diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..d13b2e497569bf4c111906077bcbd355287c9f6c 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +checkpoint-1600/tokenizer.json filter=lfs diff=lfs merge=lfs -text +checkpoint-1000/tokenizer.json filter=lfs diff=lfs merge=lfs -text +checkpoint-1200/tokenizer.json filter=lfs diff=lfs merge=lfs -text +checkpoint-1400/tokenizer.json filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md index 7b95401dc46245ac339fc25059d4a56d90b4cde5..461cef9704da0bb9725b823cead84272e366aef5 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,1290 @@ ---- -license: apache-2.0 ---- +--- +tags: +- sentence-transformers +- sentence-similarity +- feature-extraction +- generated_from_trainer +- dataset_size:86648 +- loss:MSELoss +widget: +- source_sentence: Familienberaterin + sentences: + - electric power station operator + - venue booker & promoter + - betrieblicher Aus- und Weiterbildner/betriebliche Aus- und Weiterbildnerin +- source_sentence: high school RS teacher + sentences: + - infantryman + - Schnellbedienungsrestaurantteamleiter + - drill setup operator +- source_sentence: lighting designer + sentences: + - software support manager + - 直升机维护协调员 + - bus maintenance supervisor +- source_sentence: 机场消防员 + sentences: + - Flake操作员 + - técnico en gestión de residuos peligrosos/técnica en gestión de residuos peligrosos + - 专门学校老师 +- source_sentence: Entwicklerin für mobile Anwendungen + sentences: + - fashion design expert + - Mergers-and-Acquisitions-Analyst/Mergers-and-Acquisitions-Analystin + - commercial bid manager +pipeline_tag: sentence-similarity +library_name: sentence-transformers +metrics: +- cosine_accuracy@1 +- cosine_accuracy@20 +- cosine_accuracy@50 +- cosine_accuracy@100 +- cosine_accuracy@150 +- cosine_accuracy@200 +- cosine_precision@1 +- cosine_precision@20 +- cosine_precision@50 +- cosine_precision@100 +- cosine_precision@150 +- cosine_precision@200 +- cosine_recall@1 +- cosine_recall@20 +- cosine_recall@50 +- cosine_recall@100 +- cosine_recall@150 +- cosine_recall@200 +- cosine_ndcg@1 +- cosine_ndcg@20 +- cosine_ndcg@50 +- cosine_ndcg@100 +- cosine_ndcg@150 +- cosine_ndcg@200 +- cosine_mrr@1 +- cosine_mrr@20 +- cosine_mrr@50 +- cosine_mrr@100 +- cosine_mrr@150 +- cosine_mrr@200 +- cosine_map@1 +- cosine_map@20 +- cosine_map@50 +- cosine_map@100 +- cosine_map@150 +- cosine_map@200 +- cosine_map@500 +model-index: +- name: SentenceTransformer + results: + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: full en + type: full_en + metrics: + - type: cosine_accuracy@1 + value: 0.6476190476190476 + name: Cosine Accuracy@1 + - type: cosine_accuracy@20 + value: 0.9714285714285714 + name: Cosine Accuracy@20 + - type: cosine_accuracy@50 + value: 0.9904761904761905 + name: Cosine Accuracy@50 + - type: cosine_accuracy@100 + value: 0.9904761904761905 + name: Cosine Accuracy@100 + - type: cosine_accuracy@150 + value: 0.9904761904761905 + name: Cosine Accuracy@150 + - type: cosine_accuracy@200 + value: 0.9904761904761905 + name: Cosine Accuracy@200 + - type: cosine_precision@1 + value: 0.6476190476190476 + name: Cosine Precision@1 + - type: cosine_precision@20 + value: 0.47952380952380946 + name: Cosine Precision@20 + - type: cosine_precision@50 + value: 0.28838095238095235 + name: Cosine Precision@50 + - type: cosine_precision@100 + value: 0.17304761904761906 + name: Cosine Precision@100 + - type: cosine_precision@150 + value: 0.12444444444444444 + name: Cosine Precision@150 + - type: cosine_precision@200 + value: 0.09857142857142859 + name: Cosine Precision@200 + - type: cosine_recall@1 + value: 0.06609801577496094 + name: Cosine Recall@1 + - type: cosine_recall@20 + value: 0.5122224752770898 + name: Cosine Recall@20 + - type: cosine_recall@50 + value: 0.6835205863376973 + name: Cosine Recall@50 + - type: cosine_recall@100 + value: 0.7899550177449521 + name: Cosine Recall@100 + - type: cosine_recall@150 + value: 0.8399901051245952 + name: Cosine Recall@150 + - type: cosine_recall@200 + value: 0.875868212220809 + name: Cosine Recall@200 + - type: cosine_ndcg@1 + value: 0.6476190476190476 + name: Cosine Ndcg@1 + - type: cosine_ndcg@20 + value: 0.6467537144833913 + name: Cosine Ndcg@20 + - type: cosine_ndcg@50 + value: 0.6579566361404572 + name: Cosine Ndcg@50 + - type: cosine_ndcg@100 + value: 0.7095129047395976 + name: Cosine Ndcg@100 + - type: cosine_ndcg@150 + value: 0.7310060454392588 + name: Cosine Ndcg@150 + - type: cosine_ndcg@200 + value: 0.746053293561821 + name: Cosine Ndcg@200 + - type: cosine_mrr@1 + value: 0.6476190476190476 + name: Cosine Mrr@1 + - type: cosine_mrr@20 + value: 0.7901817137111254 + name: Cosine Mrr@20 + - type: cosine_mrr@50 + value: 0.7909547501984476 + name: Cosine Mrr@50 + - type: cosine_mrr@100 + value: 0.7909547501984476 + name: Cosine Mrr@100 + - type: cosine_mrr@150 + value: 0.7909547501984476 + name: Cosine Mrr@150 + - type: cosine_mrr@200 + value: 0.7909547501984476 + name: Cosine Mrr@200 + - type: cosine_map@1 + value: 0.6476190476190476 + name: Cosine Map@1 + - type: cosine_map@20 + value: 0.5025649155749793 + name: Cosine Map@20 + - type: cosine_map@50 + value: 0.48398477448194993 + name: Cosine Map@50 + - type: cosine_map@100 + value: 0.5117703759309522 + name: Cosine Map@100 + - type: cosine_map@150 + value: 0.520199435224254 + name: Cosine Map@150 + - type: cosine_map@200 + value: 0.5249113393002316 + name: Cosine Map@200 + - type: cosine_map@500 + value: 0.5304170344184883 + name: Cosine Map@500 + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: full es + type: full_es + metrics: + - type: cosine_accuracy@1 + value: 0.11891891891891893 + name: Cosine Accuracy@1 + - type: cosine_accuracy@20 + value: 1.0 + name: Cosine Accuracy@20 + - type: cosine_accuracy@50 + value: 1.0 + name: Cosine Accuracy@50 + - type: cosine_accuracy@100 + value: 1.0 + name: Cosine Accuracy@100 + - type: cosine_accuracy@150 + value: 1.0 + name: Cosine Accuracy@150 + - type: cosine_accuracy@200 + value: 1.0 + name: Cosine Accuracy@200 + - type: cosine_precision@1 + value: 0.11891891891891893 + name: Cosine Precision@1 + - type: cosine_precision@20 + value: 0.5267567567567567 + name: Cosine Precision@20 + - type: cosine_precision@50 + value: 0.3437837837837838 + name: Cosine Precision@50 + - type: cosine_precision@100 + value: 0.21897297297297297 + name: Cosine Precision@100 + - type: cosine_precision@150 + value: 0.1658018018018018 + name: Cosine Precision@150 + - type: cosine_precision@200 + value: 0.1332972972972973 + name: Cosine Precision@200 + - type: cosine_recall@1 + value: 0.0035840147528632613 + name: Cosine Recall@1 + - type: cosine_recall@20 + value: 0.35407760203362965 + name: Cosine Recall@20 + - type: cosine_recall@50 + value: 0.5097999383006715 + name: Cosine Recall@50 + - type: cosine_recall@100 + value: 0.6076073817878247 + name: Cosine Recall@100 + - type: cosine_recall@150 + value: 0.6705429838138021 + name: Cosine Recall@150 + - type: cosine_recall@200 + value: 0.7125464731776301 + name: Cosine Recall@200 + - type: cosine_ndcg@1 + value: 0.11891891891891893 + name: Cosine Ndcg@1 + - type: cosine_ndcg@20 + value: 0.5708144272431339 + name: Cosine Ndcg@20 + - type: cosine_ndcg@50 + value: 0.535516963498245 + name: Cosine Ndcg@50 + - type: cosine_ndcg@100 + value: 0.558980163264909 + name: Cosine Ndcg@100 + - type: cosine_ndcg@150 + value: 0.5900024611410689 + name: Cosine Ndcg@150 + - type: cosine_ndcg@200 + value: 0.609478782549869 + name: Cosine Ndcg@200 + - type: cosine_mrr@1 + value: 0.11891891891891893 + name: Cosine Mrr@1 + - type: cosine_mrr@20 + value: 0.5531531531531532 + name: Cosine Mrr@20 + - type: cosine_mrr@50 + value: 0.5531531531531532 + name: Cosine Mrr@50 + - type: cosine_mrr@100 + value: 0.5531531531531532 + name: Cosine Mrr@100 + - type: cosine_mrr@150 + value: 0.5531531531531532 + name: Cosine Mrr@150 + - type: cosine_mrr@200 + value: 0.5531531531531532 + name: Cosine Mrr@200 + - type: cosine_map@1 + value: 0.11891891891891893 + name: Cosine Map@1 + - type: cosine_map@20 + value: 0.4379349002801489 + name: Cosine Map@20 + - type: cosine_map@50 + value: 0.3739269627118989 + name: Cosine Map@50 + - type: cosine_map@100 + value: 0.37629843599877466 + name: Cosine Map@100 + - type: cosine_map@150 + value: 0.3891828650842837 + name: Cosine Map@150 + - type: cosine_map@200 + value: 0.39584338663408436 + name: Cosine Map@200 + - type: cosine_map@500 + value: 0.4062909401616274 + name: Cosine Map@500 + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: full de + type: full_de + metrics: + - type: cosine_accuracy@1 + value: 0.2955665024630542 + name: Cosine Accuracy@1 + - type: cosine_accuracy@20 + value: 0.9704433497536946 + name: Cosine Accuracy@20 + - type: cosine_accuracy@50 + value: 0.9753694581280788 + name: Cosine Accuracy@50 + - type: cosine_accuracy@100 + value: 0.9901477832512315 + name: Cosine Accuracy@100 + - type: cosine_accuracy@150 + value: 0.9901477832512315 + name: Cosine Accuracy@150 + - type: cosine_accuracy@200 + value: 0.9901477832512315 + name: Cosine Accuracy@200 + - type: cosine_precision@1 + value: 0.2955665024630542 + name: Cosine Precision@1 + - type: cosine_precision@20 + value: 0.42906403940886706 + name: Cosine Precision@20 + - type: cosine_precision@50 + value: 0.29802955665024633 + name: Cosine Precision@50 + - type: cosine_precision@100 + value: 0.19433497536945815 + name: Cosine Precision@100 + - type: cosine_precision@150 + value: 0.14824302134646963 + name: Cosine Precision@150 + - type: cosine_precision@200 + value: 0.1197783251231527 + name: Cosine Precision@200 + - type: cosine_recall@1 + value: 0.01108543831680986 + name: Cosine Recall@1 + - type: cosine_recall@20 + value: 0.26675038089672504 + name: Cosine Recall@20 + - type: cosine_recall@50 + value: 0.40921566733257536 + name: Cosine Recall@50 + - type: cosine_recall@100 + value: 0.5097664540706716 + name: Cosine Recall@100 + - type: cosine_recall@150 + value: 0.5728593162394238 + name: Cosine Recall@150 + - type: cosine_recall@200 + value: 0.6120176690658915 + name: Cosine Recall@200 + - type: cosine_ndcg@1 + value: 0.2955665024630542 + name: Cosine Ndcg@1 + - type: cosine_ndcg@20 + value: 0.46962753993631184 + name: Cosine Ndcg@20 + - type: cosine_ndcg@50 + value: 0.444898497416845 + name: Cosine Ndcg@50 + - type: cosine_ndcg@100 + value: 0.466960324034805 + name: Cosine Ndcg@100 + - type: cosine_ndcg@150 + value: 0.49816218513136795 + name: Cosine Ndcg@150 + - type: cosine_ndcg@200 + value: 0.5165485300965951 + name: Cosine Ndcg@200 + - type: cosine_mrr@1 + value: 0.2955665024630542 + name: Cosine Mrr@1 + - type: cosine_mrr@20 + value: 0.5046767633988724 + name: Cosine Mrr@20 + - type: cosine_mrr@50 + value: 0.50477528556636 + name: Cosine Mrr@50 + - type: cosine_mrr@100 + value: 0.5049589761635289 + name: Cosine Mrr@100 + - type: cosine_mrr@150 + value: 0.5049589761635289 + name: Cosine Mrr@150 + - type: cosine_mrr@200 + value: 0.5049589761635289 + name: Cosine Mrr@200 + - type: cosine_map@1 + value: 0.2955665024630542 + name: Cosine Map@1 + - type: cosine_map@20 + value: 0.33658821160388247 + name: Cosine Map@20 + - type: cosine_map@50 + value: 0.2853400586620685 + name: Cosine Map@50 + - type: cosine_map@100 + value: 0.2817732307206079 + name: Cosine Map@100 + - type: cosine_map@150 + value: 0.2931317333364438 + name: Cosine Map@150 + - type: cosine_map@200 + value: 0.2988160532231927 + name: Cosine Map@200 + - type: cosine_map@500 + value: 0.31093362375086947 + name: Cosine Map@500 + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: full zh + type: full_zh + metrics: + - type: cosine_accuracy@1 + value: 0.6601941747572816 + name: Cosine Accuracy@1 + - type: cosine_accuracy@20 + value: 0.970873786407767 + name: Cosine Accuracy@20 + - type: cosine_accuracy@50 + value: 0.9902912621359223 + name: Cosine Accuracy@50 + - type: cosine_accuracy@100 + value: 0.9902912621359223 + name: Cosine Accuracy@100 + - type: cosine_accuracy@150 + value: 0.9902912621359223 + name: Cosine Accuracy@150 + - type: cosine_accuracy@200 + value: 0.9902912621359223 + name: Cosine Accuracy@200 + - type: cosine_precision@1 + value: 0.6601941747572816 + name: Cosine Precision@1 + - type: cosine_precision@20 + value: 0.44805825242718444 + name: Cosine Precision@20 + - type: cosine_precision@50 + value: 0.27126213592233006 + name: Cosine Precision@50 + - type: cosine_precision@100 + value: 0.16650485436893206 + name: Cosine Precision@100 + - type: cosine_precision@150 + value: 0.1211003236245955 + name: Cosine Precision@150 + - type: cosine_precision@200 + value: 0.09529126213592234 + name: Cosine Precision@200 + - type: cosine_recall@1 + value: 0.06611246215014785 + name: Cosine Recall@1 + - type: cosine_recall@20 + value: 0.48409390608352504 + name: Cosine Recall@20 + - type: cosine_recall@50 + value: 0.6568473638827299 + name: Cosine Recall@50 + - type: cosine_recall@100 + value: 0.7685416895166794 + name: Cosine Recall@100 + - type: cosine_recall@150 + value: 0.8277686060133904 + name: Cosine Recall@150 + - type: cosine_recall@200 + value: 0.8616979590623105 + name: Cosine Recall@200 + - type: cosine_ndcg@1 + value: 0.6601941747572816 + name: Cosine Ndcg@1 + - type: cosine_ndcg@20 + value: 0.6231250904534316 + name: Cosine Ndcg@20 + - type: cosine_ndcg@50 + value: 0.6383496204608501 + name: Cosine Ndcg@50 + - type: cosine_ndcg@100 + value: 0.6917257705456975 + name: Cosine Ndcg@100 + - type: cosine_ndcg@150 + value: 0.7167434657424917 + name: Cosine Ndcg@150 + - type: cosine_ndcg@200 + value: 0.7303448958665071 + name: Cosine Ndcg@200 + - type: cosine_mrr@1 + value: 0.6601941747572816 + name: Cosine Mrr@1 + - type: cosine_mrr@20 + value: 0.8015776699029126 + name: Cosine Mrr@20 + - type: cosine_mrr@50 + value: 0.8020876238109248 + name: Cosine Mrr@50 + - type: cosine_mrr@100 + value: 0.8020876238109248 + name: Cosine Mrr@100 + - type: cosine_mrr@150 + value: 0.8020876238109248 + name: Cosine Mrr@150 + - type: cosine_mrr@200 + value: 0.8020876238109248 + name: Cosine Mrr@200 + - type: cosine_map@1 + value: 0.6601941747572816 + name: Cosine Map@1 + - type: cosine_map@20 + value: 0.4750205237443607 + name: Cosine Map@20 + - type: cosine_map@50 + value: 0.45785161483741715 + name: Cosine Map@50 + - type: cosine_map@100 + value: 0.4848085275553208 + name: Cosine Map@100 + - type: cosine_map@150 + value: 0.4937216396074153 + name: Cosine Map@150 + - type: cosine_map@200 + value: 0.49777622471594557 + name: Cosine Map@200 + - type: cosine_map@500 + value: 0.5039795405740248 + name: Cosine Map@500 + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: mix es + type: mix_es + metrics: + - type: cosine_accuracy@1 + value: 0.6297451898075923 + name: Cosine Accuracy@1 + - type: cosine_accuracy@20 + value: 0.9105564222568903 + name: Cosine Accuracy@20 + - type: cosine_accuracy@50 + value: 0.9495579823192928 + name: Cosine Accuracy@50 + - type: cosine_accuracy@100 + value: 0.9729589183567343 + name: Cosine Accuracy@100 + - type: cosine_accuracy@150 + value: 0.983359334373375 + name: Cosine Accuracy@150 + - type: cosine_accuracy@200 + value: 0.9901196047841914 + name: Cosine Accuracy@200 + - type: cosine_precision@1 + value: 0.6297451898075923 + name: Cosine Precision@1 + - type: cosine_precision@20 + value: 0.11167446697867915 + name: Cosine Precision@20 + - type: cosine_precision@50 + value: 0.04850754030161208 + name: Cosine Precision@50 + - type: cosine_precision@100 + value: 0.02535101404056163 + name: Cosine Precision@100 + - type: cosine_precision@150 + value: 0.0172300225342347 + name: Cosine Precision@150 + - type: cosine_precision@200 + value: 0.0130811232449298 + name: Cosine Precision@200 + - type: cosine_recall@1 + value: 0.24340068840848872 + name: Cosine Recall@1 + - type: cosine_recall@20 + value: 0.8288215338137336 + name: Cosine Recall@20 + - type: cosine_recall@50 + value: 0.8986566129311838 + name: Cosine Recall@50 + - type: cosine_recall@100 + value: 0.9398509273704282 + name: Cosine Recall@100 + - type: cosine_recall@150 + value: 0.9576876408389668 + name: Cosine Recall@150 + - type: cosine_recall@200 + value: 0.9695267810712429 + name: Cosine Recall@200 + - type: cosine_ndcg@1 + value: 0.6297451898075923 + name: Cosine Ndcg@1 + - type: cosine_ndcg@20 + value: 0.7010427232190379 + name: Cosine Ndcg@20 + - type: cosine_ndcg@50 + value: 0.7200844211181043 + name: Cosine Ndcg@50 + - type: cosine_ndcg@100 + value: 0.7290848607488584 + name: Cosine Ndcg@100 + - type: cosine_ndcg@150 + value: 0.7325985285606116 + name: Cosine Ndcg@150 + - type: cosine_ndcg@200 + value: 0.7347463892077523 + name: Cosine Ndcg@200 + - type: cosine_mrr@1 + value: 0.6297451898075923 + name: Cosine Mrr@1 + - type: cosine_mrr@20 + value: 0.7036709577939534 + name: Cosine Mrr@20 + - type: cosine_mrr@50 + value: 0.7049808414398148 + name: Cosine Mrr@50 + - type: cosine_mrr@100 + value: 0.7053260954286938 + name: Cosine Mrr@100 + - type: cosine_mrr@150 + value: 0.7054145837924506 + name: Cosine Mrr@150 + - type: cosine_mrr@200 + value: 0.7054541569954363 + name: Cosine Mrr@200 + - type: cosine_map@1 + value: 0.6297451898075923 + name: Cosine Map@1 + - type: cosine_map@20 + value: 0.6194189058349782 + name: Cosine Map@20 + - type: cosine_map@50 + value: 0.6244340507841626 + name: Cosine Map@50 + - type: cosine_map@100 + value: 0.6256943736433496 + name: Cosine Map@100 + - type: cosine_map@150 + value: 0.6260195205413376 + name: Cosine Map@150 + - type: cosine_map@200 + value: 0.6261650797332174 + name: Cosine Map@200 + - type: cosine_map@500 + value: 0.6263452093477304 + name: Cosine Map@500 + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: mix de + type: mix_de + metrics: + - type: cosine_accuracy@1 + value: 0.5564222568902756 + name: Cosine Accuracy@1 + - type: cosine_accuracy@20 + value: 0.8866354654186167 + name: Cosine Accuracy@20 + - type: cosine_accuracy@50 + value: 0.9381175247009881 + name: Cosine Accuracy@50 + - type: cosine_accuracy@100 + value: 0.9594383775351014 + name: Cosine Accuracy@100 + - type: cosine_accuracy@150 + value: 0.9708788351534061 + name: Cosine Accuracy@150 + - type: cosine_accuracy@200 + value: 0.9776391055642226 + name: Cosine Accuracy@200 + - type: cosine_precision@1 + value: 0.5564222568902756 + name: Cosine Precision@1 + - type: