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--- |
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base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 |
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datasets: [] |
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language: [] |
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library_name: sentence-transformers |
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metrics: |
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- cosine_accuracy |
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- cosine_accuracy_threshold |
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- cosine_f1 |
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- cosine_f1_threshold |
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- cosine_precision |
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- cosine_recall |
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- cosine_ap |
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- dot_accuracy |
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- dot_accuracy_threshold |
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- dot_f1 |
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- dot_f1_threshold |
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- dot_precision |
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- dot_recall |
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- dot_ap |
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- manhattan_accuracy |
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- manhattan_accuracy_threshold |
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- manhattan_f1 |
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- manhattan_f1_threshold |
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- manhattan_precision |
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- manhattan_recall |
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- manhattan_ap |
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- euclidean_accuracy |
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- euclidean_accuracy_threshold |
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- euclidean_f1 |
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- euclidean_f1_threshold |
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- euclidean_precision |
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- euclidean_recall |
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- euclidean_ap |
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- max_accuracy |
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- max_accuracy_threshold |
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- max_f1 |
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- max_f1_threshold |
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- max_precision |
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- max_recall |
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- max_ap |
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pipeline_tag: sentence-similarity |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:285122 |
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- loss:SoftmaxLoss |
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- dataset_size:13912 |
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- dataset_size:42735 |
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widget: |
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- source_sentence: Chef de file figuration |
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sentences: |
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- des données massives gérer une base de données numériques créer une documentation |
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technique relation clientrecueillir et analyser les besoins client identifier |
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les besoins en logiciel apporter une assistance technique aux équipes assurer |
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un rôle de support avant-vente développement commercialprésenter et valoriser |
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un produit ou un service répondre à un appel d'offre stratégie de développementpiloter |
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une activité concevoir et gérer un projet managementanimer, coordonner une équipe |
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allouer et organiser les ressources d'un projet selon les besoins et contraintes |
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conseil, transmissionconseiller une organisation, une structure accompagner l'appropriation |
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d'un outil |
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- • accès à l'emploi ce métier est accessible avec un diplôme de niveau cap à bac |
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(bac professionnel, brevet de compagnon,...) en sellerie, en confection de chaussures, |
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en maroquinerie,... compétences savoir-faire production, fabricationréaliser la |
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préparation de pièces par parage sélectionner les matières premières à mobiliser |
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réaliser les opérations de coupe de matériaux (positionnement de gabarit,...) |
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manuellement ou à l'aide d'une machine assembler (collage, couture) les pièces |
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de l'article (tige, soufflet) manuellement ou à |
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- ec responsable marketing définition définit et met en oeuvre la stratégie marketing |
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(tarifs, promotion, communication, gammes de produits, supports techniques,...) |
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pour l'ensemble des produits de l'entreprise. • peut diriger un service ou coordonner |
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l'activité d'une équipe. • accès à l'emploi ce métier est accessible à partir |
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d'un master (m1, master professionnel, diplôme d'école de commerce,...) dans un |
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secteur technique ou commercial, complété par une expérience professionnelle en |
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tant que chef de produit ou chef de groupe. la pratique d'une langue étrangère |
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- source_sentence: Rayonneur / Rayonneuse de roue |
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sentences: |
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- si coach en développement personnel transition démographique définition met en |
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place des actions de développement personnel, selon la méthode utilisée, afin |
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de favoriser le bien-être de la personne. • peut mettre en place des actions de |
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conseil, coaching. • peut diriger une entreprise. • accès à l'emploi ce métier |
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est accessible sans diplôme particulier. des formations spécifiques peuvent en |
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faciliter l'exercice. l'enseignement de ces pratiques ne donne pas systématiquement |
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lieu à des diplômes nationaux. ces diplômes, à eux seuls, ne donnent pas droit |
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à l'exercice d'une profession de santé. pour |
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- d'un produit opérer des choix techniques, esthétiques, économiques pour un produit |
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créer, élaborer et identifier des concepts innovants dessiner des avant-projets |
