          self._by_orth = PreshMap()
          self.strings = StringStore()
          self.get_lex_attr = get_lex_attr
          self.morphology = Morphology(self.strings, tag_map, lemmatizer)
          self.serializer_freqs = serializer_freqs
          
          self.length = 1
 
</s> remove     NO_TAG
    ADJ
    ADP
    ADV
    AUX
    CONJ
    DET
    INTJ
    NOUN
    NUM
    PART
    PRON
    PROPN
    PUNCT
    SCONJ
    SYM
    VERB
    X
    EOL
    SPACE
    N_UNIV_TAGS
 </s> add     NO_TAG = EMPTY_VALUE
    ADJ = POS_adj
    ADP = POS_adp
    ADV = POS_adv
    AUX = POS_aux
    CONJ = POS_conj
    DET = POS_det
    INTJ = POS_intj
    NOUN = POS_noun
    NUM = POS_num
    PART = POS_part
    PRON = POS_pron
    PROPN = POS_propn
    PUNCT = POS_punct
    SCONJ = POS_sconj
    SYM = POS_sym
    VERB = POS_verb
    X = POS_x
    EOL = POS_eol
    SPACE = POS_space </s> remove # Google universal tag set
 </s> add from .symbols cimport *

 </s> remove from .attrs cimport FLAG13, FLAG14, FLAG15, FLAG16, FLAG17, FLAG18, FLAG19, FLAG20, FLAG21, FLAG22, FLAG23, FLAG24, FLAG25
 </s> add from .attrs cimport FLAG14, FLAG15, FLAG16, FLAG17, FLAG18, FLAG19, FLAG20, FLAG21, FLAG22, FLAG23, FLAG24, FLAG25 </s> remove     FLAG13 = 13
    FLAG14
 </s> add     FLAG14 = 14
        # Load strings in a special order, so that we have an onset number for
        # the vocabulary. This way, when words are added in order, the orth ID
        # is the frequency rank of the word, plus a certain offset. The structural
        # strings are loaded first, because the vocab is open-class, and these
        # symbols are closed class.
        #for attr_name in sorted(ATTR_NAMES.keys()):
        #    _ = self.strings[attr_name]
        #for univ_pos_name in sorted(UNIV_POS_NAMES.keys()):
        #    _ = self.strings[pos_name]
        #for morph_name in sorted(UNIV_MORPH_NAMES.keys()):
        #    _ = self.strings[morph_name]
        #for entity_type_name in sorted(ENTITY_TYPES.keys()):
        #    _ = self.strings[entity_type_name]
        #for tag_name in sorted(TAG_MAP.keys()):
        #    _ = self.strings[tag_name]
          self._by_orth = PreshMap()
          self.strings = StringStore()
        # Load strings in a special order, so that we have an onset number for
        # the vocabulary. This way, when words are added in order, the orth ID
        # is the frequency rank of the word, plus a certain offset. The structural
        # strings are loaded first, because the vocab is open-class, and these
        # symbols are closed class.
        #for attr_name in sorted(ATTR_NAMES.keys()):
        #    _ = self.strings[attr_name]
        #for univ_pos_name in sorted(UNIV_POS_NAMES.keys()):
        #    _ = self.strings[pos_name]
        #for morph_name in sorted(UNIV_MORPH_NAMES.keys()):
        #    _ = self.strings[morph_name]
        #for entity_type_name in sorted(ENTITY_TYPES.keys()):
        #    _ = self.strings[entity_type_name]
        #for tag_name in sorted(TAG_MAP.keys()):
        #    _ = self.strings[tag_name]
          self.get_lex_attr = get_lex_attr
          self.morphology = Morphology(self.strings, tag_map, lemmatizer)
          self.serializer_freqs = serializer_freqs
          
          self.length = 1
keep add keep keep keep keep keep
 * Refactor symbols, so that frequency rank can be derived from the orth id of a word. 
spacy/vocab.pyx