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.
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
defis_my_product(text):products=[u"spaCy",u"Thinc",u"displaCy"]returntextinproductsMY_PRODUCT=nlp.vocab.add_flag(is_my_product)doc=nlp(u"I like spaCy")assertdoc[2].check_flag(MY_PRODUCT)==True
Name
Type
Description
flag_getter
dict
A function f(unicode) -> 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.
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.
Example
nlp.vocab.get_vector(u"apple")
Name
Type
Description
orth
int / unicode
The hash value of a word, or its unicode string.
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(u"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.
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.