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(roughs, croquis) à partir du concept, des thèmes définis maîtriser la pensée |
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additive en plus de la pensée soustractive conception élaborer des processus et |
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des modes opératoires techniques production, fabricationsélectionner des matériaux |
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ou matières pour un projet réaliser les travaux de montage, d'assemblage intégrer |
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le jumeau numérique aux processus industriels prévention des risques |
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- routière peut en faciliter l'accès. les permis d, d1, d1e, de (précédemment d |
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et ed) pour la conduite de véhicules transport en commun de plus de 9 places complétés |
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par la formation initiale minimum obligatoire -fimo- option « voyageurs » sont |
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exigés. un renouvellement périodique de la fimo par la formation continue obligatoire |
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-fco- est exigé. une carte chronotachygraphe est obligatoire pour la conduite |
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de véhicules de transport de voyageurs de plus de 9 places. des permis ou habilita |
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- source_sentence: Ingénieur / Ingénieure d'études BTP en génie climatique et énergétique |
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sentences: |
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- apporter une assistance technique aux équipes développement commercial établir |
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un devis management animer, coordonner une équipe conseil, transmission transmettre |
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une technique, un savoir-faire organisationrespecter les règles de qualité, hygiène, |
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sécurité, santé et environnement (qhsse) alerter, demander un appui ou un arbitrage |
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adapter et optimiser sa pratique au contexte et aux risques professionnels (gestes, |
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postures, ergonomie) communication communiquer à l'oral en milieu professionnel |
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savoir-être professionnels travailler en équipe faire preuve d'autonomie faire |
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preuve de rigueur et de précision savoirs domaines d'expertise travaux |
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- gestion de l'exploitation agricole • bp responsable d'exploitation agricole (bp |
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rea) • bts techniques et services en matériels agricoles • but spécialité génie |
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biologique parcours agronomie • compétences savoir-faire production, fabricationpréparer |
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le matériel, les matériaux et les outillages monter et régler une installation, |
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une machine régler un équipement d'irrigation et d'arrosage adapter, ajuster un |
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article ou une production en fonction du besoin entretenir un arbre, une plantation |
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identifier une maladie, un parasite ou une carence sur un végétal, |
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- dans leurs démarches renseigner les clients sur les services de l'établissement |
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et offres touristiques réaliser des courses à la demande d'un client ou pour les |
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besoins de l'établissement développement commercial développer et fidéliser la |
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relation client stratégie de développementsuperviser le service de la bagagerie, |
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le stationnement des véhicules et organiser le transport des bagages management |
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organiser le travail d'une équipe gestion des ressources humaines recruter et |
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intégrer une personne conseil, transmission transmettre une technique, un savoir-faire |
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fiche emploi - juin 2024 maintenance, réparation surveiller un espace, un local, |
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un lieu |
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- source_sentence: Détective privé |
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sentences: |
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- sécurité, santé et environnement (qhsse) réaliser la mise en conformité de fonctionnement |
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(état de référence, sécurité, environnement,...) gestion des stockscontrôler l'état |
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des stocks définir des besoins en approvisionnement communication, multimédiamener |
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un entretien, une interview, une audition rédiger un cahier des charges, des spécifications |
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techniques recherche, innovationobserver des faits, des évènements, des comportements |
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procéder à des tests, expérimentations management valoriser et partager les bonnes |
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pratiques gestion des ressources humaines cartographier et classifier les emploi |
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- 'outils et matièresutilisation d''équipements de télésurveillance utilisation |
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d''outils connectés techniques professionnelles techniques pédagogiques 3 / 4 |
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- fiche emploi - juin 2024 contextes de travail conditions de travail et risques |
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professionnelsau domicile d''un particulier déplacements professionnels en environnement |
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bruyant en environnement climatique difficile en extérieur en flux tendu en milieu |
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nucléaire port d''équipement de protection individuel (epi) : gants, chaussures, |
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casque, protections auditives port de tenue professionnelle ou d''uniforme position |
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pénible sans lumière naturelle station debout prolongée travail répétitif' |
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- juin 2024 compétences savoir-faire production, fabricationmettre en oeuvre les |
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processus et les modes opératoires techniques réaliser une intervention nécessitant |
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une habilitation monter et régler une installation, une machine appliquer un traitement, |
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un produit réaliser des opérations de traitement thermique fabriquer, façonner |
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des produits utiliser un outil, une machine, un équipement, une installation maintenance, |
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réparationentretenir un équipement, une machine, une installation contrôler la |
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conformité d'un équipement, d'une machine, d'une installation réaliser un diagnostic |
