[FIX] minor fix with grammar check

docs/Examples/HED.rst

@@ -3,7 +3,7 @@ Handwritten Equation Decipherment (HED)
 
 .. raw:: html
     
-    <p>For detailed code implementation, please view on <a class="reference external" href="https://github.com/AbductiveLearning/ABL-Package/tree/Dev/examples/hed" target="_blank">GitHub</a>.</p>
+    <p>For detailed code implementation, please view it on <a class="reference external" href="https://github.com/AbductiveLearning/ABL-Package/tree/Dev/examples/hed" target="_blank">GitHub</a>.</p>
 
 Below shows an implementation of `Handwritten Equation
 Decipherment <https://proceedings.neurips.cc/paper_files/paper/2019/file/9c19a2aa1d84e04b0bd4bc888792bd1e-Paper.pdf>`__.
@@ -54,7 +54,7 @@ First, we get the datasets of handwritten equations:
     train_data, val_data = split_equation(total_train_data, 3, 1)
     test_data = get_dataset(train=False)
 
-The dataset are shown below:
+The datasets are shown below:
 
 .. code:: ipython3
 
@@ -66,24 +66,24 @@ The dataset are shown below:
     
     true_train_equation_with_length_5 = true_train_equation[5]
     false_train_equation_with_length_5 = false_train_equation[5]
-    print(f"For each euqation length, there are {len(true_train_equation_with_length_5)} " +
-          f"true equations and {len(false_train_equation_with_length_5)} false equation " +
+    print(f"For each eqaation length, there are {len(true_train_equation_with_length_5)} " +
+          f"true equations and {len(false_train_equation_with_length_5)} false equations " +
           f"in the training set.")
     
     true_val_equation = val_data[1]
     false_val_equation = val_data[0]
     true_val_equation_with_length_5 = true_val_equation[5]
     false_val_equation_with_length_5 = false_val_equation[5]
-    print(f"For each euqation length, there are {len(true_val_equation_with_length_5)} " +
-          f"true equations and {len(false_val_equation_with_length_5)} false equation " +
+    print(f"For each equation length, there are {len(true_val_equation_with_length_5)} " +
+          f"true equations and {len(false_val_equation_with_length_5)} false equations " +
           f"in the validation set.")
     
     true_test_equation = test_data[1]
     false_test_equation = test_data[0]
     true_test_equation_with_length_5 = true_test_equation[5]
     false_test_equation_with_length_5 = false_test_equation[5]
-    print(f"For each euqation length, there are {len(true_test_equation_with_length_5)} " +
-          f"true equations and {len(false_test_equation_with_length_5)} false equation " +
+    print(f"For each equation length, there are {len(true_test_equation_with_length_5)} " +
+          f"true equations and {len(false_test_equation_with_length_5)} false equations " +
           f"in the test set.")
 
 
@@ -93,9 +93,9 @@ Out:
 
         Equations in the dataset is organized by equation length, from 5 to 26.
         
-        For each euqation length, there are 225 true equations and 225 false equation in the training set.
-        For each euqation length, there are 75 true equations and 75 false equation in the validation set.
-        For each euqation length, there are 300 true equations and 300 false equation in the test set.
+        For each equation length, there are 225 true equations and 225 false equations in the training set.
+        For each equation length, there are 75 true equations and 75 false equations in the validation set.
+        For each equation length, there are 300 true equations and 300 false equations in the test set.
     
 
 As illustrations, we show four equations in the training dataset:
@@ -276,7 +276,7 @@ Bridge Learning and Reasoning
 -----------------------------
 
 Now, the last step is to bridge the learning and reasoning part. We
-proceed this step by creating an instance of ``HedBridge``, which is
+proceed with this step by creating an instance of ``HedBridge``, which is
 derived from ``SimpleBridge`` and tailored specific for this task.
 
 .. code:: ipython3

docs/Examples/HWF.rst

@@ -3,7 +3,7 @@ Handwritten Formula (HWF)
 
 .. raw:: html
     
-    <p>For detailed code implementation, please view on <a class="reference external" href="https://github.com/AbductiveLearning/ABL-Package/tree/Dev/examples/hwf" target="_blank">GitHub</a>.</p>
+    <p>For detailed code implementation, please view it on <a class="reference external" href="https://github.com/AbductiveLearning/ABL-Package/tree/Dev/examples/hwf" target="_blank">GitHub</a>.</p>
 
 Below shows an implementation of `Handwritten
 Formula <https://arxiv.org/abs/2006.06649>`__. In this
@@ -350,7 +350,7 @@ candidate that has the highest consistency.
     confidence derived from the predicted probability. In ``examples/hwf/main.py``, we
     provide options for utilizing other forms of consistency measurement.
 
