: Cosine Map@150
+    - type: cosine_map@200
+      value: 0.5720497397197026
+      name: Cosine Map@200
+    - type: cosine_map@500
+      value: 0.5723109788233504
+      name: Cosine Map@500
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: mix zh
+      type: mix_zh
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.6085594989561587
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@20
+      value: 0.9592901878914405
+      name: Cosine Accuracy@20
+    - type: cosine_accuracy@50
+      value: 0.9791231732776617
+      name: Cosine Accuracy@50
+    - type: cosine_accuracy@100
+      value: 0.9874739039665971
+      name: Cosine Accuracy@100
+    - type: cosine_accuracy@150
+      value: 0.9911273486430062
+      name: Cosine Accuracy@150
+    - type: cosine_accuracy@200
+      value: 0.9937369519832986
+      name: Cosine Accuracy@200
+    - type: cosine_precision@1
+      value: 0.6085594989561587
+      name: Cosine Precision@1
+    - type: cosine_precision@20
+      value: 0.12656576200417535
+      name: Cosine Precision@20
+    - type: cosine_precision@50
+      value: 0.05518789144050106
+      name: Cosine Precision@50
+    - type: cosine_precision@100
+      value: 0.028747390396659713
+      name: Cosine Precision@100
+    - type: cosine_precision@150
+      value: 0.019425887265135697
+      name: Cosine Precision@150
+    - type: cosine_precision@200
+      value: 0.014705114822546978
+      name: Cosine Precision@200
+    - type: cosine_recall@1
+      value: 0.2043804056069192
+      name: Cosine Recall@1
+    - type: cosine_recall@20
+      value: 0.8346468336812805
+      name: Cosine Recall@20
+    - type: cosine_recall@50
+      value: 0.9095772442588727
+      name: Cosine Recall@50
+    - type: cosine_recall@100
+      value: 0.9475643702157271
+      name: Cosine Recall@100
+    - type: cosine_recall@150
+      value: 0.9609168406402228
+      name: Cosine Recall@150
+    - type: cosine_recall@200
+      value: 0.9697807933194154
+      name: Cosine Recall@200
+    - type: cosine_ndcg@1
+      value: 0.6085594989561587
+      name: Cosine Ndcg@1
+    - type: cosine_ndcg@20
+      value: 0.6853247290079303
+      name: Cosine Ndcg@20
+    - type: cosine_ndcg@50
+      value: 0.7066940880968873
+      name: Cosine Ndcg@50
+    - type: cosine_ndcg@100
+      value: 0.715400790265437
+      name: Cosine Ndcg@100
+    - type: cosine_ndcg@150
+      value: 0.7180808450243259
+      name: Cosine Ndcg@150
+    - type: cosine_ndcg@200
+      value: 0.7197629642909036
+      name: Cosine Ndcg@200
+    - type: cosine_mrr@1
+      value: 0.6085594989561587
+      name: Cosine Mrr@1
+    - type: cosine_mrr@20
+      value: 0.7236528792595264
+      name: Cosine Mrr@20
+    - type: cosine_mrr@50
+      value: 0.7243308740364213
+      name: Cosine Mrr@50
+    - type: cosine_mrr@100
+      value: 0.7244524590415827
+      name: Cosine Mrr@100
+    - type: cosine_mrr@150
+      value: 0.7244814620971008
+      name: Cosine Mrr@150
+    - type: cosine_mrr@200
+      value: 0.7244960285685315
+      name: Cosine Mrr@200
+    - type: cosine_map@1
+      value: 0.6085594989561587
+      name: Cosine Map@1
+    - type: cosine_map@20
+      value: 0.5652211952239553
+      name: Cosine Map@20
+    - type: cosine_map@50
+      value: 0.5716374350069462
+      name: Cosine Map@50
+    - type: cosine_map@100
+      value: 0.5730756815932735
+      name: Cosine Map@100
+    - type: cosine_map@150
+      value: 0.5733543252173214
+      name: Cosine Map@150
+    - type: cosine_map@200
+      value: 0.5734860037813889
+      name: Cosine Map@200
+    - type: cosine_map@500
+      value: 0.5736416699680624
+      name: Cosine Map@500
+---
+
+# Job - Job matching Alibaba-NLP/gte-multilingual-base pruned
+
+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:** [Unknown](https://huggingface.co/unknown) -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (2): Normalize()
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("pj-mathematician/JobGTE-multilingual-base-pruned")
+# Run inference
+sentences = [
+    'Entwicklerin für mobile Anwendungen',
+    'Mergers-and-Acquisitions-Analyst/Mergers-and-Acquisitions-Analystin',
+    'fashion design expert',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Information Retrieval
+
+* Datasets: `full_en`, `full_es`, `full_de`, `full_zh`, `mix_es`, `mix_de` and `mix_zh`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric               | full_en    | full_es    | full_de    | full_zh    | mix_es     | mix_de     | mix_zh     |
+|:---------------------|:-----------|:-----------|:-----------|:-----------|:-----------|:-----------|:-----------|
+| cosine_accuracy@1    | 0.6476     | 0.1189     | 0.2956     | 0.6602     | 0.6297     | 0.5564     | 0.6086     |
+| cosine_accuracy@20   | 0.9714     | 1.0        | 0.9704     | 0.9709     | 0.9106     | 0.8866     | 0.9593     |
+| cosine_accuracy@50   | 0.9905     | 1.0        | 0.9754     | 0.9903     | 0.9496     | 0.9381     | 0.9791     |
+| cosine_accuracy@100  | 0.9905     | 1.0        | 0.9901     | 0.9903     | 0.973      | 0.9594     | 0.9875     |
+| cosine_accuracy@150  | 0.9905     | 1.0        | 0.9901     | 0.9903     | 0.9834     | 0.9709     | 0.9911     |
+| cosine_accuracy@200  | 0.9905     | 1.0        | 0.9901     | 0.9903     | 0.9901     | 0.9776     | 0.9937     |
+| cosine_precision@1   | 0.6476     | 0.1189     | 0.2956     | 0.6602     | 0.6297     | 0.5564     | 0.6086     |
+| cosine_precision@20  | 0.4795     | 0.5268     | 0.4291     | 0.4481     | 0.1117     | 0.1095     | 0.1266     |
+| cosine_precision@50  | 0.2884     | 0.3438     | 0.298      | 0.2713     | 0.0485     | 0.0481     | 0.0552     |
+| cosine_precision@100 | 0.173      | 0.219      | 0.1943     | 0.1665     | 0.0254     | 0.0253     | 0.0287     |
+| cosine_precision@150 | 0.1244     | 0.1658     | 0.1482     | 0.1211     | 0.0172     | 0.0173     | 0.0194     |
+| cosine_precision@200 | 0.0986     | 0.1333     | 0.1198     | 0.0953     | 0.0131     | 0.0131     | 0.0147     |
+| cosine_recall@1      | 0.0661     | 0.0036     | 0.0111     | 0.0661     | 0.2434     | 0.2093     | 0.2044     |
+| cosine_recall@20     | 0.5122     | 0.3541     | 0.2668     | 0.4841     | 0.8288     | 0.7989     | 0.8346     |
+| cosine_recall@50     | 0.6835     | 0.5098     | 0.4092     | 0.6568     | 0.8987     | 0.8741     | 0.9096     |
+| cosine_recall@100    | 0.79       | 0.6076     | 0.5098     | 0.7685     | 0.9399     | 0.9173     | 0.9476     |
+| cosine_recall@150    | 0.84       | 0.6705     | 0.5729     | 0.8278     | 0.9577     | 0.9424     | 0.9609     |
+| cosine_recall@200    | 0.8759     | 0.7125     | 0.612      | 0.8617     | 0.9695     | 0.9536     | 0.9698     |
+| cosine_ndcg@1        | 0.6476     | 0.1189     | 0.2956     | 0.6602     | 0.6297     | 0.5564     | 0.6086     |
+| cosine_ndcg@20       | 0.6468     | 0.5708     | 0.4696     | 0.6231     | 0.701      | 0.6541     | 0.6853     |
+| cosine_ndcg@50       | 0.658      | 0.5355     | 0.4449     | 0.6383     | 0.7201     | 0.6748     | 0.7067     |
+| cosine_ndcg@100      | 0.7095     | 0.559      | 0.467      | 0.6917     | 0.7291     | 0.6845     | 0.7154     |
+| cosine_ndcg@150      | 0.731      | 0.59       | 0.4982     | 0.7167     | 0.7326     | 0.6894     | 0.7181     |
+| **cosine_ndcg@200**  | **0.7461** | **0.6095** | **0.5165** | **0.7303** | **0.7347** | **0.6915** | **0.7198** |
+| cosine_mrr@1         | 0.6476     | 0.1189     | 0.2956     | 0.6602     | 0.6297     | 0.5564     | 0.6086     |
+| cosine_mrr@20        | 0.7902     | 0.5532     | 0.5047     | 0.8016     | 0.7037     | 0.6477     | 0.7237     |
+| cosine_mrr@50        | 0.791      | 0.5532     | 0.5048     | 0.8021     | 0.705      | 0.6494     | 0.7243     |
+| cosine_mrr@100       | 0.791      | 0.5532     | 0.505      | 0.8021     | 0.7053     | 0.6497     | 0.7245     |
+| cosine_mrr@150       | 0.791      | 0.5532     | 0.505      | 0.8021     | 0.7054     | 0.6498     | 0.7245     |
+| cosine_mrr@200       | 0.791      | 0.5532     | 0.505      | 0.8021     | 0.7055     | 0.6498     | 0.7245     |
+| cosine_map@1         | 0.6476     | 0.1189     | 0.2956     | 0.6602     | 0.6297     | 0.5564     | 0.6086     |
+| cosine_map@20        | 0.5026     | 0.4379     | 0.3366     | 0.475      | 0.6194     | 0.5648     | 0.5652     |
+| cosine_map@50        | 0.484      | 0.3739     | 0.2853     | 0.4579     | 0.6244     | 0.57       | 0.5716     |
+| cosine_map@100       | 0.5118     | 0.3763     | 0.2818     | 0.4848     | 0.6257     | 0.5714     | 0.5731     |
+| cosine_map@150       | 0.5202     | 0.3892     | 0.2931     | 0.4937     | 0.626      | 0.5719     | 0.5734     |
+| cosine_map@200       | 0.5249     | 0.3958     | 0.2988     | 0.4978     | 0.6262     | 0.572      | 0.5735     |
+| cosine_map@500       | 0.5304     | 0.4063     | 0.3109     | 0.504      | 0.6263     | 0.5723     | 0.5736     |
+
+<!--
+## 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 Dataset
+
+#### Unnamed Dataset
+
+* Size: 86,648 training samples
+* Columns: <code>sentence</code> and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence                                                                         | label                                |
+  |:--------|:---------------------------------------------------------------------------------|:-------------------------------------|
+  | type    | string                                                                           | list                                 |
+  | details | <ul><li>min: 2 tokens</li><li>mean: 8.25 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>size: 768 elements</li></ul> |
+* Samples:
+  | sentence                                 | label                                                                                                                            |
+  |:-----------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------|
+  | <code></code>                            | <code>[-0.07171934843063354, 0.03595816716551781, -0.029780959710478783, 0.006593302357941866, 0.040611181408166885, ...]</code> |
+  | <code>airport environment officer</code> | <code>[-0.022075481712818146, 0.02999737113714218, -0.02189866080880165, 0.016531817615032196, 0.012234307825565338, ...]</code> |
+  | <code>Flake操作员</code>                    | <code>[-0.04815564677119255, 0.023524893447756767, -0.01583661139011383, 0.042527906596660614, 0.03815540298819542, ...]</code>  |
+* Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 128
+- `per_device_eval_batch_size`: 128
+- `gradient_accumulation_steps`: 2
+- `learning_rate`: 0.0001
+- `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`: 0.0001
+- `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   | -             | 0.5348                  | 0.4311                  | 0.3678                  | 0.5333                  | 0.2580                 | 0.1924                 | 0.2871                 |
+| 0.0030 | 1    | 0.0017        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 0.2959 | 100  | 0.001         | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 0.5917 | 200  | 0.0005        | 0.6702                  | 0.5287                  | 0.4566                  | 0.6809                  | 0.5864                 | 0.5302                 | 0.4739                 |
+| 0.8876 | 300  | 0.0004        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 1.1834 | 400  | 0.0004        | 0.7057                  | 0.5643                  | 0.4790                  | 0.7033                  | 0.6604                 | 0.6055                 | 0.6003                 |
+| 1.4793 | 500  | 0.0004        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 1.7751 | 600  | 0.0003        | 0.7184                  | 0.5783                  | 0.4910                  | 0.7127                  | 0.6927                 | 0.6416                 | 0.6485                 |
+| 2.0710 | 700  | 0.0003        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 2.3669 | 800  | 0.0003        | 0.7307                  | 0.5938                  | 0.5023                  | 0.7233                  | 0.7125                 | 0.6639                 | 0.6847                 |
+| 2.6627 | 900  | 0.0003        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 2.9586 | 1000 | 0.0003        | 0.7371                  | 0.6002                  | 0.5085                  | 0.7228                  | 0.7222                 | 0.6761                 | 0.6998                 |
+| 3.2544 | 1100 | 0.0003        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 3.5503 | 1200 | 0.0003        | 0.7402                  | 0.6059                  | 0.5109                  | 0.7279                  | 0.7285                 | 0.6841                 | 0.7120                 |
+| 3.8462 | 1300 | 0.0003        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 4.1420 | 1400 | 0.0003        | 0.7449                  | 0.6083                  | 0.5154                  | 0.7294                  | 0.7333                 | 0.6894                 | 0.7176                 |
+| 4.4379 | 1500 | 0.0003        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 4.7337 | 1600 | 0.0003        | 0.7461                  | 0.6095                  | 0.5165                  | 0.7303                  | 0.7347                 | 0.6915                 | 0.7198                 |
+
+
+### 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",
+}
+```
+
+#### MSELoss
+```bibtex
+@inproceedings{reimers-2020-multilingual-sentence-bert,
+    title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2020",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/2004.09813",
+}
+```
+
+<!--
+## 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-1000/1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

checkpoint-1000/README.md

@@ -0,0 +1,1284 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:86648
+- loss:MSELoss
+widget:
+- source_sentence: Familienberaterin
+  sentences:
+  - electric power station operator
+  - venue booker & promoter
+  - betrieblicher Aus- und Weiterbildner/betriebliche Aus- und Weiterbildnerin
+- source_sentence: high school RS teacher
+  sentences:
+  - infantryman
+  - Schnellbedienungsrestaurantteamleiter
+  - drill setup operator
+- source_sentence: lighting designer
+  sentences:
+  - software support manager
+  - 直升机维护协调员
+  - bus maintenance supervisor
+- source_sentence: 机场消防员
+  sentences:
+  - Flake操作员
+  - técnico en gestión de residuos peligrosos/técnica en gestión de residuos peligrosos
+  - 专门学校老师
+- source_sentence: Entwicklerin für mobile Anwendungen
+  sentences:
+  - fashion design expert
+  - Mergers-and-Acquisitions-Analyst/Mergers-and-Acquisitions-Analystin
+  - commercial bid manager
+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
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: full en
+      type: full_en
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.6285714285714286
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@20
+      value: 0.9714285714285714
+      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: 0.6285714285714286
+      name: Cosine Precision@1
+    - type: cosine_precision@20
+      value: 0.4723809523809524
+      name: Cosine Precision@20
+    - type: cosine_precision@50
+      value: 0.2838095238095238
+      name: Cosine Precision@50
+    - type: cosine_precision@100
+      value: 0.1706666666666667
+      name: Cosine Precision@100
+    - type: cosine_precision@150
+      value: 0.12285714285714286
+      name: Cosine Precision@150
+    - type: cosine_precision@200
+      value: 0.09700000000000002
+      name: Cosine Precision@200
+    - type: cosine_recall@1
+      value: 0.06568451704213447
+      name: Cosine Recall@1
+    - type: cosine_recall@20
+      value: 0.5041312032991911
+      name: Cosine Recall@20
+    - type: cosine_recall@50
+      value: 0.6762963371727007
+      name: Cosine Recall@50
+    - type: cosine_recall@100
+      value: 0.7798036464336738
+      name: Cosine Recall@100
+    - type: cosine_recall@150
+      value: 0.8311908383371492
+      name: Cosine Recall@150
+    - type: cosine_recall@200
+      value: 0.8655400214018215
+      name: Cosine Recall@200
+    - type: cosine_ndcg@1
+      value: 0.6285714285714286
+      name: Cosine Ndcg@1
+    - type: cosine_ndcg@20
+      value: 0.6385286667884668
+      name: Cosine Ndcg@20
+    - type: cosine_ndcg@50
+      value: 0.6505087993598385
+      name: Cosine Ndcg@50
+    - type: cosine_ndcg@100
+      value: 0.7009585791000247
+      name: Cosine Ndcg@100
+    - type: cosine_ndcg@150
+      value: 0.7228549618650749
+      name: Cosine Ndcg@150
+    - type: cosine_ndcg@200
+      value: 0.7370730818153396
+      name: Cosine Ndcg@200
+    - type: cosine_mrr@1
+      value: 0.6285714285714286
+      name: Cosine Mrr@1
+    - type: cosine_mrr@20
+      value: 0.7790726817042607
+      name: Cosine Mrr@20
+    - type: cosine_mrr@50
+      value: 0.7797979143260452
+      name: Cosine Mrr@50
+    - type: cosine_mrr@100
+      value: 0.7797979143260452
+      name: Cosine Mrr@100
+    - type: cosine_mrr@150
+      value: 0.7797979143260452
+      name: Cosine Mrr@150
+    - type: cosine_mrr@200
+      value: 0.7797979143260452
+      name: Cosine Mrr@200
+    - type: cosine_map@1
+      value: 0.6285714285714286
+      name: Cosine Map@1
+    - type: cosine_map@20
+      value: 0.4949002324392317
+      name: Cosine Map@20
+    - type: cosine_map@50
+      value: 0.47542864021103454
+      name: Cosine Map@50
+    - type: cosine_map@100
+      value: 0.5027685735699932
+      name: Cosine Map@100
+    - type: cosine_map@150
+      value: 0.5108956115342047
+      name: Cosine Map@150
+    - type: cosine_map@200
+      value: 0.5152152246235047
+      name: Cosine Map@200
+    - type: cosine_map@500
+      value: 0.5211733943510876
+      name: Cosine Map@500
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: full es
+      type: full_es
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.11351351351351352
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@20
+      value: 1.0
+      name: Cosine Accuracy@20
+    - type: cosine_accuracy@50
+      value: 1.0
+      name: Cosine Accuracy@50
+    - type: cosine_accuracy@100
+      value: 1.0
+      name: Cosine Accuracy@100
+    - type: cosine_accuracy@150
+      value: 1.0
+      name: Cosine Accuracy@150
+    - type: cosine_accuracy@200
+      value: 1.0
+      name: Cosine Accuracy@200
+    - type: cosine_precision@1
+      value: 0.11351351351351352
+      name: Cosine Precision@1
+    - type: cosine_precision@20
+      value: 0.5213513513513512
+      name: Cosine Precision@20
+    - type: cosine_precision@50
+      value: 0.33891891891891895
+      name: Cosine Precision@50
+    - type: cosine_precision@100
+      value: 0.2141081081081081
+      name: Cosine Precision@100
+    - type: cosine_precision@150
+      value: 0.16104504504504505
+      name: Cosine Precision@150
+    - type: cosine_precision@200
+      value: 0.13094594594594594
+      name: Cosine Precision@200
+    - type: cosine_recall@1
+      value: 0.0035045234969014166
+      name: Cosine Recall@1
+    - type: cosine_recall@20
+      value: 0.34830621955762764
+      name: Cosine Recall@20
+    - type: cosine_recall@50
+      value: 0.5043797869988105
+      name: Cosine Recall@50
+    - type: cosine_recall@100
+      value: 0.5962566893615484
+      name: Cosine Recall@100
+    - type: cosine_recall@150
+      value: 0.6539916045900668
+      name: Cosine Recall@150
+    - type: cosine_recall@200
+      value: 0.7027707655811134
+      name: Cosine Recall@200
+    - type: cosine_ndcg@1
+      value: 0.11351351351351352
+      name: Cosine Ndcg@1
+    - type: cosine_ndcg@20
+      value: 0.5638160555705326
+      name: Cosine Ndcg@20
+    - type: cosine_ndcg@50
+      value: 0.5286289587475489
+      name: Cosine Ndcg@50
+    - type: cosine_ndcg@100
+      value: 0.5494533442820461
+      name: Cosine Ndcg@100
+    - type: cosine_ndcg@150
+      value: 0.5778904564772578
+      name: Cosine Ndcg@150
+    - type: cosine_ndcg@200
+      value: 0.6002374248801999
+      name: Cosine Ndcg@200
+    - type: cosine_mrr@1
+      value: 0.11351351351351352
+      name: Cosine Mrr@1
+    - type: cosine_mrr@20
+      value: 0.55
+      name: Cosine Mrr@20
+    - type: cosine_mrr@50
+      value: 0.55
+      name: Cosine Mrr@50
+    - type: cosine_mrr@100
+      value: 0.55
+      name: Cosine Mrr@100
+    - type: cosine_mrr@150
+      value: 0.55
+      name: Cosine Mrr@150
+    - type: cosine_mrr@200
+      value: 0.55
+      name: Cosine Mrr@200
+    - type: cosine_map@1
+      value: 0.11351351351351352
+      name: Cosine Map@1
+    - type: cosine_map@20
+      value: 0.4321212731877681
+      name: Cosine Map@20
+    - type: cosine_map@50
+      value: 0.3662438776904182
+      name: Cosine Map@50
+    - type: cosine_map@100
+      value: 0.3676467044477579
+      name: Cosine Map@100
+    - type: cosine_map@150
+      value: 0.37914071893635704
+      name: Cosine Map@150
+    - type: cosine_map@200
+      value: 0.3864291047810966
+      name: Cosine Map@200
+    - type: cosine_map@500
+      value: 0.3967448814407886
+      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: 0.9605911330049262
+      name: Cosine Accuracy@20
+    - type: cosine_accuracy@50
+      value: 0.9802955665024631
+      name: Cosine Accuracy@50
+    - type: cosine_accuracy@100
+      value: 0.9852216748768473
+      name: Cosine Accuracy@100
+    - type: cosine_accuracy@150
+      value: 0.9852216748768473
+      name: Cosine Accuracy@150
+    - type: cosine_accuracy@200
+      value: 0.9901477832512315
+      name: Cosine Accuracy@200
+    - type: cosine_precision@1
+      value: 0.2955665024630542
+      name: Cosine Precision@1
+    - type: cosine_precision@20
+      value: 0.424384236453202
+      name: Cosine Precision@20
+    - type: cosine_precision@50
+      value: 0.29064039408866993
+      name: Cosine Precision@50
+    - type: cosine_precision@100
+      value: 0.19019704433497536
+      name: Cosine Precision@100
+    - type: cosine_precision@150
+      value: 0.14476190476190476
+      name: Cosine Precision@150
+    - type: cosine_precision@200
+      value: 0.1177832512315271
+      name: Cosine Precision@200
+    - type: cosine_recall@1
+      value: 0.01108543831680986
+      name: Cosine Recall@1
+    - type: cosine_recall@20
+      value: 0.2623989771425487
+      name: Cosine Recall@20
+    - type: cosine_recall@50
+      value: 0.399936827395569
+      name: Cosine Recall@50
+    - type: cosine_recall@100
+      value: 0.5011599542158983
+      name: Cosine Recall@100
+    - type: cosine_recall@150
+      value: 0.5599024076006294
+      name: Cosine Recall@150
+    - type: cosine_recall@200
+      value: 0.6019565140878311
+      name: Cosine Recall@200
+    - type: cosine_ndcg@1
+      value: 0.2955665024630542
+      name: Cosine Ndcg@1
+    - type: cosine_ndcg@20
+      value: 0.46461290935992494
+      name: Cosine Ndcg@20
+    - type: cosine_ndcg@50
+      value: 0.43636700085765784
+      name: Cosine Ndcg@50
+    - type: cosine_ndcg@100
+      value: 0.4594232150790335
+      name: Cosine Ndcg@100
+    - type: cosine_ndcg@150
+      value: 0.4887319216460325
+      name: Cosine Ndcg@150
+    - type: cosine_ndcg@200
+      value: 0.5085159310260775
+      name: Cosine Ndcg@200
+    - type: cosine_mrr@1
+      value: 0.2955665024630542
+      name: Cosine Mrr@1
+    - type: cosine_mrr@20
+      value: 0.503435229891329
+      name: Cosine Mrr@20
+    - type: cosine_mrr@50
+      value: 0.5041035247761447
+      name: Cosine Mrr@50
+    - type: cosine_mrr@100
+      value: 0.5041884576791513
+      name: Cosine Mrr@100
+    - type: cosine_mrr@150
+      value: 0.5041884576791513
+      name: Cosine Mrr@150
+    - type: cosine_mrr@200
+      value: 0.5042166068698621
+      name: Cosine Mrr@200
+    - type: cosine_map@1
+      value: 0.2955665024630542
+      name: Cosine Map@1
+    - type: cosine_map@20
+      value: 0.3326012942578798
+      name: Cosine Map@20
+    - type: cosine_map@50
+      value: 0.2779781159809199
+      name: Cosine Map@50
+    - type: cosine_map@100
+      value: 0.27530357902528746
+      name: Cosine Map@100
+    - type: cosine_map@150
+      value: 0.2859029789549631
+      name: Cosine Map@150
+    - type: cosine_map@200
+      value: 0.29192358526577794
+      name: Cosine Map@200
+    - type: cosine_map@500
+      value: 0.3037728006457777
+      name: Cosine Map@500
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: full zh
+      type: full_zh
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.6504854368932039
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@20
+      value: 0.970873786407767
+      name: Cosine Accuracy@20
+    - type: cosine_accuracy@50
+      value: 0.9805825242718447
+      name: Cosine Accuracy@50
+    - type: cosine_accuracy@100
+      value: 0.9902912621359223
+      name: Cosine Accuracy@100
+    - type: cosine_accuracy@150
+      value: 0.9902912621359223
+      name: Cosine Accuracy@150
+    - type: cosine_accuracy@200
+      value: 0.9902912621359223
+      name: Cosine Accuracy@200
+    - type: cosine_precision@1
+      value: 0.6504854368932039
+      name: Cosine Precision@1
+    - type: cosine_precision@20
+      value: 0.4461165048543689
+      name: Cosine Precision@20
+    - type: cosine_precision@50
+      value: 0.26932038834951455
+      name: Cosine Precision@50
+    - type: cosine_precision@100
+      value: 0.16601941747572818
+      name: Cosine Precision@100
+    - type: cosine_precision@150
+      value: 0.12000000000000002
+      name: Cosine Precision@150
+    - type: cosine_precision@200
+      value: 0.09475728155339808
+      name: Cosine Precision@200
+    - type: cosine_recall@1
+      value: 0.06125809321810901
+      name: Cosine Recall@1
+    - type: cosine_recall@20
+      value: 0.4798173076061309
+      name: Cosine Recall@20
+    - type: cosine_recall@50
+      value: 0.6511259115267456
+      name: Cosine Recall@50
+    - type: cosine_recall@100
+      value: 0.7667280032499174
+      name: Cosine Recall@100
+    - type: cosine_recall@150
+      value: 0.8234348132226993
+      name: Cosine Recall@150
+    - type: cosine_recall@200
+      value: 0.8570886860782638
+      name: Cosine Recall@200
+    - type: cosine_ndcg@1
+      value: 0.6504854368932039
+      name: Cosine Ndcg@1
+    - type: cosine_ndcg@20
+      value: 0.6163434250133266
+      name: Cosine Ndcg@20
+    - type: cosine_ndcg@50
+      value: 0.6306194061713684
+      name: Cosine Ndcg@50
+    - type: cosine_ndcg@100
+      value: 0.6852740031621496
+      name: Cosine Ndcg@100
+    - type: cosine_ndcg@150
+      value: 0.7087858531025408
+      name: Cosine Ndcg@150
+    - type: cosine_ndcg@200
+      value: 0.7227726687256436
+      name: Cosine Ndcg@200
+    - type: cosine_mrr@1
+      value: 0.6504854368932039
+      name: Cosine Mrr@1
+    - type: cosine_mrr@20
+      value: 0.7938511326860843
+      name: Cosine Mrr@20
+    - type: cosine_mrr@50
+      value: 0.7941135310067349
+      name: Cosine Mrr@50
+    - type: cosine_mrr@100
+      value: 0.7943002375041209
+      name: Cosine Mrr@100
+    - type: cosine_mrr@150
+      value: 0.7943002375041209
+      name: Cosine Mrr@150
+    - type: cosine_mrr@200
+      value: 0.7943002375041209
+      name: Cosine Mrr@200
+    - type: cosine_map@1
+      value: 0.6504854368932039
+      name: Cosine Map@1
+    - type: cosine_map@20
+      value: 0.4673451367444491
+      name: Cosine Map@20
+    - type: cosine_map@50
+      value: 0.4491601687897158
+      name: Cosine Map@50
+    - type: cosine_map@100
+      value: 0.4759775327060125
+      name: Cosine Map@100
+    - type: cosine_map@150
+      value: 0.484283864447002
+      name: Cosine Map@150
+    - type: cosine_map@200
+      value: 0.4885403171787604
+      name: Cosine Map@200
+    - type: cosine_map@500
+      value: 0.4948931148880558
+      name: Cosine Map@500
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: mix es
+      type: mix_es
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.6172646905876235
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@20
+      value: 0.9032761310452418
+      name: Cosine Accuracy@20
+    - type: cosine_accuracy@50
+      value: 0.9443577743109725
+      name: Cosine Accuracy@50
+    - type: cosine_accuracy@100
+      value: 0.9703588143525741
+      name: Cosine Accuracy@100
+    - type: cosine_accuracy@150
+      value: 0.9812792511700468
+      name: Cosine Accuracy@150
+    - type: cosine_accuracy@200
+      value: 0.9859594383775351
+      name: Cosine Accuracy@200
+    - type: cosine_precision@1
+      value: 0.6172646905876235
+      name: Cosine Precision@1
+    - type: cosine_precision@20
+      value: 0.10972438897555903
+      name: Cosine Precision@20
+    - type: cosine_precision@50
+      value: 0.04786271450858035
+      name: Cosine Precision@50
+    - type: cosine_precision@100
+      value: 0.025169006760270413
+      name: Cosine Precision@100
+    - type: cosine_precision@150
+      value: 0.017157219622118216
+      name: Cosine Precision@150
+    - type: cosine_precision@200
+      value: 0.013018720748829957
+      name: Cosine Precision@200
+    - type: cosine_recall@1
+      value: 0.2379838050664884
+      name: Cosine Recall@1
+    - type: cosine_recall@20
+      value: 0.8149369784315182
+      name: Cosine Recall@20
+    - type: cosine_recall@50
+      value: 0.8866788004853527
+      name: Cosine Recall@50
+    - type: cosine_recall@100
+      value: 0.9331773270930838
+      name: Cosine Recall@100
+    - type: cosine_recall@150
+      value: 0.9536141445657828
+      name: Cosine Recall@150
+    - type: cosine_recall@200
+      value: 0.9651759403709481
+      name: Cosine Recall@200
+    - type: cosine_ndcg@1
+      value: 0.6172646905876235
+      name: Cosine Ndcg@1
+    - type: cosine_ndcg@20
+      value: 0.6863945449619185
+      name: Cosine Ndcg@20
+    - type: cosine_ndcg@50
+      value: 0.7059805315894592
+      name: Cosine Ndcg@50
+    - type: cosine_ndcg@100
+      value: 0.7161349937562115
+      name: Cosine Ndcg@100
+    - type: cosine_ndcg@150
+      value: 0.7201494083175249
+      name: Cosine Ndcg@150
+    - type: cosine_ndcg@200
+      value: 0.722225937142632
+      name: Cosine Ndcg@200
+    - type: cosine_mrr@1
+      value: 0.6172646905876235
+      name: Cosine Mrr@1
+    - type: cosine_mrr@20
+      value: 0.6921361840847764
+      name: Cosine Mrr@20
+    - type: cosine_mrr@50
+      value: 0.6935275501084183
+      name: Cosine Mrr@50
+    - type: cosine_mrr@100
+      value: 0.6938924919697613
+      name: Cosine Mrr@100
+    - type: cosine_mrr@150
+      value: 0.6939819360030616
+      name: Cosine Mrr@150
+    - type: cosine_mrr@200
+      value: 0.6940082129440573
+      name: Cosine Mrr@200
+    - type: cosine_map@1
+      value: 0.6172646905876235
+      name: Cosine Map@1
+    - type: cosine_map@20
+      value: 0.6028333286973904
+      name: Cosine Map@20
+    - type: cosine_map@50
+      value: 0.6079882517976847
+      name: Cosine Map@50
+    - type: cosine_map@100
+      value: 0.6094136625128228
+      name: Cosine Map@100
+    - type: cosine_map@150
+      value: 0.6097807307495342
+      name: Cosine Map@150
+    - type: cosine_map@200
+      value: 0.6099278426294548
+      name: Cosine Map@200
+    - type: cosine_map@500
+      value: 0.6101218939355526
+      name: Cosine Map@500
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: mix de
+      type: mix_de
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.5429017160686428
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@20
+      value: 0.8725949037961519
+      name: Cosine Accuracy@20
+    - type: cosine_accuracy@50
+      value: 0.9297971918876755
+      name: Cosine Accuracy@50
+    - type: cosine_accuracy@100
+      value: 0.9552782111284451
+      name: Cosine Accuracy@100
+    - type: cosine_accuracy@150
+      value: 0.968278731149246
+      name: Cosine Accuracy@150
+    - type: cosine_accuracy@200
+      value: 0.9729589183567343
+      name: Cosine Accuracy@200
+    - type: cosine_precision@1
+      value: 0.5429017160686428
+      name: Cosine Precision@1
+    - type: cosine_precision@20
+      value: 0.10709828393135724
+      name: Cosine Precision@20
+    - type: cosine_precision@50
+      value: 0.04726989079563183
+      name: Cosine Precision@50
+    - type: cosine_precision@100
+      value: 0.025002600104004166
+      name: Cosine Precision@100
+    - type: cosine_precision@150
+      value: 0.01712601837406829
+      name: Cosine Precision@150
+    - type: cosine_precision@200
+      value: 0.013044721788871557
+      name: Cosine Precision@200
+    - type: cosine_recall@1
+      value: 0.20383948691280984
+      name: Cosine Recall@1
+    - type: cosine_recall@20
+      value: 0.7817386028774485
+      name: Cosine Recall@20
+    - type: cosine_recall@50
+      value: 0.8605044201768071
+      name: Cosine Recall@50
+    - type: cosine_recall@100
+      value: 0.9077223088923557
+      name: Cosine Recall@100
+    - type: cosine_recall@150
+      value: 0.9319032761310452
+      name: Cosine Recall@150
+    - type: cosine_recall@200
+      value: 0.9461778471138845
+      name: Cosine Recall@200
+    - type: cosine_ndcg@1
+      value: 0.5429017160686428
+      name: Cosine Ndcg@1
+    - type: cosine_ndcg@20
+      value: 0.6364696194038222
+      name: Cosine Ndcg@20
+    - type: cosine_ndcg@50
+      value: 0.6580204683537704
+      name: Cosine Ndcg@50
+    - type: cosine_ndcg@100
+      value: 0.6686859699628315
+      name: Cosine Ndcg@100
+    - type: cosine_ndcg@150
+      value: 0.6734670399055159
+      name: Cosine Ndcg@150
+    - type: cosine_ndcg@200
+      value: 0.6761041848609185
+      name: Cosine Ndcg@200
+    - type: cosine_mrr@1
+      value: 0.5429017160686428
+      name: Cosine Mrr@1
+    - type: cosine_mrr@20
+      value: 0.6331176720726237
+      name: Cosine Mrr@20
+    - type: cosine_mrr@50
+      value: 0.6350347522721764
+      name: Cosine Mrr@50
+    - type: cosine_mrr@100
+      value: 0.6354157777188323
+      name: Cosine Mrr@100
+    - type: cosine_mrr@150
+      value: 0.6355194502419383
+      name: Cosine Mrr@150
+    - type: cosine_mrr@200
+      value: 0.635546462249249
+      name: Cosine Mrr@200
+    - type: cosine_map@1
+      value: 0.5429017160686428
+      name: Cosine Map@1
+    - type: cosine_map@20
+      value: 0.546038259426052
+      name: Cosine Map@20
+    - type: cosine_map@50
+      value: 0.5513401593649401
+      name: Cosine Map@50
+    - type: cosine_map@100
+      value: 0.5528890114435938
+      name: Cosine Map@100
+    - type: cosine_map@150
+      value: 0.5533285819634786
+      name: Cosine Map@150
+    - type: cosine_map@200
+      value: 0.5535297820757661
+      name: Cosine Map@200
+    - type: cosine_map@500
+      value: 0.5538215020153545
+      name: Cosine Map@500
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: mix zh
+      type: mix_zh
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.5751565762004175
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@20
+      value: 0.9514613778705637
+      name: Cosine Accuracy@20
+    - type: cosine_accuracy@50
+      value: 0.975991649269311
+      name: Cosine Accuracy@50
+    - type: cosine_accuracy@100
+      value: 0.9848643006263048
+      name: Cosine Accuracy@100
+    - type: cosine_accuracy@150
+      value: 0.9895615866388309
+      name: Cosine Accuracy@150
+    - type: cosine_accuracy@200
+      value: 0.9916492693110647
+      name: Cosine Accuracy@200
+    - type: cosine_precision@1
+      value: 0.5751565762004175
+      name: Cosine Precision@1
+    - type: cosine_precision@20
+      value: 0.123982254697286
+      name: Cosine Precision@20
+    - type: cosine_precision@50
+      value: 0.05465553235908143
+      name: Cosine Precision@50
+    - type: cosine_precision@100
+      value: 0.02851252609603341
+      name: Cosine Precision@100
+    - type: cosine_precision@150
+      value: 0.019324982602644397
+      name: Cosine Precision@150
+    - type: cosine_precision@200
+      value: 0.014634655532359089
+      name: Cosine Precision@200
+    - type: cosine_recall@1
+      value: 0.19298513768764292
+      name: Cosine Recall@1
+    - type: cosine_recall@20
+      value: 0.8174060542797494
+      name: Cosine Recall@20
+    - type: cosine_recall@50
+      value: 0.901000347947112
+      name: Cosine Recall@50
+    - type: cosine_recall@100
+      value: 0.9399095337508698
+      name: Cosine Recall@100
+    - type: cosine_recall@150
+      value: 0.9558716075156575
+      name: Cosine Recall@150
+    - type: cosine_recall@200
+      value: 0.965196590118302
+      name: Cosine Recall@200
+    - type: cosine_ndcg@1
+      value: 0.5751565762004175
+      name: Cosine Ndcg@1
+    - type: cosine_ndcg@20
+      value: 0.6621196118161056
+      name: Cosine Ndcg@20
+    - type: cosine_ndcg@50
+      value: 0.6858570871515306
+      name: Cosine Ndcg@50
+    - type: cosine_ndcg@100
+      value: 0.6947962879201968
+      name: Cosine Ndcg@100
+    - type: cosine_ndcg@150
+      value: 0.6980250427797421
+      name: Cosine Ndcg@150
+    - type: cosine_ndcg@200
+      value: 0.6997922044919449
+      name: Cosine Ndcg@200
+    - type: cosine_mrr@1
+      value: 0.5751565762004175
+      name: Cosine Mrr@1
+    - type: cosine_mrr@20
+      value: 0.6974988781113621
+      name: Cosine Mrr@20
+    - type: cosine_mrr@50
+      value: 0.6983413027160801
+      name: Cosine Mrr@50
+    - type: cosine_mrr@100
+      value: 0.6984820179753005
+      name: Cosine Mrr@100
+    - type: cosine_mrr@150
+      value: 0.6985228351798531
+      name: Cosine Mrr@150
+    - type: cosine_mrr@200
+      value: 0.6985351624205532
+      name: Cosine Mrr@200
+    - type: cosine_map@1
+      value: 0.5751565762004175
+      name: Cosine Map@1
+    - type: cosine_map@20
+      value: 0.5395939445358217
+      name: Cosine Map@20
+    - type: cosine_map@50
+      value: 0.5465541726714618
+      name: Cosine Map@50
+    - type: cosine_map@100
+      value: 0.5480058234906587
+      name: Cosine Map@100
+    - type: cosine_map@150
+      value: 0.5483452539266979
+      name: Cosine Map@150
+    - type: cosine_map@200
+      value: 0.548487754480418
+      name: Cosine Map@200
+    - type: cosine_map@500
+      value: 0.5486704400924459
+      name: Cosine Map@500
+---
+
+# SentenceTransformer
+
+This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (2): Normalize()
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    'Entwicklerin für mobile Anwendungen',
+    'Mergers-and-Acquisitions-Analyst/Mergers-and-Acquisitions-Analystin',
+    'fashion design expert',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Information Retrieval
+
+* Datasets: `full_en`, `full_es`, `full_de`, `full_zh`, `mix_es`, `mix_de` and `mix_zh`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric               | full_en    | full_es    | full_de    | full_zh    | mix_es     | mix_de     | mix_zh     |
+|:---------------------|:-----------|:-----------|:-----------|:-----------|:-----------|:-----------|:-----------|
+| cosine_accuracy@1    | 0.6286     | 0.1135     | 0.2956     | 0.6505     | 0.6173     | 0.5429     | 0.5752     |
+| cosine_accuracy@20   | 0.9714     | 1.0        | 0.9606     | 0.9709     | 0.9033     | 0.8726     | 0.9515     |
+| cosine_accuracy@50   | 0.9905     | 1.0        | 0.9803     | 0.9806     | 0.9444     | 0.9298     | 0.976      |
+| cosine_accuracy@100  | 0.9905     | 1.0        | 0.9852     | 0.9903     | 0.9704     | 0.9553     | 0.9849     |
+| cosine_accuracy@150  | 0.9905     | 1.0        | 0.9852     | 0.9903     | 0.9813     | 0.9683     | 0.9896     |
+| cosine_accuracy@200  | 0.9905     | 1.0        | 0.9901     | 0.9903     | 0.986      | 0.973      | 0.9916     |
+| cosine_precision@1   | 0.6286     | 0.1135     | 0.2956     | 0.6505     | 0.6173     | 0.5429     | 0.5752     |
+| cosine_precision@20  | 0.4724     | 0.5214     | 0.4244     | 0.4461     | 0.1097     | 0.1071     | 0.124      |
+| cosine_precision@50  | 0.2838     | 0.3389     | 0.2906     | 0.2693     | 0.0479     | 0.0473     | 0.0547     |
+| cosine_precision@100 | 0.1707     | 0.2141     | 0.1902     | 0.166      | 0.0252     | 0.025      | 0.0285     |
+| cosine_precision@150 | 0.1229     | 0.161      | 0.1448     | 0.12       | 0.0172     | 0.0171     | 0.0193     |
+| cosine_precision@200 | 0.097      | 0.1309     | 0.1178     | 0.0948     | 0.013      | 0.013      | 0.0146     |
+| cosine_recall@1      | 0.0657     | 0.0035     | 0.0111     | 0.0613     | 0.238      | 0.2038     | 0.193      |
+| cosine_recall@20     | 0.5041     | 0.3483     | 0.2624     | 0.4798     | 0.8149     | 0.7817     | 0.8174     |
+| cosine_recall@50     | 0.6763     | 0.5044     | 0.3999     | 0.6511     | 0.8867     | 0.8605     | 0.901      |
+| cosine_recall@100    | 0.7798     | 0.5963     | 0.5012     | 0.7667     | 0.9332     | 0.9077     | 0.9399     |
+| cosine_recall@150    | 0.8312     | 0.654      | 0.5599     | 0.8234     | 0.9536     | 0.9319     | 0.9559     |
+| cosine_recall@200    | 0.8655     | 0.7028     | 0.602      | 0.8571     | 0.9652     | 0.9462     | 0.9652     |
+| cosine_ndcg@1        | 0.6286     | 0.1135     | 0.2956     | 0.6505     | 0.6173     | 0.5429     | 0.5752     |
+| cosine_ndcg@20       | 0.6385     | 0.5638     | 0.4646     | 0.6163     | 0.6864     | 0.6365     | 0.6621     |
+| cosine_ndcg@50       | 0.6505     | 0.5286     | 0.4364     | 0.6306     | 0.706      | 0.658      | 0.6859     |
+| cosine_ndcg@100      | 0.701      | 0.5495     | 0.4594     | 0.6853     | 0.7161     | 0.6687     | 0.6948     |
+| cosine_ndcg@150      | 0.7229     | 0.5779     | 0.4887     | 0.7088     | 0.7201     | 0.6735     | 0.698      |
+| **cosine_ndcg@200**  | **0.7371** | **0.6002** | **0.5085** | **0.7228** | **0.7222** | **0.6761** | **0.6998** |
+| cosine_mrr@1         | 0.6286     | 0.1135     | 0.2956     | 0.6505     | 0.6173     | 0.5429     | 0.5752     |
+| cosine_mrr@20        | 0.7791     | 0.55       | 0.5034     | 0.7939     | 0.6921     | 0.6331     | 0.6975     |
+| cosine_mrr@50        | 0.7798     | 0.55       | 0.5041     | 0.7941     | 0.6935     | 0.635      | 0.6983     |
+| cosine_mrr@100       | 0.7798     | 0.55       | 0.5042     | 0.7943     | 0.6939     | 0.6354     | 0.6985     |
+| cosine_mrr@150       | 0.7798     | 0.55       | 0.5042     | 0.7943     | 0.694      | 0.6355     | 0.6985     |
+| cosine_mrr@200       | 0.7798     | 0.55       | 0.5042     | 0.7943     | 0.694      | 0.6355     | 0.6985     |
+| cosine_map@1         | 0.6286     | 0.1135     | 0.2956     | 0.6505     | 0.6173     | 0.5429     | 0.5752     |
+| cosine_map@20        | 0.4949     | 0.4321     | 0.3326     | 0.4673     | 0.6028     | 0.546      | 0.5396     |
+| cosine_map@50        | 0.4754     | 0.3662     | 0.278      | 0.4492     | 0.608      | 0.5513     | 0.5466     |
+| cosine_map@100       | 0.5028     | 0.3676     | 0.2753     | 0.476      | 0.6094     | 0.5529     | 0.548      |
+| cosine_map@150       | 0.5109     | 0.3791     | 0.2859     | 0.4843     | 0.6098     | 0.5533     | 0.5483     |
+| cosine_map@200       | 0.5152     | 0.3864     | 0.2919     | 0.4885     | 0.6099     | 0.5535     | 0.5485     |
+| cosine_map@500       | 0.5212     | 0.3967     | 0.3038     | 0.4949     | 0.6101     | 0.5538     | 0.5487     |
+
+<!--
+## 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 Dataset
+
+#### Unnamed Dataset
+
+* Size: 86,648 training samples
+* Columns: <code>sentence</code> and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence                                                                         | label                                |
+  |:--------|:---------------------------------------------------------------------------------|:-------------------------------------|
+  | type    | string                                                                           | list                                 |
+  | details | <ul><li>min: 2 tokens</li><li>mean: 8.25 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>size: 768 elements</li></ul> |
+* Samples:
+  | sentence                                 | label                                                                                                                            |
+  |:-----------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------|
+  | <code></code>                            | <code>[-0.07171934843063354, 0.03595816716551781, -0.029780959710478783, 0.006593302357941866, 0.040611181408166885, ...]</code> |
+  | <code>airport environment officer</code> | <code>[-0.022075481712818146, 0.02999737113714218, -0.02189866080880165, 0.016531817615032196, 0.012234307825565338, ...]</code> |
+  | <code>Flake操作员</code>                    | <code>[-0.04815564677119255, 0.023524893447756767, -0.01583661139011383, 0.042527906596660614, 0.03815540298819542, ...]</code>  |
+* Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 128
+- `per_device_eval_batch_size`: 128
+- `gradient_accumulation_steps`: 2
+- `learning_rate`: 0.0001
+- `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`: 0.0001
+- `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   | -             | 0.5348                  | 0.4311                  | 0.3678                  | 0.5333                  | 0.2580                 | 0.1924                 | 0.2871                 |
+| 0.0030 | 1    | 0.0017        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 0.2959 | 100  | 0.001         | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 0.5917 | 200  | 0.0005        | 0.6702                  | 0.5287                  | 0.4566                  | 0.6809                  | 0.5864                 | 0.5302                 | 0.4739                 |
+| 0.8876 | 300  | 0.0004        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 1.1834 | 400  | 0.0004        | 0.7057                  | 0.5643                  | 0.4790                  | 0.7033                  | 0.6604                 | 0.6055                 | 0.6003                 |
+| 1.4793 | 500  | 0.0004        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 1.7751 | 600  | 0.0003        | 0.7184                  | 0.5783                  | 0.4910                  | 0.7127                  | 0.6927                 | 0.6416                 | 0.6485                 |
+| 2.0710 | 700  | 0.0003        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 2.3669 | 800  | 0.0003        | 0.7307                  | 0.5938                  | 0.5023                  | 0.7233                  | 0.7125                 | 0.6639                 | 0.6847                 |
+| 2.6627 | 900  | 0.0003        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 2.9586 | 1000 | 0.0003        | 0.7371                  | 0.6002                  | 0.5085                  | 0.7228                  | 0.7222                 | 0.6761                 | 0.6998                 |
+
+
+### 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",
+}
+```
+
+#### MSELoss
+```bibtex
+@inproceedings{reimers-2020-multilingual-sentence-bert,
+    title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2020",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/2004.09813",
+}
+```
+
+<!--
+## 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-1000/config.json