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technique réaliser la maintenance d'un équipement |
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- source_sentence: Responsable d'élevage en production ovine |
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sentences: |
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- 'de travail et risques professionnelsau domicile d''un particulier déplacements |
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professionnels port d''équipement de protection individuel (epi) : gants, chaussures, |
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casque, protections auditives horaires et durée du travailtravail en astreinte |
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travail le week-end publics spécifiques particuliers secteurs d''activité • bâtiment |
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et travaux publics (btp) 4 / 4 -' |
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- 'des fibres synthétiques caractéristiques des fils caractéristiques des textiles |
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maille 3 / 4 - fiche emploi - juin 2024 techniques professionnellescadrage horizontal |
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(positionnement tissu, tension...) cadrage vertical (positionnement tissu, tension...) |
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maniement de crochet techniques de nouage techniques de remaillage techniques |
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de tricotage à maille cueillie techniques de tricotage à maille jetée contextes |
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de travail conditions de travail et risques professionnelsmanipulation de produits |
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à risques port d''équipement de protection individuel (epi) : gants, chaussures, |
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casque, protections audi' |
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- de gestion immobilière estimer la valeur d'un bien, d'un produit droit, contentieux |
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et négociationappliquer un cadre juridique ou réglementaire réaliser le suivi |
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des décisions prises en assemblées de copropriété traiter des dossiers de contentieux |
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réaliser la gestion administrative des contrats management animer, coordonner |
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une équipe gestion des ressources humaines gérer les ressources humaines conseil, |
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transmission assurer une médiation constructionétablir l'état d'avancement de |
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travaux piloter la préparation de travaux planifier des travaux de rénovation |
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définir les besoins en rénovation du patrimoine immobilier |
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model-index: |
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- name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 |
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results: |
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- task: |
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type: binary-classification |
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name: Binary Classification |
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dataset: |
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name: Unknown |
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type: unknown |
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metrics: |
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- type: cosine_accuracy |
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value: 0.9819417605117612 |
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name: Cosine Accuracy |
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- type: cosine_accuracy_threshold |
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value: 0.679751992225647 |
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name: Cosine Accuracy Threshold |
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- type: cosine_f1 |
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value: 0.973428767526228 |
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name: Cosine F1 |
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- type: cosine_f1_threshold |
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value: 0.6781774163246155 |
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name: Cosine F1 Threshold |
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- type: cosine_precision |
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value: 0.9712385051848953 |
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name: Cosine Precision |
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- type: cosine_recall |
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value: 0.97562893081761 |
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name: Cosine Recall |
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- type: cosine_ap |
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value: 0.9777667558930684 |
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name: Cosine Ap |
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- type: dot_accuracy |
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value: 0.9798760578396748 |
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name: Dot Accuracy |
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- type: dot_accuracy_threshold |
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value: 169.48760986328125 |
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name: Dot Accuracy Threshold |
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- type: dot_f1 |
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value: 0.9705653021442495 |
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|
name: Dot F1 |
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|
- type: dot_f1_threshold |
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value: 169.48760986328125 |
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name: Dot F1 Threshold |
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- type: dot_precision |
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value: 0.9626836813611755 |
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name: Dot Precision |
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- type: dot_recall |
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value: 0.9785770440251572 |
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name: Dot Recall |
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- type: dot_ap |
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value: 0.9774347462977395 |
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name: Dot Ap |
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- type: manhattan_accuracy |
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value: 0.9756113813553675 |
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name: Manhattan Accuracy |
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- type: manhattan_accuracy_threshold |
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value: 160.50274658203125 |
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name: Manhattan Accuracy Threshold |
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- type: manhattan_f1 |
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value: 0.9638031364039846 |
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|
name: Manhattan F1 |
|
|
- type: manhattan_f1_threshold |
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|
value: 165.2382354736328 |
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name: Manhattan F1 Threshold |
|
|
- type: manhattan_precision |
|
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value: 0.9673332013462681 |
