diff --git a/.gitattributes b/.gitattributes
index a6344aac8c09253b3b630fb776ae94478aa0275b..fbe64eb06e3ef6e98ca7bb23d41d9ae883457c06 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -33,3 +33,8 @@ 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-4800/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+checkpoint-4000/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+checkpoint-4200/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+checkpoint-4600/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+checkpoint-4400/tokenizer.json filter=lfs diff=lfs merge=lfs -text
diff --git a/README.md b/README.md
index 7b95401dc46245ac339fc25059d4a56d90b4cde5..25ec93dbcb84459d063031a54850e7776fe2d97c 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,1444 @@
----
-license: apache-2.0
----
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:124788
+- loss:GISTEmbedLoss
+base_model: BAAI/bge-m3
+widget:
+- source_sentence: 其他机械、设备和有形货物租赁服务代表
+ sentences:
+ - 其他机械和设备租赁服务工作人员
+ - 电子和电信设备及零部件物流经理
+ - 工业主厨
+- source_sentence: 公交车司机
+ sentences:
+ - 表演灯光设计师
+ - 乙烯基地板安装工
+ - 国际巴士司机
+- source_sentence: online communication manager
+ sentences:
+ - trades union official
+ - social media manager
+ - budget manager
+- source_sentence: Projektmanagerin
+ sentences:
+ - Projektmanager/Projektmanagerin
+ - Category-Manager
+ - Infanterist
+- source_sentence: Volksvertreter
+ sentences:
+ - Parlamentarier
+ - Oberbürgermeister
+ - Konsul
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy@1
+- cosine_accuracy@20
+- cosine_accuracy@50
+- cosine_accuracy@100
+- cosine_accuracy@150
+- cosine_accuracy@200
+- cosine_precision@1
+- cosine_precision@20
+- cosine_precision@50
+- cosine_precision@100
+- cosine_precision@150
+- cosine_precision@200
+- cosine_recall@1
+- cosine_recall@20
+- cosine_recall@50
+- cosine_recall@100
+- cosine_recall@150
+- cosine_recall@200
+- cosine_ndcg@1
+- cosine_ndcg@20
+- cosine_ndcg@50
+- cosine_ndcg@100
+- cosine_ndcg@150
+- cosine_ndcg@200
+- cosine_mrr@1
+- cosine_mrr@20
+- cosine_mrr@50
+- cosine_mrr@100
+- cosine_mrr@150
+- cosine_mrr@200
+- cosine_map@1
+- cosine_map@20
+- cosine_map@50
+- cosine_map@100
+- cosine_map@150
+- cosine_map@200
+- cosine_map@500
+model-index:
+- name: SentenceTransformer based on BAAI/bge-m3
+ results:
+ - task:
+ type: information-retrieval
+ name: Information Retrieval
+ dataset:
+ name: full en
+ type: full_en
+ metrics:
+ - type: cosine_accuracy@1
+ value: 0.6476190476190476
+ name: Cosine Accuracy@1
+ - type: cosine_accuracy@20
+ value: 0.9904761904761905
+ name: Cosine Accuracy@20
+ - type: cosine_accuracy@50
+ value: 0.9904761904761905
+ name: Cosine Accuracy@50
+ - type: cosine_accuracy@100
+ value: 0.9904761904761905
+ name: Cosine Accuracy@100
+ - type: cosine_accuracy@150
+ value: 0.9904761904761905
+ name: Cosine Accuracy@150
+ - type: cosine_accuracy@200
+ value: 0.9904761904761905
+ name: Cosine Accuracy@200
+ - type: cosine_precision@1
+ value: 0.6476190476190476
+ name: Cosine Precision@1
+ - type: cosine_precision@20
+ value: 0.5061904761904762
+ name: Cosine Precision@20
+ - type: cosine_precision@50
+ value: 0.30647619047619057
+ name: Cosine Precision@50
+ - type: cosine_precision@100
+ value: 0.1858095238095238
+ name: Cosine Precision@100
+ - type: cosine_precision@150
+ value: 0.13250793650793652
+ name: Cosine Precision@150
+ - type: cosine_precision@200
+ value: 0.10247619047619047
+ name: Cosine Precision@200
+ - type: cosine_recall@1
+ value: 0.06690172806447445
+ name: Cosine Recall@1
+ - type: cosine_recall@20
+ value: 0.5391510592522911
+ name: Cosine Recall@20
+ - type: cosine_recall@50
+ value: 0.7199711948587544
+ name: Cosine Recall@50
+ - type: cosine_recall@100
+ value: 0.8253770621157605
+ name: Cosine Recall@100
+ - type: cosine_recall@150
+ value: 0.8719997123512196
+ name: Cosine Recall@150
+ - type: cosine_recall@200
+ value: 0.9006382758109558
+ name: Cosine Recall@200
+ - type: cosine_ndcg@1
+ value: 0.6476190476190476
+ name: Cosine Ndcg@1
+ - type: cosine_ndcg@20
+ value: 0.6822066814233797
+ name: Cosine Ndcg@20
+ - type: cosine_ndcg@50
+ value: 0.6975329548006446
+ name: Cosine Ndcg@50
+ - type: cosine_ndcg@100
+ value: 0.7519637922809941
+ name: Cosine Ndcg@100
+ - type: cosine_ndcg@150
+ value: 0.7724946802449859
+ name: Cosine Ndcg@150
+ - type: cosine_ndcg@200
+ value: 0.7827357067553371
+ name: Cosine Ndcg@200
+ - type: cosine_mrr@1
+ value: 0.6476190476190476
+ name: Cosine Mrr@1
+ - type: cosine_mrr@20
+ value: 0.7999999999999998
+ name: Cosine Mrr@20
+ - type: cosine_mrr@50
+ value: 0.7999999999999998
+ name: Cosine Mrr@50
+ - type: cosine_mrr@100
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+ name: Cosine Mrr@100
+ - type: cosine_mrr@150
+ value: 0.7999999999999998
+ name: Cosine Mrr@150
+ - type: cosine_mrr@200
+ value: 0.7999999999999998
+ name: Cosine Mrr@200
+ - type: cosine_map@1
+ value: 0.6476190476190476
+ name: Cosine Map@1
+ - type: cosine_map@20
+ value: 0.5391784054866918
+ name: Cosine Map@20
+ - type: cosine_map@50
+ value: 0.5258287715484311
+ name: Cosine Map@50
+ - type: cosine_map@100
+ value: 0.5580109313638075
+ name: Cosine Map@100
+ - type: cosine_map@150
+ value: 0.5665715227835532
+ name: Cosine Map@150
+ - type: cosine_map@200
+ value: 0.569529009182472
+ name: Cosine Map@200
+ - type: cosine_map@500
+ value: 0.5743595458034346
+ name: Cosine Map@500
+ - task:
+ type: information-retrieval
+ name: Information Retrieval
+ dataset:
+ name: full es
+ type: full_es
+ metrics:
+ - type: cosine_accuracy@1
+ value: 0.11351351351351352
+ name: Cosine Accuracy@1
+ - type: cosine_accuracy@20
+ value: 1.0
+ name: Cosine Accuracy@20
+ - type: cosine_accuracy@50
+ value: 1.0
+ name: Cosine Accuracy@50
+ - type: cosine_accuracy@100
+ value: 1.0
+ name: Cosine Accuracy@100
+ - type: cosine_accuracy@150
+ value: 1.0
+ name: Cosine Accuracy@150
+ - type: cosine_accuracy@200
+ value: 1.0
+ name: Cosine Accuracy@200
+ - type: cosine_precision@1
+ value: 0.11351351351351352
+ name: Cosine Precision@1
+ - type: cosine_precision@20
+ value: 0.5667567567567567
+ name: Cosine Precision@20
+ - type: cosine_precision@50
+ value: 0.3902702702702703
+ name: Cosine Precision@50
+ - type: cosine_precision@100
+ value: 0.25254054054054054
+ name: Cosine Precision@100
+ - type: cosine_precision@150
+ value: 0.19005405405405407
+ name: Cosine Precision@150
+ - type: cosine_precision@200
+ value: 0.1507837837837838
+ name: Cosine Precision@200
+ - type: cosine_recall@1
+ value: 0.0035155918996302815
+ name: Cosine Recall@1
+ - type: cosine_recall@20
+ value: 0.37958552840441906
+ name: Cosine Recall@20
+ - type: cosine_recall@50
+ value: 0.5635730197468752
+ name: Cosine Recall@50
+ - type: cosine_recall@100
+ value: 0.672698242387141
+ name: Cosine Recall@100
+ - type: cosine_recall@150
+ value: 0.7360036980055802
+ name: Cosine Recall@150
+ - type: cosine_recall@200
+ value: 0.7697561816436992
+ name: Cosine Recall@200
+ - type: cosine_ndcg@1
+ value: 0.11351351351351352
+ name: Cosine Ndcg@1
+ - type: cosine_ndcg@20
+ value: 0.6136401766234348
+ name: Cosine Ndcg@20
+ - type: cosine_ndcg@50
+ value: 0.5908459924766464
+ name: Cosine Ndcg@50
+ - type: cosine_ndcg@100
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+ name: Cosine Ndcg@100
+ - type: cosine_ndcg@150
+ value: 0.6488575731321932
+ name: Cosine Ndcg@150
+ - type: cosine_ndcg@200
+ value: 0.665316090087272
+ name: Cosine Ndcg@200
+ - type: cosine_mrr@1
+ value: 0.11351351351351352
+ name: Cosine Mrr@1
+ - type: cosine_mrr@20
+ value: 0.5536036036036036
+ name: Cosine Mrr@20
+ - type: cosine_mrr@50
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+ name: Cosine Mrr@50
+ - type: cosine_mrr@100
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+ name: Cosine Mrr@100
+ - type: cosine_mrr@150
+ value: 0.5536036036036036
+ name: Cosine Mrr@150
+ - type: cosine_mrr@200
+ value: 0.5536036036036036
+ name: Cosine Mrr@200
+ - type: cosine_map@1
+ value: 0.11351351351351352
+ name: Cosine Map@1
+ - type: cosine_map@20
+ value: 0.48095830339282386
+ name: Cosine Map@20
+ - type: cosine_map@50
+ value: 0.43038606337879926
+ name: Cosine Map@50
+ - type: cosine_map@100
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+ name: Cosine Map@100
+ - type: cosine_map@150
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+ name: Cosine Map@150
+ - type: cosine_map@200
+ value: 0.4550924585301385
+ name: Cosine Map@200
+ - type: cosine_map@500
+ value: 0.4677023132311536
+ name: Cosine Map@500
+ - task:
+ type: information-retrieval
+ name: Information Retrieval
+ dataset:
+ name: full de
+ type: full_de
+ metrics:
+ - type: cosine_accuracy@1
+ value: 0.2955665024630542
+ name: Cosine Accuracy@1
+ - type: cosine_accuracy@20
+ value: 0.9852216748768473
+ name: Cosine Accuracy@20
+ - type: cosine_accuracy@50
+ value: 0.9901477832512315
+ name: Cosine Accuracy@50
+ - type: cosine_accuracy@100
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+ name: Cosine Accuracy@100
+ - type: cosine_accuracy@150
+ value: 0.9901477832512315
+ name: Cosine Accuracy@150
+ - type: cosine_accuracy@200
+ value: 0.9901477832512315
+ name: Cosine Accuracy@200
+ - type: cosine_precision@1
+ value: 0.2955665024630542
+ name: Cosine Precision@1
+ - type: cosine_precision@20
+ value: 0.5403940886699506
+ name: Cosine Precision@20
+ - type: cosine_precision@50
+ value: 0.38275862068965516
+ name: Cosine Precision@50
+ - type: cosine_precision@100
+ value: 0.2503448275862069
+ name: Cosine Precision@100
+ - type: cosine_precision@150
+ value: 0.187816091954023
+ name: Cosine Precision@150
+ - type: cosine_precision@200
+ value: 0.15027093596059116
+ name: Cosine Precision@200
+ - type: cosine_recall@1
+ value: 0.01108543831680986
+ name: Cosine Recall@1
+ - type: cosine_recall@20
+ value: 0.3432684453555553
+ name: Cosine Recall@20
+ - type: cosine_recall@50
+ value: 0.5339871522541048
+ name: Cosine Recall@50
+ - type: cosine_recall@100
+ value: 0.6498636280219438
+ name: Cosine Recall@100
+ - type: cosine_recall@150
+ value: 0.7100921836539074
+ name: Cosine Recall@150
+ - type: cosine_recall@200
+ value: 0.7513351913056898
+ name: Cosine Recall@200
+ - type: cosine_ndcg@1
+ value: 0.2955665024630542
+ name: Cosine Ndcg@1
+ - type: cosine_ndcg@20
+ value: 0.5647628262992046
+ name: Cosine Ndcg@20
+ - type: cosine_ndcg@50
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+ name: Cosine Ndcg@50
+ - type: cosine_ndcg@100
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+ name: Cosine Ndcg@100
+ - type: cosine_ndcg@150
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+ name: Cosine Ndcg@150
+ - type: cosine_ndcg@200
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+ name: Cosine Ndcg@200
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+ - type: cosine_mrr@20
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+ - type: cosine_mrr@100
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+ name: Cosine Mrr@100
+ - type: cosine_mrr@150
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+ - type: cosine_mrr@200
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+ name: Cosine Mrr@200
+ - type: cosine_map@1
+ value: 0.2955665024630542
+ name: Cosine Map@1
+ - type: cosine_map@20
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+ name: Cosine Map@200
+ - type: cosine_map@500
+ value: 0.4188841643667456
+ name: Cosine Map@500
+ - task:
+ type: information-retrieval
+ name: Information Retrieval
+ dataset:
+ name: full zh
+ type: full_zh
+ metrics:
+ - type: cosine_accuracy@1
+ value: 0.6796116504854369
+ name: Cosine Accuracy@1
+ - type: cosine_accuracy@20
+ value: 0.9902912621359223
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+ - type: cosine_accuracy@50
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+ - type: cosine_accuracy@100
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+ - type: cosine_accuracy@150
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+ name: Cosine Accuracy@150
+ - type: cosine_accuracy@200
+ value: 0.9902912621359223
+ name: Cosine Accuracy@200
+ - type: cosine_precision@1
+ value: 0.6796116504854369
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+ - type: cosine_precision@100
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+ name: Cosine Precision@100
+ - type: cosine_precision@150
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+ - type: cosine_recall@150
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+ name: Cosine Mrr@100
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+ name: Cosine Mrr@150
+ - type: cosine_mrr@200
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+ name: Cosine Mrr@200
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+ name: Cosine Map@1
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+ name: Cosine Map@20
+ - type: cosine_map@50
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+ name: Cosine Map@50
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+ name: Cosine Map@100
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+ name: Cosine Map@150
+ - type: cosine_map@200
+ value: 0.5228375783347256
+ name: Cosine Map@200
+ - type: cosine_map@500
+ value: 0.52765377953199
+ name: Cosine Map@500
+ - task:
+ type: information-retrieval
+ name: Information Retrieval
+ dataset:
+ name: mix es
+ type: mix_es
+ metrics:
+ - type: cosine_accuracy@1
+ value: 0.7394695787831513
+ name: Cosine Accuracy@1
+ - type: cosine_accuracy@20
+ value: 0.9635985439417577
+ name: Cosine Accuracy@20
+ - type: cosine_accuracy@50
+ value: 0.982839313572543
+ name: Cosine Accuracy@50
+ - type: cosine_accuracy@100
+ value: 0.9927197087883516
+ name: Cosine Accuracy@100
+ - type: cosine_accuracy@150
+ value: 0.9947997919916797
+ name: Cosine Accuracy@150
+ - type: cosine_accuracy@200
+ value: 0.9963598543941757
+ name: Cosine Accuracy@200
+ - type: cosine_precision@1
+ value: 0.7394695787831513
+ name: Cosine Precision@1
+ - type: cosine_precision@20
+ value: 0.12488299531981278
+ name: Cosine Precision@20
+ - type: cosine_precision@50
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+ name: Cosine Precision@50
+ - type: cosine_precision@100
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+ name: Cosine Precision@100
+ - type: cosine_precision@150
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+ - type: cosine_precision@200
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+ name: Cosine Precision@200
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+ name: Cosine Recall@1
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+ name: Cosine Recall@20
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+ name: Cosine Recall@50
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+ name: Cosine Mrr@100
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+ name: Cosine Mrr@150
+ - type: cosine_mrr@200
+ value: 0.8067455244059942
+ name: Cosine Mrr@200
+ - type: cosine_map@1
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+ name: Cosine Map@1
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+ value: 0.7439811728319751
+ name: Cosine Map@20
+ - type: cosine_map@50
+ value: 0.7464542457655368
+ name: Cosine Map@50
+ - type: cosine_map@100
+ value: 0.7469341154545359
+ name: Cosine Map@100
+ - type: cosine_map@150
+ value: 0.7470471963812441
+ name: Cosine Map@150
+ - type: cosine_map@200
+ value: 0.7471010455519603
+ name: Cosine Map@200
+ - type: cosine_map@500
+ value: 0.7471920688836787
+ name: Cosine Map@500
+ - task:
+ type: information-retrieval
+ name: Information Retrieval
+ dataset:
+ name: mix de
+ type: mix_de
+ metrics:
+ - type: cosine_accuracy@1
+ value: 0.6926677067082684
+ name: Cosine Accuracy@1
+ - type: cosine_accuracy@20
+ value: 0.9641185647425897
+ name: Cosine Accuracy@20
+ - type: cosine_accuracy@50
+ value: 0.983879355174207
+ name: Cosine Accuracy@50
+ - type: cosine_accuracy@100
+ value: 0.9921996879875195
+ name: Cosine Accuracy@100
+ - type: cosine_accuracy@150
+ value: 0.9932397295891836
+ name: Cosine Accuracy@150
+ - type: cosine_accuracy@200
+ value: 0.9942797711908476
+ name: Cosine Accuracy@200
+ - type: cosine_precision@1
+ value: 0.6926677067082684
+ name: Cosine Precision@1
+ - type: cosine_precision@20
+ value: 0.12797711908476336
+ name: Cosine Precision@20
+ - type: cosine_precision@50
+ value: 0.053281331253250144
+ name: Cosine Precision@50
+ - type: cosine_precision@100
+ value: 0.027051482059282376
+ name: Cosine Precision@100
+ - type: cosine_precision@150
+ value: 0.018110591090310275
+ name: Cosine Precision@150
+ - type: cosine_precision@200
+ value: 0.013619344773790953
+ name: Cosine Precision@200
+ - type: cosine_recall@1
+ value: 0.2603830819899463
+ name: Cosine Recall@1
+ - type: cosine_recall@20
+ value: 0.928479805858901
+ name: Cosine Recall@20
+ - type: cosine_recall@50
+ value: 0.9650286011440458
+ name: Cosine Recall@50
+ - type: cosine_recall@100
+ value: 0.9796325186340786
+ name: Cosine Recall@100
+ - type: cosine_recall@150
+ value: 0.9837060149072628
+ name: Cosine Recall@150
