diff --git a/spacy/ml/parser_model.pyx b/spacy/ml/parser_model.pyx
index 055fa0bad..91558683b 100644
--- a/spacy/ml/parser_model.pyx
+++ b/spacy/ml/parser_model.pyx
@@ -3,7 +3,6 @@ cimport numpy as np
 from libc.math cimport exp
 from libc.string cimport memset, memcpy
 from libc.stdlib cimport calloc, free, realloc
-from thinc.backends.linalg cimport Vec, VecVec
 from thinc.backends.cblas cimport saxpy, sgemm
 
 import numpy
@@ -102,11 +101,10 @@ cdef void predict_states(CBlas cblas, ActivationsC* A, StateC** states,
     sum_state_features(cblas, A.unmaxed,
         W.feat_weights, A.token_ids, n.states, n.feats, n.hiddens * n.pieces)
     for i in range(n.states):
-        VecVec.add_i(&A.unmaxed[i*n.hiddens*n.pieces],
-            W.feat_bias, 1., n.hiddens * n.pieces)
+        saxpy(cblas)(n.hiddens * n.pieces, 1., W.feat_bias, 1, &A.unmaxed[i*n.hiddens*n.pieces], 1)
         for j in range(n.hiddens):
             index = i * n.hiddens * n.pieces + j * n.pieces
-            which = Vec.arg_max(&A.unmaxed[index], n.pieces)
+            which = _arg_max(&A.unmaxed[index], n.pieces)
             A.hiddens[i*n.hiddens + j] = A.unmaxed[index + which]
     memset(A.scores, 0, n.states * n.classes * sizeof(float))
     if W.hidden_weights == NULL:
@@ -119,8 +117,7 @@ cdef void predict_states(CBlas cblas, ActivationsC* A, StateC** states,
             0.0, A.scores, n.classes)
         # Add bias
         for i in range(n.states):
-            VecVec.add_i(&A.scores[i*n.classes],
-                W.hidden_bias, 1., n.classes)
+            saxpy(cblas)(n.classes, 1., W.hidden_bias, 1, &A.scores[i*n.classes], 1)
     # Set unseen classes to minimum value
     i = 0
     min_ = A.scores[0]
@@ -158,7 +155,8 @@ cdef void cpu_log_loss(float* d_scores,
     """Do multi-label log loss"""
     cdef double max_, gmax, Z, gZ
     best = arg_max_if_gold(scores, costs, is_valid, O)
-    guess = Vec.arg_max(scores, O)
+    guess = _arg_max(scores, O)
+
     if best == -1 or guess == -1:
         # These shouldn't happen, but if they do, we want to make sure we don't
         # cause an OOB access.
@@ -488,3 +486,15 @@ cdef class precompute_hiddens:
             return d_best.reshape((d_best.shape + (1,)))
  
         return state_vector, backprop_relu
+
+cdef inline int _arg_max(const float* scores, const int n_classes) nogil:
+    if n_classes == 2:
+        return 0 if scores[0] > scores[1] else 1
+    cdef int i
+    cdef int best = 0
+    cdef float mode = scores[0]
+    for i in range(1, n_classes):
+        if scores[i] > mode:
+            mode = scores[i]
+            best = i
+    return best