cosine_precision@20 + value: 0.109464378575143 + name: Cosine Precision@20 + - type: cosine_precision@50 + value: 0.048060322412896525 + name: Cosine Precision@50 + - type: cosine_precision@100 + value: 0.025273010920436823 + name: Cosine Precision@100 + - type: cosine_precision@150 + value: 0.017313225862367825 + name: Cosine Precision@150 + - type: cosine_precision@200 + value: 0.013143525741029644 + name: Cosine Precision@200 + - type: cosine_recall@1 + value: 0.20931703934824059 + name: Cosine Recall@1 + - type: cosine_recall@20 + value: 0.7988992893049055 + name: Cosine Recall@20 + - type: cosine_recall@50 + value: 0.8741029641185647 + name: Cosine Recall@50 + - type: cosine_recall@100 + value: 0.9173426937077482 + name: Cosine Recall@100 + - type: cosine_recall@150 + value: 0.9424076963078523 + name: Cosine Recall@150 + - type: cosine_recall@200 + value: 0.953631478592477 + name: Cosine Recall@200 + - type: cosine_ndcg@1 + value: 0.5564222568902756 + name: Cosine Ndcg@1 + - type: cosine_ndcg@20 + value: 0.6541310877479573 + name: Cosine Ndcg@20 + - type: cosine_ndcg@50 + value: 0.674790854916742 + name: Cosine Ndcg@50 + - type: cosine_ndcg@100 + value: 0.6844997445798996 + name: Cosine Ndcg@100 + - type: cosine_ndcg@150 + value: 0.6894214573457343 + name: Cosine Ndcg@150 + - type: cosine_ndcg@200 + value: 0.6914881284159038 + name: Cosine Ndcg@200 + - type: cosine_mrr@1 + value: 0.5564222568902756 + name: Cosine Mrr@1 + - type: cosine_mrr@20 + value: 0.6476945170199107 + name: Cosine Mrr@20 + - type: cosine_mrr@50 + value: 0.6493649946597936 + name: Cosine Mrr@50 + - type: cosine_mrr@100 + value: 0.6496801333421218 + name: Cosine Mrr@100 + - type: cosine_mrr@150 + value: 0.6497778366579644 + name: Cosine Mrr@150 + - type: cosine_mrr@200 + value: 0.6498156890114056 + name: Cosine Mrr@200 + - type: cosine_map@1 + value: 0.5564222568902756 + name: Cosine Map@1 + - type: cosine_map@20 + value: 0.5648326970643027 + name: Cosine Map@20 + - type: cosine_map@50 + value: 0.57003456255067 + name: Cosine Map@50 + - type: cosine_map@100 + value: 0.5714370828517599 + name: Cosine Map@100 + - type: cosine_map@150 + value: 0.5719002990233493 + name: Cosine Map@150 + - type: cosine_map@200 + value: 0.5720497397197026 + name: Cosine Map@200 + - type: cosine_map@500 + value: 0.5723109788233504 + name: Cosine Map@500 + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: mix zh + type: mix_zh + metrics: + - type: cosine_accuracy@1 + value: 0.6085594989561587 + name: Cosine Accuracy@1 + - type: cosine_accuracy@20 + value: 0.9592901878914405 + name: Cosine Accuracy@20 + - type: cosine_accuracy@50 + value: 0.9791231732776617 + name: Cosine Accuracy@50 + - type: cosine_accuracy@100 + value: 0.9874739039665971 + name: Cosine Accuracy@100 + - type: cosine_accuracy@150 + value: 0.9911273486430062 + name: Cosine Accuracy@150 + - type: cosine_accuracy@200 + value: 0.9937369519832986 + name: Cosine Accuracy@200 + - type: cosine_precision@1 + value: 0.6085594989561587 + name: Cosine Precision@1 + - type: cosine_precision@20 + value: 0.12656576200417535 + name: Cosine Precision@20 + - type: cosine_precision@50 + value: 0.05518789144050106 + name: Cosine Precision@50 + - type: cosine_precision@100 + value: 0.028747390396659713 + name: Cosine Precision@100 + - type: cosine_precision@150 + value: 0.019425887265135697 + name: Cosine Precision@150 + - type: cosine_precision@200 + value: 0.014705114822546978 + name: Cosine Precision@200 + - type: cosine_recall@1 + value: 0.2043804056069192 + name: Cosine Recall@1 + - type: cosine_recall@20 + value: 0.8346468336812805 + name: Cosine Recall@20 + - type: cosine_recall@50 + value: 0.9095772442588727 + name: Cosine Recall@50 + - type: cosine_recall@100 + value: 0.9475643702157271 + name: Cosine Recall@100 + - type: cosine_recall@150 + value: 0.9609168406402228 + name: Cosine Recall@150 + - type: cosine_recall@200 + value: 0.9697807933194154 + name: Cosine Recall@200 + - type: cosine_ndcg@1 + value: 0.6085594989561587 + name: Cosine Ndcg@1 + - type: cosine_ndcg@20 + value: 0.6853247290079303 + name: Cosine Ndcg@20 + - type: cosine_ndcg@50 + value: 0.7066940880968873 + name: Cosine Ndcg@50 + - type: cosine_ndcg@100 + value: 0.715400790265437 + name: Cosine Ndcg@100 + - type: cosine_ndcg@150 + value: 0.7180808450243259 + name: Cosine Ndcg@150 + - type: cosine_ndcg@200 + value: 0.7197629642909036 + name: Cosine Ndcg@200 + - type: cosine_mrr@1 + value: 0.6085594989561587 + name: Cosine Mrr@1 + - type: cosine_mrr@20 + value: 0.7236528792595264 + name: Cosine Mrr@20 + - type: cosine_mrr@50 + value: 0.7243308740364213 + name: Cosine Mrr@50 + - type: cosine_mrr@100 + value: 0.7244524590415827 + name: Cosine Mrr@100 + - type: cosine_mrr@150 + value: 0.7244814620971008 + name: Cosine Mrr@150 + - type: cosine_mrr@200 + value: 0.7244960285685315 + name: Cosine Mrr@200 + - type: cosine_map@1 + value: 0.6085594989561587 + name: Cosine Map@1 + - type: cosine_map@20 + value: 0.5652211952239553 + name: Cosine Map@20 + - type: cosine_map@50 + value: 0.5716374350069462 + name: Cosine Map@50 + - type: cosine_map@100 + value: 0.5730756815932735 + name: Cosine Map@100 + - type: cosine_map@150 + value: 0.5733543252173214 + name: Cosine Map@150 + - type: cosine_map@200 + value: 0.5734860037813889 + name: Cosine Map@200 + - type: cosine_map@500 + value: 0.5736416699680624 + name: Cosine Map@500 +--- + +# Job - Job matching Alibaba-NLP/gte-multilingual-base pruned + +Top performing model on [TalentCLEF 2025](https://talentclef.github.io/talentclef/) Task A. Use it for multilingual job title matching + +## Model Details + +### Model Description +- **Model Type:** Sentence Transformer + +- **Maximum Sequence Length:** 512 tokens +- **Output Dimensionality:** 768 dimensions +- **Similarity Function:** Cosine Similarity + + + + +### Model Sources + +- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) +- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) +- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) + +### Full Model Architecture + +``` +SentenceTransformer( + (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel + (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) + (2): Normalize() +) +``` + +## Usage + +### Direct Usage (Sentence Transformers) + +First install the Sentence Transformers library: + +```bash +pip install -U sentence-transformers +``` + +Then you can load this model and run inference. +```python +from sentence_transformers import SentenceTransformer + +# Download from the 🤗 Hub +model = SentenceTransformer("pj-mathematician/JobGTE-multilingual-base-pruned") +# Run inference +sentences = [ + 'Entwicklerin für mobile Anwendungen', + 'Mergers-and-Acquisitions-Analyst/Mergers-and-Acquisitions-Analystin', + 'fashion design expert', +] +embeddings = model.encode(sentences) +print(embeddings.shape) +# [3, 768] + +# Get the similarity scores for the embeddings +similarities = model.similarity(embeddings, embeddings) +print(similarities.shape) +# [3, 3] +``` + + + + + + + +## 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 | 0.1189 | 0.2956 | 0.6602 | 0.6297 | 0.5564 | 0.6086 | +| cosine_accuracy@20 | 0.9714 | 1.0 | 0.9704 | 0.9709 | 0.9106 | 0.8866 | 0.9593 | +| cosine_accuracy@50 | 0.9905 | 1.0 | 0.9754 | 0.9903 | 0.9496 | 0.9381 | 0.9791 | +| cosine_accuracy@100 | 0.9905 | 1.0 | 0.9901 | 0.9903 | 0.973 | 0.9594 | 0.9875 | +| cosine_accuracy@150 | 0.9905 | 1.0 | 0.9901 | 0.9903 | 0.9834 | 0.9709 | 0.9911 | +| cosine_accuracy@200 | 0.9905 | 1.0 | 0.9901 | 0.9903 | 0.9901 | 0.9776 | 0.9937 | +| cosine_precision@1 | 0.6476 | 0.1189 | 0.2956 | 0.6602 | 0.6297 | 0.5564 | 0.6086 | +| cosine_precision@20 | 0.4795 | 0.5268 | 0.4291 | 0.4481 | 0.1117 | 0.1095 | 0.1266 | +| cosine_precision@50 | 0.2884 | 0.3438 | 0.298 | 0.2713 | 0.0485 | 0.0481 | 0.0552 | +| cosine_precision@100 | 0.173 | 0.219 | 0.1943 | 0.1665 | 0.0254 | 0.0253 | 0.0287 | +| cosine_precision@150 | 0.1244 | 0.1658 | 0.1482 | 0.1211 | 0.0172 | 0.0173 | 0.0194 | +| cosine_precision@200 | 0.0986 | 0.1333 | 0.1198 | 0.0953 | 0.0131 | 0.0131 | 0.0147 | +| cosine_recall@1 | 0.0661 | 0.0036 | 0.0111 | 0.0661 | 0.2434 | 0.2093 | 0.2044 | +| cosine_recall@20 | 0.5122 | 0.3541 | 0.2668 | 0.4841 | 0.8288 | 0.7989 | 0.8346 | +| cosine_recall@50 | 0.6835 | 0.5098 | 0.4092 | 0.6568 | 0.8987 | 0.8741 | 0.9096 | +| cosine_recall@100 | 0.79 | 0.6076 | 0.5098 | 0.7685 | 0.9399 | 0.9173 | 0.9476 | +| cosine_recall@150 | 0.84 | 0.6705 | 0.5729 | 0.8278 | 0.9577 | 0.9424 | 0.9609 | +| cosine_recall@200 | 0.8759 | 0.7125 | 0.612 | 0.8617 | 0.9695 | 0.9536 | 0.9698 | +| cosine_ndcg@1 | 0.6476 | 0.1189 | 0.2956 | 0.6602 | 0.6297 | 0.5564 | 0.6086 | +| cosine_ndcg@20 | 0.6468 | 0.5708 | 0.4696 | 0.6231 | 0.701 | 0.6541 | 0.6853 | +| cosine_ndcg@50 | 0.658 | 0.5355 | 0.4449 | 0.6383 | 0.7201 | 0.6748 | 0.7067 | +| cosine_ndcg@100 | 0.7095 | 0.559 | 0.467 | 0.6917 | 0.7291 | 0.6845 | 0.7154 | +| cosine_ndcg@150 | 0.731 | 0.59 | 0.4982 | 0.7167 | 0.7326 | 0.6894 | 0.7181 | +| **cosine_ndcg@200** | **0.7461** | **0.6095** | **0.5165** | **0.7303** | **0.7347** | **0.6915** | **0.7198** | +| cosine_mrr@1 | 0.6476 | 0.1189 | 0.2956 | 0.6602 | 0.6297 | 0.5564 | 0.6086 | +| cosine_mrr@20 | 0.7902 | 0.5532 | 0.5047 | 0.8016 | 0.7037 | 0.6477 | 0.7237 | +| cosine_mrr@50 | 0.791 | 0.5532 | 0.5048 | 0.8021 | 0.705 | 0.6494 | 0.7243 | +| cosine_mrr@100 | 0.791 | 0.5532 | 0.505 | 0.8021 | 0.7053 | 0.6497 | 0.7245 | +| cosine_mrr@150 | 0.791 | 0.5532 | 0.505 | 0.8021 | 0.7054 | 0.6498 | 0.7245 | +| cosine_mrr@200 | 0.791 | 0.5532 | 0.505 | 0.8021 | 0.7055 | 0.6498 | 0.7245 | +| cosine_map@1 | 0.6476 | 0.1189 | 0.2956 | 0.6602 | 0.6297 | 0.5564 | 0.6086 | +| cosine_map@20 | 0.5026 | 0.4379 | 0.3366 | 0.475 | 0.6194 | 0.5648 | 0.5652 | +| cosine_map@50 | 0.484 | 0.3739 | 0.2853 | 0.4579 | 0.6244 | 0.57 | 0.5716 | +| cosine_map@100 | 0.5118 | 0.3763 | 0.2818 | 0.4848 | 0.6257 | 0.5714 | 0.5731 | +| cosine_map@150 | 0.5202 | 0.3892 | 0.2931 | 0.4937 | 0.626 | 0.5719 | 0.5734 | +| cosine_map@200 | 0.5249 | 0.3958 | 0.2988 | 0.4978 | 0.6262 | 0.572 | 0.5735 | +| cosine_map@500 | 0.5304 | 0.4063 | 0.3109 | 0.504 | 0.6263 | 0.5723 | 0.5736 | + + + + + +## Training Details + +### Training Dataset + +#### Unnamed Dataset + +* Size: 86,648 training samples +* Columns: sentence and label +* Approximate statistics based on the first 1000 samples: + | | sentence | label | + |:--------|:---------------------------------------------------------------------------------|:-------------------------------------| + | type | string | list | + | details | | | +* Samples: + | sentence | label | + |:-----------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------| + | | [-0.07171934843063354, 0.03595816716551781, -0.029780959710478783, 0.006593302357941866, 0.040611181408166885, ...] | + | airport environment officer | [-0.022075481712818146, 0.02999737113714218, -0.02189866080880165, 0.016531817615032196, 0.012234307825565338, ...] | + | Flake操作员 | [-0.04815564677119255, 0.023524893447756767, -0.01583661139011383, 0.042527906596660614, 0.03815540298819542, ...] | +* Loss: [MSELoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss) + +### Training Hyperparameters +#### Non-Default Hyperparameters + +- `eval_strategy`: steps +- `per_device_train_batch_size`: 128 +- `per_device_eval_batch_size`: 128 +- `gradient_accumulation_steps`: 2 +- `learning_rate`: 0.0001 +- `num_train_epochs`: 5 +- `warmup_ratio`: 0.05 +- `log_on_each_node`: False +- `fp16`: True +- `dataloader_num_workers`: 4 +- `ddp_find_unused_parameters`: True +- `batch_sampler`: no_duplicates + +#### All Hyperparameters +
Click to expand + +- `overwrite_output_dir`: False +- `do_predict`: False +- `eval_strategy`: steps +- `prediction_loss_only`: True +- `per_device_train_batch_size`: 128 +- `per_device_eval_batch_size`: 128 +- `per_gpu_train_batch_size`: None +- `per_gpu_eval_batch_size`: None +- `gradient_accumulation_steps`: 2 +- `eval_accumulation_steps`: None +- `torch_empty_cache_steps`: None +- `learning_rate`: 0.0001 +- `weight_decay`: 0.0 +- `adam_beta1`: 0.9 +- `adam_beta2`: 0.999 +- `adam_epsilon`: 1e-08 +- `max_grad_norm`: 1.0 +- `num_train_epochs`: 5 +- `max_steps`: -1 +- `lr_scheduler_type`: linear +- `lr_scheduler_kwargs`: {} +- `warmup_ratio`: 0.05 +- `warmup_steps`: 0 +- `log_level`: passive +- `log_level_replica`: warning +- `log_on_each_node`: False +- `logging_nan_inf_filter`: True +- `save_safetensors`: True +- `save_on_each_node`: False +- `save_only_model`: False +- `restore_callback_states_from_checkpoint`: False +- `no_cuda`: False +- `use_cpu`: False +- `use_mps_device`: False +- `seed`: 42 +- `data_seed`: None +- `jit_mode_eval`: False +- `use_ipex`: False +- `bf16`: False +- `fp16`: True +- `fp16_opt_level`: O1 +- `half_precision_backend`: auto +- `bf16_full_eval`: False +- `fp16_full_eval`: False +- `tf32`: None +- `local_rank`: 0 +- `ddp_backend`: None +- `tpu_num_cores`: None +- `tpu_metrics_debug`: False +- `debug`: [] +- `dataloader_drop_last`: True +- `dataloader_num_workers`: 4 +- `dataloader_prefetch_factor`: None +- `past_index`: -1 +- `disable_tqdm`: False +- `remove_unused_columns`: True +- `label_names`: None +- `load_best_model_at_end`: False +- `ignore_data_skip`: False +- `fsdp`: [] +- `fsdp_min_num_params`: 0 +- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} +- `tp_size`: 0 +- `fsdp_transformer_layer_cls_to_wrap`: None +- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} +- `deepspeed`: None +- `label_smoothing_factor`: 0.0 +- `optim`: adamw_torch +- `optim_args`: None +- `adafactor`: False +- `group_by_length`: False +- `length_column_name`: length +- `ddp_find_unused_parameters`: True +- `ddp_bucket_cap_mb`: None +- `ddp_broadcast_buffers`: False +- `dataloader_pin_memory`: True +- `dataloader_persistent_workers`: False +- `skip_memory_metrics`: True +- `use_legacy_prediction_loop`: False +- `push_to_hub`: False +- `resume_from_checkpoint`: None +- `hub_model_id`: None +- `hub_strategy`: every_save +- `hub_private_repo`: None +- `hub_always_push`: False +- `gradient_checkpointing`: False +- `gradient_checkpointing_kwargs`: None +- `include_inputs_for_metrics`: False +- `include_for_metrics`: [] +- `eval_do_concat_batches`: True +- `fp16_backend`: auto +- `push_to_hub_model_id`: None +- `push_to_hub_organization`: None +- `mp_parameters`: +- `auto_find_batch_size`: False +- `full_determinism`: False +- `torchdynamo`: None +- `ray_scope`: last +- `ddp_timeout`: 1800 +- `torch_compile`: False +- `torch_compile_backend`: None +- `torch_compile_mode`: None +- `include_tokens_per_second`: False +- `include_num_input_tokens_seen`: False +- `neftune_noise_alpha`: None +- `optim_target_modules`: None +- `batch_eval_metrics`: False +- `eval_on_start`: False +- `use_liger_kernel`: False +- `eval_use_gather_object`: False +- `average_tokens_across_devices`: False +- `prompts`: None +- `batch_sampler`: no_duplicates +- `multi_dataset_batch_sampler`: proportional + +
+ +### Training Logs +| Epoch | Step | Training Loss | full_en_cosine_ndcg@200 | full_es_cosine_ndcg@200 | full_de_cosine_ndcg@200 | full_zh_cosine_ndcg@200 | mix_es_cosine_ndcg@200 | mix_de_cosine_ndcg@200 | mix_zh_cosine_ndcg@200 | +|:------:|:----:|:-------------:|:-----------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:| +| -1 | -1 | - | 0.5348 | 0.4311 | 0.3678 | 0.5333 | 0.2580 | 0.1924 | 0.2871 | +| 0.0030 | 1 | 0.0017 | - | - | - | - | - | - | - | +| 0.2959 | 100 | 0.001 | - | - | - | - | - | - | - | +| 0.5917 | 200 | 0.0005 | 0.6702 | 0.5287 | 0.4566 | 0.6809 | 0.5864 | 0.5302 | 0.4739 | +| 0.8876 | 300 | 0.0004 | - | - | - | - | - | - | - | +| 1.1834 | 400 | 0.0004 | 0.7057 | 0.5643 | 0.4790 | 0.7033 | 0.6604 | 0.6055 | 0.6003 | +| 1.4793 | 500 | 0.0004 | - | - | - | - | - | - | - | +| 1.7751 | 600 | 0.0003 | 0.7184 | 0.5783 | 0.4910 | 0.7127 | 0.6927 | 0.6416 | 0.6485 | +| 2.0710 | 700 | 0.0003 | - | - | - | - | - | - | - | +| 2.3669 | 800 | 0.0003 | 0.7307 | 0.5938 | 0.5023 | 0.7233 | 0.7125 | 0.6639 | 0.6847 | +| 2.6627 | 900 | 0.0003 | - | - | - | - | - | - | - | +| 2.9586 | 1000 | 0.0003 | 0.7371 | 0.6002 | 0.5085 | 0.7228 | 0.7222 | 0.6761 | 0.6998 | +| 3.2544 | 1100 | 0.0003 | - | - | - | - | - | - | - | +| 3.5503 | 1200 | 0.0003 | 0.7402 | 0.6059 | 0.5109 | 0.7279 | 0.7285 | 0.6841 | 0.7120 | +| 3.8462 | 1300 | 0.0003 | - | - | - | - | - | - | - | +| 4.1420 | 1400 | 0.0003 | 0.7449 | 0.6083 | 0.5154 | 0.7294 | 0.7333 | 0.6894 | 0.7176 | +| 4.4379 | 1500 | 0.0003 | - | - | - | - | - | - | - | +| 4.7337 | 1600 | 0.0003 | 0.7461 | 0.6095 | 0.5165 | 0.7303 | 0.7347 | 0.6915 | 0.7198 | + + +### Framework Versions +- Python: 3.11.11 +- Sentence Transformers: 4.1.0 +- Transformers: 4.51.3 +- PyTorch: 2.6.0+cu124 +- Accelerate: 1.6.0 +- Datasets: 3.5.0 +- Tokenizers: 0.21.1 + +## Citation + +### BibTeX + +#### Sentence Transformers +```bibtex +@inproceedings{reimers-2019-sentence-bert, + title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", + author = "Reimers, Nils and Gurevych, Iryna", + booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", + month = "11", + year = "2019", + publisher = "Association for Computational Linguistics", + url = "https://arxiv.org/abs/1908.10084", +} +``` + +#### MSELoss +```bibtex +@inproceedings{reimers-2020-multilingual-sentence-bert, + title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation", + author = "Reimers, Nils and Gurevych, Iryna", + booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing", + month = "11", + year = "2020", + publisher = "Association for Computational Linguistics", + url = "https://arxiv.org/abs/2004.09813", +} +``` + + + + + + \ No newline at end of file diff --git a/checkpoint-1000/1_Pooling/config.json b/checkpoint-1000/1_Pooling/config.json new file mode 100644 index 0000000000000000000000000000000000000000..1b013adee922cdde26976d6e46f4ec75a651dfdf --- /dev/null +++ b/checkpoint-1000/1_Pooling/config.json @@ -0,0 +1,10 @@ +{ + "word_embedding_dimension": 768, + "pooling_mode_cls_token": true, + "pooling_mode_mean_tokens": false, + "pooling_mode_max_tokens": false, + "pooling_mode_mean_sqrt_len_tokens": false, + "pooling_mode_weightedmean_tokens": false, + "pooling_mode_lasttoken": false, + "include_prompt": true +} \ No newline at end