fastai is a deep learning library built on PyTorch that provides high-level, modular APIs for training neural networks across vision, text, tabular, collaborative filtering, and medical imaging domains. Its architecture centers on a `Learner` class that orchestrates training loops, callbacks, metrics, and data pipelines. Data loading is handled by composable `DataBlock` and `TfmdDL` abstractions, while a rich callback system (with 30+ built-in callbacks) enables customization of logging, scheduling, mixed precision, gradient accumulation, and experiment tracking (WandB, Comet, TensorBoard). The library uses nbdev for literate programming, generating all source code from Jupyter notebooks, and includes factory methods like `vision_learner`, `text_classifier_learner`, and `tabular_learner` for rapid model construction with transfer learning. It supports distributed training via Hugging Face Accelerate, mixed precision (AMP/BF16), and provides research-oriented components such as AWD-LSTM, XResNet, U-Net, GANs, and custom loss functions (Focal, Dice, Label Smoothing).
BLURR is a library that integrates Hugging Face transformers with fastai for NLP tasks. It provides high-level, mid-level, and low-level APIs to train and deploy transformer models for text classification, token classification, question answering, summarization, translation, and language modeling. The library leverages fastai's data block API and learner system, wrapping Hugging Face models to work seamlessly within the fastai training loop. Key components include custom data blocks for text processing, model wrappers for Hugging Face integration, specialized loss functions, and callbacks for optimization. The repository includes extensive examples and notebooks demonstrating usage with standard datasets like GLUE, SQuAD, and CNN/DailyMail.