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=[u"hello", u"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. |
RETURNS |
Vocab |
The newly constructed object. |
Vocab.__len__
Get the current number of lexemes in the vocabulary.
Example
doc = nlp(u"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[u"apple"]
assert nlp.vocab[apple] == nlp.vocab[u"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[u"apple"]
oov = nlp.vocab.strings[u"dskfodkfos"]
assert apple in nlp.vocab
assert oov not in nlp.vocab
Name |
Type |
Description |
string |
unicode |
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 = [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
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.
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.
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.
Example
if nlp.vocab.has_vector(u"apple"):
vector = nlp.vocab.get_vector(u"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 |
unicode / 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 |
unicode / 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[u"apple"]
assert type(apple_id) == int
PERSON = nlp.vocab.strings[u"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. |