{"id":478509,"date":"2023-08-09T09:33:56","date_gmt":"2023-08-09T09:33:56","guid":{"rendered":""},"modified":"2023-09-05T11:16:56","modified_gmt":"2023-09-05T11:16:56","slug":"pre-trained-language-models","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/fr\/wiki\/pre-trained-language-models\/","title":{"rendered":"Mod\u00e8les de langage pr\u00e9-entra\u00een\u00e9s"},"content":{"rendered":"<p>Les mod\u00e8les linguistiques pr\u00e9-entra\u00een\u00e9s (PLM) constituent un \u00e9l\u00e9ment crucial de la technologie moderne de traitement du langage naturel (NLP). Ils repr\u00e9sentent un domaine de l&#039;intelligence artificielle qui permet aux ordinateurs de comprendre, d&#039;interpr\u00e9ter et de g\u00e9n\u00e9rer le langage humain. Les PLM sont con\u00e7us pour g\u00e9n\u00e9raliser d&#039;une t\u00e2che linguistique \u00e0 une autre en exploitant un vaste corpus de donn\u00e9es textuelles.<\/p>\n<h2>L&#039;histoire de l&#039;origine des mod\u00e8les linguistiques pr\u00e9-entra\u00een\u00e9s et sa premi\u00e8re mention<\/h2>\n<p>L\u2019id\u00e9e d\u2019utiliser des m\u00e9thodes statistiques pour comprendre le langage remonte au d\u00e9but des ann\u00e9es 1950. La v\u00e9ritable avanc\u00e9e a eu lieu avec l\u2019introduction des int\u00e9grations de mots, telles que Word2Vec, au d\u00e9but des ann\u00e9es 2010. Par la suite, les mod\u00e8les de transformateurs, introduits par Vaswani et al. en 2017, est devenu la base des PLM. BERT (Bidirectionnel Encoder Representations from Transformers) et GPT (Generative Pre-trained Transformer) ont suivi comme certains des mod\u00e8les les plus influents dans ce domaine.<\/p>\n<h2>Informations d\u00e9taill\u00e9es sur les mod\u00e8les linguistiques pr\u00e9-entra\u00een\u00e9s<\/h2>\n<p>Les mod\u00e8les linguistiques pr\u00e9-entra\u00een\u00e9s fonctionnent en s&#039;entra\u00eenant sur de grandes quantit\u00e9s de donn\u00e9es textuelles. Ils d\u00e9veloppent une compr\u00e9hension math\u00e9matique des relations entre les mots, les phrases et m\u00eame des documents entiers. Cela leur permet de g\u00e9n\u00e9rer des pr\u00e9dictions ou des analyses qui peuvent \u00eatre appliqu\u00e9es \u00e0 diverses t\u00e2ches de PNL, notamment\u00a0:<\/p>\n<ul>\n<li>Classement du texte<\/li>\n<li>Analyse des sentiments<\/li>\n<li>Reconnaissance d&#039;entit\u00e9 nomm\u00e9e<\/li>\n<li>Traduction automatique<\/li>\n<li>R\u00e9sum\u00e9 du texte<\/li>\n<\/ul>\n<h2>La structure interne des mod\u00e8les de langage pr\u00e9-entra\u00een\u00e9s<\/h2>\n<p>Les PLM utilisent souvent une architecture de transformateur, compos\u00e9e de\u00a0:<\/p>\n<ol>\n<li><strong>Couche d&#039;entr\u00e9e<\/strong>: Encodage du texte d\u2019entr\u00e9e en vecteurs.<\/li>\n<li><strong>Blocs transformateurs<\/strong>: Plusieurs couches qui traitent l&#039;entr\u00e9e, contenant des m\u00e9canismes d&#039;attention et des r\u00e9seaux de neurones \u00e0 action directe.<\/li>\n<li><strong>Couche de sortie<\/strong>: Produire le r\u00e9sultat final, comme une pr\u00e9diction ou un texte g\u00e9n\u00e9r\u00e9.<\/li>\n<\/ol>\n<h2>Analyse des principales caract\u00e9ristiques des mod\u00e8les linguistiques pr\u00e9-entra\u00een\u00e9s<\/h2>\n<p>Voici les principales caract\u00e9ristiques des PLM\u00a0:<\/p>\n<ul>\n<li><strong>Polyvalence<\/strong>: Applicable \u00e0 plusieurs t\u00e2ches PNL.<\/li>\n<li><strong>Apprentissage par transfert<\/strong>: Capacit\u00e9 \u00e0 g\u00e9n\u00e9raliser dans divers domaines.<\/li>\n<li><strong>\u00c9volutivit\u00e9<\/strong>: Traitement efficace de grandes quantit\u00e9s de donn\u00e9es.<\/li>\n<li><strong>Complexit\u00e9<\/strong>: N\u00e9cessite des ressources informatiques importantes pour la formation.