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  3. Working with Hugging Face
  • 1

    Getting Started with Hugging Face

    Gratuito

    Start your journey with the Hugging Face platform by understanding what Hugging Face is and common use cases. Then, you'll learn about the Hugging Face Hub including models and datasets available, how to search for them, navigate model, or dataset, cards, and download. Lastly, you'll learn about the high-level components of transformers and LLMs.

  • 2

    Building Pipelines with Hugging Face

    It's time to dive into the Hugging Face ecosystem! You'll start by learning the basics of the pipeline module and Auto classes from the transformers library. Then, you'll learn at a high level what natural language processing and tokenization is. Finally, you'll start using the pipeline module for several text-based tasks, including text classification.

  • 3

    Building Pipelines for Image and Audio

    In this chapter, you'll apply pipeline methodologies to new tasks using image and audio data. Specifically, you will learn ways to process these types of data in preparation for tasks such as classification, question and answering and automatic speech recognition.

  • 4

    Fine-tuning and Embeddings

    Explore the different frameworks for fine-tuning, text generation, and embeddings. Start with the basics of fine-tuning a pre-trained model on a specific dataset and task to improve performance. Then, use Auto classes to generate the text from prompts and images. Finally, you will explore how to generate and use embeddings.

Initializing

Ejercicio

Visual question and answering

Visual question and answering is an ML task that attempts to provide the best answer for a question about an image. The model will analyze the content of the image and return a label as the answer.

For example, if asking about the clothes a model is wearing, the model may return clothing items as the label. Such a task can be beneficial for people who are visually impaired or as a classification method (similar to image classification but more open-ended).

pipeline from the transformers library and both the question and image are already loaded for you.

Instrucciones

100 XP
  • Create a visual question and answering pipeline by setting the task to visual-question-answering and save as vqa.
  • Use the vqa pipeline to get an answer for the image and question, then save as results.