Add text_column_name and label_column_name to run_ner and run_ner_no_trainer args (#12083)

* Add text_column_name and label_column_name to run_ner args

* Minor fix: grouping for text and label column name

examples/pytorch/token-classification/run_ner.py

@@ -106,6 +106,12 @@ class DataTrainingArguments:
         default=None,
         metadata={"help": "An optional input test data file to predict on (a csv or JSON file)."},
     )
+    text_column_name: Optional[str] = field(
+        default=None, metadata={"help": "The column name of text to input in the file (a csv or JSON file)."}
+    )
+    label_column_name: Optional[str] = field(
+        default=None, metadata={"help": "The column name of label to input in the file (a csv or JSON file)."}
+    )
     overwrite_cache: bool = field(
         default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
     )
@@ -249,10 +255,20 @@ def main():
     else:
         column_names = datasets["validation"].column_names
         features = datasets["validation"].features
-    text_column_name = "tokens" if "tokens" in column_names else column_names[0]
-    label_column_name = (
-        f"{data_args.task_name}_tags" if f"{data_args.task_name}_tags" in column_names else column_names[1]
-    )
+
+    if data_args.text_column_name is not None:
+        text_column_name = data_args.text_column_name
+    elif "tokens" in column_names:
+        text_column_name = "tokens"
+    else:
+        text_column_name = column_names[0]
+
+    if data_args.label_column_name is not None:
+        label_column_name = data_args.label_column_name
+    elif f"{data_args.task_name}_tags" in column_names:
+        label_column_name = f"{data_args.task_name}_tags"
+    else:
+        label_column_name = column_names[1]
 
     # In the event the labels are not a `Sequence[ClassLabel]`, we will need to go through the dataset to get the
     # unique labels.

examples/pytorch/token-classification/run_ner_no_trainer.py

@@ -75,6 +75,12 @@ def parse_args():
     parser.add_argument(
         "--validation_file", type=str, default=None, help="A csv or a json file containing the validation data."
     )
+    parser.add_argument(
+        "--text_column_name", type=str, default=None, help="The column name of text to input in the file (a csv or JSON file)."
+    )
+    parser.add_argument(
+        "--label_column_name", type=str, default=None, help="The column name of label to input in the file (a csv or JSON file)."
+    )
     parser.add_argument(
         "--max_length",
         type=int,
@@ -259,8 +265,20 @@ def main():
     else:
         column_names = raw_datasets["validation"].column_names
         features = raw_datasets["validation"].features
-    text_column_name = "tokens" if "tokens" in column_names else column_names[0]
-    label_column_name = f"{args.task_name}_tags" if f"{args.task_name}_tags" in column_names else column_names[1]
+
+    if data_args.text_column_name is not None:
+        text_column_name = data_args.text_column_name
+    elif "tokens" in column_names:
+        text_column_name = "tokens"
+    else:
+        text_column_name = column_names[0]
+
+    if data_args.label_column_name is not None:
+        label_column_name = data_args.label_column_name
+    elif f"{data_args.task_name}_tags" in column_names:
+        label_column_name = f"{data_args.task_name}_tags"
+    else:
+        label_column_name = column_names[1]
 
     # In the event the labels are not a `Sequence[ClassLabel]`, we will need to go through the dataset to get the
     # unique labels.