<\/li>\n<\/ul>\n<h2>Types de mod\u00e8les linguistiques pr\u00e9-entra\u00een\u00e9s<\/h2>\n<table>\n<thead>\n<tr>\n<th>Mod\u00e8le<\/th>\n<th>Description<\/th>\n<th>Ann\u00e9e d&#039;introduction<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>BERTE<\/td>\n<td>Compr\u00e9hension bidirectionnelle du texte<\/td>\n<td>2018<\/td>\n<\/tr>\n<tr>\n<td>Google\u00a0Tag<\/td>\n<td>G\u00e9n\u00e8re un texte coh\u00e9rent<\/td>\n<td>2018<\/td>\n<\/tr>\n<tr>\n<td>T5<\/td>\n<td>Transfert texte \u00e0 texte\u00a0; applicable \u00e0 diverses t\u00e2ches de PNL<\/td>\n<td>2019<\/td>\n<\/tr>\n<tr>\n<td>RoBERTa<\/td>\n<td>Version robustement optimis\u00e9e de BERT<\/td>\n<td>2019<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Fa\u00e7ons d&#039;utiliser des mod\u00e8les linguistiques pr\u00e9-entra\u00een\u00e9s, des probl\u00e8mes et leurs solutions<\/h2>\n<p><strong>Les usages<\/strong>:<\/p>\n<ul>\n<li><strong>Commercial<\/strong>: Support client, cr\u00e9ation de contenu, etc.<\/li>\n<li><strong>Acad\u00e9mique<\/strong>: Recherche, analyse de donn\u00e9es, etc.<\/li>\n<li><strong>Personnel<\/strong>: Recommandations de contenu personnalis\u00e9es.<\/li>\n<\/ul>\n<p><strong>Probl\u00e8mes et solutions<\/strong>:<\/p>\n<ul>\n<li><strong>Co\u00fbt de calcul \u00e9lev\u00e9<\/strong>: Utilisez des mod\u00e8les plus l\u00e9gers ou du mat\u00e9riel optimis\u00e9.<\/li>\n<li><strong>Biais dans les donn\u00e9es de formation<\/strong>: Surveiller et organiser les donn\u00e9es de formation.<\/li>\n<li><strong>Probl\u00e8mes de confidentialit\u00e9 des donn\u00e9es<\/strong>: Mettre en \u0153uvre des techniques de pr\u00e9servation de la vie priv\u00e9e.<\/li>\n<\/ul>\n<h2>Principales caract\u00e9ristiques et comparaisons avec des termes similaires<\/h2>\n<ul>\n<li><strong>PLM et mod\u00e8les PNL traditionnels<\/strong>:\n<ul>\n<li>Plus polyvalent et performant<\/li>\n<li>N\u00e9cessite plus de ressources<\/li>\n<li>Mieux comprendre le contexte<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<h2>Perspectives et technologies du futur li\u00e9es aux mod\u00e8les linguistiques pr\u00e9-entra\u00een\u00e9s<\/h2>\n<p>Les avanc\u00e9es futures pourraient inclure\u00a0:<\/p>\n<ul>\n<li>Algorithmes de formation plus efficaces<\/li>\n<li>Meilleure compr\u00e9hension des nuances du langage<\/li>\n<li>Int\u00e9gration avec d&#039;autres domaines de l&#039;IA tels que la vision et le raisonnement<\/li>\n<\/ul>\n<h2>Comment les serveurs proxy peuvent \u00eatre utilis\u00e9s ou associ\u00e9s \u00e0 des mod\u00e8les de langage pr\u00e9-entra\u00een\u00e9s<\/h2>\n<p>Les serveurs proxy comme ceux fournis par OneProxy peuvent aider les PLM en\u00a0:<\/p>\n<ul>\n<li>Faciliter la collecte de donn\u00e9es pour la formation<\/li>\n<li>Permettre une formation distribu\u00e9e sur diff\u00e9rents sites<\/li>\n<li>Am\u00e9liorer la s\u00e9curit\u00e9 et la confidentialit\u00e9<\/li>\n<\/ul>\n<h2>Liens connexes<\/h2>\n<ul>\n<li><a href=\"https:\/\/arxiv.org\/abs\/1810.04805\" target=\"_new\" rel=\"noopener nofollow\">BERT expliqu\u00e9<\/a><\/li>\n<li><a href=\"https:\/\/openai.com\/blog\/better-language-models\" target=\"_new\" rel=\"noopener nofollow\">GPT-2\u00a0:\u00a0de meilleurs mod\u00e8les linguistiques<\/a><\/li>\n<li><a href=\"https:\/\/oneproxy.pro\/fr\/\" target=\"_new\" rel=\"noopener\">Services OneProxy<\/a><\/li>\n<li><a href=\"https:\/\/arxiv.org\/abs\/1706.03762\" target=\"_new\" rel=\"noopener nofollow\">Mod\u00e8les de transformateurs<\/a><\/li>\n<\/ul>\n<p>Dans l\u2019ensemble, les mod\u00e8les linguistiques pr\u00e9-entra\u00een\u00e9s continuent d\u2019\u00eatre une force motrice dans l\u2019avancement de la compr\u00e9hension du langage naturel et ont des applications qui s\u2019\u00e9tendent au-del\u00e0 des fronti\u00e8res du langage, offrant des opportunit\u00e9s et des d\u00e9fis passionnants pour la recherche et le d\u00e9veloppement futurs.