of file diff --git a/checkpoint-1000/README.md b/checkpoint-1000/README.md new file mode 100644 index 0000000000000000000000000000000000000000..c130ed8085eba0e82853f1c3a37e93168d5b7f51 --- /dev/null +++ b/checkpoint-1000/README.md @@ -0,0 +1,1284 @@ +--- +tags: +- sentence-transformers +- sentence-similarity +- feature-extraction +- generated_from_trainer +- dataset_size:86648 +- loss:MSELoss +widget: +- source_sentence: Familienberaterin + sentences: + - electric power station operator + - venue booker & promoter + - betrieblicher Aus- und Weiterbildner/betriebliche Aus- und Weiterbildnerin +- source_sentence: high school RS teacher + sentences: + - infantryman + - Schnellbedienungsrestaurantteamleiter + - drill setup operator +- source_sentence: lighting designer + sentences: + - software support manager + - 直升机维护协调员 + - bus maintenance supervisor +- source_sentence: 机场消防员 + sentences: + - Flake操作员 + - técnico en gestión de residuos peligrosos/técnica en gestión de residuos peligrosos + - 专门学校老师 +- source_sentence: Entwicklerin für mobile Anwendungen + sentences: + - fashion design expert + - Mergers-and-Acquisitions-Analyst/Mergers-and-Acquisitions-Analystin + - commercial bid manager +pipeline_tag: sentence-similarity +library_name: sentence-transformers +metrics: +- cosine_accuracy@1 +- cosine_accuracy@20 +- cosine_accuracy@50 +- cosine_accuracy@100 +- cosine_accuracy@150 +- cosine_accuracy@200 +- cosine_precision@1 +- cosine_precision@20 +- cosine_precision@50 +- cosine_precision@100 +- cosine_precision@150 +- cosine_precision@200 +- cosine_recall@1 +- cosine_recall@20 +- cosine_recall@50 +- cosine_recall@100 +- cosine_recall@150 +- cosine_recall@200 +- cosine_ndcg@1 +- cosine_ndcg@20 +- cosine_ndcg@50 +- cosine_ndcg@100 +- cosine_ndcg@150 +- cosine_ndcg@200 +- cosine_mrr@1 +- cosine_mrr@20 +- cosine_mrr@50 +- cosine_mrr@100 +- cosine_mrr@150 +- cosine_mrr@200 +- cosine_map@1 +- cosine_map@20 +- cosine_map@50 +- cosine_map@100 +- cosine_map@150 +- cosine_map@200 +- cosine_map@500 +model-index: +- name: SentenceTransformer + results: + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: full en + type: full_en + metrics: + - type: cosine_accuracy@1 + value: 0.6285714285714286 + name: Cosine Accuracy@1 + - type: cosine_accuracy@20 + value: 0.9714285714285714 + name: Cosine Accuracy@20 + - type: cosine_accuracy@50 + value: 0.9904761904761905 + name: Cosine Accuracy@50 + - type: cosine_accuracy@100 + value: 0.9904761904761905 + name: Cosine Accuracy@100 + - type: cosine_accuracy@150 + value: 0.9904761904761905 + name: Cosine Accuracy@150 + - type: cosine_accuracy@200 + value: 0.9904761904761905 + name: Cosine Accuracy@200 + - type: cosine_precision@1 + value: 0.6285714285714286 + name: Cosine Precision@1 + - type: cosine_precision@20 + value: 0.4723809523809524 + name: Cosine Precision@20 + - type: cosine_precision@50 + value: 0.2838095238095238 + name: Cosine Precision@50 + - type: cosine_precision@100 + value: 0.1706666666666667 + name: Cosine Precision@100 + - type: cosine_precision@150 + value: 0.12285714285714286 + name: Cosine Precision@150 + - type: cosine_precision@200 + value: 0.09700000000000002 + name: Cosine Precision@200 + - type: cosine_recall@1 + value: 0.06568451704213447 + name: Cosine Recall@1 + - type: cosine_recall@20 + value: 0.5041312032991911 + name: Cosine Recall@20 + - type: cosine_recall@50 + value: 0.6762963371727007 + name: Cosine Recall@50 + - type: cosine_recall@100 + value: 0.7798036464336738 + name: Cosine Recall@100 + - type: cosine_recall@150 + value: 0.8311908383371492 + name: Cosine Recall@150 + - type: cosine_recall@200 + value: 0.8655400214018215 + name: Cosine Recall@200 + - type: cosine_ndcg@1 + value: 0.6285714285714286 + name: Cosine Ndcg@1 + - type: cosine_ndcg@20 + value: 0.6385286667884668 + name: Cosine Ndcg@20 + - type: cosine_ndcg@50 + value: 0.6505087993598385 + name: Cosine Ndcg@50 + - type: cosine_ndcg@100 + value: 0.7009585791000247 + name: Cosine Ndcg@100 + - type: cosine_ndcg@150 + value: 0.7228549618650749 + name: Cosine Ndcg@150 + - type: cosine_ndcg@200 + value: 0.7370730818153396 + name: Cosine Ndcg@200 + - type: cosine_mrr@1 + value: 0.6285714285714286 + name: Cosine Mrr@1 + - type: cosine_mrr@20 + value: 0.7790726817042607 + name: Cosine Mrr@20 + - type: cosine_mrr@50 + value: 0.7797979143260452 + name: Cosine Mrr@50 + - type: cosine_mrr@100 + value: 0.7797979143260452 + name: Cosine Mrr@100 + - type: cosine_mrr@150 + value: 0.7797979143260452 + name: Cosine Mrr@150 + - type: cosine_mrr@200 + value: 0.7797979143260452 + name: Cosine Mrr@200 + - type: cosine_map@1 + value: 0.6285714285714286 + name: Cosine Map@1 + - type: cosine_map@20 + value: 0.4949002324392317 + name: Cosine Map@20 + - type: cosine_map@50 + value: 0.47542864021103454 + name: Cosine Map@50 + - type: cosine_map@100 + value: 0.5027685735699932 + name: Cosine Map@100 + - type: cosine_map@150 + value: 0.5108956115342047 + name: Cosine Map@150 + - type: cosine_map@200 + value: 0.5152152246235047 + name: Cosine Map@200 + - type: cosine_map@500 + value: 0.5211733943510876 + name: Cosine Map@500 + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: full es + type: full_es + metrics: + - type: cosine_accuracy@1 + value: 0.11351351351351352 + name: Cosine Accuracy@1 + - type: cosine_accuracy@20 + value: 1.0 + name: Cosine Accuracy@20 + - type: cosine_accuracy@50 + value: 1.0 + name: Cosine Accuracy@50 + - type: cosine_accuracy@100 + value: 1.0 + name: Cosine Accuracy@100 + - type: cosine_accuracy@150 + value: 1.0 + name: Cosine Accuracy@150 + - type: cosine_accuracy@200 + value: 1.0 + name: Cosine Accuracy@200 + - type: cosine_precision@1 + value: 0.11351351351351352 + name: Cosine Precision@1 + - type: cosine_precision@20 + value: 0.5213513513513512 + name: Cosine Precision@20 + - type: cosine_precision@50 + value: 0.33891891891891895 + name: Cosine Precision@50 + - type: cosine_precision@100 + value: 0.2141081081081081 + name: Cosine Precision@100 + - type: cosine_precision@150 + value: 0.16104504504504505 + name: Cosine Precision@150 + - type: cosine_precision@200 + value: 0.13094594594594594 + name: Cosine Precision@200 + - type: cosine_recall@1 + value: 0.0035045234969014166 + name: Cosine Recall@1 + - type: cosine_recall@20 + value: 0.34830621955762764 + name: Cosine Recall@20 + - type: cosine_recall@50 + value: 0.5043797869988105 + name: Cosine Recall@50 + - type: cosine_recall@100 + value: 0.5962566893615484 + name: Cosine Recall@100 + - type: cosine_recall@150 + value: 0.6539916045900668 + name: Cosine Recall@150 + - type: cosine_recall@200 + value: 0.7027707655811134 + name: Cosine Recall@200 + - type: cosine_ndcg@1 + value: 0.11351351351351352 + name: Cosine Ndcg@1 + - type: cosine_ndcg@20 + value: 0.5638160555705326 + name: Cosine Ndcg@20 + - type: cosine_ndcg@50 + value: 0.5286289587475489 + name: Cosine Ndcg@50 + - type: cosine_ndcg@100 + value: 0.5494533442820461 + name: Cosine Ndcg@100 + - type: cosine_ndcg@150 + value: 0.5778904564772578 + name: Cosine Ndcg@150 + - type: cosine_ndcg@200 + value: 0.6002374248801999 + name: Cosine Ndcg@200 + - type: cosine_mrr@1 + value: 0.11351351351351352 + name: Cosine Mrr@1 + - type: cosine_mrr@20 + value: 0.55 + name: Cosine Mrr@20 + - type: cosine_mrr@50 + value: 0.55 + name: Cosine Mrr@50 + - type: cosine_mrr@100 + value: 0.55 + name: Cosine Mrr@100 + - type: cosine_mrr@150 + value: 0.55 + name: Cosine Mrr@150 + - type: cosine_mrr@200 + value: 0.55 + name: Cosine Mrr@200 + - type: cosine_map@1 + value: 0.11351351351351352 + name: Cosine Map@1 + - type: cosine_map@20 + value: 0.4321212731877681 + name: Cosine Map@20 + - type: cosine_map@50 + value: 0.3662438776904182 + name: Cosine Map@50 + - type: cosine_map@100 + value: 0.3676467044477579 + name: Cosine Map@100 + - type: cosine_map@150 + value: 0.37914071893635704 + name: Cosine Map@150 + - type: cosine_map@200 + value: 0.3864291047810966 + name: Cosine Map@200 + - type: cosine_map@500 + value: 0.3967448814407886 + name: Cosine Map@500 + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: full de + type: full_de + metrics: + - type: cosine_accuracy@1 + value: 0.2955665024630542 + name: Cosine Accuracy@1 + - type: cosine_accuracy@20 + value: 0.9605911330049262 + name: Cosine Accuracy@20 + - type: cosine_accuracy@50 + value: 0.9802955665024631 + name: Cosine Accuracy@50 + - type: cosine_accuracy@100 + value: 0.9852216748768473 + name: Cosine Accuracy@100 + - type: cosine_accuracy@150 + value: 0.9852216748768473 + name: Cosine Accuracy@150 + - type: cosine_accuracy@200 + value: 0.9901477832512315 + name: Cosine Accuracy@200 + - type: cosine_precision@1 + value: 0.2955665024630542 + name: Cosine Precision@1 + - type: cosine_precision@20 + value: 0.424384236453202 + name: Cosine Precision@20 + - type: cosine_precision@50 + value: 0.29064039408866993 + name: Cosine Precision@50 + - type: cosine_precision@100 + value: 0.19019704433497536 + name: Cosine Precision@100 + - type: cosine_precision@150 + value: 0.14476190476190476 + name: Cosine Precision@150 + - type: cosine_precision@200 + value: 0.1177832512315271 + name: Cosine Precision@200 + - type: cosine_recall@1 + value: 0.01108543831680986 + name: Cosine Recall@1 + - type: cosine_recall@20 + value: 0.2623989771425487 + name: Cosine Recall@20 + - type: cosine_recall@50 + value: 0.399936827395569 + name: Cosine Recall@50 + - type: cosine_recall@100 + value: 0.5011599542158983 + name: Cosine Recall@100 + - type: cosine_recall@150 + value: 0.5599024076006294 + name: Cosine Recall@150 + - type: cosine_recall@200 + value: 0.6019565140878311 + name: Cosine Recall@200 + - type: cosine_ndcg@1 + value: 0.2955665024630542 + name: Cosine Ndcg@1 + - type: cosine_ndcg@20 + value: 0.46461290935992494 + name: Cosine Ndcg@20 + - type: cosine_ndcg@50 + value: 0.43636700085765784 + name: Cosine Ndcg@50 + - type: cosine_ndcg@100 + value: 0.4594232150790335 + name: Cosine Ndcg@100 + - type: cosine_ndcg@150 + value: 0.4887319216460325 + name: Cosine Ndcg@150 + - type: cosine_ndcg@200 + value: 0.5085159310260775 + name: Cosine Ndcg@200 + - type: cosine_mrr@1 + value: 0.2955665024630542 + name: Cosine Mrr@1 + - type: cosine_mrr@20 + value: 0.503435229891329 + name: Cosine Mrr@20 + - type: cosine_mrr@50 + value: 0.5041035247761447 + name: Cosine Mrr@50 + - type: cosine_mrr@100 + value: 0.5041884576791513 + name: Cosine Mrr@100 + - type: cosine_mrr@150 + value: 0.5041884576791513 + name: Cosine Mrr@150 + - type: cosine_mrr@200 + value: 0.5042166068698621 + name: Cosine Mrr@200 + - type: cosine_map@1 + value: 0.2955665024630542 + name: Cosine Map@1 + - type: cosine_map@20 + value: 0.3326012942578798 + name: Cosine Map@20 + - type: cosine_map@50 + value: 0.2779781159809199 + name: Cosine Map@50 + - type: cosine_map@100 + value: 0.27530357902528746 + name: Cosine Map@100 + - type: cosine_map@150 + value: 0.2859029789549631 + name: Cosine Map@150 + - type: cosine_map@200 + value: 0.29192358526577794 + name: Cosine Map@200 + - type: cosine_map@500 + value: 0.3037728006457777 + name: Cosine Map@500 + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: full zh + type: full_zh + metrics: + - type: cosine_accuracy@1 + value: 0.6504854368932039 + name: Cosine Accuracy@1 + - type: cosine_accuracy@20 + value: 0.970873786407767 + name: Cosine Accuracy@20 + - type: cosine_accuracy@50 + value: 0.9805825242718447 + name: Cosine Accuracy@50 + - type: cosine_accuracy@100 + value: 0.9902912621359223 + name: Cosine Accuracy@100 + - type: cosine_accuracy@150 + value: 0.9902912621359223 + name: Cosine Accuracy@150 + - type: cosine_accuracy@200 + value: 0.9902912621359223 + name: Cosine Accuracy@200 + - type: cosine_precision@1 + value: 0.6504854368932039 + name: Cosine Precision@1 + - type: cosine_precision@20 + value: 0.4461165048543689 + name: Cosine Precision@20 + - type: cosine_precision@50 + value: 0.26932038834951455 + name: Cosine Precision@50 + - type: cosine_precision@100 + value: 0.16601941747572818 + name: Cosine Precision@100 + - type: cosine_precision@150 + value: 0.12000000000000002 + name: Cosine Precision@150 + - type: cosine_precision@200 + value: 0.09475728155339808 + name: Cosine Precision@200 + - type: cosine_recall@1 + value: 0.06125809321810901 + name: Cosine Recall@1 + - type: cosine_recall@20 + value: 0.4798173076061309 + name: Cosine Recall@20 + - type: cosine_recall@50 + value: 0.6511259115267456 + name: Cosine Recall@50 + - type: cosine_recall@100 + value: 0.7667280032499174 + name: Cosine Recall@100 + - type: cosine_recall@150 + value: 0.8234348132226993 + name: Cosine Recall@150 + - type: cosine_recall@200 + value: 0.8570886860782638 + name: Cosine Recall@200 + - type: cosine_ndcg@1 + value: 0.6504854368932039 + name: Cosine Ndcg@1 + - type: cosine_ndcg@20 + value: 0.6163434250133266 + name: Cosine Ndcg@20 + - type: cosine_ndcg@50 + value: 0.6306194061713684 + name: Cosine Ndcg@50 + - type: cosine_ndcg@100 + value: 0.6852740031621496 + name: Cosine Ndcg@100 + - type: cosine_ndcg@150 + value: 0.7087858531025408 + name: Cosine Ndcg@150 + - type: cosine_ndcg@200 + value: 0.7227726687256436 + name: Cosine Ndcg@200 + - type: cosine_mrr@1 + value: 0.6504854368932039 + name: Cosine Mrr@1 + - type: cosine_mrr@20 + value: 0.7938511326860843 + name: Cosine Mrr@20 + - type: cosine_mrr@50 + value: 0.7941135310067349 + name: Cosine Mrr@50 + - type: cosine_mrr@100 + value: 0.7943002375041209 + name: Cosine Mrr@100 + - type: cosine_mrr@150 + value: 0.7943002375041209 + name: Cosine Mrr@150 + - type: cosine_mrr@200 + value: 0.7943002375041209 + name: Cosine Mrr@200 + - type: cosine_map@1 + value: 0.6504854368932039 + name: Cosine Map@1 + - type: cosine_map@20 + value: 0.4673451367444491 + name: Cosine Map@20 + - type: cosine_map@50 + value: 0.4491601687897158 + name: Cosine Map@50 + - type: cosine_map@100 + value: 0.4759775327060125 + name: Cosine Map@100 + - type: cosine_map@150 + value: 0.484283864447002 + name: Cosine Map@150 + - type: cosine_map@200 + value: 0.4885403171787604 + name: Cosine Map@200 + - type: cosine_map@500 + value: 0.4948931148880558 + name: Cosine Map@500 + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: mix es + type: mix_es + metrics: + - type: cosine_accuracy@1 + value: 0.6172646905876235 + name: Cosine Accuracy@1 + - type: cosine_accuracy@20 + value: 0.9032761310452418 + name: Cosine Accuracy@20 + - type: cosine_accuracy@50 + value: 0.9443577743109725 + name: Cosine Accuracy@50 + - type: cosine_accuracy@100 + value: 0.9703588143525741 + name: Cosine Accuracy@100 + - type: cosine_accuracy@150 + value: 0.9812792511700468 + name: Cosine Accuracy@150 + - type: cosine_accuracy@200 + value: 0.9859594383775351 + name: Cosine Accuracy@200 + - type: cosine_precision@1 + value: 0.6172646905876235 + name: Cosine Precision@1 + - type: cosine_precision@20 + value: 0.10972438897555903 + name: Cosine Precision@20 + - type: cosine_precision@50 + value: 0.04786271450858035 + name: Cosine Precision@50 + - type: cosine_precision@100 + value: 0.025169006760270413 + name: Cosine Precision@100 + - type: cosine_precision@150 + value: 0.017157219622118216 + name: Cosine Precision@150 + - type: cosine_precision@200 + value: 0.013018720748829957 + name: Cosine Precision@200 + - type: cosine_recall@1 + value: 0.2379838050664884 + name: Cosine Recall@1 + - type: cosine_recall@20 + value: 0.8149369784315182 + name: Cosine Recall@20 + - type: cosine_recall@50 + value: 0.8866788004853527 + name: Cosine Recall@50 + - type: cosine_recall@100 + value: 0.9331773270930838 + name: Cosine Recall@100 + - type: cosine_recall@150 + value: 0.9536141445657828 + name: Cosine Recall@150 + - type: cosine_recall@200 + value: 0.9651759403709481 + name: Cosine Recall@200 + - type: cosine_ndcg@1 + value: 0.6172646905876235 + name: Cosine Ndcg@1 + - type: cosine_ndcg@20 + value: 0.6863945449619185 + name: Cosine Ndcg@20 + - type: cosine_ndcg@50 + value: 0.7059805315894592 + name: Cosine Ndcg@50 + - type: cosine_ndcg@100 + value: 0.7161349937562115 + name: Cosine Ndcg@100 + - type: cosine_ndcg@150 + value: 0.7201494083175249 + name: Cosine Ndcg@150 + - type: cosine_ndcg@200 + value: 0.722225937142632 + name: Cosine Ndcg@200 + - type: cosine_mrr@1 + value: 0.6172646905876235 + name: Cosine Mrr@1 + - type: cosine_mrr@20 + value: 0.6921361840847764 + name: Cosine Mrr@20 + - type: cosine_mrr@50 + value: 0.6935275501084183 + name: Cosine Mrr@50 + - type: cosine_mrr@100 + value: 0.6938924919697613 + name: Cosine Mrr@100 + - type: cosine_mrr@150 + value: 0.6939819360030616 + name: Cosine Mrr@150 + - type: cosine_mrr@200 + value: 0.6940082129440573 + name: Cosine Mrr@200 + - type: cosine_map@1 + value: 0.6172646905876235 + name: Cosine Map@1 + - type: cosine_map@20 + value: 0.6028333286973904 + name: Cosine Map@20 + - type: cosine_map@50 + value: 0.6079882517976847 + name: Cosine Map@50 + - type: cosine_map@100 + value: 0.6094136625128228 + name: Cosine Map@100 + - type: cosine_map@150 + value: 0.6097807307495342 + name: Cosine Map@150 + - type: cosine_map@200 + value: 0.6099278426294548 + name: Cosine Map@200 + - type: cosine_map@500 + value: 0.6101218939355526 + name: Cosine Map@500 + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: mix de + type: mix_de + metrics: + - type: cosine_accuracy@1 + value: 0.5429017160686428 + name: Cosine Accuracy@1 + - type: cosine_accuracy@20 + value: 0.8725949037961519 + name: Cosine Accuracy@20 + - type: cosine_accuracy@50 + value: 0.9297971918876755 + name: Cosine Accuracy@50 + - type: cosine_accuracy@100 + value: 0.9552782111284451 + name: Cosine Accuracy@100 + - type: cosine_accuracy@150 + value: 0.968278731149246 + name: Cosine Accuracy@150 + - type: cosine_accuracy@200 + value: 0.9729589183567343 + name: Cosine Accuracy@200 + - type: cosine_precision@1 + value: 0.5429017160686428 + name: Cosine Precision@1 + - type: cosine_precision@20 + value: 0.10709828393135724 + name: Cosine Precision@20 + - type: cosine_precision@50 + value: 0.04726989079563183 + name: Cosine Precision@50 + - type: cosine_precision@100 + value: 0.025002600104004166 + name: Cosine Precision@100 + - type: cosine_precision@150 + value: 0.01712601837406829 + name: Cosine Precision@150 + - type: cosine_precision@200 + value: 0.013044721788871557 + name: Cosine Precision@200 + - type: