Merge pull request #5 from JohnSnowLabs/0.2rc1

0.2rc1

Author: C-K-Loan

README.md

@@ -9,7 +9,7 @@ Take a look at our official Spark NLU page: [https://nlu.johnsnowlabs.com/](http
 
 
 ## NLU in action 
-<img src="http://ckl-it.de/wp-content/uploads/2020/08/My-Video6.gif" width="1800" height="500"/>
+<img src="http://ckl-it.de/wp-content/uploads/2020/09/git_final.gif" width="1800" height="500"/>
 
 
 
@@ -36,13 +36,14 @@ nlu.load('bert elmo albert xlnet glove use').predict('I love NLU! <3')
 ## What kind of models does NLU provide? 
 NLU provides everything a data scientist might want to wish for in one line of code!  
 - The 100+ of the latest NLP word embeddings ( BERT, ELMO, ALBERT, XLNET, GLOVE, BIOBERT, ELECTRA, COVIDBERT) and different variations of them 
-- The 10+ of the latest NLP sentence embeddings ( BERT, ELECTRA, USE) and different variations of them 
+- The 45+ of the latest NLP sentence embeddings ( BERT, ELECTRA, USE) and different variations of them 
+- 50 + Classifiers
 - Generation of Sentence, Chunk and Document from these embeddings 
 - Language Classification of 20 languages 
 - 36 pretrained NER models 
 - 34 Part of Speech (POS) models
 - 34 Lemmatizer models    
-- Emotion models for 5 categories 
+- Emotion (joy, fear, suprise, sadness) 
 - Labeled and Unlabeled Dependency parsing 
 - Spell Checking 
 - Stopword removers for 41  languages 
@@ -80,7 +81,7 @@ Working with text data can sometimes be quite a dirty Job. NLU helps you keep yo
 - Stopword Cleaners
 - Pattern Cleaners
 - Slang Cleaner 
-
+- Tokenization 
 ## Where can I see NLUs entire offer? 
 Checkout the [NLU Namespace](https://nlu.johnsnowlabs.com/docs/en/namespace) for everything that NLU has to offer! 
 
@@ -166,3 +167,9 @@ The following are Collab examples which showcase each NLU component and some app
 
 # Simple NLU Demos
 - [NLU different output levels Demo](https://colab.research.google.com/drive/1C4N3wpC17YzZf9fXHDNAJ5JvSmfbq7zT?usp=sharing)
+
+
+# NLU on Medium
+- [Introduction to NLU](https://medium.com/@christian.kasim.loan/1-line-of-code-350-nlp-models-with-john-snow-labs-nlu-in-python-2f1c55bba619?sk=1379426a00ca970a6e101a8f74f66bd2)
+- [One line BERT Word Embeddings and t-SNE plotting with NLU](https://medium.com/@christian.kasim.loan/1-line-to-bert-word-embeddings-with-nlu-f50d2b08cddc?sk=ec38a4ee6c87984f2b3cc4328b27135a)
+- [BERT, ALBERT, ELECTRA, ELMO, XLNET, GLOVE Word Embeddings in one line and plotting with t-SNE](https://medium.com/@christian.kasim.loan/1-line-of-code-for-bert-albert-elmo-electra-xlnet-glove-part-of-speech-with-nlu-and-t-sne-9ebcd5379cd?sk=58137630075d8f8b23b66b17c3aaa72d)

docs/Gemfile.lock

@@ -67,7 +67,7 @@ GEM
       jekyll-theme-time-machine (= 0.1.1)
       jekyll-titles-from-headings (= 0.5.1)
       jemoji (= 0.10.1)
-      kramdown (>= 2.3.0)
+      kramdown (= 1.17.0)
       liquid (= 4.0.0)
       listen (= 3.1.5)
       mercenary (~> 0.3)
@@ -185,9 +185,6 @@ GEM
       jekyll-seo-tag (~> 2.0)
     jekyll-titles-from-headings (0.5.1)
       jekyll (~> 3.3)
-    jekyll-toc (0.13.1)
-      jekyll (>= 3.7)
-      nokogiri (~> 1.9)
     jekyll-watch (2.2.1)
       listen (~> 3.0)
     jemoji (0.10.1)
@@ -206,7 +203,7 @@ GEM
       jekyll (~> 3.5)
       jekyll-feed (~> 0.9)
       jekyll-seo-tag (~> 2.1)
-    minitest (5.14.1)
+    minitest (5.14.2)
     multipart-post (2.1.1)
     nokogiri (1.10.10)
       mini_portile2 (~> 2.4.0)
@@ -252,7 +249,6 @@ PLATFORMS
 
