--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:124788 - loss:GISTEmbedLoss base_model: BAAI/bge-small-en-v1.5 widget: - source_sentence: 其他机械、设备和有形货物租赁服务代表 sentences: - 其他机械和设备租赁服务工作人员 - 电子和电信设备及零部件物流经理 - 工业主厨 - source_sentence: 公交车司机 sentences: - 表演灯光设计师 - 乙烯基地板安装工 - 国际巴士司机 - source_sentence: online communication manager sentences: - trades union official - social media manager - budget manager - source_sentence: Projektmanagerin sentences: - Projektmanager/Projektmanagerin - Category-Manager - Infanterist - source_sentence: Volksvertreter sentences: - Parlamentarier - Oberbürgermeister - Konsul pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy@1 - cosine_accuracy@20 - cosine_accuracy@50 - cosine_accuracy@100 - cosine_accuracy@150 - cosine_accuracy@200 - cosine_precision@1 - cosine_precision@20 - cosine_precision@50 - cosine_precision@100 - cosine_precision@150 - cosine_precision@200 - cosine_recall@1 - cosine_recall@20 - cosine_recall@50 - cosine_recall@100 - cosine_recall@150 - cosine_recall@200 - cosine_ndcg@1 - cosine_ndcg@20 - cosine_ndcg@50 - cosine_ndcg@100 - cosine_ndcg@150 - cosine_ndcg@200 - cosine_mrr@1 - cosine_mrr@20 - cosine_mrr@50 - cosine_mrr@100 - cosine_mrr@150 - cosine_mrr@200 - cosine_map@1 - cosine_map@20 - cosine_map@50 - cosine_map@100 - cosine_map@150 - cosine_map@200 - cosine_map@500 model-index: - name: SentenceTransformer based on BAAI/bge-small-en-v1.5 results: - task: type: information-retrieval name: Information Retrieval dataset: name: full en type: full_en metrics: - type: cosine_accuracy@1 value: 0.6571428571428571 name: Cosine Accuracy@1 - type: cosine_accuracy@20 value: 0.9904761904761905 name: Cosine Accuracy@20 - type: cosine_accuracy@50 value: 0.9904761904761905 name: Cosine Accuracy@50 - type: cosine_accuracy@100 value: 0.9904761904761905 name: Cosine Accuracy@100 - type: cosine_accuracy@150 value: 0.9904761904761905 name: Cosine Accuracy@150 - type: cosine_accuracy@200 value: 0.9904761904761905 name: Cosine Accuracy@200 - type: cosine_precision@1 value: 0.6571428571428571 name: Cosine Precision@1 - type: cosine_precision@20 value: 0.5047619047619047 name: Cosine Precision@20 - type: cosine_precision@50 value: 0.30857142857142855 name: Cosine Precision@50 - type: cosine_precision@100 value: 0.18666666666666668 name: Cosine Precision@100 - type: cosine_precision@150 value: 0.13269841269841268 name: Cosine Precision@150 - type: cosine_precision@200 value: 0.1029047619047619 name: Cosine Precision@200 - type: cosine_recall@1 value: 0.0680237860830842 name: Cosine Recall@1 - type: cosine_recall@20 value: 0.539060339827615 name: Cosine Recall@20 - type: cosine_recall@50 value: 0.7269844521994231 name: Cosine Recall@50 - type: cosine_recall@100 value: 0.8337131628681403 name: Cosine Recall@100 - type: cosine_recall@150 value: 0.879935375805825 name: Cosine Recall@150 - type: cosine_recall@200 value: 0.9050529457831012 name: Cosine Recall@200 - type: cosine_ndcg@1 value: 0.6571428571428571 name: Cosine Ndcg@1 - type: cosine_ndcg@20 value: 0.686462471196106 name: Cosine Ndcg@20 - type: cosine_ndcg@50 value: 0.7052824081502371 name: Cosine Ndcg@50 - type: cosine_ndcg@100 value: 0.7601614355798527 name: Cosine Ndcg@100 - type: cosine_ndcg@150 value: 0.7798476891938094 name: Cosine Ndcg@150 - type: cosine_ndcg@200 value: 0.7898871141566125 name: Cosine Ndcg@200 - type: cosine_mrr@1 value: 0.6571428571428571 name: Cosine Mrr@1 - type: cosine_mrr@20 value: 0.8095238095238095 name: Cosine Mrr@20 - type: cosine_mrr@50 value: 0.8095238095238095 name: Cosine Mrr@50 - type: cosine_mrr@100 value: 0.8095238095238095 name: Cosine Mrr@100 - type: cosine_mrr@150 value: 0.8095238095238095 name: Cosine Mrr@150 - type: cosine_mrr@200 value: 0.8095238095238095 name: Cosine Mrr@200 - type: cosine_map@1 value: 0.6571428571428571 name: Cosine Map@1 - type: cosine_map@20 value: 0.5451065538458748 name: Cosine Map@20 - type: cosine_map@50 value: 0.5347802076206865 name: Cosine Map@50 - type: cosine_map@100 value: 0.567702602098158 name: Cosine Map@100 - type: cosine_map@150 value: 0.5756725358487015 name: Cosine Map@150 - type: cosine_map@200 value: 0.5789669196636947 name: Cosine Map@200 - type: cosine_map@500 value: 0.5832808543489026 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: 0.4913513513513514 name: Cosine Precision@20 - type: cosine_precision@50 value: 0.316972972972973 name: Cosine Precision@50 - type: cosine_precision@100 value: 0.19843243243243244 name: Cosine Precision@100 - type: cosine_precision@150 value: 0.146990990990991 name: Cosine Precision@150 - type: cosine_precision@200 value: 0.11778378378378378 name: Cosine Precision@200 - type: cosine_recall@1 value: 0.002992884071419607 name: Cosine Recall@1 - type: cosine_recall@20 value: 0.32341666838263944 name: Cosine Recall@20 - type: cosine_recall@50 value: 0.4630260221149236 name: Cosine Recall@50 - type: cosine_recall@100 value: 0.5419804526017848 name: Cosine Recall@100 - type: cosine_recall@150 value: 0.5826718468403144 name: Cosine Recall@150 - type: cosine_recall@200 value: 0.6149262657286421 name: Cosine Recall@200 - type: cosine_ndcg@1 value: 0.11351351351351352 name: Cosine Ndcg@1 - type: cosine_ndcg@20 value: 0.5389058089458943 name: Cosine Ndcg@20 - type: cosine_ndcg@50 value: 0.5002442028172164 name: Cosine Ndcg@50 - type: cosine_ndcg@100 value: 0.5138591255215345 name: Cosine Ndcg@100 - type: cosine_ndcg@150 value: 0.5346372349516221 name: Cosine Ndcg@150 - type: cosine_ndcg@200 value: 0.5502474315848075 name: Cosine Ndcg@200 - type: cosine_mrr@1 value: 0.11351351351351352 name: Cosine Mrr@1 - type: cosine_mrr@20 value: 0.5444744744744745 name: Cosine Mrr@20 - type: cosine_mrr@50 value: 0.5444744744744745 name: Cosine Mrr@50 - type: cosine_mrr@100 value: 0.5444744744744745 name: Cosine Mrr@100 - type: cosine_mrr@150 value: 0.5444744744744745 name: Cosine Mrr@150 - type: cosine_mrr@200 value: 0.5444744744744745 name: Cosine Mrr@200 - type: cosine_map@1 value: 0.11351351351351352 name: Cosine Map@1 - type: cosine_map@20 value: 0.40352984921129137 name: Cosine Map@20 - type: cosine_map@50 value: 0.3418539578142162 name: Cosine Map@50 - type: cosine_map@100 value: 0.339373689987275 name: Cosine Map@100 - type: cosine_map@150 value: 0.3478760829213016 name: Cosine Map@150 - type: cosine_map@200 value: 0.3533435915341769 name: Cosine Map@200 - type: cosine_map@500 value: 0.363222785830563 name: Cosine Map@500 - task: type: information-retrieval name: Information Retrieval dataset: name: full de type: full_de metrics: - type: cosine_accuracy@1 value: 0.2955665024630542 name: Cosine Accuracy@1 - type: cosine_accuracy@20 value: 0.9211822660098522 name: Cosine Accuracy@20 - type: cosine_accuracy@50 value: 0.9655172413793104 name: Cosine Accuracy@50 - type: cosine_accuracy@100 value: 0.9753694581280788 name: Cosine Accuracy@100 - type: cosine_accuracy@150 value: 0.9852216748768473 name: Cosine Accuracy@150 - type: cosine_accuracy@200 value: 0.9852216748768473 name: Cosine Accuracy@200 - type: cosine_precision@1 value: 0.2955665024630542 name: Cosine Precision@1 - type: cosine_precision@20 value: 0.424384236453202 name: Cosine Precision@20 - type: cosine_precision@50 value: 0.28167487684729065 name: Cosine Precision@50 - type: cosine_precision@100 value: 0.17995073891625615 name: Cosine Precision@100 - type: cosine_precision@150 value: 0.13589490968801315 name: Cosine Precision@150 - type: cosine_precision@200 value: 0.1108128078817734 name: Cosine Precision@200 - type: cosine_recall@1 value: 0.01108543831680986 name: Cosine Recall@1 - type: cosine_recall@20 value: 0.2600945586038909 name: Cosine Recall@20 - type: cosine_recall@50 value: 0.3844030994839744 name: Cosine