-    Also, during process of inconsistency minimization, we can
+    Also, during the process of inconsistency minimization, we can
     leverage `ZOOpt library <https://github.com/polixir/ZOOpt>`__ for
     acceleration. Options for this are also available in ``examples/hwf/main.py``. Those
     interested are encouraged to explore these features.
@@ -373,7 +373,7 @@ Bridge Learning and Reasoning
 -----------------------------
 
 Now, the last step is to bridge the learning and reasoning part. We
-proceed this step by creating an instance of ``SimpleBridge``.
+proceed with this step by creating an instance of ``SimpleBridge``.
 
 .. code:: ipython3
 

docs/Examples/MNISTAdd.rst

@@ -3,7 +3,7 @@ MNIST Addition
 
 .. raw:: html
     
-    <p>For detailed code implementation, please view on <a class="reference external" href="https://github.com/AbductiveLearning/ABL-Package/tree/Dev/examples/mnist_add" target="_blank">GitHub</a>.</p>
+    <p>For detailed code implementation, please view it on <a class="reference external" href="https://github.com/AbductiveLearning/ABL-Package/tree/Dev/examples/mnist_add" target="_blank">GitHub</a>.</p>
 
 Below shows an implementation of `MNIST
 Addition <https://arxiv.org/abs/1805.10872>`__. In this task, pairs of
@@ -301,7 +301,7 @@ candidate that has the highest consistency.
     confidence derived from the predicted probability. In ``examples/mnist_add/main.py``, we
     provide options for utilizing other forms of consistency measurement.
 
-    Also, during process of inconsistency minimization, we can leverage
+    Also, during the process of inconsistency minimization, we can leverage
     `ZOOpt library <https://github.com/polixir/ZOOpt>`__ for acceleration.
     Options for this are also available in ``examples/mnist_add/main.py``. Those interested are
     encouraged to explore these features.
@@ -324,7 +324,7 @@ Bridge Learning and Reasoning
 -----------------------------
 
 Now, the last step is to bridge the learning and reasoning part. We
-proceed this step by creating an instance of ``SimpleBridge``.
+proceed with this step by creating an instance of ``SimpleBridge``.
 
 .. code:: ipython3
 

docs/Examples/Zoo.rst

@@ -3,7 +3,7 @@ Zoo
 
 .. raw:: html
     
-    <p>For detailed code implementation, please view on <a class="reference external" href="https://github.com/AbductiveLearning/ABL-Package/tree/Dev/examples/zoo" target="_blank">GitHub</a>.</p>
+    <p>For detailed code implementation, please view it on <a class="reference external" href="https://github.com/AbductiveLearning/ABL-Package/tree/Dev/examples/zoo" target="_blank">GitHub</a>.</p>
 
 Below shows an implementation of
 `Zoo <https://archive.ics.uci.edu/dataset/111/zoo>`__ dataset. In this task,
@@ -49,9 +49,9 @@ into labeled/unlabeled/test data
     X, y = load_and_preprocess_dataset(dataset_id=62)
     X_label, y_label, X_unlabel, y_unlabel, X_test, y_test = split_dataset(X, y, test_size=0.3)
 
-Zoo dataset consist of tabular data. The attributes contains 17 boolean
+Zoo dataset consists of tabular data. The attributes contain 17 boolean
 values (e.g., hair, feathers, eggs, milk, airborne, aquatic, etc.) and
-the target is a integer value in range [0,6] representing 7 classes
+the target is an integer value in the range [0,6] representing 7 classes
 (e.g., mammal, bird, reptile, fish, amphibian, insect, and other). Below
 is an illustration:
 
@@ -202,7 +202,7 @@ Bridging Learning and Reasoning
 -------------------------------
 
 Now, the last step is to bridge the learning and reasoning part. We
-proceed this step by creating an instance of ``SimpleBridge``.
+proceed with this step by creating an instance of ``SimpleBridge``.
 
 .. code:: ipython3
 

docs/Intro/Basics.rst

@@ -60,8 +60,8 @@ to obtain the reasoning result. During training,
 alongside the aforementioned forward flow (i.e., prediction --> deduction reasoning), 
 there also exists a reverse flow, which starts from the reasoning result and 
 involves abductive reasoning ``KBBase.abduce_candidates`` to generate possible revised pseudo-labels. 
-Subsequently, these pseudo-labels are processed to minimize inconsistencies with the learning part, 
-which in turn revise the outcomes of the learning model, and then 
+Subsequently, these pseudo-labels are processed to minimize inconsistencies with the learning part.
+They in turn revise the outcomes of the learning model, which are then
 fed back for further training ``ABLModel.train``.  
 