cosine_recall@1 + value: 0.20383948691280984 + name: Cosine Recall@1 + - type: cosine_recall@20 + value: 0.7817386028774485 + name: Cosine Recall@20 + - type: cosine_recall@50 + value: 0.8605044201768071 + name: Cosine Recall@50 + - type: cosine_recall@100 + value: 0.9077223088923557 + name: Cosine Recall@100 + - type: cosine_recall@150 + value: 0.9319032761310452 + name: Cosine Recall@150 + - type: cosine_recall@200 + value: 0.9461778471138845 + name: Cosine Recall@200 + - type: cosine_ndcg@1 + value: 0.5429017160686428 + name: Cosine Ndcg@1 + - type: cosine_ndcg@20 + value: 0.6364696194038222 + name: Cosine Ndcg@20 + - type: cosine_ndcg@50 + value: 0.6580204683537704 + name: Cosine Ndcg@50 + - type: cosine_ndcg@100 + value: 0.6686859699628315 + name: Cosine Ndcg@100 + - type: cosine_ndcg@150 + value: 0.6734670399055159 + name: Cosine Ndcg@150 + - type: cosine_ndcg@200 + value: 0.6761041848609185 + name: Cosine Ndcg@200 + - type: cosine_mrr@1 + value: 0.5429017160686428 + name: Cosine Mrr@1 + - type: cosine_mrr@20 + value: 0.6331176720726237 + name: Cosine Mrr@20 + - type: cosine_mrr@50 + value: 0.6350347522721764 + name: Cosine Mrr@50 + - type: cosine_mrr@100 + value: 0.6354157777188323 + name: Cosine Mrr@100 + - type: cosine_mrr@150 + value: 0.6355194502419383 + name: Cosine Mrr@150 + - type: cosine_mrr@200 + value: 0.635546462249249 + name: Cosine Mrr@200 + - type: cosine_map@1 + value: 0.5429017160686428 + name: Cosine Map@1 + - type: cosine_map@20 + value: 0.546038259426052 + name: Cosine Map@20 + - type: cosine_map@50 + value: 0.5513401593649401 + name: Cosine Map@50 + - type: cosine_map@100 + value: 0.5528890114435938 + name: Cosine Map@100 + - type: cosine_map@150 + value: 0.5533285819634786 + name: Cosine Map@150 + - type: cosine_map@200 + value: 0.5535297820757661 + name: Cosine Map@200 + - type: cosine_map@500 + value: 0.5538215020153545 + name: Cosine Map@500 + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: mix zh + type: mix_zh + metrics: + - type: cosine_accuracy@1 + value: 0.5751565762004175 + name: Cosine Accuracy@1 + - type: cosine_accuracy@20 + value: 0.9514613778705637 + name: Cosine Accuracy@20 + - type: cosine_accuracy@50 + value: 0.975991649269311 + name: Cosine Accuracy@50 + - type: cosine_accuracy@100 + value: 0.9848643006263048 + name: Cosine Accuracy@100 + - type: cosine_accuracy@150 + value: 0.9895615866388309 + name: Cosine Accuracy@150 + - type: cosine_accuracy@200 + value: 0.9916492693110647 + name: Cosine Accuracy@200 + - type: cosine_precision@1 + value: 0.5751565762004175 + name: Cosine Precision@1 + - type: cosine_precision@20 + value: 0.123982254697286 + name: Cosine Precision@20 + - type: cosine_precision@50 + value: 0.05465553235908143 + name: Cosine Precision@50 + - type: cosine_precision@100 + value: 0.02851252609603341 + name: Cosine Precision@100 + - type: cosine_precision@150 + value: 0.019324982602644397 + name: Cosine Precision@150 + - type: cosine_precision@200 + value: 0.014634655532359089 + name: Cosine Precision@200 + - type: cosine_recall@1 + value: 0.19298513768764292 + name: Cosine Recall@1 + - type: cosine_recall@20 + value: 0.8174060542797494 + name: Cosine Recall@20 + - type: cosine_recall@50 + value: 0.901000347947112 + name: Cosine Recall@50 + - type: cosine_recall@100 + value: 0.9399095337508698 + name: Cosine Recall@100 + - type: cosine_recall@150 + value: 0.9558716075156575 + name: Cosine Recall@150 + - type: cosine_recall@200 + value: 0.965196590118302 + name: Cosine Recall@200 + - type: cosine_ndcg@1 + value: 0.5751565762004175 + name: Cosine Ndcg@1 + - type: cosine_ndcg@20 + value: 0.6621196118161056 + name: Cosine Ndcg@20 + - type: cosine_ndcg@50 + value: 0.6858570871515306 + name: Cosine Ndcg@50 + - type: cosine_ndcg@100 + value: 0.6947962879201968 + name: Cosine Ndcg@100 + - type: cosine_ndcg@150 + value: 0.6980250427797421 + name: Cosine Ndcg@150 + - type: cosine_ndcg@200 + value: 0.6997922044919449 + name: Cosine Ndcg@200 + - type: cosine_mrr@1 + value: 0.5751565762004175 + name: Cosine Mrr@1 + - type: cosine_mrr@20 + value: 0.6974988781113621 + name: Cosine Mrr@20 + - type: cosine_mrr@50 + value: 0.6983413027160801 + name: Cosine Mrr@50 + - type: cosine_mrr@100 + value: 0.6984820179753005 + name: Cosine Mrr@100 + - type: cosine_mrr@150 + value: 0.6985228351798531 + name: Cosine Mrr@150 + - type: cosine_mrr@200 + value: 0.6985351624205532 + name: Cosine Mrr@200 + - type: cosine_map@1 + value: 0.5751565762004175 + name: Cosine Map@1 + - type: cosine_map@20 + value: 0.5395939445358217 + name: Cosine Map@20 + - type: cosine_map@50 + value: 0.5465541726714618 + name: Cosine Map@50 + - type: cosine_map@100 + value: 0.5480058234906587 + name: Cosine Map@100 + - type: cosine_map@150 + value: 0.5483452539266979 + name: Cosine Map@150 + - type: cosine_map@200 + value: 0.548487754480418 + name: Cosine Map@200 + - type: cosine_map@500 + value: 0.5486704400924459 + name: Cosine Map@500 +--- + +# SentenceTransformer + +This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. + +## Model Details + +### Model Description +- **Model Type:** Sentence Transformer + +- **Maximum Sequence Length:** 512 tokens +- **Output Dimensionality:** 768 dimensions +- **Similarity Function:** Cosine Similarity + + + + +### Model Sources + +- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) +- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) +- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) + +### Full Model Architecture + +``` +SentenceTransformer( + (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel + (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) + (2): Normalize() +) +``` + +## Usage + +### Direct Usage (Sentence Transformers) + +First install the Sentence Transformers library: + +```bash +pip install -U sentence-transformers +``` + +Then you can load this model and run inference. +```python +from sentence_transformers import SentenceTransformer + +# Download from the 🤗 Hub +model = SentenceTransformer("sentence_transformers_model_id") +# Run inference +sentences = [ + 'Entwicklerin für mobile Anwendungen', + 'Mergers-and-Acquisitions-Analyst/Mergers-and-Acquisitions-Analystin', + 'fashion design expert', +] +embeddings = model.encode(sentences) +print(embeddings.shape) +# [3, 768] + +# Get the similarity scores for the embeddings +similarities = model.similarity(embeddings, embeddings) +print(similarities.shape) +# [3, 3] +``` + + + + + + + +## 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 | 0.6286 | 0.1135 | 0.2956 | 0.6505 | 0.6173 | 0.5429 | 0.5752 | +| cosine_accuracy@20 | 0.9714 | 1.0 | 0.9606 | 0.9709 | 0.9033 | 0.8726 | 0.9515 | +| cosine_accuracy@50 | 0.9905 | 1.0 | 0.9803 | 0.9806 | 0.9444 | 0.9298 | 0.976 | +| cosine_accuracy@100 | 0.9905 | 1.0 | 0.9852 | 0.9903 | 0.9704 | 0.9553 | 0.9849 | +| cosine_accuracy@150 | 0.9905 | 1.0 | 0.9852 | 0.9903 | 0.9813 | 0.9683 | 0.9896 | +| cosine_accuracy@200 | 0.9905 | 1.0 | 0.9901 | 0.9903 | 0.986 | 0.973 | 0.9916 | +| cosine_precision@1 | 0.6286 | 0.1135 | 0.2956 | 0.6505 | 0.6173 | 0.5429 | 0.5752 | +| cosine_precision@20 | 0.4724 | 0.5214 | 0.4244 | 0.4461 | 0.1097 | 0.1071 | 0.124 | +| cosine_precision@50 | 0.2838 | 0.3389 | 0.2906 | 0.2693 | 0.0479 | 0.0473 | 0.0547 | +| cosine_precision@100 | 0.1707 | 0.2141 | 0.1902 | 0.166 | 0.0252 | 0.025 | 0.0285 | +| cosine_precision@150 | 0.1229 | 0.161 | 0.1448 | 0.12 | 0.0172 | 0.0171 | 0.0193 | +| cosine_precision@200 | 0.097 | 0.1309 | 0.1178 | 0.0948 | 0.013 | 0.013 | 0.0146 | +| cosine_recall@1 | 0.0657 | 0.0035 | 0.0111 | 0.0613 | 0.238 | 0.2038 | 0.193 | +| cosine_recall@20 | 0.5041 | 0.3483 | 0.2624 | 0.4798 | 0.8149 | 0.7817 | 0.8174 | +| cosine_recall@50 | 0.6763 | 0.5044 | 0.3999 | 0.6511 | 0.8867 | 0.8605 | 0.901 | +| cosine_recall@100 | 0.7798 | 0.5963 | 0.5012 | 0.7667 | 0.9332 | 0.9077 | 0.9399 | +| cosine_recall@150 | 0.8312 | 0.654 | 0.5599 | 0.8234 | 0.9536 | 0.9319 | 0.9559 | +| cosine_recall@200 | 0.8655 | 0.7028 | 0.602 | 0.8571 | 0.9652 | 0.9462 | 0.9652 | +| cosine_ndcg@1 | 0.6286 | 0.1135 | 0.2956 | 0.6505 | 0.6173 | 0.5429 | 0.5752 | +| cosine_ndcg@20 | 0.6385 | 0.5638 | 0.4646 | 0.6163 | 0.6864 | 0.6365 | 0.6621 | +| cosine_ndcg@50 | 0.6505 | 0.5286 | 0.4364 | 0.6306 | 0.706 | 0.658 | 0.6859 | +| cosine_ndcg@100 | 0.701 | 0.5495 | 0.4594 | 0.6853 | 0.7161 | 0.6687 | 0.6948 | +| cosine_ndcg@150 | 0.7229 | 0.5779 | 0.4887 | 0.7088 | 0.7201 | 0.6735 | 0.698 | +| **cosine_ndcg@200** | **0.7371** | **0.6002** | **0.5085** | **0.7228** | **0.7222** | **0.6761** | **0.6998** | +| cosine_mrr@1 | 0.6286 | 0.1135 | 0.2956 | 0.6505 | 0.6173 | 0.5429 | 0.5752 | +| cosine_mrr@20 | 0.7791 | 0.55 | 0.5034 | 0.7939 | 0.6921 | 0.6331 | 0.6975 | +| cosine_mrr@50 | 0.7798 | 0.55 | 0.5041 | 0.7941 | 0.6935 | 0.635 | 0.6983 | +| cosine_mrr@100 | 0.7798 | 0.55 | 0.5042 | 0.7943 | 0.6939 | 0.6354 | 0.6985 | +| cosine_mrr@150 | 0.7798 | 0.55 | 0.5042 | 0.7943 | 0.694 | 0.6355 | 0.6985 | +| cosine_mrr@200 | 0.7798 | 0.55 | 0.5042 | 0.7943 | 0.694 | 0.6355 | 0.6985 | +| cosine_map@1 | 0.6286 | 0.1135 | 0.2956 | 0.6505 | 0.6173 | 0.5429 | 0.5752 | +| cosine_map@20 | 0.4949 | 0.4321 | 0.3326 | 0.4673 | 0.6028 | 0.546 | 0.5396 | +| cosine_map@50 | 0.4754 | 0.3662 | 0.278 | 0.4492 | 0.608 | 0.5513 | 0.5466 | +| cosine_map@100 | 0.5028 | 0.3676 | 0.2753 | 0.476 | 0.6094 | 0.5529 | 0.548 | +| cosine_map@150 | 0.5109 | 0.3791 | 0.2859 | 0.4843 | 0.6098 | 0.5533 | 0.5483 | +| cosine_map@200 | 0.5152 | 0.3864 | 0.2919 | 0.4885 | 0.6099 | 0.5535 | 0.5485 | +| cosine_map@500 | 0.5212 | 0.3967 | 0.3038 | 0.4949 | 0.6101 | 0.5538 | 0.5487 | + + + + + +## Training Details + +### Training Dataset + +#### Unnamed Dataset + +* Size: 86,648 training samples +* Columns: sentence and label +* Approximate statistics based on the first 1000 samples: + | | sentence | label | + |:--------|:---------------------------------------------------------------------------------|:-------------------------------------| + | type | string | list | + | details | | | +* Samples: + | sentence | label | + |:-----------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------| + | | [-0.07171934843063354, 0.03595816716551781, -0.029780959710478783, 0.006593302357941866, 0.040611181408166885, ...] | + | airport environment officer | [-0.022075481712818146, 0.02999737113714218, -0.02189866080880165, 0.016531817615032196, 0.012234307825565338, ...] | + | Flake操作员 | [-0.04815564677119255, 0.023524893447756767, -0.01583661139011383, 0.042527906596660614, 0.03815540298819542, ...] | +* Loss: [MSELoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss) + +### Training Hyperparameters +#### Non-Default Hyperparameters + +- `eval_strategy`: steps +- `per_device_train_batch_size`: 128 +- `per_device_eval_batch_size`: 128 +- `gradient_accumulation_steps`: 2 +- `learning_rate`: 0.0001 +- `num_train_epochs`: 5 +- `warmup_ratio`: 0.05 +- `log_on_each_node`: False +- `fp16`: True +- `dataloader_num_workers`: 4 +- `ddp_find_unused_parameters`: True +- `batch_sampler`: no_duplicates + +#### All Hyperparameters +
Click to expand + +- `overwrite_output_dir`: False +- `do_predict`: False +- `eval_strategy`: steps +- `prediction_loss_only`: True +- `per_device_train_batch_size`: 128 +- `per_device_eval_batch_size`: 128 +- `per_gpu_train_batch_size`: None +- `per_gpu_eval_batch_size`: None +- `gradient_accumulation_steps`: 2 +- `eval_accumulation_steps`: None +- `torch_empty_cache_steps`: None +- `learning_rate`: 0.0001 +- `weight_decay`: 0.0 +- `adam_beta1`: 0.9 +- `adam_beta2`: 0.999 +- `adam_epsilon`: 1e-08 +- `max_grad_norm`: 1.0 +- `num_train_epochs`: 5 +- `max_steps`: -1 +- `lr_scheduler_type`: linear +- `lr_scheduler_kwargs`: {} +- `warmup_ratio`: 0.05 +- `warmup_steps`: 0 +- `log_level`: passive +- `log_level_replica`: warning +- `log_on_each_node`: False +- `logging_nan_inf_filter`: True +- `save_safetensors`: True +- `save_on_each_node`: False +- `save_only_model`: False +- `restore_callback_states_from_checkpoint`: False +- `no_cuda`: False +- `use_cpu`: False +- `use_mps_device`: False +- `seed`: 42 +- `data_seed`: None +- `jit_mode_eval`: False +- `use_ipex`: False +- `bf16`: False +- `fp16`: True +- `fp16_opt_level`: O1 +- `half_precision_backend`: auto +- `bf16_full_eval`: False +- `fp16_full_eval`: False +- `tf32`: None +- `local_rank`: 0 +- `ddp_backend`: None +- `tpu_num_cores`: None +- `tpu_metrics_debug`: False +- `debug`: [] +- `dataloader_drop_last`: True +- `dataloader_num_workers`: 4 +- `dataloader_prefetch_factor`: None +- `past_index`: -1 +- `disable_tqdm`: False +- `remove_unused_columns`: True +- `label_names`: None +- `load_best_model_at_end`: False +- `ignore_data_skip`: False +- `fsdp`: [] +- `fsdp_min_num_params`: 0 +- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} +- `tp_size`: 0 +- `fsdp_transformer_layer_cls_to_wrap`: None +- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} +- `deepspeed`: None +- `label_smoothing_factor`: 0.0 +- `optim`: adamw_torch +- `optim_args`: None +- `adafactor`: False +- `group_by_length`: False +- `length_column_name`: length +- `ddp_find_unused_parameters`: True +- `ddp_bucket_cap_mb`: None +- `ddp_broadcast_buffers`: False +- `dataloader_pin_memory`: True +- `dataloader_persistent_workers`: False +- `skip_memory_metrics`: True +- `use_legacy_prediction_loop`: False +- `push_to_hub`: False +- `resume_from_checkpoint`: None +- `hub_model_id`: None +- `hub_strategy`: every_save +- `hub_private_repo`: None +- `hub_always_push`: False +- `gradient_checkpointing`: False +- `gradient_checkpointing_kwargs`: None +- `include_inputs_for_metrics`: False +- `include_for_metrics`: [] +- `eval_do_concat_batches`: True +- `fp16_backend`: auto +- `push_to_hub_model_id`: None +- `push_to_hub_organization`: None +- `mp_parameters`: +- `auto_find_batch_size`: False +- `full_determinism`: False +- `torchdynamo`: None +- `ray_scope`: last +- `ddp_timeout`: 1800 +- `torch_compile`: False +- `torch_compile_backend`: None +- `torch_compile_mode`: None +- `include_tokens_per_second`: False +- `include_num_input_tokens_seen`: False +- `neftune_noise_alpha`: None +- `optim_target_modules`: None +- `batch_eval_metrics`: False +- `eval_on_start`: False +- `use_liger_kernel`: False +- `eval_use_gather_object`: False +- `average_tokens_across_devices`: False +- `prompts`: None +- `batch_sampler`: no_duplicates +- `multi_dataset_batch_sampler`: proportional + +
+ +### Training Logs +| Epoch | Step | Training Loss | full_en_cosine_ndcg@200 | full_es_cosine_ndcg@200 | full_de_cosine_ndcg@200 | full_zh_cosine_ndcg@200 | mix_es_cosine_ndcg@200 | mix_de_cosine_ndcg@200 | mix_zh_cosine_ndcg@200 | +|:------:|:----:|:-------------:|:-----------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:| +| -1 | -1 | - | 0.5348 | 0.4311 | 0.3678 | 0.5333 | 0.2580 | 0.1924 | 0.2871 | +| 0.0030 | 1 | 0.0017 | - | - | - | - | - | - | - | +| 0.2959 | 100 | 0.001 | - | - | - | - | - | - | - | +| 0.5917 | 200 | 0.0005 | 0.6702 | 0.5287 | 0.4566 | 0.6809 | 0.5864 | 0.5302 | 0.4739 | +| 0.8876 | 300 | 0.0004 | - | - | - | - | - | - | - | +| 1.1834 | 400 | 0.0004 | 0.7057 | 0.5643 | 0.4790 | 0.7033 | 0.6604 | 0.6055 | 0.6003 | +| 1.4793 | 500 | 0.0004 | - | - | - | - | - | - | - | +| 1.7751 | 600 | 0.0003 | 0.7184 | 0.5783 | 0.4910 | 0.7127 | 0.6927 | 0.6416 | 0.6485 | +| 2.0710 | 700 | 0.0003 | - | - | - | - | - | - | - | +| 2.3669 | 800 | 0.0003 | 0.7307 | 0.5938 | 0.5023 | 0.7233 | 0.7125 | 0.6639 | 0.6847 | +| 2.6627 | 900 | 0.0003 | - | - | - | - | - | - | - | +| 2.9586 | 1000 | 0.0003 | 0.7371 | 0.6002 | 0.5085 | 0.7228 | 0.7222 | 0.6761 | 0.6998 | + + +### Framework Versions +- Python: 3.11.11 +- Sentence Transformers: 4.1.0 +- Transformers: 4.51.3 +- PyTorch: 2.6.0+cu124 +- Accelerate: 1.6.0 +- Datasets: 3.5.0 +- Tokenizers: 0.21.1 + +## Citation + +### BibTeX + +#### Sentence Transformers +```bibtex +@inproceedings{reimers-2019-sentence-bert, + title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", + author = "Reimers, Nils and Gurevych, Iryna", + booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", + month = "11", + year = "2019", + publisher = "Association for Computational Linguistics", + url = "https://arxiv.org/abs/1908.10084", +} +``` + +#### MSELoss +```bibtex +@inproceedings{reimers-2020-multilingual-sentence-bert, + title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation", + author = "Reimers, Nils and Gurevych, Iryna", + booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing", + month = "11", + year = "2020", + publisher = "Association for Computational Linguistics", + url = "https://arxiv.org/abs/2004.09813", +} +``` + + + + + + \ No newline at end of file diff --git a/checkpoint-1000/config.json b/checkpoint-1000/config.json new file mode 100644 index 0000000000000000000000000000000000000000..281db00437139c18374483e9e7ade1288b0866e1 --- /dev/null +++ b/checkpoint-1000/config.json @@ -0,0 +1,49 @@ +{ + "architectures": [ + "NewModel" + ], + "attention_probs_dropout_prob": 0.0, + "auto_map": { + "AutoConfig": "configuration.NewConfig", + "AutoModel": "Alibaba-NLP/new-impl--modeling.NewModel", + "AutoModelForMaskedLM": "Alibaba-NLP/new-impl--modeling.NewForMaskedLM", + "AutoModelForMultipleChoice": "Alibaba-NLP/new-impl--modeling.NewForMultipleChoice", + "AutoModelForQuestionAnswering": "Alibaba-NLP/new-impl--modeling.NewForQuestionAnswering", + "AutoModelForSequenceClassification": "Alibaba-NLP/new-impl--modeling.NewForSequenceClassification", + "AutoModelForTokenClassification": "Alibaba-NLP/new-impl--modeling.NewForTokenClassification" + }, + "classifier_dropout": 0.0, + "hidden_act": "gelu", + "hidden_dropout_prob": 0.1, + "hidden_size": 768, + "id2label": { + "0": "LABEL_0" + }, + "initializer_range": 0.02, + "intermediate_size": 3072, + "label2id": { + "LABEL_0": 0 + }, + "layer_norm_eps": 1e-12, + "layer_norm_type": "layer_norm", + "logn_attention_clip1": false, + "logn_attention_scale": false, + "max_position_embeddings": 8192, + "model_type": "new", + "num_attention_heads": 12, + "num_hidden_layers": 3, + "pack_qkv": true, + "pad_token_id": 1, + "position_embedding_type": "rope", + 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"include_prompt": true +} \ No newline at end of file diff --git a/checkpoint-1400/README.md