修复crf中typo; 以及可能导致数值不稳定的地方

fastNLP/modules/decoder/CRF.py

@@ -205,7 +205,7 @@ class ConditionalRandomField(nn.Module):
 
         return log_sum_exp(alpha, 1)
 
-    def _glod_score(self, logits, tags, mask):
+    def _gold_score(self, logits, tags, mask):
         """
         Compute the score for the gold path.
         :param logits: FloatTensor, max_len x batch_size x num_tags
@@ -244,7 +244,7 @@ class ConditionalRandomField(nn.Module):
         tags = tags.transpose(0, 1).long()
         mask = mask.transpose(0, 1).float()
         all_path_score = self._normalizer_likelihood(feats, mask)
-        gold_path_score = self._glod_score(feats, tags, mask)
+        gold_path_score = self._gold_score(feats, tags, mask)
 
         return all_path_score - gold_path_score
 
@@ -284,7 +284,8 @@ class ConditionalRandomField(nn.Module):
             score = prev_score + trans_score + cur_score
             best_score, best_dst = score.max(1)
             vpath[i] = best_dst
-            vscore = best_score * mask[i].view(batch_size, 1) + vscore * (1 - mask[i]).view(batch_size, 1)
+            best_score.masked_fill(mask[i].eq(0).view(batch_size, 1), 0) + \
+                                vscore.masked_fill(mask[i].view(batch_size, 1), 0)
 
         vscore += transitions[:n_tags, n_tags+1].view(1, -1)