chore: update example in runkit and readme

README.md

@@ -69,33 +69,33 @@ If you’re using winkNLP in the browser use the [wink-eng-lite-web-model](https
 The "Hello World!" in winkNLP is given below:
 
 ```javascript
-// Load wink-nlp package  & helpers.
+// Load wink-nlp package.
 const winkNLP = require( 'wink-nlp' );
-// Load "its" helper to extract item properties.
-const its = require( 'wink-nlp/src/its.js' );
-// Load "as" reducer helper to reduce a collection.
-const as = require( 'wink-nlp/src/as.js' );
-// Load english language model — light version.
-const model = require( 'wink-eng-lite-model' );
+// Load english language model.
+const model = require( 'wink-eng-lite-web-model' );
 // Instantiate winkNLP.
 const nlp = winkNLP( model );
-
+// Obtain "its" helper to extract item properties.
+const its = nlp.its;
+// Obtain "as" reducer helper to reduce a collection.
+const as = nlp.as;
+
 // NLP Code.
 const text = 'Hello   World🌎! How are you?';
 const doc = nlp.readDoc( text );
-
+ 
 console.log( doc.out() );
 // -> Hello   World🌎! How are you?
-
+ 
 console.log( doc.sentences().out() );
 // -> [ 'Hello   World🌎!', 'How are you?' ]
-
+ 
 console.log( doc.entities().out( its.detail ) );
 // -> [ { value: '🌎', type: 'EMOJI' } ]
-
+ 
 console.log( doc.tokens().out() );
 // -> [ 'Hello', 'World', '🌎', '!', 'How', 'are', 'you', '?' ]
-
+ 
 console.log( doc.tokens().out( its.type, as.freqTable ) );
 // -> [ [ 'word', 5 ], [ 'punctuation', 2 ], [ 'emoji', 1 ] ]
 ```

runkit-example.js

@@ -1,20 +1,29 @@
+// Load wink-nlp package.
 const winkNLP = require( 'wink-nlp' );
-const its = require( 'wink-nlp/src/its.js' );
-// Use web model for RunKit.
+// Load english language model — light version.
 const model = require( 'wink-eng-lite-web-model' );
+// Instantiate winkNLP.
 const nlp = winkNLP( model );
+// Obtain "its" helper to extract item properties.
+const its = nlp.its;
+// Obtain "as" reducer helper to reduce a collection.
+const as = nlp.as;
 
-const text = 'Its quarterly profits jumped 76% to $1.13 billion for the three months to December, from $639million of previous year.';
+// NLP Code.
+const text = 'Hello   World🌎! How are you?';
 const doc = nlp.readDoc( text );
-// Print tokens.
-console.log( doc.tokens().out() );
-// Print each token's type.
-console.log( doc.tokens().out( its.type ) );
-// Print details of each entity.
+
+console.log( doc.out() );
+// -> Hello   World🌎! How are you?
+
+console.log( doc.sentences().out() );
+// -> [ 'Hello   World🌎!', 'How are you?' ]
+
 console.log( doc.entities().out( its.detail ) );
-// Markup entities along with their type for highlighting them in the text.
-doc.entities().each( ( e ) => {
-  e.markup( '<mark>', `<sub style="font-weight:900"> ${e.out(its.type)}</sub></mark>` );
-} );
-// Render them as HTML via RunKit
-doc.out( its.markedUpText );
+// -> [ { value: '🌎', type: 'EMOJI' } ]
+
+console.log( doc.tokens().out() );
+// -> [ 'Hello', 'World', '🌎', '!', 'How', 'are', 'you', '?' ]
+
+console.log( doc.tokens().out( its.type, as.freqTable ) );
+// -> [ [ 'word', 5 ], [ 'punctuation', 2 ], [ 'emoji', 1 ] ]