+ - type: cosine_recall@200
+ value: 0.9862194487779511
+ name: Cosine Recall@200
+ - type: cosine_ndcg@1
+ value: 0.6926677067082684
+ name: Cosine Ndcg@1
+ - type: cosine_ndcg@20
+ value: 0.7967328692326251
+ name: Cosine Ndcg@20
+ - type: cosine_ndcg@50
+ value: 0.8068705787791701
+ name: Cosine Ndcg@50
+ - type: cosine_ndcg@100
+ value: 0.810158579950017
+ name: Cosine Ndcg@100
+ - type: cosine_ndcg@150
+ value: 0.8109641919896999
+ name: Cosine Ndcg@150
+ - type: cosine_ndcg@200
+ value: 0.8114360342473703
+ name: Cosine Ndcg@200
+ - type: cosine_mrr@1
+ value: 0.6926677067082684
+ name: Cosine Mrr@1
+ - type: cosine_mrr@20
+ value: 0.7766838069642311
+ name: Cosine Mrr@20
+ - type: cosine_mrr@50
+ value: 0.7773792960985305
+ name: Cosine Mrr@50
+ - type: cosine_mrr@100
+ value: 0.7775026273925645
+ name: Cosine Mrr@100
+ - type: cosine_mrr@150
+ value: 0.7775124036000293
+ name: Cosine Mrr@150
+ - type: cosine_mrr@200
+ value: 0.7775182983569378
+ name: Cosine Mrr@200
+ - type: cosine_map@1
+ value: 0.6926677067082684
+ name: Cosine Map@1
+ - type: cosine_map@20
+ value: 0.7210301157895639
+ name: Cosine Map@20
+ - type: cosine_map@50
+ value: 0.7237555751939095
+ name: Cosine Map@50
+ - type: cosine_map@100
+ value: 0.7242426468613273
+ name: Cosine Map@100
+ - type: cosine_map@150
+ value: 0.7243265313145111
+ name: Cosine Map@150
+ - type: cosine_map@200
+ value: 0.7243628241480395
+ name: Cosine Map@200
+ - type: cosine_map@500
+ value: 0.7244144669299598
+ name: Cosine Map@500
+ - task:
+ type: information-retrieval
+ name: Information Retrieval
+ dataset:
+ name: mix zh
+ type: mix_zh
+ metrics:
+ - type: cosine_accuracy@1
+ value: 0.17888715548621945
+ name: Cosine Accuracy@1
+ - type: cosine_accuracy@20
+ value: 1.0
+ name: Cosine Accuracy@20
+ - type: cosine_accuracy@50
+ value: 1.0
+ name: Cosine Accuracy@50
+ - type: cosine_accuracy@100
+ value: 1.0
+ name: Cosine Accuracy@100
+ - type: cosine_accuracy@150
+ value: 1.0
+ name: Cosine Accuracy@150
+ - type: cosine_accuracy@200
+ value: 1.0
+ name: Cosine Accuracy@200
+ - type: cosine_precision@1
+ value: 0.17888715548621945
+ name: Cosine Precision@1
+ - type: cosine_precision@20
+ value: 0.15439417576703063
+ name: Cosine Precision@20
+ - type: cosine_precision@50
+ value: 0.0617576703068123
+ name: Cosine Precision@50
+ - type: cosine_precision@100
+ value: 0.03087883515340615
+ name: Cosine Precision@100
+ - type: cosine_precision@150
+ value: 0.020585890102270757
+ name: Cosine Precision@150
+ - type: cosine_precision@200
+ value: 0.015439417576703075
+ name: Cosine Precision@200
+ - type: cosine_recall@1
+ value: 0.05768764083896689
+ name: Cosine Recall@1
+ - type: cosine_recall@20
+ value: 1.0
+ name: Cosine Recall@20
+ - type: cosine_recall@50
+ value: 1.0
+ name: Cosine Recall@50
+ - type: cosine_recall@100
+ value: 1.0
+ name: Cosine Recall@100
+ - type: cosine_recall@150
+ value: 1.0
+ name: Cosine Recall@150
+ - type: cosine_recall@200
+ value: 1.0
+ name: Cosine Recall@200
+ - type: cosine_ndcg@1
+ value: 0.17888715548621945
+ name: Cosine Ndcg@1
+ - type: cosine_ndcg@20
+ value: 0.5443156532634228
+ name: Cosine Ndcg@20
+ - type: cosine_ndcg@50
+ value: 0.5443156532634228
+ name: Cosine Ndcg@50
+ - type: cosine_ndcg@100
+ value: 0.5443156532634228
+ name: Cosine Ndcg@100
+ - type: cosine_ndcg@150
+ value: 0.5443156532634228
+ name: Cosine Ndcg@150
+ - type: cosine_ndcg@200
+ value: 0.5443156532634228
+ name: Cosine Ndcg@200
+ - type: cosine_mrr@1
+ value: 0.17888715548621945
+ name: Cosine Mrr@1
+ - type: cosine_mrr@20
+ value: 0.4002437442375043
+ name: Cosine Mrr@20
+ - type: cosine_mrr@50
+ value: 0.4002437442375043
+ name: Cosine Mrr@50
+ - type: cosine_mrr@100
+ value: 0.4002437442375043
+ name: Cosine Mrr@100
+ - type: cosine_mrr@150
+ value: 0.4002437442375043
+ name: Cosine Mrr@150
+ - type: cosine_mrr@200
+ value: 0.4002437442375043
+ name: Cosine Mrr@200
+ - type: cosine_map@1
+ value: 0.17888715548621945
+ name: Cosine Map@1
+ - type: cosine_map@20
+ value: 0.32718437256695937
+ name: Cosine Map@20
+ - type: cosine_map@50
+ value: 0.32718437256695937
+ name: Cosine Map@50
+ - type: cosine_map@100
+ value: 0.32718437256695937
+ name: Cosine Map@100
+ - type: cosine_map@150
+ value: 0.32718437256695937
+ name: Cosine Map@150
+ - type: cosine_map@200
+ value: 0.32718437256695937
+ name: Cosine Map@200
+ - type: cosine_map@500
+ value: 0.32718437256695937
+ name: Cosine Map@500
+---
+
+# Job - Job matching finetuned BAAI/bge-m3
+
+Top performing model on [TalentCLEF 2025](https://talentclef.github.io/talentclef/) Task A. Use it for multilingual job title matching
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3)
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 1024 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Datasets:**
+ - full_en
+ - full_de
+ - full_es
+ - full_zh
+ - mix
+
+
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+ (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
+ (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+ (2): Normalize()
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("sentence_transformers_model_id")
+# Run inference
+sentences = [
+ 'Volksvertreter',
+ 'Parlamentarier',
+ 'Oberbürgermeister',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 1024]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+
+
+
+
+
+
+## Evaluation
+
+### Metrics
+
+#### Information Retrieval
+
+* Datasets: `full_en`, `full_es`, `full_de`, `full_zh`, `mix_es`, `mix_de` and `mix_zh`
+* Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric | full_en | full_es | full_de | full_zh | mix_es | mix_de | mix_zh |
+|:---------------------|:-----------|:-----------|:-----------|:-----------|:-----------|:-----------|:-----------|
+| cosine_accuracy@1 | 0.6476 | 0.1135 | 0.2956 | 0.6796 | 0.7395 | 0.6927 | 0.1789 |
+| cosine_accuracy@20 | 0.9905 | 1.0 | 0.9852 | 0.9903 | 0.9636 | 0.9641 | 1.0 |
+| cosine_accuracy@50 | 0.9905 | 1.0 | 0.9901 | 0.9903 | 0.9828 | 0.9839 | 1.0 |
+| cosine_accuracy@100 | 0.9905 | 1.0 | 0.9901 | 0.9903 | 0.9927 | 0.9922 | 1.0 |
+| cosine_accuracy@150 | 0.9905 | 1.0 | 0.9901 | 0.9903 | 0.9948 | 0.9932 | 1.0 |
+| cosine_accuracy@200 | 0.9905 | 1.0 | 0.9901 | 0.9903 | 0.9964 | 0.9943 | 1.0 |
+| cosine_precision@1 | 0.6476 | 0.1135 | 0.2956 | 0.6796 | 0.7395 | 0.6927 | 0.1789 |
+| cosine_precision@20 | 0.5062 | 0.5668 | 0.5404 | 0.4709 | 0.1249 | 0.128 | 0.1544 |
+| cosine_precision@50 | 0.3065 | 0.3903 | 0.3828 | 0.2804 | 0.0517 | 0.0533 | 0.0618 |
+| cosine_precision@100 | 0.1858 | 0.2525 | 0.2503 | 0.1732 | 0.0263 | 0.0271 | 0.0309 |
+| cosine_precision@150 | 0.1325 | 0.1901 | 0.1878 | 0.1239 | 0.0176 | 0.0181 | 0.0206 |
+| cosine_precision@200 | 0.1025 | 0.1508 | 0.1503 | 0.0977 | 0.0133 | 0.0136 | 0.0154 |
+| cosine_recall@1 | 0.0669 | 0.0035 | 0.0111 | 0.0643 | 0.2854 | 0.2604 | 0.0577 |
+| cosine_recall@20 | 0.5392 | 0.3796 | 0.3433 | 0.5119 | 0.9226 | 0.9285 | 1.0 |
+| cosine_recall@50 | 0.72 | 0.5636 | 0.534 | 0.6727 | 0.9548 | 0.965 | 1.0 |
+| cosine_recall@100 | 0.8254 | 0.6727 | 0.6499 | 0.788 | 0.9705 | 0.9796 | 1.0 |
+| cosine_recall@150 | 0.872 | 0.736 | 0.7101 | 0.8329 | 0.9766 | 0.9837 | 1.0 |
+| cosine_recall@200 | 0.9006 | 0.7698 | 0.7513 | 0.8687 | 0.9811 | 0.9862 | 1.0 |
+| cosine_ndcg@1 | 0.6476 | 0.1135 | 0.2956 | 0.6796 | 0.7395 | 0.6927 | 0.1789 |
+| cosine_ndcg@20 | 0.6822 | 0.6136 | 0.5648 | 0.6515 | 0.8119 | 0.7967 | 0.5443 |
+| cosine_ndcg@50 | 0.6975 | 0.5908 | 0.5522 | 0.6599 | 0.8208 | 0.8069 | 0.5443 |
+| cosine_ndcg@100 | 0.752 | 0.6168 | 0.5796 | 0.7157 | 0.8243 | 0.8102 | 0.5443 |
+| cosine_ndcg@150 | 0.7725 | 0.6489 | 0.6112 | 0.7357 | 0.8255 | 0.811 | 0.5443 |
+| **cosine_ndcg@200** | **0.7827** | **0.6653** | **0.6309** | **0.7501** | **0.8262** | **0.8114** | **0.5443** |
+| cosine_mrr@1 | 0.6476 | 0.1135 | 0.2956 | 0.6796 | 0.7395 | 0.6927 | 0.1789 |
+| cosine_mrr@20 | 0.8 | 0.5536 | 0.5164 | 0.8217 | 0.8059 | 0.7767 | 0.4002 |
+| cosine_mrr@50 | 0.8 | 0.5536 | 0.5166 | 0.8217 | 0.8066 | 0.7774 | 0.4002 |
+| cosine_mrr@100 | 0.8 | 0.5536 | 0.5166 | 0.8217 | 0.8067 | 0.7775 | 0.4002 |
+| cosine_mrr@150 | 0.8 | 0.5536 | 0.5166 | 0.8217 | 0.8067 | 0.7775 | 0.4002 |
+| cosine_mrr@200 | 0.8 | 0.5536 | 0.5166 | 0.8217 | 0.8067 | 0.7775 | 0.4002 |
+| cosine_map@1 | 0.6476 | 0.1135 | 0.2956 | 0.6796 | 0.7395 | 0.6927 | 0.1789 |
+| cosine_map@20 | 0.5392 | 0.481 | 0.4222 | 0.5012 | 0.744 | 0.721 | 0.3272 |
+| cosine_map@50 | 0.5258 | 0.4304 | 0.3791 | 0.4813 | 0.7465 | 0.7238 | 0.3272 |
+| cosine_map@100 | 0.558 | 0.4335 | 0.3829 | 0.5105 | 0.7469 | 0.7242 | 0.3272 |
+| cosine_map@150 | 0.5666 | 0.4485 | 0.3981 | 0.5184 | 0.747 | 0.7243 | 0.3272 |
+| cosine_map@200 | 0.5695 | 0.4551 | 0.4056 | 0.5228 | 0.7471 | 0.7244 | 0.3272 |
+| cosine_map@500 | 0.5744 | 0.4677 | 0.4189 | 0.5277 | 0.7472 | 0.7244 | 0.3272 |
+
+
+
+
+
+## Training Details
+
+### Training Datasets
+full_en
+
+#### full_en
+
+* Dataset: full_en
+* Size: 28,880 training samples
+* Columns: anchor and positive
+* Approximate statistics based on the first 1000 samples:
+ | | anchor | positive |
+ |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+ | type | string | string |
+ | details | - min: 3 tokens
- mean: 5.68 tokens
- max: 11 tokens
| - min: 3 tokens
- mean: 5.76 tokens
- max: 12 tokens
|
+* Samples:
+ | anchor | positive |
+ |:-----------------------------------------|:-----------------------------------------|
+ | air commodore | flight lieutenant |
+ | command and control officer | flight officer |
+ | air commodore | command and control officer |
+* Loss: [GISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
+ ```json
+ {'guide': SentenceTransformer(
+ (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
+ (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+ (2): Normalize()
+ ), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
+ ```
+full_de
+
+#### full_de
+
+* Dataset: full_de
+* Size: 23,023 training samples
+* Columns: anchor and positive
+* Approximate statistics based on the first 1000 samples:
+ | | anchor | positive |
+ |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+ | type | string | string |
+ | details | - min: 3 tokens
- mean: 7.99 tokens
- max: 30 tokens
| - min: 3 tokens
- mean: 8.19 tokens
- max: 30 tokens
|
+* Samples:
+ | anchor | positive |
+ |:----------------------------------|:-----------------------------------------------------|
+ | Staffelkommandantin | Kommodore |
+ | Luftwaffenoffizierin | Luftwaffenoffizier/Luftwaffenoffizierin |
+ | Staffelkommandantin | Luftwaffenoffizierin |
+* Loss: [GISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
+ ```json
+ {'guide': SentenceTransformer(
+ (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
+ (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+ (2): Normalize()
+ ), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
+ ```
+full_es
+
+#### full_es
+
+* Dataset: full_es
+* Size: 20,724 training samples
+* Columns: anchor and positive
+* Approximate statistics based on the first 1000 samples:
+ | | anchor | positive |
+ |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+ | type | string | string |
+ | details | - min: 3 tokens
- mean: 9.13 tokens
- max: 32 tokens
| - min: 3 tokens
- mean: 8.84 tokens
- max: 32 tokens
|
+* Samples:
+ | anchor | positive |
+ |:------------------------------------|:-------------------------------------------|
+ | jefe de escuadrón | instructor |
+ | comandante de aeronave | instructor de simulador |
+ | instructor | oficial del Ejército del Aire |
+* Loss: [GISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
+ ```json
+ {'guide': SentenceTransformer(
+ (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
+ (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+ (2): Normalize()
+ ), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
+ ```
+full_zh
+
+#### full_zh
+
+* Dataset: full_zh
+* Size: 30,401 training samples
+* Columns: anchor and positive
+* Approximate statistics based on the first 1000 samples:
+ | | anchor | positive |
+ |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+ | type | string | string |
+ | details | - min: 5 tokens
- mean: 7.15 tokens
- max: 14 tokens
| - min: 5 tokens
- mean: 7.46 tokens
- max: 21 tokens
|
+* Samples:
+ | anchor | positive |
+ |:------------------|:---------------------|
+ | 技术总监 | 技术和运营总监 |
+ | 技术总监 | 技术主管 |
+ | 技术总监 | 技术艺术总监 |
+* Loss: [GISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
+ ```json
+ {'guide': SentenceTransformer(
+ (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
+ (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+ (2): Normalize()
+ ), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
+ ```
+mix
+
+#### mix
+
+* Dataset: mix
+* Size: 21,760 training samples
+* Columns: anchor and positive
+* Approximate statistics based on the first 1000 samples:
+ | | anchor | positive |
+ |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+ | type | string | string |
+ | details | - min: 2 tokens
- mean: 6.71 tokens
- max: 19 tokens
| - min: 2 tokens
- mean: 7.69 tokens
- max: 19 tokens
|
+* Samples:
+ | anchor | positive |
+ |:------------------------------------------|:----------------------------------------------------------------|
+ | technical manager | Technischer Direktor für Bühne, Film und Fernsehen |
+ | head of technical | directora técnica |
+ | head of technical department | 技术艺术总监 |
+* Loss: [GISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
+ ```json
+ {'guide': SentenceTransformer(
+ (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
+ (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+ (2): Normalize()
+ ), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
+ ```
+Click to expand
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 64
+- `per_device_eval_batch_size`: 128
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 2
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 5e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 5
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.05
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: False
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: True
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: True
+- `dataloader_num_workers`: 4
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `tp_size`: 0
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: True
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`:
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+InformationRetrievalEvaluator](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 | 0.1243 | 0.2956 | 0.6408 | 0.7358 | 0.6947 | 0.1815 |
+| cosine_accuracy@20 | 0.9905 | 1.0 | 0.9754 | 0.9903 | 0.9626 | 0.9678 | 1.0 |
+| cosine_accuracy@50 | 0.9905 | 1.0 | 0.9852 | 0.9903 | 0.9802 | 0.9844 | 1.0 |
+| cosine_accuracy@100 | 0.9905 | 1.0 | 0.9852 | 0.9903 | 0.9927 | 0.9901 | 1.0 |
+| cosine_accuracy@150 | 0.9905 | 1.0 | 0.9901 | 0.9903 | 0.9948 | 0.9932 | 1.0 |
+| cosine_accuracy@200 | 0.9905 | 1.0 | 0.9901 | 0.9903 | 0.9958 | 0.9932 | 1.0 |
+| cosine_precision@1 | 0.6476 | 0.1243 | 0.2956 | 0.6408 | 0.7358 | 0.6947 | 0.1815 |
+| cosine_precision@20 | 0.499 | 0.5719 | 0.5399 | 0.465 | 0.1244 | 0.1277 | 0.1544 |
+| cosine_precision@50 | 0.3027 | 0.3883 | 0.383 | 0.2761 | 0.0516 | 0.0532 | 0.0618 |
+| cosine_precision@100 | 0.1845 | 0.2514 | 0.251 | 0.171 | 0.0262 | 0.027 | 0.0309 |
+| cosine_precision@150 | 0.1316 | 0.1886 | 0.1874 | 0.1229 | 0.0176 | 0.0181 | 0.0206 |
+| cosine_precision@200 | 0.1017 | 0.1508 | 0.1491 | 0.0969 | 0.0133 | 0.0136 | 0.0154 |
+| cosine_recall@1 | 0.0669 | 0.0037 | 0.0111 | 0.0574 | 0.284 | 0.2606 | 0.0587 |
+| cosine_recall@20 | 0.5288 | 0.3813 | 0.3393 | 0.4979 | 0.919 | 0.9266 | 1.0 |
+| cosine_recall@50 | 0.7129 | 0.559 | 0.532 | 0.6612 | 0.9522 | 0.9633 | 1.0 |
+| cosine_recall@100 | 0.8216 | 0.6713 | 0.6497 | 0.7797 | 0.9686 | 0.9771 | 1.0 |
+| cosine_recall@150 | 0.8669 | 0.7296 | 0.7095 | 0.8272 | 0.9763 | 0.9822 | 1.0 |
+| cosine_recall@200 | 0.8882 | 0.7647 | 0.7446 | 0.8638 | 0.9802 | 0.9847 | 1.0 |
+| cosine_ndcg@1 | 0.6476 | 0.1243 | 0.2956 | 0.6408 | 0.7358 | 0.6947 | 0.1815 |
+| cosine_ndcg@20 | 0.6737 | 0.6163 | 0.5621 | 0.6374 | 0.809 | 0.7917 | 0.5447 |
+| cosine_ndcg@50 | 0.6897 | 0.5876 | 0.5506 | 0.6458 | 0.8181 | 0.8018 | 0.5447 |
+| cosine_ndcg@100 | 0.7455 | 0.6146 | 0.5784 | 0.7027 | 0.8217 | 0.805 | 0.5447 |
+| cosine_ndcg@150 | 0.7658 | 0.645 | 0.6092 | 0.7238 | 0.8232 | 0.806 | 0.5447 |
+| **cosine_ndcg@200** | **0.7747** | **0.6628** | **0.6263** | **0.7384** | **0.8239** | **0.8065** | **0.5447** |
+| cosine_mrr@1 | 0.6476 | 0.1243 | 0.2956 | 0.6408 | 0.7358 | 0.6947 | 0.1815 |
+| cosine_mrr@20 | 0.7969 | 0.5586 | 0.5127 | 0.7984 | 0.8035 | 0.7751 | 0.4037 |
+| cosine_mrr@50 | 0.7969 | 0.5586 | 0.5131 | 0.7984 | 0.8042 | 0.7757 | 0.4037 |
+| cosine_mrr@100 | 0.7969 | 0.5586 | 0.5131 | 0.7984 | 0.8043 | 0.7758 | 0.4037 |
+| cosine_mrr@150 | 0.7969 | 0.5586 | 0.5131 | 0.7984 | 0.8044 | 0.7758 | 0.4037 |
