Update README.md

README.md

@@ -155,62 +155,65 @@ In this respect, we carried out three actions:
 ## How to use
 Data provided within this repository can be straightforwardly loaded via the *datasets* library as follows:
 
-```
+```python
 from datasets import load_dataset
 dataset = load_dataset("MLNTeam-Unical/NFT-70M_transactions")
+```
 
+Complementary data involving textual and visual embeddings can be integrated as follows:
 
-```
-if you want to .... 
-```
+```python
 from datasets import load_dataset
 import numpy as np
 
 
 transactions_dataset=load_dataset("MLNTeam-Unical/NFT-70M_transactions")
-
 image_dataset=load_dataset("MLNTeam-Unical/NFT-70M_image")
-
 text_dataset=load_dataset("MLNTeam-Unical/NFT-70M_text")
 
 
-################################################################################
-#compute a mapping from image_id to the row_index within the image dataset
+# Mapping from image_id to the row_index within the image dataset
 image_id2row_index={int(id):k for k,id in enumerate(image_dataset["train"]["id"])}
-#compute a mapping from text_id to row_index within the text dataset
+
+# Mapping from text_id to row_index within the text dataset
 text_id2row_index={int(id):k for k,id in enumerate(text_dataset["train"]["id"])}
 
 
 def get_image_embedding(image_id,image_id2row_index,image_dataset):
-  #if the mapping contains the image, the embedding is present
+  # If the mapping contains the image, the embedding exists
   idx_emb=image_id2row_index.get(int(image_id),None)
   
   if idx_emb:
-    #if the embedding is present it is returned
+    # If the embedding exists, return it
     return np.array(image_dataset["train"].select([idx_emb])["emb"][0])
   else:
-    #otherwise None is returned 
     return None
   
 def get_text_embedding(text_id,text_id2row_index,text_dataset):
-  #if the mapping contains the image, the embedding is present
+  # If the mapping contains the image, the embedding exists
   idx_emb=text_id2row_index.get(int(text_id),None)
+
   if idx_emb:
-    #if the embedding is present it is returned
+    # If the embedding exists, return it
     return np.array(text_dataset["train"].select([idx_emb])["emb"][0])
   else:
-    #otherwise None is returned 
     return None
-#### USAGE EXAMPLE  #########
 
+### USAGE EXAMPLE ###
+
+# Select transaction_id
 transaction_id=120
 
+# Get the image_id (e.g., collection_image or nft_image)
 id_image=transactions_dataset["train"].select([transaction_id])["collection_image"][0]
 
+# Get the image
 image_embedding=get_image_embedding(id_image,image_id2row_index,image_dataset)
 
+# Get the text_id
 id_text=transactions_dataset["train"].select([transaction_id])["collection_description"][0]
 
+# Get the text
 text_embedding=get_text_embedding(id_text,text_id2row_index,text_dataset)
 ```