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.

This exercise is part of the course

Multi-Modal Models with Hugging Face

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

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

# Load the tokenizer of the pretrained model
tokenizer = ____

Edit and Run Code

This exercise is part of the course

Multi-Modal Models with Hugging Face

IntermediateSkill Level

5.0+

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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: Loading and preprocessing data Exercise 5: Text tokenizing

Current Exercise

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: Image-text similarity Exercise 2: Zero-shot learning with CLIP Exercise 3: Automated caption quality assessment Exercise 4: Multimodal sentiment analysis Exercise 5: Vision Language models: multi-modal setup Exercise 6: Vision Language models: multi-modal sentiment Exercise 7: Audio-visual emotion analysis Exercise 8: Video audio splitting Exercise 9: CLIP CLAP: Multi-modal emotion classification

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: Multimodal QA tasks Exercise 2: Image-text to text with ViLT Exercise 3: Document-text to text with LayoutLM Exercise 4: Text-guided image editing Exercise 5: Custom image editing Exercise 6: Image inpainting Exercise 7: Multimodal video generation Exercise 8: Video generation Exercise 9: Assessing video generation performance Exercise 10: Congratulations!