@@ -0,0 +1,49 @@
+{
+  "architectures": [
+    "NewModel"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "auto_map": {
+    "AutoConfig": "configuration.NewConfig",
+    "AutoModel": "Alibaba-NLP/new-impl--modeling.NewModel",
+    "AutoModelForMaskedLM": "Alibaba-NLP/new-impl--modeling.NewForMaskedLM",
+    "AutoModelForMultipleChoice": "Alibaba-NLP/new-impl--modeling.NewForMultipleChoice",
+    "AutoModelForQuestionAnswering": "Alibaba-NLP/new-impl--modeling.NewForQuestionAnswering",
+    "AutoModelForSequenceClassification": "Alibaba-NLP/new-impl--modeling.NewForSequenceClassification",
+    "AutoModelForTokenClassification": "Alibaba-NLP/new-impl--modeling.NewForTokenClassification"
+  },
+  "classifier_dropout": 0.0,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "layer_norm_type": "layer_norm",
+  "logn_attention_clip1": false,
+  "logn_attention_scale": false,
+  "max_position_embeddings": 8192,
+  "model_type": "new",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 3,
+  "pack_qkv": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "rope",
+  "rope_scaling": {
+    "factor": 8.0,
+    "type": "ntk"
+  },
+  "rope_theta": 20000,
+  "torch_dtype": "float32",
+  "transformers_version": "4.51.3",
+  "type_vocab_size": 1,
+  "unpad_inputs": false,
+  "use_memory_efficient_attention": false,
+  "vocab_size": 250048
+}

checkpoint-1000/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-1000/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-1000/sentence_bert_config.json

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

checkpoint-1000/special_tokens_map.json

@@ -0,0 +1,51 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

checkpoint-1000/tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:883b037111086fd4dfebbbc9b7cee11e1517b5e0c0514879478661440f137085
+size 17082987

checkpoint-1000/tokenizer_config.json

@@ -0,0 +1,62 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "250001": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "extra_special_tokens": {},
+  "mask_token": "<mask>",
+  "max_length": 512,
+  "model_max_length": 512,
+  "pad_to_multiple_of": null,
+  "pad_token": "<pad>",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "</s>",
+  "stride": 0,
+  "tokenizer_class": "XLMRobertaTokenizerFast",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "<unk>"
+}