|
|
name: Manhattan Precision |
|
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- type: manhattan_recall |
|
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value: 0.9602987421383647 |
|
|
name: Manhattan Recall |
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|
- type: manhattan_ap |
|
|
value: 0.9781788393145018 |
|
|
name: Manhattan Ap |
|
|
- type: euclidean_accuracy |
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|
value: 0.9826747517825015 |
|
|
name: Euclidean Accuracy |
|
|
- type: euclidean_accuracy_threshold |
|
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value: 12.798349380493164 |
|
|
name: Euclidean Accuracy Threshold |
|
|
- type: euclidean_f1 |
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value: 0.9744797801334903 |
|
|
name: Euclidean F1 |
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- type: euclidean_f1_threshold |
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value: 12.857450485229492 |
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|
name: Euclidean F1 Threshold |
|
|
- type: euclidean_precision |
|
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value: 0.9733333333333334 |
|
|
name: Euclidean Precision |
|
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- type: euclidean_recall |
|
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value: 0.97562893081761 |
|
|
name: Euclidean Recall |
|
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- type: euclidean_ap |
|
|
value: 0.9783322008283785 |
|
|
name: Euclidean Ap |
|
|
- type: max_accuracy |
|
|
value: 0.9826747517825015 |
|
|
name: Max Accuracy |
|
|
- type: max_accuracy_threshold |
|
|
value: 169.48760986328125 |
|
|
name: Max Accuracy Threshold |
|
|
- type: max_f1 |
|
|
value: 0.9744797801334903 |
|
|
name: Max F1 |
|
|
- type: max_f1_threshold |
|
|
value: 169.48760986328125 |
|
|
name: Max F1 Threshold |
|
|
- type: max_precision |
|
|
value: 0.9733333333333334 |
|
|
name: Max Precision |
|
|
- type: max_recall |
|
|
value: 0.9785770440251572 |
|
|
name: Max Recall |
|
|
- type: max_ap |
|
|
value: 0.9783322008283785 |
|
|
name: Max Ap |
|
|
- type: cosine_accuracy |
|
|
value: 0.834924965893588 |
|
|
name: Cosine Accuracy |
|
|
- type: cosine_accuracy_threshold |
|
|
value: 0.9927449226379395 |
|
|
name: Cosine Accuracy Threshold |
|
|
- type: cosine_f1 |
|
|
value: 0.5193370165745856 |
|
|
name: Cosine F1 |
|
|
- type: cosine_f1_threshold |
|
|
value: 0.7801194190979004 |
|
|
name: Cosine F1 Threshold |
|
|
- type: cosine_precision |
|
|
value: 0.4292237442922374 |
|
|
name: Cosine Precision |
|
|
- type: cosine_recall |
|
|
value: 0.6573426573426573 |
|
|
name: Cosine Recall |
|
|
- type: cosine_ap |
|
|
value: 0.543556418876974 |
|
|
name: Cosine Ap |
|
|
- type: dot_accuracy |
|
|
value: 0.8376534788540245 |
|
|
name: Dot Accuracy |
|
|
- type: dot_accuracy_threshold |
|
|
value: 247.44024658203125 |
|
|
name: Dot Accuracy Threshold |
|
|
- type: dot_f1 |
|
|
value: 0.5101010101010102 |
|
|
name: Dot F1 |
|
|
- type: dot_f1_threshold |
|
|
value: 180.7263641357422 |
|
|
name: Dot F1 Threshold |
|
|
- type: dot_precision |
|
|
value: 0.39920948616600793 |
|
|
name: Dot Precision |
|
|
- type: dot_recall |
|
|
value: 0.7062937062937062 |
|
|
name: Dot Recall |
|
|
- type: dot_ap |
|
|
value: 0.53020619109288 |
|
|
name: Dot Ap |
|
|
- type: manhattan_accuracy |
|
|
value: 0.8362892223738063 |
|
|
name: Manhattan Accuracy |
|
|
- type: manhattan_accuracy_threshold |
|
|
value: 24.471851348876953 |
|
|
name: Manhattan Accuracy Threshold |
|
|
- type: manhattan_f1 |
|
|
value: 0.5027027027027028 |
|
|
name: Manhattan F1 |
|
|
- type: manhattan_f1_threshold |
|
|
value: 122.65769958496094 |
|
|
name: Manhattan F1 Threshold |
|
|
- type: manhattan_precision |
|
|
value: 0.40969162995594716 |
|
|
name: Manhattan Precision |
|
|
- type: manhattan_recall |
|
|
value: 0.6503496503496503 |
|
|
name: Manhattan Recall |
|
|
- type: manhattan_ap |
|
|
value: 0.5317396236146636 |
|
|
name: Manhattan Ap |
|
|
- type: euclidean_accuracy |
|
|
value: 0.8362892223738063 |
|
|
name: Euclidean Accuracy |
|
|
- type: euclidean_accuracy_threshold |
|
|
value: 1.9895026683807373 |
|
|
name: Euclidean Accuracy Threshold |
|
|
- type: euclidean_f1 |
|
|
value: 0.5251396648044693 |
|
|
name: Euclidean F1 |
|
|
- type: euclidean_f1_threshold |
|
|
value: 10.453689575195312 |
|
|
name: Euclidean F1 Threshold |
|
|
- type: euclidean_precision |
|
|
value: 0.4372093023255814 |
|
|
name: Euclidean Precision |
|
|
- type: euclidean_recall |
|
|
value: 0.6573426573426573 |
|
|
name: Euclidean Recall |
|
|
- type: euclidean_ap |
|
|
value: 0.5439869162730425 |
|
|
name: Euclidean Ap |
|
|
- type: max_accuracy |
|
|
value: 0.8376534788540245 |
|
|
name: Max Accuracy |
|
|
- type: max_accuracy_threshold |
|
|
value: 247.44024658203125 |
|
|
name: Max Accuracy Threshold |
|
|
- type: max_f1 |
|
|
value: 0.5251396648044693 |
|
|
name: Max F1 |
|
|
- type: max_f1_threshold |
|
|
value: 180.7263641357422 |
|
|
name: Max F1 Threshold |
|
|
- type: max_precision |
|
|
value: 0.4372093023255814 |
|
|
name: Max Precision |
|
|
- type: max_recall |
|
|
value: 0.7062937062937062 |
|
|
name: Max Recall |
|
|
- type: max_ap |
|
|
value: 0.5439869162730425 |
|
|
name: Max Ap |
|
|
- type: cosine_accuracy |
|
|
value: 0.9099590723055935 |
|
|
name: Cosine Accuracy |
|
|
- type: cosine_accuracy_threshold |
|
|
value: 0.8935878872871399 |
|
|
name: Cosine Accuracy Threshold |
|
|
- type: cosine_f1 |
|
|
value: 0.7555555555555556 |
|
|
name: Cosine F1 |
|
|
- type: cosine_f1_threshold |
|
|
value: 0.7638504505157471 |
|
|
name: Cosine F1 Threshold |
|
|
- type: cosine_precision |
|
|
value: 0.8031496062992126 |
|
|
name: Cosine Precision |
|
|
- type: cosine_recall |
|
|
value: 0.7132867132867133 |
|
|
name: Cosine Recall |
|
|
- type: cosine_ap |
|
|
value: 0.7999057173309585 |
|
|
name: Cosine Ap |
|
|
- type: dot_accuracy |
|
|
value: 0.91268758526603 |
|
|
name: Dot Accuracy |
|
|
- type: dot_accuracy_threshold |
|
|
value: 227.50296020507812 |
|
|
name: Dot Accuracy Threshold |
|
|
- type: dot_f1 |
|
|
value: 0.7575757575757576 |
|
|
name: Dot F1 |
|
|
- type: dot_f1_threshold |
|
|
value: 227.50296020507812 |
|
|
name: Dot F1 Threshold |
|
|
- type: dot_precision |
|
|
value: 0.8264462809917356 |
|
|
name: Dot Precision |
|
|
- type: dot_recall |
|
|
value: 0.6993006993006993 |
|
|
name: Dot Recall |
|
|
- type: dot_ap |
|
|
value: 0.7880743512191776 |
|
|
name: Dot Ap |
|
|
- type: manhattan_accuracy |
|
|
value: 0.9113233287858117 |
|
|
name: Manhattan Accuracy |
|
|
- type: manhattan_accuracy_threshold |
|
|
value: 109.26994323730469 |
|
|
name: Manhattan Accuracy Threshold |
|
|
- type: manhattan_f1 |
|
|
value: 0.7555555555555556 |
|
|
name: Manhattan F1 |
|
|
- type: manhattan_f1_threshold |
|
|
value: 121.61300659179688 |
|
|
name: Manhattan F1 Threshold |
|
|
- type: manhattan_precision |
|
|
value: 0.8031496062992126 |
|
|
name: Manhattan Precision |
|
|
- type: manhattan_recall |
|
|
value: 0.7132867132867133 |
|
|
name: Manhattan Recall |
|
|
- type: manhattan_ap |
|
|
value: 0.7968619528822352 |
|
|
name: Manhattan Ap |
|
|
- type: euclidean_accuracy |
|
|
value: 0.9099590723055935 |
|
|
name: Euclidean Accuracy |
|
|
- type: euclidean_accuracy_threshold |
|
|
value: 7.680914402008057 |
|
|
name: Euclidean Accuracy Threshold |
|
|
- type: euclidean_f1 |
|
|
value: 0.7555555555555556 |
|
|
name: Euclidean F1 |
|
|
- type: euclidean_f1_threshold |
|
|
value: 11.580322265625 |
|
|
name: Euclidean F1 Threshold |
|
|
- type: euclidean_precision |
|
|
value: 0.8031496062992126 |
|
|
name: Euclidean Precision |
|
|
- type: euclidean_recall |
|
|
value: 0.7132867132867133 |
|
|
name: Euclidean Recall |
|
|
- type: euclidean_ap |
|
|
value: 0.8007213057530033 |
|
|
name: Euclidean Ap |
|
|
- type: max_accuracy |
|
|
value: 0.91268758526603 |
|
|
name: Max Accuracy |
|
|
- type: max_accuracy_threshold |
|
|
value: 227.50296020507812 |
|
|
name: Max Accuracy Threshold |
|
|
- type: max_f1 |
|
|
value: 0.7575757575757576 |