+| cosine_mrr@200 | 0.7969 | 0.5586 | 0.5131 | 0.7984 | 0.8044 | 0.7758 | 0.4037 |
+| cosine_map@1 | 0.6476 | 0.1243 | 0.2956 | 0.6408 | 0.7358 | 0.6947 | 0.1815 |
+| cosine_map@20 | 0.5299 | 0.4831 | 0.4209 | 0.4903 | 0.7407 | 0.7123 | 0.3267 |
+| cosine_map@50 | 0.517 | 0.4269 | 0.3779 | 0.4683 | 0.7433 | 0.7152 | 0.3267 |
+| cosine_map@100 | 0.5496 | 0.4303 | 0.3816 | 0.4974 | 0.7438 | 0.7157 | 0.3267 |
+| cosine_map@150 | 0.558 | 0.445 | 0.3962 | 0.5056 | 0.7439 | 0.7158 | 0.3267 |
+| cosine_map@200 | 0.561 | 0.4523 | 0.403 | 0.51 | 0.744 | 0.7158 | 0.3267 |
+| cosine_map@500 | 0.5665 | 0.4644 | 0.4167 | 0.5152 | 0.7441 | 0.7159 | 0.3267 |
+
+
+
+
+
+## Training Details
+
+### Training Datasets
+full_en
+
+#### full_en
+
+* Dataset: full_en
+* Size: 28,880 training samples
+* Columns: anchor and positive
+* Approximate statistics based on the first 1000 samples:
+ | | anchor | positive |
+ |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+ | type | string | string |
+ | details | - min: 3 tokens
- mean: 5.68 tokens
- max: 11 tokens
| - min: 3 tokens
- mean: 5.76 tokens
- max: 12 tokens
|
+* Samples:
+ | anchor | positive |
+ |:-----------------------------------------|:-----------------------------------------|
+ | air commodore | flight lieutenant |
+ | command and control officer | flight officer |
+ | air commodore | command and control officer |
+* Loss: [GISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
+ ```json
+ {'guide': SentenceTransformer(
+ (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
+ (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+ (2): Normalize()
+ ), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
+ ```
+full_de
+
+#### full_de
+
+* Dataset: full_de
+* Size: 23,023 training samples
+* Columns: anchor and positive
+* Approximate statistics based on the first 1000 samples:
+ | | anchor | positive |
+ |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+ | type | string | string |
+ | details | - min: 3 tokens
- mean: 7.99 tokens
- max: 30 tokens
| - min: 3 tokens
- mean: 8.19 tokens
- max: 30 tokens
|
+* Samples:
+ | anchor | positive |
+ |:----------------------------------|:-----------------------------------------------------|
+ | Staffelkommandantin | Kommodore |
+ | Luftwaffenoffizierin | Luftwaffenoffizier/Luftwaffenoffizierin |
+ | Staffelkommandantin | Luftwaffenoffizierin |
+* Loss: [GISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
+ ```json
+ {'guide': SentenceTransformer(
+ (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
+ (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+ (2): Normalize()
+ ), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
+ ```
+full_es
+
+#### full_es
+
+* Dataset: full_es
+* Size: 20,724 training samples
+* Columns: anchor and positive
+* Approximate statistics based on the first 1000 samples:
+ | | anchor | positive |
+ |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+ | type | string | string |
+ | details | - min: 3 tokens
- mean: 9.13 tokens
- max: 32 tokens
| - min: 3 tokens
- mean: 8.84 tokens
- max: 32 tokens
|
+* Samples:
+ | anchor | positive |
+ |:------------------------------------|:-------------------------------------------|
+ | jefe de escuadrón | instructor |
+ | comandante de aeronave | instructor de simulador |
+ | instructor | oficial del Ejército del Aire |
+* Loss: [GISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
+ ```json
+ {'guide': SentenceTransformer(
+ (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
+ (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+ (2): Normalize()
+ ), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
+ ```
+full_zh
+
+#### full_zh
+
+* Dataset: full_zh
+* Size: 30,401 training samples
+* Columns: anchor and positive
+* Approximate statistics based on the first 1000 samples:
+ | | anchor | positive |
+ |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+ | type | string | string |
+ | details | - min: 5 tokens
- mean: 7.15 tokens
- max: 14 tokens
| - min: 5 tokens
- mean: 7.46 tokens
- max: 21 tokens
|
+* Samples:
+ | anchor | positive |
+ |:------------------|:---------------------|
+ | 技术总监 | 技术和运营总监 |
+ | 技术总监 | 技术主管 |
+ | 技术总监 | 技术艺术总监 |
+* Loss: [GISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
+ ```json
+ {'guide': SentenceTransformer(
+ (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
+ (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+ (2): Normalize()
+ ), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
+ ```
+mix
+
+#### mix
+
+* Dataset: mix
+* Size: 21,760 training samples
+* Columns: anchor and positive
+* Approximate statistics based on the first 1000 samples:
+ | | anchor | positive |
+ |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+ | type | string | string |
+ | details | - min: 2 tokens
- mean: 6.71 tokens
- max: 19 tokens
| - min: 2 tokens
- mean: 7.69 tokens
- max: 19 tokens
|
+* Samples:
+ | anchor | positive |
+ |:------------------------------------------|:----------------------------------------------------------------|
+ | technical manager | Technischer Direktor für Bühne, Film und Fernsehen |
+ | head of technical | directora técnica |
+ | head of technical department | 技术艺术总监 |
+* Loss: [GISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
+ ```json
+ {'guide': SentenceTransformer(
+ (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
+ (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+ (2): Normalize()
+ ), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
+ ```
+Click to expand
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 64
+- `per_device_eval_batch_size`: 128
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 2
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 5e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 5
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.05
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: False
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: True
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: True
+- `dataloader_num_workers`: 4
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `tp_size`: 0
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: True
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`:
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false
+ },
+ "cls_token": {
+ "content": "",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false
+ },
+ "eos_token": {
+ "content": "",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false
+ },
+ "mask_token": {
+ "content": "",
+ "lstrip": true,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false
+ },
+ "pad_token": {
+ "content": "",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false
+ },
+ "sep_token": {
+ "content": "",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false
+ },
+ "unk_token": {
+ "content": "",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false
+ }
+}
diff --git a/checkpoint-4200/tokenizer.json b/checkpoint-4200/tokenizer.json
new file mode 100644
index 0000000000000000000000000000000000000000..225ca0f803c45cce8b17cafe2dde2c7682e91938
--- /dev/null
+++ b/checkpoint-4200/tokenizer.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d9a6af42442a3e3e9f05f618eae0bb2d98ca4f6a6406cb80ef7a4fa865204d61
+size 17083052
diff --git a/checkpoint-4200/tokenizer_config.json b/checkpoint-4200/tokenizer_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..95bd7c849ee6a47d5c92805af18d187239c1ba4a
--- /dev/null
+++ b/checkpoint-4200/tokenizer_config.json
@@ -0,0 +1,56 @@
+{
+ "added_tokens_decoder": {
+ "0": {
+ "content": "",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "1": {
+ "content": "",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "2": {
+ "content": "",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "3": {
+ "content": "",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "250001": {
+ "content": "",
+ "lstrip": true,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ }
+ },
+ "bos_token": "",
+ "clean_up_tokenization_spaces": true,
+ "cls_token": "",
+ "eos_token": "",
+ "extra_special_tokens": {},
+ "mask_token": "",
+ "model_max_length": 8192,
+ "pad_token": "",
+ "sep_token": "",
+ "sp_model_kwargs": {},
+ "tokenizer_class": "XLMRobertaTokenizer",
+ "unk_token": ""
+}
diff --git a/checkpoint-4200/trainer_state.json b/checkpoint-4200/trainer_state.json
new file mode 100644
index 0000000000000000000000000000000000000000..2fec3eba360af2a37e1df3ec63c26c2072c1c162
--- /dev/null
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diff --git a/checkpoint-4400/1_Pooling/config.json b/checkpoint-4400/1_Pooling/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..553a16bda12e2a6d2bb35de78c6ea264b7856e6a
--- /dev/null
+++ b/checkpoint-4400/1_Pooling/config.json
@@ -0,0 +1,10 @@
+{
+ "word_embedding_dimension": 1024,
+ "pooling_mode_cls_token": true,
+ "pooling_mode_mean_tokens": false,
+ "pooling_mode_max_tokens": false,
+ "pooling_mode_mean_sqrt_len_tokens": false,
+ "pooling_mode_weightedmean_tokens": false,
+ "pooling_mode_lasttoken": false,
+ "include_prompt": true
+}
\ No newline at end of file
diff --git a/checkpoint-4400/README.md b/checkpoint-4400/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..ddcca7ea09b334267dbc974aa720f69e61b2cc4e
--- /dev/null
+++ b/checkpoint-4400/README.md
@@ -0,0 +1,1440 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:124788
+- loss:GISTEmbedLoss
+base_model: BAAI/bge-m3
+widget:
+- source_sentence: 其他机械、设备和有形货物租赁服务代表
+ sentences:
+ - 其他机械和设备租赁服务工作人员
+ - 电子和电信设备及零部件物流经理
+ - 工业主厨
+- source_sentence: 公交车司机
+ sentences:
+ - 表演灯光设计师
+ - 乙烯基地板安装工
+ - 国际巴士司机
+- source_sentence: online communication manager
+ sentences:
+ - trades union official
+ - social media manager
+ - budget manager
+- source_sentence: Projektmanagerin
+ sentences:
+ - Projektmanager/Projektmanagerin
+ - Category-Manager
+ - Infanterist
+- source_sentence: Volksvertreter
+ sentences:
+ - Parlamentarier
+ - Oberbürgermeister
+ - Konsul
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy@1
+- cosine_accuracy@20
+- cosine_accuracy@50
+- cosine_accuracy@100
+- cosine_accuracy@150
+- cosine_accuracy@200
+- cosine_precision@1
+- cosine_precision@20
+- cosine_precision@50
+- cosine_precision@100
+- cosine_precision@150
+- cosine_precision@200
+- cosine_recall@1
+- cosine_recall@20
+- cosine_recall@50
+- cosine_recall@100
+- cosine_recall@150
+- cosine_recall@200
+- cosine_ndcg@1
+- cosine_ndcg@20
+- cosine_ndcg@50
+- cosine_ndcg@100
+- cosine_ndcg@150
+- cosine_ndcg@200
+- cosine_mrr@1
+- cosine_mrr@20
+- cosine_mrr@50
+- cosine_mrr@100
+- cosine_mrr@150
+- cosine_mrr@200
+- cosine_map@1
+- cosine_map@20
+- cosine_map@50
+- cosine_map@100
+- cosine_map@150
+- cosine_map@200
+- cosine_map@500
+model-index:
+- name: SentenceTransformer based on BAAI/bge-m3
+ results:
+ - task:
+ type: information-retrieval
+ name: Information Retrieval
+ dataset:
+ name: full en
+ type: full_en
+ metrics:
+ - type: cosine_accuracy@1
+ value: 0.6571428571428571
+ name: Cosine Accuracy@1
+ - type: cosine_accuracy@20
+ value: 0.9904761904761905
+ name: Cosine Accuracy@20
+ - type: cosine_accuracy@50
+ value: 0.9904761904761905
+ name: Cosine Accuracy@50
+ - type: cosine_accuracy@100
+ value: 0.9904761904761905
+ name: Cosine Accuracy@100
+ - type: cosine_accuracy@150
+ value: 0.9904761904761905
+ name: Cosine Accuracy@150
+ - type: cosine_accuracy@200
+ value: 0.9904761904761905
+ name: Cosine Accuracy@200
+ - type: cosine_precision@1
+ value: 0.6571428571428571
+ name: Cosine Precision@1
+ - type: cosine_precision@20
+ value: 0.501904761904762
+ name: Cosine Precision@20
+ - type: cosine_precision@50
+ value: 0.30514285714285716
+ name: Cosine Precision@50
+ - type: cosine_precision@100
+ value: 0.18476190476190474
+ name: Cosine Precision@100
+ - type: cosine_precision@150
+ value: 0.13238095238095238
+ name: Cosine Precision@150
+ - type: cosine_precision@200
+ value: 0.10223809523809524
+ name: Cosine Precision@200
+ - type: cosine_recall@1
+ value: 0.06749696615971254
+ name: Cosine Recall@1
+ - type: cosine_recall@20
+ value: 0.5348166179254283
+ name: Cosine Recall@20
+ - type: cosine_recall@50
+ value: 0.7176194992567407
+ name: Cosine Recall@50
+ - type: cosine_recall@100
+ value: 0.8203546241789754
+ name: Cosine Recall@100
+ - type: cosine_recall@150
+ value: 0.8712408549365904
+ name: Cosine Recall@150
+ - type: cosine_recall@200
+ value: 0.8993000584751492
+ name: Cosine Recall@200
+ - type: cosine_ndcg@1
+ value: 0.6571428571428571
+ name: Cosine Ndcg@1
+ - type: cosine_ndcg@20
+ value: 0.6791929962471466
+ name: Cosine Ndcg@20
+ - type: cosine_ndcg@50
+ value: 0.6958143211009435
+ name: Cosine Ndcg@50
+ - type: cosine_ndcg@100
+ value: 0.7493655431536407
+ name: Cosine Ndcg@100
+ - type: cosine_ndcg@150
+ value: 0.7715718645271473
+ name: Cosine Ndcg@150
+ - type: cosine_ndcg@200
+ value: 0.7814931000676181
+ name: Cosine Ndcg@200
+ - type: cosine_mrr@1
+ value: 0.6571428571428571
+ name: Cosine Mrr@1
+ - type: cosine_mrr@20
+ value: 0.8026984126984127
+ name: Cosine Mrr@20
+ - type: cosine_mrr@50
+ value: 0.8026984126984127
+ name: Cosine Mrr@50
+ - type: cosine_mrr@100
+ value: 0.8026984126984127
+ name: Cosine Mrr@100
+ - type: cosine_mrr@150
+ value: 0.8026984126984127
+ name: Cosine Mrr@150
+ - type: cosine_mrr@200
+ value: 0.8026984126984127
+ name: Cosine Mrr@200
+ - type: cosine_map@1
+ value: 0.6571428571428571
+ name: Cosine Map@1
+ - type: cosine_map@20
+ value: 0.5371258373378305
+ name: Cosine Map@20
+ - type: cosine_map@50
+ value: 0.5243155763407285
+ name: Cosine Map@50
+ - type: cosine_map@100
+ value: 0.5561427452138551
+ name: Cosine Map@100
+ - type: cosine_map@150
+ value: 0.5652920456249697
+ name: Cosine Map@150
+ - type: cosine_map@200
+ value: 0.5681007357520309
+ name: Cosine Map@200
+ - type: cosine_map@500
+ value: 0.5730541345190991
+ name: Cosine Map@500
+ - task:
+ type: information-retrieval
+ name: Information Retrieval
+ dataset:
+ name: full es
+ type: full_es
+ metrics:
+ - type: cosine_accuracy@1
+ value: 0.10810810810810811
+ 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: 0.10810810810810811
+ name: Cosine Precision@1
+ - type: cosine_precision@20
+ value: 0.5667567567567569
+ name: Cosine Precision@20
+ - type: cosine_precision@50
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+ name: Cosine Precision@50
+ - type: cosine_precision@100
+ value: 0.25156756756756754
+ name: Cosine Precision@100
+ - type: cosine_precision@150
+ value: 0.18954954954954953
+ name: Cosine Precision@150
+ - type: cosine_precision@200
+ value: 0.15067567567567566
+ name: Cosine Precision@200
+ - type: cosine_recall@1
+ value: 0.0033677005752683685
+ name: Cosine Recall@1
+ - type: cosine_recall@20
+ value: 0.3790230473715137
+ name: Cosine Recall@20
+ - type: cosine_recall@50
+ value: 0.5587328778405388
+ name: Cosine Recall@50
+ - type: cosine_recall@100
+ value: 0.670664457795493
+ name: Cosine Recall@100
+ - type: cosine_recall@150
+ value: 0.7335635895457856
+ name: Cosine Recall@150
+ - type: cosine_recall@200
+ value: 0.766278425246947
+ name: Cosine Recall@200
+ - type: cosine_ndcg@1
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+ name: Cosine Ndcg@1
+ - type: cosine_ndcg@20
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+ name: Cosine Ndcg@20
+ - type: cosine_ndcg@50
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+ name: Cosine Ndcg@50
+ - type: cosine_ndcg@100
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+ name: Cosine Ndcg@100
+ - type: cosine_ndcg@150
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+ name: Cosine Ndcg@150
+ - type: cosine_ndcg@200
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+ name: Cosine Ndcg@200
+ - type: cosine_mrr@1
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+ name: Cosine Mrr@1
+ - type: cosine_mrr@20
+ value: 0.5509009009009009
+ name: Cosine Mrr@20
+ - type: cosine_mrr@50
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+ name: Cosine Mrr@50
+ - type: cosine_mrr@100
+ value: 0.5509009009009009
+ name: Cosine Mrr@100
+ - type: cosine_mrr@150
+ value: 0.5509009009009009
+ name: Cosine Mrr@150
+ - type: cosine_mrr@200
+ value: 0.5509009009009009
+ name: Cosine Mrr@200
+ - type: cosine_map@1
+ value: 0.10810810810810811
+ name: Cosine Map@1
+ - type: cosine_map@20
+ value: 0.48105434805966624
+ name: Cosine Map@20
+ - type: cosine_map@50
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+ name: Cosine Map@50
+ - type: cosine_map@100
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+ name: Cosine Map@100
+ - type: cosine_map@150
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+ - type: cosine_map@200
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+ name: Cosine Map@200
+ - type: cosine_map@500
+ value: 0.4666628908850396
+ name: Cosine Map@500
+ - task:
+ type: information-retrieval
+ name: Information Retrieval
+ dataset:
+ name: full de
+ type: full_de
+ metrics:
+ - type: cosine_accuracy@1
+ value: 0.2955665024630542
+ name: Cosine Accuracy@1
+ - type: cosine_accuracy@20