Recall@50 - type: cosine_recall@100 value: 0.4672649807153451 name: Cosine Recall@100 - type: cosine_recall@150 value: 0.5171228717670064 name: Cosine Recall@150 - type: cosine_recall@200 value: 0.5533299912627624 name: Cosine Recall@200 - type: cosine_ndcg@1 value: 0.2955665024630542 name: Cosine Ndcg@1 - type: cosine_ndcg@20 value: 0.4593107411252075 name: Cosine Ndcg@20 - type: cosine_ndcg@50 value: 0.42313178566078624 name: Cosine Ndcg@50 - type: cosine_ndcg@100 value: 0.4367043857530601 name: Cosine Ndcg@100 - type: cosine_ndcg@150 value: 0.4621847371016286 name: Cosine Ndcg@150 - type: cosine_ndcg@200 value: 0.48019099347834654 name: Cosine Ndcg@200 - type: cosine_mrr@1 value: 0.2955665024630542 name: Cosine Mrr@1 - type: cosine_mrr@20 value: 0.4892678749821603 name: Cosine Mrr@20 - type: cosine_mrr@50 value: 0.49065090899064223 name: Cosine Mrr@50 - type: cosine_mrr@100 value: 0.49080251743966435 name: Cosine Mrr@100 - type: cosine_mrr@150 value: 0.4908799208299932 name: Cosine Mrr@150 - type: cosine_mrr@200 value: 0.4908799208299932 name: Cosine Mrr@200 - type: cosine_map@1 value: 0.2955665024630542 name: Cosine Map@1 - type: cosine_map@20 value: 0.3228620941051522 name: Cosine Map@20 - type: cosine_map@50 value: 0.2644260812747752 name: Cosine Map@50 - type: cosine_map@100 value: 0.2576011230547815 name: Cosine Map@100 - type: cosine_map@150 value: 0.2666548881846307 name: Cosine Map@150 - type: cosine_map@200 value: 0.27224102651692533 name: Cosine Map@200 - type: cosine_map@500 value: 0.28312561300678324 name: Cosine Map@500 - task: type: information-retrieval name: Information Retrieval dataset: name: full zh type: full_zh metrics: - type: cosine_accuracy@1 value: 0.3300970873786408 name: Cosine Accuracy@1 - type: cosine_accuracy@20 value: 0.7184466019417476 name: Cosine Accuracy@20 - type: cosine_accuracy@50 value: 0.8155339805825242 name: Cosine Accuracy@50 - type: cosine_accuracy@100 value: 0.8932038834951457 name: Cosine Accuracy@100 - type: cosine_accuracy@150 value: 0.9223300970873787 name: Cosine Accuracy@150 - type: cosine_accuracy@200 value: 0.9320388349514563 name: Cosine Accuracy@200 - type: cosine_precision@1 value: 0.3300970873786408 name: Cosine Precision@1 - type: cosine_precision@20 value: 0.16796116504854372 name: Cosine Precision@20 - type: cosine_precision@50 value: 0.09262135922330093 name: Cosine Precision@50 - type: cosine_precision@100 value: 0.05815533980582525 name: Cosine Precision@100 - type: cosine_precision@150 value: 0.04563106796116505 name: Cosine Precision@150 - type: cosine_precision@200 value: 0.03771844660194174 name: Cosine Precision@200 - type: cosine_recall@1 value: 0.02573649124630195 name: Cosine Recall@1 - type: cosine_recall@20 value: 0.17402459309945448 name: Cosine Recall@20 - type: cosine_recall@50 value: 0.23816219248808224 name: Cosine Recall@50 - type: cosine_recall@100 value: 0.28291725637657983 name: Cosine Recall@100 - type: cosine_recall@150 value: 0.32619122038725784 name: Cosine Recall@150 - type: cosine_recall@200 value: 0.3543394793587958 name: Cosine Recall@200 - type: cosine_ndcg@1 value: 0.3300970873786408 name: Cosine Ndcg@1 - type: cosine_ndcg@20 value: 0.23956118764265208 name: Cosine Ndcg@20 - type: cosine_ndcg@50 value: 0.2341910409667355 name: Cosine Ndcg@50 - type: cosine_ndcg@100 value: 0.2559822552765659 name: Cosine Ndcg@100 - type: cosine_ndcg@150 value: 0.27344655996496936 name: Cosine Ndcg@150 - type: cosine_ndcg@200 value: 0.28432223965649855 name: Cosine Ndcg@200 - type: cosine_mrr@1 value: 0.3300970873786408 name: Cosine Mrr@1 - type: cosine_mrr@20 value: 0.43064643766798927 name: Cosine Mrr@20 - type: cosine_mrr@50 value: 0.43374387043765733 name: Cosine Mrr@50 - type: cosine_mrr@100 value: 0.4348781442268605 name: Cosine Mrr@100 - type: cosine_mrr@150 value: 0.4351279925655956 name: Cosine Mrr@150 - type: cosine_mrr@200 value: 0.4351822313246128 name: Cosine Mrr@200 - type: cosine_map@1 value: 0.3300970873786408 name: Cosine Map@1 - type: cosine_map@20 value: 0.14301006319225865 name: Cosine Map@20 - type: cosine_map@50 value: 0.12425793473074002 name: Cosine Map@50 - type: cosine_map@100 value: 0.12962575663735706 name: Cosine Map@100 - type: cosine_map@150 value: 0.13242860022521366 name: Cosine Map@150 - type: cosine_map@200 value: 0.13374255185989983 name: Cosine Map@200 - type: cosine_map@500 value: 0.13779434547799502 name: Cosine Map@500 - task: type: information-retrieval name: Information Retrieval dataset: name: mix es type: mix_es metrics: - type: cosine_accuracy@1 value: 0.40977639105564223 name: Cosine Accuracy@1 - type: cosine_accuracy@20 value: 0.7618304732189287 name: Cosine Accuracy@20 - type: cosine_accuracy@50 value: 0.8512740509620385 name: Cosine Accuracy@50 - type: cosine_accuracy@100 value: 0.9105564222568903 name: Cosine Accuracy@100 - type: cosine_accuracy@150 value: 0.9381175247009881 name: Cosine Accuracy@150 - type: cosine_accuracy@200 value: 0.9542381695267811 name: Cosine Accuracy@200 - type: cosine_precision@1 value: 0.40977639105564223 name: Cosine Precision@1 - type: cosine_precision@20 value: 0.0890015600624025 name: Cosine Precision@20 - type: cosine_precision@50 value: 0.04168486739469579 name: Cosine Precision@50 - type: cosine_precision@100 value: 0.022854914196567863 name: Cosine Precision@100 - type: cosine_precision@150 value: 0.01585370081469925 name: Cosine Precision@150 - type: cosine_precision@200 value: 0.012220488819552783 name: Cosine Precision@200 - type: cosine_recall@1 value: 0.15567317930812472 name: Cosine Recall@1 - type: cosine_recall@20 value: 0.6574783943738703 name: Cosine Recall@20 - type: cosine_recall@50 value: 0.7691404799049105 name: Cosine Recall@50 - type: cosine_recall@100 value: 0.8454015303469281 name: Cosine Recall@100 - type: cosine_recall@150 value: 0.8795148948815096 name: Cosine Recall@150 - type: cosine_recall@200 value: 0.9035051878265606 name: Cosine Recall@200 - type: cosine_ndcg@1 value: 0.40977639105564223 name: Cosine Ndcg@1 - type: cosine_ndcg@20 value: 0.5094055696124096 name: Cosine Ndcg@20 - type: cosine_ndcg@50 value: 0.5398029704628499 name: Cosine Ndcg@50 - type: cosine_ndcg@100 value: 0.5563939454831869 name: Cosine Ndcg@100 - type: cosine_ndcg@150 value: 0.5630335952477792 name: Cosine Ndcg@150 - type: cosine_ndcg@200 value: 0.5674217099859529 name: Cosine Ndcg@200 - type: cosine_mrr@1 value: 0.40977639105564223 name: Cosine Mrr@1 - type: cosine_mrr@20 value: 0.4963374711503733 name: Cosine Mrr@20 - type: cosine_mrr@50 value: 0.49930745416180927 name: Cosine Mrr@50 - type: cosine_mrr@100 value: 0.5001571935146001 name: Cosine Mrr@100 - type: cosine_mrr@150 value: 0.5003842041203103 name: Cosine Mrr@150 - type: cosine_mrr@200 value: 0.5004783417497985 name: Cosine Mrr@200 - type: cosine_map@1 value: 0.40977639105564223 name: Cosine Map@1 - type: cosine_map@20 value: 0.4236549905504724 name: Cosine Map@20 - type: cosine_map@50 value: 0.4311498037279026 name: Cosine Map@50 - type: cosine_map@100 value: 0.43327838927965695 name: Cosine Map@100 - type: cosine_map@150 value: 0.4338451382952763 name: Cosine Map@150 - type: cosine_map@200 value: 0.4341307997461715 name: Cosine Map@200 - type: cosine_map@500 value: 0.4345995592976099 name: Cosine Map@500 - task: type: information-retrieval name: Information Retrieval dataset: name: mix de type: mix_de metrics: - type: cosine_accuracy@1 value: 0.2912116484659386 name: Cosine Accuracy@1 - type: cosine_accuracy@20 value: 0.6526261050442018 name: Cosine Accuracy@20 - type: cosine_accuracy@50 value: 0.7550702028081123 name: Cosine Accuracy@50 - type: cosine_accuracy@100 value: 0.8460738429537181 name: Cosine Accuracy@100 - type: cosine_accuracy@150 value: 0.8876755070202809 name: Cosine Accuracy@150 - type: cosine_accuracy@200 value: 0.9173166926677067 name: Cosine Accuracy@200 - type: cosine_precision@1 value: 0.2912116484659386 name: Cosine Precision@1 - type: cosine_precision@20 value: 0.07308892355694228 name: Cosine Precision@20 - type: cosine_precision@50 value: 0.03583983359334374 name: Cosine Precision@50 - type: cosine_precision@100 value: 0.02058242329693188 name: Cosine Precision@100 - type: cosine_precision@150 value: 0.014609117698041255 name: Cosine Precision@150 - type: cosine_precision@200 value: 0.011515860634425378 name: Cosine Precision@200 - type: cosine_recall@1 value: 0.10977639105564223 name: Cosine Recall@1 - type: cosine_recall@20 value: 0.5342520367481365 name: Cosine Recall@20 - type: cosine_recall@50 value: 0.6529207834980065 name: Cosine Recall@50 - type: cosine_recall@100 value: 0.7505633558675681 name: Cosine Recall@100 - type: cosine_recall@150 value: 0.7989166233315999 name: Cosine Recall@150 - type: cosine_recall@200 value: 0.8393482405962905 name: Cosine Recall@200 - type: cosine_ndcg@1 value: 0.2912116484659386 name: Cosine Ndcg@1 - type: cosine_ndcg@20 value: 0.39027078330836906 name: Cosine Ndcg@20 - type: cosine_ndcg@50 value: 0.4224011615840446 name: Cosine Ndcg@50 - type: cosine_ndcg@100 value: 0.4438393956774872 name: Cosine Ndcg@100 - type: cosine_ndcg@150 value: 0.45327900259303716 name: Cosine Ndcg@150 - type: cosine_ndcg@200 value: 0.4606831999024183 name: Cosine Ndcg@200 - type: cosine_mrr@1 value: 0.2912116484659386 name: Cosine Mrr@1 - type: cosine_mrr@20 value: 0.37544207546115405 name: Cosine Mrr@20 - type: cosine_mrr@50 value: 0.37870409367323543 name: Cosine Mrr@50 - type: cosine_mrr@100 value: 0.37999194359776256 name: Cosine Mrr@100 - type: cosine_mrr@150 value: 0.3803335431113417 name: Cosine Mrr@150 - type: cosine_mrr@200 value: 0.3805079454038972 name: Cosine Mrr@200 - type: cosine_map@1 value: 0.2912116484659386 name: Cosine Map@1 - type: cosine_map@20 value: 0.3075927383942124 name: Cosine Map@20 - type: cosine_map@50 value: 0.31502827814698436 name: Cosine Map@50 - type: cosine_map@100 value: 0.31767149302992986 name: Cosine Map@100 - type: cosine_map@150 value: 0.31842095656425334 name: Cosine Map@150 - type: cosine_map@200 value: 0.3189017921904424 name: Cosine Map@200 - type: cosine_map@500 value: 0.31963709557315734 name: Cosine Map@500 - task: type: information-retrieval name: Information Retrieval dataset: name: mix zh type: mix_zh metrics: - type: cosine_accuracy@1 value: 0.09498956158663883 name: Cosine Accuracy@1 - type: cosine_accuracy@20 value: 0.35281837160751567 name: Cosine Accuracy@20 - type: cosine_accuracy@50 value: 0.48851774530271397 name: Cosine Accuracy@50 - type: cosine_accuracy@100 value: 0.5960334029227558 name: Cosine Accuracy@100 - type: cosine_accuracy@150 value: 0.657098121085595 name: Cosine Accuracy@150 - type: cosine_accuracy@200 value: 0.7025052192066806 name: Cosine Accuracy@200 - type: cosine_precision@1 value: 0.09498956158663883 name: Cosine Precision@1 - type: cosine_precision@20 value: 0.03102818371607516 name: Cosine Precision@20 - type: cosine_precision@50 value: 0.018528183716075158 name: Cosine Precision@50 - type: cosine_precision@100 value: 0.011550104384133612 name: Cosine Precision@100 - type: cosine_precision@150 value: 0.008601252609603338 name: Cosine Precision@150 - type: cosine_precision@200 value: 0.007074634655532359 name: Cosine Precision@200 - type: cosine_recall@1 value: 0.03218510786360473 name: Cosine Recall@1 - type: cosine_recall@20 value: 0.20682473406899293 name: Cosine Recall@20 - type: cosine_recall@50 value: 0.30616239188786165 name: Cosine Recall@50 - type: cosine_recall@100 value: 0.38175970109686186 name: Cosine Recall@100 - type: cosine_recall@150 value: 0.4266063558339132 name: Cosine Recall@150 - type: cosine_recall@200 