move xnli_compute_metrics to data/metrics

transformers/__init__.py

@@ -29,7 +29,7 @@ from .data import (is_sklearn_available,
                    xnli_output_modes, xnli_processors, xnli_tasks_num_labels)
 
 if is_sklearn_available():
-    from .data import glue_compute_metrics
+    from .data import glue_compute_metrics, xnli_compute_metrics
 
 # Tokenizers
 from .tokenization_utils import (PreTrainedTokenizer)

transformers/data/__init__.py

@@ -4,4 +4,4 @@ from .processors import xnli_output_modes, xnli_processors, xnli_tasks_num_label
 
 from .metrics import is_sklearn_available
 if is_sklearn_available():
-    from .metrics import glue_compute_metrics
+    from .metrics import glue_compute_metrics, xnli_compute_metrics

transformers/data/metrics/__init__.py

@@ -81,3 +81,11 @@ if _has_sklearn:
             return {"acc": simple_accuracy(preds, labels)}
         else:
             raise KeyError(task_name)
+
+
+    def xnli_compute_metrics(task_name, preds, labels):
+        assert len(preds) == len(labels)
+        if task_name == "xnli":
+            return {"acc": simple_accuracy(preds, labels)}
+        else:
+            raise KeyError(task_name)

transformers/data/processors/xnli.py

@@ -73,13 +73,6 @@ class XnliProcessor(DataProcessor):
         """See base class."""
         return ["contradiction", "entailment", "neutral"]
 
-def xnli_compute_metrics(task_name, preds, labels):
-    assert len(preds) == len(labels)
-    if task_name == "xnli":
-        return {"acc": simple_accuracy(preds, labels)}
-    else:
-        raise ValueError('{} is not a supported task.'.format(task_name))
-
 xnli_processors = {
     "xnli": XnliProcessor,
 }