 .. image:: ../_static/img/usage.png

docs/Intro/Reasoning.rst

@@ -11,7 +11,7 @@ Reasoning part
 ===============
 
 In this section, we will look at how to build the reasoning part, which 
-leverage domain knowledge and perform deductive or abductive reasoning.
+leverages domain knowledge and performs deductive or abductive reasoning.
 In ABL-Package, building the reasoning part involves two steps:
 
 1. Build a knowledge base by creating a subclass of ``KBBase``, which
@@ -292,7 +292,7 @@ base and pseudo-labels predicted by the learning part, and then return **only
 one** candidate that has the highest consistency.
 
 We can create a reasoner simply by instantiating class
-``Reasoner`` and passing our knowledge base as an parameter. As an
+``Reasoner`` and passing our knowledge base as a parameter. As an
 example for MNIST Addition, the reasoner definition would be:
 
 .. code:: python

examples/hwf/hwf.ipynb

@@ -355,7 +355,7 @@
    "source": [
     "Note: During creating reasoner, the definition of \"consistency\" can be customized within the `dist_func` parameter. In the code above, we employ a consistency measurement based on confidence, which calculates the consistency between the data example and candidates based on the confidence derived from the predicted probability. In `main.py`, we provide options for utilizing other forms of consistency measurement.\n",
     "\n",
-    "Note: Also, during process of inconsistency minimization, we can leverage [ZOOpt library](https://github.com/polixir/ZOOpt) for acceleration. Options for this are also available in `main.py`. Those interested are encouraged to explore these features."
+    "Note: Also, during the process of inconsistency minimization, we can leverage [ZOOpt library](https://github.com/polixir/ZOOpt) for acceleration. Options for this are also available in `main.py`. Those interested are encouraged to explore these features."
    ]
   },
   {
@@ -389,7 +389,7 @@
    "source": [
     "## Bridge Learning and Reasoning\n",
     "\n",
-    "Now, the last step is to bridge the learning and reasoning part. We proceed this step by creating an instance of `SimpleBridge`."
+    "Now, the last step is to bridge the learning and reasoning part. We proceed with this step by creating an instance of `SimpleBridge`."
    ]
   },
   {

examples/mnist_add/mnist_add.ipynb

@@ -390,7 +390,7 @@
    "source": [
     "Note: During creating reasoner, the definition of \"consistency\" can be customized within the `dist_func` parameter. In the code above, we employ a consistency measurement based on confidence, which calculates the consistency between the data example and candidates based on the confidence derived from the predicted probability. In `main.py`, we provide options for utilizing other forms of consistency measurement.\n",
     "\n",
-    "Note: Also, during process of inconsistency minimization, one can leverage [ZOOpt library](https://github.com/polixir/ZOOpt) for acceleration. Options for this are also available in `main.py`. Those interested are encouraged to explore these features."
+    "Note: Also, during the process of inconsistency minimization, one can leverage [ZOOpt library](https://github.com/polixir/ZOOpt) for acceleration. Options for this are also available in `main.py`. Those interested are encouraged to explore these features."
    ]
   },
   {
@@ -424,7 +424,7 @@
    "source": [
     "## Bridging Learning and Reasoning\n",
     "\n",
-    "Now, the last step is to bridge the learning and reasoning part. We proceed this step by creating an instance of `SimpleBridge`."
+    "Now, the last step is to bridge the learning and reasoning part. We proceed with this step by creating an instance of `SimpleBridge`."
    ]
   },
   {

examples/zoo/zoo.ipynb

@@ -56,7 +56,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Zoo dataset consist of tabular data. The attributes contains 17 boolean values (e.g., hair, feathers, eggs, milk, airborne, aquatic, etc.) and the target is a integer value in range [0,6] representing 7 classes (e.g., mammal, bird, reptile, fish, amphibian, insect, and other). Below is an illustration:"
+    "Zoo dataset consists of tabular data. The attributes contain 17 boolean values (e.g., hair, feathers, eggs, milk, airborne, aquatic, etc.) and the target is an integer value in the range [0,6] representing 7 classes (e.g., mammal, bird, reptile, fish, amphibian, insect, and other). Below is an illustration:"
    ]
   },
   {
@@ -275,7 +275,7 @@
    "source": [
     "## Bridging Learning and Reasoning\n",
     "\n",
-    "Now, the last step is to bridge the learning and reasoning part. We proceed this step by creating an instance of `SimpleBridge`."
+    "Now, the last step is to bridge the learning and reasoning part. We proceed with this step by creating an instance of `SimpleBridge`."
    ]
   },
   {