Removing the dependency to pandas and using the csv module to load data.

examples/run_swag.py

@@ -14,13 +14,12 @@
 # limitations under the License.
 """BERT finetuning runner."""
 
-import pandas as pd
-
 import logging
 import os
 import argparse
 import random
 from tqdm import tqdm, trange
+import csv
 
 import numpy as np
 import torch
@@ -100,25 +99,28 @@ class InputFeatures(object):
 
 
 def read_swag_examples(input_file, is_training):
-    input_df = pd.read_csv(input_file)
+    with open(input_file, 'r') as f:
+        reader = csv.reader(f)
+        lines = list(reader)
 
-    if is_training and 'label' not in input_df.columns:
+    if is_training and lines[0][-1] != 'label':
         raise ValueError(
-            "For training, the input file must contain a label column.")
+            "For training, the input file must contain a label column."
+        )
 
     examples = [
         SwagExample(
-            swag_id = row['fold-ind'],
-            context_sentence = row['sent1'],
-            start_ending = row['sent2'], # in the swag dataset, the
+            swag_id = line[2],
+            context_sentence = line[4],
+            start_ending = line[5], # in the swag dataset, the
                                          # common beginning of each
                                          # choice is stored in "sent2".
-            ending_0 = row['ending0'],
-            ending_1 = row['ending1'],
-            ending_2 = row['ending2'],
-            ending_3 = row['ending3'],
-            label = row['label'] if is_training else None
-        ) for _, row in input_df.iterrows()
+            ending_0 = line[7],
+            ending_1 = line[8],
+            ending_2 = line[9],
+            ending_3 = line[10],
+            label = int(line[11]) if is_training else None
+        ) for line in lines[1:] # we skip the line with the column names
     ]
 
     return examples