changing code-block in docs

docs/source/data_transformations.rst

@@ -124,7 +124,9 @@ Running data transformations
 Once you have made the :ref:`transform file<Transform File>` with all the transform operations, 
 you can run data transformations with the following terminal command.
 
->>> python data_transformations.py \
+.. code-block:: console
+
+  $ python data_transformations.py \
         --transform_file 'transform_file.yml'
 
 

docs/source/index.rst

@@ -27,10 +27,12 @@ Installation
 To use multi-task-NLP, you can clone the repository into the desired location on your system
 with the following terminal command.
 
->>> cd /desired/location/
->>> git clone https://github.com/hellohaptik/multi-task-NLP.git
->>> cd multi-task-NLP
->>> pip install -r requirements.txt 
+.. code-block:: console
+
+   $ cd /desired/location/
+   $ git clone https://github.com/hellohaptik/multi-task-NLP.git
+   $ cd multi-task-NLP
+   $ pip install -r requirements.txt 
 
 NOTE:- The library is built and tested using ``Python 3.7.3``. It is recommended to install the requirements in a virtual environment.
 
@@ -42,6 +44,10 @@ and with **no requirement to code!!**
 .. toctree::
    quickstart
 
+Examples
+--------
+
+
 Step by Step Guide
 ------------------
 A complete guide explaining all the components of multi-task-NLP in sequential order.

docs/source/quickstart.rst

@@ -49,10 +49,12 @@ Step 2 - Run data preparation
 
 After defining the task file in :ref:`Step 1<Step 1 - Define your task file>`, run the following command to prepare the data.
 
->>> python data_preparation.py \ 
-    --task_file 'sample_task_file.yml' \
-    --data_dir 'data' \
-    --max_seq_len 50 
+.. code-block:: console
+  
+  $ python data_preparation.py \ 
+      --task_file 'sample_task_file.yml' \
+      --data_dir 'data' \
+      --max_seq_len 50 
 
 For knowing about the ``data_preparation.py`` script and its arguments, refer :ref:`here<Running data preparation>`.
 
@@ -61,20 +63,22 @@ Step 3 - Run train
 
 Finally you can start your training using the following command.
 
->>> python train.py \
-    --data_dir 'data/bert-base-uncased_prepared_data' \
-    --task_file 'sample_task_file.yml' \
-    --out_dir 'sample_out' \
-    --epochs 5 \
-    --train_batch_size 4 \
-    --eval_batch_size 8 \
-    --grad_accumulation_steps 2 \
-    --log_per_updates 25 \
-    --save_per_updates 1000 \
-    --eval_while_train True \
-    --test_while_train True \
-    --max_seq_len 50 \
-    --silent True 
+.. code-block:: console
+  
+  $ python train.py \
+      --data_dir 'data/bert-base-uncased_prepared_data' \
+      --task_file 'sample_task_file.yml' \
+      --out_dir 'sample_out' \
+      --epochs 5 \
+      --train_batch_size 4 \
+      --eval_batch_size 8 \
+      --grad_accumulation_steps 2 \
+      --log_per_updates 25 \
+      --save_per_updates 1000 \
+      --eval_while_train True \
+      --test_while_train True \
+      --max_seq_len 50 \
+      --silent True 
 
 For knowing about the ``train.py`` script and its arguments, refer :ref:`here<Running train>`.
 

docs/source/training.rst

@@ -25,10 +25,12 @@ The script takes the following arguments,
 
 You can use the following terminal command with your own argument values to run.
 
->>> python data_preparation.py \ 
-    --task_file 'sample_task_file.yml' \
-    --data_dir 'data' \
-    --max_seq_len 50 
+.. code-block:: console
+
+  $ python data_preparation.py \ 
+        --task_file 'sample_task_file.yml' \
+        --data_dir 'data' \
+        --max_seq_len 50 
 
 Running train
 -------------
@@ -54,27 +56,31 @@ available
 
 You can use the following terminal command with your own argument values to run.
 
->>> python train.py \
-    --data_dir 'data/bert-base-uncased_prepared_data' \
-    --task_file 'sample_task_file.yml' \
-    --out_dir 'sample_out' \
-    --epochs 5 \
-    --train_batch_size 4 \
-    --eval_batch_size 8 \
-    --grad_accumulation_steps 2 \
-    --log_per_updates 25 \
-    --save_per_updates 1000 \
-    --eval_while_train True \
-    --test_while_train True \
-    --max_seq_len 50 \
-    --silent True 
+.. code-block:: console
+
+  $ python train.py \
+        --data_dir 'data/bert-base-uncased_prepared_data' \
+        --task_file 'sample_task_file.yml' \
+        --out_dir 'sample_out' \
+        --epochs 5 \
+        --train_batch_size 4 \
+        --eval_batch_size 8 \
+        --grad_accumulation_steps 2 \
+        --log_per_updates 25 \
+        --save_per_updates 1000 \
+        --eval_while_train True \
+        --test_while_train True \
+        --max_seq_len 50 \
+        --silent True 
 
 Logs and tensorboard
 --------------------
 
 - Logs for the training should be saved in a time-stamp named directory (eg. 05_05-17_30). 
 - The tensorboard logs are also present in the same directory and tensorboard can be started with the following command
 
->>> tensorboard --logdir 05_05-17_30/tb_logs
+.. code-block:: console
+
+  $ tensorboard --logdir 05_05-17_30/tb_logs