+ value: 0.9852216748768473
+ name: Cosine Accuracy@20
+ - type: cosine_accuracy@50
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+ name: Cosine Accuracy@50
+ - type: cosine_accuracy@100
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+ name: Cosine Accuracy@100
+ - type: cosine_accuracy@150
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+ name: Cosine Accuracy@150
+ - type: cosine_accuracy@200
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+ name: Cosine Accuracy@200
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+ name: Cosine Precision@20
+ - type: cosine_precision@50
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+ name: Cosine Precision@50
+ - type: cosine_precision@100
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+ name: Cosine Precision@100
+ - type: cosine_precision@150
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+ name: Cosine Precision@150
+ - type: cosine_precision@200
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+ name: Cosine Precision@200
+ - type: cosine_recall@1
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+ name: Cosine Recall@1
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+ name: Cosine Recall@50
+ - type: cosine_recall@100
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+ name: Cosine Recall@100
+ - type: cosine_recall@150
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+ name: Cosine Recall@150
+ - type: cosine_recall@200
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+ name: Cosine Recall@200
+ - type: cosine_ndcg@1
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+ name: Cosine Ndcg@1
+ - type: cosine_ndcg@20
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+ name: Cosine Ndcg@50
+ - type: cosine_ndcg@100
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+ - type: cosine_ndcg@150
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+ - type: cosine_ndcg@200
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+ name: Cosine Ndcg@200
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+ - type: cosine_mrr@200
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+ name: Cosine Mrr@200
+ - type: cosine_map@1
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+ name: Cosine Map@1
+ - type: cosine_map@20
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+ name: Cosine Map@20
+ - type: cosine_map@50
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+ name: Cosine Map@50
+ - type: cosine_map@100
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+ - type: cosine_map@200
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+ name: Cosine Map@200
+ - type: cosine_map@500
+ value: 0.4176754500966089
+ 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
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+ name: Cosine Recall@1
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+ name: Cosine Recall@20
+ - type: cosine_recall@50
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+ name: Cosine Recall@50
+ - type: cosine_recall@100
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+ name: Cosine Recall@100
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+ name: Cosine Mrr@50
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+ name: Cosine Mrr@200
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+ name: Cosine Map@200
+ - type: cosine_map@500
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+ name: Cosine Map@500
+ - task:
+ type: information-retrieval
+ name: Information Retrieval
+ dataset:
+ name: mix es
+ type: mix_es
+ metrics:
+ - type: cosine_accuracy@1
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+ name: Cosine Accuracy@1
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+ name: Cosine Accuracy@50
+ - type: cosine_accuracy@100
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+ name: Cosine Accuracy@100
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+ name: Cosine Accuracy@150
+ - type: cosine_accuracy@200
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+ name: Cosine Accuracy@200
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+ name: Cosine Precision@1
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+ name: Cosine Precision@20
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+ - type: cosine_precision@100
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+ name: Cosine Precision@100
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+ name: Cosine Recall@50
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+ name: Cosine Map@150
+ - type: cosine_map@200
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+ name: Cosine Map@200
+ - type: cosine_map@500
+ value: 0.7456455633479137
+ name: Cosine Map@500
+ - task:
+ type: information-retrieval
+ name: Information Retrieval
+ dataset:
+ name: mix de
+ type: mix_de
+ metrics:
+ - type: cosine_accuracy@1
+ value: 0.6942277691107644
+ name: Cosine Accuracy@1
+ - type: cosine_accuracy@20
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+ name: Cosine Precision@20
+ - type: cosine_precision@50
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+ name: Cosine Precision@50
+ - type: cosine_precision@100
+ value: 0.0270306812272491
+ name: Cosine Precision@100
+ - type: cosine_precision@150
+ value: 0.018110591090310275
+ name: Cosine Precision@150
+ - type: cosine_precision@200
+ value: 0.013616744669786796
+ name: Cosine Precision@200
+ - type: cosine_recall@1
+ value: 0.26120644825793027
+ name: Cosine Recall@1
+ - type: cosine_recall@20
+ value: 0.927873114924597
+ name: Cosine Recall@20
+ - type: cosine_recall@50
+ value: 0.9637285491419657
+ name: Cosine Recall@50
+ - type: cosine_recall@100
+ value: 0.9789391575663027
+ name: Cosine Recall@100
+ - type: cosine_recall@150
+ value: 0.9837926850407349
+ name: Cosine Recall@150
+ - type: cosine_recall@200
+ value: 0.9862194487779511
+ name: Cosine Recall@200
+ - type: cosine_ndcg@1
+ value: 0.6942277691107644
+ name: Cosine Ndcg@1
+ - type: cosine_ndcg@20
+ value: 0.7952836406043297
+ name: Cosine Ndcg@20
+ - type: cosine_ndcg@50
+ value: 0.8052399503452229
+ name: Cosine Ndcg@50
+ - type: cosine_ndcg@100
+ value: 0.8086752401344494
+ name: Cosine Ndcg@100
+ - type: cosine_ndcg@150
+ value: 0.8096382458419952
+ name: Cosine Ndcg@150
+ - type: cosine_ndcg@200
+ value: 0.810085192105751
+ name: Cosine Ndcg@200
+ - type: cosine_mrr@1
+ value: 0.6942277691107644
+ name: Cosine Mrr@1
+ - type: cosine_mrr@20
+ value: 0.7761581892584265
+ name: Cosine Mrr@20
+ - type: cosine_mrr@50
+ value: 0.7766868481375114
+ name: Cosine Mrr@50
+ - type: cosine_mrr@100
+ value: 0.7768104145556238
+ name: Cosine Mrr@100
+ - type: cosine_mrr@150
+ value: 0.7768244234791826
+ name: Cosine Mrr@150
+ - type: cosine_mrr@200
+ value: 0.7768305684544853
+ name: Cosine Mrr@200
+ - type: cosine_map@1
+ value: 0.6942277691107644
+ name: Cosine Map@1
+ - type: cosine_map@20
+ value: 0.7188197545745756
+ name: Cosine Map@20
+ - type: cosine_map@50
+ value: 0.7215707141808124
+ name: Cosine Map@50
+ - type: cosine_map@100
+ value: 0.7220898692554206
+ name: Cosine Map@100
+ - type: cosine_map@150
+ value: 0.7221900369972237
+ name: Cosine Map@150
+ - type: cosine_map@200
+ value: 0.7222223600003219
+ name: Cosine Map@200
+ - type: cosine_map@500
+ value: 0.7222810622423789
+ name: Cosine Map@500
+ - task:
+ type: information-retrieval
+ name: Information Retrieval
+ dataset:
+ name: mix zh
+ type: mix_zh
+ metrics:
+ - type: cosine_accuracy@1
+ value: 0.18200728029121166
+ 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: 0.18200728029121166
+ name: Cosine Precision@1
+ - type: cosine_precision@20
+ value: 0.15439417576703063
+ name: Cosine Precision@20
+ - type: cosine_precision@50
+ value: 0.0617576703068123
+ name: Cosine Precision@50
+ - type: cosine_precision@100
+ value: 0.03087883515340615
+ name: Cosine Precision@100
+ - type: cosine_precision@150
+ value: 0.020585890102270757
+ name: Cosine Precision@150
+ - type: cosine_precision@200
+ value: 0.015439417576703075
+ name: Cosine Precision@200
+ - type: cosine_recall@1
+ value: 0.05850234009360374
+ name: Cosine Recall@1
+ - type: cosine_recall@20
+ value: 1.0
+ name: Cosine Recall@20
+ - type: cosine_recall@50
+ value: 1.0
+ name: Cosine Recall@50
+ - type: cosine_recall@100
+ value: 1.0
+ name: Cosine Recall@100
+ - type: cosine_recall@150
+ value: 1.0
+ name: Cosine Recall@150
+ - type: cosine_recall@200
+ value: 1.0
+ name: Cosine Recall@200
+ - type: cosine_ndcg@1
+ value: 0.18200728029121166
+ name: Cosine Ndcg@1
+ - type: cosine_ndcg@20
+ value: 0.5450053067257837
+ name: Cosine Ndcg@20
+ - type: cosine_ndcg@50
+ value: 0.5450053067257837
+ name: Cosine Ndcg@50
+ - type: cosine_ndcg@100
+ value: 0.5450053067257837
+ name: Cosine Ndcg@100
+ - type: cosine_ndcg@150
+ value: 0.5450053067257837
+ name: Cosine Ndcg@150
+ - type: cosine_ndcg@200
+ value: 0.5450053067257837
+ name: Cosine Ndcg@200
+ - type: cosine_mrr@1
+ value: 0.18200728029121166
+ name: Cosine Mrr@1
+ - type: cosine_mrr@20
+ value: 0.40246777114951904
+ name: Cosine Mrr@20
+ - type: cosine_mrr@50
+ value: 0.40246777114951904
+ name: Cosine Mrr@50
+ - type: cosine_mrr@100
+ value: 0.40246777114951904
+ name: Cosine Mrr@100
+ - type: cosine_mrr@150
+ value: 0.40246777114951904
+ name: Cosine Mrr@150
+ - type: cosine_mrr@200
+ value: 0.40246777114951904
+ name: Cosine Mrr@200
+ - type: cosine_map@1
+ value: 0.18200728029121166
+ name: Cosine Map@1
+ - type: cosine_map@20
+ value: 0.3277096647667185
+ name: Cosine Map@20
+ - type: cosine_map@50
+ value: 0.3277096647667185
+ name: Cosine Map@50
+ - type: cosine_map@100
+ value: 0.3277096647667185
+ name: Cosine Map@100
+ - type: cosine_map@150
+ value: 0.3277096647667185
+ name: Cosine Map@150
+ - type: cosine_map@200
+ value: 0.3277096647667185
+ name: Cosine Map@200
+ - type: cosine_map@500
+ value: 0.3277096647667185
+ name: Cosine Map@500
+---
+
+# SentenceTransformer based on BAAI/bge-m3
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) on the full_en, full_de, full_es, full_zh and mix datasets. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3)
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 1024 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Datasets:**
+ - full_en
+ - full_de
+ - full_es
+ - full_zh
+ - mix
+
+
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+ (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
+ (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+ (2): Normalize()
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("sentence_transformers_model_id")
+# Run inference
+sentences = [
+ 'Volksvertreter',
+ 'Parlamentarier',
+ 'Oberbürgermeister',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 1024]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+
+
+
+
+
+
+## Evaluation
+
+### Metrics
+
+#### Information Retrieval
+
+* Datasets: `full_en`, `full_es`, `full_de`, `full_zh`, `mix_es`, `mix_de` and `mix_zh`
+* Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric | full_en | full_es | full_de | full_zh | mix_es | mix_de | mix_zh |
+|:---------------------|:-----------|:-----------|:-----------|:-----------|:-----------|:-----------|:----------|
+| cosine_accuracy@1 | 0.6571 | 0.1081 | 0.2956 | 0.6602 | 0.7358 | 0.6942 | 0.182 |
+| cosine_accuracy@20 | 0.9905 | 1.0 | 0.9852 | 0.9903 | 0.9615 | 0.9667 | 1.0 |
+| cosine_accuracy@50 | 0.9905 | 1.0 | 0.9852 | 0.9903 | 0.9834 | 0.9834 | 1.0 |
+| cosine_accuracy@100 | 0.9905 | 1.0 | 0.9901 | 0.9903 | 0.9922 | 0.9917 | 1.0 |
+| cosine_accuracy@150 | 0.9905 | 1.0 | 0.9901 | 0.9903 | 0.9943 | 0.9932 | 1.0 |
+| cosine_accuracy@200 | 0.9905 | 1.0 | 0.9901 | 0.9903 | 0.9948 | 0.9943 | 1.0 |
+| cosine_precision@1 | 0.6571 | 0.1081 | 0.2956 | 0.6602 | 0.7358 | 0.6942 | 0.182 |
+| cosine_precision@20 | 0.5019 | 0.5668 | 0.5438 | 0.4704 | 0.1248 | 0.1278 | 0.1544 |
+| cosine_precision@50 | 0.3051 | 0.3878 | 0.3828 | 0.2794 | 0.0517 | 0.0532 | 0.0618 |
+| cosine_precision@100 | 0.1848 | 0.2516 | 0.2493 | 0.1724 | 0.0263 | 0.027 | 0.0309 |
+| cosine_precision@150 | 0.1324 | 0.1895 | 0.1869 | 0.1239 | 0.0177 | 0.0181 | 0.0206 |
+| cosine_precision@200 | 0.1022 | 0.1507 | 0.1503 | 0.0976 | 0.0133 | 0.0136 | 0.0154 |
+| cosine_recall@1 | 0.0675 | 0.0034 | 0.0111 | 0.0655 | 0.284 | 0.2612 | 0.0585 |
+| cosine_recall@20 | 0.5348 | 0.379 | 0.3451 | 0.5049 | 0.9221 | 0.9279 | 1.0 |
+| cosine_recall@50 | 0.7176 | 0.5587 | 0.5334 | 0.6723 | 0.9548 | 0.9637 | 1.0 |
+| cosine_recall@100 | 0.8204 | 0.6707 | 0.6499 | 0.784 | 0.9711 | 0.9789 | 1.0 |
+| cosine_recall@150 | 0.8712 | 0.7336 | 0.7092 | 0.8346 | 0.9778 | 0.9838 | 1.0 |
+| cosine_recall@200 | 0.8993 | 0.7663 | 0.7496 | 0.868 | 0.9823 | 0.9862 | 1.0 |
+| cosine_ndcg@1 | 0.6571 | 0.1081 | 0.2956 | 0.6602 | 0.7358 | 0.6942 | 0.182 |
+| cosine_ndcg@20 | 0.6792 | 0.613 | 0.5671 | 0.6489 | 0.8104 | 0.7953 | 0.545 |
+| cosine_ndcg@50 | 0.6958 | 0.5878 | 0.552 | 0.6583 | 0.8195 | 0.8052 | 0.545 |
+| cosine_ndcg@100 | 0.7494 | 0.6149 | 0.5787 | 0.7133 | 0.823 | 0.8087 | 0.545 |
+| cosine_ndcg@150 | 0.7716 | 0.6471 | 0.6099 | 0.7351 | 0.8243 | 0.8096 | 0.545 |
+| **cosine_ndcg@200** | **0.7815** | **0.6634** | **0.6301** | **0.7489** | **0.8251** | **0.8101** | **0.545** |
+| cosine_mrr@1 | 0.6571 | 0.1081 | 0.2956 | 0.6602 | 0.7358 | 0.6942 | 0.182 |
+| cosine_mrr@20 | 0.8027 | 0.5509 | 0.5163 | 0.8106 | 0.8035 | 0.7762 | 0.4025 |
+| cosine_mrr@50 | 0.8027 | 0.5509 | 0.5163 | 0.8106 | 0.8042 | 0.7767 | 0.4025 |
+| cosine_mrr@100 | 0.8027 | 0.5509 | 0.5164 | 0.8106 | 0.8044 | 0.7768 | 0.4025 |
+| cosine_mrr@150 | 0.8027 | 0.5509 | 0.5164 | 0.8106 | 0.8044 | 0.7768 | 0.4025 |
+| cosine_mrr@200 | 0.8027 | 0.5509 | 0.5164 | 0.8106 | 0.8044 | 0.7768 | 0.4025 |
+| cosine_map@1 | 0.6571 | 0.1081 | 0.2956 | 0.6602 | 0.7358 | 0.6942 | 0.182 |
+| cosine_map@20 | 0.5371 | 0.4811 | 0.4243 | 0.5021 | 0.7424 | 0.7188 | 0.3277 |
+| cosine_map@50 | 0.5243 | 0.4292 | 0.3787 | 0.4804 | 0.7449 | 0.7216 | 0.3277 |
+| cosine_map@100 | 0.5561 | 0.4322 | 0.3818 | 0.5097 | 0.7454 | 0.7221 | 0.3277 |
+| cosine_map@150 | 0.5653 | 0.4473 | 0.3964 | 0.5183 | 0.7455 | 0.7222 | 0.3277 |
+| cosine_map@200 | 0.5681 | 0.4541 | 0.4044 | 0.5226 | 0.7456 | 0.7222 | 0.3277 |
+| cosine_map@500 | 0.5731 | 0.4667 | 0.4177 | 0.5275 | 0.7456 | 0.7223 | 0.3277 |
+
+
+
+
+
+## Training Details
+
+### Training Datasets
+full_en
+
+#### full_en
+
+* Dataset: full_en
+* Size: 28,880 training samples
+* Columns: anchor and positive
+* Approximate statistics based on the first 1000 samples:
+ | | anchor | positive |
+ |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+ | type | string | string |
+ | details | - min: 3 tokens
- mean: 5.68 tokens
- max: 11 tokens
| - min: 3 tokens
- mean: 5.76 tokens
- max: 12 tokens
|
+* Samples:
+ | anchor | positive |
+ |:-----------------------------------------|:-----------------------------------------|
+ | air commodore | flight lieutenant |
+ | command and control officer | flight officer |
+ | air commodore | command and control officer |
+* Loss: [GISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
+ ```json
+ {'guide': SentenceTransformer(
+ (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
+ (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+ (2): Normalize()
+ ), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
+ ```
+full_de
+
+#### full_de
+
+* Dataset: full_de
+* Size: 23,023 training samples
+* Columns: anchor and positive
+* Approximate statistics based on the first 1000 samples:
+ | | anchor | positive |
+ |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+ | type | string | string |
+ | details | - min: 3 tokens
- mean: 7.99 tokens
- max: 30 tokens
| - min: 3 tokens
- mean: 8.19 tokens
- max: 30 tokens
|
+* Samples:
+ | anchor | positive |
+ |:----------------------------------|:-----------------------------------------------------|
+ | Staffelkommandantin | Kommodore |
+ | Luftwaffenoffizierin | Luftwaffenoffizier/Luftwaffenoffizierin |
+ | Staffelkommandantin | Luftwaffenoffizierin |
+* Loss: [GISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
+ ```json
+ {'guide': SentenceTransformer(
+ (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
+ (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+ (2): Normalize()
+ ), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
+ ```
+full_es
+
+#### full_es
+
+* Dataset: full_es
+* Size: 20,724 training samples
+* Columns: anchor and positive
+* Approximate statistics based on the first 1000 samples:
+ | | anchor | positive |
+ |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+ | type | string | string |
+ | details | - min: 3 tokens
- mean: 9.13 tokens
- max: 32 tokens
| - min: 3 tokens
- mean: 8.84 tokens
- max: 32 tokens
|
+* Samples:
+ | anchor | positive |
+ |:------------------------------------|:-------------------------------------------|
+ | jefe de escuadrón | instructor |
+ | comandante de aeronave | instructor de simulador |
+ | instructor | oficial del Ejército del Aire |
+* Loss: [GISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
