Quanti modelli!?

Hugging Face ospita un numero enorme di modelli progettati per svolgere compiti diversi. In questo esercizio userai le API di Hugging Face Hub per trovare il numero totale di modelli supportati per diversi task.

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Modelli multi-modali con Hugging Face

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Istruzioni dell'esercizio

Usa il metodo corretto di api per elencare tutti i modelli associati al task "text-to-image".

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Find the list of models for a task
models = api.____(task="____")
print(f"Task: text-to-image, Models: {len(list(models))}")

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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!?

Esercizio in corso

Exercise 3: Trovare il modello text-to-image più popolare Exercise 4: Preprocessing di diverse modalità Exercise 5: Tokenizzazione del testo 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!