b/checkpoint-1400/README.md new file mode 100644 index 0000000000000000000000000000000000000000..a9705ea10639b77d21cbc15c930f039129f72d8a --- /dev/null +++ b/checkpoint-1400/README.md @@ -0,0 +1,1288 @@ +--- +tags: +- sentence-transformers +- sentence-similarity +- feature-extraction +- generated_from_trainer +- dataset_size:86648 +- loss:MSELoss +widget: +- source_sentence: Familienberaterin + sentences: + - electric power station operator + - venue booker & promoter + - betrieblicher Aus- und Weiterbildner/betriebliche Aus- und Weiterbildnerin +- source_sentence: high school RS teacher + sentences: + - infantryman + - Schnellbedienungsrestaurantteamleiter + - drill setup operator +- source_sentence: lighting designer + sentences: + - software support manager + - 直升机维护协调员 + - bus maintenance supervisor +- source_sentence: 机场消防员 + sentences: + - Flake操作员 + - técnico en gestión de residuos peligrosos/técnica en gestión de residuos peligrosos + - 专门学校老师 +- source_sentence: Entwicklerin für mobile Anwendungen + sentences: + - fashion design expert + - Mergers-and-Acquisitions-Analyst/Mergers-and-Acquisitions-Analystin + - commercial bid manager +pipeline_tag: sentence-similarity +library_name: sentence-transformers +metrics: +- cosine_accuracy@1 +- cosine_accuracy@20 +- cosine_accuracy@50 +- cosine_accuracy@100 +- cosine_accuracy@150 +- cosine_accuracy@200 +- cosine_precision@1 +- cosine_precision@20 +- cosine_precision@50 +- cosine_precision@100 +- cosine_precision@150 +- cosine_precision@200 +- cosine_recall@1 +- cosine_recall@20 +- cosine_recall@50 +- cosine_recall@100 +- cosine_recall@150 +- cosine_recall@200 +- cosine_ndcg@1 +- cosine_ndcg@20 +- cosine_ndcg@50 +- cosine_ndcg@100 +- cosine_ndcg@150 +- cosine_ndcg@200 +- cosine_mrr@1 +- cosine_mrr@20 +- cosine_mrr@50 +- cosine_mrr@100 +- cosine_mrr@150 +- cosine_mrr@200 +- cosine_map@1 +- cosine_map@20 +- cosine_map@50 +- cosine_map@100 +- cosine_map@150 +- cosine_map@200 +- cosine_map@500 +model-index: +- name: SentenceTransformer + results: + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: full en + type: full_en + metrics: + - type: cosine_accuracy@1 + value: 0.638095238095238 + name: Cosine Accuracy@1 + - type: cosine_accuracy@20 + value: 0.9619047619047619 + name: Cosine Accuracy@20 + - type: cosine_accuracy@50 + value: 0.9904761904761905 + name: Cosine Accuracy@50 + - type: cosine_accuracy@100 + value: 0.9904761904761905 + name: Cosine Accuracy@100 + - type: cosine_accuracy@150 + value: 0.9904761904761905 + name: Cosine Accuracy@150 + - type: cosine_accuracy@200 + value: 0.9904761904761905 + name: Cosine Accuracy@200 + - type: cosine_precision@1 + value: 0.638095238095238 + name: Cosine Precision@1 + - type: cosine_precision@20 + value: 0.4766666666666666 + name: Cosine Precision@20 + - type: cosine_precision@50 + value: 0.28723809523809524 + name: Cosine Precision@50 + - type: cosine_precision@100 + value: 0.172952380952381 + name: Cosine Precision@100 + - type: cosine_precision@150 + value: 0.12419047619047618 + name: Cosine Precision@150 + - type: cosine_precision@200 + value: 0.09828571428571428 + name: Cosine Precision@200 + - type: cosine_recall@1 + value: 0.06587125840534644 + name: Cosine Recall@1 + - type: cosine_recall@20 + value: 0.5075382961558268 + name: Cosine Recall@20 + - type: cosine_recall@50 + value: 0.6815180199385792 + name: Cosine Recall@50 + - type: cosine_recall@100 + value: 0.7892546849949126 + name: Cosine Recall@100 + - type: cosine_recall@150 + value: 0.837763491705966 + name: Cosine Recall@150 + - type: cosine_recall@200 + value: 0.8747531461107081 + name: Cosine Recall@200 + - type: cosine_ndcg@1 + value: 0.638095238095238 + name: Cosine Ndcg@1 + - type: cosine_ndcg@20 + value: 0.6437588496803061 + name: Cosine Ndcg@20 + - type: cosine_ndcg@50 + value: 0.6565500770575415 + name: Cosine Ndcg@50 + - type: cosine_ndcg@100 + value: 0.7088213416976051 + name: Cosine Ndcg@100 + - type: cosine_ndcg@150 + value: 0.7298707409128666 + name: Cosine Ndcg@150 + - type: cosine_ndcg@200 + value: 0.7449419847756586 + name: Cosine Ndcg@200 + - type: cosine_mrr@1 + value: 0.638095238095238 + name: Cosine Mrr@1 + - type: cosine_mrr@20 + value: 0.7865079365079365 + name: Cosine Mrr@20 + - type: cosine_mrr@50 + value: 0.7877959183673469 + name: Cosine Mrr@50 + - type: cosine_mrr@100 + value: 0.7877959183673469 + name: Cosine Mrr@100 + - type: cosine_mrr@150 + value: 0.7877959183673469 + name: Cosine Mrr@150 + - type: cosine_mrr@200 + value: 0.7877959183673469 + name: Cosine Mrr@200 + - type: cosine_map@1 + value: 0.638095238095238 + name: Cosine Map@1 + - type: cosine_map@20 + value: 0.4998912029710938 + name: Cosine Map@20 + - type: cosine_map@50 + value: 0.4824988798112498 + name: Cosine Map@50 + - type: cosine_map@100 + value: 0.510770369728262 + name: Cosine Map@100 + - type: cosine_map@150 + value: 0.5189335101114453 + name: Cosine Map@150 + - type: cosine_map@200 + value: 0.5235615593885471 + name: Cosine Map@200 + - type: cosine_map@500 + value: 0.5292082683302094 + name: Cosine Map@500 + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: full es + type: full_es + metrics: + - type: cosine_accuracy@1 + value: 0.11891891891891893 + name: Cosine Accuracy@1 + - type: cosine_accuracy@20 + value: 1.0 + name: Cosine Accuracy@20 + - type: cosine_accuracy@50 + value: 1.0 + name: Cosine Accuracy@50 + - type: cosine_accuracy@100 + value: 1.0 + name: Cosine Accuracy@100 + - type: cosine_accuracy@150 + value: 1.0 + name: Cosine Accuracy@150 + - type: cosine_accuracy@200 + value: 1.0 + name: Cosine Accuracy@200 + - type: cosine_precision@1 + value: 0.11891891891891893 + name: Cosine Precision@1 + - type: cosine_precision@20 + value: 0.5278378378378379 + name: Cosine Precision@20 + - type: cosine_precision@50 + value: 0.34324324324324323 + name: Cosine Precision@50 + - type: cosine_precision@100 + value: 0.21778378378378382 + name: Cosine Precision@100 + - type: cosine_precision@150 + value: 0.16486486486486487 + name: Cosine Precision@150 + - type: cosine_precision@200 + value: 0.1328918918918919 + name: Cosine Precision@200 + - type: cosine_recall@1 + value: 0.0035840147528632613 + name: Cosine Recall@1 + - type: cosine_recall@20 + value: 0.3543566274863611 + name: Cosine Recall@20 + - type: cosine_recall@50 + value: 0.5098461049513731 + name: Cosine Recall@50 + - type: cosine_recall@100 + value: 0.6026389252991667 + name: Cosine Recall@100 + - type: cosine_recall@150 + value: 0.6669011609932756 + name: Cosine Recall@150 + - type: cosine_recall@200 + value: 0.7113409830611916 + name: Cosine Recall@200 + - type: cosine_ndcg@1 + value: 0.11891891891891893 + name: Cosine Ndcg@1 + - type: cosine_ndcg@20 + value: 0.5711957180482146 + name: Cosine Ndcg@20 + - type: cosine_ndcg@50 + value: 0.5349550041043327 + name: Cosine Ndcg@50 + - type: cosine_ndcg@100 + value: 0.5565423240177232 + name: Cosine Ndcg@100 + - type: cosine_ndcg@150 + value: 0.5877749295399255 + name: Cosine Ndcg@150 + - type: cosine_ndcg@200 + value: 0.6082665694710195 + name: Cosine Ndcg@200 + - type: cosine_mrr@1 + value: 0.11891891891891893 + name: Cosine Mrr@1 + - type: cosine_mrr@20 + value: 0.5527027027027027 + name: Cosine Mrr@20 + - type: cosine_mrr@50 + value: 0.5527027027027027 + name: Cosine Mrr@50 + - type: cosine_mrr@100 + value: 0.5527027027027027 + name: Cosine Mrr@100 + - type: cosine_mrr@150 + value: 0.5527027027027027 + name: Cosine Mrr@150 + - type: cosine_mrr@200 + value: 0.5527027027027027 + name: Cosine Mrr@200 + - type: cosine_map@1 + value: 0.11891891891891893 + name: Cosine Map@1 + - type: cosine_map@20 + value: 0.43847997732650607 + name: Cosine Map@20 + - type: cosine_map@50 + value: 0.3732694210069731 + name: Cosine Map@50 + - type: cosine_map@100 + value: 0.375118481783653 + name: Cosine Map@100 + - type: cosine_map@150 + value: 0.3878279775328886 + name: Cosine Map@150 + - type: cosine_map@200 + value: 0.3947963463478377 + name: Cosine Map@200 + - type: cosine_map@500 + value: 0.40522877653342115 + name: Cosine Map@500 + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: full de + type: full_de + metrics: + - type: cosine_accuracy@1 + value: 0.2955665024630542 + name: Cosine Accuracy@1 + - type: cosine_accuracy@20 + value: 0.9704433497536946 + name: Cosine Accuracy@20 + - type: cosine_accuracy@50 + value: 0.9753694581280788 + name: Cosine Accuracy@50 + - type: cosine_accuracy@100 + value: 0.9901477832512315 + name: Cosine Accuracy@100 + - type: cosine_accuracy@150 + value: 0.9901477832512315 + name: Cosine Accuracy@150 + - type: cosine_accuracy@200 + value: 0.9901477832512315 + name: Cosine Accuracy@200 + - type: cosine_precision@1 + value: 0.2955665024630542 + name: Cosine Precision@1 + - type: cosine_precision@20 + value: 0.4268472906403941 + name: Cosine Precision@20 + - type: cosine_precision@50 + value: 0.2961576354679803 + name: Cosine Precision@50 + - type: cosine_precision@100 + value: 0.19325123152709361 + name: Cosine Precision@100 + - type: cosine_precision@150 + value: 0.1477832512315271 + name: Cosine Precision@150 + - type: cosine_precision@200 + value: 0.11955665024630542 + name: Cosine Precision@200 + - type: cosine_recall@1 + value: 0.01108543831680986 + name: Cosine Recall@1 + - type: cosine_recall@20 + value: 0.2650930635637711 + name: Cosine Recall@20 + - type: cosine_recall@50 + value: 0.40640295677523314 + name: Cosine Recall@50 + - type: cosine_recall@100 + value: 0.5077941189728509 + name: Cosine Recall@100 + - type: cosine_recall@150 + value: 0.5715831784599764 + name: Cosine Recall@150 + - type: cosine_recall@200 + value: 0.6110348630292376 + name: Cosine Recall@200 + - type: cosine_ndcg@1 + value: 0.2955665024630542 + name: Cosine Ndcg@1 + - type: cosine_ndcg@20 + value: 0.46792018771474997 + name: Cosine Ndcg@20 + - type: cosine_ndcg@50 + value: 0.4426383293801955 + name: Cosine Ndcg@50 + - type: cosine_ndcg@100 + value: 0.46518720126873364 + name: Cosine Ndcg@100 + - type: cosine_ndcg@150 + value: 0.49680037168814867 + name: Cosine Ndcg@150 + - type: cosine_ndcg@200 + value: 0.5153612080009081 + name: Cosine Ndcg@200 + - type: cosine_mrr@1 + value: 0.2955665024630542 + name: Cosine Mrr@1 + - type: cosine_mrr@20 + value: 0.5045345800887666 + name: Cosine Mrr@20 + - type: cosine_mrr@50 + value: 0.5046677181529392 + name: Cosine Mrr@50 + - type: cosine_mrr@100 + value: 0.5048549281619237 + name: Cosine Mrr@100 + - type: cosine_mrr@150 + value: 0.5048549281619237 + name: Cosine Mrr@150 + - type: cosine_mrr@200 + value: 0.5048549281619237 + name: Cosine Mrr@200 + - type: cosine_map@1 + value: 0.2955665024630542 + name: Cosine Map@1 + - type: cosine_map@20 + value: 0.3352975238416978 + name: Cosine Map@20 + - type: cosine_map@50 + value: 0.2835629650407853 + name: Cosine Map@50 + - type: cosine_map@100 + value: 0.2802505215242132 + name: Cosine Map@100 + - type: cosine_map@150 + value: 0.29173450273639756 + name: Cosine Map@150 + - type: cosine_map@200 + value: 0.2974766350207053 + name: Cosine Map@200 + - type: cosine_map@500 + value: 0.30952701831389734 + name: Cosine Map@500 + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: full zh + type: full_zh + metrics: + - type: cosine_accuracy@1 + value: 0.6601941747572816 + name: Cosine Accuracy@1 + - type: cosine_accuracy@20 + value: 0.970873786407767 + name: Cosine Accuracy@20 + - type: cosine_accuracy@50 + value: 0.9902912621359223 + name: Cosine Accuracy@50 + - type: cosine_accuracy@100 + value: 0.9902912621359223 + name: Cosine Accuracy@100 + - type: cosine_accuracy@150 + value: 0.9902912621359223 + name: Cosine Accuracy@150 + - type: cosine_accuracy@200 + value: 0.9902912621359223 + name: Cosine Accuracy@200 + - type: cosine_precision@1 + value: 0.6601941747572816 + name: Cosine Precision@1 + - type: cosine_precision@20 + value: 0.4451456310679612 + name: Cosine Precision@20 + - type: cosine_precision@50 + value: 0.27048543689320387 + name: Cosine Precision@50 + - type: cosine_precision@100 + value: 0.16611650485436896 + name: Cosine Precision@100 + - type: cosine_precision@150 + value: 0.12084142394822009 + name: Cosine Precision@150 + - type: cosine_precision@200 + value: 0.09519417475728156 + name: Cosine Precision@200 + - type: cosine_recall@1 + value: 0.06611246215014785 + name: Cosine Recall@1 + - type: cosine_recall@20 + value: 0.48185419008936636 + name: Cosine Recall@20 + - type: cosine_recall@50 + value: 0.6551920812816043 + name: Cosine Recall@50 + - type: cosine_recall@100 + value: 0.764654034617116 + name: Cosine Recall@100 + - type: cosine_recall@150 + value: 0.8281168342114908 + name: Cosine Recall@150 + - type: cosine_recall@200 + value: 0.8609375188843946 + name: Cosine Recall@200 + - type: cosine_ndcg@1 + value: 0.6601941747572816 + name: Cosine Ndcg@1 + - type: cosine_ndcg@20 + value: 0.6209192881378345 + name: Cosine Ndcg@20 + - type: cosine_ndcg@50 + value: 0.6371304923469949 + name: Cosine Ndcg@50 + - type: cosine_ndcg@100 + value: 0.6900404048312746 + name: Cosine Ndcg@100 + - type: cosine_ndcg@150 + value: 0.7159480635761921 + name: Cosine Ndcg@150 + - type: cosine_ndcg@200 + value: 0.7294173160030438 + name: Cosine Ndcg@200 + - type: cosine_mrr@1 + value: 0.6601941747572816 + name: Cosine Mrr@1 + - type: cosine_mrr@20 + value: 0.8015419760137065 + name: Cosine Mrr@20 + - type: cosine_mrr@50 + value: 0.8020274129069105 + name: Cosine Mrr@50 + - type: cosine_mrr@100 + value: 0.8020274129069105 + name: Cosine Mrr@100 + - type: cosine_mrr@150 + value: 0.8020274129069105 + name: Cosine Mrr@150 + - type: cosine_mrr@200 + value: 0.8020274129069105 + name: Cosine Mrr@200 + - type: cosine_map@1 + value: 0.6601941747572816 + name: Cosine Map@1 + - type: cosine_map@20 + value: 0.47238295031349775 + name: Cosine Map@20 + - type: cosine_map@50 + value: 0.4561669025825994 + name: Cosine Map@50 + - type: cosine_map@100 + value: 0.48307171830860945 + name: Cosine Map@100 + - type: cosine_map@150 + value: 0.4920233958725791 + name: Cosine Map@150 + - type: cosine_map@200 + value: 0.496106859156668 + name: Cosine Map@200 + - type: cosine_map@500 + value: 0.5023110925949719 + name: Cosine Map@500 + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: mix es + type: mix_es + metrics: + - type: cosine_accuracy@1 + value: 0.6297451898075923 + name: Cosine Accuracy@1 + - type: cosine_accuracy@20 + value: 0.9079563182527302 + name: Cosine Accuracy@20 + - type: cosine_accuracy@50 + value: 0.9485179407176287 + name: Cosine Accuracy@50 + - type: cosine_accuracy@100 + value: 0.9734789391575663 + name: Cosine Accuracy@100 + - type: cosine_accuracy@150 + value: 0.9817992719708788 + name: Cosine Accuracy@150 + - type: cosine_accuracy@200 + value: 0.9890795631825273 + name: Cosine Accuracy@200 + - type: cosine_precision@1 + value: 0.6297451898075923 + name: Cosine Precision@1 + - type: cosine_precision@20 + value: 0.11144045761830473 + name: Cosine Precision@20 + - type: cosine_precision@50 + value: 0.04842433697347895 + name: Cosine Precision@50 + - type: cosine_precision@100 + value: 0.025314612584503383 + name: Cosine Precision@100 + - type: cosine_precision@150 + value: 0.017216155312879178 + name: Cosine Precision@150 + - type: cosine_precision@200 + value: 0.013070722828913158 + name: Cosine Precision@200 + - type: cosine_recall@1 + value: 0.24340068840848872 + name: Cosine Recall@1 + - type: cosine_recall@20 + value: 0.827157467251071 + name: Cosine Recall@20 + - type: cosine_recall@50 + value: 0.8970792165019934 + name: Cosine Recall@50 + - type: cosine_recall@100 + value: 0.9385508753683481 + name: Cosine Recall@100 + - type: cosine_recall@150 + value: 0.9569249436644133 + name: Cosine Recall@150 + - type: cosine_recall@200 + value: 0.9686600797365229 + name: Cosine Recall@200 + - type: cosine_ndcg@1 + value: 0.6297451898075923 + name: Cosine Ndcg@1 + - type: cosine_ndcg@20 + value: 0.6994116361658315 + name: Cosine Ndcg@20 + - type: cosine_ndcg@50 + value: 0.7184754763821674 + name: Cosine Ndcg@50 + - type: cosine_ndcg@100 + value: 0.7275271174143362 + name: Cosine Ndcg@100 + - type: cosine_ndcg@150 + value: 0.7311486978502827 + name: Cosine Ndcg@150 + - type: cosine_ndcg@200 + value: 0.733282433801573 + name: Cosine Ndcg@200 + - type: cosine_mrr@1 + value: 0.6297451898075923 + name: Cosine Mrr@1 + - type: cosine_mrr@20 + value: 0.7026675306443272 + name: Cosine Mrr@20 + - type: cosine_mrr@50 + value: 0.7040534682065075 + name: Cosine Mrr@50 + - type: cosine_mrr@100 + value: 0.7044148840240123 + name: Cosine Mrr@100 + - type: cosine_mrr@150 + value: 0.7044856803226204 + name: Cosine Mrr@150 + - type: cosine_mrr@200 + value: 0.704528165280555 + name: Cosine Mrr@200 + - type: cosine_map@1 + value: 0.6297451898075923 + name: Cosine Map@1 + - type: cosine_map@20 + value: 0.6176093380717337 + name: Cosine Map@20 + - type: cosine_map@50 + value: 0.6226112093265134 + name: Cosine Map@50 + - type: cosine_map@100 + value: 0.6238596600766622 + name: Cosine Map@100 + - type: cosine_map@150 + value: 0.6242075803658665 + name: Cosine Map@150 + - type: cosine_map@200 + value: 0.6243509834359291 + name: Cosine Map@200 + - type: cosine_map@500 + value: 0.6245346885039931 + name: Cosine Map@500 + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: mix de + type: mix_de + metrics: + - type: cosine_accuracy@1 + value: 0.5538221528861155 + name: Cosine Accuracy@1 + - type: cosine_accuracy@20 + value: 0.8814352574102964 + name: Cosine Accuracy@20 + - type: cosine_accuracy@50 + value: 0.9349973998959958 + name: Cosine Accuracy@50 + - type: cosine_accuracy@100 + value: 0.9589183567342694 + name: Cosine Accuracy@100 + - type: cosine_accuracy@150 + value: 0.96931877275091 + name: Cosine Accuracy@150 + - type: cosine_accuracy@200 + value: 0.9765990639625585 + name: Cosine Accuracy@200 + - type: cosine_precision@1 + value: 0.5538221528861155 + name: Cosine Precision@1 + - type: cosine_precision@20 + value: 0.10912636505460219 + name: Cosine Precision@20 + - type: cosine_precision@50 + value: 0.047935517420696835 + name: Cosine Precision@50 + - type: cosine_precision@100 + value: 0.025257410296411865 + name: Cosine Precision@100 + - type: cosine_precision@150 + value: 0.017257756976945746 + name: Cosine Precision@150 + - type: cosine_precision@200 + value: 0.013122724908996361 + name: Cosine Precision@200 + - type: