[DOC] resolve comments in Bri, Eva, Quick

docs/Intro/Bridge.rst

@@ -35,6 +35,8 @@ Bridging machine learning and reasoning to train the model is the fundamental id
 | test(test_data)                   | Test the model.                                                                      |
 +-----------------------------------+--------------------------------------------------------------------------------------+
 
+where ``train_data`` and ``test_data`` are both in the form of ``(X, gt_pseudo_label, Y)``. They will be used to construct ``ListData`` instances which are referred to as ``data_samples`` in the ``train`` and ``test`` methods respectively. More details can be found in `preparing datasets <Datasets.html>`_.
+
 
 ``SimpleBridge`` inherits from ``BaseBridge`` and provides a basic implementation. Besides the ``model`` and ``reasoner``, ``SimpleBridge`` has an extra initialization arguments, ``metric_list``, which will be used to evaluate model performance. Its training process involves several Abductive Learning loops and each loop consists of the following five steps:
 

docs/Intro/Evaluation.rst

@@ -10,8 +10,14 @@
 Evaluation Metrics
 ==================
 
-ABL-Package seperates the evaluation process as an independent class from the ``BaseBridge`` which accounts for training and testing. To customize our own metrics, we need to inherit from ``BaseMetric`` and implement the ``process`` and ``compute_metrics`` methods. The ``process`` method accepts a batch of model prediction. After processing this batch, we save the information to ``self.results`` property. The input results of ``compute_metrics`` is all the information saved in ``process`` and it uses these information to calculate and return a dict that holds the evaluation results. 
+ABL-Package seperates the evaluation process from model training and testing as an independent class, ``BaseMetric``. The training and testing processes are implemented in the ``BaseBridge`` class, so metrics are used by this class and its sub-classes. After building a ``bridge`` with a list of ``BaseMetric`` instances, these metrics will be used by the ``bridge.valid`` method to evaluate the model performance during training and testing. 
 
+To customize our own metrics, we need to inherit from ``BaseMetric`` and implement the ``process`` and ``compute_metrics`` methods. 
+
+- The ``process`` method accepts a batch of model prediction and saves the information to ``self.results`` property after processing this batch. 
+- The ``compute_metrics`` method uses all the information saved in ``self.results`` to calculate and return a dict that holds the evaluation results. 
+
+Besides, we can assign a ``str`` to the ``prefix`` argument of the ``__init__`` method. This string is automatically prefixed to the output metric names. For example, if we set ``prefix="mnist_add"``, the output metric name will be ``character_accuracy``.
 We provide two basic metrics, namely ``SymbolMetric`` and ``SemanticsMetric``, which are used to evaluate the accuracy of the machine learning model's predictions and the accuracy of the ``logic_forward`` results, respectively. Using ``SymbolMetric`` as an example, the following code shows how to implement a custom metrics.
 
 .. code:: python

docs/Intro/Quick-Start.rst

@@ -15,7 +15,7 @@ Working with Data
 -----------------
 
 ABL-Package assumes data to be in the form of ``(X, gt_pseudo_label, Y)``  where ``X`` is the input of the machine learning model, 
-``gt_pseudo_label`` is the ground truth label of each element in ``X`` and ``Y`` is the ground truth reasoning result of each instance in ``X``. 
+``gt_pseudo_label`` is the ground truth label of each element in ``X`` and ``Y`` is the ground truth reasoning result of each instance in ``X``. Note that ``gt_pseudo_label`` is only used to evaluate the performance of the machine learning part but not to train the model. If elements in ``X`` are unlabeled, ``gt_pseudo_label`` can be ``None``.
 
 In the MNIST Addition task, the data loading looks like