diff --git a/scripts/lang_adapt/madx_run_clm.py b/scripts/lang_adapt/madx_run_clm.py
index d0c59b6..25f9d27 100644
--- a/scripts/lang_adapt/madx_run_clm.py
+++ b/scripts/lang_adapt/madx_run_clm.py
@@ -651,7 +651,29 @@ def load_data(data_args, model_args):
     #
     # In distributed training, the load_dataset function guarantee that only one local process can concurrently
     # download the dataset.
-    if data_args.dataset_name is not None:
+    if data_args.dataset_name is not None and data_args.train_file is not None:
+        # Create a dataset from the provided file
+        data_files = {}
+        dataset_args = {}
+        if data_args.train_file is not None:
+            data_files["train"] = data_args.train_file
+        extension = (
+            data_args.train_file.split(".")[-1]
+            if data_args.train_file is not None
+            else data_args.validation_file.split(".")[-1]
+        )
+        if extension == "txt":
+            extension = "text"
+            dataset_args["keep_linebreaks"] = data_args.keep_linebreaks
+        raw_datasets = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir, **dataset_args)
+
+        # Downloading and loading a dataset from the hub.
+        raw_hf_datasets = load_dataset(
+            data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir
+        )
+        raw_datasets['train'] = datasets.concatenate_datasets(raw_datasets['train'], raw_hf_datasets['train']).shuffle(seed=42)
+
+    elif data_args.dataset_name is not None:
         # Downloading and loading a dataset from the hub.
         raw_datasets = load_dataset(
             data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir
@@ -680,7 +702,7 @@ def load_data(data_args, model_args):
         elif data_args.max_eval_samples is not None :                
             raw_datasets = raw_datasets['train'].train_test_split(test_size = data_args.max_eval_samples)
         else:
-            raw_datasets = raw_datasets['train'].train_test_split(test_size = data.args.validation_split_percentage/100.0)
+            raw_datasets = raw_datasets['train'].train_test_split(test_size = data_args.validation_split_percentage/100.0)
         raw_datasets['validation'] = raw_datasets['test']
     # 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.