//- 💫 DOCS > API > VOCAB
include ../_includes/_mixins
p
| The #[code Vocab] object provides a lookup table that allows you to
| access #[+api("lexeme") #[code Lexeme]] objects, as well as the
| #[+api("stringstore") #[code StringStore]]. It also owns underlying
| C-data that is shared between #[code Doc] objects.
+h(2, "init") Vocab.__init__
+tag method
p Create the vocabulary.
+aside-code("Example").
from spacy.vocab import Vocab
vocab = Vocab(strings=[u'hello', u'world'])
+table(["Name", "Type", "Description"])
+row
+cell #[code lex_attr_getters]
+cell dict
+cell
| A dictionary mapping attribute IDs to functions to compute them.
| Defaults to #[code None].
+row
+cell #[code tag_map]
+cell dict
+cell
| A dictionary mapping fine-grained tags to coarse-grained
| parts-of-speech, and optionally morphological attributes.
+row
+cell #[code lemmatizer]
+cell object
+cell A lemmatizer. Defaults to #[code None].
+row
+cell #[code strings]
+cell #[code StringStore] or list
+cell
| A #[+api("stringstore") #[code StringStore]] that maps
| strings to hash values, and vice versa, or a list of strings.
+row("foot")
+cell returns
+cell #[code Vocab]
+cell The newly constructed object.
+h(2, "len") Vocab.__len__
+tag method
p Get the current number of lexemes in the vocabulary.
+aside-code("Example").
doc = nlp(u'This is a sentence.')
assert len(nlp.vocab) > 0
+table(["Name", "Type", "Description"])
+row("foot")
+cell returns
+cell int
+cell The number of lexems in the vocabulary.
+h(2, "getitem") Vocab.__getitem__
+tag method
p
| Retrieve a lexeme, given an int ID or a unicode string. If a previously
| unseen unicode string is given, a new lexeme is created and stored.
+aside-code("Example").
apple = nlp.vocab.strings['apple']
assert nlp.vocab[apple] == nlp.vocab[u'apple']
+table(["Name", "Type", "Description"])
+row
+cell #[code id_or_string]
+cell int / unicode
+cell The hash value of a word, or its unicode string.
+row("foot")
+cell returns
+cell #[code Lexeme]
+cell The lexeme indicated by the given ID.
+h(2, "iter") Vocab.__iter__
+tag method
p Iterate over the lexemes in the vocabulary.
+aside-code("Example").
stop_words = (lex for lex in nlp.vocab if lex.is_stop)
+table(["Name", "Type", "Description"])
+row("foot")
+cell yields
+cell #[code Lexeme]
+cell An entry in the vocabulary.
+h(2, "contains") Vocab.__contains__
+tag method
p
| Check whether the string has an entry in the vocabulary. To get the ID
| for a given string, you need to look it up in
| #[+api("vocab#attributes") #[code vocab.strings]].
+aside-code("Example").
apple = nlp.vocab.strings['apple']
oov = nlp.vocab.strings['dskfodkfos']
assert apple in nlp.vocab
assert oov not in nlp.vocab
+table(["Name", "Type", "Description"])
+row
+cell #[code string]
+cell unicode
+cell The ID string.
+row("foot")
+cell returns
+cell bool
+cell Whether the string has an entry in the vocabulary.
+h(2, "add_flag") Vocab.add_flag
+tag method
p
| Set a new boolean flag to words in the vocabulary. The #[code flag_getter]
| function will be called over the words currently in the vocab, and then
| applied to new words as they occur. You'll then be able to access the flag
| value on each token, using #[code token.check_flag(flag_id)].
+aside-code("Example").
def is_my_product(text):
products = [u'spaCy', u'Thinc', u'displaCy']
return text in products
MY_PRODUCT = nlp.vocab.add_flag(is_my_product)
doc = nlp(u'I like spaCy')
assert doc[2].check_flag(MY_PRODUCT) == True
+table(["Name", "Type", "Description"])
+row
+cell #[code flag_getter]
+cell dict
+cell A function #[code f(unicode) -> bool], to get the flag value.
+row
+cell #[code flag_id]
+cell int
+cell
| An integer between 1 and 63 (inclusive), specifying the bit at
| which the flag will be stored. If #[code -1], the lowest
| available bit will be chosen.
+row("foot")
+cell returns
+cell int
+cell The integer ID by which the flag value can be checked.
+h(2, "add_flag") Vocab.clear_vectors
+tag method
+tag-new(2)
p
| Drop the current vector table. Because all vectors must be the same
| width, you have to call this to change the size of the vectors.
