diff --git a/templates/adding_a_new_example_script/{{cookiecutter.directory_name}}/run_{{cookiecutter.example_shortcut}}.py b/templates/adding_a_new_example_script/{{cookiecutter.directory_name}}/run_{{cookiecutter.example_shortcut}}.py
index f9bace8887..f64076bd95 100755
--- a/templates/adding_a_new_example_script/{{cookiecutter.directory_name}}/run_{{cookiecutter.example_shortcut}}.py
+++ b/templates/adding_a_new_example_script/{{cookiecutter.directory_name}}/run_{{cookiecutter.example_shortcut}}.py
@@ -27,6 +27,7 @@ import sys
 from dataclasses import dataclass, field
 from typing import Optional
 
+import datasets
 from datasets import load_dataset
 
 import transformers
@@ -226,16 +227,19 @@ def main():
         datefmt="%m/%d/%Y %H:%M:%S",
         handlers=[logging.StreamHandler(sys.stdout)],
     )
-    logger.setLevel(logging.INFO if training_args.should_log else logging.WARN)
+
+    log_level = training_args.get_process_log_level()
+    logger.setLevel(log_level)
+    datasets.utils.logging.set_verbosity(log_level)
+    transformers.utils.logging.set_verbosity(log_level)
+    transformers.utils.logging.enable_default_handler()
+    transformers.utils.logging.enable_explicit_format()
 
     # Log on each process the small summary:
     logger.warning(
         f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
         + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
     )
-    # Set the verbosity to info of the Transformers logger (on main process only):
-    if training_args.should_log:
-        transformers.utils.logging.set_verbosity_info()
     logger.info(f"Training/evaluation parameters {training_args}")
 
     # Set seed before initializing model.
@@ -252,7 +256,7 @@ def main():
     # download the dataset.
     if data_args.dataset_name is not None:
         # Downloading and loading a dataset from the hub.
-        datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)
+        raw_datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)
     else:
         data_files = {}
         if data_args.train_file is not None:
@@ -266,7 +270,7 @@ def main():
             extension = data_args.test_file.split(".")[-1]
         if extension == "txt":
             extension = "text"
-        datasets = load_dataset(extension, data_files=data_files)
+        raw_datasets = load_dataset(extension, data_files=data_files)
     # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
     # https://huggingface.co/docs/datasets/loading_datasets.html.
 
@@ -348,20 +352,20 @@ def main():
     # Preprocessing the datasets.
     # First we tokenize all the texts.
     if training_args.do_train:
-        column_names = datasets["train"].column_names
+        column_names = raw_datasets["train"].column_names
     elif training_args.do_eval:
-        column_names = datasets["validation"].column_names
+        column_names = raw_datasets["validation"].column_names
     elif training_args.do_predict:
-        column_names = datasets["test"].column_names
+        column_names = raw_datasets["test"].column_names
     text_column_name = "text" if "text" in column_names else column_names[0]
 
     def tokenize_function(examples):
         return tokenizer(examples[text_column_name], padding="max_length", truncation=True)
 
     if training_args.do_train:
-        if "train" not in datasets:
+        if "train" not in raw_datasets:
             raise ValueError("--do_train requires a train dataset")
-        train_dataset = datasets["train"]
+        train_dataset = raw_datasets["train"]
         if data_args.max_train_samples is not None:
             # Select Sample from Dataset
             train_dataset = train_dataset.select(range(data_args.max_train_samples))
@@ -375,9 +379,9 @@ def main():
         )
 
     if training_args.do_eval:
-        if "validation" not in datasets:
+        if "validation" not in raw_datasets:
             raise ValueError("--do_eval requires a validation dataset")
-        eval_dataset = datasets["validation"]
+        eval_dataset = raw_datasets["validation"]
         # Selecting samples from dataset
         if data_args.max_eval_samples is not None:
             eval_dataset = eval_dataset.select(range(data_args.max_eval_samples))
@@ -391,9 +395,9 @@ def main():
         )
 
     if training_args.do_predict:
-        if "test" not in datasets:
+        if "test" not in raw_datasets:
             raise ValueError("--do_predict requires a test dataset")
-        predict_dataset = datasets["test"]
+        predict_dataset = raw_datasets["test"]
         # Selecting samples from dataset
         if data_args.max_predict_samples is not None:
             predict_dataset = predict_dataset.select(range(data_args.max_predict_samples))
@@ -754,7 +758,7 @@ def main():
 
     # Preprocessing the datasets.
     # First we tokenize all the texts.
-    column_names = datasets["train"].column_names
+    column_names = raw_datasets["train"].column_names
     text_column_name = "text" if "text" in column_names else column_names[0]
 
     padding = "max_length" if args.pad_to_max_length else False