diff --git a/examples/language-modeling/README.md b/examples/language-modeling/README.md
index 3442d4efa042..a491c5998aae 100644
--- a/examples/language-modeling/README.md
+++ b/examples/language-modeling/README.md
@@ -91,6 +91,7 @@ to that word). This technique has been refined for Chinese in [this paper](https
 
 To fine-tune a model using whole word masking, use the following script:
 
+```bash
 python run_mlm_wwm.py \
     --model_name_or_path roberta-base \
     --dataset_name wikitext \
diff --git a/examples/language-modeling/run_mlm_wwm.py b/examples/language-modeling/run_mlm_wwm.py
index ecc4c55e7c2a..7adad187a00d 100644
--- a/examples/language-modeling/run_mlm_wwm.py
+++ b/examples/language-modeling/run_mlm_wwm.py
@@ -90,6 +90,12 @@ class DataTrainingArguments:
     Arguments pertaining to what data we are going to input our model for training and eval.
     """
 
+    dataset_name: Optional[str] = field(
+        default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
+    )
+    dataset_config_name: Optional[str] = field(
+        default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
+    )
     train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."})
     validation_file: Optional[str] = field(
         default=None,
@@ -200,15 +206,19 @@ def main():
     #
     # In distributed training, the load_dataset function guarantee that only one local process can concurrently
     # download the dataset.
-    data_files = {}
-    if data_args.train_file is not None:
-        data_files["train"] = data_args.train_file
-    if data_args.validation_file is not None:
-        data_files["validation"] = data_args.validation_file
-    extension = data_args.train_file.split(".")[-1]
-    if extension == "txt":
-        extension = "text"
-    datasets = load_dataset(extension, data_files=data_files)
+    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)
+    else:
+        data_files = {}
+        if data_args.train_file is not None:
+            data_files["train"] = data_args.train_file
+        if data_args.validation_file is not None:
+            data_files["validation"] = data_args.validation_file
+        extension = data_args.train_file.split(".")[-1]
+        if extension == "txt":
+            extension = "text"
+        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.
 
@@ -278,7 +288,7 @@ def tokenize_function(examples):
     # Add the chinese references if provided
     if data_args.train_ref_file is not None:
         tokenized_datasets["train"] = add_chinese_references(tokenized_datasets["train"], data_args.train_ref_file)
-    if data_args.valid_ref_file is not None:
+    if data_args.validation_ref_file is not None:
         tokenized_datasets["validation"] = add_chinese_references(
             tokenized_datasets["validation"], data_args.validation_ref_file
         )