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Vectors Store, save and load word vectors class spacy/vectors.pyx 2

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__

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

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=1, dtype=float32]
keys A sequence of keys aligned with the data. Iterable[Union[str, int]]
name A name to identify the vectors table. str
mode 3.2 Vectors mode: "default" or "floret" (default: "default"). str
minn 3.2 The floret char ngram minn (default: 0). int
maxn 3.2 The floret char ngram maxn (default: 0). int
hash_count 3.2 The floret hash count. Supported values: 1--4 (default: 1). int
hash_seed 3.2 The floret hash seed (default: 0). int
bow 3.2 The floret BOW string (default: "<"). str
eow 3.2 The floret EOW string (default: ">"). str

Vectors.__getitem__

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

Example

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__

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

Example

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__

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

Example

for key in nlp.vocab.vectors:
   print(key, nlp.vocab.strings[key])
Name Description
YIELDS A key in the table. int

Vectors.__len__

Return the number of vectors in the table.

Example

vectors = Vectors(shape=(3, 300))
assert len(vectors) == 3
Name Description
RETURNS The number of vectors in the table. int

Vectors.__contains__

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

Example

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

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

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

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

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

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

Example

for key in nlp.vocab.vectors.keys():
    print(key, nlp.vocab.strings[key])
Name Description
RETURNS The keys. Iterable[int]

Vectors.values

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

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

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

Example

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

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

Example

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

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

Example

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

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

Example

vectors = Vectors(shape=(500, 300))
assert vectors.size == 150000
Name Description
RETURNS The vector size. int

Vectors.is_full

Whether the vectors table is full and has 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

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

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

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

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

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 tuple The most similar entries as a (keys, best_rows, scores) tuple. Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]

Vectors.get_batch

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

Example

words = ["cat", "dog"]
vectors = nlp.vocab.vectors.get_batch(words)
Name Description
keys The keys. Iterable[Union[int, str]]

Vectors.to_ops

Change the embedding matrix to use different Thinc ops.

Example

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

Save the current state to a directory.

Example

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

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

Example

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

Serialize the current state to a binary string.

Example

vectors_bytes = vectors.to_bytes()
Name Description
RETURNS The serialized form of the Vectors object. bytes

Vectors.from_bytes

Load state from a binary string.

Example

fron 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

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]]