NLP Architect by Intel AI Lab: A Python library for exploring the state-of-the-art deep learning topologies and techniques for natural language processing and natural language understanding
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NLP Architect by Intel® AI LAB

NLP Architect is an open-source Python library for exploring 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 current version of NLP Architect includes these features that we found interesting from both research perspectives and practical applications:

  • NLP core models and NLU modules that provide best in class performance: Intent Extraction (IE), Name Entity Recognition (NER), Word Chunker, Dependency parser (BIST)
  • Modules that address semantic understanding: co-locations, most common word sense, noun phrase embedding representation (NP2Vec), relation identification and cross document coreference.
  • Components instrumental for conversational AI: ChatBot applications (Memory Networks for Dialog, Key-Value Memory Networks), Intent Extraction.
  • End-to-end DL applications using and new topologies: Q&A, Machine Reading Comprehension, Language modeling using Temporal Convolution Networks (TCN), Unsupervised Cross-lingual embeddings, Sparse and quantized GNMT.
  • Solutions using one or more models: Set Term expansion which uses the included word chunker as a noun phrase extractor and NP2Vec, Topics and trend analysis for analyzing temporal corpora.

The library consists of core modules (topologies), data pipelines, utilities and end-to-end model examples with training and inference scripts. We look at these as a set of building blocks that were needed for implementing NLP use cases based on our pragmatic research experience. Each of the models includes algorithm descriptions and results in the documentation.

Some of the components, with provided pre-trained models, are exposed as REST service APIs through NLP Architect server. NLP Architect server is designed to provide predictions across different models in NLP Architect. It also includes a web front-end exposing the model annotations for visualizations. The server supports extensions via a template for developers to add a new service. For detailed documentation see this page.

NLP Architect server in action

NLP Architect utilizes the following open source deep learning frameworks:


Framework documentation on NLP models, algorithms, and modules, and instructions on how to contribute can be found at our main documentation site.



To get started, clone our repository:

git clone
cd nlp-architect

Installing NLP Architect within a virtual environment to ensure a self-contained environment is recommended. To create and activate a new virtual environment:

python3 -m venv .nlp_architect_env
source .nlp_architect_env/bin/activate

Make sure pip and setuptools and venv are up to date before installing.

pip3 install -U pip setuptools


NLP Architect supports CPU, GPU and Intel Optimized Tensorflow (MKL-DNN). Select the desired backend using a dedicated environment variable.

CPU (default):


MKL-DNN (Linux OS only):


GPU (Linux OS only):



NLP Architect is installed using pip and it is recommended to install in development mode.


pip3 install .

Development mode:

pip3 install -e .

Once installed, the nlp_architect command provides additional options to work with the library, issue nlp_architect -h to see all options.


Package Description
nlp_architect.api Model server API interfaces
nlp_architect.common Common packages
nlp_architect.contrib Framework extensions Datasets, data loaders and data classes
nlp_architect.models NLP, NLU and End-to-End neural models
nlp_architect.pipelines End-to-end NLP apps
nlp_architect.utils Misc. I/O, metric, pre-processing and text utilities
examples Example files for each model
solutions Solution applications
server API Server and demos UI
tutorials Misc. Jupyter tutorials

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 3.5+. We encourage researchers and developers to contribute their work into the library.


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

  author       = {Izsak, Peter and
                  Bethke, Anna and
                  Korat, Daniel and
                  Yaccobi, Amit and
                  Mamou, Jonathan and
                  Guskin, Shira and
                  Nittur Sridhar, Sharath and
                  Keller, Andy and
                  Pereg, Oren and
                  Eirew, Alon and
                  Tsabari, Sapir and
                  Green, Yael and
                  Kothapalli, Chinnikrishna and
                  Eavani, Harini and
                  Wasserblat, Moshe and
                  Liu, Yinyin and
                  Boudoukh, Guy and
                  Zafrir, Ofir and
                  Tewani, Maneesh},
  title        = {NLP Architect by Intel AI Lab},
  month        = nov,
  year         = 2018,
  doi          = {10.5281/zenodo.1477518},
  url          = {}


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. 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 the NLP Architect development team through Github issues or email: