Fixes an issue in text-classification where MNLI eval/test datasets…

… are not being preprocessed. (#10621)

* Fix MNLI tests

* Linter fix

examples/text-classification/run_glue.py

@@ -374,45 +374,32 @@ def preprocess_function(examples):
             result["label"] = [(label_to_id[l] if l != -1 else -1) for l in examples["label"]]
         return result
 
+    datasets = datasets.map(preprocess_function, batched=True, load_from_cache_file=not data_args.overwrite_cache)
     if training_args.do_train:
         if "train" not in datasets:
             raise ValueError("--do_train requires a train dataset")
         train_dataset = datasets["train"]
         if data_args.max_train_samples is not None:
             train_dataset = train_dataset.select(range(data_args.max_train_samples))
-        train_dataset = train_dataset.map(
-            preprocess_function,
-            batched=True,
-            load_from_cache_file=not data_args.overwrite_cache,
-        )
 
     if training_args.do_eval:
         if "validation" not in datasets and "validation_matched" not in datasets:
             raise ValueError("--do_eval requires a validation dataset")
         eval_dataset = datasets["validation_matched" if data_args.task_name == "mnli" else "validation"]
         if data_args.max_val_samples is not None:
             eval_dataset = eval_dataset.select(range(data_args.max_val_samples))
-        eval_dataset = eval_dataset.map(
-            preprocess_function,
-            batched=True,
-            load_from_cache_file=not data_args.overwrite_cache,
-        )
 
     if training_args.do_predict or data_args.task_name is not None or data_args.test_file is not None:
         if "test" not in datasets and "test_matched" not in datasets:
             raise ValueError("--do_predict requires a test dataset")
         test_dataset = datasets["test_matched" if data_args.task_name == "mnli" else "test"]
         if data_args.max_test_samples is not None:
             test_dataset = test_dataset.select(range(data_args.max_test_samples))
-        test_dataset = test_dataset.map(
-            preprocess_function,
-            batched=True,
-            load_from_cache_file=not data_args.overwrite_cache,
-        )
 
     # Log a few random samples from the training set:
-    for index in random.sample(range(len(train_dataset)), 3):
-        logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")
+    if training_args.do_train:
+        for index in random.sample(range(len(train_dataset)), 3):
+            logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")
 
     # Get the metric function
     if data_args.task_name is not None:
@@ -447,7 +434,7 @@ def compute_metrics(p: EvalPrediction):
     trainer = Trainer(
         model=model,
         args=training_args,
-        train_dataset=train_dataset,
+        train_dataset=train_dataset if training_args.do_train else None,
         eval_dataset=eval_dataset if training_args.do_eval else None,
         compute_metrics=compute_metrics,
         tokenizer=tokenizer,