  import json
  
  from thinc.features cimport ConjFeat
  
  from ..context cimport fill_context
  from ..context cimport N_FIELDS
  from .moves cimport Move
  from .moves cimport fill_moves, transition, best_accepted
  from .moves cimport set_accept_if_valid, set_accept_if_oracle
  from .moves import get_n_moves
  from ._state cimport State
 
</s> remove     cdef weight_t score = scores[first_accept]
 </s> add     cdef weight_t score = scores[first_accept-1] </s> remove         for i, clas in enumerate(gold_classes):
            golds[i] = self.moves[clas - 1]
            assert golds[i].id == clas
 </s> add         for tok_i, clas in enumerate(gold_classes):
            golds[tok_i] = self._moves[clas]
            assert golds[tok_i].clas == clas, '%d vs %d' % (golds[tok_i].clas, clas) </s> remove     cpdef int train(self, Tokens tokens, gold_classes):
 </s> add     cpdef int train(self, Tokens tokens, gold_classes) except -1:
from .context cimport fill_context
from .context cimport N_FIELDS
  import json
  
  from thinc.features cimport ConjFeat
  
from .context cimport fill_context
from .context cimport N_FIELDS
from .context cimport fill_context
from .context cimport N_FIELDS
  from .moves cimport Move
  from .moves cimport fill_moves, transition, best_accepted
  from .moves cimport set_accept_if_valid, set_accept_if_oracle
  from .moves import get_n_moves
  from ._state cimport State
keep keep keep keep replace replace keep keep keep keep keep
 * Fixed greedy NER parsing. With static oracle, replicates accuracy from tagger. 
spacy/ner/greedy_parser.pyx