 DEPENDENCIES
   github-pages (= 192)
-  jekyll-toc
 
 BUNDLED WITH
    2.1.4

docs/_data/navigation.yml

@@ -21,17 +21,17 @@ docs-en:
     children:
       - title:  Quick Start
         url:    /docs/en/install
+      - title:  General Concepts
+        url:    /docs/en/concepts
       - title:  Simple Examples
         url:    /docs/en/examples
-      - title:  Visualization Examples
-        url:    /docs/en/viz_examples
       - title:  Notebooks
         url:    /docs/en/notebooks
+      - title:  Visualization Examples
+        url:    /docs/en/viz_examples
 
   - title:      Documentation
     children:
-      - title:  General Concepts
-        url:    /docs/en/concepts
       - title:  NLU Load Function
         url:    /docs/en/load_api
       - title:  NLU Predict Function

docs/_layouts/article.html

@@ -5,6 +5,17 @@
 <style>
 
 
+  table.standard {
+  border: 1px solid ;
+  text-align: left;
+  border-collapse: collapse;
+  overflow: auto;
+  max-width: max-content;
+  margin-left: auto;
+  margin-right: auto;
+}
+
+
 
   table.steelBlueCols {
   border: 1px solid #1C6EA4;

docs/_layouts/landing.html

@@ -208,20 +208,31 @@ <h1>{{ _section.title }}</h1>
         </b></b></li><li><b><b><a class="button button--outline-info button--pill button--lg" href="#sarcasm-classifier">Sarcasm Classifer</a>
         </b></b></li><li><b><b><a class="button button--outline-info button--pill button--lg" href="#cyberbullying-classifier">Cyberbullying Classifier</a>
         </b></b></li><li><b><b><a class="button button--outline-info button--pill button--lg" href="#spam-classifier">Spam Classifier</a>
+        </b></b></li><li><b><b><a class="button button--outline-info button--pill button--lg" href="#toxic-classifier">Toxic Classifier</a>
+
         </b></b></li><li><b><b><a class="button button--outline-info button--pill button--lg" href="#fake-news-classifier">Fake News Classifier</a>
         </b></b></li><li><b><b><a class="button button--outline-info button--pill button--lg" href="#question-classifier-6-class">Classify Question (6 class)</a>
         </b></b></li><li><b><b><a class="button button--outline-info button--pill button--lg" href="#question-classifier-50-class">Classify Question (50 class)</a>
         </b></b></li><li><b><b><a class="button button--outline-info button--pill button--lg" href="#emotion-classifier">Multi Class Emotion detection</a>
+        </b></b></li><li><b><b><a class="button button--outline-info button--pill button--lg" href="#e2e-classifier">E2E Classifier</a>
+
         </b></b></li><li><b><b><a class="button button--outline-info button--pill button--lg" href="#sentiment-classifier">Sentiment detection</a>
         </b></b></li><li><b><b><a class="button button--outline-info button--pill button--lg" href="#spell-checking">Spell checking</a>
 
