A Deep Learning NLP/NLU library by Intel® AI Lab
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 Networks.
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:
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Core NLP models used in many NLP tasks and useful in many NLP applications
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Novel NLU models showcasing novel topologies and techniques
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Optimized NLP/NLU models showcasing different optimization algorithms on neural NLP/NLU models
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Model-oriented design:
- Train and run models from command-line.
- API for using models for inference in python.
- Procedures to define custom processes for training, inference or anything related to processing.
- CLI sub-system for running procedures
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Based on optimized Deep Learning frameworks:
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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 using pip
Install core library only
pip install nlp-architect
Install from source (Github)
Includes core library, examples, solutions and tutorials:
git clone https://github.com/IntelLabs/nlp-architect.git
cd nlp-architect
pip install -e . # install in developer mode
Running Examples and Solutions
To run provided examples and solutions please install the library with [all] flag which will install extra packages required. (requires installation from source)
pip install .[all]
Models
NLP models that provide best (or near) in class performance:
- Word chunking
- Named Entity Recognition
- Dependency parsing
- Intent Extraction
- Sentiment classification
- Language models
- Transformers (for NLP tasks)
Natural Language Understanding (NLU) models that address semantic understanding:
- Aspect Based Sentiment Analysis (ABSA)
- Joint intent detection and slot tagging
- Noun phrase embedding representation (NP2Vec)
- Most common word sense detection
- Relation identification
- Cross document coreference
- Noun phrase semantic segmentation
Optimizing NLP/NLU models and misc. optimization techniques:
- Quantized BERT (8bit)
- Knowledge Distillation using Transformers
- Sparse and Quantized Neural Machine Translation (GNMT)
Solutions (End-to-end applications) using one or more models:
- Term Set expansion - uses the included word chunker as a noun phrase extractor and NP2Vec to create semantic term sets
- Topics and trend analysis - analyzing trending phrases in temporal corpora
- Aspect Based Sentiment Analysis (ABSA)
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
- Extensive model documentation and tutorials
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: [email protected]
![Problem running bist FileNotFoundError: [Errno 2] No such file or directory: (...) params.json](https://avatars.githubusercontent.com/u/35842763?v=4)
![question: [Quantization] Which files to change to make inference faster for Q8BERT?](https://avatars.githubusercontent.com/u/9082467?v=4)
![question: [Q8Bert experiment Setting]](https://avatars.githubusercontent.com/u/32248418?v=4)
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