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.
Vocab.__init__ method
Create the vocabulary.
| 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__ method
Get the current number of lexemes in the vocabulary.
| Name | Description |
|---|---|
| RETURNS | The number of lexemes in the vocabulary. int |
Vocab.__getitem__ 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.
| 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__ method
Iterate over the lexemes in the vocabulary.
| Name | Description |
|---|---|
| YIELDS | An entry in the vocabulary. Lexeme |
Vocab.__contains__ 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.
| Name | Description |
|---|---|
string | The ID string. str |
| RETURNS | Whether the string has an entry in the vocabulary. bool |
Vocab.add_flag 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).
| 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 method
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.
| Name | Description |
|---|---|
| keyword-only | |
width | The new width. int |
shape | The new shape. int |
Vocab.prune_vectors method
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.
| 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 methodv3.3
Remove any duplicate rows from the current vector table, maintaining the mappings for all words in the vectors.
Vocab.get_vector method
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.
| 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 method
Set a vector for a word in the vocabulary. Words can be referenced by string or hash value.
| 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 method
Check whether a word has a vector. Returns False if no vectors are loaded.
Words can be looked up by string or hash value.
| 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 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] |
| keyword-only | |
exclude | String names of serialization fields to exclude. Iterable[str] |
Vocab.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] |
| keyword-only | |
exclude | String names of serialization fields to exclude. Iterable[str] |
| RETURNS | The modified Vocab object. Vocab |
Vocab.to_bytes method
Serialize the current state to a binary string.
| Name | Description |
|---|---|
| keyword-only | |
exclude | String names of serialization fields to exclude. Iterable[str] |
| RETURNS | The serialized form of the Vocab object. Vocab |
Vocab.from_bytes method
Load state from a binary string.
| 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
| 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 v3.0 | A function that yields base noun phrases used for Doc.noun_chunks. Optional[Callable[[Union[Doc,Span], Iterator[Tuple[int, int, int]]]]] |
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.
| Name | Description |
|---|---|
strings | The strings in the StringStore. |
vectors | The word vectors, if available. |
lookups | The lookup tables, if available. |