cosine_recall@1 + value: 0.20845033801352056 + name: Cosine Recall@1 + - type: cosine_recall@20 + value: 0.7964725255676894 + name: Cosine Recall@20 + - type: cosine_recall@50 + value: 0.8717888715548621 + name: Cosine Recall@50 + - type: cosine_recall@100 + value: 0.9166493326399723 + name: Cosine Recall@100 + - type: cosine_recall@150 + value: 0.9388542208355001 + name: Cosine Recall@150 + - type: cosine_recall@200 + value: 0.9522447564569249 + name: Cosine Recall@200 + - type: cosine_ndcg@1 + value: 0.5538221528861155 + name: Cosine Ndcg@1 + - type: cosine_ndcg@20 + value: 0.6518455599845957 + name: Cosine Ndcg@20 + - type: cosine_ndcg@50 + value: 0.6725307652410174 + name: Cosine Ndcg@50 + - type: cosine_ndcg@100 + value: 0.6825987388473841 + name: Cosine Ndcg@100 + - type: cosine_ndcg@150 + value: 0.6869902480321315 + name: Cosine Ndcg@150 + - type: cosine_ndcg@200 + value: 0.6894230866781552 + name: Cosine Ndcg@200 + - type: cosine_mrr@1 + value: 0.5538221528861155 + name: Cosine Mrr@1 + - type: cosine_mrr@20 + value: 0.6451894555975591 + name: Cosine Mrr@20 + - type: cosine_mrr@50 + value: 0.6470013120502346 + name: Cosine Mrr@50 + - type: cosine_mrr@100 + value: 0.6473603615547494 + name: Cosine Mrr@100 + - type: cosine_mrr@150 + value: 0.6474490009158033 + name: Cosine Mrr@150 + - type: cosine_mrr@200 + value: 0.647492473181411 + name: Cosine Mrr@200 + - type: cosine_map@1 + value: 0.5538221528861155 + name: Cosine Map@1 + - type: cosine_map@20 + value: 0.5627871995310985 + name: Cosine Map@20 + - type: cosine_map@50 + value: 0.5679148655306163 + name: Cosine Map@50 + - type: cosine_map@100 + value: 0.5693421440886408 + name: Cosine Map@100 + - type: cosine_map@150 + value: 0.5697579274072834 + name: Cosine Map@150 + - type: cosine_map@200 + value: 0.569931742725807 + name: Cosine Map@200 + - type: cosine_map@500 + value: 0.5702007325952348 + name: Cosine Map@500 + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: mix zh + type: mix_zh + metrics: + - type: cosine_accuracy@1 + value: 0.6033402922755741 + name: Cosine Accuracy@1 + - type: cosine_accuracy@20 + value: 0.9592901878914405 + name: Cosine Accuracy@20 + - type: cosine_accuracy@50 + value: 0.9775574112734864 + name: Cosine Accuracy@50 + - type: cosine_accuracy@100 + value: 0.9869519832985386 + name: Cosine Accuracy@100 + - type: cosine_accuracy@150 + value: 0.9911273486430062 + name: Cosine Accuracy@150 + - type: cosine_accuracy@200 + value: 0.9937369519832986 + name: Cosine Accuracy@200 + - type: cosine_precision@1 + value: 0.6033402922755741 + name: Cosine Precision@1 + - type: cosine_precision@20 + value: 0.1262787056367432 + name: Cosine Precision@20 + - type: cosine_precision@50 + value: 0.055156576200417556 + name: Cosine Precision@50 + - type: cosine_precision@100 + value: 0.028684759916492702 + name: Cosine Precision@100 + - type: cosine_precision@150 + value: 0.019394572025052192 + name: Cosine Precision@150 + - type: cosine_precision@200 + value: 0.014694676409185809 + name: Cosine Precision@200 + - type: cosine_recall@1 + value: 0.2026406700467243 + name: Cosine Recall@1 + - type: cosine_recall@20 + value: 0.8327331245650661 + name: Cosine Recall@20 + - type: cosine_recall@50 + value: 0.9090553235908142 + name: Cosine Recall@50 + - type: cosine_recall@100 + value: 0.9454766875434933 + name: Cosine Recall@100 + - type: cosine_recall@150 + value: 0.9593510786360473 + name: Cosine Recall@150 + - type: cosine_recall@200 + value: 0.9690848990953375 + name: Cosine Recall@200 + - type: cosine_ndcg@1 + value: 0.6033402922755741 + name: Cosine Ndcg@1 + - type: cosine_ndcg@20 + value: 0.6828284711666521 + name: Cosine Ndcg@20 + - type: cosine_ndcg@50 + value: 0.7045660706215972 + name: Cosine Ndcg@50 + - type: cosine_ndcg@100 + value: 0.7129279365518828 + name: Cosine Ndcg@100 + - type: cosine_ndcg@150 + value: 0.7157293364418106 + name: Cosine Ndcg@150 + - type: cosine_ndcg@200 + value: 0.7175794784000445 + name: Cosine Ndcg@200 + - type: cosine_mrr@1 + value: 0.6033402922755741 + name: Cosine Mrr@1 + - type: cosine_mrr@20 + value: 0.7204416409571621 + name: Cosine Mrr@20 + - type: cosine_mrr@50 + value: 0.7210752869689329 + name: Cosine Mrr@50 + - type: cosine_mrr@100 + value: 0.7212211062865328 + name: Cosine Mrr@100 + - type: cosine_mrr@150 + value: 0.7212542072796881 + name: Cosine Mrr@150 + - type: cosine_mrr@200 + value: 0.7212683301539606 + name: Cosine Mrr@200 + - type: cosine_map@1 + value: 0.6033402922755741 + name: Cosine Map@1 + - type: cosine_map@20 + value: 0.5625523429259808 + name: Cosine Map@20 + - type: cosine_map@50 + value: 0.5690763342890433 + name: Cosine Map@50 + - type: cosine_map@100 + value: 0.5704513498606978 + name: Cosine Map@100 + - type: cosine_map@150 + value: 0.5707437921606868 + name: Cosine Map@150 + - type: cosine_map@200 + value: 0.5708914357578326 + name: Cosine Map@200 + - type: cosine_map@500 + value: 0.5710537045348917 + name: Cosine Map@500 +--- + +# SentenceTransformer + +This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. + +## Model Details + +### Model Description +- **Model Type:** Sentence Transformer + +- **Maximum Sequence Length:** 512 tokens +- **Output Dimensionality:** 768 dimensions +- **Similarity Function:** Cosine Similarity + + + + +### Model Sources + +- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) +- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) +- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) + +### Full Model Architecture + +``` +SentenceTransformer( + (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel + (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) + (2): Normalize() +) +``` + +## Usage + +### Direct Usage (Sentence Transformers) + +First install the Sentence Transformers library: + +```bash +pip install -U sentence-transformers +``` + +Then you can load this model and run inference. +```python +from sentence_transformers import SentenceTransformer + +# Download from the 🤗 Hub +model = SentenceTransformer("sentence_transformers_model_id") +# Run inference +sentences = [ + 'Entwicklerin für mobile Anwendungen', + 'Mergers-and-Acquisitions-Analyst/Mergers-and-Acquisitions-Analystin', + 'fashion design expert', +] +embeddings = model.encode(sentences) +print(embeddings.shape) +# [3, 768] + +# Get the similarity scores for the embeddings +similarities = model.similarity(embeddings, embeddings) +print(similarities.shape) +# [3, 3] +``` + + + + + + + +## 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 | 0.6381 | 0.1189 | 0.2956 | 0.6602 | 0.6297 | 0.5538 | 0.6033 | +| cosine_accuracy@20 | 0.9619 | 1.0 | 0.9704 | 0.9709 | 0.908 | 0.8814 | 0.9593 | +| cosine_accuracy@50 | 0.9905 | 1.0 | 0.9754 | 0.9903 | 0.9485 | 0.935 | 0.9776 | +| cosine_accuracy@100 | 0.9905 | 1.0 | 0.9901 | 0.9903 | 0.9735 | 0.9589 | 0.987 | +| cosine_accuracy@150 | 0.9905 | 1.0 | 0.9901 | 0.9903 | 0.9818 | 0.9693 | 0.9911 | +| cosine_accuracy@200 | 0.9905 | 1.0 | 0.9901 | 0.9903 | 0.9891 | 0.9766 | 0.9937 | +| cosine_precision@1 | 0.6381 | 0.1189 | 0.2956 | 0.6602 | 0.6297 | 0.5538 | 0.6033 | +| cosine_precision@20 | 0.4767 | 0.5278 | 0.4268 | 0.4451 | 0.1114 | 0.1091 | 0.1263 | +| cosine_precision@50 | 0.2872 | 0.3432 | 0.2962 | 0.2705 | 0.0484 | 0.0479 | 0.0552 | +| cosine_precision@100 | 0.173 | 0.2178 | 0.1933 | 0.1661 | 0.0253 | 0.0253 | 0.0287 | +| cosine_precision@150 | 0.1242 | 0.1649 | 0.1478 | 0.1208 | 0.0172 | 0.0173 | 0.0194 | +| cosine_precision@200 | 0.0983 | 0.1329 | 0.1196 | 0.0952 | 0.0131 | 0.0131 | 0.0147 | +| cosine_recall@1 | 0.0659 | 0.0036 | 0.0111 | 0.0661 | 0.2434 | 0.2085 | 0.2026 | +| cosine_recall@20 | 0.5075 | 0.3544 | 0.2651 | 0.4819 | 0.8272 | 0.7965 | 0.8327 | +| cosine_recall@50 | 0.6815 | 0.5098 | 0.4064 | 0.6552 | 0.8971 | 0.8718 | 0.9091 | +| cosine_recall@100 | 0.7893 | 0.6026 | 0.5078 | 0.7647 | 0.9386 | 0.9166 | 0.9455 | +| cosine_recall@150 | 0.8378 | 0.6669 | 0.5716 | 0.8281 | 0.9569 | 0.9389 | 0.9594 | +| cosine_recall@200 | 0.8748 | 0.7113 | 0.611 | 0.8609 | 0.9687 | 0.9522 | 0.9691 | +| cosine_ndcg@1 | 0.6381 | 0.1189 | 0.2956 | 0.6602 | 0.6297 | 0.5538 | 0.6033 | +| cosine_ndcg@20 | 0.6438 | 0.5712 | 0.4679 | 0.6209 | 0.6994 | 0.6518 | 0.6828 | +| cosine_ndcg@50 | 0.6566 | 0.535 | 0.4426 | 0.6371 | 0.7185 | 0.6725 | 0.7046 | +| cosine_ndcg@100 | 0.7088 | 0.5565 | 0.4652 | 0.69 | 0.7275 | 0.6826 | 0.7129 | +| cosine_ndcg@150 | 0.7299 | 0.5878 | 0.4968 | 0.7159 | 0.7311 | 0.687 | 0.7157 | +| **cosine_ndcg@200** | **0.7449** | **0.6083** | **0.5154** | **0.7294** | **0.7333** | **0.6894** | **0.7176** | +| cosine_mrr@1 | 0.6381 | 0.1189 | 0.2956 | 0.6602 | 0.6297 | 0.5538 | 0.6033 | +| cosine_mrr@20 | 0.7865 | 0.5527 | 0.5045 | 0.8015 | 0.7027 | 0.6452 | 0.7204 | +| cosine_mrr@50 | 0.7878 | 0.5527 | 0.5047 | 0.802 | 0.7041 | 0.647 | 0.7211 | +| cosine_mrr@100 | 0.7878 | 0.5527 | 0.5049 | 0.802 | 0.7044 | 0.6474 | 0.7212 | +| cosine_mrr@150 | 0.7878 | 0.5527 | 0.5049 | 0.802 | 0.7045 | 0.6474 | 0.7213 | +| cosine_mrr@200 | 0.7878 | 0.5527 | 0.5049 | 0.802 | 0.7045 | 0.6475 | 0.7213 | +| cosine_map@1 | 0.6381 | 0.1189 | 0.2956 | 0.6602 | 0.6297 | 0.5538 | 0.6033 | +| cosine_map@20 | 0.4999 | 0.4385 | 0.3353 | 0.4724 | 0.6176 | 0.5628 | 0.5626 | +| cosine_map@50 | 0.4825 | 0.3733 | 0.2836 | 0.4562 | 0.6226 | 0.5679 | 0.5691 | +| cosine_map@100 | 0.5108 | 0.3751 | 0.2803 | 0.4831 | 0.6239 | 0.5693 | 0.5705 | +| cosine_map@150 | 0.5189 | 0.3878 | 0.2917 | 0.492 | 0.6242 | 0.5698 | 0.5707 | +| cosine_map@200 | 0.5236 | 0.3948 | 0.2975 | 0.4961 | 0.6244 | 0.5699 | 0.5709 | +| cosine_map@500 | 0.5292 | 0.4052 | 0.3095 | 0.5023 | 0.6245 | 0.5702 | 0.5711 | + + + + + +## Training Details + +### Training Dataset + +#### Unnamed Dataset + +* Size: 86,648 training samples +* Columns: sentence and label +* Approximate statistics based on the first 1000 samples: + | | sentence | label | + |:--------|:---------------------------------------------------------------------------------|:-------------------------------------| + | type | string | list | + | details |
  • min: 2 tokens
  • mean: 8.25 tokens
  • max: 54 tokens
|
  • size: 768 elements
| +* Samples: + | sentence | label | + |:-----------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------| + | | [-0.07171934843063354, 0.03595816716551781, -0.029780959710478783, 0.006593302357941866, 0.040611181408166885, ...] | + | airport environment officer | [-0.022075481712818146, 0.02999737113714218, -0.02189866080880165, 0.016531817615032196, 0.012234307825565338, ...] | + | Flake操作员 | [-0.04815564677119255, 0.023524893447756767, -0.01583661139011383, 0.042527906596660614, 0.03815540298819542, ...] | +* Loss: [MSELoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss) + +### Training Hyperparameters +#### Non-Default Hyperparameters + +- `eval_strategy`: steps +- `per_device_train_batch_size`: 128 +- `per_device_eval_batch_size`: 128 +- `gradient_accumulation_steps`: 2 +- `learning_rate`: 0.0001 +- `num_train_epochs`: 5 +- `warmup_ratio`: 0.05 +- `log_on_each_node`: False +- `fp16`: True +- `dataloader_num_workers`: 4 +- `ddp_find_unused_parameters`: True +- `batch_sampler`: no_duplicates + +#### All Hyperparameters +
Click to expand + +- `overwrite_output_dir`: False +- `do_predict`: False +- `eval_strategy`: steps +- `prediction_loss_only`: True +- `per_device_train_batch_size`: 128 +- `per_device_eval_batch_size`: 128 +- `per_gpu_train_batch_size`: None +- `per_gpu_eval_batch_size`: None +- `gradient_accumulation_steps`: 2 +- `eval_accumulation_steps`: None +- `torch_empty_cache_steps`: None +- `learning_rate`: 0.0001 +- `weight_decay`: 0.0 +- `adam_beta1`: 0.9 +- `adam_beta2`: 0.999 +- `adam_epsilon`: 1e-08 +- `max_grad_norm`: 1.0 +- `num_train_epochs`: 5 +- `max_steps`: -1 +- `lr_scheduler_type`: linear +- `lr_scheduler_kwargs`: {} +- `warmup_ratio`: 0.05 +- `warmup_steps`: 0 +- `log_level`: passive +- `log_level_replica`: warning +- `log_on_each_node`: False +- `logging_nan_inf_filter`: True +- `save_safetensors`: True +- `save_on_each_node`: False +- `save_only_model`: False +- `restore_callback_states_from_checkpoint`: False +- `no_cuda`: False +- `use_cpu`: False +- `use_mps_device`: False +- `seed`: 42 +- `data_seed`: None +- `jit_mode_eval`: False +- `use_ipex`: False +- `bf16`: False +- `fp16`: True +- `fp16_opt_level`: O1 +- `half_precision_backend`: auto +- `bf16_full_eval`: False +- `fp16_full_eval`: False +- `tf32`: None +- `local_rank`: 0 +- `ddp_backend`: None +- `tpu_num_cores`: None +- `tpu_metrics_debug`: False +- `debug`: [] +- `dataloader_drop_last`: True +- `dataloader_num_workers`: 4 +- `dataloader_prefetch_factor`: None +- `past_index`: -1 +- `disable_tqdm`: False +- `remove_unused_columns`: True +- `label_names`: None +- `load_best_model_at_end`: False +- `ignore_data_skip`: False +- `fsdp`: [] +- `fsdp_min_num_params`: 0 +- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} +- `tp_size`: 0 +- `fsdp_transformer_layer_cls_to_wrap`: None +- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} +- `deepspeed`: None +- `label_smoothing_factor`: 0.0 +- `optim`: adamw_torch +- `optim_args`: None +- `adafactor`: False +- `group_by_length`: False +- `length_column_name`: length +- `ddp_find_unused_parameters`: True +- `ddp_bucket_cap_mb`: None +- `ddp_broadcast_buffers`: False +- `dataloader_pin_memory`: True +- `dataloader_persistent_workers`: False +- `skip_memory_metrics`: True +- `use_legacy_prediction_loop`: False +- `push_to_hub`: False +- `resume_from_checkpoint`: None +- `hub_model_id`: None +- `hub_strategy`: every_save +- `hub_private_repo`: None +- `hub_always_push`: False +- `gradient_checkpointing`: False +- `gradient_checkpointing_kwargs`: None +- `include_inputs_for_metrics`: False +- `include_for_metrics`: [] +- `eval_do_concat_batches`: True +- `fp16_backend`: auto +- `push_to_hub_model_id`: None +- `push_to_hub_organization`: None +- `mp_parameters`: +- `auto_find_batch_size`: False +- `full_determinism`: False +- `torchdynamo`: None +- `ray_scope`: last +- `ddp_timeout`: 1800 +- `torch_compile`: False +- `torch_compile_backend`: None +- `torch_compile_mode`: None +- `include_tokens_per_second`: False +- `include_num_input_tokens_seen`: False +- `neftune_noise_alpha`: None +- `optim_target_modules`: None +- `batch_eval_metrics`: False +- `eval_on_start`: False +- `use_liger_kernel`: False +- `eval_use_gather_object`: False +- `average_tokens_across_devices`: False +- `prompts`: None +- `batch_sampler`: no_duplicates +- `multi_dataset_batch_sampler`: proportional + +
+ +### Training Logs +| Epoch | Step | Training Loss | full_en_cosine_ndcg@200 | full_es_cosine_ndcg@200 | full_de_cosine_ndcg@200 | full_zh_cosine_ndcg@200 | mix_es_cosine_ndcg@200 | mix_de_cosine_ndcg@200 | mix_zh_cosine_ndcg@200 | +|:------:|:----:|:-------------:|:-----------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:| +| -1 | -1 | - | 0.5348 | 0.4311 | 0.3678 | 0.5333 | 0.2580 | 0.1924 | 0.2871 | +| 0.0030 | 1 | 0.0017 | - | - | - | - | - | - | - | +| 0.2959 | 100 | 0.001 | - | - | - | - | - | - | - | +| 0.5917 | 200 | 0.0005 | 0.6702 | 0.5287 | 0.4566 | 0.6809 | 0.5864 | 0.5302 | 0.4739 | +| 0.8876 | 300 | 0.0004 | - | - | - | - | - | - | - | +| 1.1834 | 400 | 0.0004 | 0.7057 | 0.5643 | 0.4790 | 0.7033 | 0.6604 | 0.6055 | 0.6003 | +| 1.4793 | 500 | 0.0004 | - | - | - | - | - | - | - | +| 1.7751 | 600 | 0.0003 | 0.7184 | 0.5783 | 0.4910 | 0.7127 | 0.6927 | 0.6416 | 0.6485 | +| 2.0710 | 700 | 0.0003 | - | - | - | - | - | - | - | +| 2.3669 | 800 | 0.0003 | 0.7307 | 0.5938 | 0.5023 | 0.7233 | 0.7125 | 0.6639 | 0.6847 | +| 2.6627 | 900 | 0.0003 | - | - | - | - | - | - | - | +| 2.9586 | 1000 | 0.0003 | 0.7371 | 0.6002 | 0.5085 | 0.7228 | 0.7222 | 0.6761 | 0.6998 | +| 3.2544 | 1100 | 0.0003 | - | - | - | - | - | - | - | +| 3.5503 | 1200 | 0.0003 | 0.7402 | 0.6059 | 0.5109 | 0.7279 | 0.7285 | 0.6841 | 0.7120 | +| 3.8462 | 1300 | 0.0003 | - | - | - | - | - | - | - | +| 4.1420 | 1400 | 0.0003 | 0.7449 | 0.6083 | 0.5154 | 0.7294 | 0.7333 | 0.6894 | 0.7176 | + + +### Framework Versions +- Python: 3.11.11 +- Sentence Transformers: 4.1.0 +- Transformers: 4.51.3 +- PyTorch: 2.6.0+cu124 +- Accelerate: 1.6.0 +- Datasets: 3.5.0 +- Tokenizers: 0.21.1 + +## Citation + +### BibTeX + +#### Sentence Transformers +```bibtex +@inproceedings{reimers-2019-sentence-bert, + title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", + author = "Reimers, Nils and Gurevych, Iryna", + booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", + month = "11", + year = "2019", + publisher = "Association for Computational Linguistics", + url = "https://arxiv.org/abs/1908.10084", +} +``` + +#### MSELoss +```bibtex +@inproceedings{reimers-2020-multilingual-sentence-bert, + title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation", + author = "Reimers, Nils and Gurevych, Iryna", + booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing", + month = "11", + year = "2020", + publisher = "Association for Computational Linguistics", + url = "https://arxiv.org/abs/2004.09813", +} +``` + + + + + + \ No newline at end of file diff --git a/checkpoint-1400/config.json b/checkpoint-1400/config.json new file mode 100644 index 0000000000000000000000000000000000000000..281db00437139c18374483e9e7ade1288b0866e1 --- /dev/null +++ b/checkpoint-1400/config.json @@ -0,0 +1,49 @@ +{ + "architectures": [ + "NewModel" + ], + "attention_probs_dropout_prob": 0.0, + "auto_map": { + "AutoConfig": "configuration.NewConfig", + "AutoModel": "Alibaba-NLP/new-impl--modeling.NewModel", + "AutoModelForMaskedLM": "Alibaba-NLP/new-impl--modeling.NewForMaskedLM", + "AutoModelForMultipleChoice": "Alibaba-NLP/new-impl--modeling.NewForMultipleChoice", + "AutoModelForQuestionAnswering": "Alibaba-NLP/new-impl--modeling.NewForQuestionAnswering", + "AutoModelForSequenceClassification": "Alibaba-NLP/new-impl--modeling.NewForSequenceClassification", + "AutoModelForTokenClassification": "Alibaba-NLP/new-impl--modeling.NewForTokenClassification" + }, + "classifier_dropout": 0.0, + "hidden_act": "gelu", + "hidden_dropout_prob": 0.1, + "hidden_size": 768, + "id2label": { + "0": "LABEL_0" + }, + "initializer_range": 0.02, + "intermediate_size": 3072, + "label2id": { + "LABEL_0": 0 + }, + "layer_norm_eps": 1e-12, + "layer_norm_type": 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b/checkpoint-1600/tokenizer.