update docstrings (#1583)

huggingface · Dec 18, 2020 · 441e204 · 441e204
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Showing 4 changed files with 8 additions and 8 deletions.
diff --git a/metrics/accuracy/accuracy.py b/metrics/accuracy/accuracy.py
@@ -30,8 +30,8 @@
 
 _KWARGS_DESCRIPTION = """
 Args:
-    predictions: Ground truth labels.
-    references: Predicted labels, as returned by a model.
+    predictions: Predicted labels, as returned by a model.
+    references: Ground truth labels.
     normalize: If False, return the number of correctly classified samples.
         Otherwise, return the fraction of correctly classified samples.
     sample_weight: Sample weights.

diff --git a/metrics/f1/f1.py b/metrics/f1/f1.py
@@ -26,8 +26,8 @@
 
 _KWARGS_DESCRIPTION = """
 Args:
-    predictions: Ground truth labels.
-    references: Predicted labels, as returned by a model.
+    predictions: Predicted labels, as returned by a model.
+    references: Ground truth labels.
     labels: The set of labels to include when average != 'binary', and
         their order if average is None. Labels present in the data can
         be excluded, for example to calculate a multiclass average ignoring

diff --git a/metrics/precision/precision.py b/metrics/precision/precision.py
@@ -28,8 +28,8 @@
 
 _KWARGS_DESCRIPTION = """
 Args:
-    predictions: Ground truth labels.
-    references: Predicted labels, as returned by a model.
+    predictions: Predicted labels, as returned by a model.
+    references: Ground truth labels.
     labels: The set of labels to include when average != 'binary', and
         their order if average is None. Labels present in the data can
         be excluded, for example to calculate a multiclass average ignoring

diff --git a/metrics/recall/recall.py b/metrics/recall/recall.py
@@ -28,8 +28,8 @@
 
 _KWARGS_DESCRIPTION = """
 Args:
-    predictions: Ground truth labels.
-    references: Predicted labels, as returned by a model.
+    predictions: Predicted labels, as returned by a model.
+    references: Ground truth labels.
     labels: The set of labels to include when average != 'binary', and
         their order if average is None. Labels present in the data can
         be excluded, for example to calculate a multiclass average ignoring