Document VQA with LayoutLM

From VQA with images to VQA with documents! In this exercise, you will use the layoutlm-document-qa model to determine the total amount of training hours provided to employees in the years 2012-2013 from this document image:

Newsletter from an ICT company

The dataset (dataset) has been loaded and the pipeline module (pipeline) has been imported.

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Multi-Modal Models with Hugging Face

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Istruzioni dell'esercizio

Load the pipeline using the 'document-question-answering' task and 'impira/layoutlm-document-qa' checkpoint.
Process the document in data point 61 of the test set from dataset with an appropriate prompt to find how many days of formal training were provided to employees in 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

Multi-Modal Models with 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

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

Esercizio in corso

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!