|
|
name: Max F1 |
|
|
- type: max_f1_threshold |
|
|
value: 227.50296020507812 |
|
|
name: Max F1 Threshold |
|
|
- type: max_precision |
|
|
value: 0.8264462809917356 |
|
|
name: Max Precision |
|
|
- type: max_recall |
|
|
value: 0.7132867132867133 |
|
|
name: Max Recall |
|
|
- type: max_ap |
|
|
value: 0.8007213057530033 |
|
|
name: Max Ap |
|
|
- type: cosine_accuracy |
|
|
value: 0.8808888888888889 |
|
|
name: Cosine Accuracy |
|
|
- type: cosine_accuracy_threshold |
|
|
value: 0.7635923624038696 |
|
|
name: Cosine Accuracy Threshold |
|
|
- type: cosine_f1 |
|
|
value: 0.7021494370522007 |
|
|
name: Cosine F1 |
|
|
- type: cosine_f1_threshold |
|
|
value: 0.5529835224151611 |
|
|
name: Cosine F1 Threshold |
|
|
- type: cosine_precision |
|
|
value: 0.6819085487077535 |
|
|
name: Cosine Precision |
|
|
- type: cosine_recall |
|
|
value: 0.7236286919831224 |
|
|
name: Cosine Recall |
|
|
- type: cosine_ap |
|
|
value: 0.7361317958466017 |
|
|
name: Cosine Ap |
|
|
- type: dot_accuracy |
|
|
value: 0.8786666666666667 |
|
|
name: Dot Accuracy |
|
|
- type: dot_accuracy_threshold |
|
|
value: 217.53868103027344 |
|
|
name: Dot Accuracy Threshold |
|
|
- type: dot_f1 |
|
|
value: 0.70042194092827 |
|
|
name: Dot F1 |
|
|
- type: dot_f1_threshold |
|
|
value: 164.10406494140625 |
|
|
name: Dot F1 Threshold |
|
|
- type: dot_precision |
|
|
value: 0.70042194092827 |
|
|
name: Dot Precision |
|
|
- type: dot_recall |
|
|
value: 0.70042194092827 |
|
|
name: Dot Recall |
|
|
- type: dot_ap |
|
|
value: 0.7299140356412122 |
|
|
name: Dot Ap |
|
|
- type: manhattan_accuracy |
|
|
value: 0.8782222222222222 |
|
|
name: Manhattan Accuracy |
|
|
- type: manhattan_accuracy_threshold |
|
|
value: 146.0133056640625 |
|
|
name: Manhattan Accuracy Threshold |
|
|
- type: manhattan_f1 |
|
|
value: 0.7016460905349794 |
|
|
name: Manhattan F1 |
|
|
- type: manhattan_f1_threshold |
|
|
value: 180.20339965820312 |
|
|
name: Manhattan F1 Threshold |
|
|
- type: manhattan_precision |
|
|
value: 0.6847389558232931 |
|
|
name: Manhattan Precision |
|
|
- type: manhattan_recall |
|
|
value: 0.7194092827004219 |
|
|
name: Manhattan Recall |
|
|
- type: manhattan_ap |
|
|
value: 0.7262306820321688 |
|
|
name: Manhattan Ap |
|
|
- type: euclidean_accuracy |
|
|
value: 0.8804444444444445 |
|
|
name: Euclidean Accuracy |
|
|
- type: euclidean_accuracy_threshold |
|
|
value: 13.764719009399414 |
|
|
name: Euclidean Accuracy Threshold |
|
|
- type: euclidean_f1 |
|
|
value: 0.7046413502109705 |
|
|
name: Euclidean F1 |
|
|
- type: euclidean_f1_threshold |
|
|
value: 15.242857933044434 |
|
|
name: Euclidean F1 Threshold |
|
|
- type: euclidean_precision |
|
|
value: 0.7046413502109705 |
|
|
name: Euclidean Precision |
|
|
- type: euclidean_recall |
|
|
value: 0.7046413502109705 |
|
|
name: Euclidean Recall |
|
|
- type: euclidean_ap |
|
|
value: 0.7390591372297478 |
|
|
name: Euclidean Ap |
|
|
- type: max_accuracy |
|
|
value: 0.8808888888888889 |
|
|
name: Max Accuracy |
|
|
- type: max_accuracy_threshold |
|
|
value: 217.53868103027344 |
|
|
name: Max Accuracy Threshold |
|
|
- type: max_f1 |
|
|
value: 0.7046413502109705 |
|
|
name: Max F1 |
|
|
- type: max_f1_threshold |
|
|
value: 180.20339965820312 |
|
|
name: Max F1 Threshold |
|
|
- type: max_precision |
|
|
value: 0.7046413502109705 |
|
|
name: Max Precision |
|
|
- type: max_recall |
|
|
value: 0.7236286919831224 |
|
|
name: Max Recall |
|
|
- type: max_ap |
|
|
value: 0.7390591372297478 |
|
|
name: Max Ap |
|
|
- type: cosine_accuracy |
|
|
value: 0.9315555555555556 |
|
|
name: Cosine Accuracy |
|
|
- type: cosine_accuracy_threshold |
|
|
value: 0.6300285458564758 |
|
|
name: Cosine Accuracy Threshold |
|
|
- type: cosine_f1 |
|
|
value: 0.8316008316008316 |
|
|
name: Cosine F1 |
|
|
- type: cosine_f1_threshold |
|
|
value: 0.5284727811813354 |
|
|
name: Cosine F1 Threshold |
|
|
- type: cosine_precision |
|
|
value: 0.7843137254901961 |
|
|
name: Cosine Precision |
|
|
- type: cosine_recall |
|
|
value: 0.8849557522123894 |
|
|
name: Cosine Recall |
|
|
- type: cosine_ap |
|
|
value: 0.8866647506496831 |
|
|
name: Cosine Ap |
|
|
- type: dot_accuracy |
|
|
value: 0.9293333333333333 |
|
|
name: Dot Accuracy |
|
|
- type: dot_accuracy_threshold |
|
|
value: 199.23004150390625 |
|
|
name: Dot Accuracy Threshold |
|
|
- type: dot_f1 |
|
|
value: 0.8273684210526315 |
|
|
name: Dot F1 |
|
|
- type: dot_f1_threshold |
|
|
value: 165.896240234375 |
|
|
name: Dot F1 Threshold |
|
|
- type: dot_precision |
|
|
value: 0.7891566265060241 |
|
|
name: Dot Precision |
|
|
- type: dot_recall |
|
|
value: 0.8694690265486725 |
|
|
name: Dot Recall |
|
|
- type: dot_ap |
|
|
value: 0.8866771855777393 |
|
|
name: Dot Ap |
|
|
- type: manhattan_accuracy |
|
|
value: 0.9288888888888889 |
|
|
name: Manhattan Accuracy |
|
|
- type: manhattan_accuracy_threshold |
|
|
value: 176.44253540039062 |
|
|
name: Manhattan Accuracy Threshold |
|
|
- type: manhattan_f1 |
|
|
value: 0.8210290827740493 |
|
|
name: Manhattan F1 |
|
|
- type: manhattan_f1_threshold |
|
|
value: 176.44253540039062 |
|
|
name: Manhattan F1 Threshold |
|
|
- type: manhattan_precision |
|
|
value: 0.830316742081448 |
|
|
name: Manhattan Precision |
|
|
- type: manhattan_recall |
|
|
value: 0.8119469026548672 |
|
|
name: Manhattan Recall |
|
|
- type: manhattan_ap |
|
|
value: 0.8726245245116421 |
|
|
name: Manhattan Ap |
|
|
- type: euclidean_accuracy |
|
|
value: 0.932 |
|
|
name: Euclidean Accuracy |
|
|
- type: euclidean_accuracy_threshold |
|
|
value: 14.744226455688477 |
|
|
name: Euclidean Accuracy Threshold |
|
|
- type: euclidean_f1 |
|
|
value: 0.8336842105263158 |
|
|
name: Euclidean F1 |
|
|
- type: euclidean_f1_threshold |
|
|
value: 16.632638931274414 |
|
|
name: Euclidean F1 Threshold |
|
|
- type: euclidean_precision |
|
|
value: 0.7951807228915663 |
|
|
name: Euclidean Precision |
|
|
- type: euclidean_recall |
|
|
value: 0.8761061946902655 |
|
|
name: Euclidean Recall |
|
|
- type: euclidean_ap |
|
|
value: 0.8886002371862958 |
|
|
name: Euclidean Ap |
|
|
- type: max_accuracy |
|
|
value: 0.932 |
|
|
name: Max Accuracy |
|
|
- type: max_accuracy_threshold |
|
|
value: 199.23004150390625 |
|
|
name: Max Accuracy Threshold |
|
|
- type: max_f1 |
|
|
value: 0.8336842105263158 |
|
|
name: Max F1 |
|
|
- type: max_f1_threshold |
|
|
value: 176.44253540039062 |
|
|
name: Max F1 Threshold |
|
|
- type: max_precision |
|
|
value: 0.830316742081448 |
|
|
name: Max Precision |
|
|
- type: max_recall |
|
|
value: 0.8849557522123894 |
|
|
name: Max Recall |
|
|
- type: max_ap |
|
|
value: 0.8886002371862958 |
|
|
name: Max Ap |
|
|
--- |
|
|
|
|
|
# SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 |
|
|
|
|
|
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2). 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:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) <!-- at revision bf3bf13ab40c3157080a7ab344c831b9ad18b5eb --> |
|
|
- **Maximum Sequence Length:** 128 tokens |
|
|
- **Output Dimensionality:** 384 tokens |
|
|
- **Similarity Function:** Cosine Similarity |
|
|
<!-- - **Training Dataset:** Unknown --> |
|
|
<!-- - **Language:** Unknown --> |
|
|
<!-- - **License:** Unknown --> |
|
|
|
|
|
### 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': 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}) |
|
|
) |
|
|
``` |
|
|
|
|
|
## 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("training_job_matching_sentence-transformers-paraphrase-multilingual-MiniLM-L12-v2-2024-09-03_13-14-25") |
|
|
# Run inference |
|
|
sentences = [ |
|
|
"Responsable d'élevage en production ovine", |
|
|
"de gestion immobilière estimer la valeur d'un bien, d'un produit droit, contentieux et négociationappliquer un cadre juridique ou réglementaire réaliser le suivi des décisions prises en assemblées de copropriété traiter des dossiers de contentieux réaliser la gestion administrative des contrats management animer, coordonner une équipe gestion des ressources humaines gérer les ressources humaines conseil, transmission assurer une médiation constructionétablir l'état d'avancement de travaux piloter la préparation de travaux planifier des travaux de rénovation définir les besoins en rénovation du patrimoine immobilier", |
