Updated docs links

Author: Peter Izsak

docs/_modules/nlp_architect/api/intent_extraction_api.html

@@ -255,12 +255,12 @@ <h1>Source code for nlp_architect.api.intent_extraction_api</h1><div class="high
                 <span class="k">if</span> <span class="n">agreed</span> <span class="ow">is</span> <span class="kc">False</span><span class="p">:</span>
                     <span class="n">sys</span><span class="o">.</span><span class="n">exit</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
             <span class="n">download_unlicensed_file</span><span class="p">(</span>
-                <span class="s2">&quot;https://s3-us-west-2.amazonaws.com/nlp-architect-data&quot;</span> <span class="s2">&quot;/models/intent/&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;https://d2zs9tzlek599f.cloudfront.net&quot;</span> <span class="s2">&quot;/models/intent/&quot;</span><span class="p">,</span>
                 <span class="s2">&quot;model_info.dat&quot;</span><span class="p">,</span>
                 <span class="n">IntentExtractionApi</span><span class="o">.</span><span class="n">pretrained_model_info</span><span class="p">,</span>
             <span class="p">)</span>
             <span class="n">download_unlicensed_file</span><span class="p">(</span>
-                <span class="s2">&quot;https://s3-us-west-2.amazonaws.com/nlp-architect-data&quot;</span> <span class="s2">&quot;/models/intent/&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;https://d2zs9tzlek599f.cloudfront.net&quot;</span> <span class="s2">&quot;/models/intent/&quot;</span><span class="p">,</span>
                 <span class="s2">&quot;model.h5&quot;</span><span class="p">,</span>
                 <span class="n">IntentExtractionApi</span><span class="o">.</span><span class="n">pretrained_model</span><span class="p">,</span>
             <span class="p">)</span>

docs/_modules/nlp_architect/api/ner_api.html

@@ -254,12 +254,12 @@ <h1>Source code for nlp_architect.api.ner_api</h1><div class="highlight"><pre>
                 <span class="k">if</span> <span class="n">agreed</span> <span class="ow">is</span> <span class="kc">False</span><span class="p">:</span>
                     <span class="n">sys</span><span class="o">.</span><span class="n">exit</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
             <span class="n">download_unlicensed_file</span><span class="p">(</span>
-                <span class="s2">&quot;https://s3-us-west-2.amazonaws.com/nlp-architect-data&quot;</span> <span class="s2">&quot;/models/ner/&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;https://d2zs9tzlek599f.cloudfront.net&quot;</span> <span class="s2">&quot;/models/ner/&quot;</span><span class="p">,</span>
                 <span class="s2">&quot;model_v4.h5&quot;</span><span class="p">,</span>
                 <span class="bp">self</span><span class="o">.</span><span class="n">pretrained_model</span><span class="p">,</span>
             <span class="p">)</span>
             <span class="n">download_unlicensed_file</span><span class="p">(</span>
-                <span class="s2">&quot;https://s3-us-west-2.amazonaws.com/nlp-architect-data&quot;</span> <span class="s2">&quot;/models/ner/&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;https://d2zs9tzlek599f.cloudfront.net&quot;</span> <span class="s2">&quot;/models/ner/&quot;</span><span class="p">,</span>
                 <span class="s2">&quot;model_info_v4.dat&quot;</span><span class="p">,</span>
                 <span class="bp">self</span><span class="o">.</span><span class="n">pretrained_model_info</span><span class="p">,</span>
             <span class="p">)</span>

docs/_modules/nlp_architect/models/absa/utils.html

@@ -221,7 +221,7 @@ <h1>Source code for nlp_architect.models.absa.utils</h1><div class="highlight"><
         <span class="n">makedirs</span><span class="p">(</span><span class="n">rerank_model_dir</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
         <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;dowloading pre-trained reranking model..&quot;</span><span class="p">)</span>
         <span class="n">download_unlicensed_file</span><span class="p">(</span>
-            <span class="s2">&quot;https://s3-us-west-2.amazonaws.com/nlp-architect-data/models/&quot;</span> <span class="s2">&quot;absa/&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;https://d2zs9tzlek599f.cloudfront.net/models/&quot;</span> <span class="s2">&quot;absa/&quot;</span><span class="p">,</span>
             <span class="s2">&quot;rerank_model.h5&quot;</span><span class="p">,</span>
             <span class="n">rerank_model_full_path</span><span class="p">,</span>
         <span class="p">)</span>

docs/_modules/nlp_architect/models/pretrained_models.html

@@ -203,7 +203,7 @@ <h1>Source code for nlp_architect.models.pretrained_models</h1><div class="highl
 