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:883b037111086fd4dfebbbc9b7cee11e1517b5e0c0514879478661440f137085 +size 17082987 diff --git a/checkpoint-1690/1_Pooling/config.json b/checkpoint-1690/1_Pooling/config.json new file mode 100644 index 0000000000000000000000000000000000000000..1b013adee922cdde26976d6e46f4ec75a651dfdf --- /dev/null +++ b/checkpoint-1690/1_Pooling/config.json @@ -0,0 +1,10 @@ +{ + "word_embedding_dimension": 768, + "pooling_mode_cls_token": true, + "pooling_mode_mean_tokens": false, + "pooling_mode_max_tokens": false, + "pooling_mode_mean_sqrt_len_tokens": false, + "pooling_mode_weightedmean_tokens": false, + "pooling_mode_lasttoken": false, + "include_prompt": true +} \ No newline at end of file diff --git a/checkpoint-1690/README.md b/checkpoint-1690/README.md new file mode 100644 index 0000000000000000000000000000000000000000..461cef9704da0bb9725b823cead84272e366aef5 --- /dev/null +++ b/checkpoint-1690/README.md @@ -0,0 +1,1290 @@ +--- +tags: +- sentence-transformers +- sentence-similarity +- feature-extraction +- generated_from_trainer +- dataset_size:86648 +- loss:MSELoss +widget: +- source_sentence: Familienberaterin + sentences: + - electric power station operator + - venue booker & promoter + - betrieblicher Aus- und Weiterbildner/betriebliche Aus- und Weiterbildnerin +- source_sentence: high school RS teacher + sentences: + - infantryman + - Schnellbedienungsrestaurantteamleiter + - drill setup operator +- source_sentence: lighting designer + sentences: + - software support manager + - 直升机维护协调员 + - bus maintenance supervisor +- source_sentence: 机场消防员 + sentences: + - Flake操作员 + - técnico en gestión de residuos peligrosos/técnica en gestión de residuos peligrosos + - 专门学校老师 +- source_sentence: Entwicklerin für mobile Anwendungen + sentences: + - fashion design expert + - Mergers-and-Acquisitions-Analyst/Mergers-and-Acquisitions-Analystin + - commercial bid manager +pipeline_tag: sentence-similarity +library_name: sentence-transformers +metrics: +- cosine_accuracy@1 +- cosine_accuracy@20 +- cosine_accuracy@50 +- cosine_accuracy@100 +- cosine_accuracy@150 +- cosine_accuracy@200 +- cosine_precision@1 +- cosine_precision@20 +- cosine_precision@50 +- cosine_precision@100 +- cosine_precision@150 +- cosine_precision@200 +- cosine_recall@1 +- cosine_recall@20 +- cosine_recall@50 +- cosine_recall@100 +- cosine_recall@150 +- cosine_recall@200 +- cosine_ndcg@1 +- cosine_ndcg@20 +- cosine_ndcg@50 +- cosine_ndcg@100 +- cosine_ndcg@150 +- cosine_ndcg@200 +- cosine_mrr@1 +- cosine_mrr@20 +- cosine_mrr@50 +- cosine_mrr@100 +- cosine_mrr@150 +- cosine_mrr@200 +- cosine_map@1 +- cosine_map@20 +- cosine_map@50 +- cosine_map@100 +- cosine_map@150 +- cosine_map@200 +- cosine_map@500 +model-index: +- name: SentenceTransformer + results: + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: full en + type: full_en + metrics: + - type: cosine_accuracy@1 + value: 0.6476190476190476 + name: Cosine Accuracy@1 + - type: cosine_accuracy@20 + value: 0.9714285714285714 + name: Cosine Accuracy@20 + - type: cosine_accuracy@50 + value: 0.9904761904761905 + name: Cosine Accuracy@50 + - type: cosine_accuracy@100 + value: 0.9904761904761905 + name: Cosine Accuracy@100 + - type: cosine_accuracy@150 + value: 0.9904761904761905 + name: Cosine Accuracy@150 + - type: cosine_accuracy@200 + value: 0.9904761904761905 + name: Cosine Accuracy@200 + - type: cosine_precision@1 + value: 0.6476190476190476 + name: Cosine Precision@1 + - type: cosine_precision@20 + value: 0.47952380952380946 + name: Cosine Precision@20 + - type: cosine_precision@50 + value: 0.28838095238095235 + name: Cosine Precision@50 + - type: cosine_precision@100 + value: 0.17304761904761906 + name: Cosine Precision@100 + - type: cosine_precision@150 + value: 0.12444444444444444 + name: Cosine Precision@150 + - type: cosine_precision@200 + value: 0.09857142857142859 + name: Cosine Precision@200 + - type: cosine_recall@1 + value: 0.06609801577496094 + name: Cosine Recall@1 + - type: cosine_recall@20 + value: 0.5122224752770898 + name: Cosine Recall@20 + - type: cosine_recall@50 + value: 0.6835205863376973 + name: Cosine Recall@50 + - type: cosine_recall@100 + value: 0.7899550177449521 + name: Cosine Recall@100 + - type: cosine_recall@150 + value: 0.8399901051245952 + name: Cosine Recall@150 + - type: cosine_recall@200 + value: 0.875868212220809 + name: Cosine Recall@200 + - type: cosine_ndcg@1 + value: 0.6476190476190476 + name: Cosine Ndcg@1 + - type: cosine_ndcg@20 + value: 0.6467537144833913 + name: Cosine Ndcg@20 + - type: cosine_ndcg@50 + value: 0.6579566361404572 + name: Cosine Ndcg@50 + - type: cosine_ndcg@100 + value: 0.7095129047395976 + name: Cosine Ndcg@100 + - type: cosine_ndcg@150 + value: 0.7310060454392588 + name: Cosine Ndcg@150 + - type: cosine_ndcg@200 + value: 0.746053293561821 + name: Cosine Ndcg@200 + - type: cosine_mrr@1 + value: 0.6476190476190476 + name: Cosine Mrr@1 + - type: cosine_mrr@20 + value: 0.7901817137111254 + name: Cosine Mrr@20 + - type: cosine_mrr@50 + value: 0.7909547501984476 + name: Cosine Mrr@50 + - type: cosine_mrr@100 + value: 0.7909547501984476 + name: Cosine Mrr@100 + - type: cosine_mrr@150 + value: 0.7909547501984476 + name: Cosine Mrr@150 + - type: cosine_mrr@200 + value: 0.7909547501984476 + name: Cosine Mrr@200 + - type: cosine_map@1 + value: 0.6476190476190476 + name: Cosine Map@1 + - type: cosine_map@20 + value: 0.5025649155749793 + name: Cosine Map@20 + - type: cosine_map@50 + value: 0.48398477448194993 + name: Cosine Map@50 + - type: cosine_map@100 + value: 0.5117703759309522 + name: Cosine Map@100 + - type: cosine_map@150 + value: 0.520199435224254 + name: Cosine Map@150 + - type: cosine_map@200 + value: 0.5249113393002316 + name: Cosine Map@200 + - type: cosine_map@500 + value: 0.5304170344184883 + name: Cosine Map@500 + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: full es + type: full_es + metrics: + - type: cosine_accuracy@1 + value: 0.11891891891891893 + name: Cosine Accuracy@1 + - type: cosine_accuracy@20 + value: 1.0 + name: Cosine Accuracy@20 + - type: cosine_accuracy@50 + value: 1.0 + name: Cosine Accuracy@50 + - type: cosine_accuracy@100 + value: 1.0 + name: Cosine Accuracy@100 + - type: cosine_accuracy@150 + value: 1.0 + name: Cosine Accuracy@150 + - type: cosine_accuracy@200 + value: 1.0 + name: Cosine Accuracy@200 + - type: cosine_precision@1 + value: 0.11891891891891893 + name: Cosine Precision@1 + - type: cosine_precision@20 + value: 0.5267567567567567 + name: Cosine Precision@20 + - type: cosine_precision@50 + value: 0.3437837837837838 + name: Cosine Precision@50 + - type: cosine_precision@100 + value: 0.21897297297297297 + name: Cosine Precision@100 + - type: cosine_precision@150 + value: 0.1658018018018018 + name: Cosine Precision@150 + - type: cosine_precision@200 + value: 0.1332972972972973 + name: Cosine Precision@200 + - type: cosine_recall@1 + value: 0.0035840147528632613 + name: Cosine Recall@1 + - type: cosine_recall@20 + value: 0.35407760203362965 + name: Cosine Recall@20 + - type: cosine_recall@50 + value: 0.5097999383006715 + name: Cosine Recall@50 + - type: cosine_recall@100 + value: 0.6076073817878247 + name: Cosine Recall@100 + - type: cosine_recall@150 + value: 0.6705429838138021 + name: Cosine Recall@150 + - type: cosine_recall@200 + value: 0.7125464731776301 + name: Cosine Recall@200 + - type: cosine_ndcg@1 + value: 0.11891891891891893 + name: Cosine Ndcg@1 + - type: cosine_ndcg@20 + value: 0.5708144272431339 + name: Cosine Ndcg@20 + - type: cosine_ndcg@50 + value: 0.535516963498245 + name: Cosine Ndcg@50 + - type: cosine_ndcg@100 + value: 0.558980163264909 + name: Cosine Ndcg@100 + - type: cosine_ndcg@150 + value: 0.5900024611410689 + name: Cosine Ndcg@150 + - type: cosine_ndcg@200 + value: 0.609478782549869 + name: Cosine Ndcg@200 + - type: cosine_mrr@1 + value: 0.11891891891891893 + name: Cosine Mrr@1 + - type: cosine_mrr@20 + value: 0.5531531531531532 + name: Cosine Mrr@20 + - type: cosine_mrr@50 + value: 0.5531531531531532 + name: Cosine Mrr@50 + - type: cosine_mrr@100 + value: 0.5531531531531532 + name: Cosine Mrr@100 + - type: cosine_mrr@150 + value: 0.5531531531531532 + name: Cosine Mrr@150 + - type: cosine_mrr@200 + value: 0.5531531531531532 + name: Cosine Mrr@200 + - type: cosine_map@1 + value: 0.11891891891891893 + name: Cosine Map@1 + - type: cosine_map@20 + value: 0.4379349002801489 + name: Cosine Map@20 + - type: cosine_map@50 + value: 0.3739269627118989 + name: Cosine Map@50 + - type: cosine_map@100 + value: 0.37629843599877466 + name: Cosine Map@100 + - type: cosine_map@150 + value: 0.3891828650842837 + name: Cosine Map@150 + - type: cosine_map@200 + value: 0.39584338663408436 + name: Cosine Map@200 + - type: cosine_map@500 + value: 0.4062909401616274 + name: Cosine Map@500 + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: full de + type: full_de + metrics: + - type: cosine_accuracy@1 + value: 0.2955665024630542 + name: Cosine Accuracy@1 + - type: cosine_accuracy@20 + value: 0.9704433497536946 + name: Cosine Accuracy@20 + - type: cosine_accuracy@50 + value: 0.9753694581280788 + name: Cosine Accuracy@50 + - type: cosine_accuracy@100 + value: 0.9901477832512315 + name: Cosine Accuracy@100 + - type: cosine_accuracy@150 + value: 0.9901477832512315 + name: Cosine Accuracy@150 + - type: cosine_accuracy@200 + value: 0.9901477832512315 + name: Cosine Accuracy@200 + - type: cosine_precision@1 + value: 0.2955665024630542 + name: Cosine Precision@1 + - type: cosine_precision@20 + value: 0.42906403940886706 + name: Cosine Precision@20 + - type: cosine_precision@50 + value: 0.29802955665024633 + name: Cosine Precision@50 + - type: cosine_precision@100 + value: 0.19433497536945815 + name: Cosine Precision@100 + - type: cosine_precision@150 + value: 0.14824302134646963 + name: Cosine Precision@150 + - type: cosine_precision@200 + value: 0.1197783251231527 + name: Cosine Precision@200 + - type: cosine_recall@1 + value: 0.01108543831680986 + name: Cosine Recall@1 + - type: cosine_recall@20 + value: 0.26675038089672504 + name: Cosine Recall@20 + - type: cosine_recall@50 + value: 0.40921566733257536 + name: Cosine Recall@50 + - type: cosine_recall@100 + value: 0.5097664540706716 + name: Cosine Recall@100 + - type: cosine_recall@150 + value: 0.5728593162394238 + name: Cosine Recall@150 + - type: cosine_recall@200 + value: 0.6120176690658915 + name: Cosine Recall@200 + - type: cosine_ndcg@1 + value: 0.2955665024630542 + name: Cosine Ndcg@1 + - type: cosine_ndcg@20 + value: 0.46962753993631184 + name: Cosine Ndcg@20 + - type: cosine_ndcg@50 + value: 0.444898497416845 + name: Cosine Ndcg@50 + - type: cosine_ndcg@100 + value: 0.466960324034805 + name: Cosine Ndcg@100 + - type: cosine_ndcg@150 + value: 0.49816218513136795 + name: Cosine Ndcg@150 + - type: cosine_ndcg@200 + value: 0.5165485300965951 + name: Cosine Ndcg@200 + - type: cosine_mrr@1 + value: 0.2955665024630542 + name: Cosine Mrr@1 + - type: cosine_mrr@20 + value: 0.5046767633988724 + name: Cosine Mrr@20 + - type: cosine_mrr@50 + value: 0.50477528556636 + name: Cosine Mrr@50 + - type: cosine_mrr@100 + value: 0.5049589761635289 + name: Cosine Mrr@100 + - type: cosine_mrr@150 + value: 0.5049589761635289 + name: Cosine Mrr@150 + - type: cosine_mrr@200 + value: 0.5049589761635289 + name: Cosine Mrr@200 + - type: cosine_map@1 + value: 0.2955665024630542 + name: Cosine Map@1 + - type: cosine_map@20 + value: 0.33658821160388247 + name: Cosine Map@20 + - type: cosine_map@50 + value: 0.2853400586620685 + name: Cosine Map@50 + - type: cosine_map@100 + value: 0.2817732307206079 + name: Cosine Map@100 + - type: cosine_map@150 + value: 0.2931317333364438 + name: Cosine Map@150 + - type: cosine_map@200 + value: 0.2988160532231927 + name: Cosine Map@200 + - type: cosine_map@500 + value: 0.31093362375086947 + name: Cosine Map@500 + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: full zh + type: full_zh + metrics: + - type: cosine_accuracy@1 + value: 0.6601941747572816 + name: Cosine Accuracy@1 + - type: cosine_accuracy@20 + value: 0.970873786407767 + name: Cosine Accuracy@20 + - type: cosine_accuracy@50 + value: 0.9902912621359223 + name: Cosine Accuracy@50 + - type: cosine_accuracy@100 + value: 0.9902912621359223 + name: Cosine Accuracy@100 + - type: cosine_accuracy@150 + value: 0.9902912621359223 + name: Cosine Accuracy@150 + - type: cosine_accuracy@200 + value: 0.9902912621359223 + name: Cosine Accuracy@200 + - type: cosine_precision@1 + value: 0.6601941747572816 + name: Cosine Precision@1 + - type: cosine_precision@20 + value: 0.44805825242718444 + name: Cosine Precision@20 + - type: cosine_precision@50 + value: 0.27126213592233006 + name: Cosine Precision@50 + - type: cosine_precision@100 + value: 0.16650485436893206 + name: Cosine Precision@100 + - type: cosine_precision@150 + value: 0.1211003236245955 + name: Cosine Precision@150 + - type: cosine_precision@200 + value: 0.09529126213592234 + name: Cosine Precision@200 + - type: cosine_recall@1 + value: 0.06611246215014785 + name: Cosine Recall@1 + - type: cosine_recall@20 + value: 0.48409390608352504 + name: Cosine Recall@20 + - type: cosine_recall@50 + value: 0.6568473638827299 + name: Cosine Recall@50 + - type: cosine_recall@100 + value: 0.7685416895166794 + name: Cosine Recall@100 + - type: cosine_recall@150 + value: 0.8277686060133904 + name: Cosine Recall@150 + - type: cosine_recall@200 + value: 0.8616979590623105 + name: Cosine Recall@200 + - type: cosine_ndcg@1 + value: 0.6601941747572816 + name: Cosine Ndcg@1 + - type: cosine_ndcg@20 + value: 0.6231250904534316 + name: Cosine Ndcg@20 + - type: cosine_ndcg@50 + value: 0.6383496204608501 + name: Cosine Ndcg@50 + - type: cosine_ndcg@100 + value: 0.6917257705456975 + name: Cosine Ndcg@100 + - type: cosine_ndcg@150 + value: 0.7167434657424917 + name: Cosine Ndcg@150 + - type: cosine_ndcg@200 + value: 0.7303448958665071 + name: Cosine Ndcg@200 + - type: cosine_mrr@1 + value: 0.6601941747572816 + name: Cosine Mrr@1 + - type: cosine_mrr@20 + value: 0.8015776699029126 + name: Cosine Mrr@20 + - type: cosine_mrr@50 + value: 0.8020876238109248 + name: Cosine Mrr@50 + - type: cosine_mrr@100 + value: 0.8020876238109248 + name: Cosine Mrr@100 + - type: cosine_mrr@150 + value: 0.8020876238109248 + name: Cosine Mrr@150 + - type: cosine_mrr@200 + value: 0.8020876238109248 + name: Cosine Mrr@200 + - type: cosine_map@1 + value: 0.6601941747572816 + name: Cosine Map@1 + - type: cosine_map@20 + value: 0.4750205237443607 + name: Cosine Map@20 + - type: cosine_map@50 + value: 0.45785161483741715 + name: Cosine Map@50 + - type: cosine_map@100 + value: 0.4848085275553208 + name: Cosine Map@100 + - type: cosine_map@150 + value: 0.4937216396074153 + name: Cosine Map@150 + - type: cosine_map@200 + value: 0.49777622471594557 + name: Cosine Map@200 + - type: cosine_map@500 + value: 0.5039795405740248 + name: Cosine Map@500 + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: mix es + type: mix_es + metrics: + - type: cosine_accuracy@1 + value: 0.6297451898075923 + name: Cosine Accuracy@1 + - type: cosine_accuracy@20 + value: 0.9105564222568903 + name: Cosine Accuracy@20 + - type: cosine_accuracy@50 + value: 0.9495579823192928 + name: Cosine Accuracy@50 + - type: cosine_accuracy@100 + value: 0.9729589183567343 + name: Cosine Accuracy@100 + - type: cosine_accuracy@150 + value: 0.983359334373375 + name: Cosine Accuracy@150 + - type: cosine_accuracy@200 + value: 0.9901196047841914 + name: Cosine Accuracy@200 + - type: cosine_precision@1 + value: 0.6297451898075923 + name: Cosine Precision@1 + - type: cosine_precision@20 + value: 0.11167446697867915 + name: Cosine Precision@20 + - type: cosine_precision@50 + value: 0.04850754030161208 + name: Cosine Precision@50 + - type: cosine_precision@100 + value: 0.02535101404056163 + name: Cosine Precision@100 + - type: cosine_precision@150 + value: 0.0172300225342347 + name: Cosine Precision@150 + - type: cosine_precision@200 + value: 0.0130811232449298 + name: Cosine Precision@200 + - type: cosine_recall@1 + value: 0.24340068840848872 + name: Cosine Recall@1 + - type: cosine_recall@20 + value: 0.8288215338137336 + name: Cosine Recall@20 + - type: cosine_recall@50 + value: 0.8986566129311838 + name: Cosine Recall@50 + - type: cosine_recall@100 + value: 0.9398509273704282 + name: Cosine Recall@100 + - type: cosine_recall@150 + value: 0.9576876408389668 + name: Cosine Recall@150 + - type: cosine_recall@200 + value: 0.9695267810712429 + name: Cosine Recall@200 + - type: cosine_ndcg@1 + value: 0.6297451898075923 + name: Cosine Ndcg@1 + - type: cosine_ndcg@20 + value: 0.7010427232190379 + name: Cosine Ndcg@20 + - type: cosine_ndcg@50 + value: 0.7200844211181043 + name: Cosine Ndcg@50 + - type: cosine_ndcg@100 + value: 0.7290848607488584 + name: Cosine Ndcg@100 + - type: cosine_ndcg@150 + value: 0.7325985285606116 + name: Cosine Ndcg@150 + - type: cosine_ndcg@200 + value: 0.7347463892077523 + name: Cosine Ndcg@200 + - type: cosine_mrr@1 + value: 0.6297451898075923 + name: Cosine Mrr@1 + - type: cosine_mrr@20 + value: 0.7036709577939534 + name: Cosine Mrr@20 + - type: cosine_mrr@50 + value: 0.7049808414398148 + name: Cosine Mrr@50 + - type: cosine_mrr@100 + value: 0.7053260954286938 + name: Cosine Mrr@100 + - type: cosine_mrr@150 + value: 0.7054145837924506 + name: Cosine Mrr@150 + - type: cosine_mrr@200 + value: 0.7054541569954363 + name: Cosine Mrr@200 + - type: cosine_map@1 + value: 0.6297451898075923 + name: Cosine Map@1 + - type: cosine_map@20 + value: 0.6194189058349782 + name: Cosine Map@20 + - type: cosine_map@50 + value: 0.6244340507841626 + name: Cosine Map@50 + - type: cosine_map@100 + value: 0.6256943736433496 + name: Cosine Map@100 + - type: cosine_map@150 + value: 0.6260195205413376 + name: Cosine Map@150 + - type: cosine_map@200 + value: 0.6261650797332174 + name: Cosine Map@200 + - type: cosine_map@500 + value: 0.6263452093477304 + name: Cosine Map@500 + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: mix de + type: mix_de + metrics: + - type: cosine_accuracy@1 + value: 0.5564222568902756 + name: Cosine Accuracy@1 + - type: cosine_accuracy@20 + value: 0.8866354654186167 + name: Cosine Accuracy@20 + - type: cosine_accuracy@50 + value: 0.9381175247009881 + name: Cosine Accuracy@50 + - type: cosine_accuracy@100 + value: 0.9594383775351014 + name: Cosine Accuracy@100 + - type: cosine_accuracy@150 + value: 0.9708788351534061 + name: Cosine Accuracy@150 + - type: cosine_accuracy@200 + value: 0.9776391055642226 + name: Cosine Accuracy@200 + - type: cosine_precision@1 + value: 0.5564222568902756 + name: Cosine Precision@1 + - type: cosine_precision@20 + value: 0.109464378575143 + name: Cosine Precision@20 + - type: cosine_precision@50 + value: 0.048060322412896525 + name: Cosine Precision@50 + - type: cosine_precision@100 + value: 0.025273010920436823 + name: Cosine Precision@100 + - type: cosine_precision@150 + value: 0.017313225862367825 + name: Cosine Precision@150 + - type: cosine_precision@200 + value: 0.013143525741029644 + name: Cosine Precision@200 + - type: cosine_recall@1 + value: 0.20931703934824059 + name: Cosine Recall@1 + - type: cosine_recall@20 + value: 0.7988992893049055 + name: Cosine Recall@20 + - type: cosine_recall@50 + value: 0.8741029641185647 + name: Cosine Recall@50 + - type: cosine_recall@100 + value: 0.9173426937077482 + name: Cosine Recall@100 + - type: cosine_recall@150 + value: 0.9424076963078523 + name: Cosine Recall@150 + - type: cosine_recall@200 + value: 0.953631478592477 + name: Cosine Recall@200 + - type: cosine_ndcg@1 + value: 0.5564222568902756 + name: Cosine Ndcg@1 + - type: cosine_ndcg@20 + value: 0.6541310877479573 + name: Cosine Ndcg@20 + - type: cosine_ndcg@50 + value: 0.674790854916742 + name: Cosine Ndcg@50 + - type: cosine_ndcg@100 + value: 0.6844997445798996 + name: Cosine Ndcg@100 + - type: cosine_ndcg@150 + value: 0.6894214573457343 + name: Cosine Ndcg@150 + - type: cosine_ndcg@200 + value: 0.6914881284159038 + name: Cosine Ndcg@200 + - type: cosine_mrr@1 + value: 0.5564222568902756 + name: Cosine Mrr@1 + - type: cosine_mrr@20 + value: 0.6476945170199107 + name: Cosine Mrr@20 + - type: cosine_mrr@50 + value: 0.6493649946597936 + name: Cosine Mrr@50 + - type: cosine_mrr@100 + value: 0.6496801333421218 + name: Cosine Mrr@100 + - type: cosine_mrr@150 + value: 0.6497778366579644 + name: Cosine Mrr@150 + - type: cosine_mrr@200 + value: 0.6498156890114056 + name: Cosine Mrr@200 + - type: cosine_map@1 + value: 0.5564222568902756 + name: Cosine Map@1 + - type: cosine_map@20 + value: 0.5648326970643027 + name: Cosine Map@20 + - type: cosine_map@50 + value: 0.57003456255067 + name: Cosine Map@50 + - type: cosine_map@100 + value: 0.5714370828517599 + name: Cosine Map@100 + - type: cosine_map@150 + value: 0.5719002990233493 + name: Cosine Map@150 + - type: cosine_map@200 + value: 0.5720497397197026 + name: Cosine Map@200 + - type: cosine_map@500 + value: 0.5723109788233504 + name: Cosine Map@500 + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: mix zh + type: mix_zh + metrics: + - type: cosine_accuracy@1 + value: 0.6085594989561587 + name: Cosine Accuracy@1 + - type: cosine_accuracy@20 + value: 0.9592901878914405 + name: Cosine Accuracy@20 + - type: cosine_accuracy@50 + value: 0.9791231732776617 + name: Cosine Accuracy@50 + - type: cosine_accuracy@100 + value: 0.9874739039665971 + name: Cosine Accuracy@100 + - type: cosine_accuracy@150 + value: 0.9911273486430062 + name: Cosine Accuracy@150 + - type: cosine_accuracy@200 + value: 0.9937369519832986 + name: Cosine Accuracy@200 + - type: cosine_precision@1 + value: 0.6085594989561587 + name: Cosine Precision@1 + - type: cosine_precision@20 + value: 0.12656576200417535 + name: Cosine Precision@20 + - type: cosine_precision@50 + value: 0.05518789144050106 + name: Cosine Precision@50 + - type: cosine_precision@100 + value: 0.028747390396659713 + name: Cosine Precision@100 + - type: cosine_precision@150 + value: 0.019425887265135697 + name: Cosine Precision@150 + - type: cosine_precision@200 + value: 0.014705114822546978 + name: Cosine Precision@200 + - type: cosine_recall@1 + value: 0.2043804056069192 + name: Cosine Recall@1 + - type: cosine_recall@20 + value: 0.8346468336812805 + name: Cosine Recall@20 + - type: cosine_recall@50 + value: 0.9095772442588727 + name: Cosine Recall@50 + - type: cosine_recall@100 + value: 0.9475643702157271 + name: Cosine Recall@100 + - type: cosine_recall@150 + value: 0.9609168406402228 + name: Cosine Recall@150 + - type: cosine_recall@200 + value: 0.9697807933194154 + name: Cosine Recall@200 + - type: cosine_ndcg@1 + value: 0.6085594989561587 + name: Cosine Ndcg@1 + - type: cosine_ndcg@20 + value: 0.6853247290079303 + name: Cosine Ndcg@20 + - type: cosine_ndcg@50 + value: 0.7066940880968873 + name: Cosine Ndcg@50 + - type: cosine_ndcg@100 + value: 0.715400790265437 + name: Cosine Ndcg@100 + - type: cosine_ndcg@150 + value: 0.7180808450243259 + name: Cosine Ndcg@150 + - type: cosine_ndcg@200 + value: 0.7197629642909036 + name: Cosine Ndcg@200 + - type: cosine_mrr@1 + value: 0.6085594989561587 + name: Cosine Mrr@1 + - type: cosine_mrr@20 + value: 0.7236528792595264 + name: Cosine Mrr@20 + - type: cosine_mrr@50 + value: 0.7243308740364213 + name: Cosine Mrr@50 + - type: cosine_mrr@100 + value: 0.7244524590415827 + name: Cosine Mrr@100 + - type: cosine_mrr@150 + value: 0.7244814620971008 + name: Cosine Mrr@150 + - type: cosine_mrr@200 + value: 0.7244960285685315 + name: Cosine Mrr@200 + - type: cosine_map@1 + value: 0.6085594989561587 + name: Cosine Map@1 + - type: cosine_map@20 + value: 0.5652211952239553 + name: Cosine Map@20 + - type: cosine_map@50 + value: 0.5716374350069462 + name: Cosine Map@50 + - type: cosine_map@100 + value: 0.5730756815932735 + name: Cosine Map@100 + - type: cosine_map@150 + value: 0.5733543252173214 + name: Cosine Map@150 + - type: cosine_map@200 + value: 0.5734860037813889 + name: Cosine Map@200 + - type: cosine_map@500 + value: 0.5736416699680624 + name: Cosine Map@500 +--- + +# Job - Job matching Alibaba-NLP/gte-multilingual-base pruned + +Top performing model on [TalentCLEF 2025](https://talentclef.github.io/talentclef/) Task A. Use it for multilingual job title matching + +## Model Details + +### Model Description +- **Model Type:** Sentence Transformer + +- **Maximum Sequence Length:** 512 tokens +- **Output Dimensionality:** 768 dimensions +- **Similarity Function:** Cosine Similarity + + + + +### Model Sources + +- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) +- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) +- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) + +### Full Model Architecture + +``` +SentenceTransformer( + (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel + (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) + (2): Normalize() +) +``` + +## Usage + +### Direct Usage (Sentence Transformers) + +First install the Sentence Transformers library: + +```bash +pip install -U sentence-transformers +``` + +Then you can load this model and run inference. +```python +from sentence_transformers import SentenceTransformer + +# Download from the 🤗 Hub +model = SentenceTransformer("pj-mathematician/JobGTE-multilingual-base-pruned") +# Run inference +sentences = [ + 'Entwicklerin für mobile Anwendungen', + 'Mergers-and-Acquisitions-Analyst/Mergers-and-Acquisitions-Analystin', + 'fashion design expert', +] +embeddings = model.encode(sentences) +print(embeddings.shape) +# [3, 768] + +# Get the similarity scores for the embeddings +similarities = model.similarity(embeddings, embeddings) +print(similarities.shape) +# [3, 3] +``` + + + + + + + +## 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 | 0.1189 | 0.2956 | 0.6602 | 0.6297 | 0.5564 | 0.6086 | +| cosine_accuracy@20 | 0.9714 | 1.0 | 0.9704 | 0.9709 | 0.9106 | 0.8866 | 0.9593 | +| cosine_accuracy@50 | 0.9905 | 1.0 | 0.9754 | 0.9903 | 0.9496 | 0.9381 | 0.9791 | +| cosine_accuracy@100 | 0.9905 | 1.0 | 0.9901 | 0.9903 | 0.973 | 0.9594 | 0.9875 | +| cosine_accuracy@150 | 0.9905 | 1.0 | 0.9901 | 0.9903 | 0.9834 | 0.9709 | 0.9911 | +| cosine_accuracy@200 | 0.9905 | 1.0 | 0.9901 | 0.9903 | 0.9901 | 0.9776 | 0.9937 | +| cosine_precision@1 | 0.6476 | 0.1189 | 0.2956 | 0.6602 | 0.6297 | 0.5564 | 0.6086 | +| cosine_precision@20 | 0.4795 | 0.5268 | 0.4291 | 0.4481 | 0.1117 | 0.1095 | 0.1266 | +| cosine_precision@50 | 0.2884 | 0.3438 | 0.298 | 0.2713 | 0.0485 | 0.0481 | 0.0552 | +| cosine_precision@100 | 0.173 | 0.219 | 0.1943 | 0.1665 | 0.0254 | 0.0253 | 0.0287 | +| cosine_precision@150 | 0.1244 | 0.1658 | 0.1482 | 0.1211 | 0.0172 | 0.0173 | 0.0194 | +| cosine_precision@200 | 0.0986 | 0.1333 | 0.1198 | 0.0953 | 0.0131 | 0.0131 | 0.0147 | +| cosine_recall@1 | 0.0661 | 0.0036 | 0.0111 | 0.0661 | 0.2434 | 0.2093 | 0.2044 | +| cosine_recall@20 | 0.5122 | 0.3541 | 0.2668 | 0.4841 | 0.8288 | 0.7989 | 0.8346 | +| cosine_recall@50 | 0.6835 | 0.5098 | 0.4092 | 0.6568 | 0.8987 | 0.8741 | 0.9096 | +| cosine_recall@100 | 0.79 | 0.6076 | 0.5098 | 0.7685 | 0.9399 | 0.9173 | 0.9476 | +| cosine_recall@150 | 0.84 | 0.6705 | 0.5729 | 0.8278 | 0.9577 | 0.9424 | 0.9609 | +| cosine_recall@200 | 0.8759 | 0.7125 | 0.612 | 0.8617 | 0.9695 | 0.9536 | 0.9698 | +| cosine_ndcg@1 | 0.6476 | 0.1189 | 0.2956 | 0.6602 | 0.6297 | 0.5564 | 0.6086 | +| cosine_ndcg@20 | 0.6468 | 0.5708 | 0.4696 | 0.6231 | 0.701 | 0.6541 | 0.6853 | +| cosine_ndcg@50 | 0.658 | 0.5355 | 0.4449 | 0.6383 | 0.7201 | 0.6748 | 0.7067 | +| cosine_ndcg@100 | 0.7095 | 0.559 | 0.467 | 0.6917 | 0.7291 | 0.6845 | 0.7154 | +| cosine_ndcg@150 | 0.731 | 0.59 | 0.4982 | 0.7167 | 0.7326 | 0.6894 | 0.7181 | +| **cosine_ndcg@200** | **0.7461** | **0.6095** | **0.5165** | **0.7303** | **0.7347** | **0.6915** | **0.7198** | +| cosine_mrr@1 | 0.6476 | 0.1189 | 0.2956 | 0.6602 | 0.6297 | 0.5564 | 0.6086 | +| cosine_mrr@20 | 0.7902 | 0.5532 | 0.5047 | 0.8016 | 0.7037 | 0.6477 | 0.7237 | +| cosine_mrr@50 | 0.791 | 0.5532 | 0.5048 | 0.8021 | 0.705 | 0.6494 | 0.7243 | +| cosine_mrr@100 | 0.791 | 0.5532 | 0.505 | 0.8021 | 0.7053 | 0.6497 | 0.7245 | +| cosine_mrr@150 | 0.791 | 0.5532 | 0.505 | 0.8021 | 0.7054 | 0.6498 | 0.7245 | +| cosine_mrr@200 | 0.791 | 0.5532 | 0.505 | 0.8021 | 0.7055 | 0.6498 | 0.7245 | +| cosine_map@1 | 0.6476 | 0.1189 | 0.2956 | 0.6602 | 0.6297 | 0.5564 | 0.6086 | +| cosine_map@20 | 0.5026 | 0.4379 | 0.3366 | 0.475 | 0.6194 | 0.5648 | 0.5652 | +| cosine_map@50 | 0.484 | 0.3739 | 0.2853 | 0.4579 | 0.6244 | 0.57 | 0.5716 | +| cosine_map@100 | 0.5118 | 0.3763 | 0.2818 | 0.4848 | 0.6257 | 0.5714 | 0.5731 | +| cosine_map@150 | 0.5202 | 0.3892 | 0.2931 | 0.4937 | 0.626 | 0.5719 | 0.5734 | +| cosine_map@200 | 0.5249 | 0.3958 | 0.2988 | 0.4978 | 0.6262 | 0.572 | 0.5735 | +| cosine_map@500 | 0.5304 | 0.4063 | 0.3109 | 0.504 | 0.6263 | 0.5723 | 0.5736 | + + + + + +## Training Details + +### Training Dataset + +#### Unnamed Dataset + +* Size: 86,648 training samples +* Columns: sentence and label +* Approximate statistics based on the first 1000 samples: + | | sentence | label | + |:--------|:---------------------------------------------------------------------------------|:-------------------------------------| + | type | string | list | + | details |
  • min: 2 tokens
  • mean: 8.25 tokens
  • max: 54 tokens
|
  • size: 768 elements
| +* Samples: + | sentence | label | + |:-----------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------| + | | [-0.07171934843063354, 0.03595816716551781, -0.029780959710478783, 0.006593302357941866, 0.040611181408166885, ...] | + | airport environment officer | [-0.022075481712818146, 0.02999737113714218, -0.02189866080880165, 0.016531817615032196, 0.012234307825565338, ...] | + | Flake操作员 | [-0.04815564677119255, 0.023524893447756767, -0.01583661139011383, 0.042527906596660614, 0.03815540298819542, ...] | +* Loss: [MSELoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss) + +### Training Hyperparameters +#### Non-Default Hyperparameters + +- `eval_strategy`: steps +- `per_device_train_batch_size`: 128 +- `per_device_eval_batch_size`: 128 +- `gradient_accumulation_steps`: 2 +- `learning_rate`: 0.0001 +- `num_train_epochs`: 5 +- `warmup_ratio`: 0.05 +- `log_on_each_node`: False +- `fp16`: True +- `dataloader_num_workers`: 4 +- `ddp_find_unused_parameters`: True +- `batch_sampler`: no_duplicates + +#### All Hyperparameters +
Click to expand + +- `overwrite_output_dir`: False +- `do_predict`: False +- `eval_strategy`: steps +- `prediction_loss_only`: True +- `per_device_train_batch_size`: 128 +- `per_device_eval_batch_size`: 128 +- `per_gpu_train_batch_size`: None +- `per_gpu_eval_batch_size`: None +- `gradient_accumulation_steps`: 2 +- `eval_accumulation_steps`: None +- `torch_empty_cache_steps`: None +- `learning_rate`: 0.0001 +- `weight_decay`: 0.0 +- `adam_beta1`: 0.9 +- `adam_beta2`: 0.999 +- `adam_epsilon`: 1e-08 +- `max_grad_norm`: 1.0 +- `num_train_epochs`: 5 +- `max_steps`: -1 +- `lr_scheduler_type`: linear +- `lr_scheduler_kwargs`: {} +- `warmup_ratio`: 0.05 +- `warmup_steps`: 0 +- `log_level`: passive +- `log_level_replica`: warning +- `log_on_each_node`: False +- `logging_nan_inf_filter`: True +- `save_safetensors`: True +- `save_on_each_node`: False +- `save_only_model`: False +- `restore_callback_states_from_checkpoint`: False +- `no_cuda`: False +- `use_cpu`: False +- `use_mps_device`: False +- `seed`: 42 +- `data_seed`: None +- `jit_mode_eval`: False +- `use_ipex`: False +- `bf16`: False +- `fp16`: True +- `fp16_opt_level`: O1 +- `half_precision_backend`: auto +- `bf16_full_eval`: False +- `fp16_full_eval`: False +- `tf32`: None +- `local_rank`: 0 +- `ddp_backend`: None +- `tpu_num_cores`: None +- `tpu_metrics_debug`: False +- `debug`: [] +- `dataloader_drop_last`: True +- `dataloader_num_workers`: 4 +- `dataloader_prefetch_factor`: None +- `past_index`: -1 +- `disable_tqdm`: False +- `remove_unused_columns`: True +- `label_names`: None +- `load_best_model_at_end`: False +- `ignore_data_skip`: False +- `fsdp`: [] +- `fsdp_min_num_params`: 0 +- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} +- `tp_size`: 0 +- `fsdp_transformer_layer_cls_to_wrap`: None +- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} +- `deepspeed`: None +- `label_smoothing_factor`: 0.0 +- `optim`: adamw_torch +- `optim_args`: None +- `adafactor`: False +- `group_by_length`: False +- `length_column_name`: length +- `ddp_find_unused_parameters`: True +- `ddp_bucket_cap_mb`: None +- `ddp_broadcast_buffers`: False +- `dataloader_pin_memory`: True +- `dataloader_persistent_workers`: False +- `skip_memory_metrics`: True +- `use_legacy_prediction_loop`: False +- `push_to_hub`: False +- `resume_from_checkpoint`: None +- `hub_model_id`: None +- `hub_strategy`: every_save +- `hub_private_repo`: None +- `hub_always_push`: False +- `gradient_checkpointing`: False +- `gradient_checkpointing_kwargs`: None +- `include_inputs_for_metrics`: False +- `include_for_metrics`: [] +- `eval_do_concat_batches`: True +- `fp16_backend`: auto +- `push_to_hub_model_id`: None +- `push_to_hub_organization`: None +- `mp_parameters`: +- `auto_find_batch_size`: False +- `full_determinism`: False +- `torchdynamo`: None +- `ray_scope`: last +- `ddp_timeout`: 1800 +- `torch_compile`: False +- `torch_compile_backend`: None +- `torch_compile_mode`: None +- `include_tokens_per_second`: False +- `include_num_input_tokens_seen`: False +- `neftune_noise_alpha`: None +- `optim_target_modules`: None +- `batch_eval_metrics`: False +- `eval_on_start`: False +- `use_liger_kernel`: False +- `eval_use_gather_object`: False +- `average_tokens_across_devices`: False +- `prompts`: None +- `batch_sampler`: no_duplicates +- `multi_dataset_batch_sampler`: proportional + +
+ +### Training Logs +| Epoch | Step | Training Loss | full_en_cosine_ndcg@200 | full_es_cosine_ndcg@200 | full_de_cosine_ndcg@200 | full_zh_cosine_ndcg@200 | mix_es_cosine_ndcg@200 | mix_de_cosine_ndcg@200 | mix_zh_cosine_ndcg@200 | +|:------:|:----:|:-------------:|:-----------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:| +| -1 | -1 | - | 0.5348 | 0.4311 | 0.3678 | 0.5333 | 0.2580 | 0.1924 | 0.2871 | +| 0.0030 | 1 | 0.0017 | - | - | - | - | - | - | - | +| 0.2959 | 100 | 0.001 | - | - | - | - | - | - | - | +| 0.5917 | 200 | 0.0005 | 0.6702 | 0.5287 | 0.4566 | 0.6809 | 0.5864 | 0.5302 | 0.4739 | +| 0.8876 | 300 | 0.0004 | - | - | - | - | - | - | - | +| 1.1834 | 400 | 0.0004 | 0.7057 | 0.5643 | 0.4790 | 0.7033 | 0.6604 | 0.6055 | 0.6003 | +| 1.4793 | 500 | 0.0004 | - | - | - | - | - | - | - | +| 1.7751 | 600 | 0.0003 | 0.7184 | 0.5783 | 0.4910 | 0.7127 | 0.6927 | 0.6416 | 0.6485 | +| 2.0710 | 700 | 0.0003 | - | - | - | - | - | - | - | +| 2.3669 | 800 | 0.0003 | 0.7307 | 0.5938 | 0.5023 | 0.7233 | 0.7125 | 0.6639 | 0.6847 | +| 2.6627 | 900 | 0.0003 | - | - | - | - | - | - | - | +| 2.9586 | 1000 | 0.0003 | 0.7371 | 0.6002 | 0.5085 | 0.7228 | 0.7222 | 0.6761 | 0.6998 | +| 3.2544 | 1100 | 0.0003 | - | - | - | - | - | - | - | +| 3.5503 | 1200 | 0.0003 | 0.7402 | 0.6059 | 0.5109 | 0.7279 | 0.7285 | 0.6841 | 0.7120 | +| 3.8462 | 1300 | 0.0003 | - | - | - | - | - | - | - | +| 4.1420 | 1400 | 0.0003 | 0.7449 | 0.6083 | 0.5154 | 0.7294 | 0.7333 | 0.6894 | 0.7176 | +| 4.4379 | 1500 | 0.0003 | - | - | - | - | - | - | - | +| 4.7337 | 1600 | 0.0003 | 0.7461 | 0.6095 | 0.5165 | 0.7303 | 0.7347 | 0.6915 | 0.7198 | + + +### Framework Versions +- Python: 3.11.11 +- Sentence Transformers: 4.1.0 +- Transformers: 4.51.3 +- PyTorch: 2.6.0+cu124 +- Accelerate: 1.6.0 +- Datasets: 3.5.0 +- Tokenizers: 0.21.1 + +## Citation + +### BibTeX + +#### Sentence Transformers +```bibtex +@inproceedings{reimers-2019-sentence-bert, + title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", + author = "Reimers, Nils and Gurevych, Iryna", + booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", + month = "11", + year = "2019", + publisher = "Association for Computational Linguistics", + url = "https://arxiv.org/abs/1908.10084", +} +``` + +#### MSELoss +```bibtex +@inproceedings{reimers-2020-multilingual-sentence-bert, + title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation", + author = "Reimers, Nils and Gurevych, Iryna", + booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing", + month = "11", + year = "2020", + publisher = "Association for Computational Linguistics", + url = "https://arxiv.org/abs/2004.09813", +} +``` + + + + + + \ No newline at end of file diff --git a/checkpoint-1690/config.json 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