checkpoint-1000/trainer_state.json

@@ -0,0 +1,1446 @@
+{
+  "best_global_step": null,
+  "best_metric": null,
+  "best_model_checkpoint": null,
+  "epoch": 2.9585798816568047,
+  "eval_steps": 200,
+  "global_step": 1000,
+  "is_hyper_param_search": false,
+  "is_local_process_zero": true,
+  "is_world_process_zero": true,
+  "log_history": [
+    {
+      "epoch": 0.0029585798816568047,
+      "grad_norm": 0.003932574763894081,
+      "learning_rate": 0.0,
+      "loss": 0.0017,
+      "step": 1
+    },
+    {
+      "epoch": 0.2958579881656805,
+      "grad_norm": 0.0002998154377564788,
+      "learning_rate": 9.912772585669783e-05,
+      "loss": 0.001,
+      "step": 100
+    },
+    {
+      "epoch": 0.591715976331361,
+      "grad_norm": 0.00026975994114764035,
+      "learning_rate": 9.289719626168225e-05,
+      "loss": 0.0005,
+      "step": 200
+    },
+    {
+      "epoch": 0.591715976331361,
+      "eval_full_de_cosine_accuracy@1": 0.2955665024630542,
+      "eval_full_de_cosine_accuracy@100": 0.9802955665024631,
+      "eval_full_de_cosine_accuracy@150": 0.9901477832512315,
+      "eval_full_de_cosine_accuracy@20": 0.9556650246305419,
+      "eval_full_de_cosine_accuracy@200": 0.9901477832512315,
+      "eval_full_de_cosine_accuracy@50": 0.9704433497536946,
+      "eval_full_de_cosine_map@1": 0.2955665024630542,
+      "eval_full_de_cosine_map@100": 0.23912170495699045,
+      "eval_full_de_cosine_map@150": 0.2480105904337085,
+      "eval_full_de_cosine_map@20": 0.308500931390137,
+      "eval_full_de_cosine_map@200": 0.253032960716972,
+      "eval_full_de_cosine_map@50": 0.24325745555408396,
+      "eval_full_de_cosine_map@500": 0.2637582116936768,
+      "eval_full_de_cosine_mrr@1": 0.2955665024630542,
+      "eval_full_de_cosine_mrr@100": 0.5005668219316932,
+      "eval_full_de_cosine_mrr@150": 0.5006561680149542,
+      "eval_full_de_cosine_mrr@20": 0.4999630364162379,
+      "eval_full_de_cosine_mrr@200": 0.5006561680149542,
+      "eval_full_de_cosine_mrr@50": 0.500417913595253,
+      "eval_full_de_cosine_ndcg@1": 0.2955665024630542,
+      "eval_full_de_cosine_ndcg@100": 0.4132573069016215,
+      "eval_full_de_cosine_ndcg@150": 0.4386619476207025,
+      "eval_full_de_cosine_ndcg@20": 0.4413841049902018,
+      "eval_full_de_cosine_ndcg@200": 0.45655433596891026,
+      "eval_full_de_cosine_ndcg@50": 0.396035545928819,
+      "eval_full_de_cosine_precision@1": 0.2955665024630542,
+      "eval_full_de_cosine_precision@100": 0.1660591133004926,
+      "eval_full_de_cosine_precision@150": 0.1265353037766831,
+      "eval_full_de_cosine_precision@20": 0.3982758620689655,
+      "eval_full_de_cosine_precision@200": 0.1033743842364532,
+      "eval_full_de_cosine_precision@50": 0.25733990147783253,
+      "eval_full_de_cosine_recall@1": 0.01108543831680986,
+      "eval_full_de_cosine_recall@100": 0.4368237340812292,
+      "eval_full_de_cosine_recall@150": 0.4866824517136917,
+      "eval_full_de_cosine_recall@20": 0.24291574566277963,
+      "eval_full_de_cosine_recall@200": 0.5237567328396163,
+      "eval_full_de_cosine_recall@50": 0.3508390750999705,
+      "eval_full_en_cosine_accuracy@1": 0.6095238095238096,
+      "eval_full_en_cosine_accuracy@100": 0.9809523809523809,
+      "eval_full_en_cosine_accuracy@150": 0.9904761904761905,
+      "eval_full_en_cosine_accuracy@20": 0.9619047619047619,
+      "eval_full_en_cosine_accuracy@200": 0.9904761904761905,
+      "eval_full_en_cosine_accuracy@50": 0.9714285714285714,
+      "eval_full_en_cosine_map@1": 0.6095238095238096,
+      "eval_full_en_cosine_map@100": 0.4212871549773475,
+      "eval_full_en_cosine_map@150": 0.4292041397533778,
+      "eval_full_en_cosine_map@20": 0.41834185162590387,
+      "eval_full_en_cosine_map@200": 0.43360983440296147,
+      "eval_full_en_cosine_map@50": 0.3950487272048726,
+      "eval_full_en_cosine_map@500": 0.44048129620249793,
+      "eval_full_en_cosine_mrr@1": 0.6095238095238096,
+      "eval_full_en_cosine_mrr@100": 0.7651908878224667,
+      "eval_full_en_cosine_mrr@150": 0.7652709198352718,
+      "eval_full_en_cosine_mrr@20": 0.7647869674185462,
+      "eval_full_en_cosine_mrr@200": 0.7652709198352718,
+      "eval_full_en_cosine_mrr@50": 0.7650443676759465,
+      "eval_full_en_cosine_ndcg@1": 0.6095238095238096,
+      "eval_full_en_cosine_ndcg@100": 0.6311923978676361,
+      "eval_full_en_cosine_ndcg@150": 0.6538048550548475,
+      "eval_full_en_cosine_ndcg@20": 0.5708801066213397,
+      "eval_full_en_cosine_ndcg@200": 0.6702447487259725,
+      "eval_full_en_cosine_ndcg@50": 0.5759834478645489,
+      "eval_full_en_cosine_precision@1": 0.6095238095238096,
+      "eval_full_en_cosine_precision@100": 0.15038095238095237,
+      "eval_full_en_cosine_precision@150": 0.10958730158730158,
+      "eval_full_en_cosine_precision@20": 0.40714285714285714,
+      "eval_full_en_cosine_precision@200": 0.08747619047619049,
+      "eval_full_en_cosine_precision@50": 0.2398095238095238,
+      "eval_full_en_cosine_recall@1": 0.06442722624702855,
+      "eval_full_en_cosine_recall@100": 0.6967385317394644,
+      "eval_full_en_cosine_recall@150": 0.7500891135070625,
+      "eval_full_en_cosine_recall@20": 0.4346349989024896,
+      "eval_full_en_cosine_recall@200": 0.7922657853216979,
+      "eval_full_en_cosine_recall@50": 0.5835601213490562,
+      "eval_full_es_cosine_accuracy@1": 0.12432432432432433,
+      "eval_full_es_cosine_accuracy@100": 1.0,
+      "eval_full_es_cosine_accuracy@150": 1.0,
+      "eval_full_es_cosine_accuracy@20": 1.0,
+      "eval_full_es_cosine_accuracy@200": 1.0,
+      "eval_full_es_cosine_accuracy@50": 1.0,
+      "eval_full_es_cosine_map@1": 0.12432432432432433,
+      "eval_full_es_cosine_map@100": 0.30626153255960936,
+      "eval_full_es_cosine_map@150": 0.31525172417568115,
+      "eval_full_es_cosine_map@20": 0.3754636987030811,
+      "eval_full_es_cosine_map@200": 0.3203321736620439,
+      "eval_full_es_cosine_map@50": 0.3089347159262503,
+      "eval_full_es_cosine_map@500": 0.33123215907886244,
+      "eval_full_es_cosine_mrr@1": 0.12432432432432433,
+      "eval_full_es_cosine_mrr@100": 0.554954954954955,
+      "eval_full_es_cosine_mrr@150": 0.554954954954955,
+      "eval_full_es_cosine_mrr@20": 0.554954954954955,
+      "eval_full_es_cosine_mrr@200": 0.554954954954955,
+      "eval_full_es_cosine_mrr@50": 0.554954954954955,
+      "eval_full_es_cosine_ndcg@1": 0.12432432432432433,
+      "eval_full_es_cosine_ndcg@100": 0.48680034537634215,
+      "eval_full_es_cosine_ndcg@150": 0.512345996720451,
+      "eval_full_es_cosine_ndcg@20": 0.5146195558295508,
+      "eval_full_es_cosine_ndcg@200": 0.5287187750886941,
+      "eval_full_es_cosine_ndcg@50": 0.46964512631645533,
+      "eval_full_es_cosine_precision@1": 0.12432432432432433,
+      "eval_full_es_cosine_precision@100": 0.1852972972972973,
+      "eval_full_es_cosine_precision@150": 0.1388828828828829,
+      "eval_full_es_cosine_precision@20": 0.4651351351351351,
+      "eval_full_es_cosine_precision@200": 0.11194594594594595,
+      "eval_full_es_cosine_precision@50": 0.29437837837837844,
+      "eval_full_es_cosine_recall@1": 0.0035399794657678236,
+      "eval_full_es_cosine_recall@100": 0.5157476179053113,
+      "eval_full_es_cosine_recall@150": 0.5689714281780028,
+      "eval_full_es_cosine_recall@20": 0.30618326173570504,
+      "eval_full_es_cosine_recall@200": 0.603294577927924,
+      "eval_full_es_cosine_recall@50": 0.4305380966092272,
+      "eval_full_zh_cosine_accuracy@1": 0.6504854368932039,
+      "eval_full_zh_cosine_accuracy@100": 0.9902912621359223,
+      "eval_full_zh_cosine_accuracy@150": 0.9902912621359223,
+      "eval_full_zh_cosine_accuracy@20": 0.9805825242718447,
+      "eval_full_zh_cosine_accuracy@200": 0.9902912621359223,
+      "eval_full_zh_cosine_accuracy@50": 0.9805825242718447,
+      "eval_full_zh_cosine_map@1": 0.6504854368932039,
+      "eval_full_zh_cosine_map@100": 0.4341400410992279,
+      "eval_full_zh_cosine_map@150": 0.4421470493216924,
+      "eval_full_zh_cosine_map@20": 0.4370709010270343,
+      "eval_full_zh_cosine_map@200": 0.446164032411221,
+      "eval_full_zh_cosine_map@50": 0.4129724014671616,
+      "eval_full_zh_cosine_map@500": 0.45382091491269705,
+      "eval_full_zh_cosine_mrr@1": 0.6504854368932039,
+      "eval_full_zh_cosine_mrr@100": 0.7916435506241332,
+      "eval_full_zh_cosine_mrr@150": 0.7916435506241332,
+      "eval_full_zh_cosine_mrr@20": 0.7915048543689321,
+      "eval_full_zh_cosine_mrr@200": 0.7916435506241332,
+      "eval_full_zh_cosine_mrr@50": 0.7915048543689321,
+      "eval_full_zh_cosine_ndcg@1": 0.6504854368932039,
+      "eval_full_zh_cosine_ndcg@100": 0.6435966486586578,
+      "eval_full_zh_cosine_ndcg@150": 0.6661105297536296,
+      "eval_full_zh_cosine_ndcg@20": 0.5891740583683294,
+      "eval_full_zh_cosine_ndcg@200": 0.6809059849503412,
+      "eval_full_zh_cosine_ndcg@50": 0.5940858700081013,
+      "eval_full_zh_cosine_precision@1": 0.6504854368932039,
+      "eval_full_zh_cosine_precision@100": 0.14951456310679612,
+      "eval_full_zh_cosine_precision@150": 0.109126213592233,
+      "eval_full_zh_cosine_precision@20": 0.4174757281553398,
+      "eval_full_zh_cosine_precision@200": 0.08660194174757284,
+      "eval_full_zh_cosine_precision@50": 0.24388349514563107,
+      "eval_full_zh_cosine_recall@1": 0.06297195936535313,
+      "eval_full_zh_cosine_recall@100": 0.7066962990102679,
+      "eval_full_zh_cosine_recall@150": 0.7572705548910881,
+      "eval_full_zh_cosine_recall@20": 0.45216321099583096,
+      "eval_full_zh_cosine_recall@200": 0.7957063099867823,
+      "eval_full_zh_cosine_recall@50": 0.5985947222599055,
+      "eval_mix_de_cosine_accuracy@1": 0.38845553822152884,
+      "eval_mix_de_cosine_accuracy@100": 0.8803952158086323,
+      "eval_mix_de_cosine_accuracy@150": 0.9121164846593863,
+      "eval_mix_de_cosine_accuracy@20": 0.734269370774831,
+      "eval_mix_de_cosine_accuracy@200": 0.9355174206968279,
+      "eval_mix_de_cosine_accuracy@50": 0.8117524700988039,
+      "eval_mix_de_cosine_map@1": 0.38845553822152884,
+      "eval_mix_de_cosine_map@100": 0.39390468588701283,
+      "eval_mix_de_cosine_map@150": 0.39462204836530157,
+      "eval_mix_de_cosine_map@20": 0.38515411365076746,
+      "eval_mix_de_cosine_map@200": 0.3950134124984197,
+      "eval_mix_de_cosine_map@50": 0.39147682232706005,
+      "eval_mix_de_cosine_map@500": 0.39564242254391563,
+      "eval_mix_de_cosine_mrr@1": 0.38845553822152884,
+      "eval_mix_de_cosine_mrr@100": 0.47910662936458426,
+      "eval_mix_de_cosine_mrr@150": 0.4793689523443691,
+      "eval_mix_de_cosine_mrr@20": 0.4755813854666515,
+      "eval_mix_de_cosine_mrr@200": 0.47950783378595774,
+      "eval_mix_de_cosine_mrr@50": 0.47811704545297706,
+      "eval_mix_de_cosine_ndcg@1": 0.38845553822152884,
+      "eval_mix_de_cosine_ndcg@100": 0.5158211232743959,
+      "eval_mix_de_cosine_ndcg@150": 0.5242022471749376,
+      "eval_mix_de_cosine_ndcg@20": 0.47089223070507885,
+      "eval_mix_de_cosine_ndcg@200": 0.5302273876532717,
+      "eval_mix_de_cosine_ndcg@50": 0.49701321688193106,
+      "eval_mix_de_cosine_precision@1": 0.38845553822152884,
+      "eval_mix_de_cosine_precision@100": 0.021476859074362975,
+      "eval_mix_de_cosine_precision@150": 0.015122204888195528,
+      "eval_mix_de_cosine_precision@20": 0.08216328653146125,
+      "eval_mix_de_cosine_precision@200": 0.011799271970878837,
+      "eval_mix_de_cosine_precision@50": 0.038169526781071245,
+      "eval_mix_de_cosine_recall@1": 0.14673253596810537,
+      "eval_mix_de_cosine_recall@100": 0.7834026694401108,
+      "eval_mix_de_cosine_recall@150": 0.8256557279835054,
+      "eval_mix_de_cosine_recall@20": 0.6032587970185473,
+      "eval_mix_de_cosine_recall@200": 0.8584343724626179,
+      "eval_mix_de_cosine_recall@50": 0.6989859594383776,
+      "eval_mix_es_cosine_accuracy@1": 0.4638585543421737,
+      "eval_mix_es_cosine_accuracy@100": 0.9136765470618825,
+      "eval_mix_es_cosine_accuracy@150": 0.9365574622984919,
+      "eval_mix_es_cosine_accuracy@20": 0.7857514300572023,
+      "eval_mix_es_cosine_accuracy@200": 0.9542381695267811,
+      "eval_mix_es_cosine_accuracy@50": 0.8647945917836714,
+      "eval_mix_es_cosine_map@1": 0.4638585543421737,
+      "eval_mix_es_cosine_map@100": 0.4536657081231957,
+      "eval_mix_es_cosine_map@150": 0.45437606495676935,
+      "eval_mix_es_cosine_map@20": 0.4451761994811194,
+      "eval_mix_es_cosine_map@200": 0.4547015177344374,
+      "eval_mix_es_cosine_map@50": 0.4514863355267052,
+      "eval_mix_es_cosine_map@500": 0.4551697792251131,
+      "eval_mix_es_cosine_mrr@1": 0.4638585543421737,
+      "eval_mix_es_cosine_mrr@100": 0.5475382320931931,
+      "eval_mix_es_cosine_mrr@150": 0.5477257595682632,
+      "eval_mix_es_cosine_mrr@20": 0.5443176030174267,
+      "eval_mix_es_cosine_mrr@200": 0.5478279203164989,
+      "eval_mix_es_cosine_mrr@50": 0.5468380835249812,
+      "eval_mix_es_cosine_ndcg@1": 0.4638585543421737,
+      "eval_mix_es_cosine_ndcg@100": 0.5736815761872325,
+      "eval_mix_es_cosine_ndcg@150": 0.5816308035747072,
+      "eval_mix_es_cosine_ndcg@20": 0.5312553934197572,
+      "eval_mix_es_cosine_ndcg@200": 0.5864336371127215,
+      "eval_mix_es_cosine_ndcg@50": 0.5579803424628411,
+      "eval_mix_es_cosine_precision@1": 0.4638585543421737,
+      "eval_mix_es_cosine_precision@100": 0.022371294851794076,
+      "eval_mix_es_cosine_precision@150": 0.015656092910383078,
+      "eval_mix_es_cosine_precision@20": 0.08827353094123765,
+      "eval_mix_es_cosine_precision@200": 0.012106084243369737,
+      "eval_mix_es_cosine_precision@50": 0.040811232449297984,
+      "eval_mix_es_cosine_recall@1": 0.17768739321001412,
+      "eval_mix_es_cosine_recall@100": 0.8300398682613971,
+      "eval_mix_es_cosine_recall@150": 0.8706881608597676,
+      "eval_mix_es_cosine_recall@20": 0.6605551841121264,
+      "eval_mix_es_cosine_recall@200": 0.89693534408043,
+      "eval_mix_es_cosine_recall@50": 0.759268751702449,
+      "eval_mix_zh_cosine_accuracy@1": 0.26148225469728603,
+      "eval_mix_zh_cosine_accuracy@100": 0.947286012526096,
+      "eval_mix_zh_cosine_accuracy@150": 0.9634655532359081,
+      "eval_mix_zh_cosine_accuracy@20": 0.7964509394572025,
+      "eval_mix_zh_cosine_accuracy@200": 0.9770354906054279,
+      "eval_mix_zh_cosine_accuracy@50": 0.906054279749478,
+      "eval_mix_zh_cosine_map@1": 0.26148225469728603,
+      "eval_mix_zh_cosine_map@100": 0.28749631900572015,
+      "eval_mix_zh_cosine_map@150": 0.288385962001369,
+      "eval_mix_zh_cosine_map@20": 0.27295959762752364,
+      "eval_mix_zh_cosine_map@200": 0.2887678508836165,
+      "eval_mix_zh_cosine_map@50": 0.28444963805440326,
+      "eval_mix_zh_cosine_map@500": 0.28928883002088956,
+      "eval_mix_zh_cosine_mrr@1": 0.26148225469728603,
+      "eval_mix_zh_cosine_mrr@100": 0.39683287872204204,
+      "eval_mix_zh_cosine_mrr@150": 0.39696910415421693,
+      "eval_mix_zh_cosine_mrr@20": 0.39252482645039777,
+      "eval_mix_zh_cosine_mrr@200": 0.3970482418572872,
+      "eval_mix_zh_cosine_mrr@50": 0.3962445974483728,
+      "eval_mix_zh_cosine_ndcg@1": 0.26148225469728603,
+      "eval_mix_zh_cosine_ndcg@100": 0.4593523494555993,
+      "eval_mix_zh_cosine_ndcg@150": 0.4684878552004546,
+      "eval_mix_zh_cosine_ndcg@20": 0.3939592969070947,
+      "eval_mix_zh_cosine_ndcg@200": 0.47387019447635725,
+      "eval_mix_zh_cosine_ndcg@50": 0.4391803566109192,
+      "eval_mix_zh_cosine_precision@1": 0.26148225469728603,
+      "eval_mix_zh_cosine_precision@100": 0.025161795407098127,
+      "eval_mix_zh_cosine_precision@150": 0.017682672233820456,
+      "eval_mix_zh_cosine_precision@20": 0.08815240083507307,
+      "eval_mix_zh_cosine_precision@200": 0.013692588726513574,
+      "eval_mix_zh_cosine_precision@50": 0.04498956158663884,
+      "eval_mix_zh_cosine_recall@1": 0.08735212247738343,
+      "eval_mix_zh_cosine_recall@100": 0.8295183417834775,
+      "eval_mix_zh_cosine_recall@150": 0.8744905060145144,
+      "eval_mix_zh_cosine_recall@20": 0.5815724724127646,
+      "eval_mix_zh_cosine_recall@200": 0.9029314544189283,
+      "eval_mix_zh_cosine_recall@50": 0.7417213440699871,
+      "eval_runtime": 9.6673,
+      "eval_samples_per_second": 0.0,
+      "eval_sequential_score": 0.47387019447635725,
+      "eval_steps_per_second": 0.0,
+      "step": 200
+    },
+    {
+      "epoch": 0.8875739644970414,
+      "grad_norm": 0.00019490200793370605,
+      "learning_rate": 8.666666666666667e-05,
+      "loss": 0.0004,
+      "step": 300
+    },
+    {
+      "epoch": 1.183431952662722,
+      "grad_norm": 0.00019593666365835816,
+      "learning_rate": 8.04361370716511e-05,
+      "loss": 0.0004,
+      "step": 400
+    },
+    {
+      "epoch": 1.183431952662722,
+      "eval_full_de_cosine_accuracy@1": 0.2955665024630542,
+      "eval_full_de_cosine_accuracy@100": 0.9852216748768473,
+      "eval_full_de_cosine_accuracy@150": 0.9852216748768473,
+      "eval_full_de_cosine_accuracy@20": 0.9507389162561576,
+      "eval_full_de_cosine_accuracy@200": 0.9852216748768473,
+      "eval_full_de_cosine_accuracy@50": 0.9802955665024631,
+      "eval_full_de_cosine_map@1": 0.2955665024630542,
+      "eval_full_de_cosine_map@100": 0.2567419215438217,
+      "eval_full_de_cosine_map@150": 0.265533796232109,
+      "eval_full_de_cosine_map@20": 0.32048995864445406,
+      "eval_full_de_cosine_map@200": 0.2708551550258729,
+      "eval_full_de_cosine_map@50": 0.2606298564929459,
+      "eval_full_de_cosine_map@500": 0.2822869226313566,
+      "eval_full_de_cosine_mrr@1": 0.2955665024630542,
+      "eval_full_de_cosine_mrr@100": 0.5045788271072928,
+      "eval_full_de_cosine_mrr@150": 0.5045788271072928,
+      "eval_full_de_cosine_mrr@20": 0.5034406890318217,
+      "eval_full_de_cosine_mrr@200": 0.5045788271072928,
+      "eval_full_de_cosine_mrr@50": 0.5044953337450152,
+      "eval_full_de_cosine_ndcg@1": 0.2955665024630542,
+      "eval_full_de_cosine_ndcg@100": 0.4359687320861737,
+      "eval_full_de_cosine_ndcg@150": 0.46083576855515557,
+      "eval_full_de_cosine_ndcg@20": 0.4529243844878547,
+      "eval_full_de_cosine_ndcg@200": 0.478958125597508,
+      "eval_full_de_cosine_ndcg@50": 0.41681830054243046,
+      "eval_full_de_cosine_precision@1": 0.2955665024630542,
+      "eval_full_de_cosine_precision@100": 0.17866995073891623,
+      "eval_full_de_cosine_precision@150": 0.13484400656814452,
+      "eval_full_de_cosine_precision@20": 0.41305418719211817,
+      "eval_full_de_cosine_precision@200": 0.10938423645320196,
+      "eval_full_de_cosine_precision@50": 0.27536945812807884,
+      "eval_full_de_cosine_recall@1": 0.01108543831680986,
+      "eval_full_de_cosine_recall@100": 0.469544736236114,
+      "eval_full_de_cosine_recall@150": 0.518314283934692,
+      "eval_full_de_cosine_recall@20": 0.24971697618676908,
+      "eval_full_de_cosine_recall@200": 0.5568747053184373,
+      "eval_full_de_cosine_recall@50": 0.377731758436709,
+      "eval_full_en_cosine_accuracy@1": 0.638095238095238,
+      "eval_full_en_cosine_accuracy@100": 0.9809523809523809,
+      "eval_full_en_cosine_accuracy@150": 0.9904761904761905,
+      "eval_full_en_cosine_accuracy@20": 0.9619047619047619,
+      "eval_full_en_cosine_accuracy@200": 0.9904761904761905,
+      "eval_full_en_cosine_accuracy@50": 0.9809523809523809,
+      "eval_full_en_cosine_map@1": 0.638095238095238,
+      "eval_full_en_cosine_map@100": 0.4641764995443202,
+      "eval_full_en_cosine_map@150": 0.4720056673047399,
+      "eval_full_en_cosine_map@20": 0.45544156984456047,
+      "eval_full_en_cosine_map@200": 0.4759301061204395,
+      "eval_full_en_cosine_map@50": 0.43603273590332614,
+      "eval_full_en_cosine_map@500": 0.48186919678908585,
+      "eval_full_en_cosine_mrr@1": 0.638095238095238,
+      "eval_full_en_cosine_mrr@100": 0.777095588456426,
+      "eval_full_en_cosine_mrr@150": 0.7771762987066277,
+      "eval_full_en_cosine_mrr@20": 0.7764695621838479,
+      "eval_full_en_cosine_mrr@200": 0.7771762987066277,
+      "eval_full_en_cosine_mrr@50": 0.777095588456426,
+      "eval_full_en_cosine_ndcg@1": 0.638095238095238,
+      "eval_full_en_cosine_ndcg@100": 0.6716130331252141,
+      "eval_full_en_cosine_ndcg@150": 0.6926366359514627,
+      "eval_full_en_cosine_ndcg@20": 0.6045447897465399,
+      "eval_full_en_cosine_ndcg@200": 0.7057049689923274,
+      "eval_full_en_cosine_ndcg@50": 0.616645155946133,
+      "eval_full_en_cosine_precision@1": 0.638095238095238,
+      "eval_full_en_cosine_precision@100": 0.16285714285714287,
+      "eval_full_en_cosine_precision@150": 0.1175873015873016,
+      "eval_full_en_cosine_precision@20": 0.44,
+      "eval_full_en_cosine_precision@200": 0.09252380952380952,
+      "eval_full_en_cosine_precision@50": 0.2638095238095238,
+      "eval_full_en_cosine_recall@1": 0.06587125840534644,
+      "eval_full_en_cosine_recall@100": 0.7504235744205895,
+      "eval_full_en_cosine_recall@150": 0.7988070365630658,
+      "eval_full_en_cosine_recall@20": 0.46630153865605645,
+      "eval_full_en_cosine_recall@200": 0.8309271201681789,
+      "eval_full_en_cosine_recall@50": 0.6382197782219897,
+      "eval_full_es_cosine_accuracy@1": 0.11891891891891893,
+      "eval_full_es_cosine_accuracy@100": 1.0,
+      "eval_full_es_cosine_accuracy@150": 1.0,
+      "eval_full_es_cosine_accuracy@20": 1.0,
+      "eval_full_es_cosine_accuracy@200": 1.0,
+      "eval_full_es_cosine_accuracy@50": 1.0,
+      "eval_full_es_cosine_map@1": 0.11891891891891893,
+      "eval_full_es_cosine_map@100": 0.33925112142258473,
+      "eval_full_es_cosine_map@150": 0.3495637811043188,
+      "eval_full_es_cosine_map@20": 0.4061377984909505,
+      "eval_full_es_cosine_map@200": 0.3553587904962262,
+      "eval_full_es_cosine_map@50": 0.3393000005892038,
+      "eval_full_es_cosine_map@500": 0.3673477186343514,
+      "eval_full_es_cosine_mrr@1": 0.11891891891891893,
+      "eval_full_es_cosine_mrr@100": 0.5527027027027027,
+      "eval_full_es_cosine_mrr@150": 0.5527027027027027,
+      "eval_full_es_cosine_mrr@20": 0.5527027027027027,
+      "eval_full_es_cosine_mrr@200": 0.5527027027027027,
+      "eval_full_es_cosine_mrr@50": 0.5527027027027027,
+      "eval_full_es_cosine_ndcg@1": 0.11891891891891893,
+      "eval_full_es_cosine_ndcg@100": 0.5197929449881334,
+      "eval_full_es_cosine_ndcg@150": 0.5468931458857987,
+      "eval_full_es_cosine_ndcg@20": 0.5388533101278794,
+      "eval_full_es_cosine_ndcg@200": 0.564264362976638,
+      "eval_full_es_cosine_ndcg@50": 0.4987694462893664,
+      "eval_full_es_cosine_precision@1": 0.11891891891891893,
+      "eval_full_es_cosine_precision@100": 0.2010810810810811,
+      "eval_full_es_cosine_precision@150": 0.150990990990991,
+      "eval_full_es_cosine_precision@20": 0.49324324324324315,
+      "eval_full_es_cosine_precision@200": 0.12159459459459462,
+      "eval_full_es_cosine_precision@50": 0.31762162162162166,
+      "eval_full_es_cosine_recall@1": 0.003585791969830351,
+      "eval_full_es_cosine_recall@100": 0.5579278113476547,
+      "eval_full_es_cosine_recall@150": 0.6134526766095394,
+      "eval_full_es_cosine_recall@20": 0.3278801797081431,
+      "eval_full_es_cosine_recall@200": 0.6494087695214673,
+      "eval_full_es_cosine_recall@50": 0.4648123261874251,
+      "eval_full_zh_cosine_accuracy@1": 0.6504854368932039,
+      "eval_full_zh_cosine_accuracy@100": 0.9902912621359223,
+      "eval_full_zh_cosine_accuracy@150": 0.9902912621359223,
+      "eval_full_zh_cosine_accuracy@20": 0.970873786407767,
+      "eval_full_zh_cosine_accuracy@200": 0.9902912621359223,
+      "eval_full_zh_cosine_accuracy@50": 0.9902912621359223,
+      "eval_full_zh_cosine_map@1": 0.6504854368932039,
+      "eval_full_zh_cosine_map@100": 0.4540673637374807,
+      "eval_full_zh_cosine_map@150": 0.4618461018630378,
+      "eval_full_zh_cosine_map@20": 0.44892279062424895,
+      "eval_full_zh_cosine_map@200": 0.4667217834039844,
+      "eval_full_zh_cosine_map@50": 0.4305094126014137,
+      "eval_full_zh_cosine_map@500": 0.4739392963854547,
+      "eval_full_zh_cosine_mrr@1": 0.6504854368932039,
+      "eval_full_zh_cosine_mrr@100": 0.7962001570321734,
+      "eval_full_zh_cosine_mrr@150": 0.7962001570321734,
+      "eval_full_zh_cosine_mrr@20": 0.7957119741100325,
+      "eval_full_zh_cosine_mrr@200": 0.7962001570321734,
+      "eval_full_zh_cosine_mrr@50": 0.7962001570321734,
+      "eval_full_zh_cosine_ndcg@1": 0.6504854368932039,
+      "eval_full_zh_cosine_ndcg@100": 0.6638909331450039,
+      "eval_full_zh_cosine_ndcg@150": 0.6853311981200142,
+      "eval_full_zh_cosine_ndcg@20": 0.5999086133003301,
+      "eval_full_zh_cosine_ndcg@200": 0.7033198205460287,
+      "eval_full_zh_cosine_ndcg@50": 0.6129532658410302,
+      "eval_full_zh_cosine_precision@1": 0.6504854368932039,
+      "eval_full_zh_cosine_precision@100": 0.15786407766990293,
+      "eval_full_zh_cosine_precision@150": 0.11411003236245955,
+      "eval_full_zh_cosine_precision@20": 0.4305825242718446,
+      "eval_full_zh_cosine_precision@200": 0.0913592233009709,
+      "eval_full_zh_cosine_precision@50": 0.25825242718446595,
+      "eval_full_zh_cosine_recall@1": 0.060986081165882694,
+      "eval_full_zh_cosine_recall@100": 0.738289597577811,
+      "eval_full_zh_cosine_recall@150": 0.7878105561736307,
+      "eval_full_zh_cosine_recall@20": 0.4605261938478766,
+      "eval_full_zh_cosine_recall@200": 0.8324956345368711,
+      "eval_full_zh_cosine_recall@50": 0.6279780571130372,
+      "eval_mix_de_cosine_accuracy@1": 0.46021840873634945,
+      "eval_mix_de_cosine_accuracy@100": 0.9313572542901716,
+      "eval_mix_de_cosine_accuracy@150": 0.9495579823192928,
+      "eval_mix_de_cosine_accuracy@20": 0.8091523660946438,
+      "eval_mix_de_cosine_accuracy@200": 0.9625585023400937,
+      "eval_mix_de_cosine_accuracy@50": 0.8835153406136246,
+      "eval_mix_de_cosine_map@1": 0.46021840873634945,
+      "eval_mix_de_cosine_map@100": 0.4723217628710669,
+      "eval_mix_de_cosine_map@150": 0.47290246629177823,
+      "eval_mix_de_cosine_map@20": 0.4642437490768453,
+      "eval_mix_de_cosine_map@200": 0.47323000409233096,
+      "eval_mix_de_cosine_map@50": 0.47019746688105846,
+      "eval_mix_de_cosine_map@500": 0.47363146569190606,
+      "eval_mix_de_cosine_mrr@1": 0.46021840873634945,
+      "eval_mix_de_cosine_mrr@100": 0.5561629113405923,
+      "eval_mix_de_cosine_mrr@150": 0.5563168524767522,
+      "eval_mix_de_cosine_mrr@20": 0.5531152893840248,
+      "eval_mix_de_cosine_mrr@200": 0.5563946576872058,
+      "eval_mix_de_cosine_mrr@50": 0.5554363671701441,
+      "eval_mix_de_cosine_ndcg@1": 0.46021840873634945,
+      "eval_mix_de_cosine_ndcg@100": 0.5944999375964086,