|
|
"de travail et risques professionnelsau domicile d'un particulier déplacements professionnels port d'équipement de protection individuel (epi) : gants, chaussures, casque, protections auditives horaires et durée du travailtravail en astreinte travail le week-end publics spécifiques particuliers secteurs d'activité • bâtiment et travaux publics (btp) 4 / 4 -", |
|
|
] |
|
|
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] |
|
|
``` |
|
|
|
|
|
<!-- |
|
|
### Direct Usage (Transformers) |
|
|
|
|
|
<details><summary>Click to see the direct usage in Transformers</summary> |
|
|
|
|
|
</details> |
|
|
--> |
|
|
|
|
|
<!-- |
|
|
### Downstream Usage (Sentence Transformers) |
|
|
|
|
|
You can finetune this model on your own dataset. |
|
|
|
|
|
<details><summary>Click to expand</summary> |
|
|
|
|
|
</details> |
|
|
--> |
|
|
|
|
|
<!-- |
|
|
### Out-of-Scope Use |
|
|
|
|
|
*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
|
|
--> |
|
|
|
|
|
## Evaluation |
|
|
|
|
|
### Metrics |
|
|
|
|
|
#### Binary Classification |
|
|
|
|
|
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) |
|
|
|
|
|
| Metric | Value | |
|
|
|:-----------------------------|:-----------| |
|
|
| cosine_accuracy | 0.9819 | |
|
|
| cosine_accuracy_threshold | 0.6798 | |
|
|
| cosine_f1 | 0.9734 | |
|
|
| cosine_f1_threshold | 0.6782 | |
|
|
| cosine_precision | 0.9712 | |
|
|
| cosine_recall | 0.9756 | |
|
|
| cosine_ap | 0.9778 | |
|
|
| dot_accuracy | 0.9799 | |
|
|
| dot_accuracy_threshold | 169.4876 | |
|
|
| dot_f1 | 0.9706 | |
|
|
| dot_f1_threshold | 169.4876 | |
|
|
| dot_precision | 0.9627 | |
|
|
| dot_recall | 0.9786 | |
|
|
| dot_ap | 0.9774 | |
|
|
| manhattan_accuracy | 0.9756 | |
|
|
| manhattan_accuracy_threshold | 160.5027 | |
|
|
| manhattan_f1 | 0.9638 | |
|
|
| manhattan_f1_threshold | 165.2382 | |
|
|
| manhattan_precision | 0.9673 | |
|
|
| manhattan_recall | 0.9603 | |
|
|
| manhattan_ap | 0.9782 | |
|
|
| euclidean_accuracy | 0.9827 | |
|
|
| euclidean_accuracy_threshold | 12.7983 | |
|
|
| euclidean_f1 | 0.9745 | |
|
|
| euclidean_f1_threshold | 12.8575 | |
|
|
| euclidean_precision | 0.9733 | |
|
|
| euclidean_recall | 0.9756 | |
|
|
| euclidean_ap | 0.9783 | |
|
|
| max_accuracy | 0.9827 | |
|
|
| max_accuracy_threshold | 169.4876 | |
|
|
| max_f1 | 0.9745 | |
|
|
| max_f1_threshold | 169.4876 | |
|
|
| max_precision | 0.9733 | |
|
|
| max_recall | 0.9786 | |
|
|
| **max_ap** | **0.9783** | |
|
|
|
|
|
#### Binary Classification |
|
|
|
|
|
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) |
|
|
|
|
|
| Metric | Value | |
|
|
|:-----------------------------|:----------| |
|
|
| cosine_accuracy | 0.8349 | |
|
|
| cosine_accuracy_threshold | 0.9927 | |
|
|
| cosine_f1 | 0.5193 | |
|
|
| cosine_f1_threshold | 0.7801 | |
|
|
| cosine_precision | 0.4292 | |
|
|
| cosine_recall | 0.6573 | |
|
|
| cosine_ap | 0.5436 | |
|
|
| dot_accuracy | 0.8377 | |
|
|
| dot_accuracy_threshold | 247.4402 | |
|
|
| dot_f1 | 0.5101 | |
|
|
| dot_f1_threshold | 180.7264 | |
|
|
| dot_precision | 0.3992 | |
|
|
| dot_recall | 0.7063 | |
|
|
| dot_ap | 0.5302 | |
|
|
| manhattan_accuracy | 0.8363 | |
|
|
| manhattan_accuracy_threshold | 24.4719 | |
|
|
| manhattan_f1 | 0.5027 | |
|
|
| manhattan_f1_threshold | 122.6577 | |
|
|
| manhattan_precision | 0.4097 | |
|
|
| manhattan_recall | 0.6503 | |
|
|
| manhattan_ap | 0.5317 | |
|
|
| euclidean_accuracy | 0.8363 | |
|
|
| euclidean_accuracy_threshold | 1.9895 | |
|
|
| euclidean_f1 | 0.5251 | |
|
|
| euclidean_f1_threshold | 10.4537 | |
|
|
| euclidean_precision | 0.4372 | |
|
|
| euclidean_recall | 0.6573 | |
|
|
| euclidean_ap | 0.544 | |
|
|
| max_accuracy | 0.8377 | |
|
|
| max_accuracy_threshold | 247.4402 | |
|
|
| max_f1 | 0.5251 | |
|
|
| max_f1_threshold | 180.7264 | |
|
|
| max_precision | 0.4372 | |
|
|
| max_recall | 0.7063 | |
|
|
| **max_ap** | **0.544** | |
|
|
|
|
|
#### Binary Classification |
|
|
|
|
|
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) |
|
|
|
|
|
| Metric | Value | |
|
|
|:-----------------------------|:-----------| |
|
|
| cosine_accuracy | 0.91 | |
|
|
| cosine_accuracy_threshold | 0.8936 | |
|
|
| cosine_f1 | 0.7556 | |
|
|
| cosine_f1_threshold | 0.7639 | |
|
|
| cosine_precision | 0.8031 | |
|
|
| cosine_recall | 0.7133 | |
|
|
| cosine_ap | 0.7999 | |
|
|
| dot_accuracy | 0.9127 | |
|
|
| dot_accuracy_threshold | 227.503 | |
|
|
| dot_f1 | 0.7576 | |
|
|
| dot_f1_threshold | 227.503 | |
|
|
| dot_precision | 0.8264 | |
|
|
| dot_recall | 0.6993 | |
|
|
| dot_ap | 0.7881 | |
|
|
| manhattan_accuracy | 0.9113 | |
|
|
| manhattan_accuracy_threshold | 109.2699 | |
|
|
| manhattan_f1 | 0.7556 | |
|
|
| manhattan_f1_threshold | 121.613 | |
|
|
| manhattan_precision | 0.8031 | |
|
|
| manhattan_recall | 0.7133 | |
|
|
| manhattan_ap | 0.7969 | |
|
|
| euclidean_accuracy | 0.91 | |
|
|
| euclidean_accuracy_threshold | 7.6809 | |
|
|
| euclidean_f1 | 0.7556 | |
|
|
| euclidean_f1_threshold | 11.5803 | |
|
|
| euclidean_precision | 0.8031 | |
|
|
| euclidean_recall | 0.7133 | |
|
|
| euclidean_ap | 0.8007 | |
|
|
| max_accuracy | 0.9127 | |
|
|
| max_accuracy_threshold | 227.503 | |
|
|
| max_f1 | 0.7576 | |
|
|
| max_f1_threshold | 227.503 | |
|
|
| max_precision | 0.8264 | |
|
|
| max_recall | 0.7133 | |
|
|
| **max_ap** | **0.8007** | |
|
|
|
|
|
#### Binary Classification |
|
|
|
|
|
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) |
|
|
|
|
|
| Metric | Value | |
|
|
|:-----------------------------|:-----------| |
|
|
| cosine_accuracy | 0.8809 | |
|
|
| cosine_accuracy_threshold | 0.7636 | |
|
|
| cosine_f1 | 0.7021 | |
|
|
| cosine_f1_threshold | 0.553 | |
|
|
| cosine_precision | 0.6819 | |
|
|
| cosine_recall | 0.7236 | |
|
|
| cosine_ap | 0.7361 | |
|
|
| dot_accuracy | 0.8787 | |
|
|
| dot_accuracy_threshold | 217.5387 | |
|
|
| dot_f1 | 0.7004 | |
|
|
| dot_f1_threshold | 164.1041 | |
|
|
| dot_precision | 0.7004 | |
|
|
| dot_recall | 0.7004 | |
|
|
| dot_ap | 0.7299 | |
|
|
| manhattan_accuracy | 0.8782 | |
|
|
| manhattan_accuracy_threshold | 146.0133 | |
|
|
| manhattan_f1 | 0.7016 | |
|
|
| manhattan_f1_threshold | 180.2034 | |
|
|
| manhattan_precision | 0.6847 | |
|
|
| manhattan_recall | 0.7194 | |
|
|
| manhattan_ap | 0.7262 | |
|
|
| euclidean_accuracy | 0.8804 | |
|
|
| euclidean_accuracy_threshold | 13.7647 | |
|
|
| euclidean_f1 | 0.7046 | |
|
|
| euclidean_f1_threshold | 15.2429 | |
|
|
| euclidean_precision | 0.7046 | |
|
|
| euclidean_recall | 0.7046 | |
|
|
| euclidean_ap | 0.7391 | |
|
|
| max_accuracy | 0.8809 | |
|
|
| max_accuracy_threshold | 217.5387 | |
|
|
| max_f1 | 0.7046 | |
|
|
| max_f1_threshold | 180.2034 | |
|
|
| max_precision | 0.7046 | |
|
|
| max_recall | 0.7236 | |
|
|
| **max_ap** | **0.7391** | |
|
|
|
|
|
#### Binary Classification |
|
|
|
|
|
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) |
|
|
|
|
|
| Metric | Value | |
|
|
|:-----------------------------|:-----------| |
|
|
| cosine_accuracy | 0.9316 | |
|
|
| cosine_accuracy_threshold | 0.63 | |
|
|
| cosine_f1 | 0.8316 | |
|
|
| cosine_f1_threshold | 0.5285 | |
|
|
| cosine_precision | 0.7843 | |
|
|
| cosine_recall | 0.885 | |
|
|
| cosine_ap | 0.8867 | |
|
|
| dot_accuracy | 0.9293 | |
|
|
| dot_accuracy_threshold | 199.23 | |
|
|
| dot_f1 | 0.8274 | |
|
|
| dot_f1_threshold | 165.8962 | |
|
|
| dot_precision | 0.7892 | |
|
|
| dot_recall | 0.8695 | |
|
|
| dot_ap | 0.8867 | |
|
|
| manhattan_accuracy | 0.9289 | |
|
|
| manhattan_accuracy_threshold | 176.4425 | |
|
|
| manhattan_f1 | 0.821 | |
|
|
| manhattan_f1_threshold | 176.4425 | |
|
|
| manhattan_precision | 0.8303 | |
|
|
| manhattan_recall | 0.8119 | |
|
|
| manhattan_ap | 0.8726 | |
|
|
| euclidean_accuracy | 0.932 | |
|
|
| euclidean_accuracy_threshold | 14.7442 | |
|
|
| euclidean_f1 | 0.8337 | |
|
|
| euclidean_f1_threshold | 16.6326 | |
|
|
| euclidean_precision | 0.7952 | |
|
|
| euclidean_recall | 0.8761 | |
|
|
| euclidean_ap | 0.8886 | |
|
|
