add better documentation for examples

README.md

@@ -8,7 +8,14 @@
 ![Python package](https://github.com/pavanchhatpar/copynet-tf/workflows/Python%20package/badge.svg)
 ![Upload Python Package](https://github.com/pavanchhatpar/copynet-tf/workflows/Upload%20Python%20Package/badge.svg)
 
-## Environment to run examples
+## Run the examples
+ - It is recommended to run examples using the package
+ - Instructions to run an example are detailed in its own folders respectively
+ - The example has a Google colab link for end-to-end demo on running with
+ package and source as well.
+ - Refer next section to setup the local environment only if running via source
+
+## Environment to run examples from source
 ### Setup
 - Copy `sample.env` to `.env` and enter appropriate values for the variables
  - A brief description of each is provided as a comment in that file
@@ -50,9 +57,6 @@
  - It is recommended to run the examples in this environment to ensure all
  correct dependencies are met
 
-## Run examples
- - Instructions to run an example are detailed in its own folders respectively
-
 ## References
  - Incorporating Copying Mechanism in Sequence-to-Sequence Learning: ([paper](https://arxiv.org/abs/1603.06393))
  - AllenNLP implementation: ([blog](https://medium.com/@epwalsh10/incorporating-a-copy-mechanism-into-sequence-to-sequence-models-40917280b89d)) ([code](https://github.com/epwalsh/nlp-models))

examples/greetings/README.md

@@ -1,5 +1,11 @@
-## Greetings dataset
+# Greetings dataset
 
+## Google Colab link (Recommended)
+ - [Running with the package](https://colab.research.google.com/drive/1fztwQ7baqrLhaKGnL4v_p_SjCP0yVESI?usp=sharing) (Recommended method)
+ - [Running from source](https://colab.research.google.com/drive/1by8Ob-6JVN9_RSuDFEKAwJYOHIgPnN4f?usp=sharing)
+
+
+## Refer only when running locally
 ### Pre-requisites
 You should have the environment already setup as detailed in the main README
 
@@ -17,5 +23,5 @@ python preprocess.py
 Refer `train.ipynb`
 
 #### Output format
- - Top 3 sequences are printed for each example as beam width is set to 3
- - Its log probability is seen printed next to it
+ - Top n sequences are printed for each example as per the beam width set
+ - Its probability is seen printed next to it