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Update URLs to IntelLabs
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.github/workflows/build_docs.yml

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README.md

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<p align="center">
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<br>
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<img src="https://raw.githubusercontent.com/NervanaSystems/nlp-architect/master/docs-source/source/assets/nlp_architect_logo_trans.png" width="400"/>
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<img src="https://raw.githubusercontent.com/IntelLabs/nlp-architect/master/docs-source/source/assets/nlp_architect_logo_trans.png" width="400"/>
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<br>
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<p>
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<h2 align="center">
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A Deep Learning NLP/NLU library by <a href="https://www.intel.ai/research/">Intel® AI Lab</a>
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</h2>
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<p align="center">
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<a href="https://github.com/NervanaSystems/nlp-architect/blob/master/LICENSE">
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<img alt="GitHub" src="https://img.shields.io/github/license/NervanaSystems/nlp-architect.svg?color=blue&style=flat-square">
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<a href="https://github.com/IntelLabs/nlp-architect/blob/master/LICENSE">
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<img alt="GitHub" src="https://img.shields.io/github/license/IntelLabs/nlp-architect.svg?color=blue&style=flat-square">
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</a>
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<a href="http://nlp_architect.nervanasys.com">
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<img alt="Website" src="https://img.shields.io/website/http/nlp_architect.nervanasys.com.svg?down_color=red&down_message=offline&style=flat-square&up_message=online">
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<a href="https://intellabs.github.io/nlp-architect">
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<img alt="Website" src="https://img.shields.io/website/http/intellabs.github.io/nlp-architect.svg?down_color=red&down_message=offline&style=flat-square&up_message=online">
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</a>
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<a href="https://github.com/NervanaSystems/nlp-architect/releases">
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<img alt="GitHub release" src="https://img.shields.io/github/release/NervanaSystems/nlp-architect.svg?style=flat-square">
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<a href="https://github.com/IntelLabs/nlp-architect/releases">
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<img alt="GitHub release" src="https://img.shields.io/github/release/IntelLabs/nlp-architect.svg?style=flat-square">
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</a>
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<a href="https://pepy.tech/project/nlp-architect">
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<img src="https://pepy.tech/badge/nlp-architect">
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<a href="#overview">Overview</a> |
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<a href="#models">Models</a> |
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<a href="#installing-nlp-architect">Installation</a> |
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<a href="https://github.com/NervanaSystems/nlp-architect/tree/master/examples">Examples</a> <a href="http://nlp_architect.nervanasys.com/"></a> |
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<a href="http://nlp_architect.nervanasys.com">Documentation</a> |
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<a href="https://github.com/NervanaSystems/nlp-architect/tree/master/tutorials">Tutorials</a> |
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<a href="http://nlp_architect.nervanasys.com/developer_guide.html">Contributing</a>
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<a href="https://github.com/IntelLabs/nlp-architect/tree/master/examples">Examples</a> <a href="https://intellabs.github.io/nlp-architect/"></a> |
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<a href="https://intellabs.github.io/nlp-architect">Documentation</a> |
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<a href="https://github.com/IntelLabs/nlp-architect/tree/master/tutorials">Tutorials</a> |
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<a href="https://intellabs.github.io/nlp-architect/developer_guide.html">Contributing</a>
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</h4>
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NLP Architect is an open source Python library for exploring state-of-the-art
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Includes core library, examples, solutions and tutorials:
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```sh
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git clone https://github.com/NervanaSystems/nlp-architect.git
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git clone https://github.com/IntelLabs/nlp-architect.git
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cd nlp-architect
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pip install -e . # install in developer mode
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```
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NLP models that provide best (or near) in class performance:
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* [Word chunking](http://nlp_architect.nervanasys.com/tagging/sequence_tagging.html#word-chunker)
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* [Named Entity Recognition](http://nlp_architect.nervanasys.com/tagging/sequence_tagging.html#named-entity-recognition)
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* [Dependency parsing](http://nlp_architect.nervanasys.com/bist_parser.html)
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* [Intent Extraction](http://nlp_architect.nervanasys.com/intent.html)
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* [Sentiment classification](http://nlp_architect.nervanasys.com/sentiment.html#supervised-sentiment)
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* [Language models](http://nlp_architect.nervanasys.com/lm.html#language-modeling-with-tcn)
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* [Transformers](http://nlp_architect.nervanasys.com/transformers.html) (for NLP tasks)
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* [Word chunking](https://intellabs.github.io/nlp-architect/tagging/sequence_tagging.html#word-chunker)