| max_accuracy | 0.932 | |
|
|
| max_accuracy_threshold | 199.23 | |
|
|
| max_f1 | 0.8337 | |
|
|
| max_f1_threshold | 176.4425 | |
|
|
| max_precision | 0.8303 | |
|
|
| max_recall | 0.885 | |
|
|
| **max_ap** | **0.8886** | |
|
|
|
|
|
<!-- |
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|
## Bias, Risks and Limitations |
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|
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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<!-- |
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|
### Recommendations |
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|
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|
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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|
--> |
|
|
|
|
|
## Training Details |
|
|
|
|
|
### Training Dataset |
|
|
|
|
|
#### Unnamed Dataset |
|
|
|
|
|
|
|
|
* Size: 42,735 training samples |
|
|
* Columns: <code>name</code>, <code>fiche</code>, and <code>label</code> |
|
|
* Approximate statistics based on the first 1000 samples: |
|
|
| | name | fiche | label | |
|
|
|:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------| |
|
|
| type | string | string | int | |
|
|
| details | <ul><li>min: 3 tokens</li><li>mean: 9.44 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 107.63 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>0: ~78.20%</li><li>1: ~21.80%</li></ul> | |
|
|
* Samples: |
|
|
| name | fiche | label | |
|
|
|:---------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| |
|
|
| <code>Front End Angular Developer</code> | <code>communication WCF is used. The layer concept enables the reduction of dependencies (dependency injection) of the different tasks (separation of concerns). The entities are exchanged with the database via object-relational mapping (ORM) and processed using the CRUD methods. Through the consistent use of the MVVM pattern, we avoid code-behind. The user interface of the application is realized using the PRISM framework as a "Composite Application UI".Main tasks Software developerIn cooperation with a team located in Germany and respecting the software development guidelines and customers</code> | <code>0</code> | |
|
|
| <code>SCM : Administrateur des ventes</code> | <code>CHEF DE PROJET CONFIRME MAÎTRISANT ANGULAR 4.SON RÔLE SERA L'ENCADREMENT D'UNE EQUIPE ET LA GESTION TOTALE DU DÉVELOPPEMENT D'UNE APPLICATION MOBILE ANDROID.ESPRIT D'ÉQUIPE OBLIGATOIRE.</code> | <code>0</code> | |
|
|
| <code>Talent Acquisition Junior</code> | <code>Pilotage et suivi de toutes les activités du call center (commandes clients et interactions bénéficiaires de la carte).Assurer le calcul et le suivi des Kpi’s du call center.Veiller à la conformité des process et des procédures pour le call center.Contrôle de la prise en charge et la saisie des demandes et réclamations.Pilotage et suivi des projets de la direction clientèle.Assurer toutes demandes ou actions émanant de la Direction Clientèle.Assurer le maintien d’une bonne qualité de service.Augmenter la satisfaction</code> | <code>0</code> | |
|
|
* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss) |
|
|
|
|
|
### Evaluation Dataset |
|
|
|
|
|
#### Unnamed Dataset |
|
|
|
|
|
|
|
|
* Size: 2,250 evaluation samples |
|
|
* Columns: <code>name</code>, <code>fiche</code>, and <code>label</code> |
|
|
* Approximate statistics based on the first 1000 samples: |
|
|
| | name | fiche | label | |
|
|
|:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------| |
|
|
| type | string | string | int | |
|
|
| details | <ul><li>min: 3 tokens</li><li>mean: 9.31 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 109.04 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>0: ~80.30%</li><li>1: ~19.70%</li></ul> | |
|
|
* Samples: |
|
|
| name | fiche | label | |
|
|
|:---------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| |
|
|
| <code>1way com</code> | <code>Nous somme a la recherche de Profils en Telco avec connaissance en Produit! 😃Vous avez une connaissances dans la télécommunication? Emission ou réception (orange, sfr, boygues, free..)Vous voulez travailler dans un environnement stable, accueillant et sans pression?Vous êtes passionnés? Postulez maintenant et profitez d'un salaire motivant et pleins d'avantages:- Salaire 1100 a 1300 (selon le profil)- Primes et challenges- Tickets repas- Transport assuré- Samedi dimanche off- Titularisation- Convention</code> | <code>1</code> | |
|
|
| <code>Senior Front end Web Developer</code> | <code>As part of our growth in Tunis, we are looking to hire a Sénior Front-End Web Developer, who is passionate by Web Development and would like to have a career in an international company, in the Private Banking sector, within an exciting work environment.You will take part, throughout the software development life cycle (SDLC), to the requirement analysis, development and the support of different applications for private banks.You will perform AngularJS frontend development.You will integrate a highly motivated development team working on providing solutions for the private banking sector in which you will integrate the existing global</code> | <code>1</code> | |
|
|
| <code>DÉVELOPPEUR FULLSTACK RUBY ET ANGULAR</code> | <code>professionnel et d'évolution de carrière.- Projets stimulants et variés.- Esprit d'équipe et culture d'entreprise positive.- Salaire compétitif et avantages sociaux attractifs.Rejoignez MCOM et contribuez à révolutionner le commerce mobile avec nous!</code> | <code>0</code> | |
|
|
* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss) |
|
|
|
|
|
### Training Hyperparameters |
|
|
#### Non-Default Hyperparameters |
|
|
|
|
|
- `eval_strategy`: steps |
|
|
- `per_device_train_batch_size`: 64 |
|
|
- `per_device_eval_batch_size`: 32 |
|
|
- `num_train_epochs`: 5 |
|
|
- `warmup_ratio`: 0.1 |
|
|
- `bf16`: True |
|
|
|
|
|
#### All Hyperparameters |
|
|
<details><summary>Click to expand</summary> |
|
|
|
|
|
- `overwrite_output_dir`: False |
|
|
- `do_predict`: False |
|
|
- `eval_strategy`: steps |
|
|
- `prediction_loss_only`: True |
|
|
- `per_device_train_batch_size`: 64 |
|
|
- `per_device_eval_batch_size`: 32 |
|
|
- `per_gpu_train_batch_size`: None |
|
|
- `per_gpu_eval_batch_size`: None |
|
|
- `gradient_accumulation_steps`: 1 |
|
|
- `eval_accumulation_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`: 0.1 |
|
|
- `warmup_steps`: 0 |
|
|
- `log_level`: passive |
|
|
- `log_level_replica`: warning |
|
|
- `log_on_each_node`: True |
|
|
- `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`: True |
|
|
- `fp16`: False |
|
|
- `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`: False |
|
|
- `dataloader_num_workers`: 0 |
|
|
- `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} |
|
|
- `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`: None |
|
|
- `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`: False |
|
|
- `hub_always_push`: False |
|
|
- `gradient_checkpointing`: False |
|
|
- `gradient_checkpointing_kwargs`: None |
|
|
- `include_inputs_for_metrics`: False |
|
|
- `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 |
|
|
- `dispatch_batches`: None |
|
|
- `split_batches`: 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 |
|
|
- `batch_sampler`: batch_sampler |
|
|
- `multi_dataset_batch_sampler`: proportional |
|
|
|
|
|
</details> |
|
|
|
|
|
### Training Logs |
|
|
<details><summary>Click to expand</summary> |
|
|
|
|
|
| Epoch | Step | Training Loss | loss | max_ap | |
|
|
|:------:|:-----:|:-------------:|:------:|:------:| |
|
|
| 0 | 0 | - | - | 0.6610 | |
|
|
| 0.0673 | 300 | 0.638 | - | - | |
|
|
| 0.1346 | 600 | 0.5642 | - | - | |
|
|
| 0.2020 | 900 | 0.4942 | - | - | |
|
|
| 0.2244 | 1000 | - | 0.4283 | 0.7756 | |
|
|
| 0.2693 | 1200 | 0.4323 | - | - | |
|
|
| 0.3366 | 1500 | 0.3986 | - | - | |
|
|
| 0.4039 | 1800 | 0.3798 | - | - | |
|
|
| 0.4488 | 2000 | - | 0.3481 | 0.8517 | |
|
|
| 0.4713 | 2100 | 0.3532 | - | - | |
|
|
| 0.5386 | 2400 | 0.3407 | - | - | |
|
|
| 0.6059 | 2700 | 0.323 | - | - | |
|
|
