Buckets:
| <meta charset="utf-8" /><meta name="hf:doc:metadata" content="{"title":"Glossaire","local":"glossaire","sections":[],"depth":1}"> | |
| <link href="/docs/course/pr_1069/fr/_app/immutable/assets/0.e3b0c442.css" rel="modulepreload"> | |
| <link rel="modulepreload" href="/docs/course/pr_1069/fr/_app/immutable/entry/start.cea6db46.js"> | |
| <link rel="modulepreload" href="/docs/course/pr_1069/fr/_app/immutable/chunks/scheduler.37c15a92.js"> | |
| <link rel="modulepreload" href="/docs/course/pr_1069/fr/_app/immutable/chunks/singletons.2b29b91f.js"> | |
| <link rel="modulepreload" href="/docs/course/pr_1069/fr/_app/immutable/chunks/index.18351ede.js"> | |
| <link rel="modulepreload" href="/docs/course/pr_1069/fr/_app/immutable/chunks/paths.f6fdf97f.js"> | |
| <link rel="modulepreload" href="/docs/course/pr_1069/fr/_app/immutable/entry/app.3f6640b1.js"> | |
| <link rel="modulepreload" href="/docs/course/pr_1069/fr/_app/immutable/chunks/index.2bf4358c.js"> | |
| <link rel="modulepreload" href="/docs/course/pr_1069/fr/_app/immutable/nodes/0.b777de11.js"> | |
| <link rel="modulepreload" href="/docs/course/pr_1069/fr/_app/immutable/chunks/each.e59479a4.js"> | |
| <link rel="modulepreload" href="/docs/course/pr_1069/fr/_app/immutable/nodes/82.49bbf58d.js"> | |
| <link rel="modulepreload" href="/docs/course/pr_1069/fr/_app/immutable/chunks/getInferenceSnippets.24b50994.js"><!-- HEAD_svelte-u9bgzb_START --><meta name="hf:doc:metadata" content="{"title":"Glossaire","local":"glossaire","sections":[],"depth":1}"><!-- HEAD_svelte-u9bgzb_END --> <p></p> <h1 class="relative group"><a id="glossaire" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#glossaire"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>Glossaire</span></h1> <table data-svelte-h="svelte-1erofq1"><thead><tr><th>Original</th> <th>Français</th></tr></thead> <tbody><tr><td>Accuracy</td> <td>Précision</td></tr> <tr><td>Backward Pass</td> <td>Passe arrière</td></tr> <tr><td>Batch</td> <td><em>Batch</em></td></tr> <tr><td>Benchmark</td> <td><em>Benchmark</em></td></tr> <tr><td>Cache</td> <td>Cache</td></tr> <tr><td>Chapter</td> <td>Chapitre</td></tr> <tr><td>Checkpoint</td> <td><em>Checkpoint</em> (plus rarement « point de sauvegarde »)</td></tr> <tr><td>Colab Notebook</td> <td><em>Notebook</em> Google Colab</td></tr> <tr><td>Colator function</td> <td>Fonction d’assemblement</td></tr> <tr><td>Command</td> <td>Commande</td></tr> <tr><td>Configuration</td> <td>Configuration</td></tr> <tr><td>Course</td> <td>Cours</td></tr> <tr><td>Dataloader</td> <td>Chargeur de données</td></tr> <tr><td>Dependency</td> <td>Dépendances</td></tr> <tr><td>Deployment</td> <td>Déploiement</td></tr> <tr><td>Development</td> <td>Développement</td></tr> <tr><td>Dictionary</td> <td>Dictionnaire</td></tr> <tr><td>Download</td> <td>Télécharger</td></tr> <tr><td>Feature</td> <td>Variable</td></tr> <tr><td>Field</td> <td>Champ</td></tr> <tr><td>Fine-tuning</td> <td>Finetuning</td></tr> <tr><td>Folder</td> <td>Dossier</td></tr> <tr><td>Forward Pass</td> <td>Passe avant</td></tr> <tr><td>Google</td> <td><em>Google</em></td></tr> <tr><td>Hugging