NLP Architect by Intel® AI Lab

NLP Architect is an open-source Python library for exploring the state-of-the-art deep learning topologies and techniques for natural language processing and natural language understanding. It is intended to be a platform for future research and collaboration.

The library includes our past and ongoing NLP research and development efforts as part of Intel AI Lab.

NLP Architect can be downloaded from Github:

Library Overview

Research driven NLP/NLU models

The library contains state-of-art and novel NLP and NLU models in a variety of topics:

  • Dependency parsing
  • Intent detection and Slot tagging model for Intent based applications
  • Memory Networks for goal-oriented dialog
  • Noun phrase embedding vectors model
  • Noun phrase semantic segmentation
  • Named Entity Recognition
  • Word Chunking
  • Reading comprehension
  • Language modeling using Temporal Convolution Network
  • Unsupervised Crosslingual Word Embedding
  • Aspect Based Sentiment Analysis
  • Supervised sentiment analysis
  • Sparse and quantized neural machine translation
  • Relation Identification and cross document coreference

Quick Install

Select the desired configuration of your system:

Install from
Create virtualenv?
Install in developer mode?

Run the following commands to install NLP Architect:

It is recommended to install NLP Architect in development mode to utilize all its features, examples and solutions.

How can NLP Architect be used

  • Train models using provided algorithms, reference datasets and configurations
  • Train models using your own data
  • Create new/extend models based on existing models or topologies
  • Explore how deep learning models tackle various NLP tasks
  • Experiment and optimize state-of-the-art deep learning algorithms
  • integrate modules and utilities from the library to solutions

Deep Learning frameworks

Because of the current research nature of the library, several open source deep learning frameworks are used in this repository including:

Overtime the list of models and frameworks included in this space will change, though all generally run with Python 3.6+

Using the Models

Each of the models includes a comprehensive description on algorithms, network topologies, reference dataset descriptions and loader, and evaluation results. Overtime the list of models included in this space will grow.

Contributing to the library

We welcome collaboration, suggestions, and critiques. For information on how to become a developer on this project, please see the developer guide.