+ ```json
+ {'guide': SentenceTransformer(
+ (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
+ (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+ (2): Normalize()
+ ), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
+ ```
+full_zh
+
+#### full_zh
+
+* Dataset: full_zh
+* Size: 30,401 training samples
+* Columns: anchor and positive
+* Approximate statistics based on the first 1000 samples:
+ | | anchor | positive |
+ |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+ | type | string | string |
+ | details | - min: 5 tokens
- mean: 7.15 tokens
- max: 14 tokens
| - min: 5 tokens
- mean: 7.46 tokens
- max: 21 tokens
|
+* Samples:
+ | anchor | positive |
+ |:------------------|:---------------------|
+ | 技术总监 | 技术和运营总监 |
+ | 技术总监 | 技术主管 |
+ | 技术总监 | 技术艺术总监 |
+* Loss: [GISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
+ ```json
+ {'guide': SentenceTransformer(
+ (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
+ (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+ (2): Normalize()
+ ), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
+ ```
+mix
+
+#### mix
+
+* Dataset: mix
+* Size: 21,760 training samples
+* Columns: anchor and positive
+* Approximate statistics based on the first 1000 samples:
+ | | anchor | positive |
+ |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+ | type | string | string |
+ | details | - min: 2 tokens
- mean: 6.71 tokens
- max: 19 tokens
| - min: 2 tokens
- mean: 7.69 tokens
- max: 19 tokens
|
+* Samples:
+ | anchor | positive |
+ |:------------------------------------------|:----------------------------------------------------------------|
+ | technical manager | Technischer Direktor für Bühne, Film und Fernsehen |
+ | head of technical | directora técnica |
+ | head of technical department | 技术艺术总监 |
+* Loss: [GISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
+ ```json
+ {'guide': SentenceTransformer(
+ (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
+ (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+ (2): Normalize()
+ ), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
+ ```
+Click to expand
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 64
+- `per_device_eval_batch_size`: 128
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 2
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 5e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 5
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.05
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: False
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: True
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: True
+- `dataloader_num_workers`: 4
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `tp_size`: 0
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: True
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`:
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false
+ },
+ "cls_token": {
+ "content": "",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false
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+ "eos_token": {
+ "content": "",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false
+ },
+ "mask_token": {
+ "content": "",
+ "lstrip": true,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false
+ },
+ "pad_token": {
+ "content": "",
+ "lstrip": false,
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+ "rstrip": false,
+ "single_word": false
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+ "sep_token": {
+ "content": "",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false
+ },
+ "unk_token": {
+ "content": "",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false
+ }
+}
diff --git a/checkpoint-4400/tokenizer.json b/checkpoint-4400/tokenizer.json
new file mode 100644
index 0000000000000000000000000000000000000000..225ca0f803c45cce8b17cafe2dde2c7682e91938
--- /dev/null
+++ b/checkpoint-4400/tokenizer.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d9a6af42442a3e3e9f05f618eae0bb2d98ca4f6a6406cb80ef7a4fa865204d61
+size 17083052
diff --git a/checkpoint-4400/tokenizer_config.json b/checkpoint-4400/tokenizer_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..95bd7c849ee6a47d5c92805af18d187239c1ba4a
--- /dev/null
+++ b/checkpoint-4400/tokenizer_config.json
@@ -0,0 +1,56 @@
+{
+ "added_tokens_decoder": {
+ "0": {
+ "content": "",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "1": {
+ "content": "",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "2": {
+ "content": "",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
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+ "3": {
+ "content": "",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "250001": {
+ "content": "",
+ "lstrip": true,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ }
+ },
+ "bos_token": "",
+ "clean_up_tokenization_spaces": true,
+ "cls_token": "",
+ "eos_token": "",
+ "extra_special_tokens": {},
+ "mask_token": "",
+ "model_max_length": 8192,
+ "pad_token": "",
+ "sep_token": "",
+ "sp_model_kwargs": {},
+ "tokenizer_class": "XLMRobertaTokenizer",
+ "unk_token": ""
+}
diff --git a/checkpoint-4600/1_Pooling/config.json b/checkpoint-4600/1_Pooling/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..553a16bda12e2a6d2bb35de78c6ea264b7856e6a
--- /dev/null
+++ b/checkpoint-4600/1_Pooling/config.json
@@ -0,0 +1,10 @@
+{
+ "word_embedding_dimension": 1024,
+ "pooling_mode_cls_token": true,
+ "pooling_mode_mean_tokens": false,
+ "pooling_mode_max_tokens": false,
+ "pooling_mode_mean_sqrt_len_tokens": false,
+ "pooling_mode_weightedmean_tokens": false,
+ "pooling_mode_lasttoken": false,
+ "include_prompt": true
+}
\ No newline at end of file
diff --git a/checkpoint-4600/README.md b/checkpoint-4600/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..26e9975048a11a8ebf65aefe53a4c0371f7a8787
--- /dev/null
+++ b/checkpoint-4600/README.md
@@ -0,0 +1,1442 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:124788
+- loss:GISTEmbedLoss
+base_model: BAAI/bge-m3
+widget:
+- source_sentence: 其他机械、设备和有形货物租赁服务代表
+ sentences:
+ - 其他机械和设备租赁服务工作人员
+ - 电子和电信设备及零部件物流经理
+ - 工业主厨
+- source_sentence: 公交车司机
+ sentences:
+ - 表演灯光设计师
+ - 乙烯基地板安装工
+ - 国际巴士司机
+- source_sentence: online communication manager
+ sentences:
+ - trades union official
+ - social media manager
+ - budget manager
+- source_sentence: Projektmanagerin
+ sentences:
+ - Projektmanager/Projektmanagerin
+ - Category-Manager
+ - Infanterist
+- source_sentence: Volksvertreter
+ sentences:
+ - Parlamentarier
+ - Oberbürgermeister
+ - Konsul
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy@1
+- cosine_accuracy@20
+- cosine_accuracy@50
+- cosine_accuracy@100
+- cosine_accuracy@150
+- cosine_accuracy@200
+- cosine_precision@1
+- cosine_precision@20
+- cosine_precision@50
+- cosine_precision@100
+- cosine_precision@150
+- cosine_precision@200
+- cosine_recall@1
+- cosine_recall@20
+- cosine_recall@50
+- cosine_recall@100
+- cosine_recall@150
+- cosine_recall@200
+- cosine_ndcg@1
+- cosine_ndcg@20
+- cosine_ndcg@50
+- cosine_ndcg@100
+- cosine_ndcg@150
+- cosine_ndcg@200
+- cosine_mrr@1
+- cosine_mrr@20
+- cosine_mrr@50
+- cosine_mrr@100
+- cosine_mrr@150
+- cosine_mrr@200
+- cosine_map@1
+- cosine_map@20
+- cosine_map@50
+- cosine_map@100
+- cosine_map@150
+- cosine_map@200
+- cosine_map@500
+model-index:
+- name: SentenceTransformer based on BAAI/bge-m3
+ results:
+ - task:
+ type: information-retrieval
+ name: Information Retrieval
+ dataset:
+ name: full en
+ type: full_en
+ metrics:
+ - type: cosine_accuracy@1
+ value: 0.6476190476190476
+ name: Cosine Accuracy@1
+ - type: cosine_accuracy@20
+ value: 0.9904761904761905
+ name: Cosine Accuracy@20
+ - type: cosine_accuracy@50
+ value: 0.9904761904761905
+ name: Cosine Accuracy@50
+ - type: cosine_accuracy@100
+ value: 0.9904761904761905
+ name: Cosine Accuracy@100
+ - type: cosine_accuracy@150
+ value: 0.9904761904761905
+ name: Cosine Accuracy@150
+ - type: cosine_accuracy@200
+ value: 0.9904761904761905
+ name: Cosine Accuracy@200
+ - type: cosine_precision@1
+ value: 0.6476190476190476
+ name: Cosine Precision@1
+ - type: cosine_precision@20
+ value: 0.5033333333333333
+ name: Cosine Precision@20
+ - type: cosine_precision@50
+ value: 0.3051428571428572
+ name: Cosine Precision@50
+ - type: cosine_precision@100
+ value: 0.18504761904761904
+ name: Cosine Precision@100
+ - type: cosine_precision@150
+ value: 0.13263492063492063
+ name: Cosine Precision@150
+ - type: cosine_precision@200
+ value: 0.10238095238095238
+ name: Cosine Precision@200
+ - type: cosine_recall@1
+ value: 0.06690172806447445
+ name: Cosine Recall@1
+ - type: cosine_recall@20
+ value: 0.5361893486281004
+ name: Cosine Recall@20
+ - type: cosine_recall@50
+ value: 0.7178301231768206
+ name: Cosine Recall@50
+ - type: cosine_recall@100
+ value: 0.8209713456799689
+ name: Cosine Recall@100
+ - type: cosine_recall@150
+ value: 0.8719838465781551
+ name: Cosine Recall@150
+ - type: cosine_recall@200
+ value: 0.9002628694890553
+ name: Cosine Recall@200
+ - type: cosine_ndcg@1
+ value: 0.6476190476190476
+ name: Cosine Ndcg@1
+ - type: cosine_ndcg@20
+ value: 0.6792043770713534
+ name: Cosine Ndcg@20
+ - type: cosine_ndcg@50
+ value: 0.6952356840844034
+ name: Cosine Ndcg@50
+ - type: cosine_ndcg@100
+ value: 0.7491776279498115
+ name: Cosine Ndcg@100
+ - type: cosine_ndcg@150
+ value: 0.7714889294157944
+ name: Cosine Ndcg@150
+ - type: cosine_ndcg@200
+ value: 0.7814307168109694
+ name: Cosine Ndcg@200
+ - type: cosine_mrr@1
+ value: 0.6476190476190476
+ name: Cosine Mrr@1
+ - type: cosine_mrr@20
+ value: 0.7979365079365079
+ name: Cosine Mrr@20
+ - type: cosine_mrr@50
+ value: 0.7979365079365079
+ name: Cosine Mrr@50
+ - type: cosine_mrr@100
+ value: 0.7979365079365079
+ name: Cosine Mrr@100
+ - type: cosine_mrr@150
+ value: 0.7979365079365079
+ name: Cosine Mrr@150
+ - type: cosine_mrr@200
+ value: 0.7979365079365079
+ name: Cosine Mrr@200
+ - type: cosine_map@1
+ value: 0.6476190476190476
+ name: Cosine Map@1
+ - type: cosine_map@20
+ value: 0.5373325378117988
+ name: Cosine Map@20
+ - type: cosine_map@50
+ value: 0.5240005650356997
+ name: Cosine Map@50
+ - type: cosine_map@100
+ value: 0.5562356661851569
+ name: Cosine Map@100
+ - type: cosine_map@150
+ value: 0.5654875568184526
+ name: Cosine Map@150
+ - type: cosine_map@200
+ value: 0.5682618444726486
+ name: Cosine Map@200
+ - type: cosine_map@500
+ value: 0.5731371282665402
+ 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: 0.5654054054054054
+ name: Cosine Precision@20
+ - type: cosine_precision@50
+ value: 0.3897297297297298
+ name: Cosine Precision@50
+ - type: cosine_precision@100
+ value: 0.25324324324324327
+ name: Cosine Precision@100
+ - type: cosine_precision@150
+ value: 0.1901981981981982
+ name: Cosine Precision@150
+ - type: cosine_precision@200
+ value: 0.15102702702702703
+ name: Cosine Precision@200
+ - type: cosine_recall@1
+ value: 0.0034454146142631225
+ name: Cosine Recall@1
+ - type: cosine_recall@20
+ value: 0.37952988347964417
+ name: Cosine Recall@20
+ - type: cosine_recall@50
+ value: 0.5627963647085502
+ name: Cosine Recall@50
+ - type: cosine_recall@100
+ value: 0.6735817765534955
+ name: Cosine Recall@100
+ - type: cosine_recall@150
+ value: 0.735181694329396
+ name: Cosine Recall@150
+ - type: cosine_recall@200
+ value: 0.7691645515769362
+ name: Cosine Recall@200
+ - type: cosine_ndcg@1
+ value: 0.11351351351351352
+ name: Cosine Ndcg@1
+ - type: cosine_ndcg@20
+ value: 0.6127764701851742
+ name: Cosine Ndcg@20
+ - type: cosine_ndcg@50
+ value: 0.5903129713737418
+ name: Cosine Ndcg@50
+ - type: cosine_ndcg@100
+ value: 0.6173381508468064
+ name: Cosine Ndcg@100
+ - type: cosine_ndcg@150
+ value: 0.6486192970671256
+ name: Cosine Ndcg@150
+ - type: cosine_ndcg@200
+ value: 0.6654238285942606
+ name: Cosine Ndcg@200
+ - type: cosine_mrr@1
+ value: 0.11351351351351352
+ 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: 0.11351351351351352
+ name: Cosine Map@1
+ - type: cosine_map@20
+ value: 0.47962787853124583
+ name: Cosine Map@20
+ - type: cosine_map@50
+ value: 0.43007675118643823
+ name: Cosine Map@50
+ - type: cosine_map@100
+ value: 0.4338635407926422
+ name: Cosine Map@100
+ - type: cosine_map@150
+ value: 0.4486007040723234
+ name: Cosine Map@150
+ - type: cosine_map@200
+ value: 0.4552653077040697
+ name: Cosine Map@200
+ - type: cosine_map@500
+ value: 0.46787303947870823
+ name: Cosine Map@500
+ - task:
+ type: information-retrieval
+ name: Information Retrieval
+ dataset:
+ name: full de
+ type: full_de
+ metrics:
+ - type: cosine_accuracy@1
+ value: 0.2955665024630542
+ name: Cosine Accuracy@1
+ - type: cosine_accuracy@20
+ value: 0.9852216748768473
+ name: Cosine Accuracy@20
+ - type: cosine_accuracy@50
+ value: 0.9901477832512315
+ name: Cosine Accuracy@50
+ - type: cosine_accuracy@100
+ value: 0.9901477832512315
+ name: Cosine Accuracy@100
+ - type: cosine_accuracy@150
+ value: 0.9901477832512315
+ name: Cosine Accuracy@150
+ - type: cosine_accuracy@200
+ value: 0.9901477832512315
+ name: Cosine Accuracy@200
+ - type: cosine_precision@1
+ value: 0.2955665024630542
+ name: Cosine Precision@1
+ - type: cosine_precision@20
+ value: 0.5406403940886699
+ name: Cosine Precision@20
+ - type: cosine_precision@50
+ value: 0.38216748768472913
+ name: Cosine Precision@50
+ - type: cosine_precision@100
+ value: 0.24970443349753693
+ name: Cosine Precision@100
+ - type: cosine_precision@150
+ value: 0.18712643678160917
+ name: Cosine Precision@150
+ - type: cosine_precision@200
+ value: 0.14992610837438422
+ name: Cosine Precision@200
+ - type: cosine_recall@1
+ value: 0.01108543831680986
+ name: Cosine Recall@1
+ - type: cosine_recall@20
+ value: 0.3424896767056911
+ name: Cosine Recall@20
+ - type: cosine_recall@50
+ value: 0.5329881987535446
+ name: Cosine Recall@50
+ - type: cosine_recall@100
+ value: 0.64863278001433
+ name: Cosine Recall@100
+ - type: cosine_recall@150
+ value: 0.7085620603885778
+ name: Cosine Recall@150
+ - type: cosine_recall@200
+ value: 0.7485450670219227
+ name: Cosine Recall@200
+ - type: cosine_ndcg@1
+ value: 0.2955665024630542
+ name: Cosine Ndcg@1
+ - type: cosine_ndcg@20
+ value: 0.5645228585682827
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+ - type: cosine_ndcg@50
+ value: 0.5512955891986082
+ name: Cosine Ndcg@50
+ - type: cosine_ndcg@100
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+ name: Cosine Ndcg@100
+ - type: cosine_ndcg@150
+ value: 0.6098517448142098
+ name: Cosine Ndcg@150
+ - type: cosine_ndcg@200
+ value: 0.6294320172892106
+ name: Cosine Ndcg@200
+ - type: cosine_mrr@1
+ value: 0.2955665024630542
+ name: Cosine Mrr@1
+ - type: cosine_mrr@20
+ value: 0.5164545268058354
+ name: Cosine Mrr@20
+ - type: cosine_mrr@50
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+ name: Cosine Mrr@50
+ - type: cosine_mrr@100
+ value: 0.5165841612367403
+ name: Cosine Mrr@100
+ - type: cosine_mrr@150
+ value: 0.5165841612367403
+ name: Cosine Mrr@150
+ - type: cosine_mrr@200
+ value: 0.5165841612367403
+ name: Cosine Mrr@200
+ - type: cosine_map@1
+ value: 0.2955665024630542
+ name: Cosine Map@1
+ - type: cosine_map@20
+ value: 0.4226075638788329
+ name: Cosine Map@20
+ - type: cosine_map@50
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+ name: Cosine Map@50
+ - type: cosine_map@100
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+ name: Cosine Map@100
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+ - type: cosine_map@200
+ value: 0.40449418055036124
+ name: Cosine Map@200
+ - type: cosine_map@500
+ value: 0.41779880211141207
+ name: Cosine Map@500
+ - task:
+ type: information-retrieval
+ name: Information Retrieval
+ dataset:
+ name: full zh
+ type: full_zh
+ metrics:
+ - type: cosine_accuracy@1
+ value: 0.6796116504854369
+ name: Cosine Accuracy@1
+ - type: cosine_accuracy@20
+ value: 0.9902912621359223
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+ - type: cosine_accuracy@50
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+ name: Cosine Accuracy@50
+ - type: cosine_accuracy@100
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+ name: Cosine Accuracy@100
+ - type: cosine_accuracy@150
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+ name: Cosine Accuracy@150
+ - type: cosine_accuracy@200
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+ name: Cosine Accuracy@200
+ - type: cosine_precision@1
+ value: 0.6796116504854369
+ name: Cosine Precision@1
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+ name: Cosine Precision@20
+ - type: cosine_precision@50
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+ name: Cosine Precision@100
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+ name: Cosine Recall@1
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+ - type: cosine_recall@50
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+ name: Cosine Recall@100
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+ name: Cosine Ndcg@1
+ - type: cosine_ndcg@20
+ value: 0.6530045751641177
+ name: Cosine Ndcg@20
+ - type: cosine_ndcg@50