 <span class="kn">from</span> <span class="nn">nlp_architect</span> <span class="kn">import</span> <span class="n">LIBRARY_OUT</span>
 
-<span class="n">S3_PREFIX</span> <span class="o">=</span> <span class="s2">&quot;https://s3-us-west-2.amazonaws.com/nlp-architect-data/&quot;</span>
+<span class="n">S3_PREFIX</span> <span class="o">=</span> <span class="s2">&quot;https://d2zs9tzlek599f.cloudfront.net/&quot;</span>
 
 
 <div class="viewcode-block" id="PretrainedModel"><a class="viewcode-back" href="../../../generated_api/nlp_architect.models.html#nlp_architect.models.pretrained_models.PretrainedModel">[docs]</a><span class="k">class</span> <span class="nc">PretrainedModel</span><span class="p">:</span>

docs/_modules/nlp_architect/pipelines/spacy_bist.html

@@ -344,7 +344,7 @@ <h1>Source code for nlp_architect.pipelines.spacy_bist</h1><div class="highlight
         <span class="n">zip_path</span> <span class="o">=</span> <span class="n">SpacyBISTParser</span><span class="o">.</span><span class="n">dir</span> <span class="o">/</span> <span class="s2">&quot;bist-pretrained.zip&quot;</span>
         <span class="n">makedirs</span><span class="p">(</span><span class="n">SpacyBISTParser</span><span class="o">.</span><span class="n">dir</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
         <span class="n">download_unlicensed_file</span><span class="p">(</span>
-            <span class="s2">&quot;https://s3-us-west-2.amazonaws.com/nlp-architect-data/models/dep_parse/&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;https://d2zs9tzlek599f.cloudfront.net/models/dep_parse/&quot;</span><span class="p">,</span>
             <span class="s2">&quot;bist-pretrained.zip&quot;</span><span class="p">,</span>
             <span class="n">zip_path</span><span class="p">,</span>
         <span class="p">)</span>

docs/_sources/model_zoo.rst.txt

@@ -27,27 +27,27 @@ NLP Architect Model Zoo
      - Links
    * - :doc:`Sparse GNMT <sparse_gnmt>`
      - 90% sparse GNMT model and a 2x2 block sparse translating German to English trained on Europarl-v7 [#]_ , Common Crawl and News Commentary 11 datasets
-     -  | `model <https://s3-us-west-2.amazonaws.com/nlp-architect-data/models/sparse_gnmt/gnmt_sparse.zip>`_
-        | `2x2 block sparse model <https://s3-us-west-2.amazonaws.com/nlp-architect-data/models/sparse_gnmt/gnmt_blocksparse2x2.zip>`_
+     -  | `model <https://d2zs9tzlek599f.cloudfront.net/models/sparse_gnmt/gnmt_sparse.zip>`_
+        | `2x2 block sparse model <https://d2zs9tzlek599f.cloudfront.net/models/sparse_gnmt/gnmt_blocksparse2x2.zip>`_
    * - :doc:`Intent Extraction <intent>`
      - A :py:class:`MultiTaskIntentModel <nlp_architect.models.intent_extraction.MultiTaskIntentModel>` intent extraction and slot tagging model, trained on SNIPS NLU dataset
-     - | `model <https://s3-us-west-2.amazonaws.com/nlp-architect-data/models/intent/model.h5>`_
-       | `params <https://s3-us-west-2.amazonaws.com/nlp-architect-data/models/intent/model_info.dat>`_
+     - | `model <https://d2zs9tzlek599f.cloudfront.net/models/intent/model.h5>`_
+       | `params <https://d2zs9tzlek599f.cloudfront.net/models/intent/model_info.dat>`_
    * - :doc:`Named Entity Recognition <ner_crf>`
      - A :py:class:`NERCRF <nlp_architect.models.ner_crf.NERCRF>` model trained on CoNLL 2003 dataset
-     - | `model <https://s3-us-west-2.amazonaws.com/nlp-architect-data/models/ner/model.h5>`_
-       | `params <https://s3-us-west-2.amazonaws.com/nlp-architect-data/models/ner/model_info.dat>`_
+     - | `model <https://d2zs9tzlek599f.cloudfront.net/models/ner/model.h5>`_
+       | `params <https://d2zs9tzlek599f.cloudfront.net/models/ner/model_info.dat>`_
    * - :doc:`Dependency parser <bist_parser>`
      - Graph-based dependency parser using BiLSTM feature extractors
-     - `model <https://s3-us-west-2.amazonaws.com/nlp-architect-data/models/dep_parse/bist-pretrained.zip>`_
+     - `model <https://d2zs9tzlek599f.cloudfront.net/models/dep_parse/bist-pretrained.zip>`_
    * - :doc:`Machine comprehension <reading_comprehension>`
      - Match LSTM model trained on SQuAD dataset
-     - | `model <https://s3-us-west-2.amazonaws.com/nlp-architect-data/models/mrc/mrc_model.zip>`_
-       | `data <https://s3-us-west-2.amazonaws.com/nlp-architect-data/models/mrc/mrc_data.zip>`_
+     - | `model <https://d2zs9tzlek599f.cloudfront.net/models/mrc/mrc_model.zip>`_
+       | `data <https://d2zs9tzlek599f.cloudfront.net/models/mrc/mrc_data.zip>`_
    * - :doc:`Word chunker <chunker>`
      - A word chunker model trained on CoNLL 2000 dataset
-     - | `model <https://s3-us-west-2.amazonaws.com/nlp-architect-data/models/chunker/model.h5>`_
-       | `params <https://s3-us-west-2.amazonaws.com/nlp-architect-data/models/chunker/model_info.dat.params>`_
+     - | `model <https://d2zs9tzlek599f.cloudfront.net/models/chunker/model.h5>`_
+       | `params <https://d2zs9tzlek599f.cloudfront.net/models/chunker/model_info.dat.params>`_
 
