update desc for map in all examples (#12226)

* update desc for map in all examples

* added plm

* suggestions

examples/pytorch/language-modeling/run_mlm.py

@@ -45,10 +45,12 @@ from transformers import (
 )
 from transformers.trainer_utils import get_last_checkpoint
 from transformers.utils import check_min_version
+from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
 check_min_version("4.8.0.dev0")
+require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt")
 
 logger = logging.getLogger(__name__)
 MODEL_CONFIG_CLASSES = list(MODEL_FOR_MASKED_LM_MAPPING.keys())
@@ -380,6 +382,7 @@ def main():
             num_proc=data_args.preprocessing_num_workers,
             remove_columns=[text_column_name],
             load_from_cache_file=not data_args.overwrite_cache,
+            desc="Running tokenizer on dataset line_by_line",
         )
     else:
         # Otherwise, we tokenize every text, then concatenate them together before splitting them in smaller parts.
@@ -394,6 +397,7 @@ def main():
             num_proc=data_args.preprocessing_num_workers,
             remove_columns=column_names,
             load_from_cache_file=not data_args.overwrite_cache,
+            desc="Running tokenizer on every text in dataset",
         )
 
         # Main data processing function that will concatenate all texts from our dataset and generate chunks of
@@ -424,6 +428,7 @@ def main():
             batched=True,
             num_proc=data_args.preprocessing_num_workers,
             load_from_cache_file=not data_args.overwrite_cache,
+            desc=f"Grouping texts in chunks of {max_seq_length}",
         )
 
     if training_args.do_train: