[Docs] Fix spelling and grammar mistakes (#28825)

* Fix typos and grammar mistakes in docs and examples

* Fix typos in docstrings and comments

* Fix spelling of `tokenizer` in model tests

* Remove erroneous spaces in decorators

* Remove extra spaces in Markdown link texts

examples/pytorch/question-answering/run_qa.py

@@ -378,7 +378,7 @@ def main():
         )
 
     # Preprocessing the datasets.
-    # Preprocessing is slighlty different for training and evaluation.
+    # Preprocessing is slightly different for training and evaluation.
     if training_args.do_train:
         column_names = raw_datasets["train"].column_names
     elif training_args.do_eval:

examples/pytorch/question-answering/run_qa_beam_search.py

@@ -354,7 +354,7 @@ def main():
     )
 
     # Preprocessing the datasets.
-    # Preprocessing is slighlty different for training and evaluation.
+    # Preprocessing is slightly different for training and evaluation.
     if training_args.do_train:
         column_names = raw_datasets["train"].column_names
     elif training_args.do_eval:

examples/pytorch/question-answering/run_qa_beam_search_no_trainer.py

@@ -119,7 +119,7 @@ def parse_args():
         default=384,
         help=(
             "The maximum total input sequence length after tokenization. Sequences longer than this will be truncated,"
-            " sequences shorter will be padded if `--pad_to_max_lengh` is passed."
+            " sequences shorter will be padded if `--pad_to_max_length` is passed."
         ),
     )
     parser.add_argument(
@@ -385,7 +385,7 @@ def main():
     )
 
     # Preprocessing the datasets.
-    # Preprocessing is slighlty different for training and evaluation.
+    # Preprocessing is slightly different for training and evaluation.
     column_names = raw_datasets["train"].column_names
 
     question_column_name = "question" if "question" in column_names else column_names[0]
@@ -508,7 +508,7 @@ def main():
         raise ValueError("--do_train requires a train dataset")
     train_dataset = raw_datasets["train"]
     if args.max_train_samples is not None:
-        # We will select sample from whole data if agument is specified
+        # We will select sample from whole data if argument is specified
         train_dataset = train_dataset.select(range(args.max_train_samples))
     # Create train feature from dataset
     with accelerator.main_process_first():
@@ -877,7 +877,7 @@ def main():
                     commit_message=f"Training in progress epoch {epoch}", blocking=False, auto_lfs_prune=True
                 )
 
-    # intialize all lists to collect the batches
+    # initialize all lists to collect the batches
     all_start_top_log_probs = []
     all_start_top_index = []
     all_end_top_log_probs = []
@@ -936,7 +936,7 @@ def main():
     logger.info(f"Evaluation metrics: {eval_metric}")
 
     if args.do_predict:
-        # intialize all lists to collect the batches
+        # initialize all lists to collect the batches
 
         all_start_top_log_probs = []
         all_start_top_index = []

examples/pytorch/question-answering/run_qa_no_trainer.py

@@ -123,7 +123,7 @@ def parse_args():
         default=384,
         help=(
             "The maximum total input sequence length after tokenization. Sequences longer than this will be truncated,"
-            " sequences shorter will be padded if `--pad_to_max_lengh` is passed."
+            " sequences shorter will be padded if `--pad_to_max_length` is passed."
         ),
     )
     parser.add_argument(
@@ -460,7 +460,7 @@ def main():
         model = AutoModelForQuestionAnswering.from_config(config, trust_remote_code=args.trust_remote_code)
 
     # Preprocessing the datasets.
-    # Preprocessing is slighlty different for training and evaluation.
+    # Preprocessing is slightly different for training and evaluation.
 
     column_names = raw_datasets["train"].column_names
 
@@ -561,7 +561,7 @@ def main():
         raise ValueError("--do_train requires a train dataset")
     train_dataset = raw_datasets["train"]
     if args.max_train_samples is not None:
-        # We will select sample from whole data if agument is specified
+        # We will select sample from whole data if argument is specified
         train_dataset = train_dataset.select(range(args.max_train_samples))
 
     # Create train feature from dataset

examples/pytorch/question-answering/run_seq2seq_qa.py

@@ -559,7 +559,7 @@ def main():
             raise ValueError("--do_train requires a train dataset")
         train_dataset = raw_datasets["train"]
         if data_args.max_train_samples is not None:
-            # We will select sample from whole data if agument is specified
+            # We will select sample from whole data if argument is specified
             max_train_samples = min(len(train_dataset), data_args.max_train_samples)
             train_dataset = train_dataset.select(range(max_train_samples))
         # Create train feature from dataset