value: 0.4677598005103224 name: Cosine Recall@200 - type: cosine_ndcg@1 value: 0.09498956158663883 name: Cosine Ndcg@1 - type: cosine_ndcg@20 value: 0.13726194438538974 name: Cosine Ndcg@20 - type: cosine_ndcg@50 value: 0.16515347653846224 name: Cosine Ndcg@50 - type: cosine_ndcg@100 value: 0.18245718935168395 name: Cosine Ndcg@100 - type: cosine_ndcg@150 value: 0.1915123607890909 name: Cosine Ndcg@150 - type: cosine_ndcg@200 value: 0.1993072789458329 name: Cosine Ndcg@200 - type: cosine_mrr@1 value: 0.09498956158663883 name: Cosine Mrr@1 - type: cosine_mrr@20 value: 0.15082760305134044 name: Cosine Mrr@20 - type: cosine_mrr@50 value: 0.1552139914541245 name: Cosine Mrr@50 - type: cosine_mrr@100 value: 0.1567682757261486 name: Cosine Mrr@100 - type: cosine_mrr@150 value: 0.1572599746321091 name: Cosine Mrr@150 - type: cosine_mrr@200 value: 0.15752063728764779 name: Cosine Mrr@200 - type: cosine_map@1 value: 0.09498956158663883 name: Cosine Map@1 - type: cosine_map@20 value: 0.08696228866764828 name: Cosine Map@20 - type: cosine_map@50 value: 0.0925585898977933 name: Cosine Map@50 - type: cosine_map@100 value: 0.09443690504503688 name: Cosine Map@100 - type: cosine_map@150 value: 0.09508196706389692 name: Cosine Map@150 - type: cosine_map@200 value: 0.09552658777692054 name: Cosine Map@200 - type: cosine_map@500 value: 0.09647934265199021 name: Cosine Map@500 --- # SentenceTransformer based on BAAI/bge-small-en-v1.5 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) on the full_en, full_de, full_es, full_zh and mix datasets. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 384 dimensions - **Similarity Function:** Cosine Similarity - **Training Datasets:** - full_en - full_de - full_es - full_zh - mix ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("sentence_transformers_model_id") # Run inference sentences = [ 'Volksvertreter', 'Parlamentarier', 'Oberbürgermeister', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 384] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Information Retrieval * Datasets: `full_en`, `full_es`, `full_de`, `full_zh`, `mix_es`, `mix_de` and `mix_zh` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | full_en | full_es | full_de | full_zh | mix_es | mix_de | mix_zh | |:---------------------|:-----------|:-----------|:-----------|:-----------|:-----------|:-----------|:-----------| | cosine_accuracy@1 | 0.6571 | 0.1135 | 0.2956 | 0.3301 | 0.4098 | 0.2912 | 0.095 | | cosine_accuracy@20 | 0.9905 | 1.0 | 0.9212 | 0.7184 | 0.7618 | 0.6526 | 0.3528 | | cosine_accuracy@50 | 0.9905 | 1.0 | 0.9655 | 0.8155 | 0.8513 | 0.7551 | 0.4885 | | cosine_accuracy@100 | 0.9905 | 1.0 | 0.9754 | 0.8932 | 0.9106 | 0.8461 | 0.596 | | cosine_accuracy@150 | 0.9905 | 1.0 | 0.9852 | 0.9223 | 0.9381 | 0.8877 | 0.6571 | | cosine_accuracy@200 | 0.9905 | 1.0 | 0.9852 | 0.932 | 0.9542 | 0.9173 | 0.7025 | | cosine_precision@1 | 0.6571 | 0.1135 | 0.2956 | 0.3301 | 0.4098 | 0.2912 | 0.095 | | cosine_precision@20 | 0.5048 | 0.4914 | 0.4244 | 0.168 | 0.089 | 0.0731 | 0.031 | | cosine_precision@50 | 0.3086 | 0.317 | 0.2817 | 0.0926 | 0.0417 | 0.0358 | 0.0185 | | cosine_precision@100 | 0.1867 | 0.1984 | 0.18 | 0.0582 | 0.0229 | 0.0206 | 0.0116 | | cosine_precision@150 | 0.1327 | 0.147 | 0.1359 | 0.0456 | 0.0159 | 0.0146 | 0.0086 | | cosine_precision@200 | 0.1029 | 0.1178 | 0.1108 | 0.0377 | 0.0122 | 0.0115 | 0.0071 | | cosine_recall@1 | 0.068 | 0.003 | 0.0111 | 0.0257 | 0.1557 | 0.1098 | 0.0322 | | cosine_recall@20 | 0.5391 | 0.3234 | 0.2601 | 0.174 | 0.6575 | 0.5343 | 0.2068 | | cosine_recall@50 | 0.727 | 0.463 | 0.3844 | 0.2382 | 0.7691 | 0.6529 | 0.3062 | | cosine_recall@100 | 0.8337 | 0.542 | 0.4673 | 0.2829 | 0.8454 | 0.7506 | 0.3818 | | cosine_recall@150 | 0.8799 | 0.5827 | 0.5171 | 0.3262 | 0.8795 | 0.7989 | 0.4266 | | cosine_recall@200 | 0.9051 | 0.6149 | 0.5533 | 