Query on Hugging Face's Transformer Library | Julio Herrera
Hello to everyone here, I introduce myself as Julio Herrera from London. I’m a beginner and a new learner, please help to solve my query. How does Hugging Face’s Transformer library handle the fine-tuning of large pre-trained language models on domain-specific datasets, and what strategies can be employed?I am working on a multi-modal machine learning project that involves integrating text, image, and audio data. Using the Hugging Face Transformers library, how can I fine-tune a pre-trained model to handle these different data modalities simultaneously? Specifically, what steps should I take to preprocess and encode each type of data, and how can I design a model architecture that effectively combines these modalities for downstream tasks such as classification or generation? Additionally, what are some best practices for optimizing performance and handling the increased computational complexity of multi-modal inputs?Thank you in advance