         </b></b></li><li><b><b><a class="button button--outline-info button--pill button--lg" href="#language-classifier">Language Classification</a>
-        </b></b></li><li><b><b><a class="button button--outline-info button--pill button--lg" href="#word-embeddings-bert">Bert Word Embeddings</a>
-        </b></b></li><li><b><b><a class="button button--outline-info button--pill button--lg" href="#word-embeddings-elmo">Elmo Word Embeddings</a>
-        </b></b></li><li><b><b><a class="button button--outline-info button--pill button--lg" href="#word-embeddings-albert">Albert Word Embeddings</a>
-        </b></b></li><li><b><b><a class="button button--outline-info button--pill button--lg" href="#word-embeddings-xlnet">Xlnet Word Embeddings</a>
-        </b></b></li><li><b><b><a class="button button--outline-info button--pill button--lg" href="#word-embeddings-glove">Glove Word Embeddings</a>
+
+        </b></b></li><li><b><b><a class="button button--outline-info button--pill button--lg" href="#word-embeddings-bert">BERT Word Embeddings</a>
+        </b></b></li><li><b><b><a class="button button--outline-info button--pill button--lg" href="#word-embeddings-elmo">ELMO Word Embeddings</a>
+        </b></b></li><li><b><b><a class="button button--outline-info button--pill button--lg" href="#word-embeddings-electra">Electra Word Embeddings</a>
+
+        </b></b></li><li><b><b><a class="button button--outline-info button--pill button--lg" href="#word-embeddings-albert">ALBERT Word Embeddings</a>
+        </b></b></li><li><b><b><a class="button button--outline-info button--pill button--lg" href="#word-embeddings-xlnet">XLNET Word Embeddings</a>
+        </b></b></li><li><b><b><a class="button button--outline-info button--pill button--lg" href="#word-embeddings-glove">GLOVE Word Embeddings</a>
+
         </b></b></li><li><b><b><a class="button button--outline-info button--pill button--lg" href="#sentence-embeddings-use">USE Sentence Embeddings</a>
+        </b></b></li><li><b><b><a class="button button--outline-info button--pill button--lg" href="#sentence-embeddings-bert">BERT Sentence Embeddings</a>
+        </b></b></li><li><b><b><a class="button button--outline-info button--pill button--lg" href="#sentence-embeddings-electra">ELECTRA Sentence Embeddings</a>
+
         </b></b></li><li><b><b><a class="button button--outline-info button--pill button--lg" href="#dependency-parsing-unlabeled">Dependency Parsing Unlabeled</a>
         </b></b></li><li><b><b><a class="button button--outline-info button--pill button--lg" href="#dependency-parsing-labeled">Dependency Parsing Labeled</a>
         </b></b></li><li><b><b><a class="button button--outline-info button--pill button--lg" href="#lemmatization">Lemmatization</a>
@@ -269,15 +280,15 @@ <h3 style="margin-left:-10px">NLU Features</h3>
             <ul>
               <li> - State-of-the-art <strong>Deep Learning</strong> algorithms</li>
               <li> - Achieve high accuracy with a <strong>one line of code</strong></li>
-              <li> - <strong>244 +</strong> unique NLU components</li>
+              <li> - <strong>350 +</strong> NLP Models</li>
               <li> - <strong>176 +</strong> unique NLP models and algorithms</li>
               <li> - <strong>68 +</strong> unique NLP pipelines consisting of composed NLU components</li>
-              <li> - <strong>60 +</strong> languages supported</li>
-              <li> - <strong>14 +</strong>  embeddings  <strong> BERT, ELMO, ALBERT, XLNET, GLOVE, USE </strong></li>
-              <li> - <strong>12 +</strong>  Pre-trained Classifiers : <strong> Emotion, Sarcasm, Language, Question</strong> </li>
+              <li> - <strong>50 +</strong> languages supported</li>
+              <li> - <strong>14 +</strong>  embeddings  <strong> BERT, ELMO, ALBERT, XLNET, GLOVE, USE, ELECTRA </strong></li>
+              <li> - <strong>50 +</strong>  Pre-trained Classifiers : <strong> Emotion, Sarcasm, Language, Question, E2E, Toxic</strong> </li>
               <li> - <strong>36 +</strong>  Pre-Trained  <strong> NER (Named Entity Recognition)  </strong> models </li>
               <li> - <strong>34 +</strong>  Pre-Trained  <strong> POS (Part of Speech)  </strong> models </li>
-              <li> - <strong>34 +</strong>  Pre-Trained  <strong> Lemmatizer  </strong> models </li>
+              <li> - <strong>3 +</strong>  Pre-Trained  <strong> Lemmatizer  </strong> models </li>
               <li> - Dependency parsing untyped and typed</li>
               <li> - Spell Checking</li>
               <li> - Multi-lingual NER models in <strong>Dutch, English, French, German, Italian, Norwegian, Polish, Portuguese, Russian, Spanish</strong></li>