Metadata-Version: 2.1
Name: nlp-architect
Version: 0.4
Summary: Intel AI Lab's open-source NLP and NLU research library
Home-page: https://github.com/NervanaSystems/nlp-architect
Author: Intel AI Lab
Author-email: nlp_architect@intel.com
License: Apache 2.0
Description: <p align="center">
            <br>
            <a href="https://github.com/NervanaSystems/nlp-architect/">
                <img src="https://raw.githubusercontent.com/NervanaSystems/nlp-architect/master/assets/nlp_architect_header.png" width="400"/>
            </a>
            <br>
        <p>
        <h2 align="center">
        A Deep Learning NLP/NLU library by <a href="https://www.intel.ai/research/">Intel® AI Lab</a>
        </h2>
        <p align="center">
            <a href="https://github.com/NervanaSystems/nlp-architect/blob/master/LICENSE">
                <img alt="GitHub" src="https://img.shields.io/github/license/NervanaSystems/nlp-architect.svg?color=blue&style=popout">
            </a>
            <a href="http://nlp_architect.nervanasys.com">
                <img alt="Website" src="https://img.shields.io/website/http/nlp_architect.nervanasys.com.svg?down_color=red&down_message=offline&style=popout&up_message=online">
            </a>
            <a href="https://doi.org/10.5281/zenodo.1477518">
                <img src="https://zenodo.org/badge/DOI/10.5281/zenodo.1477518.svg" alt="DOI">
            </a>
            <a href="https://pepy.tech/project/nlp-architect">
                <img src="https://pepy.tech/badge/nlp-architect"/>
            </a>
            <a href="https://github.com/NervanaSystems/nlp-architect/blob/master/LICENSE">
                <img alt="GitHub release" src="https://img.shields.io/github/release/NervanaSystems/nlp-architect.svg?style=popout">
            </a>
        </p>
        
        <h4 align="center">
          <a href="#overview">Overview</a> |
          <a href="#models">Models</a> |
          <a href="#installing-nlp-architect">Installation</a> |
          <a href="https://github.com/NervanaSystems/nlp-architect/tree/master/examples">Examples</a> <a href="http://nlp_architect.nervanasys.com/"></a> |
          <a href="http://nlp_architect.nervanasys.com">Documentation</a> |
          <a href="https://github.com/NervanaSystems/nlp-architect/tree/master/tutorials">Tutorials</a> |
          <a href="http://nlp_architect.nervanasys.com/developer_guide.html">Contributing</a>
        </h4>
        
        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. NLP Architect's main purpose is to provide easy usage of NLP and NLU models while providing state-of-art and robust implementation.
        
        ## 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
        * Simple REST API server ([doc](http://nlp_architect.nervanasys.com/service.html)):
          * 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:
          * [TensorFlow]
          * [Intel-Optimized TensorFlow with MKL-DNN]
          * [Dynet]
        * Documentation [website](http://nlp_architect.nervanasys.com/) and [tutorials](http://nlp_architect.nervanasys.com/tutorials.html)
        * 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
        
        ```sh
        git clone https://github.com/NervanaSystems/nlp-architect.git
        cd nlp-architect
        ```
        
        Install (in develop mode)
        
        ```sh
        pip install -e .
        ```
        
        ### Install from pypi (using `pip install`)
        
        Includes only core library
        
        ```sh
        pip install nlp-architect
        ```
        
        ### Further installation options
        
        Refer to our full [installation instructions](http://nlp_architect.nervanasys.com/installation.html) page on our website for complete details on how to install NLP Architect and other backend installations such as MKL-DNN or GPU backends.
        
        ## Models
        
        NLP models that provide best (or near) in class performance:
        
        * [Word chunking](http://nlp_architect.nervanasys.com/chunker.html)
        * [Named Entity Recognition](http://nlp_architect.nervanasys.com/ner_crf.html)
        * [Dependency parsing](http://nlp_architect.nervanasys.com/bist_parser.html)
        * [Intent Extraction](http://nlp_architect.nervanasys.com/intent.html)
        * [Sentiment classification](http://nlp_architect.nervanasys.com/supervised_sentiment.html)
        * [Language models](http://nlp_architect.nervanasys.com/tcn.html)
        
        Natural Language Understanding (NLU) models that address semantic understanding:
        
