VQA su documenti con LayoutLM

Dal VQA su immagini al VQA su documenti! In questo esercizio userai il modello layoutlm-document-qa per determinare il numero totale di ore di formazione erogate ai dipendenti negli anni 2012-2013 partendo da questa immagine del documento:

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Il insieme di dati (dataset) è stato caricato e il modulo della pipeline (pipeline) è stato importato.

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Istruzioni dell'esercizio

Carica la pipeline usando il task 'document-question-answering' e il checkpoint 'impira/layoutlm-document-qa'.
Elabora il documento al punto dati 61 del set test da dataset con un prompt appropriato per trovare quanti giorni di formazione formale sono stati forniti ai dipendenti nel 2012-2013.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Load the document-question-answering pipeline with the pretrained model
pipe = ____

# Process datapoint 61 to find the amount of training days
result = ____(dataset["____"][61]["____"], "____")

print(result)

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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: Hugging Face model navigation Exercise 2: How many models!?Exercise 3: Finding the most popular text-to-image model Exercise 4: Preprocessing different modalities Exercise 5: Text tokenizing Exercise 6: Image preprocessing Exercise 7: Audio preprocessing Exercise 8: Pipeline tasks and evaluations Exercise 9: Pipeline caption generation Exercise 10: Passing keyword arguments Exercise 11: Model evaluation on a custom dataset

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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 con i Vision Language Transformers (ViLT)Exercise 3: VQA su documenti con LayoutLM

Esercizio in corso

Exercise 4: Modifica delle immagini con modelli di diffusione Exercise 5: Modifica personalizzata di immagini Exercise 6: Image inpainting Exercise 7: Generazione di video Exercise 8: Crea un video!Exercise 9: Valutare le prestazioni della generazione video Exercise 10: Congratulazioni!