NielsRogge/Transformers-Tutorials
Transformers Tutorials
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.
Features
- Transformer Implementations - Serves as a comprehensive guide for implementing and deploying transformer architectures across various ML domains.
- Transformer Tutorials - Offers a comprehensive collection of tutorials and notebooks for implementing transformer-based architectures across various modalities.
- Computer Vision - Provides a wide range of computer vision implementations including object detection, segmentation, and depth estimation.
- Implementation Guides - Provides implementation examples for deploying models that combine text, image, and audio inputs.
- Model Fine-Tuning - Provides practical demonstrations for adapting pre-trained transformer models to custom datasets using training loops.
- Model Fine-tuning Resources - Provides code samples and educational materials for optimizing pre-trained models using training loops and accelerators.
- Multimodal Processing - Implements techniques for combining text, image, and audio data into shared latent spaces for cross-modal analysis.
- Natural Language Processing - Implements natural language processing tasks such as text classification, document information extraction, and image captioning.
- Pre-trained Model Application - Demonstrates how to use weights from pre-trained transformer networks to perform predictions on new data.
- Supervised Fine-Tuning - Includes resources for optimizing pre-trained models through supervised fine-tuning on custom labeled datasets.
- Object Detection - Provides implementation examples for identifying and locating specific objects within images using bounding boxes.
- Depth Estimation - Implements algorithms to calculate the distance of objects from the camera using single-image depth estimation.
- Image Segmentation - Provides guides for isolating specific objects or semantic regions within images using segmentation techniques.
- Synthetic Media Generators - Includes examples for generating synthetic images from text prompts and creating descriptive captions for media.
- Hardware Acceleration - Provides examples for utilizing GPUs to accelerate the training and inference of transformer models.
- Data Preparation Tools - Offers utilities and examples for cleaning and transforming raw data into formats suitable for transformer model ingestion.
- Tensor Conversion Utilities - Provides examples for preparing PyTorch datasets by converting raw files into tensors and batches.
- Text Classifiers - Demonstrates the categorization of text sequences and audio signals into predefined labels.
- Zero-Shot Inference - Covers methods for performing classification or segmentation on new categories without specific training data.
- Document Parsing and Extraction - Demonstrates how to parse document images to classify layouts and extract structured information.
- Audio Event Classification - Implements models to categorize audio files and signals into specific labels using transformer architectures.
- Dataset Tensor Mappings - Provides examples for converting raw input files into tensors and batches for efficient model processing.
- Dataset Preparation Tutorials - Includes guides for converting raw files into tensors and batches for PyTorch machine learning pipelines.
