fastNLP is a modular and extensible NLP framework designed to reduce engineering boilerplate in user projects, such as data processing loops and training cycles. It targets NLP practitioners and researchers seeking a streamlined workflow for tasks like text classification, offering features for efficient training and multi-framework compatibility.

How It Works

fastNLP provides a high-level API for data handling, model training, and evaluation. It abstracts away complex engineering tasks, allowing users to focus on model logic. Key components include DataSet and DataBundle for data management, Trainer and Evaluator for streamlined training and evaluation loops, and support for distributed training and mixed-precision (fp16) out-of-the-box. Its modular design and backend abstraction enable compatibility with PyTorch, PaddlePaddle, and Jittor.

Quick Start & Requirements

• Install: pip install fastNLP>=1.0.0alpha
• Prerequisites: PyTorch (>=1.6.0) or PaddlePaddle (>=2.2.0) with paddlenlp (>=2.3.3).
• Documentation: fastNLP Documentation
• Tutorials: 10 Minute Quick Start (Torch), Quick Start (Paddle)

Highlighted Details

• Supports PyTorch, PaddlePaddle, and Jittor backends.
• Built-in support for fp16, multi-GPU, and ZeRO optimization.
• cache_results decorator for efficient data preprocessing.
• Trainer and Evaluator classes simplify training and evaluation loops.
• apply_field and apply_field_more for efficient data transformation.

Maintenance & Community

The project is currently in incubation. Further community and maintenance details are not explicitly provided in the README.

Licensing & Compatibility

The README does not explicitly state a license. Compatibility for commercial use or closed-source linking is not specified.

Limitations & Caveats

Version 1.0.0+ features a redesigned architecture, making it incompatible with older versions, requiring code adjustments for prior fastNLP users. The project is noted as being "currently still in incubation."