[HANS] Fix label_list for RoBERTa/BART (class flipping) (#5196)

* fix weirdness in roberta/bart for mnli trained checkpoints

* black compliance

* isort code check

examples/adversarial/run_hans.py

@@ -33,7 +33,7 @@ from transformers import (
     default_data_collator,
     set_seed,
 )
-from utils_hans import HansDataset, InputFeatures, hans_processors
+from utils_hans import HansDataset, InputFeatures, hans_processors, hans_tasks_num_labels
 
 
 logger = logging.getLogger(__name__)
@@ -130,9 +130,7 @@ def main():
     set_seed(training_args.seed)
 
     try:
-        processor = hans_processors[data_args.task_name]()
+        num_labels = hans_tasks_num_labels[data_args.task_name]
-        label_list = processor.get_labels()
-        num_labels = len(label_list)
     except KeyError:
         raise ValueError("Task not found: %s" % (data_args.task_name))
 
@@ -214,6 +212,7 @@ def main():
 
         pair_ids = [ex.pairID for ex in eval_dataset]
         output_eval_file = os.path.join(training_args.output_dir, "hans_predictions.txt")
+        label_list = eval_dataset.get_labels()
         if trainer.is_world_master():
             with open(output_eval_file, "w") as writer:
                 writer.write("pairID,gold_label\n")

examples/adversarial/utils_hans.py

@@ -22,7 +22,17 @@ from typing import List, Optional, Union
 import tqdm
 from filelock import FileLock
 
-from transformers import DataProcessor, PreTrainedTokenizer, is_tf_available, is_torch_available
+from transformers import (
+    BartTokenizer,
+    BartTokenizerFast,
+    DataProcessor,
+    PreTrainedTokenizer,
+    RobertaTokenizer,
+    RobertaTokenizerFast,
+    XLMRobertaTokenizer,
+    is_tf_available,
+    is_torch_available,
+)
 
 
 logger = logging.getLogger(__name__)
@@ -105,6 +115,17 @@ if is_torch_available():
                     "dev" if evaluate else "train", tokenizer.__class__.__name__, str(max_seq_length), task,
                 ),
             )
+            label_list = processor.get_labels()
+            if tokenizer.__class__ in (
+                RobertaTokenizer,
+                RobertaTokenizerFast,
+                XLMRobertaTokenizer,
+                BartTokenizer,
+                BartTokenizerFast,
+            ):
+                # HACK(label indices are swapped in RoBERTa pretrained model)
+                label_list[1], label_list[2] = label_list[2], label_list[1]
+            self.label_list = label_list
 
             # Make sure only the first process in distributed training processes the dataset,
             # and the others will use the cache.
@@ -116,7 +137,6 @@ if is_torch_available():
                     self.features = torch.load(cached_features_file)
                 else:
                     logger.info(f"Creating features from dataset file at {data_dir}")
-                    label_list = processor.get_labels()
 
                     examples = (
                         processor.get_dev_examples(data_dir) if evaluate else processor.get_train_examples(data_dir)
@@ -133,6 +153,9 @@ if is_torch_available():
         def __getitem__(self, i) -> InputFeatures:
             return self.features[i]
 
+        def get_labels(self):
+            return self.label_list
+
 
 if is_tf_available():
     import tensorflow as tf
@@ -156,6 +179,16 @@ if is_tf_available():
         ):
             processor = hans_processors[task]()
             label_list = processor.get_labels()
+            if tokenizer.__class__ in (
+                RobertaTokenizer,
+                RobertaTokenizerFast,
+                XLMRobertaTokenizer,
+                BartTokenizer,
+                BartTokenizerFast,
+            ):
+                # HACK(label indices are swapped in RoBERTa pretrained model)
+                label_list[1], label_list[2] = label_list[2], label_list[1]
+            self.label_list = label_list
 
             examples = processor.get_dev_examples(data_dir) if evaluate else processor.get_train_examples(data_dir)
             self.features = hans_convert_examples_to_features(examples, label_list, max_seq_length, tokenizer)
@@ -206,6 +239,9 @@ if is_tf_available():
         def __getitem__(self, i) -> InputFeatures:
             return self.features[i]
 
+        def get_labels(self):
+            return self.label_list
+
 
 class HansProcessor(DataProcessor):
     """Processor for the HANS data set."""