Vectors

Vectors data is kept in the Vectors.data attribute, which should be an instance of numpy.ndarray (for CPU vectors) or cupy.ndarray (for GPU vectors).

As of spaCy v3.2, Vectors supports two types of vector tables:

default: A standard vector table (as in spaCy v3.1 and earlier) where each key is mapped to one row in the vector table. Multiple keys can be mapped to the same vector, and not all of the rows in the table need to be assigned – so vectors.n_keys may be greater or smaller than vectors.shape[0].
floret: Only supports vectors trained with floret, an extended version of fastText that produces compact vector tables by combining fastText's subword ngrams with Bloom embeddings. The compact tables are similar to the HashEmbed embeddings already used in many spaCy components. Each word is represented as the sum of one or more rows as determined by the settings related to character ngrams and the hash table.

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

Create a new vector store. With the default mode, you can set the vector values and keys directly on initialization, or supply a shape keyword argument to create an empty table you can add vectors to later. In floret mode, the complete vector data and settings must be provided on initialization and cannot be modified later.

Example

python

from spacy.vectors import Vectors

empty_vectors = Vectors(shape=(10000, 300))

data = numpy.zeros((3, 300), dtype='f')
keys = ["cat", "dog", "rat"]
vectors = Vectors(data=data, keys=keys)

Name	Description
keyword-only
`strings`	The string store. A new string store is created if one is not provided. Defaults to `None`. ~~Optional[StringStore]~~
`shape`	Size of the table as `(n_entries, n_columns)`, the number of entries and number of columns. Not required if you're initializing the object with `data` and `keys`. ~~Tuple[int, int]~~
`data`	The vector data. ~~numpy.ndarray[ndim=2, dtype=float32]~~
`keys`	An iterable of keys aligned with the data. ~~Iterable[Union[str, int]]~~
`name`	A name to identify the vectors table. ~~str~~
`mode` <Tag variant="new">3.2</Tag>	Vectors mode: `"default"` or `"floret"` (default: `"default"`). ~~str~~
`minn` <Tag variant="new">3.2</Tag>	The floret char ngram minn (default: `0`). ~~int~~
`maxn` <Tag variant="new">3.2</Tag>	The floret char ngram maxn (default: `0`). ~~int~~
`hash_count` <Tag variant="new">3.2</Tag>	The floret hash count. Supported values: 1--4 (default: `1`). ~~int~~
`hash_seed` <Tag variant="new">3.2</Tag>	The floret hash seed (default: `0`). ~~int~~
`bow` <Tag variant="new">3.2</Tag>	The floret BOW string (default: `"<"`). ~~str~~
`eow` <Tag variant="new">3.2</Tag>	The floret EOW string (default: `">"`). ~~str~~
`attr` <Tag variant="new">3.6</Tag>	The token attribute for the vector keys (default: `"ORTH"`). ~~Union[int, str]~~

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

Get a vector by key. If the key is not found in the table, a KeyError is raised.

Example

python

cat_id = nlp.vocab.strings["cat"]
cat_vector = nlp.vocab.vectors[cat_id]
assert cat_vector == nlp.vocab["cat"].vector

Name	Description
`key`	The key to get the vector for. ~~Union[int, str]~~
RETURNS	The vector for the key. ~~numpy.ndarray[ndim=1, dtype=float32]~~

Vectors.setitem {id="setitem",tag="method"}

Set a vector for the given key. Not supported for floret mode.

Example

python

cat_id = nlp.vocab.strings["cat"]
vector = numpy.random.uniform(-1, 1, (300,))
nlp.vocab.vectors[cat_id] = vector

Name	Description
`key`	The key to set the vector for. ~~int~~
`vector`	The vector to set. ~~numpy.ndarray[ndim=1, dtype=float32]~~

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

Iterate over the keys in the table. In floret mode, the keys table is not used.

Example

python

for key in nlp.vocab.vectors:
   print(key, nlp.vocab.strings[key])

Name	Description
YIELDS	A key in the table. ~~int~~

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

Return the number of vectors in the table.

Example

python

vectors = Vectors(shape=(3, 300))
assert len(vectors) == 3

Name	Description
RETURNS	The number of vectors in the table. ~~int~~

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

Check whether a key has been mapped to a vector entry in the table. In floret mode, returns True for all keys.

Example

python

cat_id = nlp.vocab.strings["cat"]
nlp.vocab.vectors.add(cat_id, numpy.random.uniform(-1, 1, (300,)))
assert cat_id in vectors

Name	Description
`key`	The key to check. ~~int~~
RETURNS	Whether the key has a vector entry. ~~bool~~

Vectors.add {id="add",tag="method"}

Add a key to the table, optionally setting a vector value as well. Keys can be mapped to an existing vector by setting row, or a new vector can be added. Not supported for floret mode.