+      "eval_mix_de_cosine_ndcg@150": 0.6010213315483848,
+      "eval_mix_de_cosine_ndcg@20": 0.5540701081096809,
+      "eval_mix_de_cosine_ndcg@200": 0.6054920754873866,
+      "eval_mix_de_cosine_ndcg@50": 0.5786034933790482,
+      "eval_mix_de_cosine_precision@1": 0.46021840873634945,
+      "eval_mix_de_cosine_precision@100": 0.02356214248569943,
+      "eval_mix_de_cosine_precision@150": 0.016328653146125843,
+      "eval_mix_de_cosine_precision@20": 0.09500780031201247,
+      "eval_mix_de_cosine_precision@200": 0.012597503900156008,
+      "eval_mix_de_cosine_precision@50": 0.04315132605304212,
+      "eval_mix_de_cosine_recall@1": 0.17405096203848153,
+      "eval_mix_de_cosine_recall@100": 0.857782977985786,
+      "eval_mix_de_cosine_recall@150": 0.8908389668920089,
+      "eval_mix_de_cosine_recall@20": 0.6965938637545501,
+      "eval_mix_de_cosine_recall@200": 0.9146832890859494,
+      "eval_mix_de_cosine_recall@50": 0.7862367828046456,
+      "eval_mix_es_cosine_accuracy@1": 0.5455018200728029,
+      "eval_mix_es_cosine_accuracy@100": 0.9474778991159646,
+      "eval_mix_es_cosine_accuracy@150": 0.9651586063442538,
+      "eval_mix_es_cosine_accuracy@20": 0.8569942797711908,
+      "eval_mix_es_cosine_accuracy@200": 0.9729589183567343,
+      "eval_mix_es_cosine_accuracy@50": 0.9126365054602185,
+      "eval_mix_es_cosine_map@1": 0.5455018200728029,
+      "eval_mix_es_cosine_map@100": 0.535255166612506,
+      "eval_mix_es_cosine_map@150": 0.535781036051377,
+      "eval_mix_es_cosine_map@20": 0.5274530705128716,
+      "eval_mix_es_cosine_map@200": 0.5360323324320723,
+      "eval_mix_es_cosine_map@50": 0.5335700056858728,
+      "eval_mix_es_cosine_map@500": 0.5363495198132057,
+      "eval_mix_es_cosine_mrr@1": 0.5455018200728029,
+      "eval_mix_es_cosine_mrr@100": 0.6286967011568556,
+      "eval_mix_es_cosine_mrr@150": 0.6288426810615296,
+      "eval_mix_es_cosine_mrr@20": 0.626350833432816,
+      "eval_mix_es_cosine_mrr@200": 0.6288903722545199,
+      "eval_mix_es_cosine_mrr@50": 0.6281937000449875,
+      "eval_mix_es_cosine_ndcg@1": 0.5455018200728029,
+      "eval_mix_es_cosine_ndcg@100": 0.6510862026444352,
+      "eval_mix_es_cosine_ndcg@150": 0.6568932430019802,
+      "eval_mix_es_cosine_ndcg@20": 0.6150365602568844,
+      "eval_mix_es_cosine_ndcg@200": 0.6604458038086418,
+      "eval_mix_es_cosine_ndcg@50": 0.6390345568565291,
+      "eval_mix_es_cosine_precision@1": 0.5455018200728029,
+      "eval_mix_es_cosine_precision@100": 0.02399375975039002,
+      "eval_mix_es_cosine_precision@150": 0.016543595077136417,
+      "eval_mix_es_cosine_precision@20": 0.10023400936037442,
+      "eval_mix_es_cosine_precision@200": 0.012665106604264172,
+      "eval_mix_es_cosine_precision@50": 0.04500260010400416,
+      "eval_mix_es_cosine_recall@1": 0.2100760220885026,
+      "eval_mix_es_cosine_recall@100": 0.8890448951291384,
+      "eval_mix_es_cosine_recall@150": 0.9186167446697868,
+      "eval_mix_es_cosine_recall@20": 0.7463635688284673,
+      "eval_mix_es_cosine_recall@200": 0.9383775351014041,
+      "eval_mix_es_cosine_recall@50": 0.8345207141618998,
+      "eval_mix_zh_cosine_accuracy@1": 0.4232776617954071,
+      "eval_mix_zh_cosine_accuracy@100": 0.9765135699373695,
+      "eval_mix_zh_cosine_accuracy@150": 0.9859081419624217,
+      "eval_mix_zh_cosine_accuracy@20": 0.901356993736952,
+      "eval_mix_zh_cosine_accuracy@200": 0.9890396659707724,
+      "eval_mix_zh_cosine_accuracy@50": 0.954070981210856,
+      "eval_mix_zh_cosine_map@1": 0.4232776617954071,
+      "eval_mix_zh_cosine_map@100": 0.42741180736358436,
+      "eval_mix_zh_cosine_map@150": 0.4279910729994889,
+      "eval_mix_zh_cosine_map@20": 0.41568187503158693,
+      "eval_mix_zh_cosine_map@200": 0.42824261164446553,
+      "eval_mix_zh_cosine_map@50": 0.42544885981608077,
+      "eval_mix_zh_cosine_map@500": 0.428533272446383,
+      "eval_mix_zh_cosine_mrr@1": 0.4232776617954071,
+      "eval_mix_zh_cosine_mrr@100": 0.5650479488833118,
+      "eval_mix_zh_cosine_mrr@150": 0.565124056781373,
+      "eval_mix_zh_cosine_mrr@20": 0.5629837129820784,
+      "eval_mix_zh_cosine_mrr@200": 0.5651432098126187,
+      "eval_mix_zh_cosine_mrr@50": 0.5647239642584531,
+      "eval_mix_zh_cosine_ndcg@1": 0.4232776617954071,
+      "eval_mix_zh_cosine_ndcg@100": 0.5914166160498772,
+      "eval_mix_zh_cosine_ndcg@150": 0.5971759685516622,
+      "eval_mix_zh_cosine_ndcg@20": 0.543883122464392,
+      "eval_mix_zh_cosine_ndcg@200": 0.6003485381890498,
+      "eval_mix_zh_cosine_ndcg@50": 0.5784717737270649,
+      "eval_mix_zh_cosine_precision@1": 0.4232776617954071,
+      "eval_mix_zh_cosine_precision@100": 0.027332985386221306,
+      "eval_mix_zh_cosine_precision@150": 0.01878566457898399,
+      "eval_mix_zh_cosine_precision@20": 0.10955114822546971,
+      "eval_mix_zh_cosine_precision@200": 0.014342379958246351,
+      "eval_mix_zh_cosine_precision@50": 0.05122129436325679,
+      "eval_mix_zh_cosine_recall@1": 0.14153618649965202,
+      "eval_mix_zh_cosine_recall@100": 0.9008872651356993,
+      "eval_mix_zh_cosine_recall@150": 0.9293493389004871,
+      "eval_mix_zh_cosine_recall@20": 0.722858882592703,
+      "eval_mix_zh_cosine_recall@200": 0.9460681976339596,
+      "eval_mix_zh_cosine_recall@50": 0.8446490704841435,
+      "eval_runtime": 10.0466,
+      "eval_samples_per_second": 0.0,
+      "eval_sequential_score": 0.6003485381890498,
+      "eval_steps_per_second": 0.0,
+      "step": 400
+    },
+    {
+      "epoch": 1.4792899408284024,
+      "grad_norm": 0.0002115300012519583,
+      "learning_rate": 7.420560747663552e-05,
+      "loss": 0.0004,
+      "step": 500
+    },
+    {
+      "epoch": 1.7751479289940828,
+      "grad_norm": 0.0001920880749821663,
+      "learning_rate": 6.797507788161995e-05,
+      "loss": 0.0003,
+      "step": 600
+    },
+    {
+      "epoch": 1.7751479289940828,
+      "eval_full_de_cosine_accuracy@1": 0.2955665024630542,
+      "eval_full_de_cosine_accuracy@100": 0.9852216748768473,
+      "eval_full_de_cosine_accuracy@150": 0.9852216748768473,
+      "eval_full_de_cosine_accuracy@20": 0.9458128078817734,
+      "eval_full_de_cosine_accuracy@200": 0.9852216748768473,
+      "eval_full_de_cosine_accuracy@50": 0.9852216748768473,
+      "eval_full_de_cosine_map@1": 0.2955665024630542,
+      "eval_full_de_cosine_map@100": 0.2625024440656235,
+      "eval_full_de_cosine_map@150": 0.2722157763818812,
+      "eval_full_de_cosine_map@20": 0.32183381322560267,
+      "eval_full_de_cosine_map@200": 0.27793824790648164,
+      "eval_full_de_cosine_map@50": 0.26687539986261816,
+      "eval_full_de_cosine_map@500": 0.2893817200030204,
+      "eval_full_de_cosine_mrr@1": 0.2955665024630542,
+      "eval_full_de_cosine_mrr@100": 0.5023210116527641,
+      "eval_full_de_cosine_mrr@150": 0.5023210116527641,
+      "eval_full_de_cosine_mrr@20": 0.5008673660767252,
+      "eval_full_de_cosine_mrr@200": 0.5023210116527641,
+      "eval_full_de_cosine_mrr@50": 0.5023210116527641,
+      "eval_full_de_cosine_ndcg@1": 0.2955665024630542,
+      "eval_full_de_cosine_ndcg@100": 0.44367884677993147,
+      "eval_full_de_cosine_ndcg@150": 0.47160000110152916,
+      "eval_full_de_cosine_ndcg@20": 0.4542210023439735,
+      "eval_full_de_cosine_ndcg@200": 0.4909825861195281,
+      "eval_full_de_cosine_ndcg@50": 0.42517836513872964,
+      "eval_full_de_cosine_precision@1": 0.2955665024630542,
+      "eval_full_de_cosine_precision@100": 0.18251231527093598,
+      "eval_full_de_cosine_precision@150": 0.13898193760262725,
+      "eval_full_de_cosine_precision@20": 0.41379310344827586,
+      "eval_full_de_cosine_precision@200": 0.11325123152709361,
+      "eval_full_de_cosine_precision@50": 0.283448275862069,
+      "eval_full_de_cosine_recall@1": 0.01108543831680986,
+      "eval_full_de_cosine_recall@100": 0.4813501045539941,
+      "eval_full_de_cosine_recall@150": 0.5364084146960163,
+      "eval_full_de_cosine_recall@20": 0.2542107894141876,
+      "eval_full_de_cosine_recall@200": 0.5778850032024853,
+      "eval_full_de_cosine_recall@50": 0.38875379672131,
+      "eval_full_en_cosine_accuracy@1": 0.6285714285714286,
+      "eval_full_en_cosine_accuracy@100": 0.9904761904761905,
+      "eval_full_en_cosine_accuracy@150": 0.9904761904761905,
+      "eval_full_en_cosine_accuracy@20": 0.9714285714285714,
+      "eval_full_en_cosine_accuracy@200": 0.9904761904761905,
+      "eval_full_en_cosine_accuracy@50": 0.9904761904761905,
+      "eval_full_en_cosine_map@1": 0.6285714285714286,
+      "eval_full_en_cosine_map@100": 0.4808997647658073,
+      "eval_full_en_cosine_map@150": 0.48880948507389355,
+      "eval_full_en_cosine_map@20": 0.47343714269469456,
+      "eval_full_en_cosine_map@200": 0.4927770622539844,
+      "eval_full_en_cosine_map@50": 0.4522725203086179,
+      "eval_full_en_cosine_map@500": 0.4988827324510947,
+      "eval_full_en_cosine_mrr@1": 0.6285714285714286,
+      "eval_full_en_cosine_mrr@100": 0.7724103674103674,
+      "eval_full_en_cosine_mrr@150": 0.7724103674103674,
+      "eval_full_en_cosine_mrr@20": 0.7716777666777668,
+      "eval_full_en_cosine_mrr@200": 0.7724103674103674,
+      "eval_full_en_cosine_mrr@50": 0.7724103674103674,
+      "eval_full_en_cosine_ndcg@1": 0.6285714285714286,
+      "eval_full_en_cosine_ndcg@100": 0.6843480406110959,
+      "eval_full_en_cosine_ndcg@150": 0.7049173882762053,
+      "eval_full_en_cosine_ndcg@20": 0.6209444418709248,
+      "eval_full_en_cosine_ndcg@200": 0.7183756949142358,
+      "eval_full_en_cosine_ndcg@50": 0.6294473723576711,
+      "eval_full_en_cosine_precision@1": 0.6285714285714286,
+      "eval_full_en_cosine_precision@100": 0.16647619047619047,
+      "eval_full_en_cosine_precision@150": 0.11974603174603174,
+      "eval_full_en_cosine_precision@20": 0.4561904761904762,
+      "eval_full_en_cosine_precision@200": 0.09438095238095238,
+      "eval_full_en_cosine_precision@50": 0.2714285714285714,
+      "eval_full_en_cosine_recall@1": 0.06527602031010833,
+      "eval_full_en_cosine_recall@100": 0.7653429555146114,
+      "eval_full_en_cosine_recall@150": 0.8123778494980212,
+      "eval_full_en_cosine_recall@20": 0.48745391682105155,
+      "eval_full_en_cosine_recall@200": 0.8453961465111183,
+      "eval_full_en_cosine_recall@50": 0.6514858980133492,
+      "eval_full_es_cosine_accuracy@1": 0.11891891891891893,
+      "eval_full_es_cosine_accuracy@100": 1.0,
+      "eval_full_es_cosine_accuracy@150": 1.0,
+      "eval_full_es_cosine_accuracy@20": 1.0,
+      "eval_full_es_cosine_accuracy@200": 1.0,
+      "eval_full_es_cosine_accuracy@50": 1.0,
+      "eval_full_es_cosine_map@1": 0.11891891891891893,
+      "eval_full_es_cosine_map@100": 0.3498217476058891,
+      "eval_full_es_cosine_map@150": 0.3605480020931346,
+      "eval_full_es_cosine_map@20": 0.4161905368354915,
+      "eval_full_es_cosine_map@200": 0.3668087716720678,
+      "eval_full_es_cosine_map@50": 0.3483074634920869,
+      "eval_full_es_cosine_map@500": 0.3779292241221979,
+      "eval_full_es_cosine_mrr@1": 0.11891891891891893,
+      "eval_full_es_cosine_mrr@100": 0.5527027027027027,
+      "eval_full_es_cosine_mrr@150": 0.5527027027027027,
+      "eval_full_es_cosine_mrr@20": 0.5527027027027027,
+      "eval_full_es_cosine_mrr@200": 0.5527027027027027,
+      "eval_full_es_cosine_mrr@50": 0.5527027027027027,
+      "eval_full_es_cosine_ndcg@1": 0.11891891891891893,
+      "eval_full_es_cosine_ndcg@100": 0.5331520105561902,
+      "eval_full_es_cosine_ndcg@150": 0.559793036371957,
+      "eval_full_es_cosine_ndcg@20": 0.5478489868293568,
+      "eval_full_es_cosine_ndcg@200": 0.5782925384583979,
+      "eval_full_es_cosine_ndcg@50": 0.5093924480385684,
+      "eval_full_es_cosine_precision@1": 0.11891891891891893,
+      "eval_full_es_cosine_precision@100": 0.2076216216216216,
+      "eval_full_es_cosine_precision@150": 0.15545945945945944,
+      "eval_full_es_cosine_precision@20": 0.504054054054054,
+      "eval_full_es_cosine_precision@200": 0.12545945945945947,
+      "eval_full_es_cosine_precision@50": 0.3250810810810811,
+      "eval_full_es_cosine_recall@1": 0.0035436931012884127,
+      "eval_full_es_cosine_recall@100": 0.5786140267067541,
+      "eval_full_es_cosine_recall@150": 0.6321781446534547,
+      "eval_full_es_cosine_recall@20": 0.3336169042151915,
+      "eval_full_es_cosine_recall@200": 0.670760202615299,
+      "eval_full_es_cosine_recall@50": 0.4802169473485588,
+      "eval_full_zh_cosine_accuracy@1": 0.6504854368932039,
+      "eval_full_zh_cosine_accuracy@100": 0.9902912621359223,
+      "eval_full_zh_cosine_accuracy@150": 0.9902912621359223,
+      "eval_full_zh_cosine_accuracy@20": 0.970873786407767,
+      "eval_full_zh_cosine_accuracy@200": 0.9902912621359223,
+      "eval_full_zh_cosine_accuracy@50": 0.9805825242718447,
+      "eval_full_zh_cosine_map@1": 0.6504854368932039,
+      "eval_full_zh_cosine_map@100": 0.4638450563796641,
+      "eval_full_zh_cosine_map@150": 0.4725316312725551,
+      "eval_full_zh_cosine_map@20": 0.4569063620154663,
+      "eval_full_zh_cosine_map@200": 0.47660671101600177,
+      "eval_full_zh_cosine_map@50": 0.4389770039812701,
+      "eval_full_zh_cosine_map@500": 0.48324935280787457,
+      "eval_full_zh_cosine_mrr@1": 0.6504854368932039,
+      "eval_full_zh_cosine_mrr@100": 0.7966674372014181,
+      "eval_full_zh_cosine_mrr@150": 0.7966674372014181,
+      "eval_full_zh_cosine_mrr@20": 0.7962783171521037,
+      "eval_full_zh_cosine_mrr@200": 0.7966674372014181,
+      "eval_full_zh_cosine_mrr@50": 0.7964940668824165,
+      "eval_full_zh_cosine_ndcg@1": 0.6504854368932039,
+      "eval_full_zh_cosine_ndcg@100": 0.6731573623782805,
+      "eval_full_zh_cosine_ndcg@150": 0.6988573621008486,
+      "eval_full_zh_cosine_ndcg@20": 0.6071619364096145,
+      "eval_full_zh_cosine_ndcg@200": 0.7127085059867994,
+      "eval_full_zh_cosine_ndcg@50": 0.6201924771490717,
+      "eval_full_zh_cosine_precision@1": 0.6504854368932039,
+      "eval_full_zh_cosine_precision@100": 0.16135922330097088,
+      "eval_full_zh_cosine_precision@150": 0.11754045307443368,
+      "eval_full_zh_cosine_precision@20": 0.43592233009708736,
+      "eval_full_zh_cosine_precision@200": 0.0929126213592233,
+      "eval_full_zh_cosine_precision@50": 0.2627184466019417,
+      "eval_full_zh_cosine_recall@1": 0.06125809321810901,
+      "eval_full_zh_cosine_recall@100": 0.7480807136556352,
+      "eval_full_zh_cosine_recall@150": 0.8100117014753285,
+      "eval_full_zh_cosine_recall@20": 0.46840361505881567,
+      "eval_full_zh_cosine_recall@200": 0.8429633028529663,
+      "eval_full_zh_cosine_recall@50": 0.6339158091762449,
+      "eval_mix_de_cosine_accuracy@1": 0.5039001560062403,
+      "eval_mix_de_cosine_accuracy@100": 0.9448777951118045,
+      "eval_mix_de_cosine_accuracy@150": 0.9589183567342694,
+      "eval_mix_de_cosine_accuracy@20": 0.84399375975039,
+      "eval_mix_de_cosine_accuracy@200": 0.968278731149246,
+      "eval_mix_de_cosine_accuracy@50": 0.9084763390535622,
+      "eval_mix_de_cosine_map@1": 0.5039001560062403,
+      "eval_mix_de_cosine_map@100": 0.5125971106477005,
+      "eval_mix_de_cosine_map@150": 0.5131188199194585,
+      "eval_mix_de_cosine_map@20": 0.505156594331499,
+      "eval_mix_de_cosine_map@200": 0.5133496478406526,
+      "eval_mix_de_cosine_map@50": 0.5107325975552374,
+      "eval_mix_de_cosine_map@500": 0.5136957927685452,
+      "eval_mix_de_cosine_mrr@1": 0.5039001560062403,
+      "eval_mix_de_cosine_mrr@100": 0.5985336788307954,
+      "eval_mix_de_cosine_mrr@150": 0.5986528259488063,
+      "eval_mix_de_cosine_mrr@20": 0.5958911705035703,
+      "eval_mix_de_cosine_mrr@200": 0.5987063352922788,
+      "eval_mix_de_cosine_mrr@50": 0.5979962477714235,
+      "eval_mix_de_cosine_ndcg@1": 0.5039001560062403,
+      "eval_mix_de_cosine_ndcg@100": 0.6328275670544934,
+      "eval_mix_de_cosine_ndcg@150": 0.6383837176839967,
+      "eval_mix_de_cosine_ndcg@20": 0.5959978647836432,
+      "eval_mix_de_cosine_ndcg@200": 0.6416384714660877,
+      "eval_mix_de_cosine_ndcg@50": 0.6193308358901232,
+      "eval_mix_de_cosine_precision@1": 0.5039001560062403,
+      "eval_mix_de_cosine_precision@100": 0.024368174726989083,
+      "eval_mix_de_cosine_precision@150": 0.01678280464551915,
+      "eval_mix_de_cosine_precision@20": 0.10124804992199687,
+      "eval_mix_de_cosine_precision@200": 0.012841913676547067,
+      "eval_mix_de_cosine_precision@50": 0.045325013000520026,
+      "eval_mix_de_cosine_recall@1": 0.18934824059629052,
+      "eval_mix_de_cosine_recall@100": 0.8861067776044376,
+      "eval_mix_de_cosine_recall@150": 0.9139625585023401,
+      "eval_mix_de_cosine_recall@20": 0.7398769284104697,
+      "eval_mix_de_cosine_recall@200": 0.9314265903969492,
+      "eval_mix_de_cosine_recall@50": 0.825628358467672,
+      "eval_mix_es_cosine_accuracy@1": 0.5886635465418617,
+      "eval_mix_es_cosine_accuracy@100": 0.9594383775351014,
+      "eval_mix_es_cosine_accuracy@150": 0.9729589183567343,
+      "eval_mix_es_cosine_accuracy@20": 0.8809152366094644,
+      "eval_mix_es_cosine_accuracy@200": 0.9802392095683827,
+      "eval_mix_es_cosine_accuracy@50": 0.9261570462818512,
+      "eval_mix_es_cosine_map@1": 0.5886635465418617,
+      "eval_mix_es_cosine_map@100": 0.5729158897524718,
+      "eval_mix_es_cosine_map@150": 0.5733350346731677,
+      "eval_mix_es_cosine_map@20": 0.5655989370130696,
+      "eval_mix_es_cosine_map@200": 0.5735091332370877,
+      "eval_mix_es_cosine_map@50": 0.5711765803960042,
+      "eval_mix_es_cosine_map@500": 0.5737709089431217,
+      "eval_mix_es_cosine_mrr@1": 0.5886635465418617,
+      "eval_mix_es_cosine_mrr@100": 0.6657709136674115,
+      "eval_mix_es_cosine_mrr@150": 0.6658836947979178,
+      "eval_mix_es_cosine_mrr@20": 0.663746406326751,
+      "eval_mix_es_cosine_mrr@200": 0.6659271840534754,
+      "eval_mix_es_cosine_mrr@50": 0.6652772962495554,
+      "eval_mix_es_cosine_ndcg@1": 0.5886635465418617,
+      "eval_mix_es_cosine_ndcg@100": 0.6853991490467752,
+      "eval_mix_es_cosine_ndcg@150": 0.6900742923636211,
+      "eval_mix_es_cosine_ndcg@20": 0.6519769338150208,
+      "eval_mix_es_cosine_ndcg@200": 0.6926702396087282,
+      "eval_mix_es_cosine_ndcg@50": 0.6730191518959872,
+      "eval_mix_es_cosine_precision@1": 0.5886635465418617,
+      "eval_mix_es_cosine_precision@100": 0.024664586583463342,
+      "eval_mix_es_cosine_precision@150": 0.01687640838966892,
+      "eval_mix_es_cosine_precision@20": 0.10491419656786272,
+      "eval_mix_es_cosine_precision@200": 0.012844513780551223,
+      "eval_mix_es_cosine_precision@50": 0.04624024960998441,
+      "eval_mix_es_cosine_recall@1": 0.22689002798207167,
+      "eval_mix_es_cosine_recall@100": 0.9135552088750216,
+      "eval_mix_es_cosine_recall@150": 0.9375628358467673,
+      "eval_mix_es_cosine_recall@20": 0.7807976128568952,
+      "eval_mix_es_cosine_recall@200": 0.9520714161899808,
+      "eval_mix_es_cosine_recall@50": 0.8575923036921478,
+      "eval_mix_zh_cosine_accuracy@1": 0.4979123173277662,
+      "eval_mix_zh_cosine_accuracy@100": 0.9822546972860126,
+      "eval_mix_zh_cosine_accuracy@150": 0.9879958246346555,
+      "eval_mix_zh_cosine_accuracy@20": 0.9258872651356994,
+      "eval_mix_zh_cosine_accuracy@200": 0.9911273486430062,
+      "eval_mix_zh_cosine_accuracy@50": 0.9660751565762005,
+      "eval_mix_zh_cosine_map@1": 0.4979123173277662,
+      "eval_mix_zh_cosine_map@100": 0.48398634980849026,
+      "eval_mix_zh_cosine_map@150": 0.4844678407622049,
+      "eval_mix_zh_cosine_map@20": 0.47412363249079337,
+      "eval_mix_zh_cosine_map@200": 0.4846279769270419,
+      "eval_mix_zh_cosine_map@50": 0.482345495201663,
+      "eval_mix_zh_cosine_map@500": 0.4848745509164184,
+      "eval_mix_zh_cosine_mrr@1": 0.4979123173277662,
+      "eval_mix_zh_cosine_mrr@100": 0.6315865451011656,
+      "eval_mix_zh_cosine_mrr@150": 0.6316324222127293,
+      "eval_mix_zh_cosine_mrr@20": 0.6299942347473082,
+      "eval_mix_zh_cosine_mrr@200": 0.6316505881656331,
+      "eval_mix_zh_cosine_mrr@50": 0.6313380864532873,
+      "eval_mix_zh_cosine_ndcg@1": 0.4979123173277662,
+      "eval_mix_zh_cosine_ndcg@100": 0.6418702520158802,
+      "eval_mix_zh_cosine_ndcg@150": 0.6465087052200355,
+      "eval_mix_zh_cosine_ndcg@20": 0.6027761736684122,
+      "eval_mix_zh_cosine_ndcg@200": 0.6485444879545422,
+      "eval_mix_zh_cosine_ndcg@50": 0.6312554634401243,
+      "eval_mix_zh_cosine_precision@1": 0.4979123173277662,
+      "eval_mix_zh_cosine_precision@100": 0.027964509394572033,
+      "eval_mix_zh_cosine_precision@150": 0.019102296450939454,
+      "eval_mix_zh_cosine_precision@20": 0.11761482254697284,
+      "eval_mix_zh_cosine_precision@200": 0.01448851774530272,
+      "eval_mix_zh_cosine_precision@50": 0.05312108559498958,
+      "eval_mix_zh_cosine_recall@1": 0.16643677303906948,
+      "eval_mix_zh_cosine_recall@100": 0.9219380654140571,
+      "eval_mix_zh_cosine_recall@150": 0.9448677800974251,
+      "eval_mix_zh_cosine_recall@20": 0.7755393180236603,
+      "eval_mix_zh_cosine_recall@200": 0.9555845511482255,
+      "eval_mix_zh_cosine_recall@50": 0.8757132915796799,
+      "eval_runtime": 9.6289,
+      "eval_samples_per_second": 0.0,
+      "eval_sequential_score": 0.6485444879545422,
+      "eval_steps_per_second": 0.0,
+      "step": 600
+    },
+    {
+      "epoch": 2.0710059171597632,
+      "grad_norm": 0.00018405374430585653,
+      "learning_rate": 6.174454828660436e-05,
+      "loss": 0.0003,
+      "step": 700
+    },
+    {
+      "epoch": 2.366863905325444,
+      "grad_norm": 0.00021158685558475554,
+      "learning_rate": 5.551401869158879e-05,
+      "loss": 0.0003,
+      "step": 800
+    },
+    {
+      "epoch": 2.366863905325444,
+      "eval_full_de_cosine_accuracy@1": 0.2955665024630542,
+      "eval_full_de_cosine_accuracy@100": 0.9852216748768473,
+      "eval_full_de_cosine_accuracy@150": 0.9852216748768473,
+      "eval_full_de_cosine_accuracy@20": 0.9605911330049262,
+      "eval_full_de_cosine_accuracy@200": 0.9852216748768473,
+      "eval_full_de_cosine_accuracy@50": 0.9852216748768473,
+      "eval_full_de_cosine_map@1": 0.2955665024630542,
+      "eval_full_de_cosine_map@100": 0.26989528469865864,
+      "eval_full_de_cosine_map@150": 0.280569759282424,
+      "eval_full_de_cosine_map@20": 0.32803634024001166,
+      "eval_full_de_cosine_map@200": 0.28615236999483784,
+      "eval_full_de_cosine_map@50": 0.2732961593033311,
+      "eval_full_de_cosine_map@500": 0.29766318543285797,
+      "eval_full_de_cosine_mrr@1": 0.2955665024630542,
+      "eval_full_de_cosine_mrr@100": 0.5036943461274306,
+      "eval_full_de_cosine_mrr@150": 0.5036943461274306,
+      "eval_full_de_cosine_mrr@20": 0.5028631393626662,
+      "eval_full_de_cosine_mrr@200": 0.5036943461274306,
+      "eval_full_de_cosine_mrr@50": 0.5036943461274306,
+      "eval_full_de_cosine_ndcg@1": 0.2955665024630542,
+      "eval_full_de_cosine_ndcg@100": 0.4534525483371107,
+      "eval_full_de_cosine_ndcg@150": 0.4832887800019625,
+      "eval_full_de_cosine_ndcg@20": 0.4608166745708263,
+      "eval_full_de_cosine_ndcg@200": 0.5023264900853682,
+      "eval_full_de_cosine_ndcg@50": 0.43241693487506655,
+      "eval_full_de_cosine_precision@1": 0.2955665024630542,
+      "eval_full_de_cosine_precision@100": 0.18748768472906405,
+      "eval_full_de_cosine_precision@150": 0.1430870279146141,
+      "eval_full_de_cosine_precision@20": 0.4204433497536946,
+      "eval_full_de_cosine_precision@200": 0.11603448275862069,
+      "eval_full_de_cosine_precision@50": 0.2882758620689655,
+      "eval_full_de_cosine_recall@1": 0.01108543831680986,
+      "eval_full_de_cosine_recall@100": 0.49405659749418546,
+      "eval_full_de_cosine_recall@150": 0.5537788330251293,
+      "eval_full_de_cosine_recall@20": 0.26013047047662125,
+      "eval_full_de_cosine_recall@200": 0.5949031327763369,
+      "eval_full_de_cosine_recall@50": 0.3966511112168954,
+      "eval_full_en_cosine_accuracy@1": 0.638095238095238,
+      "eval_full_en_cosine_accuracy@100": 0.9904761904761905,
+      "eval_full_en_cosine_accuracy@150": 0.9904761904761905,
+      "eval_full_en_cosine_accuracy@20": 0.9714285714285714,
+      "eval_full_en_cosine_accuracy@200": 0.9904761904761905,
+      "eval_full_en_cosine_accuracy@50": 0.9904761904761905,
+      "eval_full_en_cosine_map@1": 0.638095238095238,
+      "eval_full_en_cosine_map@100": 0.49496599786546613,
+      "eval_full_en_cosine_map@150": 0.5034594550180629,
+      "eval_full_en_cosine_map@20": 0.4869878588929393,
+      "eval_full_en_cosine_map@200": 0.5072739176309733,
+      "eval_full_en_cosine_map@50": 0.46662543266107637,
+      "eval_full_en_cosine_map@500": 0.513497188490262,
+      "eval_full_en_cosine_mrr@1": 0.638095238095238,
+      "eval_full_en_cosine_mrr@100": 0.7830671550671551,
+      "eval_full_en_cosine_mrr@150": 0.7830671550671551,
+      "eval_full_en_cosine_mrr@20": 0.7823199023199023,
+      "eval_full_en_cosine_mrr@200": 0.7830671550671551,
+      "eval_full_en_cosine_mrr@50": 0.7830671550671551,
+      "eval_full_en_cosine_ndcg@1": 0.638095238095238,
+      "eval_full_en_cosine_ndcg@100": 0.6953363920968629,
+      "eval_full_en_cosine_ndcg@150": 0.7178434762154305,
+      "eval_full_en_cosine_ndcg@20": 0.6328305552523847,
+      "eval_full_en_cosine_ndcg@200": 0.7306862991424842,
+      "eval_full_en_cosine_ndcg@50": 0.6422634260444581,
+      "eval_full_en_cosine_precision@1": 0.638095238095238,
+      "eval_full_en_cosine_precision@100": 0.16866666666666671,
+      "eval_full_en_cosine_precision@150": 0.12184126984126983,
+      "eval_full_en_cosine_precision@20": 0.46523809523809523,
+      "eval_full_en_cosine_precision@200": 0.09585714285714286,
+      "eval_full_en_cosine_precision@50": 0.27809523809523806,
+      "eval_full_en_cosine_recall@1": 0.06587125840534644,
+      "eval_full_en_cosine_recall@100": 0.7728137840947553,
+      "eval_full_en_cosine_recall@150": 0.8252655196277217,
+      "eval_full_en_cosine_recall@20": 0.49716157160369917,
+      "eval_full_en_cosine_recall@200": 0.8555531706571073,
+      "eval_full_en_cosine_recall@50": 0.6633636132155855,
+      "eval_full_es_cosine_accuracy@1": 0.11891891891891893,
+      "eval_full_es_cosine_accuracy@100": 1.0,
+      "eval_full_es_cosine_accuracy@150": 1.0,
+      "eval_full_es_cosine_accuracy@20": 1.0,
+      "eval_full_es_cosine_accuracy@200": 1.0,
+      "eval_full_es_cosine_accuracy@50": 1.0,
+      "eval_full_es_cosine_map@1": 0.11891891891891893,
+      "eval_full_es_cosine_map@100": 0.36277737080647704,
+      "eval_full_es_cosine_map@150": 0.37405867180001495,
+      "eval_full_es_cosine_map@20": 0.4282003376242628,
+      "eval_full_es_cosine_map@200": 0.38113830290272754,
+      "eval_full_es_cosine_map@50": 0.36226639114104775,
+      "eval_full_es_cosine_map@500": 0.3916826388439343,
+      "eval_full_es_cosine_mrr@1": 0.11891891891891893,
+      "eval_full_es_cosine_mrr@100": 0.5536036036036036,
+      "eval_full_es_cosine_mrr@150": 0.5536036036036036,
+      "eval_full_es_cosine_mrr@20": 0.5536036036036036,
+      "eval_full_es_cosine_mrr@200": 0.5536036036036036,
+      "eval_full_es_cosine_mrr@50": 0.5536036036036036,
+      "eval_full_es_cosine_ndcg@1": 0.11891891891891893,
+      "eval_full_es_cosine_ndcg@100": 0.5449386715947235,
+      "eval_full_es_cosine_ndcg@150": 0.5723788977348012,
+      "eval_full_es_cosine_ndcg@20": 0.5602690347910769,
+      "eval_full_es_cosine_ndcg@200": 0.593844013640946,
+      "eval_full_es_cosine_ndcg@50": 0.5249033466595285,
+      "eval_full_es_cosine_precision@1": 0.11891891891891893,
+      "eval_full_es_cosine_precision@100": 0.21189189189189192,
+      "eval_full_es_cosine_precision@150": 0.15902702702702703,
+      "eval_full_es_cosine_precision@20": 0.5178378378378379,
+      "eval_full_es_cosine_precision@200": 0.12905405405405404,
+      "eval_full_es_cosine_precision@50": 0.3364324324324324,
+      "eval_full_es_cosine_recall@1": 0.0035747235671014874,
+      "eval_full_es_cosine_recall@100": 0.5916287235670691,
+      "eval_full_es_cosine_recall@150": 0.6467712559340097,
+      "eval_full_es_cosine_recall@20": 0.34399720695046865,
+      "eval_full_es_cosine_recall@200": 0.6937965313057715,
+      "eval_full_es_cosine_recall@50": 0.5012588939840604,
+      "eval_full_zh_cosine_accuracy@1": 0.6601941747572816,
+      "eval_full_zh_cosine_accuracy@100": 0.9902912621359223,
+      "eval_full_zh_cosine_accuracy@150": 0.9902912621359223,
+      "eval_full_zh_cosine_accuracy@20": 0.970873786407767,
+      "eval_full_zh_cosine_accuracy@200": 0.9902912621359223,
+      "eval_full_zh_cosine_accuracy@50": 0.9902912621359223,
+      "eval_full_zh_cosine_map@1": 0.6601941747572816,
+      "eval_full_zh_cosine_map@100": 0.4749717781804501,
+      "eval_full_zh_cosine_map@150": 0.4832701810816351,
+      "eval_full_zh_cosine_map@20": 0.46498128288276724,
+      "eval_full_zh_cosine_map@200": 0.487762460646856,
+      "eval_full_zh_cosine_map@50": 0.4485986843158645,
+      "eval_full_zh_cosine_map@500": 0.4939522359452576,
+      "eval_full_zh_cosine_mrr@1": 0.6601941747572816,
+      "eval_full_zh_cosine_mrr@100": 0.8038261082260857,
+      "eval_full_zh_cosine_mrr@150": 0.8038261082260857,
+      "eval_full_zh_cosine_mrr@20": 0.8033980582524272,
+      "eval_full_zh_cosine_mrr@200": 0.8038261082260857,
+      "eval_full_zh_cosine_mrr@50": 0.8038261082260857,
+      "eval_full_zh_cosine_ndcg@1": 0.6601941747572816,
+      "eval_full_zh_cosine_ndcg@100": 0.6845556918348534,
+      "eval_full_zh_cosine_ndcg@150": 0.707685056330406,
+      "eval_full_zh_cosine_ndcg@20": 0.6135478065862052,
+      "eval_full_zh_cosine_ndcg@200": 0.7232611066574451,
+      "eval_full_zh_cosine_ndcg@50": 0.6308156378108885,
+      "eval_full_zh_cosine_precision@1": 0.6601941747572816,
+      "eval_full_zh_cosine_precision@100": 0.164368932038835,
+      "eval_full_zh_cosine_precision@150": 0.11870550161812297,
+      "eval_full_zh_cosine_precision@20": 0.437378640776699,
+      "eval_full_zh_cosine_precision@200": 0.09432038834951458,
+      "eval_full_zh_cosine_precision@50": 0.26679611650485435,
+      "eval_full_zh_cosine_recall@1": 0.06611246215014785,
+      "eval_full_zh_cosine_recall@100": 0.7614494349130585,
+      "eval_full_zh_cosine_recall@150": 0.8169184700467885,
+      "eval_full_zh_cosine_recall@20": 0.47220221895116143,
+      "eval_full_zh_cosine_recall@200": 0.8548694196710027,
+      "eval_full_zh_cosine_recall@50": 0.6481360616867319,
+      "eval_mix_de_cosine_accuracy@1": 0.5325013000520021,
+      "eval_mix_de_cosine_accuracy@100": 0.9516380655226209,
+      "eval_mix_de_cosine_accuracy@150": 0.9651586063442538,
+      "eval_mix_de_cosine_accuracy@20": 0.8637545501820073,
+      "eval_mix_de_cosine_accuracy@200": 0.9729589183567343,
+      "eval_mix_de_cosine_accuracy@50": 0.9251170046801872,
+      "eval_mix_de_cosine_map@1": 0.5325013000520021,
+      "eval_mix_de_cosine_map@100": 0.5374123776699551,
+      "eval_mix_de_cosine_map@150": 0.5378689641686509,
+      "eval_mix_de_cosine_map@20": 0.5304865672939455,
+      "eval_mix_de_cosine_map@200": 0.5381229578769543,
+      "eval_mix_de_cosine_map@50": 0.5358413929854877,
+      "eval_mix_de_cosine_map@500": 0.5384218703705271,
+      "eval_mix_de_cosine_mrr@1": 0.5325013000520021,
+      "eval_mix_de_cosine_mrr@100": 0.6238401145880241,
+      "eval_mix_de_cosine_mrr@150": 0.6239470104163971,
+      "eval_mix_de_cosine_mrr@20": 0.6214631528403295,
+      "eval_mix_de_cosine_mrr@200": 0.6239938982140064,
+      "eval_mix_de_cosine_mrr@50": 0.6234664391780935,
+      "eval_mix_de_cosine_ndcg@1": 0.5325013000520021,
+      "eval_mix_de_cosine_ndcg@100": 0.655555914976012,
+      "eval_mix_de_cosine_ndcg@150": 0.6605327238662299,
+      "eval_mix_de_cosine_ndcg@20": 0.6224558186311073,
+      "eval_mix_de_cosine_ndcg@200": 0.6639471441610337,
+      "eval_mix_de_cosine_ndcg@50": 0.644673644554676,
+      "eval_mix_de_cosine_precision@1": 0.5325013000520021,
+      "eval_mix_de_cosine_precision@100": 0.02476339053562143,
+      "eval_mix_de_cosine_precision@150": 0.016987346160513086,
+      "eval_mix_de_cosine_precision@20": 0.10535621424856995,
+      "eval_mix_de_cosine_precision@200": 0.013000520020800833,
+      "eval_mix_de_cosine_precision@50": 0.04671866874674988,
+      "eval_mix_de_cosine_recall@1": 0.19959265037268156,
+      "eval_mix_de_cosine_recall@100": 0.8992286358120992,
+      "eval_mix_de_cosine_recall@150": 0.9242329693187727,
+      "eval_mix_de_cosine_recall@20": 0.7690587623504941,
+      "eval_mix_de_cosine_recall@200": 0.9427543768417402,
+      "eval_mix_de_cosine_recall@50": 0.8509793725082337,
+      "eval_mix_es_cosine_accuracy@1": 0.607904316172647,
+      "eval_mix_es_cosine_accuracy@100": 0.9656786271450858,
+      "eval_mix_es_cosine_accuracy@150": 0.9776391055642226,
+      "eval_mix_es_cosine_accuracy@20": 0.8975559022360895,
+      "eval_mix_es_cosine_accuracy@200": 0.983879355174207,
+      "eval_mix_es_cosine_accuracy@50": 0.9370774830993239,
+      "eval_mix_es_cosine_map@1": 0.607904316172647,
+      "eval_mix_es_cosine_map@100": 0.5975211890190857,
+      "eval_mix_es_cosine_map@150": 0.5979584945984814,
+      "eval_mix_es_cosine_map@20": 0.5906291851909811,
+      "eval_mix_es_cosine_map@200": 0.5981011467491768,
+      "eval_mix_es_cosine_map@50": 0.5960353592752704,
+      "eval_mix_es_cosine_map@500": 0.5983366971827869,
+      "eval_mix_es_cosine_mrr@1": 0.607904316172647,
+      "eval_mix_es_cosine_mrr@100": 0.6849138427496972,
+      "eval_mix_es_cosine_mrr@150": 0.6850137837316987,
+      "eval_mix_es_cosine_mrr@20": 0.68313810383803,
+      "eval_mix_es_cosine_mrr@200": 0.6850496425615078,
+      "eval_mix_es_cosine_mrr@50": 0.6845037901150748,
+      "eval_mix_es_cosine_ndcg@1": 0.607904316172647,
+      "eval_mix_es_cosine_ndcg@100": 0.7056752373517705,
+      "eval_mix_es_cosine_ndcg@150": 0.7103953789351477,
+      "eval_mix_es_cosine_ndcg@20": 0.6752876326597782,
+      "eval_mix_es_cosine_ndcg@200": 0.712457504431498,
+      "eval_mix_es_cosine_ndcg@50": 0.6951844012171731,
+      "eval_mix_es_cosine_precision@1": 0.607904316172647,
+      "eval_mix_es_cosine_precision@100": 0.02492459698387936,
+      "eval_mix_es_cosine_precision@150": 0.017053215461951807,
+      "eval_mix_es_cosine_precision@20": 0.10816432657306292,
+      "eval_mix_es_cosine_precision@200": 0.012943317732709313,
+      "eval_mix_es_cosine_precision@50": 0.04728029121164847,
+      "eval_mix_es_cosine_recall@1": 0.23460366986108014,
+      "eval_mix_es_cosine_recall@100": 0.9238169526781072,
+      "eval_mix_es_cosine_recall@150": 0.9479805858901021,
+      "eval_mix_es_cosine_recall@20": 0.8038345343337544,
+      "eval_mix_es_cosine_recall@200": 0.9593517074016294,
+      "eval_mix_es_cosine_recall@50": 0.8761570462818512,
+      "eval_mix_zh_cosine_accuracy@1": 0.5521920668058455,
+      "eval_mix_zh_cosine_accuracy@100": 0.9843423799582464,
+      "eval_mix_zh_cosine_accuracy@150": 0.9900835073068893,
+      "eval_mix_zh_cosine_accuracy@20": 0.9420668058455115,
+      "eval_mix_zh_cosine_accuracy@200": 0.9921711899791231,
+      "eval_mix_zh_cosine_accuracy@50": 0.9749478079331941,
+      "eval_mix_zh_cosine_map@1": 0.5521920668058455,
+      "eval_mix_zh_cosine_map@100": 0.5290555813566699,
+      "eval_mix_zh_cosine_map@150": 0.5294873001921219,
+      "eval_mix_zh_cosine_map@20": 0.5203958350785768,
+      "eval_mix_zh_cosine_map@200": 0.5296267135628773,
+      "eval_mix_zh_cosine_map@50": 0.5275524955682618,
+      "eval_mix_zh_cosine_map@500": 0.5298325476614522,
+      "eval_mix_zh_cosine_mrr@1": 0.5521920668058455,
+      "eval_mix_zh_cosine_mrr@100": 0.678394992746269,
+      "eval_mix_zh_cosine_mrr@150": 0.67843915509047,
+      "eval_mix_zh_cosine_mrr@20": 0.6771753752113248,
+      "eval_mix_zh_cosine_mrr@200": 0.6784507184460267,
+      "eval_mix_zh_cosine_mrr@50": 0.6782538605101974,
+      "eval_mix_zh_cosine_ndcg@1": 0.5521920668058455,
+      "eval_mix_zh_cosine_ndcg@100": 0.6789004025721674,
+      "eval_mix_zh_cosine_ndcg@150": 0.6829363362348053,
+      "eval_mix_zh_cosine_ndcg@20": 0.6447847513126116,
+      "eval_mix_zh_cosine_ndcg@200": 0.6846976649936148,
+      "eval_mix_zh_cosine_ndcg@50": 0.6696189286501009,
+      "eval_mix_zh_cosine_precision@1": 0.5521920668058455,
+      "eval_mix_zh_cosine_precision@100": 0.028298538622129447,
+      "eval_mix_zh_cosine_precision@150": 0.019262352122477382,
+      "eval_mix_zh_cosine_precision@20": 0.12207724425887263,
+      "eval_mix_zh_cosine_precision@200": 0.014587682672233825,
+      "eval_mix_zh_cosine_precision@50": 0.05415448851774532,
+      "eval_mix_zh_cosine_recall@1": 0.1851563276667661,
+      "eval_mix_zh_cosine_recall@100": 0.9329853862212943,
+      "eval_mix_zh_cosine_recall@150": 0.9528705636743215,
+      "eval_mix_zh_cosine_recall@20": 0.8050539318023661,
+      "eval_mix_zh_cosine_recall@200": 0.962152052887961,
+      "eval_mix_zh_cosine_recall@50": 0.8926757132915798,
+      "eval_runtime": 9.8944,
+      "eval_samples_per_second": 0.0,
+      "eval_sequential_score": 0.6846976649936148,
+      "eval_steps_per_second": 0.0,
+      "step": 800
+    },
+    {
+      "epoch": 2.662721893491124,
+      "grad_norm": 0.00016998578212223947,
+      "learning_rate": 4.928348909657321e-05,
+      "loss": 0.0003,
+      "step": 900
+    },
+    {
+      "epoch": 2.9585798816568047,
+      "grad_norm": 0.00017521833069622517,
+      "learning_rate": 4.3052959501557634e-05,
+      "loss": 0.0003,
+      "step": 1000
+    },
+    {
+      "epoch": 2.9585798816568047,
+      "eval_full_de_cosine_accuracy@1": 0.2955665024630542,
+      "eval_full_de_cosine_accuracy@100": 0.9852216748768473,
+      "eval_full_de_cosine_accuracy@150": 0.9852216748768473,
+      "eval_full_de_cosine_accuracy@20": 0.9605911330049262,
+      "eval_full_de_cosine_accuracy@200": 0.9901477832512315,
+      "eval_full_de_cosine_accuracy@50": 0.9802955665024631,
+      "eval_full_de_cosine_map@1": 0.2955665024630542,
+      "eval_full_de_cosine_map@100": 0.27530357902528746,
+      "eval_full_de_cosine_map@150": 0.2859029789549631,
+      "eval_full_de_cosine_map@20": 0.3326012942578798,
+      "eval_full_de_cosine_map@200": 0.29192358526577794,
+      "eval_full_de_cosine_map@50": 0.2779781159809199,
+      "eval_full_de_cosine_map@500": 0.3037728006457777,
+      "eval_full_de_cosine_mrr@1": 0.2955665024630542,
+      "eval_full_de_cosine_mrr@100": 0.5041884576791513,
+      "eval_full_de_cosine_mrr@150": 0.5041884576791513,
+      "eval_full_de_cosine_mrr@20": 0.503435229891329,
+      "eval_full_de_cosine_mrr@200": 0.5042166068698621,
+      "eval_full_de_cosine_mrr@50": 0.5041035247761447,
+      "eval_full_de_cosine_ndcg@1": 0.2955665024630542,
+      "eval_full_de_cosine_ndcg@100": 0.4594232150790335,
+      "eval_full_de_cosine_ndcg@150": 0.4887319216460325,
+      "eval_full_de_cosine_ndcg@20": 0.46461290935992494,
+      "eval_full_de_cosine_ndcg@200": 0.5085159310260775,
+      "eval_full_de_cosine_ndcg@50": 0.43636700085765784,
+      "eval_full_de_cosine_precision@1": 0.2955665024630542,
+      "eval_full_de_cosine_precision@100": 0.19019704433497536,
+      "eval_full_de_cosine_precision@150": 0.14476190476190476,
+      "eval_full_de_cosine_precision@20": 0.424384236453202,
+      "eval_full_de_cosine_precision@200": 0.1177832512315271,
+      "eval_full_de_cosine_precision@50": 0.29064039408866993,
+      "eval_full_de_cosine_recall@1": 0.01108543831680986,
+      "eval_full_de_cosine_recall@100": 0.5011599542158983,
+      "eval_full_de_cosine_recall@150": 0.5599024076006294,
+      "eval_full_de_cosine_recall@20": 0.2623989771425487,
+      "eval_full_de_cosine_recall@200": 0.6019565140878311,
+      "eval_full_de_cosine_recall@50": 0.399936827395569,
+      "eval_full_en_cosine_accuracy@1": 0.6285714285714286,
+      "eval_full_en_cosine_accuracy@100": 0.9904761904761905,
+      "eval_full_en_cosine_accuracy@150": 0.9904761904761905,
+      "eval_full_en_cosine_accuracy@20": 0.9714285714285714,
+      "eval_full_en_cosine_accuracy@200": 0.9904761904761905,
+      "eval_full_en_cosine_accuracy@50": 0.9904761904761905,
+      "eval_full_en_cosine_map@1": 0.6285714285714286,
+      "eval_full_en_cosine_map@100": 0.5027685735699932,
+      "eval_full_en_cosine_map@150": 0.5108956115342047,
+      "eval_full_en_cosine_map@20": 0.4949002324392317,
+      "eval_full_en_cosine_map@200": 0.5152152246235047,
+      "eval_full_en_cosine_map@50": 0.47542864021103454,
+      "eval_full_en_cosine_map@500": 0.5211733943510876,
+      "eval_full_en_cosine_mrr@1": 0.6285714285714286,
+      "eval_full_en_cosine_mrr@100": 0.7797979143260452,
+      "eval_full_en_cosine_mrr@150": 0.7797979143260452,
+      "eval_full_en_cosine_mrr@20": 0.7790726817042607,
+      "eval_full_en_cosine_mrr@200": 0.7797979143260452,
+      "eval_full_en_cosine_mrr@50": 0.7797979143260452,
+      "eval_full_en_cosine_ndcg@1": 0.6285714285714286,
+      "eval_full_en_cosine_ndcg@100": 0.7009585791000247,
+      "eval_full_en_cosine_ndcg@150": 0.7228549618650749,
+      "eval_full_en_cosine_ndcg@20": 0.6385286667884668,
+      "eval_full_en_cosine_ndcg@200": 0.7370730818153396,
+      "eval_full_en_cosine_ndcg@50": 0.6505087993598385,
+      "eval_full_en_cosine_precision@1": 0.6285714285714286,
+      "eval_full_en_cosine_precision@100": 0.1706666666666667,
+      "eval_full_en_cosine_precision@150": 0.12285714285714286,
+      "eval_full_en_cosine_precision@20": 0.4723809523809524,
+      "eval_full_en_cosine_precision@200": 0.09700000000000002,
+      "eval_full_en_cosine_precision@50": 0.2838095238095238,
+      "eval_full_en_cosine_recall@1": 0.06568451704213447,
+      "eval_full_en_cosine_recall@100": 0.7798036464336738,
+      "eval_full_en_cosine_recall@150": 0.8311908383371492,
+      "eval_full_en_cosine_recall@20": 0.5041312032991911,
+      "eval_full_en_cosine_recall@200": 0.8655400214018215,
+      "eval_full_en_cosine_recall@50": 0.6762963371727007,
+      "eval_full_es_cosine_accuracy@1": 0.11351351351351352,
+      "eval_full_es_cosine_accuracy@100": 1.0,
+      "eval_full_es_cosine_accuracy@150": 1.0,
+      "eval_full_es_cosine_accuracy@20": 1.0,
+      "eval_full_es_cosine_accuracy@200": 1.0,
+      "eval_full_es_cosine_accuracy@50": 1.0,
+      "eval_full_es_cosine_map@1": 0.11351351351351352,
+      "eval_full_es_cosine_map@100": 0.3676467044477579,
+      "eval_full_es_cosine_map@150": 0.37914071893635704,
+      "eval_full_es_cosine_map@20": 0.4321212731877681,
+      "eval_full_es_cosine_map@200": 0.3864291047810966,
+      "eval_full_es_cosine_map@50": 0.3662438776904182,
+      "eval_full_es_cosine_map@500": 0.3967448814407886,
+      "eval_full_es_cosine_mrr@1": 0.11351351351351352,
+      "eval_full_es_cosine_mrr@100": 0.55,
+      "eval_full_es_cosine_mrr@150": 0.55,
+      "eval_full_es_cosine_mrr@20": 0.55,
+      "eval_full_es_cosine_mrr@200": 0.55,
+      "eval_full_es_cosine_mrr@50": 0.55,
+      "eval_full_es_cosine_ndcg@1": 0.11351351351351352,
+      "eval_full_es_cosine_ndcg@100": 0.5494533442820461,
+      "eval_full_es_cosine_ndcg@150": 0.5778904564772578,
+      "eval_full_es_cosine_ndcg@20": 0.5638160555705326,
+      "eval_full_es_cosine_ndcg@200": 0.6002374248801999,
+      "eval_full_es_cosine_ndcg@50": 0.5286289587475489,
+      "eval_full_es_cosine_precision@1": 0.11351351351351352,
+      "eval_full_es_cosine_precision@100": 0.2141081081081081,
+      "eval_full_es_cosine_precision@150": 0.16104504504504505,
+      "eval_full_es_cosine_precision@20": 0.5213513513513512,
+      "eval_full_es_cosine_precision@200": 0.13094594594594594,
+      "eval_full_es_cosine_precision@50": 0.33891891891891895,
+      "eval_full_es_cosine_recall@1": 0.0035045234969014166,
+      "eval_full_es_cosine_recall@100": 0.5962566893615484,
+      "eval_full_es_cosine_recall@150": 0.6539916045900668,
+      "eval_full_es_cosine_recall@20": 0.34830621955762764,
+      "eval_full_es_cosine_recall@200": 0.7027707655811134,
+      "eval_full_es_cosine_recall@50": 0.5043797869988105,
+      "eval_full_zh_cosine_accuracy@1": 0.6504854368932039,
+      "eval_full_zh_cosine_accuracy@100": 0.9902912621359223,
+      "eval_full_zh_cosine_accuracy@150": 0.9902912621359223,
+      "eval_full_zh_cosine_accuracy@20": 0.970873786407767,
+      "eval_full_zh_cosine_accuracy@200": 0.9902912621359223,
+      "eval_full_zh_cosine_accuracy@50": 0.9805825242718447,
+      "eval_full_zh_cosine_map@1": 0.6504854368932039,
+      "eval_full_zh_cosine_map@100": 0.4759775327060125,
+      "eval_full_zh_cosine_map@150": 0.484283864447002,
+      "eval_full_zh_cosine_map@20": 0.4673451367444491,
+      "eval_full_zh_cosine_map@200": 0.4885403171787604,
+      "eval_full_zh_cosine_map@50": 0.4491601687897158,
+      "eval_full_zh_cosine_map@500": 0.4948931148880558,
+      "eval_full_zh_cosine_mrr@1": 0.6504854368932039,
+      "eval_full_zh_cosine_mrr@100": 0.7943002375041209,
+      "eval_full_zh_cosine_mrr@150": 0.7943002375041209,
+      "eval_full_zh_cosine_mrr@20": 0.7938511326860843,
+      "eval_full_zh_cosine_mrr@200": 0.7943002375041209,
+      "eval_full_zh_cosine_mrr@50": 0.7941135310067349,
+      "eval_full_zh_cosine_ndcg@1": 0.6504854368932039,
+      "eval_full_zh_cosine_ndcg@100": 0.6852740031621496,
+      "eval_full_zh_cosine_ndcg@150": 0.7087858531025408,
+      "eval_full_zh_cosine_ndcg@20": 0.6163434250133266,
+      "eval_full_zh_cosine_ndcg@200": 0.7227726687256436,
+      "eval_full_zh_cosine_ndcg@50": 0.6306194061713684,
+      "eval_full_zh_cosine_precision@1": 0.6504854368932039,
+      "eval_full_zh_cosine_precision@100": 0.16601941747572818,
+      "eval_full_zh_cosine_precision@150": 0.12000000000000002,
+      "eval_full_zh_cosine_precision@20": 0.4461165048543689,
+      "eval_full_zh_cosine_precision@200": 0.09475728155339808,
+      "eval_full_zh_cosine_precision@50": 0.26932038834951455,
+      "eval_full_zh_cosine_recall@1": 0.06125809321810901,
+      "eval_full_zh_cosine_recall@100": 0.7667280032499174,
+      "eval_full_zh_cosine_recall@150": 0.8234348132226993,
+      "eval_full_zh_cosine_recall@20": 0.4798173076061309,
+      "eval_full_zh_cosine_recall@200": 0.8570886860782638,
+      "eval_full_zh_cosine_recall@50": 0.6511259115267456,
+      "eval_mix_de_cosine_accuracy@1": 0.5429017160686428,
+      "eval_mix_de_cosine_accuracy@100": 0.9552782111284451,
+      "eval_mix_de_cosine_accuracy@150": 0.968278731149246,
+      "eval_mix_de_cosine_accuracy@20": 0.8725949037961519,
+      "eval_mix_de_cosine_accuracy@200": 0.9729589183567343,
+      "eval_mix_de_cosine_accuracy@50": 0.9297971918876755,
+      "eval_mix_de_cosine_map@1": 0.5429017160686428,
+      "eval_mix_de_cosine_map@100": 0.5528890114435938,
+      "eval_mix_de_cosine_map@150": 0.5533285819634786,
+      "eval_mix_de_cosine_map@20": 0.546038259426052,
+      "eval_mix_de_cosine_map@200": 0.5535297820757661,
+      "eval_mix_de_cosine_map@50": 0.5513401593649401,
+      "eval_mix_de_cosine_map@500": 0.5538215020153545,
+      "eval_mix_de_cosine_mrr@1": 0.5429017160686428,
+      "eval_mix_de_cosine_mrr@100": 0.6354157777188323,
+      "eval_mix_de_cosine_mrr@150": 0.6355194502419383,
+      "eval_mix_de_cosine_mrr@20": 0.6331176720726237,
+      "eval_mix_de_cosine_mrr@200": 0.635546462249249,
+      "eval_mix_de_cosine_mrr@50": 0.6350347522721764,
+      "eval_mix_de_cosine_ndcg@1": 0.5429017160686428,
+      "eval_mix_de_cosine_ndcg@100": 0.6686859699628315,
+      "eval_mix_de_cosine_ndcg@150": 0.6734670399055159,
+      "eval_mix_de_cosine_ndcg@20": 0.6364696194038222,
+      "eval_mix_de_cosine_ndcg@200": 0.6761041848609185,
+      "eval_mix_de_cosine_ndcg@50": 0.6580204683537704,
+      "eval_mix_de_cosine_precision@1": 0.5429017160686428,
+      "eval_mix_de_cosine_precision@100": 0.025002600104004166,
+      "eval_mix_de_cosine_precision@150": 0.01712601837406829,
+      "eval_mix_de_cosine_precision@20": 0.10709828393135724,
+      "eval_mix_de_cosine_precision@200": 0.013044721788871557,
+      "eval_mix_de_cosine_precision@50": 0.04726989079563183,
+      "eval_mix_de_cosine_recall@1": 0.20383948691280984,
+      "eval_mix_de_cosine_recall@100": 0.9077223088923557,
+      "eval_mix_de_cosine_recall@150": 0.9319032761310452,
+      "eval_mix_de_cosine_recall@20": 0.7817386028774485,
+      "eval_mix_de_cosine_recall@200": 0.9461778471138845,
+      "eval_mix_de_cosine_recall@50": 0.8605044201768071,
+      "eval_mix_es_cosine_accuracy@1": 0.6172646905876235,
+      "eval_mix_es_cosine_accuracy@100": 0.9703588143525741,
+      "eval_mix_es_cosine_accuracy@150": 0.9812792511700468,
+      "eval_mix_es_cosine_accuracy@20": 0.9032761310452418,
+      "eval_mix_es_cosine_accuracy@200": 0.9859594383775351,
+      "eval_mix_es_cosine_accuracy@50": 0.9443577743109725,
+      "eval_mix_es_cosine_map@1": 0.6172646905876235,
+      "eval_mix_es_cosine_map@100": 0.6094136625128228,
+      "eval_mix_es_cosine_map@150": 0.6097807307495342,
+      "eval_mix_es_cosine_map@20": 0.6028333286973904,
+      "eval_mix_es_cosine_map@200": 0.6099278426294548,
+      "eval_mix_es_cosine_map@50": 0.6079882517976847,
+      "eval_mix_es_cosine_map@500": 0.6101218939355526,
+      "eval_mix_es_cosine_mrr@1": 0.6172646905876235,
+      "eval_mix_es_cosine_mrr@100": 0.6938924919697613,
+      "eval_mix_es_cosine_mrr@150": 0.6939819360030616,
+      "eval_mix_es_cosine_mrr@20": 0.6921361840847764,
+      "eval_mix_es_cosine_mrr@200": 0.6940082129440573,
+      "eval_mix_es_cosine_mrr@50": 0.6935275501084183,
+      "eval_mix_es_cosine_ndcg@1": 0.6172646905876235,
+      "eval_mix_es_cosine_ndcg@100": 0.7161349937562115,
+      "eval_mix_es_cosine_ndcg@150": 0.7201494083175249,
+      "eval_mix_es_cosine_ndcg@20": 0.6863945449619185,
+      "eval_mix_es_cosine_ndcg@200": 0.722225937142632,
+      "eval_mix_es_cosine_ndcg@50": 0.7059805315894592,
+      "eval_mix_es_cosine_precision@1": 0.6172646905876235,
+      "eval_mix_es_cosine_precision@100": 0.025169006760270413,
+      "eval_mix_es_cosine_precision@150": 0.017157219622118216,
+      "eval_mix_es_cosine_precision@20": 0.10972438897555903,
+      "eval_mix_es_cosine_precision@200": 0.013018720748829957,
+      "eval_mix_es_cosine_precision@50": 0.04786271450858035,
+      "eval_mix_es_cosine_recall@1": 0.2379838050664884,
+      "eval_mix_es_cosine_recall@100": 0.9331773270930838,
+      "eval_mix_es_cosine_recall@150": 0.9536141445657828,
+      "eval_mix_es_cosine_recall@20": 0.8149369784315182,
+      "eval_mix_es_cosine_recall@200": 0.9651759403709481,
+      "eval_mix_es_cosine_recall@50": 0.8866788004853527,
+      "eval_mix_zh_cosine_accuracy@1": 0.5751565762004175,
+      "eval_mix_zh_cosine_accuracy@100": 0.9848643006263048,
+      "eval_mix_zh_cosine_accuracy@150": 0.9895615866388309,
+      "eval_mix_zh_cosine_accuracy@20": 0.9514613778705637,
+      "eval_mix_zh_cosine_accuracy@200": 0.9916492693110647,
+      "eval_mix_zh_cosine_accuracy@50": 0.975991649269311,
+      "eval_mix_zh_cosine_map@1": 0.5751565762004175,
+      "eval_mix_zh_cosine_map@100": 0.5480058234906587,
+      "eval_mix_zh_cosine_map@150": 0.5483452539266979,
+      "eval_mix_zh_cosine_map@20": 0.5395939445358217,
+      "eval_mix_zh_cosine_map@200": 0.548487754480418,
+      "eval_mix_zh_cosine_map@50": 0.5465541726714618,
+      "eval_mix_zh_cosine_map@500": 0.5486704400924459,
+      "eval_mix_zh_cosine_mrr@1": 0.5751565762004175,
+      "eval_mix_zh_cosine_mrr@100": 0.6984820179753005,
+      "eval_mix_zh_cosine_mrr@150": 0.6985228351798531,
+      "eval_mix_zh_cosine_mrr@20": 0.6974988781113621,
+      "eval_mix_zh_cosine_mrr@200": 0.6985351624205532,
+      "eval_mix_zh_cosine_mrr@50": 0.6983413027160801,
+      "eval_mix_zh_cosine_ndcg@1": 0.5751565762004175,
+      "eval_mix_zh_cosine_ndcg@100": 0.6947962879201968,
+      "eval_mix_zh_cosine_ndcg@150": 0.6980250427797421,
+      "eval_mix_zh_cosine_ndcg@20": 0.6621196118161056,
+      "eval_mix_zh_cosine_ndcg@200": 0.6997922044919449,
+      "eval_mix_zh_cosine_ndcg@50": 0.6858570871515306,
+      "eval_mix_zh_cosine_precision@1": 0.5751565762004175,
+      "eval_mix_zh_cosine_precision@100": 0.02851252609603341,
+      "eval_mix_zh_cosine_precision@150": 0.019324982602644397,
+      "eval_mix_zh_cosine_precision@20": 0.123982254697286,
+      "eval_mix_zh_cosine_precision@200": 0.014634655532359089,
+      "eval_mix_zh_cosine_precision@50": 0.05465553235908143,
+      "eval_mix_zh_cosine_recall@1": 0.19298513768764292,
+      "eval_mix_zh_cosine_recall@100": 0.9399095337508698,
+      "eval_mix_zh_cosine_recall@150": 0.9558716075156575,
+      "eval_mix_zh_cosine_recall@20": 0.8174060542797494,
+      "eval_mix_zh_cosine_recall@200": 0.965196590118302,
+      "eval_mix_zh_cosine_recall@50": 0.901000347947112,
+      "eval_runtime": 9.5387,
+      "eval_samples_per_second": 0.0,
+      "eval_sequential_score": 0.6997922044919449,
+      "eval_steps_per_second": 0.0,
+      "step": 1000
+    }
+  ],
+  "logging_steps": 100,
+  "max_steps": 1690,
+  "num_input_tokens_seen": 0,
+  "num_train_epochs": 5,
+  "save_steps": 200,
+  "stateful_callbacks": {
+    "TrainerControl": {
+      "args": {
+        "should_epoch_stop": false,
+        "should_evaluate": false,
+        "should_log": false,
+        "should_save": true,
+        "should_training_stop": false
+      },
+      "attributes": {}
+    }
+  },
+  "total_flos": 0.0,
+  "train_batch_size": 128,
+  "trial_name": null,
+  "trial_params": null
+}

