Tokenizzazione del testo

In questo esercizio userai il dataset flickr, che contiene 30.000 immagini con relative didascalie, per eseguire operazioni di preprocessamento sul testo. Questo è necessario per poterlo usare con i modelli in attività come la classificazione del testo. È particolarmente utile per applicazioni multi-modali, in cui i modelli Hugging Face possono verificare l’adeguatezza di una didascalia rispetto all’immagine associata.

Il dataset (dataset) è stato caricato e AutoTokenizer è stato importato.

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Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Load the first caption from the image at index 5
text = dataset[5]["____"][0]
print(text)

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Questo esercizio fa parte del corso

Modelli multi-modali con Hugging Face

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Navigate the Hugging Face model hub, transform raw text, audio, and visual data into AI-friendly formats. Learn how to find the latest most popular models for tasks such as text generation and harness the power of pre-built pipelines.

Exercise 1: Navigare tra i modelli su Hugging Face Exercise 2: Quanti modelli!?Exercise 3: Trovare il modello text-to-image più popolare Exercise 4: Preprocessing di diverse modalità Exercise 5: Tokenizzazione del testo

Esercizio in corso

Exercise 6: Preprocessing delle immagini Exercise 7: Preprocessing dell'audio Exercise 8: Attività delle pipeline e valutazioni Exercise 9: Generazione di didascalie con pipeline Exercise 10: Passare argomenti con nome Exercise 11: Valutazione del modello su un insieme di dati personalizzato

Learn to master individual modalities with state-of-the-art models. Dive into computer vision for image classification and segmentation, explore speech recognition and text-to-speech synthesis, and learn effective fine-tuning techniques. Build practical skills with pre-trained models from Hugging Face's transformers library.

Exercise 1: Computer vision Exercise 2: Image classification Exercise 3: Object detection Exercise 4: Image background removal Exercise 5: Fine-tuning computer vision models Exercise 6: CV fine-tuning: dataset prep Exercise 7: CV fine-tuning: model classes Exercise 8: CV fine-tuning: trainer configuration Exercise 9: Speech recognition and audio generation Exercise 10: Automatic speech recognition Exercise 11: Creating speech embeddings Exercise 12: Audio denoising Exercise 13: Fine-tuning text-to-speech models Exercise 14: Fine-tuning a text-to-speech model Exercise 15: Generating new speech

Learn to fuse visual, textual, and audio information for richer AI applications. Master techniques like CLIP for zero-shot classification, build sentiment analyzers that see and read, and create emotion detectors that combine facial expressions with voice. Take your AI models beyond single-modality thinking.

Exercise 1: Zero-shot image classification Exercise 2: Zero-shot learning with CLIP Exercise 3: Automated caption quality assessment Exercise 4: Multi-modal sentiment analysis Exercise 5: Prompting Vision Language Models (VLMs)Exercise 6: Multi-modal sentiment classification with Qwen Exercise 7: Zero-shot video classification Exercise 8: Video audio splitting Exercise 9: Video sentiment analysis with CLIP CLAP

Transform ideas into reality! Master cutting-edge AI techniques to generate and manipulate visual content using text prompts. Create stunning images, edit photos intelligently, and build powerful question-answering systems for images and documents. Turn your creative vision into digital reality with multi-modal AI.

Exercise 1: Visual question-answering (VQA)Exercise 2: VQA with Vision Language Transformers (ViLTs)Exercise 3: Document VQA with LayoutLM Exercise 4: Image editing with diffusion models Exercise 5: Custom image editing Exercise 6: Image inpainting Exercise 7: Video generation Exercise 8: Build a video!Exercise 9: Assessing video generation performance Exercise 10: Congratulations!