Example

python

vector = numpy.random.uniform(-1, 1, (300,))
cat_id = nlp.vocab.strings["cat"]
nlp.vocab.vectors.add(cat_id, vector=vector)
nlp.vocab.vectors.add("dog", row=0)

Name	Description
`key`	The key to add. ~~Union[str, int]~~
keyword-only
`vector`	An optional vector to add for the key. ~~numpy.ndarray[ndim=1, dtype=float32]~~
`row`	An optional row number of a vector to map the key to. ~~int~~
RETURNS	The row the vector was added to. ~~int~~

Vectors.resize {id="resize",tag="method"}

Resize the underlying vectors array. If inplace=True, the memory is reallocated. This may cause other references to the data to become invalid, so only use inplace=True if you're sure that's what you want. If the number of vectors is reduced, keys mapped to rows that have been deleted are removed. These removed items are returned as a list of (key, row) tuples. Not supported for floret mode.

Example

python

removed = nlp.vocab.vectors.resize((10000, 300))

Name	Description
`shape`	A `(rows, dims)` tuple describing the number of rows and dimensions. ~~Tuple[int, int]~~
`inplace`	Reallocate the memory. ~~bool~~
RETURNS	The removed items as a list of `(key, row)` tuples. ~~List[Tuple[int, int]]~~

Vectors.keys {id="keys",tag="method"}

A sequence of the keys in the table. In floret mode, the keys table is not used.

Example

python

for key in nlp.vocab.vectors.keys():
    print(key, nlp.vocab.strings[key])

Name	Description
RETURNS	The keys. ~~Iterable[int]~~

Vectors.values {id="values",tag="method"}

Iterate over vectors that have been assigned to at least one key. Note that some vectors may be unassigned, so the number of vectors returned may be less than the length of the vectors table. In floret mode, the keys table is not used.

Example

python

for vector in nlp.vocab.vectors.values():
    print(vector)

Name	Description
YIELDS	A vector in the table. ~~numpy.ndarray[ndim=1, dtype=float32]~~

Vectors.items {id="items",tag="method"}

Iterate over (key, vector) pairs, in order. In floret mode, the keys table is empty.

Example

python

for key, vector in nlp.vocab.vectors.items():
   print(key, nlp.vocab.strings[key], vector)

Name	Description
YIELDS	`(key, vector)` pairs, in order. ~~Tuple[int, numpy.ndarray[ndim=1, dtype=float32]]~~

Vectors.find {id="find",tag="method"}

Look up one or more keys by row, or vice versa. Not supported for floret mode.

Example

python

row = nlp.vocab.vectors.find(key="cat")
rows = nlp.vocab.vectors.find(keys=["cat", "dog"])
key = nlp.vocab.vectors.find(row=256)
keys = nlp.vocab.vectors.find(rows=[18, 256, 985])

Name	Description
keyword-only
`key`	Find the row that the given key points to. Returns int, `-1` if missing. ~~Union[str, int]~~
`keys`	Find rows that the keys point to. Returns `numpy.ndarray`. ~~Iterable[Union[str, int]]~~
`row`	Find the first key that points to the row. Returns integer. ~~int~~
`rows`	Find the keys that point to the rows. Returns `numpy.ndarray`. ~~Iterable[int]~~
RETURNS	The requested key, keys, row or rows. ~~Union[int, numpy.ndarray[ndim=1, dtype=float32]]~~

Vectors.shape {id="shape",tag="property"}

Get (rows, dims) tuples of number of rows and number of dimensions in the vector table.

Example

python

vectors = Vectors(shape(1, 300))
vectors.add("cat", numpy.random.uniform(-1, 1, (300,)))
rows, dims = vectors.shape
assert rows == 1
assert dims == 300

Name	Description
RETURNS	A `(rows, dims)` pair. ~~Tuple[int, int]~~

Vectors.size {id="size",tag="property"}

The vector size, i.e. rows * dims.

Example

python

vectors = Vectors(shape=(500, 300))
assert vectors.size == 150000

Name	Description
RETURNS	The vector size. ~~int~~

Vectors.is_full {id="is_full",tag="property"}

Whether the vectors table is full and no slots are available for new keys. If a table is full, it can be resized using Vectors.resize. In floret mode, the table is always full and cannot be resized.

Example

python

vectors = Vectors(shape=(1, 300))
vectors.add("cat", numpy.random.uniform(-1, 1, (300,)))
assert vectors.is_full

Name	Description
RETURNS	Whether the vectors table is full. ~~bool~~

Vectors.n_keys {id="n_keys",tag="property"}

Get the number of keys in the table. Note that this is the number of all keys, not just unique vectors. If several keys are mapped to the same vectors, they will be counted individually. In floret mode, the keys table is not used.