 References
 ----------

docs/_sources/publications.rst.txt

@@ -53,4 +53,4 @@ Demos
 -----
 - NeurIPS 2018:
 
-  - Unsupervised Aspect-based Sentiment Analysis (`video <https://s3-us-west-2.amazonaws.com/nlp-architect-data/content/absa_kingsman_demo.mp4>`_)
+  - Unsupervised Aspect-based Sentiment Analysis (`video <https://d2zs9tzlek599f.cloudfront.net/content/absa_kingsman_demo.mp4>`_)

docs/_sources/sparse_gnmt.rst.txt

@@ -151,7 +151,7 @@ Run inference using our pre-trained models:
 .. code-block:: bash
 
     # Download pre-trained model zip file, e.g. gnmt_sparse.zip
-    wget https://s3-us-west-2.amazonaws.com/nlp-architect-data/models/sparse_gnmt/gnmt_sparse.zip
+    wget https://d2zs9tzlek599f.cloudfront.net/models/sparse_gnmt/gnmt_sparse.zip
 
     # Unzip checkpoint + vocabulary files
     unzip gnmt_sparse.zip -d /tmp/gnmt_sparse_checkpoint
@@ -386,5 +386,5 @@ References
 .. _`Shared Task`: http://www.statmt.org/wmt16/translation-task.html
 .. _`Model Zoo`: http://nlp_architect.nervanasys.com/model_zoo.html
 .. _`TensorFlow API`: https://www.tensorflow.org/api_docs/python/tf/quantize
-.. _`Sparse`: https://s3-us-west-2.amazonaws.com/nlp-architect-data/models/sparse_gnmt/gnmt_sparse.zip
-.. _`2x2 Block Sparse`: https://s3-us-west-2.amazonaws.com/nlp-architect-data/models/sparse_gnmt/gnmt_blocksparse2x2.zip
+.. _`Sparse`: https://d2zs9tzlek599f.cloudfront.net/models/sparse_gnmt/gnmt_sparse.zip
+.. _`2x2 Block Sparse`: https://d2zs9tzlek599f.cloudfront.net/models/sparse_gnmt/gnmt_blocksparse2x2.zip

docs/_sources/term_set_expansion.rst.txt

@@ -84,19 +84,19 @@ size, min_count, window and hs hyperparameters. Please refer to the np2vec modul
     --corpus_format txt
 
 
-A `pretrained model <https://s3-us-west-2.amazonaws.com/nlp-architect-data/models/term_set/enwiki-20171201_pretrained_set_expansion.txt.tar.gz>`__
+A `pretrained model <https://d2zs9tzlek599f.cloudfront.net/models/term_set/enwiki-20171201_pretrained_set_expansion.txt.tar.gz>`__
 on English Wikipedia dump (``enwiki-20171201-pages-articles-multistream.xml.bz2``) is available under
 Apache 2.0 license. It has been trained with hyperparameters values
-recommended above. Full English Wikipedia `raw corpus <https://s3-us-west-2.amazonaws.com/nlp-architect-data/models/term_set/enwiki-20171201.txt.gz>`_ and
-`marked corpus <https://s3-us-west-2.amazonaws.com/nlp-architect-data/models/term_set/enwiki-20171201_spacy_marked.txt.tar.gz>`_
+recommended above. Full English Wikipedia `raw corpus <https://d2zs9tzlek599f.cloudfront.net/models/term_set/enwiki-20171201.txt.gz>`_ and
+`marked corpus <https://d2zs9tzlek599f.cloudfront.net/models/term_set/enwiki-20171201_spacy_marked.txt.tar.gz>`_
 are also available under the
 `Creative Commons Attribution-Share-Alike 3.0 License <https://creativecommons.org/licenses/by-sa/3.0/>`__.
 
