Convert hans to Trainer (#5025)

* Convert hans to Trainer

* Tick box

examples/adversarial/utils_hans.py

@@ -22,15 +22,7 @@ from typing import List, Optional, Union
 import tqdm
 from filelock import FileLock
 
-from transformers import (
-    DataProcessor,
-    PreTrainedTokenizer,
-    RobertaTokenizer,
-    RobertaTokenizerFast,
-    XLMRobertaTokenizer,
-    is_tf_available,
-    is_torch_available,
-)
+from transformers import DataProcessor, PreTrainedTokenizer, is_tf_available, is_torch_available
 
 
 logger = logging.getLogger(__name__)
@@ -106,7 +98,6 @@ if is_torch_available():
             evaluate: bool = False,
         ):
             processor = hans_processors[task]()
-            output_mode = hans_output_modes[task]
 
             cached_features_file = os.path.join(
                 data_dir,
@@ -127,22 +118,12 @@ if is_torch_available():
                     logger.info(f"Creating features from dataset file at {data_dir}")
                     label_list = processor.get_labels()
 
-                    if task in ["mnli", "mnli-mm"] and tokenizer.__class__ in (
-                        RobertaTokenizer,
-                        RobertaTokenizerFast,
-                        XLMRobertaTokenizer,
-                    ):
-                        # HACK(label indices are swapped in RoBERTa pretrained model)
-                        label_list[1], label_list[2] = label_list[2], label_list[1]
                     examples = (
                         processor.get_dev_examples(data_dir) if evaluate else processor.get_train_examples(data_dir)
                     )
 
                     logger.info("Training examples: %s", len(examples))
-                    # TODO clean up all this to leverage built-in features of tokenizers
-                    self.features = hans_convert_examples_to_features(
-                        examples, label_list, max_seq_length, tokenizer, output_mode
-                    )
+                    self.features = hans_convert_examples_to_features(examples, label_list, max_seq_length, tokenizer)
                     logger.info("Saving features into cached file %s", cached_features_file)
                     torch.save(self.features, cached_features_file)
 
@@ -174,21 +155,10 @@ if is_tf_available():
             evaluate: bool = False,
         ):
             processor = hans_processors[task]()
-            output_mode = hans_output_modes[task]
             label_list = processor.get_labels()
 
-            if task in ["mnli", "mnli-mm"] and tokenizer.__class__ in (
-                RobertaTokenizer,
-                RobertaTokenizerFast,
-                XLMRobertaTokenizer,
-            ):
-                # HACK(label indices are swapped in RoBERTa pretrained model)
-                label_list[1], label_list[2] = label_list[2], label_list[1]
-
             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, output_mode
-            )
+            self.features = hans_convert_examples_to_features(examples, label_list, max_seq_length, tokenizer)
 
             def gen():
                 for (ex_index, ex) in tqdm.tqdm(enumerate(self.features), desc="convert examples to features"):
@@ -240,15 +210,6 @@ if is_tf_available():
 class HansProcessor(DataProcessor):
     """Processor for the HANS data set."""
 
-    def get_example_from_tensor_dict(self, tensor_dict):
-        """See base class."""
-        return InputExample(
-            tensor_dict["idx"].numpy(),
-            tensor_dict["premise"].numpy().decode("utf-8"),
-            tensor_dict["hypothesis"].numpy().decode("utf-8"),
-            str(tensor_dict["label"].numpy()),
-        )
-
     def get_train_examples(self, data_dir):
         """See base class."""
         return self._create_examples(self._read_tsv(os.path.join(data_dir, "heuristics_train_set.txt")), "train")
@@ -277,11 +238,7 @@ class HansProcessor(DataProcessor):
 
 
 def hans_convert_examples_to_features(
-    examples: List[InputExample],
-    label_list: List[str],
-    max_length: int,
-    tokenizer: PreTrainedTokenizer,
-    output_mode: str,
+    examples: List[InputExample], label_list: List[str], max_length: int, tokenizer: PreTrainedTokenizer,
 ):
     """
     Loads a data file into a list of ``InputFeatures``
@@ -313,19 +270,8 @@ def hans_convert_examples_to_features(
             pad_to_max_length=True,
             return_overflowing_tokens=True,
         )
-        if "num_truncated_tokens" in inputs and inputs["num_truncated_tokens"] > 0:
-            logger.info(
-                "Attention! you are cropping tokens (swag task is ok). "
-                "If you are training ARC and RACE and you are poping question + options,"
-                "you need to try to use a bigger max seq length!"
-            )
 
-        if output_mode == "classification":
-            label = label_map[example.label] if example.label in label_map else 0
-        elif output_mode == "regression":
-            label = float(example.label)
-        else:
-            raise KeyError(output_mode)
+        label = label_map[example.label] if example.label in label_map else 0
 
         pairID = int(example.pairID)
 
@@ -346,7 +292,3 @@ hans_tasks_num_labels = {
 hans_processors = {
     "hans": HansProcessor,
 }
-
-hans_output_modes = {
-    "hans": "classification",
-}