[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-1-fr-114":3,"doc-seo-191071-114":41,"doc-detail-191071-fr":108},{"code":4,"msg":5,"data":6},0,"success",[7,13,17,21,25,29,33,37],{"id":8,"doc_module":9,"doc_module_name":10,"category_name":11,"show_sort_weight":4,"slug":12},187,1,"Template","Affiches","affiches",{"id":14,"doc_module":9,"doc_module_name":10,"category_name":15,"show_sort_weight":4,"slug":16},185,"CV","cv-de4d58cbecd644d0a7e318765621a3e1",{"id":18,"doc_module":9,"doc_module_name":10,"category_name":19,"show_sort_weight":4,"slug":20},186,"Factures","factures",{"id":22,"doc_module":9,"doc_module_name":10,"category_name":23,"show_sort_weight":4,"slug":24},189,"Formulaires","formulaires",{"id":26,"doc_module":9,"doc_module_name":10,"category_name":27,"show_sort_weight":4,"slug":28},191,"Général","general-191",{"id":30,"doc_module":9,"doc_module_name":10,"category_name":31,"show_sort_weight":4,"slug":32},190,"Lettres","lettres",{"id":34,"doc_module":9,"doc_module_name":10,"category_name":35,"show_sort_weight":4,"slug":36},184,"Présentations","cv-767d690125b54e1a9d6afd8b391bba13",{"id":38,"doc_module":9,"doc_module_name":10,"category_name":39,"show_sort_weight":4,"slug":40},188,"Réseaux sociaux","reseaux-sociaux",{"code":4,"msg":42,"data":43},"ok",{"site_id":44,"language":45,"slug":46,"title":47,"keywords":48,"description":49,"schema_data":50,"social_meta":101,"head_meta":103,"extra_data":105,"updated_unix":107},114,"fr","text-preprocessing-and-model-adjustment-stages","Text Preprocessing and Model Adjustment Stages","","This document outlines the crucial stages involved in preparing text data and fine-tuning machine learning models, particularly for Natural Language Processing (NLP) tasks. The process begins with 'Prétraitement du texte' (Text Preprocessing), a foundational step that includes 'Tokenisation' (Tokenization) to break down text into individual words or sub-word units, 'Lemmatisation' (Lemmatization) to reduce words to their base or dictionary form, and 'Suppression des mots vides' (Stop word removal) to eliminate common words that do not carry significant meaning. Following preprocessing, 'Représentation du texte' (Text Representation) is addressed, encompassing 'Sac de mots' (Bag-of-words) models, which represent text as an unordered collection of its words, and 'Plongements de mots' (Word Embeddings) that capture semantic relationships between words. The document then moves to 'Pré-entraînement' (Pre-training), where models are trained on large datasets to learn general language patterns. The subsequent stage, 'Ajustement' (Adjustment), is critical for adapting pre-trained models to specific tasks, featuring 'Apprentissage zero-shot' (Zero-shot learning), where models perform tasks without explicit examples; 'Apprentissage few-shot' (Few-shot learning), where models learn from a small number of examples; and 'Apprentissage multi-shot' (Multi-shot learning), which involves training with a moderate dataset. Finally, 'Ajustement avancé' (Advanced Adjustment) suggests further refinements and optimizations for enhanced model performance. The diagram visually represents these sequential steps, highlighting their interdependence in building effective NLP