+aside-code("Example").
nlp.vocab.clear_vectors(new_dim=300)
+table(["Name", "Type", "Description"])
+row
+cell #[code new_dim]
+cell int
+cell
| Number of dimensions of the new vectors. If #[code None], size
| is not changed.
+h(2, "add_flag") Vocab.get_vector
+tag method
+tag-new(2)
p
| Retrieve a vector for a word in the vocabulary. Words can be looked up
| by string or hash value. If no vectors data is loaded, a
| #[code ValueError] is raised.
+aside-code("Example").
nlp.vocab.get_vector(u'apple')
+table(["Name", "Type", "Description"])
+row
+cell #[code orth]
+cell int / unicode
+cell The hash value of a word, or its unicode string.
+row("foot")
+cell returns
+cell #[code.u-break numpy.ndarray[ndim=1, dtype='float32']]
+cell
| A word vector. Size and shape are determined by the
| #[code Vocab.vectors] instance.
+h(2, "add_flag") Vocab.set_vector
+tag method
+tag-new(2)
p
| Set a vector for a word in the vocabulary. Words can be referenced by
| by string or hash value.
+aside-code("Example").
nlp.vocab.set_vector(u'apple', array([...]))
+table(["Name", "Type", "Description"])
+row
+cell #[code orth]
+cell int / unicode
+cell The hash value of a word, or its unicode string.
+row
+cell #[code vector]
+cell #[code.u-break numpy.ndarray[ndim=1, dtype='float32']]
+cell The vector to set.
+h(2, "add_flag") Vocab.has_vector
+tag method
+tag-new(2)
p
| Check whether a word has a vector. Returns #[code False] if no vectors
| are loaded. Words can be looked up by string or hash value.
+aside-code("Example").
if nlp.vocab.has_vector(u'apple'):
vector = nlp.vocab.get_vector(u'apple')
+table(["Name", "Type", "Description"])
+row
+cell #[code orth]
+cell int / unicode
+cell The hash value of a word, or its unicode string.
+row("foot")
+cell returns
+cell bool
+cell Whether the word has a vector.
+h(2, "to_disk") Vocab.to_disk
+tag method
+tag-new(2)
p Save the current state to a directory.
+aside-code("Example").
nlp.vocab.to_disk('/path/to/vocab')
+table(["Name", "Type", "Description"])
+row
+cell #[code path]
+cell unicode or #[code Path]
+cell
| A path to a directory, which will be created if it doesn't exist.
| Paths may be either strings or #[code Path]-like objects.
+h(2, "from_disk") Vocab.from_disk
+tag method
+tag-new(2)
p Loads state from a directory. Modifies the object in place and returns it.
+aside-code("Example").
from spacy.vocab import Vocab
vocab = Vocab().from_disk('/path/to/vocab')
+table(["Name", "Type", "Description"])
+row
+cell #[code path]
+cell unicode or #[code Path]
+cell
| A path to a directory. Paths may be either strings or
| #[code Path]-like objects.
+row("foot")
+cell returns
+cell #[code Vocab]
+cell The modified #[code Vocab] object.
+h(2, "to_bytes") Vocab.to_bytes
+tag method
p Serialize the current state to a binary string.
+aside-code("Example").
vocab_bytes = nlp.vocab.to_bytes()
+table(["Name", "Type", "Description"])
+row
+cell #[code **exclude]
+cell -
+cell Named attributes to prevent from being serialized.
+row("foot")
+cell returns
+cell bytes
+cell The serialized form of the #[code Vocab] object.
+h(2, "from_bytes") Vocab.from_bytes
+tag method
p Load state from a binary string.
+aside-code("Example").
fron spacy.vocab import Vocab
vocab_bytes = nlp.vocab.to_bytes()
vocab = Vocab()
vocab.from_bytes(vocab_bytes)
+table(["Name", "Type", "Description"])
+row
+cell #[code bytes_data]
+cell bytes
+cell The data to load from.
+row
+cell #[code **exclude]
+cell -
+cell Named attributes to prevent from being loaded.
+row("foot")
+cell returns
+cell #[code Vocab]
+cell The #[code Vocab] object.
+h(2, "attributes") Attributes
+aside-code("Example").
apple_id = nlp.vocab.strings['apple']
assert type(apple_id) == int
PERSON = nlp.vocab.strings['PERSON']
assert type(PERSON) == int
+table(["Name", "Type", "Description"])
+row
+cell #[code strings]
+cell #[code StringStore]
+cell A table managing the string-to-int mapping.
+row
+cell #[code vectors]
+tag-new(2)
+cell #[code Vectors]
+cell A table associating word IDs to word vectors.
+row
+cell #[code vectors_length]
+cell int
+cell Number of dimensions for each word vector.