This is a collection of tutorials and practical demonstrations for implementing machine learning tasks using the HuggingFace Transformers library. It serves as a guide for applying transformer architectures across computer vision, natural language processing, and audio analysis.
The repository provides implementation examples for multimodal model deployment, including the combination of text, image, and audio inputs. It includes resources for optimizing pre-trained models through fine-tuning on custom datasets and provides examples for preparing PyTorch datasets by converting raw files into tensors and batches.
The covered capabilities span various machine learning domains, including object detection, image segmentation, and depth estimation in computer vision, as well as audio signal classification and text categorization. It also covers the generation of visual content and the extraction of information from document images.