        * [Aspect Based Sentiment Analysis (ABSA)](http://nlp_architect.nervanasys.com/absa.html)
        * [Noun phrase embedding representation (NP2Vec)](http://nlp_architect.nervanasys.com/np2vec.html)
        * [Most common word sense detection](http://nlp_architect.nervanasys.com/word_sense.html)
        * [Relation identification](http://nlp_architect.nervanasys.com/identifying_semantic_relation.html)
        * [Cross document coreference](http://nlp_architect.nervanasys.com/cross_doc_coref.html)
        * [Noun phrase semantic segmentation](http://nlp_architect.nervanasys.com/np_segmentation.html)
        
        Components instrumental for conversational AI:
        
        * [Joint intent detection and slot tagging](http://nlp_architect.nervanasys.com/intent.html)
        * [Memory Networks for goal oriented dialog](http://nlp_architect.nervanasys.com/memn2n.html)
        
        End-to-end Deep Learning-based NLP models:
        
        * [Reading comprehension](http://nlp_architect.nervanasys.com/reading_comprehension.html)
        * [Sparse and Quantized Neural Machine Translation (GNMT)](http://nlp_architect.nervanasys.com/sparse_gnmt.html)
        * [Language Modeling using Temporal Convolution Network (TCN)](http://nlp_architect.nervanasys.com/tcn.html)
        * [Unsupervised Cross-lingual embeddings](http://nlp_architect.nervanasys.com/crosslingual_emb.html)
        
        Solutions (End-to-end applications) using one or more models:
        
        * [Term Set expansion](http://nlp_architect.nervanasys.com/term_set_expansion.html) - uses the included word chunker as a noun phrase extractor and NP2Vec to create semantic term sets
        * [Topics and trend analysis](http://nlp_architect.nervanasys.com/trend_analysis.html) - analyzing trending phrases in temporal corpora
        * [Aspect Based Sentiment Analysis (ABSA)](http://nlp_architect.nervanasys.com/absa_solution.html)
        
        ## Documentation
        
        Full library [documentation](http://nlp_architect.nervanasys.com/) of NLP models, algorithms, solutions and instructions
        on how to run each model can be found on our [website](http://nlp_architect.nervanasys.com/).
        
        ## NLP Architect library design philosophy
        
        NLP Architect aspires to enable quick development of state-of-art NLP/NLU algorithms and to showcase Intel AI's efforts in deep-learning software optimization (Tensorflow MKL-DNN, etc.)
        The library is designed around the life cycle of model development - pre-process, build model, train, validate, infer, save or deploy.
        
        The main design guidelines are:
        
        * Deep Learning framework agnostic
        * Develop topologies utilized in NLP models
        * NLP/NLU models implementation using included topologies
        * 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.)
        * Pythonic API for training and inference
        * REST API servers with ability to serve trained models via HTTP
        * Extensive model documentation and tutorials
        
        ## Demo UI examples
        
        Dependency parser
        <p>
          <img src="https://raw.githubusercontent.com/NervanaSystems/nlp-architect/master/assets/bist-demo-small.png" height="375"/>
        </p>
        Intent Extraction
        <p>
          <img src="https://raw.githubusercontent.com/NervanaSystems/nlp-architect/master/assets/ie-demo-small.png" height="375"/>
        <p>
        
        ## Packages
        
        | Package                 	| Description                                          	|
        |-------------------------	|------------------------------------------------------	|
        | `nlp_architect.api`      	| Model server API interfaces                          	|
        | `nlp_architect.common`   	| Common packages                                      	|
        | `nlp_architect.contrib`  	| Framework extensions                                 	|
        | `nlp_architect.data`     	| 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.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,
          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          = {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
        
        [documentation]:http://nlp_architect.nervanasys.com
        [Intel-Optimized TensorFlow with MKL-DNN]:https://software.intel.com/en-us/articles/intel-optimized-tensorflow-wheel-now-available
        [TensorFlow]:https://www.tensorflow.org/
        [Dynet]:https://dynet.readthedocs.io/en/latest/
        
Keywords: NLP NLU deep learning natural language processing tensorflow keras dynet
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: End Users/Desktop
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Environment :: Console
Classifier: Environment :: Web Environment
Classifier: Operating System :: POSIX
Classifier: Operating System :: MacOS :: MacOS X
Requires-Python: >=3.6.*
Description-Content-Type: text/markdown