+ value: 0.660508346535501
+ name: Cosine Ndcg@50
+ - type: cosine_ndcg@100
+ value: 0.7168972604426495
+ name: Cosine Ndcg@100
+ - type: cosine_ndcg@150
+ value: 0.7384624839319754
+ name: Cosine Ndcg@150
+ - type: cosine_ndcg@200
+ value: 0.7523092684328068
+ name: Cosine Ndcg@200
+ - type: cosine_mrr@1
+ value: 0.6796116504854369
+ name: Cosine Mrr@1
+ - type: cosine_mrr@20
+ value: 0.8217907227615966
+ name: Cosine Mrr@20
+ - type: cosine_mrr@50
+ value: 0.8217907227615966
+ name: Cosine Mrr@50
+ - type: cosine_mrr@100
+ value: 0.8217907227615966
+ name: Cosine Mrr@100
+ - type: cosine_mrr@150
+ value: 0.8217907227615966
+ name: Cosine Mrr@150
+ - type: cosine_mrr@200
+ value: 0.8217907227615966
+ name: Cosine Mrr@200
+ - type: cosine_map@1
+ value: 0.6796116504854369
+ name: Cosine Map@1
+ - type: cosine_map@20
+ value: 0.5041722603829375
+ name: Cosine Map@20
+ - type: cosine_map@50
+ value: 0.48337509847759624
+ name: Cosine Map@50
+ - type: cosine_map@100
+ value: 0.5132814739940859
+ name: Cosine Map@100
+ - type: cosine_map@150
+ value: 0.5217542791488864
+ name: Cosine Map@150
+ - type: cosine_map@200
+ value: 0.5259152810735493
+ name: Cosine Map@200
+ - type: cosine_map@500
+ value: 0.5307415745410603
+ name: Cosine Map@500
+ - task:
+ type: information-retrieval
+ name: Information Retrieval
+ dataset:
+ name: mix es
+ type: mix_es
+ metrics:
+ - type: cosine_accuracy@1
+ value: 0.7384295371814873
+ name: Cosine Accuracy@1
+ - type: cosine_accuracy@20
+ value: 0.9630785231409257
+ name: Cosine Accuracy@20
+ - type: cosine_accuracy@50
+ value: 0.983359334373375
+ name: Cosine Accuracy@50
+ - type: cosine_accuracy@100
+ value: 0.9921996879875195
+ name: Cosine Accuracy@100
+ - type: cosine_accuracy@150
+ value: 0.9942797711908476
+ name: Cosine Accuracy@150
+ - type: cosine_accuracy@200
+ value: 0.9963598543941757
+ name: Cosine Accuracy@200
+ - type: cosine_precision@1
+ value: 0.7384295371814873
+ name: Cosine Precision@1
+ - type: cosine_precision@20
+ value: 0.12472698907956316
+ name: Cosine Precision@20
+ - type: cosine_precision@50
+ value: 0.05177327093083725
+ name: Cosine Precision@50
+ - type: cosine_precision@100
+ value: 0.026271450858034326
+ name: Cosine Precision@100
+ - type: cosine_precision@150
+ value: 0.017635638758883684
+ name: Cosine Precision@150
+ - type: cosine_precision@200
+ value: 0.013273530941237652
+ name: Cosine Precision@200
+ - type: cosine_recall@1
+ value: 0.285115023648565
+ name: Cosine Recall@1
+ - type: cosine_recall@20
+ value: 0.9216415323279598
+ name: Cosine Recall@20
+ - type: cosine_recall@50
+ value: 0.9554082163286531
+ name: Cosine Recall@50
+ - type: cosine_recall@100
+ value: 0.9698387935517421
+ name: Cosine Recall@100
+ - type: cosine_recall@150
+ value: 0.9766337320159473
+ name: Cosine Recall@150
+ - type: cosine_recall@200
+ value: 0.9803813485872768
+ name: Cosine Recall@200
+ - type: cosine_ndcg@1
+ value: 0.7384295371814873
+ name: Cosine Ndcg@1
+ - type: cosine_ndcg@20
+ value: 0.8113619374567514
+ name: Cosine Ndcg@20
+ - type: cosine_ndcg@50
+ value: 0.8206771431445348
+ name: Cosine Ndcg@50
+ - type: cosine_ndcg@100
+ value: 0.8238555673509024
+ name: Cosine Ndcg@100
+ - type: cosine_ndcg@150
+ value: 0.8251818493639627
+ name: Cosine Ndcg@150
+ - type: cosine_ndcg@200
+ value: 0.8258365367322651
+ name: Cosine Ndcg@200
+ - type: cosine_mrr@1
+ value: 0.7384295371814873
+ name: Cosine Mrr@1
+ - type: cosine_mrr@20
+ value: 0.8055430723700101
+ name: Cosine Mrr@20
+ - type: cosine_mrr@50
+ value: 0.806216320765459
+ name: Cosine Mrr@50
+ - type: cosine_mrr@100
+ value: 0.8063554621873477
+ name: Cosine Mrr@100
+ - type: cosine_mrr@150
+ value: 0.8063737837993774
+ name: Cosine Mrr@150
+ - type: cosine_mrr@200
+ value: 0.8063859337270688
+ name: Cosine Mrr@200
+ - type: cosine_map@1
+ value: 0.7384295371814873
+ name: Cosine Map@1
+ - type: cosine_map@20
+ value: 0.7435414485170373
+ name: Cosine Map@20
+ - type: cosine_map@50
+ value: 0.7461028121794654
+ name: Cosine Map@50
+ - type: cosine_map@100
+ value: 0.7465435818030448
+ name: Cosine Map@100
+ - type: cosine_map@150
+ value: 0.7466754353712773
+ name: Cosine Map@150
+ - type: cosine_map@200
+ value: 0.7467184450113576
+ name: Cosine Map@200
+ - type: cosine_map@500
+ value: 0.746815569330807
+ name: Cosine Map@500
+ - task:
+ type: information-retrieval
+ name: Information Retrieval
+ dataset:
+ name: mix de
+ type: mix_de
+ metrics:
+ - type: cosine_accuracy@1
+ value: 0.6895475819032761
+ name: Cosine Accuracy@1
+ - type: cosine_accuracy@20
+ value: 0.9672386895475819
+ name: Cosine Accuracy@20
+ - type: cosine_accuracy@50
+ value: 0.983879355174207
+ name: Cosine Accuracy@50
+ - type: cosine_accuracy@100
+ value: 0.9921996879875195
+ name: Cosine Accuracy@100
+ - type: cosine_accuracy@150
+ value: 0.9932397295891836
+ name: Cosine Accuracy@150
+ - type: cosine_accuracy@200
+ value: 0.9942797711908476
+ name: Cosine Accuracy@200
+ - type: cosine_precision@1
+ value: 0.6895475819032761
+ name: Cosine Precision@1
+ - type: cosine_precision@20
+ value: 0.12810712428497137
+ name: Cosine Precision@20
+ - type: cosine_precision@50
+ value: 0.05329173166926679
+ name: Cosine Precision@50
+ - type: cosine_precision@100
+ value: 0.0270566822672907
+ name: Cosine Precision@100
+ - type: cosine_precision@150
+ value: 0.018107124284971396
+ name: Cosine Precision@150
+ - type: cosine_precision@200
+ value: 0.013619344773790953
+ name: Cosine Precision@200
+ - type: cosine_recall@1
+ value: 0.2593430403882822
+ name: Cosine Recall@1
+ - type: cosine_recall@20
+ value: 0.9296931877275091
+ name: Cosine Recall@20
+ - type: cosine_recall@50
+ value: 0.9652886115444618
+ name: Cosine Recall@50
+ - type: cosine_recall@100
+ value: 0.9797191887675507
+ name: Cosine Recall@100
+ - type: cosine_recall@150
+ value: 0.9835326746403189
+ name: Cosine Recall@150
+ - type: cosine_recall@200
+ value: 0.9862194487779511
+ name: Cosine Recall@200
+ - type: cosine_ndcg@1
+ value: 0.6895475819032761
+ name: Cosine Ndcg@1
+ - type: cosine_ndcg@20
+ value: 0.7960673141716103
+ name: Cosine Ndcg@20
+ - type: cosine_ndcg@50
+ value: 0.8059435010700053
+ name: Cosine Ndcg@50
+ - type: cosine_ndcg@100
+ value: 0.8091935286265849
+ name: Cosine Ndcg@100
+ - type: cosine_ndcg@150
+ value: 0.8099392087586132
+ name: Cosine Ndcg@150
+ - type: cosine_ndcg@200
+ value: 0.8104433137439041
+ name: Cosine Ndcg@200
+ - type: cosine_mrr@1
+ value: 0.6895475819032761
+ name: Cosine Mrr@1
+ - type: cosine_mrr@20
+ value: 0.77474131277538
+ name: Cosine Mrr@20
+ - type: cosine_mrr@50
+ value: 0.7752786950401535
+ name: Cosine Mrr@50
+ - type: cosine_mrr@100
+ value: 0.7753996393885253
+ name: Cosine Mrr@100
+ - type: cosine_mrr@150
+ value: 0.7754090671651442
+ name: Cosine Mrr@150
+ - type: cosine_mrr@200
+ value: 0.7754150110363532
+ name: Cosine Mrr@200
+ - type: cosine_map@1
+ value: 0.6895475819032761
+ name: Cosine Map@1
+ - type: cosine_map@20
+ value: 0.7198609917140115
+ name: Cosine Map@20
+ - type: cosine_map@50
+ value: 0.7225763770177105
+ name: Cosine Map@50
+ - type: cosine_map@100
+ value: 0.7230590007971497
+ name: Cosine Map@100
+ - type: cosine_map@150
+ value: 0.7231361328506057
+ name: Cosine Map@150
+ - type: cosine_map@200
+ value: 0.7231741651357827
+ name: Cosine Map@200
+ - type: cosine_map@500
+ value: 0.7232269917591311
+ name: Cosine Map@500
+ - task:
+ type: information-retrieval
+ name: Information Retrieval
+ dataset:
+ name: mix zh
+ type: mix_zh
+ metrics:
+ - type: cosine_accuracy@1
+ value: 0.17836713468538742
+ 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: 0.17836713468538742
+ name: Cosine Precision@1
+ - type: cosine_precision@20
+ value: 0.15439417576703063
+ name: Cosine Precision@20
+ - type: cosine_precision@50
+ value: 0.0617576703068123
+ name: Cosine Precision@50
+ - type: cosine_precision@100
+ value: 0.03087883515340615
+ name: Cosine Precision@100
+ - type: cosine_precision@150
+ value: 0.020585890102270757
+ name: Cosine Precision@150
+ - type: cosine_precision@200
+ value: 0.015439417576703075
+ name: Cosine Precision@200
+ - type: cosine_recall@1
+ value: 0.05735829433177326
+ name: Cosine Recall@1
+ - type: cosine_recall@20
+ value: 1.0
+ name: Cosine Recall@20
+ - type: cosine_recall@50
+ value: 1.0
+ name: Cosine Recall@50
+ - type: cosine_recall@100
+ value: 1.0
+ name: Cosine Recall@100
+ - type: cosine_recall@150
+ value: 1.0
+ name: Cosine Recall@150
+ - type: cosine_recall@200
+ value: 1.0
+ name: Cosine Recall@200
+ - type: cosine_ndcg@1
+ value: 0.17836713468538742
+ name: Cosine Ndcg@1
+ - type: cosine_ndcg@20
+ value: 0.5435666858139967
+ name: Cosine Ndcg@20
+ - type: cosine_ndcg@50
+ value: 0.5435666858139967
+ name: Cosine Ndcg@50
+ - type: cosine_ndcg@100
+ value: 0.5435666858139967
+ name: Cosine Ndcg@100
+ - type: cosine_ndcg@150
+ value: 0.5435666858139967
+ name: Cosine Ndcg@150
+ - type: cosine_ndcg@200
+ value: 0.5435666858139967
+ name: Cosine Ndcg@200
+ - type: cosine_mrr@1
+ value: 0.17836713468538742
+ name: Cosine Mrr@1
+ - type: cosine_mrr@20
+ value: 0.39877387158978467
+ name: Cosine Mrr@20
+ - type: cosine_mrr@50
+ value: 0.39877387158978467
+ name: Cosine Mrr@50
+ - type: cosine_mrr@100
+ value: 0.39877387158978467
+ name: Cosine Mrr@100
+ - type: cosine_mrr@150
+ value: 0.39877387158978467
+ name: Cosine Mrr@150
+ - type: cosine_mrr@200
+ value: 0.39877387158978467
+ name: Cosine Mrr@200
+ - type: cosine_map@1
+ value: 0.17836713468538742
+ name: Cosine Map@1
+ - type: cosine_map@20
+ value: 0.3263343144665112
+ name: Cosine Map@20
+ - type: cosine_map@50
+ value: 0.3263343144665112
+ name: Cosine Map@50
+ - type: cosine_map@100
+ value: 0.3263343144665112
+ name: Cosine Map@100
+ - type: cosine_map@150
+ value: 0.3263343144665112
+ name: Cosine Map@150
+ - type: cosine_map@200
+ value: 0.3263343144665112
+ name: Cosine Map@200
+ - type: cosine_map@500
+ value: 0.3263343144665112
+ name: Cosine Map@500
+---
+
+# SentenceTransformer based on BAAI/bge-m3
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) on the full_en, full_de, full_es, full_zh and mix datasets. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3)
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 1024 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Datasets:**
+ - full_en
+ - full_de
+ - full_es
+ - full_zh
+ - mix
+
+
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+ (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
+ (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+ (2): Normalize()
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("sentence_transformers_model_id")
+# Run inference
+sentences = [
+ 'Volksvertreter',
+ 'Parlamentarier',
+ 'Oberbürgermeister',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 1024]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+
+
+
+
+
+
+## Evaluation
+
+### Metrics
+
+#### Information Retrieval
+
+* Datasets: `full_en`, `full_es`, `full_de`, `full_zh`, `mix_es`, `mix_de` and `mix_zh`
+* Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric | full_en | full_es | full_de | full_zh | mix_es | mix_de | mix_zh |
+|:---------------------|:-----------|:-----------|:-----------|:-----------|:-----------|:-----------|:-----------|
+| cosine_accuracy@1 | 0.6476 | 0.1135 | 0.2956 | 0.6796 | 0.7384 | 0.6895 | 0.1784 |
+| cosine_accuracy@20 | 0.9905 | 1.0 | 0.9852 | 0.9903 | 0.9631 | 0.9672 | 1.0 |
+| cosine_accuracy@50 | 0.9905 | 1.0 | 0.9901 | 0.9903 | 0.9834 | 0.9839 | 1.0 |
+| cosine_accuracy@100 | 0.9905 | 1.0 | 0.9901 | 0.9903 | 0.9922 | 0.9922 | 1.0 |
+| cosine_accuracy@150 | 0.9905 | 1.0 | 0.9901 | 0.9903 | 0.9943 | 0.9932 | 1.0 |
+| cosine_accuracy@200 | 0.9905 | 1.0 | 0.9901 | 0.9903 | 0.9964 | 0.9943 | 1.0 |
+| cosine_precision@1 | 0.6476 | 0.1135 | 0.2956 | 0.6796 | 0.7384 | 0.6895 | 0.1784 |
+| cosine_precision@20 | 0.5033 | 0.5654 | 0.5406 | 0.4709 | 0.1247 | 0.1281 | 0.1544 |
+| cosine_precision@50 | 0.3051 | 0.3897 | 0.3822 | 0.2794 | 0.0518 | 0.0533 | 0.0618 |
+| cosine_precision@100 | 0.185 | 0.2532 | 0.2497 | 0.1731 | 0.0263 | 0.0271 | 0.0309 |
+| cosine_precision@150 | 0.1326 | 0.1902 | 0.1871 | 0.1243 | 0.0176 | 0.0181 | 0.0206 |
+| cosine_precision@200 | 0.1024 | 0.151 | 0.1499 | 0.0977 | 0.0133 | 0.0136 | 0.0154 |
+| cosine_recall@1 | 0.0669 | 0.0034 | 0.0111 | 0.0682 | 0.2851 | 0.2593 | 0.0574 |
+| cosine_recall@20 | 0.5362 | 0.3795 | 0.3425 | 0.5113 | 0.9216 | 0.9297 | 1.0 |
+| cosine_recall@50 | 0.7178 | 0.5628 | 0.533 | 0.6702 | 0.9554 | 0.9653 | 1.0 |
+| cosine_recall@100 | 0.821 | 0.6736 | 0.6486 | 0.7853 | 0.9698 | 0.9797 | 1.0 |
+| cosine_recall@150 | 0.872 | 0.7352 | 0.7086 | 0.835 | 0.9766 | 0.9835 | 1.0 |
+| cosine_recall@200 | 0.9003 | 0.7692 | 0.7485 | 0.8696 | 0.9804 | 0.9862 | 1.0 |
+| cosine_ndcg@1 | 0.6476 | 0.1135 | 0.2956 | 0.6796 | 0.7384 | 0.6895 | 0.1784 |
+| cosine_ndcg@20 | 0.6792 | 0.6128 | 0.5645 | 0.653 | 0.8114 | 0.7961 | 0.5436 |
+| cosine_ndcg@50 | 0.6952 | 0.5903 | 0.5513 | 0.6605 | 0.8207 | 0.8059 | 0.5436 |
+| cosine_ndcg@100 | 0.7492 | 0.6173 | 0.5786 | 0.7169 | 0.8239 | 0.8092 | 0.5436 |
+| cosine_ndcg@150 | 0.7715 | 0.6486 | 0.6099 | 0.7385 | 0.8252 | 0.8099 | 0.5436 |
+| **cosine_ndcg@200** | **0.7814** | **0.6654** | **0.6294** | **0.7523** | **0.8258** | **0.8104** | **0.5436** |
+| cosine_mrr@1 | 0.6476 | 0.1135 | 0.2956 | 0.6796 | 0.7384 | 0.6895 | 0.1784 |
+| cosine_mrr@20 | 0.7979 | 0.5532 | 0.5165 | 0.8218 | 0.8055 | 0.7747 | 0.3988 |
+| cosine_mrr@50 | 0.7979 | 0.5532 | 0.5166 | 0.8218 | 0.8062 | 0.7753 | 0.3988 |
+| cosine_mrr@100 | 0.7979 | 0.5532 | 0.5166 | 0.8218 | 0.8064 | 0.7754 | 0.3988 |
+| cosine_mrr@150 | 0.7979 | 0.5532 | 0.5166 | 0.8218 | 0.8064 | 0.7754 | 0.3988 |
+| cosine_mrr@200 | 0.7979 | 0.5532 | 0.5166 | 0.8218 | 0.8064 | 0.7754 | 0.3988 |
+| cosine_map@1 | 0.6476 | 0.1135 | 0.2956 | 0.6796 | 0.7384 | 0.6895 | 0.1784 |
+| cosine_map@20 | 0.5373 | 0.4796 | 0.4226 | 0.5042 | 0.7435 | 0.7199 | 0.3263 |
+| cosine_map@50 | 0.524 | 0.4301 | 0.3782 | 0.4834 | 0.7461 | 0.7226 | 0.3263 |
+| cosine_map@100 | 0.5562 | 0.4339 | 0.382 | 0.5133 | 0.7465 | 0.7231 | 0.3263 |
+| cosine_map@150 | 0.5655 | 0.4486 | 0.3969 | 0.5218 | 0.7467 | 0.7231 | 0.3263 |
+| cosine_map@200 | 0.5683 | 0.4553 | 0.4045 | 0.5259 | 0.7467 | 0.7232 | 0.3263 |
+| cosine_map@500 | 0.5731 | 0.4679 | 0.4178 | 0.5307 | 0.7468 | 0.7232 | 0.3263 |
+
+
+
+
+
+## Training Details
+
+### Training Datasets
+full_en
+
+#### full_en
+
+* Dataset: full_en
+* Size: 28,880 training samples
+* Columns: anchor and positive
+* Approximate statistics based on the first 1000 samples:
+ | | anchor | positive |
+ |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+ | type | string | string |
+ | details | - min: 3 tokens
- mean: 5.68 tokens
- max: 11 tokens
| - min: 3 tokens
- mean: 5.76 tokens
- max: 12 tokens
|
+* Samples:
+ | anchor | positive |
+ |:-----------------------------------------|:-----------------------------------------|
+ | air commodore | flight lieutenant |
+ | command and control officer | flight officer |
+ | air commodore | command and control officer |
+* Loss: [GISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
+ ```json
+ {'guide': SentenceTransformer(
+ (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
+ (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+ (2): Normalize()
+ ), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
+ ```
+full_de
+
+#### full_de
+
+* Dataset: full_de
+* Size: 23,023 training samples
+* Columns: anchor and positive
+* Approximate statistics based on the first 1000 samples:
+ | | anchor | positive |
+ |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+ | type | string | string |
+ | details | - min: 3 tokens
- mean: 7.99 tokens
- max: 30 tokens
| - min: 3 tokens
- mean: 8.19 tokens
- max: 30 tokens
|
+* Samples:
+ | anchor | positive |
+ |:----------------------------------|:-----------------------------------------------------|
+ | Staffelkommandantin | Kommodore |
+ | Luftwaffenoffizierin | Luftwaffenoffizier/Luftwaffenoffizierin |
+ | Staffelkommandantin | Luftwaffenoffizierin |
+* Loss: [GISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
+ ```json
+ {'guide': SentenceTransformer(
+ (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
+ (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+ (2): Normalize()
+ ), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
+ ```
+full_es
+
+#### full_es
+
+* Dataset: full_es
+* Size: 20,724 training samples
+* Columns: anchor and positive
+* Approximate statistics based on the first 1000 samples:
+ | | anchor | positive |
+ |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+ | type | string | string |
+ | details | - min: 3 tokens
- mean: 9.13 tokens
- max: 32 tokens
| - min: 3 tokens
- mean: 8.84 tokens
- max: 32 tokens
|
+* Samples:
+ | anchor | positive |
+ |:------------------------------------|:-------------------------------------------|
+ | jefe de escuadrón | instructor |
+ | comandante de aeronave | instructor de simulador |
