diff --git a/spacy/_ml.pxd b/spacy/_ml.pxd
index 7024e88fc..e19a3a480 100644
--- a/spacy/_ml.pxd
+++ b/spacy/_ml.pxd
@@ -18,18 +18,10 @@ cdef int arg_max(const weight_t* scores, const int n_classes) nogil
 cdef class Model:
     cdef int n_classes
     
-    cdef int regularize(self, Feature* feats, int n, int a=*) except -1
+    cdef const weight_t* score(self, atom_t* context, bint regularize) except NULL
 
     cdef int update(self, atom_t* context, class_t guess, class_t gold, int cost) except -1
-
+    
     cdef object model_loc
     cdef Extractor _extractor
     cdef LinearModel _model
-
-    cdef inline const weight_t* score(self, atom_t* context, bint regularize) except NULL:
-        cdef int n_feats
-        feats = self._extractor.get_feats(context, &n_feats)
-        if regularize:
-            self.regularize(feats, n_feats, 3)
-        return self._model.get_scores(feats, n_feats)
-
diff --git a/spacy/_ml.pyx b/spacy/_ml.pyx
index 3dffed611..a7599ecf6 100644
--- a/spacy/_ml.pyx
+++ b/spacy/_ml.pyx
@@ -33,6 +33,11 @@ cdef class Model:
         if self.model_loc and path.exists(self.model_loc):
             self._model.load(self.model_loc, freq_thresh=0)
 
+    cdef const weight_t* score(self, atom_t* context, bint regularize) except NULL:
+        cdef int n_feats
+        feats = self._extractor.get_feats(context, &n_feats)
+        return self._model.get_scores(feats, n_feats)
+
     cdef int update(self, atom_t* context, class_t guess, class_t gold, int cost) except -1:
         cdef int n_feats
         if cost == 0:
@@ -44,19 +49,6 @@ cdef class Model:
             count_feats(counts[guess], feats, n_feats, -cost)
             self._model.update(counts)
 
-    @cython.cdivision
-    @cython.boundscheck(False)
-    cdef int regularize(self, Feature* feats, int n, int a=3) except -1:
-        pass
-        # Disable this for now, while we investigate effect.
-        # Use the Zipfian corruptions technique from here:
-        # http://www.aclweb.org/anthology/N13-1077
-        # This seems good for 0.1 - 0.3 % on OOD data.
-        #cdef int i
-        #cdef long[:] zipfs = numpy.random.zipf(a, n)
-        #for i in range(n):
-        #    feats[i].value *= 1 / zipfs[i]
-
     def end_training(self):
         self._model.end_training()
         self._model.dump(self.model_loc, freq_thresh=0)