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* [Named Entity Recognition](https://intellabs.github.io/nlp-architect/tagging/sequence_tagging.html#named-entity-recognition)
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* [Dependency parsing](https://intellabs.github.io/nlp-architect/bist_parser.html)
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* [Intent Extraction](https://intellabs.github.io/nlp-architect/intent.html)
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* [Sentiment classification](https://intellabs.github.io/nlp-architect/sentiment.html#supervised-sentiment)
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* [Language models](https://intellabs.github.io/nlp-architect/lm.html#language-modeling-with-tcn)
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* [Transformers](https://intellabs.github.io/nlp-architect/transformers.html) (for NLP tasks)
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Natural Language Understanding (NLU) models that address semantic understanding:
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* [Aspect Based Sentiment Analysis (ABSA)](http://nlp_architect.nervanasys.com/absa.html)
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* [Joint intent detection and slot tagging](http://nlp_architect.nervanasys.com/intent.html)
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* [Noun phrase embedding representation (NP2Vec)](http://nlp_architect.nervanasys.com/np2vec.html)
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* [Most common word sense detection](http://nlp_architect.nervanasys.com/word_sense.html)
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* [Relation identification](http://nlp_architect.nervanasys.com/identifying_semantic_relation.html)
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* [Cross document coreference](http://nlp_architect.nervanasys.com/cross_doc_coref.html)
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* [Noun phrase semantic segmentation](http://nlp_architect.nervanasys.com/np_segmentation.html)
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* [Aspect Based Sentiment Analysis (ABSA)](https://intellabs.github.io/nlp-architect/absa.html)
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* [Joint intent detection and slot tagging](https://intellabs.github.io/nlp-architect/intent.html)
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* [Noun phrase embedding representation (NP2Vec)](https://intellabs.github.io/nlp-architect/np2vec.html)
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* [Most common word sense detection](https://intellabs.github.io/nlp-architect/word_sense.html)
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* [Relation identification](https://intellabs.github.io/nlp-architect/identifying_semantic_relation.html)
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* [Cross document coreference](https://intellabs.github.io/nlp-architect/cross_doc_coref.html)
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* [Noun phrase semantic segmentation](https://intellabs.github.io/nlp-architect/np_segmentation.html)
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Optimizing NLP/NLU models and misc. optimization techniques:
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* [Quantized BERT (8bit)](http://nlp_architect.nervanasys.com/quantized_bert.html)
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* [Knowledge Distillation using Transformers](http://nlp_architect.nervanasys.com/transformers_distillation.html)
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* [Sparse and Quantized Neural Machine Translation (GNMT)](http://nlp_architect.nervanasys.com/sparse_gnmt.html)
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* [Quantized BERT (8bit)](https://intellabs.github.io/nlp-architect/quantized_bert.html)
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* [Knowledge Distillation using Transformers](https://intellabs.github.io/nlp-architect/transformers_distillation.html)
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* [Sparse and Quantized Neural Machine Translation (GNMT)](https://intellabs.github.io/nlp-architect/sparse_gnmt.html)
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Solutions (End-to-end applications) using one or more models:
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* [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
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* [Topics and trend analysis](http://nlp_architect.nervanasys.com/trend_analysis.html) - analyzing trending phrases in temporal corpora
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* [Aspect Based Sentiment Analysis (ABSA)](http://nlp_architect.nervanasys.com/absa_solution.html)
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* [Term Set expansion](https://intellabs.github.io/nlp-architect/term_set_expansion.html) - uses the included word chunker as a noun phrase extractor and NP2Vec to create semantic term sets
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* [Topics and trend analysis](https://intellabs.github.io/nlp-architect/trend_analysis.html) - analyzing trending phrases in temporal corpora
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* [Aspect Based Sentiment Analysis (ABSA)](https://intellabs.github.io/nlp-architect/absa_solution.html)
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## Documentation
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Full library [documentation](http://nlp_architect.nervanasys.com/) of NLP models, algorithms, solutions and instructions
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on how to run each model can be found on our [website](http://nlp_architect.nervanasys.com/).
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Full library [documentation](https://intellabs.github.io/nlp-architect/) of NLP models, algorithms, solutions and instructions
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on how to run each model can be found on our [website](https://intellabs.github.io/nlp-architect/).
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## NLP Architect library design philosophy
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Contact the NLP Architect development team through Github issues or
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[documentation]:http://nlp_architect.nervanasys.com
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[documentation]:https://intellabs.github.io/nlp-architect
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[TensorFlow]:https://www.tensorflow.org/
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[PyTorch]:https://pytorch.org/
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[Dynet]:https://dynet.readthedocs.io/en/latest/

docker/Dockerfile

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WORKDIR /workspace
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RUN git clone https://github.com/IntelLabs/nlp-architect.git
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docs-source/Makefile

