diff --git a/source/wink-nlp/language-models.html.markdown.erb b/source/wink-nlp/language-models.html.markdown.erb index f8cd05f..9c39401 100644 --- a/source/wink-nlp/language-models.html.markdown.erb +++ b/source/wink-nlp/language-models.html.markdown.erb @@ -57,7 +57,7 @@ WinkNLP comes with pre-trained language models with gzipped sizes starting from Usage - Broswer and Node.js both. Recommended for Node.js version 16 and 18. + Browser and Node.js both. Recommended for Node.js version 16 and 18. diff --git a/source/wink-nlp/wink-nlp-in-browsers.html.markdown.erb b/source/wink-nlp/wink-nlp-in-browsers.html.markdown.erb index e7b1ee9..b76fb15 100644 --- a/source/wink-nlp/wink-nlp-in-browsers.html.markdown.erb +++ b/source/wink-nlp/wink-nlp-in-browsers.html.markdown.erb @@ -7,7 +7,7 @@ title: WinkNLP in browsers? WinkNLP is designed to work on Node.js and web browsers both. Apart from building a server side solution using Node.js, you can build a pure browser side NLP app with equal ease. To do this, we need to use the web version of the English lite model — [wink-eng-lite-web-model](https://www.npmjs.com/package/wink-eng-lite-web-model). We'll also need a tool that can bundle all the required modules, something like [Webpack](https://webpack.js.org/) or [Browserify](http://browserify.org/). For the purpose of this tutorial we'll use Browserify. First, lets install the required packages:
- We'll be installing Broswerify globally, refer to its documentation for more details. + We'll be installing Browserify globally, refer to its documentation for more details.
npm install wink-nlp --save
@@ -29,7 +29,7 @@ const doc = nlp.readDoc( text );
 doc.entities().each((e) => e.markup());
 document.getElementById("result").innerHTML = doc.out(its.markedUpText);
-Now, we'll use Broswerify to bundle all the required modules into a single file: +Now, we'll use Browserify to bundle all the required modules into a single file:
browserify token-counter.js -o bundle.js