Migrate metric to Evaluate in Pytorch examples (#18369)

* Migrate metric to Evaluate in pytorch examples

* Remove unused imports

examples/pytorch/text-classification/run_xnli.py

@@ -26,8 +26,9 @@ from typing import Optional
 
 import datasets
 import numpy as np
-from datasets import load_dataset, load_metric
+from datasets import load_dataset
 
+import evaluate
 import transformers
 from transformers import (
     AutoConfig,
@@ -349,7 +350,7 @@ def main():
             )
 
     # Get the metric function
-    metric = load_metric("xnli")
+    metric = evaluate.load("xnli")
 
     # You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
     # predictions and label_ids field) and has to return a dictionary string to float.