Added util to create padding mask

Project_Model/Libs/Transformer/Utils/__init__.py

@@ -1,6 +1,6 @@
 from .attention_mask import get_causal_attention_mask,get_causal_attention_mask_batched
 from .task_type import TaskType
-from .post_tokenization import truncate_sequence, pad_sequence, normalize_sequence
+from .post_tokenization import truncate_sequence, pad_sequence, normalize_sequence, create_padding_mask
 from .inference_masking import inference_masking
 from .truncate_rdf_list import truncate_rdf_list
 
@@ -10,6 +10,7 @@ __all__ = [
     "get_causal_attention_mask_batched",
     "truncate_sequence",
     "pad_sequence",
+    "create_padding_mask",
     "normalize_sequence",
     "inference_masking",
     "truncate_rdf_list"

Project_Model/Libs/Transformer/Utils/post_tokenization.py

@@ -25,15 +25,29 @@ def pad_sequence(sequence: list[int], pad_until: int, pad_token: int) -> list[in
 
     return PADDED_SEQUENCE
 
+def create_padding_mask(sequence: list[int], pad_token: int) -> list[bool]:
+
+    PADDING_MASK = [False] * len(sequence)
+
+    for i in range(0, len(sequence)):
+
+        if sequence[i] != pad_token:
+            continue
+
+        PADDING_MASK[i] = True
+
+    return PADDING_MASK
+
 
 def normalize_sequence(
     sequence: list[int],
     max_length: int,
     pad_token: int,
     end_token: int,
-) -> list[int]:
+) -> tuple[list[int], list[bool]]:
 
     new_sequence = pad_sequence(sequence, max_length, pad_token)
     new_sequence = truncate_sequence(sequence, max_length, end_token)
+    PADDING_MASK = create_padding_mask(sequence, pad_token)
 
-    return new_sequence
+    return (new_sequence, PADDING_MASK)