Vocab

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.

Note that a Vocab instance is not static. It increases in size as texts with new tokens are processed. Some models may have an empty vocab at initialization.

</Infobox>

Vocab.init {id="init",tag="method"}

Create the vocabulary.

Example

python

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

Name	Description
`lex_attr_getters`	A dictionary mapping attribute IDs to functions to compute them. Defaults to `None`. ~~Optional[Dict[str, Callable[[str], Any]]]~~
`strings`	A `StringStore` that maps strings to hash values, and vice versa, or a list of strings. ~~Union[List[str], StringStore]~~
`lookups`	A `Lookups` that stores the `lexeme_norm` and other large lookup tables. Defaults to `None`. ~~Optional[Lookups]~~
`oov_prob`	The default OOV probability. Defaults to `-20.0`. ~~float~~
`vectors_name`	A name to identify the vectors table. ~~str~~
`writing_system`	A dictionary describing the language's writing system. Typically provided by `Language.Defaults`. ~~Dict[str, Any]~~
`get_noun_chunks`	A function that yields base noun phrases used for `Doc.noun_chunks`. ~~Optional[Callable[[Union[Doc, Span], Iterator[Tuple[int, int, int]]]]]~~

Vocab.len {id="len",tag="method"}

Get the current number of lexemes in the vocabulary.

Example

python

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

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

Vocab.getitem {id="getitem",tag="method"}

Retrieve a lexeme, given an int ID or a string. If a previously unseen string is given, a new lexeme is created and stored.

Example

python

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

Name	Description
`id_or_string`	The hash value of a word, or its string. ~~Union[int, str]~~
RETURNS	The lexeme indicated by the given ID. ~~Lexeme~~

Vocab.iter {id="iter",tag="method"}

Iterate over the lexemes in the vocabulary.

Example

python

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

Name	Description
YIELDS	An entry in the vocabulary. ~~Lexeme~~

Vocab.contains {id="contains",tag="method"}

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

python

nlp("I'm eating an apple")
apple = nlp.vocab.strings["apple"]
oov = nlp.vocab.strings["dskfodkfos"]
assert apple in nlp.vocab
assert oov not in nlp.vocab

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

Vocab.add_flag {id="add_flag",tag="method"}

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

python

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	Description
`flag_getter`	A function that takes the lexeme text and returns the boolean flag value. ~~Callable[[str], bool]~~
`flag_id`	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. ~~int~~
RETURNS	The integer ID by which the flag value can be checked. ~~int~~

Vocab.reset_vectors {id="reset_vectors",tag="method",version="2"}

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
python
nlp.vocab.reset_vectors(width=300)

Name	Description
keyword-only
`width`	The new width. ~~int~~
`shape`	The new shape. ~~int~~

Vocab.prune_vectors {id="prune_vectors",tag="method",version="2"}

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

python

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

Name	Description
`nr_row`	The number of rows to keep in the vector table. ~~int~~
`batch_size`	Batch of vectors for calculating the similarities. Larger batch sizes might be faster, while temporarily requiring more memory. ~~int~~
RETURNS	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. ~~Dict[str, Tuple[str, float]]~~

Vocab.deduplicate_vectors {id="deduplicate_vectors",tag="method",version="3.3"}

Example
python
nlp.vocab.deduplicate_vectors()

Remove any duplicate rows from the current vector table, maintaining the mappings for all words in the vectors.

Vocab.get_vector {id="get_vector",tag="method",version="2"}

Retrieve a vector for a word in the vocabulary. Words can be looked up by string or hash value. If the current vectors do not contain an entry for the word, a 0-vector with the same number of dimensions (Vocab.vectors_length) as the current vectors is returned.

