diff --git a/Project_Model/Libs/BPE/Classes/NanoSocratesBPE.py b/Project_Model/Libs/BPE/Classes/NanoSocratesBPE.py
index 132217e..baa5efd 100644
--- a/Project_Model/Libs/BPE/Classes/NanoSocratesBPE.py
+++ b/Project_Model/Libs/BPE/Classes/NanoSocratesBPE.py
@@ -2,7 +2,10 @@ from collections import deque
 from .Encoder import Encoder
 from ..Errors import OutOfDictionaryException, DuplicateWordException
 
-
+# ABOUT THE DICTIONARY:
+# the string is converted into utf-char bytes, that is: each char is rappresented with a set of bytes from 1 to 4.
+# each bytes get casted into an integer; such that, if an integer has its value lower then 256,
+# then it is rappresenting an utf-char-byte, otherwise it is a token-ID.
 class NanoSocratesBatchMemoryBPE:
     """ Memory to batch training. Keeps token couple frequencies, and merge_treshold 
     """
@@ -31,6 +34,7 @@ class NanoSocratesBPE(Encoder):
         for key, value in vocabulary.items():
             if value < 256:
                 raise OutOfDictionaryException()
+                # values under 256 are used for unpaired char
             # TODO: check if they are in order
             self.__vocabulary[key] = value
             self.__reverse_vocabulary[value] = key
@@ -38,7 +42,7 @@ class NanoSocratesBPE(Encoder):
 
     @property
     def vocabulary_size(self):
-        return len(self.__vocabulary) + 255
+        return len(self.__vocabulary) + 256
 
     @property
     def vocabulary(self):
@@ -51,7 +55,7 @@ class NanoSocratesBPE(Encoder):
         Returns:
             int:
         """
-        return self.vocabulary_size + 1
+        return self.vocabulary_size
 
     # TODO: implement fit
     def fit(
@@ -64,6 +68,7 @@ class NanoSocratesBPE(Encoder):
         ENCODED_CHUNK = self.encode_intermediate(chunk_data)
         DATA_LEN_BEFORE_LAST = len(ENCODED_CHUNK) - 1
 
+        # update frequency of each couple of element
         for i in range(0, DATA_LEN_BEFORE_LAST):
             CANDIDATE_COUPLE = (ENCODED_CHUNK[i], ENCODED_CHUNK[i+1])
 
@@ -77,6 +82,7 @@ class NanoSocratesBPE(Encoder):
             frequency += 1
             memory.frequencies[CANDIDATE_COUPLE] = frequency
 
+
         if not last_batch:
             return (self, memory, ENCODED_CHUNK)
 
@@ -126,13 +132,14 @@ class NanoSocratesBPE(Encoder):
 
 
     def __round_encode(self, piece: list[int]):
-        """_summary_
-
+        """ A single round of encode that traverse all the object. Multiple round are needed for a full encode: \n
+        1) "ABAB" -> "XX"
+        2) "XX" -> "Y"
         Args:
-            piece (list[int]): _description_
+            piece (list[int]): the object to encode as a list of integer
 
         Returns:
-            _type_: _description_
+            (list[int]): the one time encoded object
         """
 
         if len(piece) == 1:
@@ -144,26 +151,31 @@ class NanoSocratesBPE(Encoder):
         index = 0
         while index < PIECE_LENGTH:
 
-            CANDIDATE_WORD = (piece[index], piece[index + 1])
+            CANDIDATE_WORD = (piece[index], piece[index + 1]) # take a tuple of consecutive element [int]
             CANDIDATE_TOKEN = self.__vocabulary.get(CANDIDATE_WORD)
 
+            # if no token to substitute the tuple, append the first element
             if CANDIDATE_TOKEN is None:
-                NEW_PIECE.append(piece[index])
+                NEW_PIECE.append(piece[index]) 
                 index += 1
 
+                # if the latter element of the tuple is the last element of the piece, append it
                 if index == PIECE_LENGTH:
-                    NEW_PIECE.append(piece[index])
+                    NEW_PIECE.append(piece[index]) 
 
                 continue
 
+            # in this case there was a candidate token to substitute the couple of element
             NEW_PIECE.append(CANDIDATE_TOKEN)
             index += 2
 
 
         return NEW_PIECE
 
+
     # TODO: Remake decode to take a list of token IDs
     def decode(self, token_ids: list[int]) -> str:
+        
 
         # deque: double ended queue
         token_stack: deque[int] = deque(token_ids)
@@ -199,7 +211,13 @@ class NanoSocratesBPE(Encoder):
         return CANDIDATE_DECODED
 
     def __learn_word(self, words: tuple[int, int]):
+        """ learn a new couple of object in the vocabulary
+        Args:
+            words (tuple[int, int]): the Pair of element to substitute with a new tokenID
 
+        Raises:
+            DuplicateWordException: it launch if there is a duplicate of the new tokenID in the dictionary
+        """
         ID = self.__next_id
 
         DUPLICATE = self.__vocabulary.get(words)