+ | instructor | oficial del Ejército del Aire |
+* Loss: [GISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
+ ```json
+ {'guide': SentenceTransformer(
+ (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
+ (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+ (2): Normalize()
+ ), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
+ ```
+full_zh
+
+#### full_zh
+
+* Dataset: full_zh
+* Size: 30,401 training samples
+* Columns: anchor and positive
+* Approximate statistics based on the first 1000 samples:
+ | | anchor | positive |
+ |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+ | type | string | string |
+ | details | - min: 5 tokens
- mean: 7.15 tokens
- max: 14 tokens
| - min: 5 tokens
- mean: 7.46 tokens
- max: 21 tokens
|
+* Samples:
+ | anchor | positive |
+ |:------------------|:---------------------|
+ | 技术总监 | 技术和运营总监 |
+ | 技术总监 | 技术主管 |
+ | 技术总监 | 技术艺术总监 |
+* Loss: [GISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
+ ```json
+ {'guide': SentenceTransformer(
+ (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
+ (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+ (2): Normalize()
+ ), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
+ ```
+mix
+
+#### mix
+
+* Dataset: mix
+* Size: 21,760 training samples
+* Columns: anchor and positive
+* Approximate statistics based on the first 1000 samples:
+ | | anchor | positive |
+ |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+ | type | string | string |
+ | details | - min: 2 tokens
- mean: 6.71 tokens
- max: 19 tokens
| - min: 2 tokens
- mean: 7.69 tokens
- max: 19 tokens
|
+* Samples:
+ | anchor | positive |
+ |:------------------------------------------|:----------------------------------------------------------------|
+ | technical manager | Technischer Direktor für Bühne, Film und Fernsehen |
+ | head of technical | directora técnica |
+ | head of technical department | 技术艺术总监 |
+* Loss: [GISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
+ ```json
+ {'guide': SentenceTransformer(
+ (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
+ (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+ (2): Normalize()
+ ), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
+ ```
+Click to expand
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 64
+- `per_device_eval_batch_size`: 128
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 2
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 5e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 5
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.05
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: False
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: True
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: True
+- `dataloader_num_workers`: 4
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `tp_size`: 0
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: True
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`:
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "1": {
+ "content": "",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "2": {
+ "content": "",
+ "lstrip": false,
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+ "rstrip": false,
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+ "special": true
+ },
+ "3": {
+ "content": "",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "250001": {
+ "content": "",
+ "lstrip": true,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ }
+ },
+ "bos_token": "",
+ "clean_up_tokenization_spaces": true,
+ "cls_token": "",
+ "eos_token": "",
+ "extra_special_tokens": {},
+ "mask_token": "",
+ "model_max_length": 8192,
+ "pad_token": "",
+ "sep_token": "",
+ "sp_model_kwargs": {},
+ "tokenizer_class": "XLMRobertaTokenizer",
+ "unk_token": ""
+}
diff --git a/checkpoint-4600/trainer_state.json b/checkpoint-4600/trainer_state.json
new file mode 100644
index 0000000000000000000000000000000000000000..4e40ccce3b587ca7a1a41e4d60106c794550c7bb
--- /dev/null
+++ b/checkpoint-4600/trainer_state.json
@@ -0,0 +1,6504 @@
+{
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diff --git a/checkpoint-4600/training_args.bin b/checkpoint-4600/training_args.bin
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index 0000000000000000000000000000000000000000..015266d3f3560b2a700cdf208fd26045e1f83844
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+version https://git-lfs.github.com/spec/v1
+oid sha256:1a948c4f5667f6700da28d0d70c0c6f024b018ee933ba85d5cc9de9d626dadca
+size 5624
diff --git a/checkpoint-4800/README.md b/checkpoint-4800/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..062c0f07d1014cdb4c055dd1594437d3d0cd7b42
--- /dev/null
+++ b/checkpoint-4800/README.md
@@ -0,0 +1,1444 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:124788
+- loss:GISTEmbedLoss
+base_model: BAAI/bge-m3
+widget:
+- source_sentence: 其他机械、设备和有形货物租赁服务代表
+ sentences:
+ - 其他机械和设备租赁服务工作人员
+ - 电子和电信设备及零部件物流经理
+ - 工业主厨
+- source_sentence: 公交车司机
+ sentences:
+ - 表演灯光设计师
+ - 乙烯基地板安装工
+ - 国际巴士司机
+- source_sentence: online communication manager
+ sentences:
+ - trades union official
+ - social media manager
+ - budget manager
+- source_sentence: Projektmanagerin
+ sentences:
+ - Projektmanager/Projektmanagerin
+ - Category-Manager
+ - Infanterist
+- source_sentence: Volksvertreter
+ sentences:
+ - Parlamentarier
+ - Oberbürgermeister
+ - Konsul
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy@1
+- cosine_accuracy@20
+- cosine_accuracy@50
+- cosine_accuracy@100
+- cosine_accuracy@150
+- cosine_accuracy@200
+- cosine_precision@1
+- cosine_precision@20
+- cosine_precision@50
+- cosine_precision@100
+- cosine_precision@150
+- cosine_precision@200
+- cosine_recall@1
+- cosine_recall@20
+- cosine_recall@50
+- cosine_recall@100
+- cosine_recall@150
+- cosine_recall@200
+- cosine_ndcg@1
+- cosine_ndcg@20
+- cosine_ndcg@50
+- cosine_ndcg@100
+- cosine_ndcg@150
+- cosine_ndcg@200
+- cosine_mrr@1
+- cosine_mrr@20
+- cosine_mrr@50
+- cosine_mrr@100
+- cosine_mrr@150
+- cosine_mrr@200
+- cosine_map@1
+- cosine_map@20
+- cosine_map@50
+- cosine_map@100
+- cosine_map@150
+- cosine_map@200
+- cosine_map@500
+model-index:
+- name: SentenceTransformer based on BAAI/bge-m3
+ results:
+ - task:
+ type: information-retrieval
+ name: Information Retrieval
+ dataset:
+ name: full en
+ type: full_en
+ metrics:
+ - type: cosine_accuracy@1
+ value: 0.6476190476190476
+ name: Cosine Accuracy@1
+ - type: cosine_accuracy@20
+ value: 0.9904761904761905
+ name: Cosine Accuracy@20
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+ 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
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+ value: 0.9904761904761905
+ name: Cosine Accuracy@200
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+ value: 0.6476190476190476
+ name: Cosine Precision@1
+ - type: cosine_precision@20
+ value: 0.5061904761904762
+ name: Cosine Precision@20
+ - type: cosine_precision@50
+ value: 0.30647619047619057
+ name: Cosine Precision@50
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+ value: 0.1858095238095238
+ name: Cosine Precision@100
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+ value: 0.13250793650793652
+ name: Cosine Precision@150
+ - type: cosine_precision@200
+ value: 0.10247619047619047
+ name: Cosine Precision@200
+ - type: cosine_recall@1
+ value: 0.06690172806447445
+ name: Cosine Recall@1
+ - type: cosine_recall@20
+ value: 0.5391510592522911
+ name: Cosine Recall@20
+ - type: cosine_recall@50
+ value: 0.7199711948587544
+ name: Cosine Recall@50
+ - type: cosine_recall@100
+ value: 0.8253770621157605
+ name: Cosine Recall@100
+ - type: cosine_recall@150
+ value: 0.8719997123512196
+ name: Cosine Recall@150
+ - type: cosine_recall@200
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+ name: Cosine Recall@200
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+ name: Cosine Map@20
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+ name: Cosine Map@200
+ - type: cosine_map@500
+ value: 0.5743595458034346
+ name: Cosine Map@500
+ - task:
+ type: information-retrieval
+ name: Information Retrieval
+ dataset:
+ name: full es
+ type: full_es
+ metrics:
+ - type: cosine_accuracy@1
+ value: 0.11351351351351352
+ name: Cosine Accuracy@1
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+ value: 1.0
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+ - type: cosine_accuracy@200
+ value: 1.0
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+ - type: cosine_precision@50
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+ name: Cosine Precision@50
+ - type: cosine_precision@100
+ value: 0.25254054054054054
+ name: Cosine Precision@100
+ - type: cosine_precision@150
+ value: 0.19005405405405407
+ name: Cosine Precision@150
+ - type: cosine_precision@200
+ value: 0.1507837837837838
+ name: Cosine Precision@200
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+ value: 0.0035155918996302815
+ name: Cosine Recall@1
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+ value: 0.37958552840441906
+ name: Cosine Recall@20
+ - type: cosine_recall@50
+ value: 0.5635730197468752
+ name: Cosine Recall@50
+ - type: cosine_recall@100
+ value: 0.672698242387141
+ name: Cosine Recall@100
+ - type: cosine_recall@150
+ value: 0.7360036980055802
+ name: Cosine Recall@150
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+ name: Cosine Recall@200
+ - type: cosine_ndcg@1
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+ value: 0.5536036036036036
+ name: Cosine Mrr@20
+ - type: cosine_mrr@50
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+ name: Cosine Mrr@50
+ - type: cosine_mrr@100
+ value: 0.5536036036036036
+ name: Cosine Mrr@100
+ - type: cosine_mrr@150
+ value: 0.5536036036036036
+ name: Cosine Mrr@150
+ - type: cosine_mrr@200
+ value: 0.5536036036036036
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+ - type: cosine_map@1
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+ - type: cosine_map@500
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+ - task:
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+ 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
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+ name: Cosine Ndcg@200
+ - type: cosine_mrr@1
+ value: 0.6796116504854369
+ name: Cosine Mrr@1
+ - type: cosine_mrr@20
+ value: 0.8216828478964402
+ name: Cosine Mrr@20
+ - type: cosine_mrr@50
+ value: 0.8216828478964402
+ name: Cosine Mrr@50
+ - type: cosine_mrr@100
+ value: 0.8216828478964402
+ name: Cosine Mrr@100
+ - type: cosine_mrr@150
+ value: 0.8216828478964402
+ name: Cosine Mrr@150
+ - type: cosine_mrr@200
+ value: 0.8216828478964402
+ name: Cosine Mrr@200
+ - type: cosine_map@1
+ value: 0.6796116504854369
+ name: Cosine Map@1
+ - type: cosine_map@20
+ value: 0.5012149610968577
+ name: Cosine Map@20
+ - type: cosine_map@50
+ value: 0.48128476255481567
+ name: Cosine Map@50
+ - type: cosine_map@100
+ value: 0.5105374388587102
+ name: Cosine Map@100
+ - type: cosine_map@150
+ value: 0.518381647971727
+ name: Cosine Map@150
+ - type: cosine_map@200
+ value: 0.5228375783347256
+ name: Cosine Map@200
+ - type: cosine_map@500
+ value: 0.52765377953199
+ name: Cosine Map@500
+ - task:
+ type: information-retrieval
+ name: Information Retrieval
+ dataset:
+ name: mix es
+ type: mix_es
+ metrics:
+ - type: cosine_accuracy@1
+ value: 0.7394695787831513
+ name: Cosine Accuracy@1
+ - type: cosine_accuracy@20
+ value: 0.9635985439417577
+ name: Cosine Accuracy@20
+ - type: cosine_accuracy@50
+ value: 0.982839313572543
+ name: Cosine Accuracy@50
+ - type: cosine_accuracy@100
+ value: 0.9927197087883516
+ name: Cosine Accuracy@100
+ - type: cosine_accuracy@150
+ value: 0.9947997919916797
+ name: Cosine Accuracy@150
+ - type: cosine_accuracy@200
+ value: 0.9963598543941757
+ name: Cosine Accuracy@200
+ - type: cosine_precision@1
+ value: 0.7394695787831513
+ name: Cosine Precision@1
+ - type: cosine_precision@20
+ value: 0.12488299531981278
+ name: Cosine Precision@20
+ - type: cosine_precision@50
+ value: 0.05174206968278733
+ name: Cosine Precision@50
+ - type: cosine_precision@100
+ value: 0.02629225169006761
+ name: Cosine Precision@100
+ - type: cosine_precision@150
+ value: 0.017635638758883684
+ name: Cosine Precision@150
+ - type: cosine_precision@200
+ value: 0.013281331253250133
+ name: Cosine Precision@200
+ - type: cosine_recall@1
+ value: 0.28537503404898107
+ name: Cosine Recall@1
+ - type: cosine_recall@20
+ value: 0.9225949037961519
+ name: Cosine Recall@20
+ - type: cosine_recall@50
+ value: 0.9548015253943491
+ name: Cosine Recall@50
+ - type: cosine_recall@100
+ value: 0.970532154619518
+ name: Cosine Recall@100
+ - type: cosine_recall@150
+ value: 0.9766337320159473
+ name: Cosine Recall@150
+ - type: cosine_recall@200
+ value: 0.9810747096550528
+ name: Cosine Recall@200
+ - type: cosine_ndcg@1
+ value: 0.7394695787831513
+ name: Cosine Ndcg@1
+ - type: cosine_ndcg@20
+ value: 0.8119072371250002
+ name: Cosine Ndcg@20
+ - type: cosine_ndcg@50
+ value: 0.8208055075822587
+ name: Cosine Ndcg@50
+ - type: cosine_ndcg@100
+ value: 0.8242798548838444
+ name: Cosine Ndcg@100
+ - type: cosine_ndcg@150
+ value: 0.8254601712767063
+ name: Cosine Ndcg@150
+ - type: cosine_ndcg@200
+ value: 0.826231823086538
+ name: Cosine Ndcg@200
+ - type: cosine_mrr@1
+ value: 0.7394695787831513
+ name: Cosine Mrr@1
+ - type: cosine_mrr@20
+ value: 0.8059183822863336
+ name: Cosine Mrr@20
+ - type: cosine_mrr@50
+ value: 0.8065662458714291
+ name: Cosine Mrr@50
+ - type: cosine_mrr@100
+ value: 0.8067209669800003
+ name: Cosine Mrr@100
+ - type: cosine_mrr@150
+ value: 0.8067371899834064
+ name: Cosine Mrr@150
+ - type: cosine_mrr@200
+ value: 0.8067455244059942
+ name: Cosine Mrr@200
+ - type: cosine_map@1
+ value: 0.7394695787831513
+ name: Cosine Map@1
+ - type: cosine_map@20
+ value: 0.7439811728319751
+ name: Cosine Map@20
+ - type: cosine_map@50
+ value: 0.7464542457655368
+ name: Cosine Map@50
+ - type: cosine_map@100
+ value: 0.7469341154545359
+ name: Cosine Map@100
+ - type: cosine_map@150
+ value: 0.7470471963812441
+ name: Cosine Map@150
+ - type: cosine_map@200
+ value: 0.7471010455519603
+ name: Cosine Map@200
+ - type: cosine_map@500
+ value: 0.7471920688836787
+ name: Cosine Map@500
+ - task:
+ type: information-retrieval
+ name: Information Retrieval
+ dataset:
+ name: mix de
+ type: mix_de
+ metrics:
+ - type: cosine_accuracy@1
+ value: 0.6926677067082684
+ name: Cosine Accuracy@1
+ - type: cosine_accuracy@20
+ value: 0.9641185647425897
+ name: Cosine Accuracy@20
+ - type: cosine_accuracy@50
+ value: 0.983879355174207
+ name: Cosine Accuracy@50
+ - type: cosine_accuracy@100
+ value: 0.9921996879875195
+ name: Cosine Accuracy@100
+ - type: cosine_accuracy@150
+ value: 0.9932397295891836
+ name: Cosine Accuracy@150
+ - type: cosine_accuracy@200
+ value: 0.9942797711908476
+ name: Cosine Accuracy@200
+ - type: cosine_precision@1
+ value: 0.6926677067082684
+ name: Cosine Precision@1
+ - type: cosine_precision@20
+ value: 0.12797711908476336
+ name: Cosine Precision@20
+ - type: cosine_precision@50
+ value: 0.053281331253250144
+ name: Cosine Precision@50
+ - type: cosine_precision@100
+ value: 0.027051482059282376
+ name: Cosine Precision@100
+ - type: cosine_precision@150
+ value: 0.018110591090310275
+ name: Cosine Precision@150
+ - type: cosine_precision@200
+ value: 0.013619344773790953
+ name: Cosine Precision@200
+ - type: cosine_recall@1
+ value: 0.2603830819899463
+ name: Cosine Recall@1
+ - type: cosine_recall@20
+ value: 0.928479805858901
+ name: Cosine Recall@20
+ - type: cosine_recall@50
+ value: 0.9650286011440458
+ name: Cosine Recall@50
+ - type: cosine_recall@100
+ value: 0.9796325186340786
+ name: Cosine Recall@100
+ - type: cosine_recall@150
+ value: 0.9837060149072628
+ name: Cosine Recall@150
+ - type: cosine_recall@200
+ value: 0.9862194487779511
+ name: Cosine Recall@200
+ - type: cosine_ndcg@1
+ value: 0.6926677067082684
+ name: Cosine Ndcg@1
+ - type: cosine_ndcg@20
+ value: 0.7967328692326251
+ name: Cosine Ndcg@20
+ - type: cosine_ndcg@50
+ value: 0.8068705787791701
+ name: Cosine Ndcg@50
+ - type: cosine_ndcg@100
+ value: 0.810158579950017
+ name: Cosine Ndcg@100
+ - type: cosine_ndcg@150
+ value: 0.8109641919896999
+ name: Cosine Ndcg@150
+ - type: cosine_ndcg@200
+ value: 0.8114360342473703
+ name: Cosine Ndcg@200
+ - type: cosine_mrr@1
+ value: 0.6926677067082684
+ name: Cosine Mrr@1
+ - type: cosine_mrr@20
+ value: 0.7766838069642311
+ name: Cosine Mrr@20
+ - type: cosine_mrr@50
+ value: 0.7773792960985305
+ name: Cosine Mrr@50
+ - type: cosine_mrr@100
+ value: 0.7775026273925645
+ name: Cosine Mrr@100
+ - type: cosine_mrr@150
+ value: 0.7775124036000293
+ name: Cosine Mrr@150
+ - type: cosine_mrr@200
+ value: 0.7775182983569378
+ name: Cosine Mrr@200
+ - type: cosine_map@1
+ value: 0.6926677067082684
+ name: Cosine Map@1
+ - type: cosine_map@20
+ value: 0.7210301157895639
+ name: Cosine Map@20
+ - type: cosine_map@50
+ value: 0.7237555751939095
+ name: Cosine Map@50
+ - type: cosine_map@100
+ value: 0.7242426468613273
