docs(README): add section on model details

README.md

@@ -2,7 +2,7 @@
 **English lite language model for winkNLP**
 
 [<img align="right" src="https://decisively.github.io/wink-logos/logo-title.png" width="100px" >](https://winkjs.org/)
-This is a pre-trained English language model for the winkjs NLP package — [Wink NLP](https://winkjs.org/wink-nlp/). The lite model package has a size of under 800KB, which expands to about 2.4MB after installation. It is an open-source language model, released under the MIT license.
+This is a pre-trained English language model for the winkjs NLP package — [Wink NLP](https://winkjs.org/wink-nlp/). The lite model package has a size of under 825KB, which expands to about 2.4MB after installation. It is an open-source language model, released under the MIT license.
 
 It contains models for the following NLP tasks:
 
@@ -11,16 +11,17 @@ It contains models for the following NLP tasks:
 3. Sentence Boundary Detection
 4. Negation Handling
 5. POS tagging
-6. Lemmatization (upcoming)
+6. Automatic mapping of British spellings to American
 7. Named Entity Recognition
 8. Sentiment Analysis
 9. Custom Entities Definition
+10. *Lemmatization (upcoming)*
 
 
 
-### Getting Started
+## Getting Started
 
-#### Installation
+### Installation
 The model must be installed along with the [wink-nlp](https://winkjs.org/wink-nlp/):
 
 ```sh
@@ -30,7 +31,7 @@ npm install wink-nlp --save
 node -e "require( 'wink-nlp/models/install' )" wink-eng-lite-model
 ```
 
-#### Example
+### Example
 We start by requiring the **wink-nlp** package and the **wink-eng-lite-model**. Then we instantiate wink-nlp using the language model:
 
 ```javascript
@@ -48,20 +49,41 @@ console.log(doc.out());
 // -> Hello   World!
 ```
 
-#### Documentation
-Check out the wink-nlp's [concepts](https://winkjs.org/wink-nlp/getting-started.html) and  [API reference](https://winkjs.org/wink-nlp/read-doc.html) to learn more about the package.
+### Documentation
+Learn how to use this model with wink NLP from the following resources:
+- [Overview](https://winkjs.org/wink-nlp/) — introduction to winkNLP.
+- [Concepts](https://winkjs.org/wink-nlp/getting-started.html) — everything you need to know to get started.
+- [API Reference](https://winkjs.org/wink-nlp/read-doc.html) — explains usage of APIs with examples.
+- [Release history](https://github.com/winkjs/wink-eng-lite-model/releases) — version history along with the details of breaking changes, if any.
+
+## About model
+### Performance
+The [winkNLP](https://winkjs.org/wink-nlp/) processes raw text at **>600,000 tokens per second** with this model, when [benchmarked](https://github.com/bestiejs/benchmark.js) using "Ch 13 of Ulysses by James Joyce" on a 2.2 GHz Intel Core i7 machine with 16GB RAM. The benchmark covered the entire NLP pipeline — tokenization, sentence boundary detection, negation handling, sentiment analysis, part-of-speech tagging, and named entity extraction.
+
+### Tokenization
+While it is trained to process English language text, it can tokenize text containing other languages such as Hindi, French and German. Such tokens are tagged as **X** (foreign word) during pos tagging.
+
+### POS Tagging
+The model follows the [Universal POS tags](https://universaldependencies.org/u/pos/) standards. It delivers an accuracy of **~94.7%** on a subset of WSJ corpus — this includes *tokenization of raw text prior to pos tagging*.
+
+### Named Entity Recognition (NER)
+The model is trained to detect **CARDINAL**, **DATE**, **DURATION**,  **EMAIL**, **EMOJI**, **EMOTICON**, **HASHTAG**, **MENTION**, **MONEY**, **ORDINAL**, **PERCENT**, **TIME**, and **URL**.
+
+### Sentiment Analysis
+It delivers a [f-score](https://en.wikipedia.org/wiki/F1_score) of **~84.5%**, when validated using Amazon Product Review [Sentiment Labelled Sentences Data Set](https://archive.ics.uci.edu/ml/machine-learning-databases/00331/) at [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/index.php).
 
 ### Storage Structure
 The model is contained in the standard [NPM tarball](https://docs.npmjs.com/cli-commands/pack.html) format. You can find it under the latest [release](https://github.com/winkjs/wink-eng-lite-model/releases). The model is stored in form of trained data in JSON and binary formats. Apart from the data, there is a tiny fraction of JS glue code, which is primarily used during model loading.
 
-### Need Help?
+
+## Need Help?
 If you spot a bug and the same has not yet been reported, raise a new [issue](https://github.com/winkjs/wink-eng-lite-model/issues).
 
-### About wink
+## About wink
 [Wink](https://winkjs.org/) is a family of open source packages for **Natural Language Processing**, **Machine Learning** and **Statistical Analysis** in NodeJS. The code is **thoroughly documented** for easy human comprehension and has a **test coverage of ~100%** for reliability to build production grade solutions.
 
 
-### Copyright & License
+## Copyright & License
 The **wink-eng-lite-model** is copyright 2020 of [GRAYPE Systems Private Limited](https://graype.in/).
 
 It is licensed under the terms of the MIT License.