0.3543 | 0.9035 | 0.8393 | 0.4678 | | cosine_ndcg@1 | 0.6571 | 0.1135 | 0.2956 | 0.3301 | 0.4098 | 0.2912 | 0.095 | | cosine_ndcg@20 | 0.6865 | 0.5389 | 0.4593 | 0.2396 | 0.5094 | 0.3903 | 0.1373 | | cosine_ndcg@50 | 0.7053 | 0.5002 | 0.4231 | 0.2342 | 0.5398 | 0.4224 | 0.1652 | | cosine_ndcg@100 | 0.7602 | 0.5139 | 0.4367 | 0.256 | 0.5564 | 0.4438 | 0.1825 | | cosine_ndcg@150 | 0.7798 | 0.5346 | 0.4622 | 0.2734 | 0.563 | 0.4533 | 0.1915 | | **cosine_ndcg@200** | **0.7899** | **0.5502** | **0.4802** | **0.2843** | **0.5674** | **0.4607** | **0.1993** | | cosine_mrr@1 | 0.6571 | 0.1135 | 0.2956 | 0.3301 | 0.4098 | 0.2912 | 0.095 | | cosine_mrr@20 | 0.8095 | 0.5445 | 0.4893 | 0.4306 | 0.4963 | 0.3754 | 0.1508 | | cosine_mrr@50 | 0.8095 | 0.5445 | 0.4907 | 0.4337 | 0.4993 | 0.3787 | 0.1552 | | cosine_mrr@100 | 0.8095 | 0.5445 | 0.4908 | 0.4349 | 0.5002 | 0.38 | 0.1568 | | cosine_mrr@150 | 0.8095 | 0.5445 | 0.4909 | 0.4351 | 0.5004 | 0.3803 | 0.1573 | | cosine_mrr@200 | 0.8095 | 0.5445 | 0.4909 | 0.4352 | 0.5005 | 0.3805 | 0.1575 | | cosine_map@1 | 0.6571 | 0.1135 | 0.2956 | 0.3301 | 0.4098 | 0.2912 | 0.095 | | cosine_map@20 | 0.5451 | 0.4035 | 0.3229 | 0.143 | 0.4237 | 0.3076 | 0.087 | | cosine_map@50 | 0.5348 | 0.3419 | 0.2644 | 0.1243 | 0.4311 | 0.315 | 0.0926 | | cosine_map@100 | 0.5677 | 0.3394 | 0.2576 | 0.1296 | 0.4333 | 0.3177 | 0.0944 | | cosine_map@150 | 0.5757 | 0.3479 | 0.2667 | 0.1324 | 0.4338 | 0.3184 | 0.0951 | | cosine_map@200 | 0.579 | 0.3533 | 0.2722 | 0.1337 | 0.4341 | 0.3189 | 0.0955 | | cosine_map@500 | 0.5833 | 0.3632 | 0.2831 | 0.1378 | 0.4346 | 0.3196 | 0.0965 | ## Training Details ### Training Datasets
full_en #### full_en * Dataset: full_en * Size: 28,880 training samples * Columns: anchor and positive * Approximate statistics based on the first 1000 samples: | | anchor | positive | |:--------|:--------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | anchor | positive | |:-----------------------------------------|:-----------------------------------------| | air commodore | flight lieutenant | | command and control officer | flight officer | | air commodore | command and control officer | * Loss: [GISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0} ```
full_de #### full_de * Dataset: full_de * Size: 23,023 training samples * Columns: anchor and positive * Approximate statistics based on the first 1000 samples: | | anchor | positive | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | anchor | positive | |:----------------------------------|:-----------------------------------------------------| | Staffelkommandantin | Kommodore | | Luftwaffenoffizierin | Luftwaffenoffizier/Luftwaffenoffizierin | | Staffelkommandantin | Luftwaffenoffizierin | * Loss: [GISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0} ```
full_es #### full_es * Dataset: full_es * Size: 20,724 training samples * Columns: anchor and positive * Approximate statistics based on the first 1000 samples: | | anchor | positive | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | anchor | positive | |:------------------------------------|:-------------------------------------------| | jefe de escuadrón | instructor | | comandante de aeronave | instructor de simulador | | instructor | oficial del Ejército del Aire | * Loss: [GISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0} ```
full_zh #### full_zh * Dataset: full_zh * Size: 30,401 training samples * Columns: anchor and positive * Approximate statistics based on the first 1000 samples: | | anchor | positive | |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | anchor | positive | |:------------------|:---------------------| | 技术总监 | 技术和运营总监 | | 技术总监 | 技术主管 | | 技术总监 | 技术艺术总监 | * Loss: [GISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0} ```
