Update README.md

README.md

@@ -75,15 +75,14 @@ def sentence_embeddings(sentences, model_name, max_length=512):
 
 
 def plot_similarity(sentences, model_name):
-    # get sentence embeddings produced by the model
+    # Get sentence embeddings produced by the model
     embeddings = sentence_embeddings(sentences, model_name)
-    # perfome similarity score using cosine similarity
+    # Perfom similarity score using cosine similarity
     corr = cosine_similarity(embeddings, embeddings)
     
-    # plot heatmap similarity
-    sns.set(font_scale=1.2)
-    # for text axis labels wrapping
-    labels = [ '\n'.join(wrap(l, 40)) for l in sentences]
+    # Plot heatmap similarity
+    sns.set(font_scale=1.2)    
+    labels = [ '\n'.join(wrap(l, 40)) for l in sentences] # for text axis labels wrapping
     g = sns.heatmap(
       corr,
       xticklabels=labels,
@@ -95,7 +94,7 @@ def plot_similarity(sentences, model_name):
     g.set_title(f"Semantic Textual Similarity ({model_short_name})")
     plt.show()
 
-    
+# Sentences to be compared    
 sent = [
     # 1. "The court shall pronounce the judgment for the dissolution or termination of the civil effects of marriage."
     "Il tribunale pronuncia la sentenza per lo scioglimento o la cessazione degli effetti civili del matrimonio",
@@ -108,9 +107,11 @@ sent = [
     "Il ricorrente ha perso la causa"
     ]
 
-
+# Perform Semantic Textual Similarity using 'Italian-Legal-BERT'
 model_name = "dlicari/Italian-Legal-BERT"
 plot_similarity(sent, model_name)
+
+# Perform Semantic Textual Similarity using 'bert-base-italian-xxl-cased'
 model_name =  'dbmdz/bert-base-italian-xxl-cased'
 plot_similarity(sent, model_name)
 ```