-A `pretrained model with grouping <https://s3-us-west-2.amazonaws.com/nlp-architect-data/models/term_set/enwiki-20171201_grouping_pretrained_set_expansion.tar.gz>`__
+A `pretrained model with grouping <https://d2zs9tzlek599f.cloudfront.net/models/term_set/enwiki-20171201_grouping_pretrained_set_expansion.tar.gz>`__
 on the same English Wikipedia dump is also
 available under
 Apache 2.0 license. It has been trained with hyperparameters values
-recommended above. `Marked corpus <https://s3-us-west-2.amazonaws.com/nlp-architect-data/models/term_set/enwiki-20171201_grouping_marked.txt.tar.gz>`_
+recommended above. `Marked corpus <https://d2zs9tzlek599f.cloudfront.net/models/term_set/enwiki-20171201_grouping_marked.txt.tar.gz>`_
 is also available under the
 `Creative Commons Attribution-Share-Alike 3.0 License <https://creativecommons.org/licenses/by-sa/3.0/>`__.
 

docs/_sources/trend_analysis.rst.txt

@@ -37,7 +37,7 @@ In this stage, the algorithm will also train a W2V model on the joint corpora to
 In the second stage the topic lists are being compared and analyzed.
 Finally the UI reads the analysis data and generates automatic reports for extracted topics, “Hot” and “Cold” trends, and topic clustering in 2D space.
 
-The noun phrase extraction module is using a pre-trained `model <https://s3-us-west-2.amazonaws.com/nlp-architect-data/models/chunker/model.h5>`__ which is available under the Apache 2.0 license.
+The noun phrase extraction module is using a pre-trained `model <https://d2zs9tzlek599f.cloudfront.net/models/chunker/model.h5>`__ which is available under the Apache 2.0 license.
 
