diff --git "a/README.md" "b/README.md" --- "a/README.md" +++ "b/README.md" @@ -1,3 +1,4 @@ +--- model-index: - name: karsar/paraphrase-multilingual-MiniLM-L12-hu-v2 results: @@ -643,7987 +644,273 @@ model-index: - type: nauc_map_at_1_max value: 68.35724812614716 - type: nauc_map_at_1_std - value: 4.038661494923131 - - type: nauc_map_at_20_diff1 - value: 77.68304529150893 - - type: nauc_map_at_20_max - value: 70.95365196926124 - - type: nauc_map_at_20_std - value: 6.459235608230074 - - type: nauc_map_at_3_diff1 - value: 77.65108311925263 - - type: nauc_map_at_3_max - value: 71.27300229679268 - - type: nauc_map_at_3_std - value: 6.421413249698873 - - type: nauc_map_at_5_diff1 - value: 77.62058612073584 - - type: nauc_map_at_5_max - value: 71.28166308466814 - - type: nauc_map_at_5_std - value: 7.148832281239676 - - type: nauc_mrr_at_1000_diff1 - value: 77.77369357560062 - - type: nauc_mrr_at_1000_max - value: 70.94830494912476 - - type: nauc_mrr_at_1000_std - value: 6.522974403262641 - - type: nauc_mrr_at_100_diff1 - 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dataset: @@ -10134,102 +2421,6 @@ model-index: type: BitextMining tags: - mteb -base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 -language: -- hu -library_name: sentence-transformers -license: apache-2.0 -metrics: -- cosine_accuracy -- dot_accuracy -- manhattan_accuracy -- euclidean_accuracy -- max_accuracy -pipeline_tag: sentence-similarity -tags: -- sentence-transformers -- sentence-similarity -- feature-extraction -- generated_from_trainer -- dataset_size:1044013 -- loss:MultipleNegativesRankingLoss -widget: -- source_sentence: Emberek várnak a lámpánál kerékpárral. - sentences: - - Az emberek piros lámpánál haladnak. - - Az emberek a kerékpárjukon vannak. - - Egy fekete kutya úszik a vízben egy teniszlabdával a szájában -- source_sentence: A kutya a vízben van. - sentences: - - Két férfi takarítja a havat a tetőről, az egyik egy emelőben ül, a másik pedig - a tetőn. - - A macska a vízben van, és dühös. - - Egy kutya van a vízben, a szájában egy faág. -- source_sentence: A nő feketét visel. - sentences: - - Egy barna kutya fröcsköl, ahogy úszik a vízben. - - Egy tetoválással rendelkező nő, aki fekete tank tetején néz a földre. - - 'Egy kékbe öltözött nő intenzív arckifejezéssel üti a teniszlabdát. A képen:' -- source_sentence: Az emberek alszanak. - sentences: - - Három ember beszélget egy városi utcán. - - A nő fehéret visel. - - Egy apa és a fia ölelgeti alvás közben. -- source_sentence: Az emberek alszanak. - sentences: - - Egy feketébe öltözött nő cigarettát és bevásárlótáskát tart a kezében, miközben - egy idősebb nő átmegy az utcán. - - Egy csoport ember ül egy nyitott, térszerű területen, mögötte nagy bokrok és egy - sor viktoriánus stílusú épület, melyek közül sokat a kép jobb oldalán lévő erős - elmosódás tesz kivehetetlenné. - - Egy apa és a fia ölelgeti alvás közben. -model-index: -- name: paraphrase-multilingual-MiniLM-L12-v2-hu - results: - - task: - type: triplet - name: Triplet - dataset: - name: all nli dev - type: all-nli-dev - metrics: - - type: cosine_accuracy - value: 0.9918 - name: Cosine Accuracy - - type: dot_accuracy - value: 0.0102 - name: Dot Accuracy - - type: manhattan_accuracy - value: 0.99 - name: Manhattan Accuracy - - type: euclidean_accuracy - value: 0.99 - name: Euclidean Accuracy - - type: max_accuracy - value: 0.9918 - name: Max Accuracy - - task: - type: triplet - name: Triplet - dataset: - name: all nli test - type: all-nli-test - metrics: - - type: cosine_accuracy - value: 0.9937878787878788 - name: Cosine Accuracy - - type: dot_accuracy - value: 0.00803030303030303 - name: Dot Accuracy - - type: manhattan_accuracy - value: 0.9928787878787879 - name: Manhattan Accuracy - - type: euclidean_accuracy - value: 0.9924242424242424 - name: Euclidean Accuracy - - type: max_accuracy - value: 0.9937878787878788 - name: Max Accuracy --- # paraphrase-multilingual-MiniLM-L12-v2-hu