Samuel-DD
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microsoft-deberta-large-squad_v2

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-tags: []
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+# Algorithmic Learning and Optimized Quantum Artificial Solutions (ALOQAS)
+<p>
+  <a href="https://huggingface.co/spaces/ALOQAS/aloqas-gradio">Démo. Gradio sur Hugging Face Spaces</a>
+</p>
+<p>
+  <a href="https://github.com/LucasAguetai/ALOQAS">Lien vers le repository GitHub</a>
+</p>
+<p>
+  <a href="https://drive.google.com/drive/folders/1MrW-UftHd0HVgLjJ_C5HmwBG3ymEY_qY?usp=drive_link">Lien vers les notebooks Google Colaboratory (sur demande)</a>
+</p>
+## Projet : Création d'un Système de Chatbot Conversationnel basé sur GPT-2
+Ce projet a pour objectif de développer un chatbot conversationnel intelligent en utilisant le modèle GPT-2 comme base. <br />
+Le chatbot sera capable d'engager des conversations naturelles avec les utilisateurs, de répondre à leurs questions et de fournir des informations utiles.
+## Membres du projet
+<ul>
+  <li><b>A</b>urélien ZUFIC</li>
+  <li><b>L</b>ucas AGUETAÏ</li>
+  <li><b>O</b>ny ANDRIATSAHAVOJAONA</li>
+  <li><b>Q</b>uentin VERMEERSCH</li>
+  <li><b>A</b>lexandre HUYNH</li>
+  <li><b>S</b>amuel DORISMOND</li>
+</ul>
+## Jeux de données traité
+Dataset TensorFlow sur des articles scientifiques : <a href="https://www.tensorflow.org/datasets/catalog/scientific_papers">scientific_papers</a>
+## Tâches du projet
+### Compréhension de GPT-2 :
+Étudiez le fonctionnement de GPT-2 en utilisant l'API TensorFlow.<br />
+Explorez comment GPT-2 génère du texte en réponse à des stimuli.
+### Collecte de Données :
+Identifiez un domaine spécifique ou une application pour votre chatbot (par exemple,
+un chatbot de service client, un chatbot éducatif, etc.).<br />
+Collectez ou préparez un ensemble de données de dialogue adapté à votre domaine
+d'application.
+### Fine-tuning de GPT-2 :
+Fine-tunez le modèle GPT-2 en utilisant l'ensemble de données de dialogue.<br />
+Optimisez le modèle pour la génération de réponses de chatbot cohérentes et
+pertinentes.<br />
+Évaluez les performances du modèle fine-tuné en utilisant des mesures de qualité de
+dialogue.
+### Intégration de Gradio :
+Utilisez la bibliothèque Gradio pour intégrer une interface utilisateur conviviale à
+votre chatbot.<br />
+Personnalisez l'interface pour qu'elle corresponde à l'esthétique de votre application.
+### Tests et Optimisation :
+Testez le chatbot avec des utilisateurs pour recueillir des commentaires et des
+données de performance.<br />
+Effectuez des ajustements en fonction des commentaires des utilisateurs pour
+améliorer la qualité des réponses du chatbot.
+### Documentation et Présentation :
+Rédigez une documentation complète expliquant comment utiliser le chatbot.<br />
+Préparez une présentation pour montrer et expliquer votre chatbot à vos pairs et
+enseignants.
+### Ressources :
+Vous pouvez utiliser l'API GPT-2 de TensorFlow pour le fine-tuning et la génération de
+réponses de chatbot.<br />
+Flask est une bibliothèque Python populaire pour le développement de serveurs web.<br />
+Gradio propose des ressources et des exemples pour développer des interfaces
+utilisateur interactives.