 Flow diagram
 ============

docs/model_zoo.html

@@ -202,44 +202,44 @@ <h1>NLP Architect Model Zoo<a class="headerlink" href="#nlp-architect-model-zoo"
 <tr class="row-even"><td><a class="reference internal" href="sparse_gnmt.html"><span class="doc">Sparse GNMT</span></a></td>
 <td>90% sparse GNMT model and a 2x2 block sparse translating German to English trained on Europarl-v7 <a class="footnote-reference" href="#id9" id="id1">[1]</a> , Common Crawl and News Commentary 11 datasets</td>
 <td><div class="first last line-block">
-<div class="line"><a class="reference external" href="https://s3-us-west-2.amazonaws.com/nlp-architect-data/models/sparse_gnmt/gnmt_sparse.zip">model</a></div>
-<div class="line"><a class="reference external" href="https://s3-us-west-2.amazonaws.com/nlp-architect-data/models/sparse_gnmt/gnmt_blocksparse2x2.zip">2x2 block sparse model</a></div>
+<div class="line"><a class="reference external" href="https://d2zs9tzlek599f.cloudfront.net/models/sparse_gnmt/gnmt_sparse.zip">model</a></div>
+<div class="line"><a class="reference external" href="https://d2zs9tzlek599f.cloudfront.net/models/sparse_gnmt/gnmt_blocksparse2x2.zip">2x2 block sparse model</a></div>
 </div>
 </td>
 </tr>
 <tr class="row-odd"><td><a class="reference internal" href="intent.html"><span class="doc">Intent Extraction</span></a></td>
 <td>A <a class="reference internal" href="generated_api/nlp_architect.models.html#nlp_architect.models.intent_extraction.MultiTaskIntentModel" title="nlp_architect.models.intent_extraction.MultiTaskIntentModel"><code class="xref py py-class docutils literal notranslate"><span class="pre">MultiTaskIntentModel</span></code></a> intent extraction and slot tagging model, trained on SNIPS NLU dataset</td>
 <td><div class="first last line-block">
-<div class="line"><a class="reference external" href="https://s3-us-west-2.amazonaws.com/nlp-architect-data/models/intent/model.h5">model</a></div>
-<div class="line"><a class="reference external" href="https://s3-us-west-2.amazonaws.com/nlp-architect-data/models/intent/model_info.dat">params</a></div>
+<div class="line"><a class="reference external" href="https://d2zs9tzlek599f.cloudfront.net/models/intent/model.h5">model</a></div>
+<div class="line"><a class="reference external" href="https://d2zs9tzlek599f.cloudfront.net/models/intent/model_info.dat">params</a></div>
 </div>
 </td>
 </tr>
 <tr class="row-even"><td><span class="xref std std-doc">Named Entity Recognition</span></td>
 <td>A <a class="reference internal" href="generated_api/nlp_architect.models.html#nlp_architect.models.ner_crf.NERCRF" title="nlp_architect.models.ner_crf.NERCRF"><code class="xref py py-class docutils literal notranslate"><span class="pre">NERCRF</span></code></a> model trained on CoNLL 2003 dataset</td>
 <td><div class="first last line-block">
-<div class="line"><a class="reference external" href="https://s3-us-west-2.amazonaws.com/nlp-architect-data/models/ner/model.h5">model</a></div>
-<div class="line"><a class="reference external" href="https://s3-us-west-2.amazonaws.com/nlp-architect-data/models/ner/model_info.dat">params</a></div>
+<div class="line"><a class="reference external" href="https://d2zs9tzlek599f.cloudfront.net/models/ner/model.h5">model</a></div>
+<div class="line"><a class="reference external" href="https://d2zs9tzlek599f.cloudfront.net/models/ner/model_info.dat">params</a></div>
 </div>
 </td>
 </tr>
 <tr class="row-odd"><td><a class="reference internal" href="bist_parser.html"><span class="doc">Dependency parser</span></a></td>
 <td>Graph-based dependency parser using BiLSTM feature extractors</td>
-<td><a class="reference external" href="https://s3-us-west-2.amazonaws.com/nlp-architect-data/models/dep_parse/bist-pretrained.zip">model</a></td>
+<td><a class="reference external" href="https://d2zs9tzlek599f.cloudfront.net/models/dep_parse/bist-pretrained.zip">model</a></td>
 </tr>
 <tr class="row-even"><td><span class="xref std std-doc">Machine comprehension</span></td>
 <td>Match LSTM model trained on SQuAD dataset</td>
 <td><div class="first last line-block">
-<div class="line"><a class="reference external" href="https://s3-us-west-2.amazonaws.com/nlp-architect-data/models/mrc/mrc_model.zip">model</a></div>
-<div class="line"><a class="reference external" href="https://s3-us-west-2.amazonaws.com/nlp-architect-data/models/mrc/mrc_data.zip">data</a></div>
+<div class="line"><a class="reference external" href="https://d2zs9tzlek599f.cloudfront.net/models/mrc/mrc_model.zip">model</a></div>
+<div class="line"><a class="reference external" href="https://d2zs9tzlek599f.cloudfront.net/models/mrc/mrc_data.zip">data</a></div>
 </div>
 </td>
 </tr>
 <tr class="row-odd"><td><span class="xref std std-doc">Word chunker</span></td>
 <td>A word chunker model trained on CoNLL 2000 dataset</td>
 <td><div class="first last line-block">
-<div class="line"><a class="reference external" href="https://s3-us-west-2.amazonaws.com/nlp-architect-data/models/chunker/model.h5">model</a></div>
-<div class="line"><a class="reference external" href="https://s3-us-west-2.amazonaws.com/nlp-architect-data/models/chunker/model_info.dat.params">params</a></div>
+<div class="line"><a class="reference external" href="https://d2zs9tzlek599f.cloudfront.net/models/chunker/model.h5">model</a></div>
+<div class="line"><a class="reference external" href="https://d2zs9tzlek599f.cloudfront.net/models/chunker/model_info.dat.params">params</a></div>
 </div>
 </td>
 </tr>

docs/publications.html

@@ -227,7 +227,7 @@ <h2>Video Tutorials<a class="headerlink" href="#video-tutorials" title="Permalin
 <h2>Demos<a class="headerlink" href="#demos" title="Permalink to this headline">¶</a></h2>
 <ul class="simple">
 <li>NeurIPS 2018:<ul>
-<li>Unsupervised Aspect-based Sentiment Analysis (<a class="reference external" href="https://s3-us-west-2.amazonaws.com/nlp-architect-data/content/absa_kingsman_demo.mp4">video</a>)</li>
+<li>Unsupervised Aspect-based Sentiment Analysis (<a class="reference external" href="https://d2zs9tzlek599f.cloudfront.net/content/absa_kingsman_demo.mp4">video</a>)</li>
 </ul>
 </li>
 </ul>