PyText is a deprecated natural language modeling framework built on PyTorch, designed for rapid experimentation and large-scale inference. It targets NLP researchers and engineers needing production-ready models for tasks like text classification and sequence tagging, offering seamless deployment via Caffe2.

How It Works

PyText leverages PyTorch for deep learning models and Caffe2 for optimized inference. It provides extensible interfaces for model components, enabling quick iteration on new architectures. The framework supports distributed and mixed-precision training, enhancing speed and memory efficiency.

Quick Start & Requirements

• Install: pip install pytext-nlp (requires Python 3.6.1+)
• Documentation: https://github.com/facebookresearch/pytext
• Example: pytext train < demo/configs/docnn.json

Highlighted Details

• Production-ready models for text classification (CNNs, self-attention) and sequence tagging (NER, joint intent-slot).
• Supports distributed training via PyTorch's C10d backend and mixed-precision training with APEX.
• Extensible components for creating new models and tasks.
• Reference implementations for semantic parsing papers.

Maintenance & Community

• Deprecated: The project is no longer actively maintained. Users are directed to migrate to torchtext.
• Community: Facebook group available at https://www.facebook.com/groups/pytext/

Licensing & Compatibility

• BSD-licensed.
• Compatible with commercial use and closed-source linking.

Limitations & Caveats

The project is officially deprecated and will not receive further active maintenance, posing a risk for long-term adoption. Users are strongly advised to migrate to torchtext.