Merge branch 'Dev' of https://github.com/AbductiveLearning/ABL-Package into Dev

docs/Examples/HED.rst

@@ -1,6 +1,10 @@
 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>
+
 Below shows an implementation of `Handwritten Equation
 Decipherment <https://proceedings.neurips.cc/paper_files/paper/2019/file/9c19a2aa1d84e04b0bd4bc888792bd1e-Paper.pdf>`__.
 In this task, the handwritten equations are given, which consist of

docs/Examples/HWF.rst

@@ -1,6 +1,10 @@
 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>
+
 Below shows an implementation of `Handwritten
 Formula <https://arxiv.org/abs/2006.06649>`__. In this
 task, handwritten images of decimal formulas and their computed results
@@ -466,5 +470,3 @@ Out:
         abl - INFO - Saving model: loop(save) [3]
         abl - INFO - Checkpoints will be saved to weights_dir/model_checkpoint_loop_2.pth
         abl - INFO - Evaluation ended, hwf/character_accuracy: 0.996 hwf/reasoning_accuracy: 0.977 
-
-More concrete examples are available in ``examples/hwf/main.py`` and ``examples/hwf/hwf.ipynb``.

docs/Examples/MNISTAdd.rst

@@ -1,6 +1,10 @@
 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>
+
 Below shows an implementation of `MNIST
 Addition <https://arxiv.org/abs/1805.10872>`__. In this task, pairs of
 MNIST handwritten images and their sums are given, alongwith a domain
@@ -375,5 +379,3 @@ Out:
         abl - INFO - Checkpoints will be saved to results/20231222_22_25_07/weights/model_checkpoint_loop_1.pth
         abl - INFO - Test start:
         abl - INFO - Evaluation ended, mnist_add/character_accuracy: 0.983 mnist_add/reasoning_accuracy: 0.967 
-
-More concrete examples are available in ``examples/mnist_add/main.py`` and ``examples/mnist_add/mnist_add.ipynb``.

docs/Examples/Zoo.rst

@@ -1,6 +1,10 @@
 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>
+
 Below shows an implementation of
 `Zoo <https://archive.ics.uci.edu/dataset/111/zoo>`__ dataset. In this task,
 attributes of animals (such as presence of hair, eggs, etc.) and their
@@ -251,4 +255,3 @@ Out:
 We may see from the results, after undergoing training with ABL, the
 model’s accuracy has improved.
 
-More concrete examples are available in ``examples/zoo/main.py`` and ``examples/zoo/zoo.ipynb``.