mirror of https://github.com/explosion/spaCy.git
461 lines
20 KiB
Plaintext
461 lines
20 KiB
Plaintext
---
|
||
title: Vectors
|
||
teaser: Store, save and load word vectors
|
||
tag: class
|
||
source: spacy/vectors.pyx
|
||
version: 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](https://github.com/explosion/floret), an extended version of
|
||
[fastText](https://fasttext.cc) that produces compact vector tables by
|
||
combining fastText's subword ngrams with Bloom embeddings. The compact tables
|
||
are similar to the [`HashEmbed`](https://thinc.ai/docs/api-layers#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` | A sequence 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"`](https://github.com/explosion/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~~ |
|
||
|
||
## 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 has no slots are available for new keys.
|
||
If a table is full, it can be resized using
|
||
[`Vectors.resize`](/api/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
|
||
> 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 {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]]~~ |
|