mix #### mix * Dataset: mix * Size: 21,760 training samples * Columns: anchor and positive * Approximate statistics based on the first 1000 samples: | | anchor | positive | |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | anchor | positive | |:------------------------------------------|:----------------------------------------------------------------| | technical manager | Technischer Direktor für Bühne, Film und Fernsehen | | head of technical | directora técnica | | head of technical department | 技术艺术总监 | * Loss: [GISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0} ```
### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 128 - `per_device_eval_batch_size`: 128 - `gradient_accumulation_steps`: 2 - `num_train_epochs`: 5 - `warmup_ratio`: 0.05 - `log_on_each_node`: False - `fp16`: True - `dataloader_num_workers`: 4 - `ddp_find_unused_parameters`: True - `batch_sampler`: no_duplicates #### All Hyperparameters
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`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 5 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.05 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: False - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: True - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: True - `dataloader_num_workers`: 4 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `tp_size`: 0 - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: True - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | Training Loss | full_en_cosine_ndcg@200 | full_es_cosine_ndcg@200 | full_de_cosine_ndcg@200 | full_zh_cosine_ndcg@200 | mix_es_cosine_ndcg@200 | mix_de_cosine_ndcg@200 | mix_zh_cosine_ndcg@200 | |:------:|:----:|:-------------:|:-----------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:| | -1 | -1 | - | 0.7322 | 0.4690 | 0.3853 | 0.2723 | 0.3209 | 0.2244 | 0.0919 | | 0.0021 | 1 | 23.8878 | - | - | - | - | - | - | - | | 0.2058 | 100 | 7.2098 | - | - | - | - | - | - | - | | 0.4115 | 200 | 4.2635 | 0.7800 | 0.5132 | 0.4268 | 0.2798 | 0.4372 | 0.2996 | 0.1447 | | 0.6173 | 300 | 4.1931 | - | - | - | - | - | - | - | | 0.8230 | 400 | 3.73 | 0.7863 | 0.5274 | 0.4451 | 0.2805 | 0.4762 | 0.3455 | 0.1648 | | 1.0309 | 500 | 3.3569 | - | - | - | - | - | - | - | | 1.2366 | 600 | 3.6464 | 0.7868 | 0.5372 | 0.4540 | 0.2813 | 0.5063 | 0.3794 | 0.1755 | | 1.4424 | 700 | 3.0772 | - | - | - | - | - | - | - | | 1.6481 | 800 | 3.114 | 0.7906 | 0.5391 | 0.4576 | 0.2832 | 0.5221 | 0.4047 | 0.1779 | | 1.8539 | 900 | 2.9246 | - | - | - | - | - | - | - | | 2.0617 | 1000 | 2.7479 | 0.7873 | 0.5423 | 0.4631 | 0.2871 | 0.5323 | 0.4143 | 0.1843 | | 2.2675 | 1100 | 3.049 | - | - | - | - | - | - | - | | 2.4733 | 1200 | 2.6137 | 0.7878 | 0.5418 | 0.4685 | 0.2870 | 0.5470 | 0.4339 | 0.1932 | | 2.6790 | 1300 | 2.8607 | - | - | - | - | - | - | - | | 2.8848 | 1400 | 2.7071 | 0.7889 | 0.5465 | 0.4714 | 0.2891 | 0.5504 | 0.4362 | 0.1944 | | 3.0926 | 1500 | 2.7012 | - | - | - | - | - | - | - | | 3.2984 | 1600 | 2.7423 | 0.7882 | 0.5471 | 0.4748 | 0.2868 | 0.5542 | 0.4454 | 0.1976 | | 3.5041 | 1700 | 2.5316 | - | - | - | - | - | - | - | | 3.7099 | 1800 | 2.6344 | 0.7900 | 0.5498 | 0.4763 | 0.2857 | 0.5639 | 0.4552 | 0.1954 | | 3.9156 | 1900 | 2.4983 | - | - | - | - | - | - | - | | 4.1235 | 2000 | 2.5423 | 0.7894 | 0.5499 | 0.4786 | 0.2870 | 0.5644 | 0.4576 | 0.1974 | | 4.3292 | 2100 | 2.5674 | - | - | - | - | - | - | - | | 4.5350 | 2200 | 2.6237 | 0.7899 | 0.5502 | 0.4802 | 0.2843 | 0.5674 | 0.4607 | 0.1993 | ### Framework Versions - Python: 3.11.11 - Sentence Transformers: 4.1.0 - Transformers: 4.51.3 - PyTorch: 2.6.0+cu124 - Accelerate: 1.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### GISTEmbedLoss ```bibtex @misc{solatorio2024gistembed, title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning}, author={Aivin V. Solatorio}, year={2024}, eprint={2402.16829}, archivePrefix={arXiv}, primaryClass={cs.LG} } ```