Example
python
nlp.vocab.get_vector("apple")

Name	Description
`orth`	The hash value of a word, or its unicode string. ~~Union[int, str]~~
RETURNS	A word vector. Size and shape are determined by the `Vocab.vectors` instance. ~~numpy.ndarray[ndim=1, dtype=float32]~~

Vocab.set_vector {id="set_vector",tag="method",version="2"}

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

Example

python

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

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

Vocab.has_vector {id="has_vector",tag="method",version="2"}

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

python

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

Name	Description
`orth`	The hash value of a word, or its unicode string. ~~Union[int, str]~~
RETURNS	Whether the word has a vector. ~~bool~~

Vocab.to_disk {id="to_disk",tag="method",version="2"}

Save the current state to a directory.

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

Name	Description
`path`	A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~
keyword-only
`exclude`	String names of serialization fields to exclude. ~~Iterable[str]~~

Vocab.from_disk {id="from_disk",tag="method",version="2"}

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

Example

python

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

Name	Description
`path`	A path to a directory. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~
keyword-only
`exclude`	String names of serialization fields to exclude. ~~Iterable[str]~~
RETURNS	The modified `Vocab` object. ~~Vocab~~

Vocab.to_bytes {id="to_bytes",tag="method"}

Serialize the current state to a binary string.

Example
python
vocab_bytes = nlp.vocab.to_bytes()

Name	Description
keyword-only
`exclude`	String names of serialization fields to exclude. ~~Iterable[str]~~
RETURNS	The serialized form of the `Vocab` object. ~~bytes~~

Vocab.from_bytes {id="from_bytes",tag="method"}

Load state from a binary string.

Example

python

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

Name	Description
`bytes_data`	The data to load from. ~~bytes~~
keyword-only
`exclude`	String names of serialization fields to exclude. ~~Iterable[str]~~
RETURNS	The `Vocab` object. ~~Vocab~~

Attributes {id="attributes"}

Example

python

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

Name	Description
`strings`	A table managing the string-to-int mapping. ~~StringStore~~
`vectors`	A table associating word IDs to word vectors. ~~Vectors~~
`vectors_length`	Number of dimensions for each word vector. ~~int~~
`lookups`	The available lookup tables in this vocab. ~~Lookups~~
`writing_system`	A dict with information about the language's writing system. ~~Dict[str, Any]~~
`get_noun_chunks` <Tag variant="new">3.0</Tag>	A function that yields base noun phrases used for `Doc.noun_chunks`. ~~Optional[Callable[[Union[Doc, Span], Iterator[Tuple[int, int, int]]]]]~~

Serialization fields {id="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

python

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

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

Vocab.__init__ {id="init",tag="method"}

Example

Vocab.__len__ {id="len",tag="method"}

Example

Vocab.__getitem__ {id="getitem",tag="method"}

Example

Vocab.__iter__ {id="iter",tag="method"}

Example

Vocab.__contains__ {id="contains",tag="method"}

Example

Vocab.add_flag {id="add_flag",tag="method"}

Example

Vocab.reset_vectors {id="reset_vectors",tag="method",version="2"}

Example

Vocab.prune_vectors {id="prune_vectors",tag="method",version="2"}

Example

Vocab.deduplicate_vectors {id="deduplicate_vectors",tag="method",version="3.3"}

Example

Vocab.get_vector {id="get_vector",tag="method",version="2"}

Example

Vocab.set_vector {id="set_vector",tag="method",version="2"}

Example

Vocab.has_vector {id="has_vector",tag="method",version="2"}

Example

Vocab.to_disk {id="to_disk",tag="method",version="2"}

Example

Vocab.from_disk {id="from_disk",tag="method",version="2"}

Example

Vocab.to_bytes {id="to_bytes",tag="method"}

Example

Vocab.from_bytes {id="from_bytes",tag="method"}

Example

Attributes {id="attributes"}

Example

Serialization fields {id="serialization-fields"}

Example

Vocab.init {id="init",tag="method"}

Vocab.len {id="len",tag="method"}

Vocab.getitem {id="getitem",tag="method"}

Vocab.iter {id="iter",tag="method"}

Vocab.contains {id="contains",tag="method"}