checkpoint-1200/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-1200/sentence_bert_config.json

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

checkpoint-1200/special_tokens_map.json

@@ -0,0 +1,51 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

checkpoint-1200/tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:883b037111086fd4dfebbbc9b7cee11e1517b5e0c0514879478661440f137085
+size 17082987

checkpoint-1200/tokenizer_config.json

@@ -0,0 +1,62 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "250001": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "extra_special_tokens": {},
+  "mask_token": "<mask>",
+  "max_length": 512,
+  "model_max_length": 512,
+  "pad_to_multiple_of": null,
+  "pad_token": "<pad>",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "</s>",
+  "stride": 0,
+  "tokenizer_class": "XLMRobertaTokenizerFast",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "<unk>"
+}

checkpoint-1400/1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

checkpoint-1400/README.md

@@ -0,0 +1,1288 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:86648
+- loss:MSELoss
+widget:
+- source_sentence: Familienberaterin
+  sentences:
+  - electric power station operator
+  - venue booker & promoter
+  - betrieblicher Aus- und Weiterbildner/betriebliche Aus- und Weiterbildnerin
+- source_sentence: high school RS teacher
+  sentences:
+  - infantryman
+  - Schnellbedienungsrestaurantteamleiter
+  - drill setup operator
+- source_sentence: lighting designer
+  sentences:
+  - software support manager
+  - 直升机维护协调员
+  - bus maintenance supervisor
+- source_sentence: 机场消防员
+  sentences:
+  - Flake操作员
+  - técnico en gestión de residuos peligrosos/técnica en gestión de residuos peligrosos
+  - 专门学校老师
+- source_sentence: Entwicklerin für mobile Anwendungen
+  sentences:
+  - fashion design expert
+  - Mergers-and-Acquisitions-Analyst/Mergers-and-Acquisitions-Analystin
+  - commercial bid manager
+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
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: full en
+      type: full_en
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.638095238095238
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@20
+      value: 0.9619047619047619
+      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: 0.638095238095238
+      name: Cosine Precision@1
+    - type: cosine_precision@20
+      value: 0.4766666666666666
+      name: Cosine Precision@20
+    - type: cosine_precision@50
+      value: 0.28723809523809524
+      name: Cosine Precision@50
+    - type: cosine_precision@100
+      value: 0.172952380952381
+      name: Cosine Precision@100
+    - type: cosine_precision@150
+      value: 0.12419047619047618
+      name: Cosine Precision@150
+    - type: cosine_precision@200
+      value: 0.09828571428571428
+      name: Cosine Precision@200
+    - type: cosine_recall@1
+      value: 0.06587125840534644
+      name: Cosine Recall@1
+    - type: cosine_recall@20
+      value: 0.5075382961558268
+      name: Cosine Recall@20
+    - type: cosine_recall@50
+      value: 0.6815180199385792
+      name: Cosine Recall@50
+    - type: cosine_recall@100
+      value: 0.7892546849949126
+      name: Cosine Recall@100
+    - type: cosine_recall@150
+      value: 0.837763491705966
+      name: Cosine Recall@150
+    - type: cosine_recall@200
+      value: 0.8747531461107081
+      name: Cosine Recall@200
+    - type: cosine_ndcg@1
+      value: 0.638095238095238
+      name: Cosine Ndcg@1
+    - type: cosine_ndcg@20
+      value: 0.6437588496803061
+      name: Cosine Ndcg@20
+    - type: cosine_ndcg@50
+      value: 0.6565500770575415
+      name: Cosine Ndcg@50
+    - type: cosine_ndcg@100
+      value: 0.7088213416976051
+      name: Cosine Ndcg@100
+    - type: cosine_ndcg@150
+      value: 0.7298707409128666
+      name: Cosine Ndcg@150
+    - type: cosine_ndcg@200
+      value: 0.7449419847756586
+      name: Cosine Ndcg@200
+    - type: cosine_mrr@1
+      value: 0.638095238095238
+      name: Cosine Mrr@1
+    - type: cosine_mrr@20
+      value: 0.7865079365079365
+      name: Cosine Mrr@20
+    - type: cosine_mrr@50
+      value: 0.7877959183673469
+      name: Cosine Mrr@50
+    - type: cosine_mrr@100
+      value: 0.7877959183673469
+      name: Cosine Mrr@100
+    - type: cosine_mrr@150
+      value: 0.7877959183673469
+      name: Cosine Mrr@150
+    - type: cosine_mrr@200
+      value: 0.7877959183673469
+      name: Cosine Mrr@200
+    - type: cosine_map@1
+      value: 0.638095238095238
+      name: Cosine Map@1
+    - type: cosine_map@20
+      value: 0.4998912029710938
+      name: Cosine Map@20
+    - type: cosine_map@50
+      value: 0.4824988798112498
+      name: Cosine Map@50
+    - type: cosine_map@100
+      value: 0.510770369728262
+      name: Cosine Map@100
+    - type: cosine_map@150
+      value: 0.5189335101114453
+      name: Cosine Map@150
+    - type: cosine_map@200
+      value: 0.5235615593885471
+      name: Cosine Map@200
+    - type: cosine_map@500
+      value: 0.5292082683302094
+      name: Cosine Map@500
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: full es
+      type: full_es
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.11891891891891893
+      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: 0.11891891891891893
+      name: Cosine Precision@1
+    - type: cosine_precision@20
+      value: 0.5278378378378379
+      name: Cosine Precision@20
+    - type: cosine_precision@50
+      value: 0.34324324324324323
+      name: Cosine Precision@50
+    - type: cosine_precision@100
+      value: 0.21778378378378382
+      name: Cosine Precision@100
+    - type: cosine_precision@150
+      value: 0.16486486486486487
+      name: Cosine Precision@150
+    - type: cosine_precision@200
+      value: 0.1328918918918919
+      name: Cosine Precision@200
+    - type: cosine_recall@1
+      value: 0.0035840147528632613
+      name: Cosine Recall@1
+    - type: cosine_recall@20
+      value: 0.3543566274863611
+      name: Cosine Recall@20
+    - type: cosine_recall@50
+      value: 0.5098461049513731
+      name: Cosine Recall@50
+    - type: cosine_recall@100
+      value: 0.6026389252991667
+      name: Cosine Recall@100
+    - type: cosine_recall@150
+      value: 0.6669011609932756
+      name: Cosine Recall@150
+    - type: cosine_recall@200
+      value: 0.7113409830611916
+      name: Cosine Recall@200
+    - type: cosine_ndcg@1
+      value: 0.11891891891891893
+      name: Cosine Ndcg@1
+    - type: cosine_ndcg@20
+      value: 0.5711957180482146
+      name: Cosine Ndcg@20
+    - type: cosine_ndcg@50
+      value: 0.5349550041043327
+      name: Cosine Ndcg@50
+    - type: cosine_ndcg@100
+      value: 0.5565423240177232
+      name: Cosine Ndcg@100
+    - type: cosine_ndcg@150
+      value: 0.5877749295399255
+      name: Cosine Ndcg@150
+    - type: cosine_ndcg@200
+      value: 0.6082665694710195
+      name: Cosine Ndcg@200
+    - type: cosine_mrr@1
+      value: 0.11891891891891893
+      name: Cosine Mrr@1
+    - type: cosine_mrr@20
+      value: 0.5527027027027027
+      name: Cosine Mrr@20
+    - type: cosine_mrr@50
+      value: 0.5527027027027027
+      name: Cosine Mrr@50
+    - type: cosine_mrr@100
+      value: 0.5527027027027027
+      name: Cosine Mrr@100
+    - type: cosine_mrr@150
+      value: 0.5527027027027027
+      name: Cosine Mrr@150
+    - type: cosine_mrr@200
+      value: 0.5527027027027027
+      name: Cosine Mrr@200
+    - type: cosine_map@1
+      value: 0.11891891891891893
+      name: Cosine Map@1
+    - type: cosine_map@20
+      value: 0.43847997732650607
+      name: Cosine Map@20
+    - type: cosine_map@50
+      value: 0.3732694210069731
+      name: Cosine Map@50
+    - type: cosine_map@100
+      value: 0.375118481783653
+      name: Cosine Map@100
+    - type: cosine_map@150
+      value: 0.3878279775328886
+      name: Cosine Map@150
+    - type: cosine_map@200
+      value: 0.3947963463478377
+      name: Cosine Map@200
+    - type: cosine_map@500
+      value: 0.40522877653342115
+      name: Cosine Map@500
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: full de
+      type: full_de
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.2955665024630542
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@20
+      value: 0.9704433497536946
+      name: Cosine Accuracy@20
+    - type: cosine_accuracy@50
+      value: 0.9753694581280788
+      name: Cosine Accuracy@50
+    - type: cosine_accuracy@100
+      value: 0.9901477832512315
+      name: Cosine Accuracy@100
+    - type: cosine_accuracy@150
+      value: 0.9901477832512315
+      name: Cosine Accuracy@150
+    - type: cosine_accuracy@200
+      value: 0.9901477832512315
+      name: Cosine Accuracy@200
+    - type: cosine_precision@1
+      value: 0.2955665024630542
+      name: Cosine Precision@1
+    - type: cosine_precision@20
+      value: 0.4268472906403941
+      name: Cosine Precision@20
+    - type: cosine_precision@50
+      value: 0.2961576354679803
+      name: Cosine Precision@50
+    - type: cosine_precision@100
+      value: 0.19325123152709361
+      name: Cosine Precision@100
+    - type: cosine_precision@150
+      value: 0.1477832512315271
+      name: Cosine Precision@150
+    - type: cosine_precision@200
+      value: 0.11955665024630542
+      name: Cosine Precision@200
+    - type: cosine_recall@1
+      value: 0.01108543831680986
+      name: Cosine Recall@1
+    - type: cosine_recall@20
+      value: 0.2650930635637711
+      name: Cosine Recall@20
+    - type: cosine_recall@50
+      value: 0.40640295677523314
+      name: Cosine Recall@50
+    - type: cosine_recall@100
+      value: 0.5077941189728509
+      name: Cosine Recall@100
+    - type: cosine_recall@150
+      value: 0.5715831784599764
+      name: Cosine Recall@150
+    - type: cosine_recall@200
+      value: 0.6110348630292376
+      name: Cosine Recall@200
+    - type: cosine_ndcg@1
+      value: 0.2955665024630542
+      name: Cosine Ndcg@1
+    - type: cosine_ndcg@20
+      value: 0.46792018771474997
+      name: Cosine Ndcg@20
+    - type: cosine_ndcg@50
+      value: 0.4426383293801955
+      name: Cosine Ndcg@50
+    - type: cosine_ndcg@100
+      value: 0.46518720126873364
+      name: Cosine Ndcg@100
+    - type: cosine_ndcg@150
+      value: 0.49680037168814867
+      name: Cosine Ndcg@150
+    - type: cosine_ndcg@200
+      value: 0.5153612080009081
+      name: Cosine Ndcg@200
+    - type: cosine_mrr@1
+      value: 0.2955665024630542
+      name: Cosine Mrr@1
+    - type: cosine_mrr@20
+      value: 0.5045345800887666
+      name: Cosine Mrr@20
+    - type: cosine_mrr@50
+      value: 0.5046677181529392
+      name: Cosine Mrr@50
+    - type: cosine_mrr@100
+      value: 0.5048549281619237
+      name: Cosine Mrr@100
+    - type: cosine_mrr@150
+      value: 0.5048549281619237
+      name: Cosine Mrr@150
+    - type: cosine_mrr@200
+      value: 0.5048549281619237
+      name: Cosine Mrr@200
+    - type: cosine_map@1
+      value: 0.2955665024630542
+      name: Cosine Map@1
+    - type: cosine_map@20
+      value: 0.3352975238416978
+      name: Cosine Map@20
+    - type: cosine_map@50
+      value: 0.2835629650407853
+      name: Cosine Map@50
+    - type: cosine_map@100
+      value: 0.2802505215242132
+      name: Cosine Map@100
+    - type: cosine_map@150
+      value: 0.29173450273639756
+      name: Cosine Map@150
+    - type: cosine_map@200
+      value: 0.2974766350207053
+      name: Cosine Map@200
+    - type: cosine_map@500
+      value: 0.30952701831389734
+      name: Cosine Map@500
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: full zh
+      type: full_zh
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.6601941747572816
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@20
+      value: 0.970873786407767
+      name: Cosine Accuracy@20
+    - type: cosine_accuracy@50
+      value: 0.9902912621359223
+      name: Cosine Accuracy@50
+    - type: cosine_accuracy@100
+      value: 0.9902912621359223
+      name: Cosine Accuracy@100
+    - type: cosine_accuracy@150
+      value: 0.9902912621359223
+      name: Cosine Accuracy@150
+    - type: cosine_accuracy@200
+      value: 0.9902912621359223
+      name: Cosine Accuracy@200
+    - type: cosine_precision@1
+      value: 0.6601941747572816
+      name: Cosine Precision@1
+    - type: cosine_precision@20
+      value: 0.4451456310679612
+      name: Cosine Precision@20
+    - type: cosine_precision@50
+      value: 0.27048543689320387
+      name: Cosine Precision@50
+    - type: cosine_precision@100
+      value: 0.16611650485436896
+      name: Cosine Precision@100
+    - type: cosine_precision@150
+      value: 0.12084142394822009
+      name: Cosine Precision@150
+    - type: cosine_precision@200
+      value: 0.09519417475728156
+      name: Cosine Precision@200
+    - type: cosine_recall@1
+      value: 0.06611246215014785
+      name: Cosine Recall@1
+    - type: cosine_recall@20
+      value: 0.48185419008936636
+      name: Cosine Recall@20
+    - type: cosine_recall@50
+      value: 0.6551920812816043
+      name: Cosine Recall@50
+    - type: cosine_recall@100
+      value: 0.764654034617116
+      name: Cosine Recall@100
+    - type: cosine_recall@150
+      value: 0.8281168342114908
+      name: Cosine Recall@150
+    - type: cosine_recall@200
+      value: 0.8609375188843946
+      name: Cosine Recall@200
+    - type: cosine_ndcg@1
+      value: 0.6601941747572816
+      name: Cosine Ndcg@1
+    - type: cosine_ndcg@20
+      value: 0.6209192881378345
+      name: Cosine Ndcg@20
+    - type: cosine_ndcg@50
+      value: 0.6371304923469949
+      name: Cosine Ndcg@50
+    - type: cosine_ndcg@100
+      value: 0.6900404048312746
+      name: Cosine Ndcg@100
+    - type: cosine_ndcg@150
+      value: 0.7159480635761921
+      name: Cosine Ndcg@150
+    - type: cosine_ndcg@200
+      value: 0.7294173160030438
+      name: Cosine Ndcg@200
+    - type: cosine_mrr@1
+      value: 0.6601941747572816
+      name: Cosine Mrr@1
+    - type: cosine_mrr@20
+      value: 0.8015419760137065
+      name: Cosine Mrr@20
+    - type: cosine_mrr@50
+      value: 0.8020274129069105
+      name: Cosine Mrr@50
+    - type: cosine_mrr@100
+      value: 0.8020274129069105
+      name: Cosine Mrr@100
+    - type: cosine_mrr@150
+      value: 0.8020274129069105
+      name: Cosine Mrr@150
+    - type: cosine_mrr@200
+      value: 0.8020274129069105
+      name: Cosine Mrr@200
+    - type: cosine_map@1
+      value: 0.6601941747572816
+      name: Cosine Map@1
+    - type: cosine_map@20
+      value: 0.47238295031349775
+      name: Cosine Map@20
+    - type: cosine_map@50
+      value: 0.4561669025825994
+      name: Cosine Map@50
+    - type: cosine_map@100
+      value: 0.48307171830860945
+      name: Cosine Map@100
+    - type: cosine_map@150
+      value: 0.4920233958725791
+      name: Cosine Map@150
+    - type: cosine_map@200
+      value: 0.496106859156668
+      name: Cosine Map@200
+    - type: cosine_map@500
+      value: 0.5023110925949719
+      name: Cosine Map@500
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: mix es
+      type: mix_es
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.6297451898075923
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@20
+      value: 0.9079563182527302
+      name: Cosine Accuracy@20
+    - type: cosine_accuracy@50
+      value: 0.9485179407176287
+      name: Cosine Accuracy@50
+    - type: cosine_accuracy@100
+      value: 0.9734789391575663
+      name: Cosine Accuracy@100
+    - type: cosine_accuracy@150
+      value: 0.9817992719708788
+      name: Cosine Accuracy@150
+    - type: cosine_accuracy@200
+      value: 0.9890795631825273
+      name: Cosine Accuracy@200
+    - type: cosine_precision@1
+      value: 0.6297451898075923
+      name: Cosine Precision@1
+    - type: cosine_precision@20
+      value: 0.11144045761830473
+      name: Cosine Precision@20
+    - type: cosine_precision@50
+      value: 0.04842433697347895
+      name: Cosine Precision@50
+    - type: cosine_precision@100
+      value: 0.025314612584503383
+      name: Cosine Precision@100
+    - type: cosine_precision@150
+      value: 0.017216155312879178
+      name: Cosine Precision@150
+    - type: cosine_precision@200
+      value: 0.013070722828913158
+      name: Cosine Precision@200
+    - type: cosine_recall@1
+      value: 0.24340068840848872
+      name: Cosine Recall@1
+    - type: cosine_recall@20
+      value: 0.827157467251071
+      name: Cosine Recall@20
+    - type: cosine_recall@50
+      value: 0.8970792165019934
+      name: Cosine Recall@50
+    - type: cosine_recall@100
+      value: 0.9385508753683481
+      name: Cosine Recall@100
+    - type: cosine_recall@150
+      value: 0.9569249436644133
+      name: Cosine Recall@150
+    - type: cosine_recall@200
+      value: 0.9686600797365229
+      name: Cosine Recall@200
+    - type: cosine_ndcg@1
+      value: 0.6297451898075923
+      name: Cosine Ndcg@1
+    - type: cosine_ndcg@20
+      value: 0.6994116361658315
+      name: Cosine Ndcg@20
+    - type: cosine_ndcg@50
+      value: 0.7184754763821674
+      name: Cosine Ndcg@50
+    - type: cosine_ndcg@100
+      value: 0.7275271174143362
+      name: Cosine Ndcg@100
+    - type: cosine_ndcg@150
+      value: 0.7311486978502827
+      name: Cosine Ndcg@150
+    - type: cosine_ndcg@200
+      value: 0.733282433801573
+      name: Cosine Ndcg@200
+    - type: cosine_mrr@1
+      value: 0.6297451898075923
+      name: Cosine Mrr@1
+    - type: cosine_mrr@20
+      value: 0.7026675306443272
+      name: Cosine Mrr@20
+    - type: cosine_mrr@50
+      value: 0.7040534682065075
+      name: Cosine Mrr@50
+    - type: cosine_mrr@100
+      value: 0.7044148840240123
+      name: Cosine Mrr@100
+    - type: cosine_mrr@150
+      value: 0.7044856803226204
+      name: Cosine Mrr@150
+    - type: cosine_mrr@200
+      value: 0.704528165280555
+      name: Cosine Mrr@200
+    - type: cosine_map@1
+      value: 0.6297451898075923
+      name: Cosine Map@1
+    - type: cosine_map@20
+      value: 0.6176093380717337
+      name: Cosine Map@20
+    - type: cosine_map@50
+      value: 0.6226112093265134
+      name: Cosine Map@50
+    - type: cosine_map@100
+      value: 0.6238596600766622
+      name: Cosine Map@100
+    - type: cosine_map@150
+      value: 0.6242075803658665
+      name: Cosine Map@150
+    - type: cosine_map@200
+      value: 0.6243509834359291
+      name: Cosine Map@200
+    - type: cosine_map@500
+      value: 0.6245346885039931
+      name: Cosine Map@500
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: mix de
+      type: mix_de
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.5538221528861155
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@20
+      value: 0.8814352574102964
+      name: Cosine Accuracy@20
+    - type: cosine_accuracy@50
+      value: 0.9349973998959958
+      name: Cosine Accuracy@50
+    - type: cosine_accuracy@100
+      value: 0.9589183567342694
+      name: Cosine Accuracy@100
+    - type: cosine_accuracy@150
+      value: 0.96931877275091
+      name: Cosine Accuracy@150
+    - type: cosine_accuracy@200
+      value: 0.9765990639625585
+      name: Cosine Accuracy@200
+    - type: cosine_precision@1
+      value: 0.5538221528861155
+      name: Cosine Precision@1
+    - type: cosine_precision@20
+      value: 0.10912636505460219
+      name: Cosine Precision@20
+    - type: cosine_precision@50
+      value: 0.047935517420696835
+      name: Cosine Precision@50
+    - type: cosine_precision@100
+      value: 0.025257410296411865
+      name: Cosine Precision@100
+    - type: cosine_precision@150
+      value: 0.017257756976945746
+      name: Cosine Precision@150
+    - type: cosine_precision@200
+      value: 0.013122724908996361
+      name: Cosine Precision@200
+    - type: cosine_recall@1
+      value: 0.20845033801352056
+      name: Cosine Recall@1
+    - type: cosine_recall@20
+      value: 0.7964725255676894
+      name: Cosine Recall@20
+    - type: cosine_recall@50
+      value: 0.8717888715548621
+      name: Cosine Recall@50
+    - type: cosine_recall@100
+      value: 0.9166493326399723
+      name: Cosine Recall@100
+    - type: cosine_recall@150
+      value: 0.9388542208355001
+      name: Cosine Recall@150
+    - type: cosine_recall@200
+      value: 0.9522447564569249
+      name: Cosine Recall@200
+    - type: cosine_ndcg@1
+      value: 0.5538221528861155
+      name: Cosine Ndcg@1
+    - type: cosine_ndcg@20
+      value: 0.6518455599845957
+      name: Cosine Ndcg@20
+    - type: cosine_ndcg@50
+      value: 0.6725307652410174
+      name: Cosine Ndcg@50
+    - type: cosine_ndcg@100
+      value: 0.6825987388473841
+      name: Cosine Ndcg@100
+    - type: cosine_ndcg@150
+      value: 0.6869902480321315
+      name: Cosine Ndcg@150
+    - type: cosine_ndcg@200
+      value: 0.6894230866781552
+      name: Cosine Ndcg@200
+    - type: cosine_mrr@1
+      value: 0.5538221528861155
+      name: Cosine Mrr@1
+    - type: cosine_mrr@20
+      value: 0.6451894555975591
+      name: Cosine Mrr@20
+    - type: cosine_mrr@50
+      value: 0.6470013120502346
+      name: Cosine Mrr@50
+    - type: cosine_mrr@100
+      value: 0.6473603615547494
+      name: Cosine Mrr@100
+    - type: cosine_mrr@150
+      value: 0.6474490009158033
+      name: Cosine Mrr@150
+    - type: cosine_mrr@200
+      value: 0.647492473181411
+      name: Cosine Mrr@200
+    - type: cosine_map@1
+      value: 0.5538221528861155
+      name: Cosine Map@1
+    - type: cosine_map@20
+      value: 0.5627871995310985
+      name: Cosine Map@20
+    - type: cosine_map@50
+      value: 0.5679148655306163
+      name: Cosine Map@50
+    - type: cosine_map@100
+      value: 0.5693421440886408
+      name: Cosine Map@100
+    - type: cosine_map@150
+      value: 0.5697579274072834
+      name: Cosine Map@150
+    - type: cosine_map@200
+      value: 0.569931742725807
+      name: Cosine Map@200
+    - type: cosine_map@500
+      value: 0.5702007325952348
+      name: Cosine Map@500
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: mix zh
+      type: mix_zh
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.6033402922755741
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@20
+      value: 0.9592901878914405
+      name: Cosine Accuracy@20
+    - type: cosine_accuracy@50
+      value: 0.9775574112734864
+      name: Cosine Accuracy@50
+    - type: cosine_accuracy@100
+      value: 0.9869519832985386
+      name: Cosine Accuracy@100
+    - type: cosine_accuracy@150
+      value: 0.9911273486430062
+      name: Cosine Accuracy@150
+    - type: cosine_accuracy@200
+      value: 0.9937369519832986
+      name: Cosine Accuracy@200
+    - type: cosine_precision@1
+      value: 0.6033402922755741
+      name: Cosine Precision@1
+    - type: cosine_precision@20
+      value: 0.1262787056367432
+      name: Cosine Precision@20
+    - type: cosine_precision@50
+      value: 0.055156576200417556
+      name: Cosine Precision@50
+    - type: cosine_precision@100
+      value: 0.028684759916492702
+      name: Cosine Precision@100
+    - type: cosine_precision@150
+      value: 0.019394572025052192
+      name: Cosine Precision@150
+    - type: cosine_precision@200
+      value: 0.014694676409185809
+      name: Cosine Precision@200
+    - type: cosine_recall@1
+      value: 0.2026406700467243
+      name: Cosine Recall@1
+    - type: cosine_recall@20
+      value: 0.8327331245650661
+      name: Cosine Recall@20
+    - type: cosine_recall@50
+      value: 0.9090553235908142
+      name: Cosine Recall@50
+    - type: cosine_recall@100
+      value: 0.9454766875434933
+      name: Cosine Recall@100
+    - type: cosine_recall@150
+      value: 0.9593510786360473
+      name: Cosine Recall@150
+    - type: cosine_recall@200
+      value: 0.9690848990953375
+      name: Cosine Recall@200
+    - type: cosine_ndcg@1
+      value: 0.6033402922755741
+      name: Cosine Ndcg@1
+    - type: cosine_ndcg@20
+      value: 0.6828284711666521
+      name: Cosine Ndcg@20
+    - type: cosine_ndcg@50
+      value: 0.7045660706215972
+      name: Cosine Ndcg@50
+    - type: cosine_ndcg@100
+      value: 0.7129279365518828
+      name: Cosine Ndcg@100
+    - type: cosine_ndcg@150
+      value: 0.7157293364418106
+      name: Cosine Ndcg@150
+    - type: cosine_ndcg@200
+      value: 0.7175794784000445
+      name: Cosine Ndcg@200
+    - type: cosine_mrr@1
+      value: 0.6033402922755741
+      name: Cosine Mrr@1
+    - type: cosine_mrr@20
+      value: 0.7204416409571621
+      name: Cosine Mrr@20
+    - type: cosine_mrr@50
+      value: 0.7210752869689329
+      name: Cosine Mrr@50
+    - type: cosine_mrr@100
+      value: 0.7212211062865328
+      name: Cosine Mrr@100
+    - type: cosine_mrr@150
+      value: 0.7212542072796881
+      name: Cosine Mrr@150
+    - type: cosine_mrr@200
+      value: 0.7212683301539606
+      name: Cosine Mrr@200
+    - type: cosine_map@1
+      value: 0.6033402922755741
+      name: Cosine Map@1
+    - type: cosine_map@20
+      value: 0.5625523429259808
+      name: Cosine Map@20
+    - type: cosine_map@50
+      value: 0.5690763342890433
+      name: Cosine Map@50
+    - type: cosine_map@100
+      value: 0.5704513498606978
+      name: Cosine Map@100
+    - type: cosine_map@150
+      value: 0.5707437921606868
+      name: Cosine Map@150
+    - type: cosine_map@200
+      value: 0.5708914357578326
+      name: Cosine Map@200
+    - type: cosine_map@500
+      value: 0.5710537045348917
+      name: Cosine Map@500
+---
+
+# SentenceTransformer
+
+This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (2): Normalize()
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    'Entwicklerin für mobile Anwendungen',
+    'Mergers-and-Acquisitions-Analyst/Mergers-and-Acquisitions-Analystin',
+    'fashion design expert',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Information Retrieval
+
+* Datasets: `full_en`, `full_es`, `full_de`, `full_zh`, `mix_es`, `mix_de` and `mix_zh`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric               | full_en    | full_es    | full_de    | full_zh    | mix_es     | mix_de     | mix_zh     |
+|:---------------------|:-----------|:-----------|:-----------|:-----------|:-----------|:-----------|:-----------|
+| cosine_accuracy@1    | 0.6381     | 0.1189     | 0.2956     | 0.6602     | 0.6297     | 0.5538     | 0.6033     |
+| cosine_accuracy@20   | 0.9619     | 1.0        | 0.9704     | 0.9709     | 0.908      | 0.8814     | 0.9593     |
+| cosine_accuracy@50   | 0.9905     | 1.0        | 0.9754     | 0.9903     | 0.9485     | 0.935      | 0.9776     |
+| cosine_accuracy@100  | 0.9905     | 1.0        | 0.9901     | 0.9903     | 0.9735     | 0.9589     | 0.987      |
+| cosine_accuracy@150  | 0.9905     | 1.0        | 0.9901     | 0.9903     | 0.9818     | 0.9693     | 0.9911     |
+| cosine_accuracy@200  | 0.9905     | 1.0        | 0.9901     | 0.9903     | 0.9891     | 0.9766     | 0.9937     |
+| cosine_precision@1   | 0.6381     | 0.1189     | 0.2956     | 0.6602     | 0.6297     | 0.5538     | 0.6033     |
+| cosine_precision@20  | 0.4767     | 0.5278     | 0.4268     | 0.4451     | 0.1114     | 0.1091     | 0.1263     |
+| cosine_precision@50  | 0.2872     | 0.3432     | 0.2962     | 0.2705     | 0.0484     | 0.0479     | 0.0552     |
+| cosine_precision@100 | 0.173      | 0.2178     | 0.1933     | 0.1661     | 0.0253     | 0.0253     | 0.0287     |
+| cosine_precision@150 | 0.1242     | 0.1649     | 0.1478     | 0.1208     | 0.0172     | 0.0173     | 0.0194     |
+| cosine_precision@200 | 0.0983     | 0.1329     | 0.1196     | 0.0952     | 0.0131     | 0.0131     | 0.0147     |
+| cosine_recall@1      | 0.0659     | 0.0036     | 0.0111     | 0.0661     | 0.2434     | 0.2085     | 0.2026     |
+| cosine_recall@20     | 0.5075     | 0.3544     | 0.2651     | 0.4819     | 0.8272     | 0.7965     | 0.8327     |
+| cosine_recall@50     | 0.6815     | 0.5098     | 0.4064     | 0.6552     | 0.8971     | 0.8718     | 0.9091     |
+| cosine_recall@100    | 0.7893     | 0.6026     | 0.5078     | 0.7647     | 0.9386     | 0.9166     | 0.9455     |
+| cosine_recall@150    | 0.8378     | 0.6669     | 0.5716     | 0.8281     | 0.9569     | 0.9389     | 0.9594     |
+| cosine_recall@200    | 0.8748     | 0.7113     | 0.611      | 0.8609     | 0.9687     | 0.9522     | 0.9691     |
+| cosine_ndcg@1        | 0.6381     | 0.1189     | 0.2956     | 0.6602     | 0.6297     | 0.5538     | 0.6033     |
+| cosine_ndcg@20       | 0.6438     | 0.5712     | 0.4679     | 0.6209     | 0.6994     | 0.6518     | 0.6828     |
+| cosine_ndcg@50       | 0.6566     | 0.535      | 0.4426     | 0.6371     | 0.7185     | 0.6725     | 0.7046     |
+| cosine_ndcg@100      | 0.7088     | 0.5565     | 0.4652     | 0.69       | 0.7275     | 0.6826     | 0.7129     |
+| cosine_ndcg@150      | 0.7299     | 0.5878     | 0.4968     | 0.7159     | 0.7311     | 0.687      | 0.7157     |
+| **cosine_ndcg@200**  | **0.7449** | **0.6083** | **0.5154** | **0.7294** | **0.7333** | **0.6894** | **0.7176** |
+| cosine_mrr@1         | 0.6381     | 0.1189     | 0.2956     | 0.6602     | 0.6297     | 0.5538     | 0.6033     |
+| cosine_mrr@20        | 0.7865     | 0.5527     | 0.5045     | 0.8015     | 0.7027     | 0.6452     | 0.7204     |
+| cosine_mrr@50        | 0.7878     | 0.5527     | 0.5047     | 0.802      | 0.7041     | 0.647      | 0.7211     |
+| cosine_mrr@100       | 0.7878     | 0.5527     | 0.5049     | 0.802      | 0.7044     | 0.6474     | 0.7212     |
+| cosine_mrr@150       | 0.7878     | 0.5527     | 0.5049     | 0.802      | 0.7045     | 0.6474     | 0.7213     |
+| cosine_mrr@200       | 0.7878     | 0.5527     | 0.5049     | 0.802      | 0.7045     | 0.6475     | 0.7213     |
+| cosine_map@1         | 0.6381     | 0.1189     | 0.2956     | 0.6602     | 0.6297     | 0.5538     | 0.6033     |
+| cosine_map@20        | 0.4999     | 0.4385     | 0.3353     | 0.4724     | 0.6176     | 0.5628     | 0.5626     |
+| cosine_map@50        | 0.4825     | 0.3733     | 0.2836     | 0.4562     | 0.6226     | 0.5679     | 0.5691     |
+| cosine_map@100       | 0.5108     | 0.3751     | 0.2803     | 0.4831     | 0.6239     | 0.5693     | 0.5705     |
+| cosine_map@150       | 0.5189     | 0.3878     | 0.2917     | 0.492      | 0.6242     | 0.5698     | 0.5707     |
+| cosine_map@200       | 0.5236     | 0.3948     | 0.2975     | 0.4961     | 0.6244     | 0.5699     | 0.5709     |
+| cosine_map@500       | 0.5292     | 0.4052     | 0.3095     | 0.5023     | 0.6245     | 0.5702     | 0.5711     |
+
+<!--
+## 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 Dataset
+
+#### Unnamed Dataset
+
+* Size: 86,648 training samples
+* Columns: <code>sentence</code> and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence                                                                         | label                                |
+  |:--------|:---------------------------------------------------------------------------------|:-------------------------------------|
+  | type    | string                                                                           | list                                 |
+  | details | <ul><li>min: 2 tokens</li><li>mean: 8.25 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>size: 768 elements</li></ul> |
+* Samples:
+  | sentence                                 | label                                                                                                                            |
+  |:-----------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------|
+  | <code></code>                            | <code>[-0.07171934843063354, 0.03595816716551781, -0.029780959710478783, 0.006593302357941866, 0.040611181408166885, ...]</code> |
+  | <code>airport environment officer</code> | <code>[-0.022075481712818146, 0.02999737113714218, -0.02189866080880165, 0.016531817615032196, 0.012234307825565338, ...]</code> |
+  | <code>Flake操作员</code>                    | <code>[-0.04815564677119255, 0.023524893447756767, -0.01583661139011383, 0.042527906596660614, 0.03815540298819542, ...]</code>  |
+* Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 128
+- `per_device_eval_batch_size`: 128
+- `gradient_accumulation_steps`: 2
+- `learning_rate`: 0.0001
+- `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`: 0.0001
+- `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   | -             | 0.5348                  | 0.4311                  | 0.3678                  | 0.5333                  | 0.2580                 | 0.1924                 | 0.2871                 |
+| 0.0030 | 1    | 0.0017        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 0.2959 | 100  | 0.001         | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 0.5917 | 200  | 0.0005        | 0.6702                  | 0.5287                  | 0.4566                  | 0.6809                  | 0.5864                 | 0.5302                 | 0.4739                 |
+| 0.8876 | 300  | 0.0004        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 1.1834 | 400  | 0.0004        | 0.7057                  | 0.5643                  | 0.4790                  | 0.7033                  | 0.6604                 | 0.6055                 | 0.6003                 |
+| 1.4793 | 500  | 0.0004        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 1.7751 | 600  | 0.0003        | 0.7184                  | 0.5783                  | 0.4910                  | 0.7127                  | 0.6927                 | 0.6416                 | 0.6485                 |
+| 2.0710 | 700  | 0.0003        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 2.3669 | 800  | 0.0003        | 0.7307                  | 0.5938                  | 0.5023                  | 0.7233                  | 0.7125                 | 0.6639                 | 0.6847                 |
+| 2.6627 | 900  | 0.0003        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 2.9586 | 1000 | 0.0003        | 0.7371                  | 0.6002                  | 0.5085                  | 0.7228                  | 0.7222                 | 0.6761                 | 0.6998                 |
+| 3.2544 | 1100 | 0.0003        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 3.5503 | 1200 | 0.0003        | 0.7402                  | 0.6059                  | 0.5109                  | 0.7279                  | 0.7285                 | 0.6841                 | 0.7120                 |
+| 3.8462 | 1300 | 0.0003        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 4.1420 | 1400 | 0.0003        | 0.7449                  | 0.6083                  | 0.5154                  | 0.7294                  | 0.7333                 | 0.6894                 | 0.7176                 |
+
+
+### 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",
+}
+```
+
+#### MSELoss
+```bibtex
+@inproceedings{reimers-2020-multilingual-sentence-bert,
+    title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2020",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/2004.09813",
+}
+```
+
+<!--
+## 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-1400/config.json

@@ -0,0 +1,49 @@
+{
+  "architectures": [
+    "NewModel"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "auto_map": {
+    "AutoConfig": "configuration.NewConfig",
+    "AutoModel": "Alibaba-NLP/new-impl--modeling.NewModel",
+    "AutoModelForMaskedLM": "Alibaba-NLP/new-impl--modeling.NewForMaskedLM",
+    "AutoModelForMultipleChoice": "Alibaba-NLP/new-impl--modeling.NewForMultipleChoice",
+    "AutoModelForQuestionAnswering": "Alibaba-NLP/new-impl--modeling.NewForQuestionAnswering",
+    "AutoModelForSequenceClassification": "Alibaba-NLP/new-impl--modeling.NewForSequenceClassification",
+    "AutoModelForTokenClassification": "Alibaba-NLP/new-impl--modeling.NewForTokenClassification"
+  },
+  "classifier_dropout": 0.0,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "layer_norm_type": "layer_norm",
+  "logn_attention_clip1": false,
+  "logn_attention_scale": false,
+  "max_position_embeddings": 8192,
+  "model_type": "new",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 3,
+  "pack_qkv": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "rope",
+  "rope_scaling": {
+    "factor": 8.0,
+    "type": "ntk"
+  },
+  "rope_theta": 20000,
+  "torch_dtype": "float32",
+  "transformers_version": "4.51.3",
+  "type_vocab_size": 1,
+  "unpad_inputs": false,
+  "use_memory_efficient_attention": false,
+  "vocab_size": 250048
+}

checkpoint-1400/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-1400/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-1400/rng_state.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:07f3badfb567c130add803d0d1e1dbe024ed6c96aa1c23295972de4b116581e0
+size 15894

checkpoint-1400/scaler.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:baba31a5e5063037a5c811de9cb04bc62c6c5f0f5fe6720b7d681afe6500d4c1
+size 988

checkpoint-1400/scheduler.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:77e90f03152a309c16a91dfe51c70cf7581a3391085d45b702ce07af5a49d6cf
+size 1064

checkpoint-1400/sentence_bert_config.json

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

checkpoint-1400/special_tokens_map.json

@@ -0,0 +1,51 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

checkpoint-1400/tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:883b037111086fd4dfebbbc9b7cee11e1517b5e0c0514879478661440f137085
+size 17082987

checkpoint-1400/tokenizer_config.json

@@ -0,0 +1,62 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "250001": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "extra_special_tokens": {},
+  "mask_token": "<mask>",
+  "max_length": 512,
+  "model_max_length": 512,
+  "pad_to_multiple_of": null,
+  "pad_token": "<pad>",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "</s>",
+  "stride": 0,
+  "tokenizer_class": "XLMRobertaTokenizerFast",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "<unk>"
+}

checkpoint-1400/training_args.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cc51e1de286671ae3aedb23a247ac2f4c1af94ae0cf98c4e47d46fef0beeda98
+size 5624

checkpoint-1600/1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

checkpoint-1600/config.json

@@ -0,0 +1,49 @@
+{
+  "architectures": [
+    "NewModel"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "auto_map": {
+    "AutoConfig": "configuration.NewConfig",
+    "AutoModel": "Alibaba-NLP/new-impl--modeling.NewModel",
+    "AutoModelForMaskedLM": "Alibaba-NLP/new-impl--modeling.NewForMaskedLM",
+    "AutoModelForMultipleChoice": "Alibaba-NLP/new-impl--modeling.NewForMultipleChoice",
+    "AutoModelForQuestionAnswering": "Alibaba-NLP/new-impl--modeling.NewForQuestionAnswering",
+    "AutoModelForSequenceClassification": "Alibaba-NLP/new-impl--modeling.NewForSequenceClassification",
+    "AutoModelForTokenClassification": "Alibaba-NLP/new-impl--modeling.NewForTokenClassification"
+  },
+  "classifier_dropout": 0.0,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "layer_norm_type": "layer_norm",
+  "logn_attention_clip1": false,
+  "logn_attention_scale": false,
+  "max_position_embeddings": 8192,
+  "model_type": "new",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 3,
+  "pack_qkv": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "rope",
+  "rope_scaling": {
+    "factor": 8.0,
+    "type": "ntk"
+  },
+  "rope_theta": 20000,
+  "torch_dtype": "float32",
+  "transformers_version": "4.51.3",
+  "type_vocab_size": 1,
+  "unpad_inputs": false,
+  "use_memory_efficient_attention": false,
+  "vocab_size": 250048
+}

checkpoint-1600/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-1600/special_tokens_map.json

