/nlp-architect

NLP Architect by Intel AI Lab: A library for exploring state-of-the-art deep learning topologies and techniques for optimizing Natural Language Processing and Natural Language Understanding neural networks

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A Deep Learning NLP/NLU library by Intel® AI Lab

GitHub Website DOI GitHub release

Overview | Models | Installation | Examples | Documentation | Tutorials | Contributing

NLP Architect is an open source Python library for exploring state-of-the-art deep learning topologies and techniques for optimizing Natural Language Processing and Natural Language Understanding neural network.

Overview

NLP Architect is an NLP library designed to be flexible, easy to extend, allow for easy and rapid integration of NLP models in applications and to showcase optimized models.

Features:

  • Core NLP models used in many NLP tasks and useful in many NLP applications

  • Novel NLU models showcasing novel topologies and techniques

  • Optimized NLP/NLU models showcasing different optimization algorithms on neural NLP/NLU models

  • Simple REST API server (doc):

    • serving trained models (for inference)
    • plug-in system for adding your own model

  • 4 Demos of models (pre-trained by us) showcasing NLP Architect (Dependency parser, NER, Intent Extraction, Q&A)

  • Based on optimized Deep Learning frameworks:

  • Documentation website and tutorials

  • Essential utilities for working with NLP models - Text/String pre-processing, IO, data-manipulation, metrics, embeddings.

Installing NLP Architect

We recommend to install NLP Architect in a new python environment, to use python 3.6+ with up-to-date pip, setuptools and h5py.

Install from source (Github)

Includes core library and all content (example scripts, datasets, tutorials)

Clone repository

git clone https://github.com/NervanaSystems/nlp-architect.git
cd nlp-architect

Install (in develop mode)

pip install -e .

Install from pypi (using pip install)

Includes only core library

pip install nlp-architect

Further installation options

Refer to our full installation instructions page on our website for complete details on how to install NLP Architect and other backend installations such as MKL-DNN or GPU backends. Users can install any deep learning backends manually before/after they install NLP Architect.

Models

NLP models that provide best (or near) in class performance:

Natural Language Understanding (NLU) models that address semantic understanding:

Components instrumental for conversational AI:

Optimizing NLP/NLU models and misc. optimization techniques:

End-to-end Deep Learning-based NLP models:

Solutions (End-to-end applications) using one or more models:

Documentation

Full library documentation of NLP models, algorithms, solutions and instructions on how to run each model can be found on our website.

NLP Architect library design philosophy

NLP Architect is a model-oriented library designed to showcase novel and different neural network optimizations. The library contains NLP/NLU related models per task, different neural network topologies (which are used in models), procedures for simplifying workflows in the library, pre-defined data processors and dataset loaders and misc utilities. The library is designed to be a tool for model development: data pre-process, build model, train, validate, infer, save or load a model.

The main design guidelines are:

  • Deep Learning framework agnostic
  • NLP/NLU models per task
  • Different topologies used in models
  • Showcase End-to-End applications (Solutions) utilizing one or more NLP Architect model
  • Generic dataset loaders, textual data processing utilities, and miscellaneous utilities that support NLP model development (loaders, text processors, io, metrics, etc.)
  • Procedures for defining processes for training, inference, optimization or any kind of elaborate script.
  • Pythonic API for using models for inference
  • REST API servers with ability to serve trained models via HTTP
  • Extensive model documentation and tutorials

Demo UI examples

Dependency parser

Intent Extraction

Packages

Package Description
nlp_architect.api Model API interfaces
nlp_architect.common Common packages
nlp_architect.cli Command line module
nlp_architect.data Datasets, loaders and data processors
nlp_architect.models NLP, NLU and End-to-End models
nlp_architect.nn Topology related models and additions (per framework)
nlp_architect.pipelines End-to-end NLP apps
nlp_architect.procedures Procedure scripts
nlp_architect.server API Server and demos UI
nlp_architect.solutions Solution applications
nlp_architect.utils Misc. I/O, metric, pre-processing and text utilities

Note

NLP Architect is an active space of research and development; Throughout future releases new models, solutions, topologies and framework additions and changes will be made. We aim to make sure all models run with Python 3.6+. We encourage researchers and developers to contribute their work into the library.

Citing

If you use NLP Architect in your research, please use the following citation:

@misc{izsak_peter_2018_1477518,
  title        = {NLP Architect by Intel AI Lab},
  month        = nov,
  year         = 2018,
  doi          = {10.5281/zenodo.1477518},
  url          = {https://doi.org/10.5281/zenodo.1477518}
}

Disclaimer

The NLP Architect is released as reference code for research purposes. It is not an official Intel product, and the level of quality and support may not be as expected from an official product. NLP Architect is intended to be used locally and has not been designed, developed or evaluated for production usage or web-deployment. Additional algorithms and environments are planned to be added to the framework. Feedback and contributions from the open source and NLP research communities are more than welcome.

Contact

Contact the NLP Architect development team through Github issues or email: nlp_architect@intel.com