Text tokenizing

In this exercise, you will use the flickr dataset, which has 30,000 images and associated captions, to perform preprocessing operations on text. This is necessary to be used by models for tasks such as text classification. This is especially useful for multi-modal applications where Hugging Face models can be used to check caption suitability for an associated image.

The dataset (dataset) has been loaded and the AutoTokenizer has been imported.

Diese Übung ist Teil des Kurses

Multi-Modal Models with Hugging Face

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

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Diese Übung ist Teil des Kurses

Multi-Modal Models with Hugging Face

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4.8+

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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

Aktuelle Übung

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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!