--- 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", } ```