| 0.6732 | 3000 | 0.3022 | 0.2953 | 0.8899 | |
|
|
| 0.7406 | 3300 | 0.2945 | - | - | |
|
|
| 0.8079 | 3600 | 0.2864 | - | - | |
|
|
| 0.8752 | 3900 | 0.2656 | - | - | |
|
|
| 0.8977 | 4000 | - | 0.2434 | 0.9199 | |
|
|
| 0.9425 | 4200 | 0.2581 | - | - | |
|
|
| 1.0099 | 4500 | 0.2486 | - | - | |
|
|
| 1.0772 | 4800 | 0.2282 | - | - | |
|
|
| 1.1221 | 5000 | - | 0.2160 | 0.9248 | |
|
|
| 1.1445 | 5100 | 0.2191 | - | - | |
|
|
| 1.2118 | 5400 | 0.2113 | - | - | |
|
|
| 1.2792 | 5700 | 0.2111 | - | - | |
|
|
| 1.3465 | 6000 | 0.2011 | 0.1882 | 0.9339 | |
|
|
| 1.4138 | 6300 | 0.1894 | - | - | |
|
|
| 1.4811 | 6600 | 0.1814 | - | - | |
|
|
| 1.5485 | 6900 | 0.1772 | - | - | |
|
|
| 1.5709 | 7000 | - | 0.1697 | 0.9409 | |
|
|
| 1.6158 | 7200 | 0.1731 | - | - | |
|
|
| 1.6831 | 7500 | 0.1707 | - | - | |
|
|
| 1.7504 | 7800 | 0.163 | - | - | |
|
|
| 1.7953 | 8000 | - | 0.1497 | 0.9411 | |
|
|
| 1.8178 | 8100 | 0.1576 | - | - | |
|
|
| 1.8851 | 8400 | 0.1518 | - | - | |
|
|
| 1.9524 | 8700 | 0.1447 | - | - | |
|
|
| 2.0197 | 9000 | 0.142 | 0.1355 | 0.9483 | |
|
|
| 2.0871 | 9300 | 0.1277 | - | - | |
|
|
| 2.1544 | 9600 | 0.1278 | - | - | |
|
|
| 2.2217 | 9900 | 0.1243 | - | - | |
|
|
| 2.2442 | 10000 | - | 0.1225 | 0.9526 | |
|
|
| 2.2890 | 10200 | 0.1228 | - | - | |
|
|
| 2.3564 | 10500 | 0.1214 | - | - | |
|
|
| 2.4237 | 10800 | 0.1173 | - | - | |
|
|
| 2.4686 | 11000 | - | 0.1082 | 0.9606 | |
|
|
| 2.4910 | 11100 | 0.1154 | - | - | |
|
|
| 2.5583 | 11400 | 0.1098 | - | - | |
|
|
| 2.6257 | 11700 | 0.1074 | - | - | |
|
|
| 2.6930 | 12000 | 0.105 | 0.1005 | 0.9656 | |
|
|
| 2.7603 | 12300 | 0.1042 | - | - | |
|
|
| 2.8276 | 12600 | 0.0998 | - | - | |
|
|
| 2.8950 | 12900 | 0.0967 | - | - | |
|
|
| 2.9174 | 13000 | - | 0.0911 | 0.9645 | |
|
|
| 2.9623 | 13200 | 0.0977 | - | - | |
|
|
| 3.0296 | 13500 | 0.0896 | - | - | |
|
|
| 3.0969 | 13800 | 0.0854 | - | - | |
|
|
| 3.1418 | 14000 | - | 0.0843 | 0.9686 | |
|
|
| 3.1643 | 14100 | 0.0848 | - | - | |
|
|
| 3.2316 | 14400 | 0.0841 | - | - | |
|
|
| 3.2989 | 14700 | 0.082 | - | - | |
|
|
| 3.3662 | 15000 | 0.0815 | 0.0790 | 0.9711 | |
|
|
| 3.4336 | 15300 | 0.0812 | - | - | |
|
|
| 3.5009 | 15600 | 0.0799 | - | - | |
|
|
| 3.5682 | 15900 | 0.0753 | - | - | |
|
|
| 3.5907 | 16000 | - | 0.0751 | 0.9725 | |
|
|
| 3.6355 | 16200 | 0.0756 | - | - | |
|
|
| 3.7029 | 16500 | 0.0737 | - | - | |
|
|
| 3.7702 | 16800 | 0.0742 | - | - | |
|
|
| 3.8151 | 17000 | - | 0.0713 | 0.9750 | |
|
|
| 3.8375 | 17100 | 0.0725 | - | - | |
|
|
| 3.9048 | 17400 | 0.0721 | - | - | |
|
|
| 3.9722 | 17700 | 0.0696 | - | - | |
|
|
| 4.0395 | 18000 | 0.0665 | 0.0664 | 0.9746 | |
|
|
| 4.1068 | 18300 | 0.0648 | - | - | |
|
|
| 4.1741 | 18600 | 0.0636 | - | - | |
|
|
| 4.2415 | 18900 | 0.0617 | - | - | |
|
|
| 4.2639 | 19000 | - | 0.0637 | 0.9757 | |
|
|
| 4.3088 | 19200 | 0.0624 | - | - | |
|
|
| 4.3761 | 19500 | 0.062 | - | - | |
|
|
| 4.4434 | 19800 | 0.0609 | - | - | |
|
|
| 4.4883 | 20000 | - | 0.0608 | 0.9774 | |
|
|
| 4.5108 | 20100 | 0.0607 | - | - | |
|
|
| 4.5781 | 20400 | 0.061 | - | - | |
|
|
| 4.6454 | 20700 | 0.0612 | - | - | |
|
|
| 4.7127 | 21000 | 0.0598 | 0.0591 | 0.9777 | |
|
|
| 4.7801 | 21300 | 0.0613 | - | - | |
|
|
| 4.8474 | 21600 | 0.0599 | - | - | |
|
|
| 4.9147 | 21900 | 0.0575 | - | - | |
|
|
| 4.9372 | 22000 | - | 0.0582 | 0.9783 | |
|
|
| 4.9820 | 22200 | 0.0593 | - | - | |
|
|
| 5.0 | 22280 | - | - | 0.5440 | |
|
|
| 0.8303 | 181 | - | - | 0.7148 | |
|
|
| 0.4587 | 100 | - | 0.2849 | 0.7360 | |
|
|
| 0.9174 | 200 | - | 0.3019 | 0.7230 | |
|
|
| 1.3761 | 300 | 0.2712 | 0.2813 | 0.7697 | |
|
|
| 1.8349 | 400 | - | 0.2667 | 0.8033 | |
|
|
| 2.2936 | 500 | - | 0.2673 | 0.7936 | |
|
|
| 2.7523 | 600 | 0.2268 | 0.2518 | 0.8078 | |
|
|
| 3.2110 | 700 | - | 0.2539 | 0.8103 | |
|
|
| 3.6697 | 800 | - | 0.2662 | 0.8118 | |
|
|
| 4.1284 | 900 | 0.1845 | 0.2688 | 0.8003 | |
|
|
| 4.5872 | 1000 | - | 0.2632 | 0.8081 | |
|
|
| 0.4587 | 100 | - | 0.2642 | 0.8101 | |
|
|
| 0.9174 | 200 | - | 0.2741 | 0.7995 | |
|
|
| 1.3761 | 300 | 0.1742 | 0.2818 | 0.7861 | |
|
|
| 1.8349 | 400 | - | 0.2595 | 0.8146 | |
|
|
| 2.2936 | 500 | - | 0.2716 | 0.8021 | |
|
|
| 2.7523 | 600 | 0.1572 | 0.2622 | 0.8013 | |
|
|
| 3.2110 | 700 | - | 0.2660 | 0.7985 | |
|
|
| 3.6697 | 800 | - | 0.2716 | 0.7986 | |
|
|
| 4.1284 | 900 | 0.1327 | 0.2724 | 0.7942 | |
|
|
| 4.5872 | 1000 | - | 0.2670 | 0.8007 | |
|
|
| 5.0 | 1090 | - | - | 0.5292 | |
|
|
| 0.1497 | 100 | - | 0.4254 | 0.5464 | |
|
|
| 0.2994 | 200 | - | 0.3918 | 0.5718 | |
|
|
| 0.4491 | 300 | 0.3988 | 0.3853 | 0.5670 | |
|
|
| 0.5988 | 400 | - | 0.3670 | 0.5780 | |
|
|
| 0.7485 | 500 | - | 0.3630 | 0.5954 | |
|
|
| 0.8982 | 600 | 0.3577 | 0.3551 | 0.6197 | |
|
|
| 1.0479 | 700 | - | 0.3463 | 0.6320 | |
|
|
| 1.1976 | 800 | - | 0.3362 | 0.6455 | |
|
|
| 1.3473 | 900 | 0.3092 | 0.3547 | 0.6496 | |
|
|
| 1.4970 | 1000 | - | 0.3403 | 0.6502 | |
|
|
| 1.6467 | 1100 | - | 0.3418 | 0.6614 | |
|
|
| 1.7964 | 1200 | 0.2901 | 0.3367 | 0.6781 | |
|
|
| 1.9461 | 1300 | - | 0.3283 | 0.6939 | |
|
|
| 2.0958 | 1400 | - | 0.3266 | 0.7053 | |
|
|
| 2.2455 | 1500 | 0.2627 | 0.3275 | 0.7074 | |
|
|
| 2.3952 | 1600 | - | 0.3174 | 0.6976 | |
|
|
| 2.5449 | 1700 | - | 0.3275 | 0.7037 | |
|
|
| 2.6946 | 1800 | 0.2319 | 0.3094 | 0.7086 | |
|
|
| 2.8443 | 1900 | - | 0.3184 | 0.7118 | |
|
|
| 2.9940 | 2000 | - | 0.3195 | 0.7076 | |
|
|
| 3.1437 | 2100 | 0.2222 | 0.3225 | 0.7178 | |
|
|
| 3.2934 | 2200 | - | 0.3214 | 0.7184 | |
|
|
| 3.4431 | 2300 | - | 0.3170 | 0.7270 | |
|
|
| 3.5928 | 2400 | 0.188 | 0.3236 | 0.7269 | |
|
|
| 3.7425 | 2500 | - | 0.3174 | 0.7345 | |
|
|
| 3.8922 | 2600 | - | 0.3196 | 0.7365 | |
|
|
| 4.0419 | 2700 | 0.1877 | 0.3174 | 0.7394 | |
|
|
| 4.1916 | 2800 | - | 0.3195 | 0.7355 | |
|
|
| 4.3413 | 2900 | - | 0.3207 | 0.7373 | |
|
|
| 4.4910 | 3000 | 0.1582 | 0.3274 | 0.7349 | |
|
|
| 4.6407 | 3100 | - | 0.3252 | 0.7350 | |
|
|
| 4.7904 | 3200 | - | 0.3210 | 0.7393 | |
|
|
| 4.9401 | 3300 | 0.1612 | 0.3205 | 0.7386 | |
|
|
| 5.0 | 3340 | - | - | 0.8142 | |
|
|
| 0.1497 | 100 | - | 0.2197 | 0.8248 | |
|
|
| 0.2994 | 200 | - | 0.2117 | 0.8303 | |
|
|
| 0.4491 | 300 | 0.2456 | 0.2299 | 0.8156 | |
|
|
| 0.5988 | 400 | - | 0.2219 | 0.8113 | |
|
|
| 0.7485 | 500 | - | 0.2149 | 0.8231 | |
|
|
| 0.8982 | 600 | 0.2397 | 0.2110 | 0.8354 | |
|
|
| 1.0479 | 700 | - | 0.2069 | 0.8479 | |
|
|
| 1.1976 | 800 | - | 0.2070 | 0.8465 | |
|
|
| 1.3473 | 900 | 0.1956 | 0.2046 | 0.8445 | |
|
|
| 1.4970 | 1000 | - | 0.2070 | 0.8412 | |
|
|
| 1.6467 | 1100 | - | 0.2001 | 0.8453 | |
|
|
| 1.7964 | 1200 | 0.185 | 0.1970 | 0.8473 | |
|
|
| 1.9461 | 1300 | - | 0.1904 | 0.8491 | |
|
|
| 2.0958 | 1400 | - | 0.1864 | 0.8691 | |
|
|
| 2.2455 | 1500 | 0.1537 | 0.1916 | 0.8570 | |
|
|
| 2.3952 | 1600 | - | 0.1886 | 0.8740 | |
|
|
| 2.5449 | 1700 | - | 0.1827 | 0.8770 | |
|
|
| 2.6946 | 1800 | 0.1363 | 0.1771 | 0.8798 | |
|
|
| 2.8443 | 1900 | - | 0.1768 | 0.8862 | |
|
|
| 2.9940 | 2000 | - | 0.1799 | 0.8912 | |
|
|
| 3.1437 | 2100 | 0.1276 | 0.1785 | 0.8838 | |
|
|
| 3.2934 | 2200 | - | 0.1772 | 0.8803 | |
|
|
| 3.4431 | 2300 | - | 0.1819 | 0.8801 | |
|
|
| 3.5928 | 2400 | 0.1048 | 0.1763 | 0.8820 | |
|
|
| 3.7425 | 2500 | - | 0.1782 | 0.8880 | |
|
|
| 3.8922 | 2600 | - | 0.1784 | 0.8833 | |
|
|
| 4.0419 | 2700 | 0.1017 | 0.1777 | 0.8885 | |
|
|
| 4.1916 | 2800 | - | 0.1805 | 0.8901 | |
|
|
| 4.3413 | 2900 | - | 0.1756 | 0.8911 | |
|
|
| 4.4910 | 3000 | 0.0853 | 0.1781 | 0.8895 | |
|
|
| 4.6407 | 3100 | - | 0.1784 | 0.8869 | |
|
|
| 4.7904 | 3200 | - | 0.1775 | 0.8879 | |
|
|
| 4.9401 | 3300 | 0.0854 | 0.1766 | 0.8883 | |
|
|
| 5.0 | 3340 | - | - | 0.8886 | |
|
|
|
|
|
</details> |
|
|
|
|
|
### Framework Versions |
|
|
- Python: 3.10.12 |
|
|
- Sentence Transformers: 3.0.1 |
|
|
- Transformers: 4.42.4 |
|
|
- PyTorch: 2.3.1+cu121 |
|
|
- Accelerate: 0.32.1 |
|
|
- Datasets: 2.21.0 |
|
|
- Tokenizers: 0.19.1 |
|
|
|
|
|
## Citation |
|
|
|
|
|
### BibTeX |
|
|
|
|
|
#### Sentence Transformers and SoftmaxLoss |
|
|
```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", |
|
|
} |
|
|
``` |
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