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# ----------------------------------------------------------------------------
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# Copyright 2016 Nervana Systems Inc.
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# Copyright 2016 Intel Corp.
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at

docs-source/source/CONTRIBUTING.rst

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1. File an issue (to track your contribution):
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* Create an issue on GitHub:
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https://github.com/NervanaSystems/nlp-architect/issues
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https://github.com/IntelLabs/nlp-architect/issues
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2. Fork NLP Architect and/or update your checked out copy of
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nlp-architect to ensure you have the
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most recent commits from the master branch, for example:
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7. Create a new `pull request <https://github.com/IntelLabs/nlp-architect/pulls>`_
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docs-source/source/absa_solution.rst

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Installing and Running
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======================
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For instructions on running the end-to-end solution UI, see here: `ABSA Solution README <https://github.com/IntelLabs/nlp-architect/tree/master/solutions/absa_solution#setup>`__
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Workflow and UI

docs-source/source/index.rst

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nlp_architect API <generated_api/nlp_architect_api_index.rst>
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developer_guide.rst
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.. _https://github.com/NervanaSystems/nlp-architect: https://github.com/NervanaSystems/nlp-architect
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.. _https://github.com/IntelLabs/nlp-architect: https://github.com/IntelLabs/nlp-architect

docs-source/source/installation.rst

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.. code:: bash
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docs-source/source/main.rst

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The library includes our past and ongoing NLP research and development efforts as part of Intel AI Lab.
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NLP Architect can be downloaded from Github: https://github.com/IntelLabs/nlp-architect
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Overview
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========

docs-source/source/quick_start.rst

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.. code:: bash
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docs-source/source/sparse_gnmt.rst

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.. _wmt16_en_de.sh: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/wmt16_en_de.sh
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.. _`WMT Shared Task: Machine Translation of News`: http://www.statmt.org/wmt16/translation-task.html
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.. _`Shared Task`: http://www.statmt.org/wmt16/translation-task.html
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.. _`Model Zoo`: https://intellabs.github.io/nlp-architect/model_zoo.html
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.. _`TensorFlow API`: https://www.tensorflow.org/api_docs/python/tf/quantize
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docs-source/source/static/install.js

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cmd.push("git clone https://github.com/IntelLabs/nlp-architect.git<br>cd nlp-architect")
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docs-source/source/tutorials.rst

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List of tutorials
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-----------------
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- `Natural Language Question/Answering Systems <https://github.com/NervanaSystems/nlp-architect/blob/master/tutorials/Question_Answering/Natural_Language_Question_Answer_Systems.ipynb>`_
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- `Intent Extraction <https://github.com/NervanaSystems/nlp-architect/blob/master/tutorials/intent_extraction/intent_extraction_demo.ipynb>`_
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- `Named Entity Recognition <https://github.com/NervanaSystems/nlp-architect/blob/master/tutorials/ner/ner_demo.ipynb>`_
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- `NP Semantic Segmentation <https://github.com/NervanaSystems/nlp-architect/blob/master/tutorials/np_semantic_segmentation/np_semantic_segmentation_demo.ipynb>`_
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- `Sentiment Analysis <https://github.com/NervanaSystems/nlp-architect/blob/master/tutorials/sentiment/Deep_Learning_Sentiment_Demo.ipynb>`_
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- `NP2Vec <https://github.com/NervanaSystems/nlp-architect/blob/master/tutorials/NP2vec/NP2vec_training.ipynb>`_
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- `Term set expansion <https://github.com/NervanaSystems/private-nlp-architect/blob/master/tutorials/Term_Set_Expansion/term_set_expansion.ipynb>`_
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- `Natural Language Question/Answering Systems <https://github.com/IntelLabs/nlp-architect/blob/master/tutorials/Question_Answering/Natural_Language_Question_Answer_Systems.ipynb>`_
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- `Intent Extraction <https://github.com/IntelLabs/nlp-architect/blob/master/tutorials/intent_extraction/intent_extraction_demo.ipynb>`_
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- `Named Entity Recognition <https://github.com/IntelLabs/nlp-architect/blob/master/tutorials/ner/ner_demo.ipynb>`_
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- `NP Semantic Segmentation <https://github.com/IntelLabs/nlp-architect/blob/master/tutorials/np_semantic_segmentation/np_semantic_segmentation_demo.ipynb>`_
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- `Sentiment Analysis <https://github.com/IntelLabs/nlp-architect/blob/master/tutorials/sentiment/Deep_Learning_Sentiment_Demo.ipynb>`_
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- `NP2Vec <https://github.com/IntelLabs/nlp-architect/blob/master/tutorials/NP2vec/NP2vec_training.ipynb>`_
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- `Term set expansion <https://github.com/IntelLabs/private-nlp-architect/blob/master/tutorials/Term_Set_Expansion/term_set_expansion.ipynb>`_