Example

python

vectors = Vectors(shape=(10, 300))
assert len(vectors) == 10
assert vectors.n_keys == 0

Name	Description
RETURNS	The number of all keys in the table. Returns `-1` for floret vectors. ~~int~~

Vectors.most_similar {id="most_similar",tag="method"}

For each of the given vectors, find the n most similar entries to it by cosine. Queries are by vector. Results are returned as a (keys, best_rows, scores) tuple. If queries is large, the calculations are performed in chunks to avoid consuming too much memory. You can set the batch_size to control the size/space trade-off during the calculations. Not supported for floret mode.

Example

python

queries = numpy.asarray([numpy.random.uniform(-1, 1, (300,))])
most_similar = nlp.vocab.vectors.most_similar(queries, n=10)

Name	Description
`queries`	An array with one or more vectors. ~~numpy.ndarray~~
keyword-only
`batch_size`	The batch size to use. Default to `1024`. ~~int~~
`n`	The number of entries to return for each query. Defaults to `1`. ~~int~~
`sort`	Whether to sort the entries returned by score. Defaults to `True`. ~~bool~~
RETURNS	The most similar entries as a `(keys, best_rows, scores)` tuple. ~~Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]~~

Vectors.get_batch {id="get_batch",tag="method",version="3.2"}

Get the vectors for the provided keys efficiently as a batch.

Example

python

words = ["cat", "dog"]
vectors = nlp.vocab.vectors.get_batch(words)

Name	Description
`keys`	The keys. ~~Iterable[Union[int, str]]~~

Vectors.to_ops {id="to_ops",tag="method"}

Change the embedding matrix to use different Thinc ops.

Example

python

from thinc.api import NumpyOps

vectors.to_ops(NumpyOps())

Name	Description
`ops`	The Thinc ops to switch the embedding matrix to. ~~Ops~~

Vectors.to_disk {id="to_disk",tag="method"}

Save the current state to a directory.

Example

python

vectors.to_disk("/path/to/vectors")

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]~~

Vectors.from_disk {id="from_disk",tag="method"}

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

Example

python

vectors = Vectors(StringStore())
vectors.from_disk("/path/to/vectors")

Name	Description
`path`	A path to a directory. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~
RETURNS	The modified `Vectors` object. ~~Vectors~~

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

Serialize the current state to a binary string.

Example
python
vectors_bytes = vectors.to_bytes()

Name	Description
RETURNS	The serialized form of the `Vectors` object. ~~bytes~~

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

Load state from a binary string.

Example

python

from spacy.vectors import Vectors
vectors_bytes = vectors.to_bytes()
new_vectors = Vectors(StringStore())
new_vectors.from_bytes(vectors_bytes)

Name	Description
`data`	The data to load from. ~~bytes~~
RETURNS	The `Vectors` object. ~~Vectors~~

Attributes {id="attributes"}

Name	Description
`data`	Stored vectors data. `numpy` is used for CPU vectors, `cupy` for GPU vectors. ~~Union[numpy.ndarray[ndim=1, dtype=float32], cupy.ndarray[ndim=1, dtype=float32]]~~
`key2row`	Dictionary mapping word hashes to rows in the `Vectors.data` table. ~~Dict[int, int]~~
`keys`	Array keeping the keys in order, such that `keys[vectors.key2row[key]] == key`. ~~Union[numpy.ndarray[ndim=1, dtype=float32], cupy.ndarray[ndim=1, dtype=float32]]~~
`attr` <Tag variant="new">3.6</Tag>	The token attribute for the vector keys. ~~int~~

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

Example

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

Example

Vectors.__setitem__ {id="setitem",tag="method"}

Example

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

Example

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

Example

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

Example

Vectors.add {id="add",tag="method"}

Example

Vectors.resize {id="resize",tag="method"}

Example

Vectors.keys {id="keys",tag="method"}

Example

Vectors.values {id="values",tag="method"}

Example

Vectors.items {id="items",tag="method"}

Example

Vectors.find {id="find",tag="method"}

Example

Vectors.shape {id="shape",tag="property"}

Example

Vectors.size {id="size",tag="property"}

Example

Vectors.is_full {id="is_full",tag="property"}

Example

Vectors.n_keys {id="n_keys",tag="property"}

Example

Vectors.most_similar {id="most_similar",tag="method"}

Example

Vectors.get_batch {id="get_batch",tag="method",version="3.2"}

Example

Vectors.to_ops {id="to_ops",tag="method"}

Example

Vectors.to_disk {id="to_disk",tag="method"}

Example

Vectors.from_disk {id="from_disk",tag="method"}

Example

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

Example

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

Example

Attributes {id="attributes"}

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

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

Vectors.setitem {id="setitem",tag="method"}

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

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

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