diff --git a/fuse/data/tokenizers/modular_tokenizer/modular_tokenizer.py b/fuse/data/tokenizers/modular_tokenizer/modular_tokenizer.py
index c34fde47..5762250c 100644
--- a/fuse/data/tokenizers/modular_tokenizer/modular_tokenizer.py
+++ b/fuse/data/tokenizers/modular_tokenizer/modular_tokenizer.py
@@ -1,29 +1,37 @@
 import random
 from torch import Tensor
-from typing import Dict
 from collections.abc import Iterable
 from tokenizers import Tokenizer, Encoding
 import tokenizers
 from warnings import warn
-from typing import Optional, List, Set, Union, Tuple, Any, Iterator
+from typing import Optional, List, Set, Union, Tuple, Any, Iterator, Dict
 import json
 import transformers
 import os
 from omegaconf import OmegaConf
-import collections
 import omegaconf
 import copy
 import traceback
 import re
 from fuse.data.tokenizers.modular_tokenizer.special_tokens import special_wrap_input
+from dataclasses import dataclass
 
 
-TypedInput = collections.namedtuple(
-    "TypedInput", ["input_type", "input_string", "max_len", "truncate_mode"]
-)
+@dataclass
+class ModularTokenizerInput:
+    input_type: str  # sub tokenizer name
+    input_string: str  # the string to tokenize
+    max_len: Optional[int] = None  # max length used for truncation only.
+    truncate_mode: Optional[
+        str
+    ] = None  # by defualt will truncate with right direction, setting to "RAND" will randomly crop a sub-sequence of length=max_len
 
 
-def list_to_tokenizer_string(lst: List[TypedInput]) -> str:
+# for backward compatibility
+TypedInput = ModularTokenizerInput
+
+
+def list_to_tokenizer_string(lst: List[ModularTokenizerInput]) -> str:
     out = ""
     # prev_tokenizer = None
     for in_named_tuple in lst:
@@ -983,7 +991,7 @@ def count_unknowns(
 
     def encode_list(
         self,
-        typed_input_list: List,
+        typed_input_list: List[ModularTokenizerInput],
         max_len: Optional[int] = None,
         padding_token_id: Optional[int] = None,
         padding_token: Optional[str] = "<PAD>",
@@ -1001,13 +1009,7 @@ def encode_list(
         """_summary_
 
         Args:
-            typed_input_list (List): list of collections.namedtuple("input_type", ["input_string", "max_len"]), with
-                input type: the name of input type,
-                input_string: the string to be encoded
-                max_len: maximal length of the encoding (in tokens). Only relevant for truncation, as we do not need to
-                pad individual sub-tokenizer encodings - we only pad the final encoding of the ModularTokenizer.
-                The smallest value between config-defined and tuple-defined is used. If None, the max_len
-                that was defined for the sub-tokenizer in the config is used.
+            typed_input_list (List): list of ModularTokenizerInput.
             max_len (Optional[int], optional): _description_. Defaults to None.
             padding_token_id (Optional[str], optional): _description_. Defaults to 0. TODO: default to None and infer it
             padding_token (Optional[str], optional): _description_. Defaults to "<PAD>".