@@ -0,0 +1,51 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

checkpoint-1600/tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:883b037111086fd4dfebbbc9b7cee11e1517b5e0c0514879478661440f137085
+size 17082987

checkpoint-1690/1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

checkpoint-1690/README.md

@@ -0,0 +1,1290 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:86648
+- loss:MSELoss
+widget:
+- source_sentence: Familienberaterin
+  sentences:
+  - electric power station operator
+  - venue booker & promoter
+  - betrieblicher Aus- und Weiterbildner/betriebliche Aus- und Weiterbildnerin
+- source_sentence: high school RS teacher
+  sentences:
+  - infantryman
+  - Schnellbedienungsrestaurantteamleiter
+  - drill setup operator
+- source_sentence: lighting designer
+  sentences:
+  - software support manager
+  - 直升机维护协调员
+  - bus maintenance supervisor
+- source_sentence: 机场消防员
+  sentences:
+  - Flake操作员
+  - técnico en gestión de residuos peligrosos/técnica en gestión de residuos peligrosos
+  - 专门学校老师
+- source_sentence: Entwicklerin für mobile Anwendungen
+  sentences:
+  - fashion design expert
+  - Mergers-and-Acquisitions-Analyst/Mergers-and-Acquisitions-Analystin
+  - commercial bid manager
+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
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: full en
+      type: full_en
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.6476190476190476
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@20
+      value: 0.9714285714285714
+      name: Cosine Accuracy@20
+    - type: cosine_accuracy@50
+      value: 0.9904761904761905
+      name: Cosine Accuracy@50
+    - type: cosine_accuracy@100
+      value: 0.9904761904761905
+      name: Cosine Accuracy@100
+    - type: cosine_accuracy@150
+      value: 0.9904761904761905
+      name: Cosine Accuracy@150
+    - type: cosine_accuracy@200
+      value: 0.9904761904761905
+      name: Cosine Accuracy@200
+    - type: cosine_precision@1
+      value: 0.6476190476190476
+      name: Cosine Precision@1
+    - type: cosine_precision@20
+      value: 0.47952380952380946
+      name: Cosine Precision@20
+    - type: cosine_precision@50
+      value: 0.28838095238095235
+      name: Cosine Precision@50
+    - type: cosine_precision@100
+      value: 0.17304761904761906
+      name: Cosine Precision@100
+    - type: cosine_precision@150
+      value: 0.12444444444444444
+      name: Cosine Precision@150
+    - type: cosine_precision@200
+      value: 0.09857142857142859
+      name: Cosine Precision@200
+    - type: cosine_recall@1
+      value: 0.06609801577496094
+      name: Cosine Recall@1
+    - type: cosine_recall@20
+      value: 0.5122224752770898
+      name: Cosine Recall@20
+    - type: cosine_recall@50
+      value: 0.6835205863376973
+      name: Cosine Recall@50
+    - type: cosine_recall@100
+      value: 0.7899550177449521
+      name: Cosine Recall@100
+    - type: cosine_recall@150
+      value: 0.8399901051245952
+      name: Cosine Recall@150
+    - type: cosine_recall@200
+      value: 0.875868212220809
+      name: Cosine Recall@200
+    - type: cosine_ndcg@1
+      value: 0.6476190476190476
+      name: Cosine Ndcg@1
+    - type: cosine_ndcg@20
+      value: 0.6467537144833913
+      name: Cosine Ndcg@20
+    - type: cosine_ndcg@50
+      value: 0.6579566361404572
+      name: Cosine Ndcg@50
+    - type: cosine_ndcg@100
+      value: 0.7095129047395976
+      name: Cosine Ndcg@100
+    - type: cosine_ndcg@150
+      value: 0.7310060454392588
+      name: Cosine Ndcg@150
+    - type: cosine_ndcg@200
+      value: 0.746053293561821
+      name: Cosine Ndcg@200
+    - type: cosine_mrr@1
+      value: 0.6476190476190476
+      name: Cosine Mrr@1
+    - type: cosine_mrr@20
+      value: 0.7901817137111254
+      name: Cosine Mrr@20
+    - type: cosine_mrr@50
+      value: 0.7909547501984476
+      name: Cosine Mrr@50
+    - type: cosine_mrr@100
+      value: 0.7909547501984476
+      name: Cosine Mrr@100
+    - type: cosine_mrr@150
+      value: 0.7909547501984476
+      name: Cosine Mrr@150
+    - type: cosine_mrr@200
+      value: 0.7909547501984476
+      name: Cosine Mrr@200
+    - type: cosine_map@1
+      value: 0.6476190476190476
+      name: Cosine Map@1
+    - type: cosine_map@20
+      value: 0.5025649155749793
+      name: Cosine Map@20
+    - type: cosine_map@50
+      value: 0.48398477448194993
+      name: Cosine Map@50
+    - type: cosine_map@100
+      value: 0.5117703759309522
+      name: Cosine Map@100
+    - type: cosine_map@150
+      value: 0.520199435224254
+      name: Cosine Map@150
+    - type: cosine_map@200
+      value: 0.5249113393002316
+      name: Cosine Map@200
+    - type: cosine_map@500
+      value: 0.5304170344184883
+      name: Cosine Map@500
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: full es
+      type: full_es
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.11891891891891893
+      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: 0.11891891891891893
+      name: Cosine Precision@1
+    - type: cosine_precision@20
+      value: 0.5267567567567567
+      name: Cosine Precision@20
+    - type: cosine_precision@50
+      value: 0.3437837837837838
+      name: Cosine Precision@50
+    - type: cosine_precision@100
+      value: 0.21897297297297297
+      name: Cosine Precision@100
+    - type: cosine_precision@150
+      value: 0.1658018018018018
+      name: Cosine Precision@150
+    - type: cosine_precision@200
+      value: 0.1332972972972973
+      name: Cosine Precision@200
+    - type: cosine_recall@1
+      value: 0.0035840147528632613
+      name: Cosine Recall@1
+    - type: cosine_recall@20
+      value: 0.35407760203362965
+      name: Cosine Recall@20
+    - type: cosine_recall@50
+      value: 0.5097999383006715
+      name: Cosine Recall@50
+    - type: cosine_recall@100
+      value: 0.6076073817878247
+      name: Cosine Recall@100
+    - type: cosine_recall@150
+      value: 0.6705429838138021
+      name: Cosine Recall@150
+    - type: cosine_recall@200
+      value: 0.7125464731776301
+      name: Cosine Recall@200
+    - type: cosine_ndcg@1
+      value: 0.11891891891891893
+      name: Cosine Ndcg@1
+    - type: cosine_ndcg@20
+      value: 0.5708144272431339
+      name: Cosine Ndcg@20
+    - type: cosine_ndcg@50
+      value: 0.535516963498245
+      name: Cosine Ndcg@50
+    - type: cosine_ndcg@100
+      value: 0.558980163264909
+      name: Cosine Ndcg@100
+    - type: cosine_ndcg@150
+      value: 0.5900024611410689
+      name: Cosine Ndcg@150
+    - type: cosine_ndcg@200
+      value: 0.609478782549869
+      name: Cosine Ndcg@200
+    - type: cosine_mrr@1
+      value: 0.11891891891891893
+      name: Cosine Mrr@1
+    - type: cosine_mrr@20
+      value: 0.5531531531531532
+      name: Cosine Mrr@20
+    - type: cosine_mrr@50
+      value: 0.5531531531531532
+      name: Cosine Mrr@50
+    - type: cosine_mrr@100
+      value: 0.5531531531531532
+      name: Cosine Mrr@100
+    - type: cosine_mrr@150
+      value: 0.5531531531531532
+      name: Cosine Mrr@150
+    - type: cosine_mrr@200
+      value: 0.5531531531531532
+      name: Cosine Mrr@200
+    - type: cosine_map@1
+      value: 0.11891891891891893
+      name: Cosine Map@1
+    - type: cosine_map@20
+      value: 0.4379349002801489
+      name: Cosine Map@20
+    - type: cosine_map@50
+      value: 0.3739269627118989
+      name: Cosine Map@50
+    - type: cosine_map@100
+      value: 0.37629843599877466
+      name: Cosine Map@100
+    - type: cosine_map@150
+      value: 0.3891828650842837
+      name: Cosine Map@150
+    - type: cosine_map@200
+      value: 0.39584338663408436
+      name: Cosine Map@200
+    - type: cosine_map@500
+      value: 0.4062909401616274
+      name: Cosine Map@500
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: full de
+      type: full_de
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.2955665024630542
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@20
+      value: 0.9704433497536946
+      name: Cosine Accuracy@20
+    - type: cosine_accuracy@50
+      value: 0.9753694581280788
+      name: Cosine Accuracy@50
+    - type: cosine_accuracy@100
+      value: 0.9901477832512315
+      name: Cosine Accuracy@100
+    - type: cosine_accuracy@150
+      value: 0.9901477832512315
+      name: Cosine Accuracy@150
+    - type: cosine_accuracy@200
+      value: 0.9901477832512315
+      name: Cosine Accuracy@200
+    - type: cosine_precision@1
+      value: 0.2955665024630542
+      name: Cosine Precision@1
+    - type: cosine_precision@20
+      value: 0.42906403940886706
+      name: Cosine Precision@20
+    - type: cosine_precision@50
+      value: 0.29802955665024633
+      name: Cosine Precision@50
+    - type: cosine_precision@100
+      value: 0.19433497536945815
+      name: Cosine Precision@100
+    - type: cosine_precision@150
+      value: 0.14824302134646963
+      name: Cosine Precision@150
+    - type: cosine_precision@200
+      value: 0.1197783251231527
+      name: Cosine Precision@200
+    - type: cosine_recall@1
+      value: 0.01108543831680986
+      name: Cosine Recall@1
+    - type: cosine_recall@20
+      value: 0.26675038089672504
+      name: Cosine Recall@20
+    - type: cosine_recall@50
+      value: 0.40921566733257536
+      name: Cosine Recall@50
+    - type: cosine_recall@100
+      value: 0.5097664540706716
+      name: Cosine Recall@100
+    - type: cosine_recall@150
+      value: 0.5728593162394238
+      name: Cosine Recall@150
+    - type: cosine_recall@200
+      value: 0.6120176690658915
+      name: Cosine Recall@200
+    - type: cosine_ndcg@1
+      value: 0.2955665024630542
+      name: Cosine Ndcg@1
+    - type: cosine_ndcg@20
+      value: 0.46962753993631184
+      name: Cosine Ndcg@20
+    - type: cosine_ndcg@50
+      value: 0.444898497416845
+      name: Cosine Ndcg@50
+    - type: cosine_ndcg@100
+      value: 0.466960324034805
+      name: Cosine Ndcg@100
+    - type: cosine_ndcg@150
+      value: 0.49816218513136795
+      name: Cosine Ndcg@150
+    - type: cosine_ndcg@200
+      value: 0.5165485300965951
+      name: Cosine Ndcg@200
+    - type: cosine_mrr@1
+      value: 0.2955665024630542
+      name: Cosine Mrr@1
+    - type: cosine_mrr@20
+      value: 0.5046767633988724
+      name: Cosine Mrr@20
+    - type: cosine_mrr@50
+      value: 0.50477528556636
+      name: Cosine Mrr@50
+    - type: cosine_mrr@100
+      value: 0.5049589761635289
+      name: Cosine Mrr@100
+    - type: cosine_mrr@150
+      value: 0.5049589761635289
+      name: Cosine Mrr@150
+    - type: cosine_mrr@200
+      value: 0.5049589761635289
+      name: Cosine Mrr@200
+    - type: cosine_map@1
+      value: 0.2955665024630542
+      name: Cosine Map@1
+    - type: cosine_map@20
+      value: 0.33658821160388247
+      name: Cosine Map@20
+    - type: cosine_map@50
+      value: 0.2853400586620685
+      name: Cosine Map@50
+    - type: cosine_map@100
+      value: 0.2817732307206079
+      name: Cosine Map@100
+    - type: cosine_map@150
+      value: 0.2931317333364438
+      name: Cosine Map@150
+    - type: cosine_map@200
+      value: 0.2988160532231927
+      name: Cosine Map@200
+    - type: cosine_map@500
+      value: 0.31093362375086947
+      name: Cosine Map@500
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: full zh
+      type: full_zh
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.6601941747572816
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@20
+      value: 0.970873786407767
+      name: Cosine Accuracy@20
+    - type: cosine_accuracy@50
+      value: 0.9902912621359223
+      name: Cosine Accuracy@50
+    - type: cosine_accuracy@100
+      value: 0.9902912621359223
+      name: Cosine Accuracy@100
+    - type: cosine_accuracy@150
+      value: 0.9902912621359223
+      name: Cosine Accuracy@150
+    - type: cosine_accuracy@200
+      value: 0.9902912621359223
+      name: Cosine Accuracy@200
+    - type: cosine_precision@1
+      value: 0.6601941747572816
+      name: Cosine Precision@1
+    - type: cosine_precision@20
+      value: 0.44805825242718444
+      name: Cosine Precision@20
+    - type: cosine_precision@50
+      value: 0.27126213592233006
+      name: Cosine Precision@50
+    - type: cosine_precision@100
+      value: 0.16650485436893206
+      name: Cosine Precision@100
+    - type: cosine_precision@150
+      value: 0.1211003236245955
+      name: Cosine Precision@150
+    - type: cosine_precision@200
+      value: 0.09529126213592234
+      name: Cosine Precision@200
+    - type: cosine_recall@1
+      value: 0.06611246215014785
+      name: Cosine Recall@1
+    - type: cosine_recall@20
+      value: 0.48409390608352504
+      name: Cosine Recall@20
+    - type: cosine_recall@50
+      value: 0.6568473638827299
+      name: Cosine Recall@50
+    - type: cosine_recall@100
+      value: 0.7685416895166794
+      name: Cosine Recall@100
+    - type: cosine_recall@150
+      value: 0.8277686060133904
+      name: Cosine Recall@150
+    - type: cosine_recall@200
+      value: 0.8616979590623105
+      name: Cosine Recall@200
+    - type: cosine_ndcg@1
+      value: 0.6601941747572816
+      name: Cosine Ndcg@1
+    - type: cosine_ndcg@20
+      value: 0.6231250904534316
+      name: Cosine Ndcg@20
+    - type: cosine_ndcg@50
+      value: 0.6383496204608501
+      name: Cosine Ndcg@50
+    - type: cosine_ndcg@100
+      value: 0.6917257705456975
+      name: Cosine Ndcg@100
+    - type: cosine_ndcg@150
+      value: 0.7167434657424917
+      name: Cosine Ndcg@150
+    - type: cosine_ndcg@200
+      value: 0.7303448958665071
+      name: Cosine Ndcg@200
+    - type: cosine_mrr@1
+      value: 0.6601941747572816
+      name: Cosine Mrr@1
+    - type: cosine_mrr@20
+      value: 0.8015776699029126
+      name: Cosine Mrr@20
+    - type: cosine_mrr@50
+      value: 0.8020876238109248
+      name: Cosine Mrr@50
+    - type: cosine_mrr@100
+      value: 0.8020876238109248
+      name: Cosine Mrr@100
+    - type: cosine_mrr@150
+      value: 0.8020876238109248
+      name: Cosine Mrr@150
+    - type: cosine_mrr@200
+      value: 0.8020876238109248
+      name: Cosine Mrr@200
+    - type: cosine_map@1
+      value: 0.6601941747572816
+      name: Cosine Map@1
+    - type: cosine_map@20
+      value: 0.4750205237443607
+      name: Cosine Map@20
+    - type: cosine_map@50
+      value: 0.45785161483741715
+      name: Cosine Map@50
+    - type: cosine_map@100
+      value: 0.4848085275553208
+      name: Cosine Map@100
+    - type: cosine_map@150
+      value: 0.4937216396074153
+      name: Cosine Map@150
+    - type: cosine_map@200
+      value: 0.49777622471594557
+      name: Cosine Map@200
+    - type: cosine_map@500
+      value: 0.5039795405740248
+      name: Cosine Map@500
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: mix es
+      type: mix_es
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.6297451898075923
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@20
+      value: 0.9105564222568903
+      name: Cosine Accuracy@20
+    - type: cosine_accuracy@50
+      value: 0.9495579823192928
+      name: Cosine Accuracy@50
+    - type: cosine_accuracy@100
+      value: 0.9729589183567343
+      name: Cosine Accuracy@100
+    - type: cosine_accuracy@150
+      value: 0.983359334373375
+      name: Cosine Accuracy@150
+    - type: cosine_accuracy@200
+      value: 0.9901196047841914
+      name: Cosine Accuracy@200
+    - type: cosine_precision@1
+      value: 0.6297451898075923
+      name: Cosine Precision@1
+    - type: cosine_precision@20
+      value: 0.11167446697867915
+      name: Cosine Precision@20
+    - type: cosine_precision@50
+      value: 0.04850754030161208
+      name: Cosine Precision@50
+    - type: cosine_precision@100
+      value: 0.02535101404056163
+      name: Cosine Precision@100
+    - type: cosine_precision@150
+      value: 0.0172300225342347
+      name: Cosine Precision@150
+    - type: cosine_precision@200
+      value: 0.0130811232449298
+      name: Cosine Precision@200
+    - type: cosine_recall@1
+      value: 0.24340068840848872
+      name: Cosine Recall@1
+    - type: cosine_recall@20
+      value: 0.8288215338137336
+      name: Cosine Recall@20
+    - type: cosine_recall@50
+      value: 0.8986566129311838
+      name: Cosine Recall@50
+    - type: cosine_recall@100
+      value: 0.9398509273704282
+      name: Cosine Recall@100
+    - type: cosine_recall@150
+      value: 0.9576876408389668
+      name: Cosine Recall@150
+    - type: cosine_recall@200
+      value: 0.9695267810712429
+      name: Cosine Recall@200
+    - type: cosine_ndcg@1
+      value: 0.6297451898075923
+      name: Cosine Ndcg@1
+    - type: cosine_ndcg@20
+      value: 0.7010427232190379
+      name: Cosine Ndcg@20
+    - type: cosine_ndcg@50
+      value: 0.7200844211181043
+      name: Cosine Ndcg@50
+    - type: cosine_ndcg@100
+      value: 0.7290848607488584
+      name: Cosine Ndcg@100
+    - type: cosine_ndcg@150
+      value: 0.7325985285606116
+      name: Cosine Ndcg@150
+    - type: cosine_ndcg@200
+      value: 0.7347463892077523
+      name: Cosine Ndcg@200
+    - type: cosine_mrr@1
+      value: 0.6297451898075923
+      name: Cosine Mrr@1
+    - type: cosine_mrr@20
+      value: 0.7036709577939534
+      name: Cosine Mrr@20
+    - type: cosine_mrr@50
+      value: 0.7049808414398148
+      name: Cosine Mrr@50
+    - type: cosine_mrr@100
+      value: 0.7053260954286938
+      name: Cosine Mrr@100
+    - type: cosine_mrr@150
+      value: 0.7054145837924506
+      name: Cosine Mrr@150
+    - type: cosine_mrr@200
+      value: 0.7054541569954363
+      name: Cosine Mrr@200
+    - type: cosine_map@1
+      value: 0.6297451898075923
+      name: Cosine Map@1
+    - type: cosine_map@20
+      value: 0.6194189058349782
+      name: Cosine Map@20
+    - type: cosine_map@50
+      value: 0.6244340507841626
+      name: Cosine Map@50
+    - type: cosine_map@100
+      value: 0.6256943736433496
+      name: Cosine Map@100
+    - type: cosine_map@150
+      value: 0.6260195205413376
+      name: Cosine Map@150
+    - type: cosine_map@200
+      value: 0.6261650797332174
+      name: Cosine Map@200
+    - type: cosine_map@500
+      value: 0.6263452093477304
+      name: Cosine Map@500
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: mix de
+      type: mix_de
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.5564222568902756
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@20
+      value: 0.8866354654186167
+      name: Cosine Accuracy@20
+    - type: cosine_accuracy@50
+      value: 0.9381175247009881
+      name: Cosine Accuracy@50
+    - type: cosine_accuracy@100
+      value: 0.9594383775351014
+      name: Cosine Accuracy@100
+    - type: cosine_accuracy@150
+      value: 0.9708788351534061
+      name: Cosine Accuracy@150
+    - type: cosine_accuracy@200
+      value: 0.9776391055642226
+      name: Cosine Accuracy@200
+    - type: cosine_precision@1
+      value: 0.5564222568902756
+      name: Cosine Precision@1
+    - type: cosine_precision@20
+      value: 0.109464378575143
+      name: Cosine Precision@20
+    - type: cosine_precision@50
+      value: 0.048060322412896525
+      name: Cosine Precision@50
+    - type: cosine_precision@100
+      value: 0.025273010920436823
+      name: Cosine Precision@100
+    - type: cosine_precision@150
+      value: 0.017313225862367825
+      name: Cosine Precision@150
+    - type: cosine_precision@200
+      value: 0.013143525741029644
+      name: Cosine Precision@200
+    - type: cosine_recall@1
+      value: 0.20931703934824059
+      name: Cosine Recall@1
+    - type: cosine_recall@20
+      value: 0.7988992893049055
+      name: Cosine Recall@20
+    - type: cosine_recall@50
+      value: 0.8741029641185647
+      name: Cosine Recall@50
+    - type: cosine_recall@100
+      value: 0.9173426937077482
+      name: Cosine Recall@100
+    - type: cosine_recall@150
+      value: 0.9424076963078523
+      name: Cosine Recall@150
+    - type: cosine_recall@200
+      value: 0.953631478592477
+      name: Cosine Recall@200
+    - type: cosine_ndcg@1
+      value: 0.5564222568902756
+      name: Cosine Ndcg@1
+    - type: cosine_ndcg@20
+      value: 0.6541310877479573
+      name: Cosine Ndcg@20
+    - type: cosine_ndcg@50
+      value: 0.674790854916742
+      name: Cosine Ndcg@50
+    - type: cosine_ndcg@100
+      value: 0.6844997445798996
+      name: Cosine Ndcg@100
+    - type: cosine_ndcg@150
+      value: 0.6894214573457343
+      name: Cosine Ndcg@150
+    - type: cosine_ndcg@200
+      value: 0.6914881284159038
+      name: Cosine Ndcg@200
+    - type: cosine_mrr@1
+      value: 0.5564222568902756
+      name: Cosine Mrr@1
+    - type: cosine_mrr@20
+      value: 0.6476945170199107
+      name: Cosine Mrr@20
+    - type: cosine_mrr@50
+      value: 0.6493649946597936
+      name: Cosine Mrr@50
+    - type: cosine_mrr@100
+      value: 0.6496801333421218
+      name: Cosine Mrr@100
+    - type: cosine_mrr@150
+      value: 0.6497778366579644
+      name: Cosine Mrr@150
+    - type: cosine_mrr@200
+      value: 0.6498156890114056
+      name: Cosine Mrr@200
+    - type: cosine_map@1
+      value: 0.5564222568902756
+      name: Cosine Map@1
+    - type: cosine_map@20
+      value: 0.5648326970643027
+      name: Cosine Map@20
+    - type: cosine_map@50
+      value: 0.57003456255067
+      name: Cosine Map@50
+    - type: cosine_map@100
+      value: 0.5714370828517599
+      name: Cosine Map@100
+    - type: cosine_map@150
+      value: 0.5719002990233493
+      name: Cosine Map@150
+    - type: cosine_map@200
+      value: 0.5720497397197026
+      name: Cosine Map@200
+    - type: cosine_map@500
+      value: 0.5723109788233504
+      name: Cosine Map@500
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: mix zh
+      type: mix_zh
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.6085594989561587
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@20
+      value: 0.9592901878914405
+      name: Cosine Accuracy@20
+    - type: cosine_accuracy@50
+      value: 0.9791231732776617
+      name: Cosine Accuracy@50
+    - type: cosine_accuracy@100
+      value: 0.9874739039665971
+      name: Cosine Accuracy@100
+    - type: cosine_accuracy@150
+      value: 0.9911273486430062
+      name: Cosine Accuracy@150
+    - type: cosine_accuracy@200
+      value: 0.9937369519832986
+      name: Cosine Accuracy@200
+    - type: cosine_precision@1
+      value: 0.6085594989561587
+      name: Cosine Precision@1
+    - type: cosine_precision@20
+      value: 0.12656576200417535
+      name: Cosine Precision@20
+    - type: cosine_precision@50
+      value: 0.05518789144050106
+      name: Cosine Precision@50
+    - type: cosine_precision@100
+      value: 0.028747390396659713
+      name: Cosine Precision@100
+    - type: cosine_precision@150
+      value: 0.019425887265135697
+      name: Cosine Precision@150
+    - type: cosine_precision@200
+      value: 0.014705114822546978
+      name: Cosine Precision@200
+    - type: cosine_recall@1
+      value: 0.2043804056069192
+      name: Cosine Recall@1
+    - type: cosine_recall@20
+      value: 0.8346468336812805
+      name: Cosine Recall@20
+    - type: cosine_recall@50
+      value: 0.9095772442588727
+      name: Cosine Recall@50
+    - type: cosine_recall@100
+      value: 0.9475643702157271
+      name: Cosine Recall@100
+    - type: cosine_recall@150
+      value: 0.9609168406402228
+      name: Cosine Recall@150
+    - type: cosine_recall@200
+      value: 0.9697807933194154
+      name: Cosine Recall@200
+    - type: cosine_ndcg@1
+      value: 0.6085594989561587
+      name: Cosine Ndcg@1
+    - type: cosine_ndcg@20
+      value: 0.6853247290079303
+      name: Cosine Ndcg@20
+    - type: cosine_ndcg@50
+      value: 0.7066940880968873
+      name: Cosine Ndcg@50
+    - type: cosine_ndcg@100
+      value: 0.715400790265437
+      name: Cosine Ndcg@100
+    - type: cosine_ndcg@150
+      value: 0.7180808450243259
+      name: Cosine Ndcg@150
+    - type: cosine_ndcg@200
+      value: 0.7197629642909036
+      name: Cosine Ndcg@200
+    - type: cosine_mrr@1
+      value: 0.6085594989561587
+      name: Cosine Mrr@1
+    - type: cosine_mrr@20
+      value: 0.7236528792595264
+      name: Cosine Mrr@20
+    - type: cosine_mrr@50
+      value: 0.7243308740364213
+      name: Cosine Mrr@50
+    - type: cosine_mrr@100
+      value: 0.7244524590415827
+      name: Cosine Mrr@100
+    - type: cosine_mrr@150
+      value: 0.7244814620971008
+      name: Cosine Mrr@150
+    - type: cosine_mrr@200
+      value: 0.7244960285685315
+      name: Cosine Mrr@200
+    - type: cosine_map@1
+      value: 0.6085594989561587
+      name: Cosine Map@1
+    - type: cosine_map@20
+      value: 0.5652211952239553
+      name: Cosine Map@20
+    - type: cosine_map@50
+      value: 0.5716374350069462
+      name: Cosine Map@50
+    - type: cosine_map@100
+      value: 0.5730756815932735
+      name: Cosine Map@100
+    - type: cosine_map@150
+      value: 0.5733543252173214
+      name: Cosine Map@150
+    - type: cosine_map@200
+      value: 0.5734860037813889
+      name: Cosine Map@200
+    - type: cosine_map@500
+      value: 0.5736416699680624
+      name: Cosine Map@500
+---
+
+# Job - Job matching Alibaba-NLP/gte-multilingual-base pruned
+
+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:** [Unknown](https://huggingface.co/unknown) -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (2): Normalize()
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("pj-mathematician/JobGTE-multilingual-base-pruned")
+# Run inference
+sentences = [
+    'Entwicklerin für mobile Anwendungen',
+    'Mergers-and-Acquisitions-Analyst/Mergers-and-Acquisitions-Analystin',
+    'fashion design expert',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Information Retrieval
+
+* Datasets: `full_en`, `full_es`, `full_de`, `full_zh`, `mix_es`, `mix_de` and `mix_zh`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric               | full_en    | full_es    | full_de    | full_zh    | mix_es     | mix_de     | mix_zh     |
+|:---------------------|:-----------|:-----------|:-----------|:-----------|:-----------|:-----------|:-----------|
+| cosine_accuracy@1    | 0.6476     | 0.1189     | 0.2956     | 0.6602     | 0.6297     | 0.5564     | 0.6086     |
+| cosine_accuracy@20   | 0.9714     | 1.0        | 0.9704     | 0.9709     | 0.9106     | 0.8866     | 0.9593     |
+| cosine_accuracy@50   | 0.9905     | 1.0        | 0.9754     | 0.9903     | 0.9496     | 0.9381     | 0.9791     |
+| cosine_accuracy@100  | 0.9905     | 1.0        | 0.9901     | 0.9903     | 0.973      | 0.9594     | 0.9875     |
+| cosine_accuracy@150  | 0.9905     | 1.0        | 0.9901     | 0.9903     | 0.9834     | 0.9709     | 0.9911     |
+| cosine_accuracy@200  | 0.9905     | 1.0        | 0.9901     | 0.9903     | 0.9901     | 0.9776     | 0.9937     |
+| cosine_precision@1   | 0.6476     | 0.1189     | 0.2956     | 0.6602     | 0.6297     | 0.5564     | 0.6086     |
+| cosine_precision@20  | 0.4795     | 0.5268     | 0.4291     | 0.4481     | 0.1117     | 0.1095     | 0.1266     |
+| cosine_precision@50  | 0.2884     | 0.3438     | 0.298      | 0.2713     | 0.0485     | 0.0481     | 0.0552     |
+| cosine_precision@100 | 0.173      | 0.219      | 0.1943     | 0.1665     | 0.0254     | 0.0253     | 0.0287     |
+| cosine_precision@150 | 0.1244     | 0.1658     | 0.1482     | 0.1211     | 0.0172     | 0.0173     | 0.0194     |
+| cosine_precision@200 | 0.0986     | 0.1333     | 0.1198     | 0.0953     | 0.0131     | 0.0131     | 0.0147     |
+| cosine_recall@1      | 0.0661     | 0.0036     | 0.0111     | 0.0661     | 0.2434     | 0.2093     | 0.2044     |
+| cosine_recall@20     | 0.5122     | 0.3541     | 0.2668     | 0.4841     | 0.8288     | 0.7989     | 0.8346     |
+| cosine_recall@50     | 0.6835     | 0.5098     | 0.4092     | 0.6568     | 0.8987     | 0.8741     | 0.9096     |
+| cosine_recall@100    | 0.79       | 0.6076     | 0.5098     | 0.7685     | 0.9399     | 0.9173     | 0.9476     |
+| cosine_recall@150    | 0.84       | 0.6705     | 0.5729     | 0.8278     | 0.9577     | 0.9424     | 0.9609     |
+| cosine_recall@200    | 0.8759     | 0.7125     | 0.612      | 0.8617     | 0.9695     | 0.9536     | 0.9698     |
+| cosine_ndcg@1        | 0.6476     | 0.1189     | 0.2956     | 0.6602     | 0.6297     | 0.5564     | 0.6086     |
+| cosine_ndcg@20       | 0.6468     | 0.5708     | 0.4696     | 0.6231     | 0.701      | 0.6541     | 0.6853     |
+| cosine_ndcg@50       | 0.658      | 0.5355     | 0.4449     | 0.6383     | 0.7201     | 0.6748     | 0.7067     |
+| cosine_ndcg@100      | 0.7095     | 0.559      | 0.467      | 0.6917     | 0.7291     | 0.6845     | 0.7154     |
+| cosine_ndcg@150      | 0.731      | 0.59       | 0.4982     | 0.7167     | 0.7326     | 0.6894     | 0.7181     |
+| **cosine_ndcg@200**  | **0.7461** | **0.6095** | **0.5165** | **0.7303** | **0.7347** | **0.6915** | **0.7198** |
+| cosine_mrr@1         | 0.6476     | 0.1189     | 0.2956     | 0.6602     | 0.6297     | 0.5564     | 0.6086     |
+| cosine_mrr@20        | 0.7902     | 0.5532     | 0.5047     | 0.8016     | 0.7037     | 0.6477     | 0.7237     |
+| cosine_mrr@50        | 0.791      | 0.5532     | 0.5048     | 0.8021     | 0.705      | 0.6494     | 0.7243     |
+| cosine_mrr@100       | 0.791      | 0.5532     | 0.505      | 0.8021     | 0.7053     | 0.6497     | 0.7245     |
+| cosine_mrr@150       | 0.791      | 0.5532     | 0.505      | 0.8021     | 0.7054     | 0.6498     | 0.7245     |
+| cosine_mrr@200       | 0.791      | 0.5532     | 0.505      | 0.8021     | 0.7055     | 0.6498     | 0.7245     |
+| cosine_map@1         | 0.6476     | 0.1189     | 0.2956     | 0.6602     | 0.6297     | 0.5564     | 0.6086     |
+| cosine_map@20        | 0.5026     | 0.4379     | 0.3366     | 0.475      | 0.6194     | 0.5648     | 0.5652     |
+| cosine_map@50        | 0.484      | 0.3739     | 0.2853     | 0.4579     | 0.6244     | 0.57       | 0.5716     |
+| cosine_map@100       | 0.5118     | 0.3763     | 0.2818     | 0.4848     | 0.6257     | 0.5714     | 0.5731     |
+| cosine_map@150       | 0.5202     | 0.3892     | 0.2931     | 0.4937     | 0.626      | 0.5719     | 0.5734     |
+| cosine_map@200       | 0.5249     | 0.3958     | 0.2988     | 0.4978     | 0.6262     | 0.572      | 0.5735     |
+| cosine_map@500       | 0.5304     | 0.4063     | 0.3109     | 0.504      | 0.6263     | 0.5723     | 0.5736     |
+
+<!--
+## 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 Dataset
+
+#### Unnamed Dataset
+
+* Size: 86,648 training samples
+* Columns: <code>sentence</code> and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence                                                                         | label                                |
+  |:--------|:---------------------------------------------------------------------------------|:-------------------------------------|
+  | type    | string                                                                           | list                                 |
+  | details | <ul><li>min: 2 tokens</li><li>mean: 8.25 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>size: 768 elements</li></ul> |
+* Samples:
+  | sentence                                 | label                                                                                                                            |
+  |:-----------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------|
+  | <code></code>                            | <code>[-0.07171934843063354, 0.03595816716551781, -0.029780959710478783, 0.006593302357941866, 0.040611181408166885, ...]</code> |
+  | <code>airport environment officer</code> | <code>[-0.022075481712818146, 0.02999737113714218, -0.02189866080880165, 0.016531817615032196, 0.012234307825565338, ...]</code> |
+  | <code>Flake操作员</code>                    | <code>[-0.04815564677119255, 0.023524893447756767, -0.01583661139011383, 0.042527906596660614, 0.03815540298819542, ...]</code>  |
+* Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 128
+- `per_device_eval_batch_size`: 128
+- `gradient_accumulation_steps`: 2
+- `learning_rate`: 0.0001
+- `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`: 0.0001
+- `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   | -             | 0.5348                  | 0.4311                  | 0.3678                  | 0.5333                  | 0.2580                 | 0.1924                 | 0.2871                 |
+| 0.0030 | 1    | 0.0017        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 0.2959 | 100  | 0.001         | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 0.5917 | 200  | 0.0005        | 0.6702                  | 0.5287                  | 0.4566                  | 0.6809                  | 0.5864                 | 0.5302                 | 0.4739                 |
+| 0.8876 | 300  | 0.0004        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 1.1834 | 400  | 0.0004        | 0.7057                  | 0.5643                  | 0.4790                  | 0.7033                  | 0.6604                 | 0.6055                 | 0.6003                 |
+| 1.4793 | 500  | 0.0004        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 1.7751 | 600  | 0.0003        | 0.7184                  | 0.5783                  | 0.4910                  | 0.7127                  | 0.6927                 | 0.6416                 | 0.6485                 |
+| 2.0710 | 700  | 0.0003        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 2.3669 | 800  | 0.0003        | 0.7307                  | 0.5938                  | 0.5023                  | 0.7233                  | 0.7125                 | 0.6639                 | 0.6847                 |
+| 2.6627 | 900  | 0.0003        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 2.9586 | 1000 | 0.0003        | 0.7371                  | 0.6002                  | 0.5085                  | 0.7228                  | 0.7222                 | 0.6761                 | 0.6998                 |
+| 3.2544 | 1100 | 0.0003        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 3.5503 | 1200 | 0.0003        | 0.7402                  | 0.6059                  | 0.5109                  | 0.7279                  | 0.7285                 | 0.6841                 | 0.7120                 |
+| 3.8462 | 1300 | 0.0003        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 4.1420 | 1400 | 0.0003        | 0.7449                  | 0.6083                  | 0.5154                  | 0.7294                  | 0.7333                 | 0.6894                 | 0.7176                 |
+| 4.4379 | 1500 | 0.0003        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 4.7337 | 1600 | 0.0003        | 0.7461                  | 0.6095                  | 0.5165                  | 0.7303                  | 0.7347                 | 0.6915                 | 0.7198                 |
+
+
+### 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",
+}
+```
+
+#### MSELoss
+```bibtex
+@inproceedings{reimers-2020-multilingual-sentence-bert,
+    title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2020",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/2004.09813",
+}
+```
+
+<!--
+## 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-1690/config.json

