Vectors

class

Store, save and load word 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 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.

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`	A sequence of keys aligned with the data. Iterable[Union[str, int]]
`name`	A name to identify the vectors table. str
`mode` v3.2	Vectors mode: `"default"` or `"floret"` (default: `"default"`). str
`minn` v3.2	The floret char ngram minn (default: `0`). int
`maxn` v3.2	The floret char ngram maxn (default: `0`). int
`hash_count` v3.2	The floret hash count. Supported values: 1—4 (default: `1`). int
`hash_seed` v3.2	The floret hash seed (default: `0`). int
`bow` v3.2	The floret BOW string (default: `"<"`). str
`eow` v3.2	The floret EOW string (default: `">"`). str
`attr` v3.6	The token attribute for the vector keys (default: `"ORTH"`). Union[int, str]

Vectors.getitem method

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

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 method

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

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

Vectors.iter method

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

Name	Description
YIELDS	A key in the table. int

Vectors.len method

Return the number of vectors in the table.

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

Vectors.contains method

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

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

Vectors.add 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.

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 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.

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 method

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

Name	Description
RETURNS	The keys. Iterable[int]

Vectors.values 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.

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

Vectors.items method

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

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

Vectors.find method

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

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 property

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

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

Vectors.size property

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

Name	Description
RETURNS	The vector size. int

Vectors.is_full 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.

Name	Description
RETURNS	Whether the vectors table is full. bool

Vectors.n_keys 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.

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

Vectors.most_similar 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.

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 methodv3.2

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

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

Vectors.to_ops method

Change the embedding matrix to use different Thinc ops.

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

Vectors.to_disk method

Save the current state to a directory.

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 method

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

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 method

Serialize the current state to a binary string.

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

Vectors.from_bytes method

Load state from a binary string.

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]]
`attr` v3.6	The token attribute for the vector keys. int

Suggest edits

Other

Vectors.__init__ method

Vectors.__getitem__ method

Vectors.__setitem__ method

Vectors.__iter__ method

Vectors.__len__ method

Vectors.__contains__ method