fix metrics

metrics/bleu.py

@@ -47,14 +47,14 @@ def _get_ngrams(segment, max_order):
     return ngram_counts
 
 
-def compute_bleu(reference_corpus, translation_corpus, max_order=4, smooth=False):
+def compute_bleu(translation_corpus, reference_corpus, max_order=4, smooth=False):
     """Computes BLEU score of translated segments against one or more references.
 
     Args:
-      reference_corpus: list of lists of references for each translation. Each
-          reference should be tokenized into a list of tokens.
       translation_corpus: list of translations to score. Each translation
           should be tokenized into a list of tokens.
+      reference_corpus: list of lists of references for each translation. Each
+          reference should be tokenized into a list of tokens.
       max_order: Maximum n-gram order to use when computing BLEU score.
       smooth: Whether or not to apply Lin et al. 2004 smoothing.
 

metrics/exact_match.py

@@ -37,4 +37,4 @@ def compute_exact_match(predictions, references):
     exact_match = 0
     for prediction, ground_truths in zip(predictions, references):
         exact_match += metric_max_over_ground_truths(exact_match_score, prediction, ground_truths)
-    return {"exact_match": 100.0 * exact_match / len(predictions)}
+    return 100.0 * exact_match / len(predictions)

metrics/f1.py

@@ -53,4 +53,4 @@ def compute_f1(predictions, references):
     f1 = 0
     for prediction, ground_truths in zip(predictions, references):
         f1 += metric_max_over_ground_truths(f1_score, prediction, ground_truths)
-    return {"f1": 100.0 * f1 / len(predictions)}
+    return 100.0 * f1 / len(predictions)