Typo on doctring in ElectraTokenizer (#20192)

* chore: typo on docstring in tokenization_electra

* chore: typo on docstring in tokenization_electra

* update for check copies

src/transformers/models/electra/tokenization_electra.py

@@ -89,10 +89,10 @@ def whitespace_tokenize(text):
     return tokens
 
 
-# Copied from transformers.models.bert.tokenization_bert.BertTokenizer with Bert->Electra
+# Copied from transformers.models.bert.tokenization_bert.BertTokenizer with Bert->Electra,BERT->Electra
 class ElectraTokenizer(PreTrainedTokenizer):
     r"""
-    Construct a BERT tokenizer. Based on WordPiece.
+    Construct a Electra tokenizer. Based on WordPiece.
 
     This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
     this superclass for more information regarding those methods.
@@ -129,7 +129,7 @@ class ElectraTokenizer(PreTrainedTokenizer):
             [issue](https://github.com/huggingface/transformers/issues/328)).
         strip_accents (`bool`, *optional*):
             Whether or not to strip all accents. If this option is not specified, then it will be determined by the
-            value for `lowercase` (as in the original BERT).
+            value for `lowercase` (as in the original Electra).
     """
 
     vocab_files_names = VOCAB_FILES_NAMES
@@ -226,7 +226,7 @@ class ElectraTokenizer(PreTrainedTokenizer):
     ) -> List[int]:
         """
         Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
-        adding special tokens. A BERT sequence has the following format:
+        adding special tokens. A Electra sequence has the following format:
 
         - single sequence: `[CLS] X [SEP]`
         - pair of sequences: `[CLS] A [SEP] B [SEP]`
@@ -278,8 +278,8 @@ class ElectraTokenizer(PreTrainedTokenizer):
         self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
     ) -> List[int]:
         """
-        Create a mask from the two sequences passed to be used in a sequence-pair classification task. A BERT sequence
-        pair mask has the following format:
+        Create a mask from the two sequences passed to be used in a sequence-pair classification task. A Electra
+        sequence pair mask has the following format:
 
         ```
         0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1