systems.",{"@graph":51,"@context":100},[52,68,83],{"@type":53,"itemListElement":54},"BreadcrumbList",[55,59,62,65],{"item":56,"name":57,"@type":58,"position":9},"https://docshare.wps.com","Home","ListItem",{"item":60,"name":10,"@type":58,"position":61},"https://docshare.wps.com/fr/template/",2,{"item":63,"name":27,"@type":58,"position":64},"https://docshare.wps.com/fr/template/général/",3,{"item":66,"name":47,"@type":58,"position":67},"https://docshare.wps.com/fr/template/text-preprocessing-and-model-adjustment-stages/191071/",4,{"url":66,"name":47,"@type":69,"author":70,"headline":47,"publisher":73,"fileFormat":76,"inLanguage":45,"description":49,"dateModified":77,"datePublished":77,"encodingFormat":76,"isAccessibleForFree":78,"interactionStatistic":79},"DigitalDocument",{"name":71,"@type":72},"Lucas Martin","Person",{"url":56,"name":74,"@type":75},"DocShare","Organization","application/pdf","2026-09-03",true,{"@type":80,"interactionType":81,"userInteractionCount":4},"InteractionCounter",{"@type":82},"ViewAction",{"@type":84,"mainEntity":85},"FAQPage",[86,92,96],{"name":87,"@type":88,"acceptedAnswer":89},"Quelles sont les étapes clés du prétraitement du texte mentionnées dans le document ?","Question",{"text":90,"@type":91},"Les étapes clés du prétraitement du texte incluent la tokenisation, la lemmatisation et la suppression des mots vides.","Answer",{"name":93,"@type":88,"acceptedAnswer":94},"Qu'est-ce que l'apprentissage zero-shot dans le contexte de l'ajustement de modèle ?",{"text":95,"@type":91},"L'apprentissage zero-shot permet à un modèle d'accomplir une tâche sans avoir vu d'exemples spécifiques de cette tâche pendant l'entraînement.",{"name":97,"@type":88,"acceptedAnswer":98},"Quelle est la différence entre l'apprentissage few-shot et l'apprentissage multi-shot ?",{"text":99,"@type":91},"L'apprentissage few-shot utilise un très petit nombre d'exemples pour l'apprentissage, tandis que l'apprentissage multi-shot utilise un ensemble de données plus conséquent.","https://schema.org",{"og:url":66,"og:type":102,"og:title":47,"og:site_name":74,"og:description":49},"article",{"robots":104,"canonical":66},"index,follow",{"doc_id":106,"site_id":44},191071,1788406471,{"code":4,"msg":5,"data":109},{"doc_id":106,"user_id":110,"nickname":71,"user_avatar":111,"doc_module":9,"category_id":26,"category_name":27,"doc_title":47,"doc_description":49,"doc_content":48,"file_id":112,"file_url":113,"file_type":114,"file_size":115,"view_count":4,"is_deleted":4,"is_public":9,"is_downloadable":9,"audit_status":9,"page_count":116,"language":117,"language_code":45,"site_id":44,"html_lang":45,"table_of_contents":118,"faqs":119,"seo_title":120,"seo_description":49,"update_tm":107,"read_time":121},8796095360427,"https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d","cbCaimSrW1vqOYlf","https://ap.wps.com/l/cbCaimSrW1vqOYlf","pdf",3578035,49,"French","# Prétraitement du texte\n## Tokenisation\n## Lemmatisation\n## Suppression des mots vides\n# Représentation du texte\n## Sac de mots\n## Plongements de mots\n# Pré-entraînement\n# Ajustement\n## Apprentissage zero-shot\n## Apprentissage few-shot\n## Apprentissage multi-shot\n# Ajustement avancé","[{\"question\":\"Quelles sont les étapes clés du prétraitement du texte mentionnées dans le document ?\",\"answer\":\"Les étapes clés du prétraitement du texte incluent la tokenisation, la lemmatisation et la suppression des mots vides.\"},{\"question\":\"Qu'est-ce que l'apprentissage zero-shot dans le contexte de l'ajustement de modèle ?\",\"answer\":\"L'apprentissage zero-shot permet à un modèle d'accomplir une tâche sans avoir vu d'exemples spécifiques de cette tâche pendant l'entraînement.\"},{\"question\":\"Quelle est la différence entre l'apprentissage few-shot et l'apprentissage multi-shot ?\",\"answer\":\"L'apprentissage few-shot utilise un très petit nombre d'exemples pour l'apprentissage, tandis que l'apprentissage multi-shot utilise un ensemble de données plus conséquent.\"}]","Text Preprocessing and Model Adjustment Stages | PDF",17]