@@ -0,0 +1,49 @@
+{
+  "architectures": [
+    "NewModel"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "auto_map": {
+    "AutoConfig": "configuration.NewConfig",
+    "AutoModel": "Alibaba-NLP/new-impl--modeling.NewModel",
+    "AutoModelForMaskedLM": "Alibaba-NLP/new-impl--modeling.NewForMaskedLM",
+    "AutoModelForMultipleChoice": "Alibaba-NLP/new-impl--modeling.NewForMultipleChoice",
+    "AutoModelForQuestionAnswering": "Alibaba-NLP/new-impl--modeling.NewForQuestionAnswering",
+    "AutoModelForSequenceClassification": "Alibaba-NLP/new-impl--modeling.NewForSequenceClassification",
+    "AutoModelForTokenClassification": "Alibaba-NLP/new-impl--modeling.NewForTokenClassification"
+  },
+  "classifier_dropout": 0.0,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "layer_norm_type": "layer_norm",
+  "logn_attention_clip1": false,
+  "logn_attention_scale": false,
+  "max_position_embeddings": 8192,
+  "model_type": "new",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 3,
+  "pack_qkv": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "rope",
+  "rope_scaling": {
+    "factor": 8.0,
+    "type": "ntk"
+  },
+  "rope_theta": 20000,
+  "torch_dtype": "float32",
+  "transformers_version": "4.51.3",
+  "type_vocab_size": 1,
+  "unpad_inputs": false,
+  "use_memory_efficient_attention": false,
+  "vocab_size": 250048
+}

checkpoint-1690/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-1690/rng_state.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:be5bb4198d009f33dc93fdee19da4370d07a9d35a51ec1fd33f89c44ebac7bc0
+size 15894

checkpoint-1690/scaler.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6ff6cb721caca733f45ccb9a8b0dd8e38f26f84e94309ea829daee5b4d6a586f
+size 988

checkpoint-1690/sentence_bert_config.json

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

checkpoint-1690/special_tokens_map.json

@@ -0,0 +1,51 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

checkpoint-1690/tokenizer_config.json

@@ -0,0 +1,62 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "250001": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "extra_special_tokens": {},
+  "mask_token": "<mask>",
+  "max_length": 512,
+  "model_max_length": 512,
+  "pad_to_multiple_of": null,
+  "pad_token": "<pad>",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "</s>",
+  "stride": 0,
+  "tokenizer_class": "XLMRobertaTokenizerFast",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "<unk>"
+}

checkpoint-1690/training_args.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cc51e1de286671ae3aedb23a247ac2f4c1af94ae0cf98c4e47d46fef0beeda98
+size 5624

eval/Information-Retrieval_evaluation_full_en_results.csv

@@ -0,0 +1,9 @@
+epoch,steps,cosine-Accuracy@1,cosine-Accuracy@20,cosine-Accuracy@50,cosine-Accuracy@100,cosine-Accuracy@150,cosine-Accuracy@200,cosine-Precision@1,cosine-Recall@1,cosine-Precision@20,cosine-Recall@20,cosine-Precision@50,cosine-Recall@50,cosine-Precision@100,cosine-Recall@100,cosine-Precision@150,cosine-Recall@150,cosine-Precision@200,cosine-Recall@200,cosine-MRR@1,cosine-MRR@20,cosine-MRR@50,cosine-MRR@100,cosine-MRR@150,cosine-MRR@200,cosine-NDCG@1,cosine-NDCG@20,cosine-NDCG@50,cosine-NDCG@100,cosine-NDCG@150,cosine-NDCG@200,cosine-MAP@1,cosine-MAP@20,cosine-MAP@50,cosine-MAP@100,cosine-MAP@150,cosine-MAP@200,cosine-MAP@500
+0.591715976331361,200,0.6095238095238096,0.9619047619047619,0.9714285714285714,0.9809523809523809,0.9904761904761905,0.9904761904761905,0.6095238095238096,0.06442722624702855,0.40714285714285714,0.4346349989024896,0.2398095238095238,0.5835601213490562,0.15038095238095237,0.6967385317394644,0.10958730158730158,0.7500891135070625,0.08747619047619049,0.7922657853216979,0.6095238095238096,0.7647869674185462,0.7650443676759465,0.7651908878224667,0.7652709198352718,0.7652709198352718,0.6095238095238096,0.5708801066213397,0.5759834478645489,0.6311923978676361,0.6538048550548475,0.6702447487259725,0.6095238095238096,0.41834185162590387,0.3950487272048726,0.4212871549773475,0.4292041397533778,0.43360983440296147,0.44048129620249793
+1.183431952662722,400,0.638095238095238,0.9619047619047619,0.9809523809523809,0.9809523809523809,0.9904761904761905,0.9904761904761905,0.638095238095238,0.06587125840534644,0.44,0.46630153865605645,0.2638095238095238,0.6382197782219897,0.16285714285714287,0.7504235744205895,0.1175873015873016,0.7988070365630658,0.09252380952380952,0.8309271201681789,0.638095238095238,0.7764695621838479,0.777095588456426,0.777095588456426,0.7771762987066277,0.7771762987066277,0.638095238095238,0.6045447897465399,0.616645155946133,0.6716130331252141,0.6926366359514627,0.7057049689923274,0.638095238095238,0.45544156984456047,0.43603273590332614,0.4641764995443202,0.4720056673047399,0.4759301061204395,0.48186919678908585
+1.7751479289940828,600,0.6285714285714286,0.9714285714285714,0.9904761904761905,0.9904761904761905,0.9904761904761905,0.9904761904761905,0.6285714285714286,0.06527602031010833,0.4561904761904762,0.48745391682105155,0.2714285714285714,0.6514858980133492,0.16647619047619047,0.7653429555146114,0.11974603174603174,0.8123778494980212,0.09438095238095238,0.8453961465111183,0.6285714285714286,0.7716777666777668,0.7724103674103674,0.7724103674103674,0.7724103674103674,0.7724103674103674,0.6285714285714286,0.6209444418709248,0.6294473723576711,0.6843480406110959,0.7049173882762053,0.7183756949142358,0.6285714285714286,0.47343714269469456,0.4522725203086179,0.4808997647658073,0.48880948507389355,0.4927770622539844,0.4988827324510947
+2.366863905325444,800,0.638095238095238,0.9714285714285714,0.9904761904761905,0.9904761904761905,0.9904761904761905,0.9904761904761905,0.638095238095238,0.06587125840534644,0.46523809523809523,0.49716157160369917,0.27809523809523806,0.6633636132155855,0.16866666666666671,0.7728137840947553,0.12184126984126983,0.8252655196277217,0.09585714285714286,0.8555531706571073,0.638095238095238,0.7823199023199023,0.7830671550671551,0.7830671550671551,0.7830671550671551,0.7830671550671551,0.638095238095238,0.6328305552523847,0.6422634260444581,0.6953363920968629,0.7178434762154305,0.7306862991424842,0.638095238095238,0.4869878588929393,0.46662543266107637,0.49496599786546613,0.5034594550180629,0.5072739176309733,0.513497188490262
+2.9585798816568047,1000,0.6285714285714286,0.9714285714285714,0.9904761904761905,0.9904761904761905,0.9904761904761905,0.9904761904761905,0.6285714285714286,0.06568451704213447,0.4723809523809524,0.5041312032991911,0.2838095238095238,0.6762963371727007,0.1706666666666667,0.7798036464336738,0.12285714285714286,0.8311908383371492,0.09700000000000002,0.8655400214018215,0.6285714285714286,0.7790726817042607,0.7797979143260452,0.7797979143260452,0.7797979143260452,0.7797979143260452,0.6285714285714286,0.6385286667884668,0.6505087993598385,0.7009585791000247,0.7228549618650749,0.7370730818153396,0.6285714285714286,0.4949002324392317,0.47542864021103454,0.5027685735699932,0.5108956115342047,0.5152152246235047,0.5211733943510876
+3.5502958579881656,1200,0.6285714285714286,0.9714285714285714,0.9904761904761905,0.9904761904761905,0.9904761904761905,0.9904761904761905,0.6285714285714286,0.06568451704213447,0.47238095238095235,0.5028457675067052,0.28514285714285714,0.676933903111657,0.17142857142857143,0.7837663267176828,0.12361904761904763,0.8369671671626038,0.09742857142857143,0.8683370262861448,0.6285714285714286,0.7822472848788637,0.7830464856780646,0.7830464856780646,0.7830464856780646,0.7830464856780646,0.6285714285714286,0.6393119319266262,0.6526673690626589,0.7043574282251062,0.7269332569198788,0.7401982784576455,0.6285714285714286,0.49537416609793716,0.47813955037924555,0.5059345967521239,0.5144312336524836,0.5185744183980712,0.5244908168836407
+4.1420118343195265,1400,0.638095238095238,0.9619047619047619,0.9904761904761905,0.9904761904761905,0.9904761904761905,0.9904761904761905,0.638095238095238,0.06587125840534644,0.4766666666666666,0.5075382961558268,0.28723809523809524,0.6815180199385792,0.172952380952381,0.7892546849949126,0.12419047619047618,0.837763491705966,0.09828571428571428,0.8747531461107081,0.638095238095238,0.7865079365079365,0.7877959183673469,0.7877959183673469,0.7877959183673469,0.7877959183673469,0.638095238095238,0.6437588496803061,0.6565500770575415,0.7088213416976051,0.7298707409128666,0.7449419847756586,0.638095238095238,0.4998912029710938,0.4824988798112498,0.510770369728262,0.5189335101114453,0.5235615593885471,0.5292082683302094
+4.733727810650888,1600,0.6476190476190476,0.9714285714285714,0.9904761904761905,0.9904761904761905,0.9904761904761905,0.9904761904761905,0.6476190476190476,0.06609801577496094,0.47952380952380946,0.5122224752770898,0.28838095238095235,0.6835205863376973,0.17304761904761906,0.7899550177449521,0.12444444444444444,0.8399901051245952,0.09857142857142859,0.875868212220809,0.6476190476190476,0.7901817137111254,0.7909547501984476,0.7909547501984476,0.7909547501984476,0.7909547501984476,0.6476190476190476,0.6467537144833913,0.6579566361404572,0.7095129047395976,0.7310060454392588,0.746053293561821,0.6476190476190476,0.5025649155749793,0.48398477448194993,0.5117703759309522,0.520199435224254,0.5249113393002316,0.5304170344184883

eval/Information-Retrieval_evaluation_full_zh_results.csv

@@ -0,0 +1,9 @@
+epoch,steps,cosine-Accuracy@1,cosine-Accuracy@20,cosine-Accuracy@50,cosine-Accuracy@100,cosine-Accuracy@150,cosine-Accuracy@200,cosine-Precision@1,cosine-Recall@1,cosine-Precision@20,cosine-Recall@20,cosine-Precision@50,cosine-Recall@50,cosine-Precision@100,cosine-Recall@100,cosine-Precision@150,cosine-Recall@150,cosine-Precision@200,cosine-Recall@200,cosine-MRR@1,cosine-MRR@20,cosine-MRR@50,cosine-MRR@100,cosine-MRR@150,cosine-MRR@200,cosine-NDCG@1,cosine-NDCG@20,cosine-NDCG@50,cosine-NDCG@100,cosine-NDCG@150,cosine-NDCG@200,cosine-MAP@1,cosine-MAP@20,cosine-MAP@50,cosine-MAP@100,cosine-MAP@150,cosine-MAP@200,cosine-MAP@500
+0.591715976331361,200,0.6504854368932039,0.9805825242718447,0.9805825242718447,0.9902912621359223,0.9902912621359223,0.9902912621359223,0.6504854368932039,0.06297195936535313,0.4174757281553398,0.45216321099583096,0.24388349514563107,0.5985947222599055,0.14951456310679612,0.7066962990102679,0.109126213592233,0.7572705548910881,0.08660194174757284,0.7957063099867823,0.6504854368932039,0.7915048543689321,0.7915048543689321,0.7916435506241332,0.7916435506241332,0.7916435506241332,0.6504854368932039,0.5891740583683294,0.5940858700081013,0.6435966486586578,0.6661105297536296,0.6809059849503412,0.6504854368932039,0.4370709010270343,0.4129724014671616,0.4341400410992279,0.4421470493216924,0.446164032411221,0.45382091491269705
+1.183431952662722,400,0.6504854368932039,0.970873786407767,0.9902912621359223,0.9902912621359223,0.9902912621359223,0.9902912621359223,0.6504854368932039,0.060986081165882694,0.4305825242718446,0.4605261938478766,0.25825242718446595,0.6279780571130372,0.15786407766990293,0.738289597577811,0.11411003236245955,0.7878105561736307,0.0913592233009709,0.8324956345368711,0.6504854368932039,0.7957119741100325,0.7962001570321734,0.7962001570321734,0.7962001570321734,0.7962001570321734,0.6504854368932039,0.5999086133003301,0.6129532658410302,0.6638909331450039,0.6853311981200142,0.7033198205460287,0.6504854368932039,0.44892279062424895,0.4305094126014137,0.4540673637374807,0.4618461018630378,0.4667217834039844,0.4739392963854547
+1.7751479289940828,600,0.6504854368932039,0.970873786407767,0.9805825242718447,0.9902912621359223,0.9902912621359223,0.9902912621359223,0.6504854368932039,0.06125809321810901,0.43592233009708736,0.46840361505881567,0.2627184466019417,0.6339158091762449,0.16135922330097088,0.7480807136556352,0.11754045307443368,0.8100117014753285,0.0929126213592233,0.8429633028529663,0.6504854368932039,0.7962783171521037,0.7964940668824165,0.7966674372014181,0.7966674372014181,0.7966674372014181,0.6504854368932039,0.6071619364096145,0.6201924771490717,0.6731573623782805,0.6988573621008486,0.7127085059867994,0.6504854368932039,0.4569063620154663,0.4389770039812701,0.4638450563796641,0.4725316312725551,0.47660671101600177,0.48324935280787457
+2.366863905325444,800,0.6601941747572816,0.970873786407767,0.9902912621359223,0.9902912621359223,0.9902912621359223,0.9902912621359223,0.6601941747572816,0.06611246215014785,0.437378640776699,0.47220221895116143,0.26679611650485435,0.6481360616867319,0.164368932038835,0.7614494349130585,0.11870550161812297,0.8169184700467885,0.09432038834951458,0.8548694196710027,0.6601941747572816,0.8033980582524272,0.8038261082260857,0.8038261082260857,0.8038261082260857,0.8038261082260857,0.6601941747572816,0.6135478065862052,0.6308156378108885,0.6845556918348534,0.707685056330406,0.7232611066574451,0.6601941747572816,0.46498128288276724,0.4485986843158645,0.4749717781804501,0.4832701810816351,0.487762460646856,0.4939522359452576
+2.9585798816568047,1000,0.6504854368932039,0.970873786407767,0.9805825242718447,0.9902912621359223,0.9902912621359223,0.9902912621359223,0.6504854368932039,0.06125809321810901,0.4461165048543689,0.4798173076061309,0.26932038834951455,0.6511259115267456,0.16601941747572818,0.7667280032499174,0.12000000000000002,0.8234348132226993,0.09475728155339808,0.8570886860782638,0.6504854368932039,0.7938511326860843,0.7941135310067349,0.7943002375041209,0.7943002375041209,0.7943002375041209,0.6504854368932039,0.6163434250133266,0.6306194061713684,0.6852740031621496,0.7087858531025408,0.7227726687256436,0.6504854368932039,0.4673451367444491,0.4491601687897158,0.4759775327060125,0.484283864447002,0.4885403171787604,0.4948931148880558
+3.5502958579881656,1200,0.6601941747572816,0.970873786407767,0.9902912621359223,0.9902912621359223,0.9902912621359223,0.9902912621359223,0.6601941747572816,0.06611246215014785,0.44466019417475733,0.48241403320688186,0.2700970873786408,0.6545173174336991,0.16611650485436893,0.7666222988041391,0.11993527508090616,0.821433115232699,0.0950970873786408,0.8607757081755069,0.6601941747572816,0.802674662097849,0.8031466146329083,0.8031466146329083,0.8031466146329083,0.8031466146329083,0.6601941747572816,0.6200439246564962,0.6357468583118394,0.6892184385347752,0.7120690440507333,0.7279251789627177,0.6601941747572816,0.47064044627994783,0.454032660512398,0.48053939417933,0.488614341849449,0.49318512356249333,0.4992400242495022
+4.1420118343195265,1400,0.6601941747572816,0.970873786407767,0.9902912621359223,0.9902912621359223,0.9902912621359223,0.9902912621359223,0.6601941747572816,0.06611246215014785,0.4451456310679612,0.48185419008936636,0.27048543689320387,0.6551920812816043,0.16611650485436896,0.764654034617116,0.12084142394822009,0.8281168342114908,0.09519417475728156,0.8609375188843946,0.6601941747572816,0.8015419760137065,0.8020274129069105,0.8020274129069105,0.8020274129069105,0.8020274129069105,0.6601941747572816,0.6209192881378345,0.6371304923469949,0.6900404048312746,0.7159480635761921,0.7294173160030438,0.6601941747572816,0.47238295031349775,0.4561669025825994,0.48307171830860945,0.4920233958725791,0.496106859156668,0.5023110925949719
+4.733727810650888,1600,0.6601941747572816,0.970873786407767,0.9902912621359223,0.9902912621359223,0.9902912621359223,0.9902912621359223,0.6601941747572816,0.06611246215014785,0.44805825242718444,0.48409390608352504,0.27126213592233006,0.6568473638827299,0.16650485436893206,0.7685416895166794,0.1211003236245955,0.8277686060133904,0.09529126213592234,0.8616979590623105,0.6601941747572816,0.8015776699029126,0.8020876238109248,0.8020876238109248,0.8020876238109248,0.8020876238109248,0.6601941747572816,0.6231250904534316,0.6383496204608501,0.6917257705456975,0.7167434657424917,0.7303448958665071,0.6601941747572816,0.4750205237443607,0.45785161483741715,0.4848085275553208,0.4937216396074153,0.49777622471594557,0.5039795405740248

eval/Information-Retrieval_evaluation_mix_de_results.csv

@@ -0,0 +1,9 @@
+epoch,steps,cosine-Accuracy@1,cosine-Accuracy@20,cosine-Accuracy@50,cosine-Accuracy@100,cosine-Accuracy@150,cosine-Accuracy@200,cosine-Precision@1,cosine-Recall@1,cosine-Precision@20,cosine-Recall@20,cosine-Precision@50,cosine-Recall@50,cosine-Precision@100,cosine-Recall@100,cosine-Precision@150,cosine-Recall@150,cosine-Precision@200,cosine-Recall@200,cosine-MRR@1,cosine-MRR@20,cosine-MRR@50,cosine-MRR@100,cosine-MRR@150,cosine-MRR@200,cosine-NDCG@1,cosine-NDCG@20,cosine-NDCG@50,cosine-NDCG@100,cosine-NDCG@150,cosine-NDCG@200,cosine-MAP@1,cosine-MAP@20,cosine-MAP@50,cosine-MAP@100,cosine-MAP@150,cosine-MAP@200,cosine-MAP@500
+0.591715976331361,200,0.38845553822152884,0.734269370774831,0.8117524700988039,0.8803952158086323,0.9121164846593863,0.9355174206968279,0.38845553822152884,0.14673253596810537,0.08216328653146125,0.6032587970185473,0.038169526781071245,0.6989859594383776,0.021476859074362975,0.7834026694401108,0.015122204888195528,0.8256557279835054,0.011799271970878837,0.8584343724626179,0.38845553822152884,0.4755813854666515,0.47811704545297706,0.47910662936458426,0.4793689523443691,0.47950783378595774,0.38845553822152884,0.47089223070507885,0.49701321688193106,0.5158211232743959,0.5242022471749376,0.5302273876532717,0.38845553822152884,0.38515411365076746,0.39147682232706005,0.39390468588701283,0.39462204836530157,0.3950134124984197,0.39564242254391563
+1.183431952662722,400,0.46021840873634945,0.8091523660946438,0.8835153406136246,0.9313572542901716,0.9495579823192928,0.9625585023400937,0.46021840873634945,0.17405096203848153,0.09500780031201247,0.6965938637545501,0.04315132605304212,0.7862367828046456,0.02356214248569943,0.857782977985786,0.016328653146125843,0.8908389668920089,0.012597503900156008,0.9146832890859494,0.46021840873634945,0.5531152893840248,0.5554363671701441,0.5561629113405923,0.5563168524767522,0.5563946576872058,0.46021840873634945,0.5540701081096809,0.5786034933790482,0.5944999375964086,0.6010213315483848,0.6054920754873866,0.46021840873634945,0.4642437490768453,0.47019746688105846,0.4723217628710669,0.47290246629177823,0.47323000409233096,0.47363146569190606
+1.7751479289940828,600,0.5039001560062403,0.84399375975039,0.9084763390535622,0.9448777951118045,0.9589183567342694,0.968278731149246,0.5039001560062403,0.18934824059629052,0.10124804992199687,0.7398769284104697,0.045325013000520026,0.825628358467672,0.024368174726989083,0.8861067776044376,0.01678280464551915,0.9139625585023401,0.012841913676547067,0.9314265903969492,0.5039001560062403,0.5958911705035703,0.5979962477714235,0.5985336788307954,0.5986528259488063,0.5987063352922788,0.5039001560062403,0.5959978647836432,0.6193308358901232,0.6328275670544934,0.6383837176839967,0.6416384714660877,0.5039001560062403,0.505156594331499,0.5107325975552374,0.5125971106477005,0.5131188199194585,0.5133496478406526,0.5136957927685452
+2.366863905325444,800,0.5325013000520021,0.8637545501820073,0.9251170046801872,0.9516380655226209,0.9651586063442538,0.9729589183567343,0.5325013000520021,0.19959265037268156,0.10535621424856995,0.7690587623504941,0.04671866874674988,0.8509793725082337,0.02476339053562143,0.8992286358120992,0.016987346160513086,0.9242329693187727,0.013000520020800833,0.9427543768417402,0.5325013000520021,0.6214631528403295,0.6234664391780935,0.6238401145880241,0.6239470104163971,0.6239938982140064,0.5325013000520021,0.6224558186311073,0.644673644554676,0.655555914976012,0.6605327238662299,0.6639471441610337,0.5325013000520021,0.5304865672939455,0.5358413929854877,0.5374123776699551,0.5378689641686509,0.5381229578769543,0.5384218703705271
+2.9585798816568047,1000,0.5429017160686428,0.8725949037961519,0.9297971918876755,0.9552782111284451,0.968278731149246,0.9729589183567343,0.5429017160686428,0.20383948691280984,0.10709828393135724,0.7817386028774485,0.04726989079563183,0.8605044201768071,0.025002600104004166,0.9077223088923557,0.01712601837406829,0.9319032761310452,0.013044721788871557,0.9461778471138845,0.5429017160686428,0.6331176720726237,0.6350347522721764,0.6354157777188323,0.6355194502419383,0.635546462249249,0.5429017160686428,0.6364696194038222,0.6580204683537704,0.6686859699628315,0.6734670399055159,0.6761041848609185,0.5429017160686428,0.546038259426052,0.5513401593649401,0.5528890114435938,0.5533285819634786,0.5535297820757661,0.5538215020153545
+3.5502958579881656,1200,0.5501820072802912,0.875715028601144,0.9334373374934998,0.9578783151326054,0.968798751950078,0.9771190847633905,0.5501820072802912,0.20695961171780206,0.10808632345293812,0.7888455538221528,0.047665106604264186,0.8676980412549836,0.025169006760270413,0.91352920783498,0.017205754896862536,0.9362367828046455,0.013109724388975563,0.951291384988733,0.5501820072802912,0.6404980755674814,0.6424799446207491,0.6428438772177503,0.6429316774029018,0.6429786628088062,0.5501820072802912,0.6448940133190817,0.6665823406307751,0.6769109649623175,0.6813839836815733,0.6841263896292673,0.5501820072802912,0.5552666840642385,0.560692088371109,0.5621625672472186,0.5625833020357084,0.56278042754345,0.5630480560935588
+4.1420118343195265,1400,0.5538221528861155,0.8814352574102964,0.9349973998959958,0.9589183567342694,0.96931877275091,0.9765990639625585,0.5538221528861155,0.20845033801352056,0.10912636505460219,0.7964725255676894,0.047935517420696835,0.8717888715548621,0.025257410296411865,0.9166493326399723,0.017257756976945746,0.9388542208355001,0.013122724908996361,0.9522447564569249,0.5538221528861155,0.6451894555975591,0.6470013120502346,0.6473603615547494,0.6474490009158033,0.647492473181411,0.5538221528861155,0.6518455599845957,0.6725307652410174,0.6825987388473841,0.6869902480321315,0.6894230866781552,0.5538221528861155,0.5627871995310985,0.5679148655306163,0.5693421440886408,0.5697579274072834,0.569931742725807,0.5702007325952348
+4.733727810650888,1600,0.5564222568902756,0.8866354654186167,0.9381175247009881,0.9594383775351014,0.9708788351534061,0.9776391055642226,0.5564222568902756,0.20931703934824059,0.109464378575143,0.7988992893049055,0.048060322412896525,0.8741029641185647,0.025273010920436823,0.9173426937077482,0.017313225862367825,0.9424076963078523,0.013143525741029644,0.953631478592477,0.5564222568902756,0.6476945170199107,0.6493649946597936,0.6496801333421218,0.6497778366579644,0.6498156890114056,0.5564222568902756,0.6541310877479573,0.674790854916742,0.6844997445798996,0.6894214573457343,0.6914881284159038,0.5564222568902756,0.5648326970643027,0.57003456255067,0.5714370828517599,0.5719002990233493,0.5720497397197026,0.5723109788233504