diff --git a/docs/Examples/HED.rst b/docs/Examples/HED.rst
index 5ea92ad..2b91be5 100644
--- a/docs/Examples/HED.rst
+++ b/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
diff --git a/docs/Examples/HWF.rst b/docs/Examples/HWF.rst
index f26b7ed..c6a83e6 100644
--- a/docs/Examples/HWF.rst
+++ b/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
 
diff --git a/docs/Examples/MNISTAdd.rst b/docs/Examples/MNISTAdd.rst
index d464c44..57f22cd 100644
--- a/docs/Examples/MNISTAdd.rst
+++ b/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
 
diff --git a/docs/Examples/Zoo.rst b/docs/Examples/Zoo.rst
index 0fc5788..e32a00f 100644
--- a/docs/Examples/Zoo.rst
+++ b/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
 
diff --git a/docs/Intro/Basics.rst b/docs/Intro/Basics.rst
index 8267d97..0187729 100644
--- a/docs/Intro/Basics.rst
+++ b/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
diff --git a/docs/Intro/Reasoning.rst b/docs/Intro/Reasoning.rst
index 3bc7f68..2150436 100644
--- a/docs/Intro/Reasoning.rst
+++ b/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
diff --git a/examples/hed/hed.ipynb b/examples/hed/hed.ipynb
index a7c5156..befea83 100644
--- a/examples/hed/hed.ipynb
+++ b/examples/hed/hed.ipynb
@@ -63,7 +63,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The dataset are shown below:"
+    "The datasets are shown below:"
    ]
   },
   {
@@ -75,11 +75,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Equations in the dataset is organized by equation length, from 5 to 26.\n",
+      "Equations in the dataset are organized by equation length, from 5 to 26.\n",
       "\n",
-      "For each euqation length, there are 225 true equations and 225 false equations in the training set.\n",
-      "For each euqation length, there are 75 true equations and 75 false equations in the validation set.\n",
-      "For each euqation length, there are 300 true equations and 300 false equations in the test set.\n"
+      "For each equation length, there are 225 true equations and 225 false equations in the training set.\n",
+      "For each equation length, there are 75 true equations and 75 false equations in the validation set.\n",
+      "For each equation length, there are 300 true equations and 300 false equations in the test set.\n"
      ]
     }
    ],
@@ -87,7 +87,7 @@
     "true_train_equation = train_data[1]\n",
     "false_train_equation = train_data[0]\n",
     "print(\n",
-    "    f\"Equations in the dataset is organized by equation length, \"\n",
+    "    f\"Equations in the dataset are organized by equation length, \"\n",
     "    + f\"from {min(train_data[0].keys())} to {max(train_data[0].keys())}.\"\n",
     ")\n",
     "print()\n",
@@ -95,7 +95,7 @@
     "true_train_equation_with_length_5 = true_train_equation[5]\n",
     "false_train_equation_with_length_5 = false_train_equation[5]\n",
     "print(\n",
-    "    f\"For each euqation length, there are {len(true_train_equation_with_length_5)} \"\n",
+    "    f\"For each equation length, there are {len(true_train_equation_with_length_5)} \"\n",
     "    + f\"true equations and {len(false_train_equation_with_length_5)} false equations \"\n",
     "    + f\"in the training set.\"\n",
     ")\n",
@@ -105,7 +105,7 @@
     "true_val_equation_with_length_5 = true_val_equation[5]\n",
     "false_val_equation_with_length_5 = false_val_equation[5]\n",
     "print(\n",
-    "    f\"For each euqation length, there are {len(true_val_equation_with_length_5)} \"\n",
+    "    f\"For each equation length, there are {len(true_val_equation_with_length_5)} \"\n",
     "    + f\"true equations and {len(false_val_equation_with_length_5)} false equations \"\n",
     "    + f\"in the validation set.\"\n",
     ")\n",
@@ -115,7 +115,7 @@
     "true_test_equation_with_length_5 = true_test_equation[5]\n",
     "false_test_equation_with_length_5 = false_test_equation[5]\n",
     "print(\n",
-    "    f\"For each euqation length, there are {len(true_test_equation_with_length_5)} \"\n",
+    "    f\"For each equation length, there are {len(true_test_equation_with_length_5)} \"\n",
     "    + f\"true equations and {len(false_test_equation_with_length_5)} false equations \"\n",
     "    + f\"in the test set.\"\n",
     ")"
@@ -130,7 +130,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -142,7 +142,7 @@
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 640x480 with 5 Axes>"
       ]
@@ -159,7 +159,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 8 Axes>"
       ]
@@ -176,7 +176,7 @@
     },
     {
      "data": {
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+      "image/png": "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",
       "text/plain": [
        "<Figure size 640x480 with 5 Axes>"
       ]
@@ -193,7 +193,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 8 Axes>"
       ]
@@ -251,7 +251,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -281,7 +281,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -307,7 +307,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -332,7 +332,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -370,7 +370,7 @@
    "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 `HedBridge`, which is derived from `SimpleBridge` and tailored specific for this task."
+    "Now, the last step is to bridge the learning and reasoning part. We proceed with this step by creating an instance of `HedBridge`, which is derived from `SimpleBridge` and tailored specific for this task."
    ]
   },
   {
diff --git a/examples/hwf/hwf.ipynb b/examples/hwf/hwf.ipynb
index feca467..4e3838e 100644
--- a/examples/hwf/hwf.ipynb
+++ b/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`."
    ]
   },
   {
diff --git a/examples/mnist_add/mnist_add.ipynb b/examples/mnist_add/mnist_add.ipynb
index 802cb8b..f7a0acc 100644
--- a/examples/mnist_add/mnist_add.ipynb
+++ b/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`."
    ]
   },
   {
diff --git a/examples/zoo/zoo.ipynb b/examples/zoo/zoo.ipynb
index c429fa2..da19c2f 100644
--- a/examples/zoo/zoo.ipynb
+++ b/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`."
    ]
   },
   {