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2 changes: 1 addition & 1 deletion chapters/fr/chapter1/1.mdx
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Expand Up @@ -66,7 +66,7 @@ Actuellement, nous n'avons pas de certification pour ce cours. Cependant, nous t
Chaque chapitre de ce cours est conçu pour être complété en une semaine, avec environ 6 à 8 heures de travail par semaine. Cependant, vous pouvez prendre tout le temps nécessaire pour le suivre.

- **Où puis-je poser une question si j'en ai une ?**
Si vous avez une question sur l'une des sections du cours, il vous suffit de cliquer sur la bannière « *Ask a question* » en haut de la page pour être automatiquement redirigé vers la bonne section du [forum d’*Hugging Face*] (https://discuss.huggingface.co/) :
Si vous avez une question sur l'une des sections du cours, il vous suffit de cliquer sur la bannière « *Ask a question* » en haut de la page pour être automatiquement redirigé vers la bonne section du [forum d’*Hugging Face*](https://discuss.huggingface.co/) :

<img src="https://huggingface.co/datasets/huggingface-course/documentation-images/resolve/main/en/chapter1/forum-button.png" alt="Link to the Hugging Face forums" width="75%">

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6 changes: 4 additions & 2 deletions chapters/fr/chapter1/3.mdx
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Dans cette section, nous allons voir ce que peuvent faire les *transformers* et utiliser notre premier outil de la bibliothèque 🤗 *Transformers* : la fonction `pipeline()`.
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6 changes: 4 additions & 2 deletions chapters/fr/chapter1/8.mdx
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Si vous souhaitez utiliser un modèle pré-entraîné ou une version *finetunée* de celui-ci en production, il est important d'avoir conscience que, bien que ces modèles soient puissants, ils ont des limites. La plus importante de ces limitations est que, pour permettre le pré-entraînement des modèles sur de grandes quantités de données, les chercheurs récupèrent souvent tout le contenu qu'ils peuvent trouver et donc en prenant le meilleur et le pire de ce qui est disponible sur internet.
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12 changes: 8 additions & 4 deletions chapters/fr/chapter2/2.mdx
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{/if}
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12 changes: 8 additions & 4 deletions chapters/fr/chapter2/3.mdx
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12 changes: 8 additions & 4 deletions chapters/fr/chapter2/4.mdx
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{/if}
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12 changes: 8 additions & 4 deletions chapters/fr/chapter2/5.mdx
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{/if}
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12 changes: 8 additions & 4 deletions chapters/fr/chapter2/6.mdx
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{/if}
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13 changes: 9 additions & 4 deletions chapters/fr/chapter3/2.mdx
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{#if fw === 'pt'}


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{/if}
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6 changes: 4 additions & 2 deletions chapters/fr/chapter3/3.mdx
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6 changes: 4 additions & 2 deletions chapters/fr/chapter3/3_tf.mdx
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Une fois que vous avez fait tout le travail de prétraitement des données dans la dernière section, il ne vous reste que quelques étapes pour entraîner le modèle. Notez, cependant, que la commande `model.fit()` s'exécutera très lentement sur un CPU. Si vous n'avez pas de GPU, vous pouvez avoir accès à des GPUs ou TPUs gratuits sur [Google Colab](https://colab.research.google.com/).
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6 changes: 4 additions & 2 deletions chapters/fr/chapter3/4.mdx
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