spaCy/website/docs/api/vocab.md

title	teaser	tag	source
Vocab	A storage class for vocabulary and other data shared across a language	class	spacy/vocab.pyx

The Vocab object provides a lookup table that allows you to access Lexeme objects, as well as the StringStore. It also owns underlying C-data that is shared between Doc objects.

Vocab.__init__

Create the vocabulary.

Example

from spacy.vocab import Vocab
vocab = Vocab(strings=["hello", "world"])

Name	Type	Description
lex_attr_getters	dict	A dictionary mapping attribute IDs to functions to compute them. Defaults to None.
tag_map	dict	A dictionary mapping fine-grained tags to coarse-grained parts-of-speech, and optionally morphological attributes.
lemmatizer	object	A lemmatizer. Defaults to None.
strings	StringStore / list	A StringStore that maps strings to hash values, and vice versa, or a list of strings.
vectors_name 2.2	str	A name to identify the vectors table.
RETURNS	Vocab	The newly constructed object.

Vocab.__len__

Get the current number of lexemes in the vocabulary.

Example

doc = nlp("This is a sentence.")
assert len(nlp.vocab) > 0

Name	Type	Description
RETURNS	int	The number of lexemes in the vocabulary.

Vocab.__getitem__

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.

Example

apple = nlp.vocab.strings["apple"]
assert nlp.vocab[apple] == nlp.vocab["apple"]

Name	Type	Description
id_or_string	int / unicode	The hash value of a word, or its unicode string.
RETURNS	Lexeme	The lexeme indicated by the given ID.

Vocab.__iter__

Iterate over the lexemes in the vocabulary.

Example

stop_words = (lex for lex in nlp.vocab if lex.is_stop)

Name	Type	Description
YIELDS	Lexeme	An entry in the vocabulary.

Vocab.__contains__

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 vocab.strings.

Example

apple = nlp.vocab.strings["apple"]
oov = nlp.vocab.strings["dskfodkfos"]
assert apple in nlp.vocab
assert oov not in nlp.vocab

Name	Type	Description
string	str	The ID string.
RETURNS	bool	Whether the string has an entry in the vocabulary.

Vocab.add_flag

Set a new boolean flag to words in the vocabulary. The 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 token.check_flag(flag_id).

Example

def is_my_product(text):
    products = ["spaCy", "Thinc", "displaCy"]
    return text in products

MY_PRODUCT = nlp.vocab.add_flag(is_my_product)
doc = nlp("I like spaCy")
assert doc[2].check_flag(MY_PRODUCT) == True

Name	Type	Description
flag_getter	dict	A function f(str) -> bool, to get the flag value.
flag_id	int	An integer between 1 and 63 (inclusive), specifying the bit at which the flag will be stored. If -1, the lowest available bit will be chosen.
RETURNS	int	The integer ID by which the flag value can be checked.

Vocab.reset_vectors

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. Only one of the width and shape keyword arguments can be specified.

Example

nlp.vocab.reset_vectors(width=300)

Name	Type	Description
width	int	The new width (keyword argument only).
shape	int	The new shape (keyword argument only).

Vocab.prune_vectors

Reduce the current vector table to nr_row unique entries. Words mapped to the discarded vectors will be remapped to the closest vector among those remaining. For example, suppose the original table had vectors for the words: ['sat', 'cat', 'feline', 'reclined']. If we prune the vector table to, two rows, we would discard the vectors for "feline" and "reclined". These words would then be remapped to the closest remaining vector – so "feline" would have the same vector as "cat", and "reclined" would have the same vector as "sat". The similarities are judged by cosine. The original vectors may be large, so the cosines are calculated in minibatches, to reduce memory usage.

Example

nlp.vocab.prune_vectors(10000)
assert len(nlp.vocab.vectors) <= 1000

Name	Type	Description
nr_row	int	The number of rows to keep in the vector table.
batch_size	int	Batch of vectors for calculating the similarities. Larger batch sizes might be faster, while temporarily requiring more memory.
RETURNS	dict	A dictionary keyed by removed words mapped to (string, score) tuples, where string is the entry the removed word was mapped to, and score the similarity score between the two words.