Face</td> <td><em>Hugging Face</em></td></tr> <tr><td>Inference</td> <td>Inférence</td></tr> <tr><td>Learning rate</td> <td>Taux d’apprentissage</td></tr> <tr><td>Library</td> <td>Bibliothèque</td></tr> <tr><td>Linux</td> <td>Linux</td></tr> <tr><td>Loss function</td> <td>Fonction de perte/coût</td></tr> <tr><td>Loop</td> <td>Boucle</td></tr> <tr><td>macOS</td> <td>macOS</td></tr> <tr><td>Model</td> <td>Modèle</td></tr> <tr><td>Hugging Face Hub</td> <td><em>Hub</em> d’<em>Hugging Face</em></td></tr> <tr><td>Module</td> <td>Module</td></tr> <tr><td>Natural Language Processing</td> <td>Traitement du langage naturel</td></tr> <tr><td>Package</td> <td>Paquet</td></tr> <tr><td>Padding</td> <td>Rembourrage</td></tr> <tr><td>Parameter</td> <td>Paramètre</td></tr> <tr><td>Python</td> <td>Python</td></tr> <tr><td>PyTorch</td> <td>PyTorch</td></tr> <tr><td>Samples</td> <td>Echantillons</td></tr> <tr><td>Scheduler</td> <td>Planificateur</td></tr> <tr><td>Script</td> <td>Script</td></tr> <tr><td>Setup</td> <td>Installation</td></tr> <tr><td>TensorFlow</td> <td>TensorFlow</td></tr> <tr><td>Terminal</td> <td>Terminal</td></tr> <tr><td>Tokenizer</td> <td>Tokeniseur</td></tr> <tr><td>Train</td> <td>Entraîner</td></tr> <tr><td>Transformer</td> <td><em>Transformer</em></td></tr> <tr><td>Virtual Environment</td> <td>Environnement virtuel</td></tr> <tr><td>Weight decay</td> <td>Taux de décroissance des poids</td></tr> <tr><td>Weights</td> <td>Poids</td></tr> <tr><td>Windows</td> <td><em>Windows</em></td></tr> <tr><td>Working Environment</td> <td>Environnement de travail</td></tr></tbody></table> <p data-svelte-h="svelte-jqjvcs">A noter que les mots anglais non traduits sont indiqués en italique dans le cours.<br> | |
| De plus, les abréviations techniques comme API, GPU, TPU, etc. ne sont pas traduites.</p> <a class="!text-gray-400 !no-underline text-sm flex items-center not-prose mt-4" href="https://github.com/huggingface/course/blob/main/chapters/fr/glossary/1.mdx" target="_blank"><span data-svelte-h="svelte-1kd6by1"><</span> <span data-svelte-h="svelte-x0xyl0">></span> <span data-svelte-h="svelte-1dajgef"><span class="underline ml-1.5">Update</span> on GitHub</span></a> <p></p> | |
| <script> | |
| { | |
| __sveltekit_1sfisyd = { | |
| assets: "/docs/course/pr_1069/fr", | |
| base: "/docs/course/pr_1069/fr", | |
| env: {} | |
| }; | |
| const element = document.currentScript.parentElement; | |
| const data = [null,null]; | |
| Promise.all([ | |
| import("/docs/course/pr_1069/fr/_app/immutable/entry/start.cea6db46.js"), | |
| import("/docs/course/pr_1069/fr/_app/immutable/entry/app.3f6640b1.js") | |
| ]).then(([kit, app]) => { | |
| kit.start(app, element, { | |
| node_ids: [0, 82], | |
| data, | |
| form: null, | |
| error: null | |
| }); | |
| }); | |
| } | |
| </script> | |
Xet Storage Details
- Size:
- 6.88 kB
- Xet hash:
- 77c87db7ca8e837b64abcfa1e3bbf97636e9f1001ec98e08f904f83c3c59e6f3
·
Xet efficiently stores files, intelligently splitting them into unique chunks and accelerating uploads and downloads. More info.