docs/CNAME

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examples/chunker/README.md

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## Documentation
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Full documentation of this example and the neural network model can be found here: [https://intellabs.github.io/nlp-architect/chunker.html](https://intellabs.github.io/nlp-architect/chunker.html)
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examples/sparse_gnmt/README.md

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## Results & Pre-Trained Models
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The following table presents some of our experiments and results.
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Furthermore, some of our pre-trained models are offered in the form of checkpoints in our [Model Zoo](https://intellabs.github.io/nlp-architect/model_zoo.html).
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| Model | Sparsity | BLEU| Non-Zero Parameters | Data Type |

licenses/neon-license.txt

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licenses/ngraph_python-license.txt

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https://github.com/NervanaSystems/ngraph-python/blob/master/LICENSE
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https://github.com/IntelLabs/ngraph-python/blob/master/LICENSE
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Apache License

server/angular-ui/dist/angular-ui/main.24b2853170a2cf16e1bc.js

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setup.py

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keywords="NLP NLU deep learning natural language processing tensorflow pytorch",
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author_email="[email protected]",
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url="https://github.com/NervanaSystems/nlp-architect",
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url="https://github.com/IntelLabs/nlp-architect",
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solutions/absa_solution/README.md

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## Video Demo
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[![Video Demo](https://raw.githubusercontent.com/NervanaSystems/nlp-architect/master/solutions/absa_solution/assets/video.png)](https://drive.google.com/open?id=1BLk0xkjIOqyRhNy4UQEFQpDF_KR_NMAd)
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[![Video Demo](https://raw.githubusercontent.com/IntelLabs/nlp-architect/master/solutions/absa_solution/assets/video.png)](https://drive.google.com/open?id=1BLk0xkjIOqyRhNy4UQEFQpDF_KR_NMAd)
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*Figure 1*
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## Workflow and UI
2020

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![Workflow](https://raw.githubusercontent.com/NervanaSystems/nlp-architect/master/solutions/absa_solution/assets/absa_solution_workflow.png)
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![Workflow](https://raw.githubusercontent.com/IntelLabs/nlp-architect/master/solutions/absa_solution/assets/absa_solution_workflow.png)
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*Figure 2*
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![Workflow](https://raw.githubusercontent.com/NervanaSystems/nlp-architect/master/solutions/absa_solution/assets/absa_solution_ui_3.png)
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![Workflow](https://raw.githubusercontent.com/IntelLabs/nlp-architect/master/solutions/absa_solution/assets/absa_solution_ui_3.png)
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*Figure 3*
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@@ -54,7 +54,7 @@ source absa_env/bin/activate
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- **Clone and install**:
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```bash
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git clone https://github.com/NervanaSystems/nlp-architect.git
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git clone https://github.com/IntelLabs/nlp-architect.git
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pip install -U pip
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pip install -e nlp-architect
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pip install -r nlp-architect/solutions/absa_solution/requirements.txt

solutions/absa_solution/ui.py

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Original file line numberDiff line numberDiff line change
@@ -74,7 +74,7 @@ def _create_header(train_dropdown, inference_dropdown, text_status) -> layouts.R
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"""Utility function for creating and styling the header row in the UI layout."""
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architect_logo = Div(
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text='<a href="http://nlp_architect.nervanasys.com"> <img border="0" '
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text='<a href="https://intellabs.github.io/nlp-architect"> <img border="0" '
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'src="style/nlp_architect.jpg" width="200"></a> by Intel® AI Lab',
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style={
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"margin-left": "500px",

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