+ name: Cosine Map@100
+ - type: cosine_map@150
+ value: 0.7243265313145111
+ name: Cosine Map@150
+ - type: cosine_map@200
+ value: 0.7243628241480395
+ name: Cosine Map@200
+ - type: cosine_map@500
+ value: 0.7244144669299598
+ name: Cosine Map@500
+ - task:
+ type: information-retrieval
+ name: Information Retrieval
+ dataset:
+ name: mix zh
+ type: mix_zh
+ metrics:
+ - type: cosine_accuracy@1
+ value: 0.17888715548621945
+ name: Cosine Accuracy@1
+ - type: cosine_accuracy@20
+ value: 1.0
+ name: Cosine Accuracy@20
+ - type: cosine_accuracy@50
+ value: 1.0
+ name: Cosine Accuracy@50
+ - type: cosine_accuracy@100
+ value: 1.0
+ name: Cosine Accuracy@100
+ - type: cosine_accuracy@150
+ value: 1.0
+ name: Cosine Accuracy@150
+ - type: cosine_accuracy@200
+ value: 1.0
+ name: Cosine Accuracy@200
+ - type: cosine_precision@1
+ value: 0.17888715548621945
+ name: Cosine Precision@1
+ - type: cosine_precision@20
+ value: 0.15439417576703063
+ name: Cosine Precision@20
+ - type: cosine_precision@50
+ value: 0.0617576703068123
+ name: Cosine Precision@50
+ - type: cosine_precision@100
+ value: 0.03087883515340615
+ name: Cosine Precision@100
+ - type: cosine_precision@150
+ value: 0.020585890102270757
+ name: Cosine Precision@150
+ - type: cosine_precision@200
+ value: 0.015439417576703075
+ name: Cosine Precision@200
+ - type: cosine_recall@1
+ value: 0.05768764083896689
+ name: Cosine Recall@1
+ - type: cosine_recall@20
+ value: 1.0
+ name: Cosine Recall@20
+ - type: cosine_recall@50
+ value: 1.0
+ name: Cosine Recall@50
+ - type: cosine_recall@100
+ value: 1.0
+ name: Cosine Recall@100
+ - type: cosine_recall@150
+ value: 1.0
+ name: Cosine Recall@150
+ - type: cosine_recall@200
+ value: 1.0
+ name: Cosine Recall@200
+ - type: cosine_ndcg@1
+ value: 0.17888715548621945
+ name: Cosine Ndcg@1
+ - type: cosine_ndcg@20
+ value: 0.5443156532634228
+ name: Cosine Ndcg@20
+ - type: cosine_ndcg@50
+ value: 0.5443156532634228
+ name: Cosine Ndcg@50
+ - type: cosine_ndcg@100
+ value: 0.5443156532634228
+ name: Cosine Ndcg@100
+ - type: cosine_ndcg@150
+ value: 0.5443156532634228
+ name: Cosine Ndcg@150
+ - type: cosine_ndcg@200
+ value: 0.5443156532634228
+ name: Cosine Ndcg@200
+ - type: cosine_mrr@1
+ value: 0.17888715548621945
+ name: Cosine Mrr@1
+ - type: cosine_mrr@20
+ value: 0.4002437442375043
+ name: Cosine Mrr@20
+ - type: cosine_mrr@50
+ value: 0.4002437442375043
+ name: Cosine Mrr@50
+ - type: cosine_mrr@100
+ value: 0.4002437442375043
+ name: Cosine Mrr@100
+ - type: cosine_mrr@150
+ value: 0.4002437442375043
+ name: Cosine Mrr@150
+ - type: cosine_mrr@200
+ value: 0.4002437442375043
+ name: Cosine Mrr@200
+ - type: cosine_map@1
+ value: 0.17888715548621945
+ name: Cosine Map@1
+ - type: cosine_map@20
+ value: 0.32718437256695937
+ name: Cosine Map@20
+ - type: cosine_map@50
+ value: 0.32718437256695937
+ name: Cosine Map@50
+ - type: cosine_map@100
+ value: 0.32718437256695937
+ name: Cosine Map@100
+ - type: cosine_map@150
+ value: 0.32718437256695937
+ name: Cosine Map@150
+ - type: cosine_map@200
+ value: 0.32718437256695937
+ name: Cosine Map@200
+ - type: cosine_map@500
+ value: 0.32718437256695937
+ name: Cosine Map@500
+---
+
+# Job - Job matching finetuned BAAI/bge-m3
+
+Top performing model on [TalentCLEF 2025](https://talentclef.github.io/talentclef/) Task A. Use it for multilingual job title matching
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3)
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 1024 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Datasets:**
+ - full_en
+ - full_de
+ - full_es
+ - full_zh
+ - mix
+
+
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+ (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
+ (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+ (2): Normalize()
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("pj-mathematician/JobBGE-m3")
+# Run inference
+sentences = [
+ 'Volksvertreter',
+ 'Parlamentarier',
+ 'Oberbürgermeister',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 1024]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+
+
+
+
+
+
+## Evaluation
+
+### Metrics
+
+#### Information Retrieval
+
+* Datasets: `full_en`, `full_es`, `full_de`, `full_zh`, `mix_es`, `mix_de` and `mix_zh`
+* Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric | full_en | full_es | full_de | full_zh | mix_es | mix_de | mix_zh |
+|:---------------------|:-----------|:-----------|:-----------|:-----------|:-----------|:-----------|:-----------|
+| cosine_accuracy@1 | 0.6476 | 0.1135 | 0.2956 | 0.6796 | 0.7395 | 0.6927 | 0.1789 |
+| cosine_accuracy@20 | 0.9905 | 1.0 | 0.9852 | 0.9903 | 0.9636 | 0.9641 | 1.0 |
+| cosine_accuracy@50 | 0.9905 | 1.0 | 0.9901 | 0.9903 | 0.9828 | 0.9839 | 1.0 |
+| cosine_accuracy@100 | 0.9905 | 1.0 | 0.9901 | 0.9903 | 0.9927 | 0.9922 | 1.0 |
+| cosine_accuracy@150 | 0.9905 | 1.0 | 0.9901 | 0.9903 | 0.9948 | 0.9932 | 1.0 |
+| cosine_accuracy@200 | 0.9905 | 1.0 | 0.9901 | 0.9903 | 0.9964 | 0.9943 | 1.0 |
+| cosine_precision@1 | 0.6476 | 0.1135 | 0.2956 | 0.6796 | 0.7395 | 0.6927 | 0.1789 |
+| cosine_precision@20 | 0.5062 | 0.5668 | 0.5404 | 0.4709 | 0.1249 | 0.128 | 0.1544 |
+| cosine_precision@50 | 0.3065 | 0.3903 | 0.3828 | 0.2804 | 0.0517 | 0.0533 | 0.0618 |
+| cosine_precision@100 | 0.1858 | 0.2525 | 0.2503 | 0.1732 | 0.0263 | 0.0271 | 0.0309 |
+| cosine_precision@150 | 0.1325 | 0.1901 | 0.1878 | 0.1239 | 0.0176 | 0.0181 | 0.0206 |
+| cosine_precision@200 | 0.1025 | 0.1508 | 0.1503 | 0.0977 | 0.0133 | 0.0136 | 0.0154 |
+| cosine_recall@1 | 0.0669 | 0.0035 | 0.0111 | 0.0643 | 0.2854 | 0.2604 | 0.0577 |
+| cosine_recall@20 | 0.5392 | 0.3796 | 0.3433 | 0.5119 | 0.9226 | 0.9285 | 1.0 |
+| cosine_recall@50 | 0.72 | 0.5636 | 0.534 | 0.6727 | 0.9548 | 0.965 | 1.0 |
+| cosine_recall@100 | 0.8254 | 0.6727 | 0.6499 | 0.788 | 0.9705 | 0.9796 | 1.0 |
+| cosine_recall@150 | 0.872 | 0.736 | 0.7101 | 0.8329 | 0.9766 | 0.9837 | 1.0 |
+| cosine_recall@200 | 0.9006 | 0.7698 | 0.7513 | 0.8687 | 0.9811 | 0.9862 | 1.0 |
+| cosine_ndcg@1 | 0.6476 | 0.1135 | 0.2956 | 0.6796 | 0.7395 | 0.6927 | 0.1789 |
+| cosine_ndcg@20 | 0.6822 | 0.6136 | 0.5648 | 0.6515 | 0.8119 | 0.7967 | 0.5443 |
+| cosine_ndcg@50 | 0.6975 | 0.5908 | 0.5522 | 0.6599 | 0.8208 | 0.8069 | 0.5443 |
+| cosine_ndcg@100 | 0.752 | 0.6168 | 0.5796 | 0.7157 | 0.8243 | 0.8102 | 0.5443 |
+| cosine_ndcg@150 | 0.7725 | 0.6489 | 0.6112 | 0.7357 | 0.8255 | 0.811 | 0.5443 |
+| **cosine_ndcg@200** | **0.7827** | **0.6653** | **0.6309** | **0.7501** | **0.8262** | **0.8114** | **0.5443** |
+| cosine_mrr@1 | 0.6476 | 0.1135 | 0.2956 | 0.6796 | 0.7395 | 0.6927 | 0.1789 |
+| cosine_mrr@20 | 0.8 | 0.5536 | 0.5164 | 0.8217 | 0.8059 | 0.7767 | 0.4002 |
+| cosine_mrr@50 | 0.8 | 0.5536 | 0.5166 | 0.8217 | 0.8066 | 0.7774 | 0.4002 |
+| cosine_mrr@100 | 0.8 | 0.5536 | 0.5166 | 0.8217 | 0.8067 | 0.7775 | 0.4002 |
+| cosine_mrr@150 | 0.8 | 0.5536 | 0.5166 | 0.8217 | 0.8067 | 0.7775 | 0.4002 |
+| cosine_mrr@200 | 0.8 | 0.5536 | 0.5166 | 0.8217 | 0.8067 | 0.7775 | 0.4002 |
+| cosine_map@1 | 0.6476 | 0.1135 | 0.2956 | 0.6796 | 0.7395 | 0.6927 | 0.1789 |
+| cosine_map@20 | 0.5392 | 0.481 | 0.4222 | 0.5012 | 0.744 | 0.721 | 0.3272 |
+| cosine_map@50 | 0.5258 | 0.4304 | 0.3791 | 0.4813 | 0.7465 | 0.7238 | 0.3272 |
+| cosine_map@100 | 0.558 | 0.4335 | 0.3829 | 0.5105 | 0.7469 | 0.7242 | 0.3272 |
+| cosine_map@150 | 0.5666 | 0.4485 | 0.3981 | 0.5184 | 0.747 | 0.7243 | 0.3272 |
+| cosine_map@200 | 0.5695 | 0.4551 | 0.4056 | 0.5228 | 0.7471 | 0.7244 | 0.3272 |
+| cosine_map@500 | 0.5744 | 0.4677 | 0.4189 | 0.5277 | 0.7472 | 0.7244 | 0.3272 |
+
+
+
+
+
+## Training Details
+
+### Training Datasets
+full_en
+
+#### full_en
+
+* Dataset: full_en
+* Size: 28,880 training samples
+* Columns: anchor and positive
+* Approximate statistics based on the first 1000 samples:
+ | | anchor | positive |
+ |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+ | type | string | string |
+ | details | - min: 3 tokens
- mean: 5.68 tokens
- max: 11 tokens
| - min: 3 tokens
- mean: 5.76 tokens
- max: 12 tokens
|
+* Samples:
+ | anchor | positive |
+ |:-----------------------------------------|:-----------------------------------------|
+ | air commodore | flight lieutenant |
+ | command and control officer | flight officer |
+ | air commodore | command and control officer |
+* Loss: [GISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
+ ```json
+ {'guide': SentenceTransformer(
+ (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
+ (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+ (2): Normalize()
+ ), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
+ ```
+full_de
+
+#### full_de
+
+* Dataset: full_de
+* Size: 23,023 training samples
+* Columns: anchor and positive
+* Approximate statistics based on the first 1000 samples:
+ | | anchor | positive |
+ |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+ | type | string | string |
+ | details | - min: 3 tokens
- mean: 7.99 tokens
- max: 30 tokens
| - min: 3 tokens
- mean: 8.19 tokens
- max: 30 tokens
|
+* Samples:
+ | anchor | positive |
+ |:----------------------------------|:-----------------------------------------------------|
+ | Staffelkommandantin | Kommodore |
+ | Luftwaffenoffizierin | Luftwaffenoffizier/Luftwaffenoffizierin |
+ | Staffelkommandantin | Luftwaffenoffizierin |
+* Loss: [GISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
+ ```json
+ {'guide': SentenceTransformer(
+ (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
+ (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+ (2): Normalize()
+ ), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
+ ```
+full_es
+
+#### full_es
+
+* Dataset: full_es
+* Size: 20,724 training samples
+* Columns: anchor and positive
+* Approximate statistics based on the first 1000 samples:
+ | | anchor | positive |
+ |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+ | type | string | string |
+ | details | - min: 3 tokens
- mean: 9.13 tokens
- max: 32 tokens
| - min: 3 tokens
- mean: 8.84 tokens
- max: 32 tokens
|
+* Samples:
+ | anchor | positive |
+ |:------------------------------------|:-------------------------------------------|
+ | jefe de escuadrón | instructor |
+ | comandante de aeronave | instructor de simulador |
+ | instructor | oficial del Ejército del Aire |
+* Loss: [GISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
+ ```json
+ {'guide': SentenceTransformer(
+ (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
+ (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+ (2): Normalize()
+ ), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
+ ```
+full_zh
+
+#### full_zh
+
+* Dataset: full_zh
+* Size: 30,401 training samples
+* Columns: anchor and positive
+* Approximate statistics based on the first 1000 samples:
+ | | anchor | positive |
+ |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+ | type | string | string |
+ | details | - min: 5 tokens
- mean: 7.15 tokens
- max: 14 tokens
| - min: 5 tokens
- mean: 7.46 tokens
- max: 21 tokens
|
+* Samples:
+ | anchor | positive |
+ |:------------------|:---------------------|
+ | 技术总监 | 技术和运营总监 |
+ | 技术总监 | 技术主管 |
+ | 技术总监 | 技术艺术总监 |
+* Loss: [GISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
+ ```json
+ {'guide': SentenceTransformer(
+ (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
+ (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+ (2): Normalize()
+ ), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
+ ```
+mix
+
+#### mix
+
+* Dataset: mix
+* Size: 21,760 training samples
+* Columns: anchor and positive
+* Approximate statistics based on the first 1000 samples:
+ | | anchor | positive |
+ |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+ | type | string | string |
+ | details | - min: 2 tokens
- mean: 6.71 tokens
- max: 19 tokens
| - min: 2 tokens
- mean: 7.69 tokens
- max: 19 tokens
|
+* Samples:
+ | anchor | positive |
+ |:------------------------------------------|:----------------------------------------------------------------|
+ | technical manager | Technischer Direktor für Bühne, Film und Fernsehen |
+ | head of technical | directora técnica |
+ | head of technical department | 技术艺术总监 |
+* Loss: [GISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
+ ```json
+ {'guide': SentenceTransformer(
+ (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
+ (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+ (2): Normalize()
+ ), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
+ ```
+Click to expand
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 64
+- `per_device_eval_batch_size`: 128
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 2
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 5e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 5
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.05
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: False
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: True
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: True
+- `dataloader_num_workers`: 4
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `tp_size`: 0
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: True
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`:
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false
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+ "normalized": false,
+ "rstrip": false,
+ "single_word": false
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+ "single_word": false
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+ "unk_token": {
+ "content": "",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false
+ }
+}
diff --git a/checkpoint-4800/tokenizer.json b/checkpoint-4800/tokenizer.json
new file mode 100644
index 0000000000000000000000000000000000000000..225ca0f803c45cce8b17cafe2dde2c7682e91938
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+oid sha256:d9a6af42442a3e3e9f05f618eae0bb2d98ca4f6a6406cb80ef7a4fa865204d61
+size 17083052
diff --git a/checkpoint-4800/tokenizer_config.json b/checkpoint-4800/tokenizer_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..95bd7c849ee6a47d5c92805af18d187239c1ba4a
--- /dev/null
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new file mode 100644
index 0000000000000000000000000000000000000000..c2f45a5b593dff4db28c60c03abd849fc524be2c
--- /dev/null
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diff --git a/eval/Information-Retrieval_evaluation_full_de_results.csv b/eval/Information-Retrieval_evaluation_full_de_results.csv
new file mode 100644
index 0000000000000000000000000000000000000000..acfa024d2b124f075a1f2d88afc3c7a164a817cd
--- /dev/null
+++ b/eval/Information-Retrieval_evaluation_full_de_results.csv
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diff --git a/eval/Information-Retrieval_evaluation_full_en_results.csv b/eval/Information-Retrieval_evaluation_full_en_results.csv
new file mode 100644
index 0000000000000000000000000000000000000000..dd38b081817ca88abc50b526d591f9d6861bf8d7
--- /dev/null
+++ b/eval/Information-Retrieval_evaluation_full_en_results.csv
@@ -0,0 +1,25 @@
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diff --git a/eval/Information-Retrieval_evaluation_full_es_results.csv b/eval/Information-Retrieval_evaluation_full_es_results.csv
new file mode 100644
index 0000000000000000000000000000000000000000..e23c760c3773ff33f38dba2677159588cf072fa1
--- /dev/null
+++ b/eval/Information-Retrieval_evaluation_full_es_results.csv
@@ -0,0 +1,25 @@
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diff --git a/eval/Information-Retrieval_evaluation_full_zh_results.csv b/eval/Information-Retrieval_evaluation_full_zh_results.csv
new file mode 100644
index 0000000000000000000000000000000000000000..f30b52fe2ce6e1c33d745d3feb3cd03feb8ceaad
--- /dev/null
+++ b/eval/Information-Retrieval_evaluation_full_zh_results.csv
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diff --git a/eval/Information-Retrieval_evaluation_mix_de_results.csv b/eval/Information-Retrieval_evaluation_mix_de_results.csv
new file mode 100644
index 0000000000000000000000000000000000000000..460360edfe2e58c4d2260e3f4b13d867940ecfef
--- /dev/null
+++ b/eval/Information-Retrieval_evaluation_mix_de_results.csv
@@ -0,0 +1,25 @@
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diff --git a/eval/Information-Retrieval_evaluation_mix_es_results.csv b/eval/Information-Retrieval_evaluation_mix_es_results.csv
new file mode 100644
index 0000000000000000000000000000000000000000..f95672af8e96803d5e0aaaa807283e7e85720e1d
--- /dev/null
+++ b/eval/Information-Retrieval_evaluation_mix_es_results.csv
@@ -0,0 +1,25 @@
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diff --git a/eval/Information-Retrieval_evaluation_mix_zh_results.csv b/eval/Information-Retrieval_evaluation_mix_zh_results.csv
new file mode 100644
index 0000000000000000000000000000000000000000..bdb0c11166655ae650cd8e6b74f628f98d41cdcc
--- /dev/null
+++ b/eval/Information-Retrieval_evaluation_mix_zh_results.csv
@@ -0,0 +1,25 @@
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