<\/p>","protected":false},"featured_media":469209,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-478509","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>Pre-trained Language Models<\/mark>","faq_items":[{"question":"What are Pre-trained Language Models (PLMs)?","answer":"<p>Pre-trained Language Models (PLMs) are AI systems trained on vast amounts of text data to understand and interpret human language. They can be used for various NLP tasks such as text classification, sentiment analysis, and machine translation.<\/p>"},{"question":"What was the historical development of Pre-trained Language Models?","answer":"<p>The concept of PLMs has its roots in the early 1950s, with significant advancements like Word2Vec in the early 2010s and the introduction of transformer models in 2017. Models like BERT and GPT have become landmarks in this field.<\/p>"},{"question":"How do Pre-trained Language Models work?","answer":"<p>PLMs function using a transformer architecture, comprising an input layer to encode text, several transformer blocks with attention mechanisms and feed-forward networks, and an output layer to produce the final result.<\/p>"},{"question":"What are the key features of Pre-trained Language Models?","answer":"<p>The key features include versatility across multiple NLP tasks, the ability to generalize through transfer learning, scalability to handle large data, and complexity, requiring significant computing resources.<\/p>"},{"question":"What types of Pre-trained Language Models exist?","answer":"<p>Some popular types include BERT for bidirectional understanding, GPT for text generation, T5 for various NLP tasks, and RoBERTa, a robustly optimized version of BERT.<\/p>"},{"question":"How can Pre-trained Language Models be used, and what are the problems associated with them?","answer":"<p>PLMs are used in commercial, academic, and personal applications. The main challenges include high computational costs, bias in training data, and data privacy concerns. Solutions include using optimized models and hardware, curating data, and implementing privacy-preserving techniques.<\/p>"},{"question":"What are the main characteristics of Pre-trained Language Models compared to traditional NLP Models?","answer":"<p>PLMs are more versatile, capable, and context-aware than traditional NLP models, but they require more resources for operation.<\/p>"},{"question":"What are the future prospects for Pre-trained Language Models?","answer":"<p>Future prospects include developing more efficient training algorithms, enhancing the understanding of language nuances, and integrating with other AI fields like vision and reasoning.<\/p>"},{"question":"How can proxy servers like OneProxy be associated with Pre-trained Language Models?","answer":"<p>Proxy servers provided by OneProxy can aid PLMs by facilitating data collection for training, enabling distributed training, and enhancing security and privacy measures.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/fr\/wp-json\/wp\/v2\/wiki\/478509","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/fr\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/fr\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/fr\/wp-json\/wp\/v2\/wiki\/478509\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/fr\/wp-json\/wp\/v2\/media\/469209"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/fr\/wp-json\/wp\/v2\/media?parent=478509"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}