Vocab.get_vector

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 ValueError is raised. If minn is defined, then the resulting vector uses FastText's subword features by average over ngrams of orth (introduced in spaCy v2.1).

Example

nlp.vocab.get_vector("apple")
nlp.vocab.get_vector("apple", minn=1, maxn=5)

Name	Type	Description
orth	int / unicode	The hash value of a word, or its unicode string.
minn 2.1	int	Minimum n-gram length used for FastText's ngram computation. Defaults to the length of orth.
maxn 2.1	int	Maximum n-gram length used for FastText's ngram computation. Defaults to the length of orth.
RETURNS	numpy.ndarray[ndim=1, dtype='float32']	A word vector. Size and shape are determined by the Vocab.vectors instance.

Vocab.set_vector

Set a vector for a word in the vocabulary. Words can be referenced by by string or hash value.

Example

nlp.vocab.set_vector("apple", array([...]))

Name	Type	Description
orth	int / unicode	The hash value of a word, or its unicode string.
vector	numpy.ndarray[ndim=1, dtype='float32']	The vector to set.

Vocab.has_vector

Check whether a word has a vector. Returns False if no vectors are loaded. Words can be looked up by string or hash value.

Example

if nlp.vocab.has_vector("apple"):
    vector = nlp.vocab.get_vector("apple")

Name	Type	Description
orth	int / unicode	The hash value of a word, or its unicode string.
RETURNS	bool	Whether the word has a vector.

Vocab.to_disk

Save the current state to a directory.

Example

nlp.vocab.to_disk("/path/to/vocab")

Name	Type	Description
path	str / Path	A path to a directory, which will be created if it doesn't exist. Paths may be either strings or Path-like objects.
exclude	list	String names of serialization fields to exclude.

Vocab.from_disk

Loads state from a directory. Modifies the object in place and returns it.

Example

from spacy.vocab import Vocab
vocab = Vocab().from_disk("/path/to/vocab")

Name	Type	Description
path	str / Path	A path to a directory. Paths may be either strings or Path-like objects.
exclude	list	String names of serialization fields to exclude.
RETURNS	Vocab	The modified Vocab object.

Vocab.to_bytes

Serialize the current state to a binary string.

Example

vocab_bytes = nlp.vocab.to_bytes()

Name	Type	Description
exclude	list	String names of serialization fields to exclude.
RETURNS	bytes	The serialized form of the Vocab object.

Vocab.from_bytes

Load state from a binary string.

Example

from spacy.vocab import Vocab
vocab_bytes = nlp.vocab.to_bytes()
vocab = Vocab()
vocab.from_bytes(vocab_bytes)

Name	Type	Description
bytes_data	bytes	The data to load from.
exclude	list	String names of serialization fields to exclude.
RETURNS	Vocab	The Vocab object.

Attributes

Example

apple_id = nlp.vocab.strings["apple"]
assert type(apple_id) == int
PERSON = nlp.vocab.strings["PERSON"]
assert type(PERSON) == int

Name	Type	Description
strings	StringStore	A table managing the string-to-int mapping.
vectors 2	Vectors	A table associating word IDs to word vectors.
vectors_length	int	Number of dimensions for each word vector.
lookups	Lookups	The available lookup tables in this vocab.
writing_system 2.1	dict	A dict with information about the language's writing system.

Serialization fields

During serialization, spaCy will export several data fields used to restore different aspects of the object. If needed, you can exclude them from serialization by passing in the string names via the exclude argument.

Example

data = vocab.to_bytes(exclude=["strings", "vectors"])
vocab.from_disk("./vocab", exclude=["strings"])

Name	Description
strings	The strings in the StringStore.
lexemes	The lexeme data.
vectors	The word vectors, if available.
lookups	The lookup tables, if available.