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e962784531
spaCy/website/docs/api/vocab.md
Adriane Boyd e962784531
Add Lemmatizer and simplify related components (#5848) 
* Add Lemmatizer and simplify related components

* Add `Lemmatizer` pipe with `lookup` and `rule` modes using the
`Lookups` tables.
* Reduce `Tagger` to a simple tagger that sets `Token.tag` (no pos or lemma)
* Reduce `Morphology` to only keep track of morph tags (no tag map, lemmatizer,
or morph rules)
* Remove lemmatizer from `Vocab`
* Adjust many many tests

Differences:

* No default lookup lemmas
* No special treatment of TAG in `from_array` and similar required
* Easier to modify labels in a `Tagger`
* No extra strings added from morphology / tag map

* Fix test

* Initial fix for Lemmatizer config/serialization

* Adjust init test to be more generic

* Adjust init test to force empty Lookups

* Add simple cache to rule-based lemmatizer

* Convert language-specific lemmatizers

Convert language-specific lemmatizers to component lemmatizers. Remove
previous lemmatizer class.

* Fix French and Polish lemmatizers

* Remove outdated UPOS conversions

* Update Russian lemmatizer init in tests

* Add minimal init/run tests for custom lemmatizers

* Add option to overwrite existing lemmas

* Update mode setting, lookup loading, and caching

* Make `mode` an immutable property
* Only enforce strict `load_lookups` for known supported modes
* Move caching into individual `_lemmatize` methods

* Implement strict when lang is not found in lookups

* Fix tables/lookups in make_lemmatizer

* Reallow provided lookups and allow for stricter checks

* Add lookups asset to all Lemmatizer pipe tests

* Rename lookups in lemmatizer init test

* Clean up merge

* Refactor lookup table loading

* Add helper from `load_lemmatizer_lookups` that loads required and
optional lookups tables based on settings provided by a config.

Additional slight refactor of lookups:

* Add `Lookups.set_table` to set a table from a provided `Table`
* Reorder class definitions to be able to specify type as `Table`

* Move registry assets into test methods

* Refactor lookups tables config

Use class methods within `Lemmatizer` to provide the config for
particular modes and to load the lookups from a config.

* Add pipe and score to lemmatizer

* Simplify Tagger.score

* Add missing import

* Clean up imports and auto-format

* Remove unused kwarg

* Tidy up and auto-format

* Update docstrings for Lemmatizer

Update docstrings for Lemmatizer.

Additionally modify `is_base_form` API to take `Token` instead of
individual features.

* Update docstrings

* Remove tag map values from Tagger.add_label

* Update API docs

* Fix relative link in Lemmatizer API docs
2020-08-07 15:27:13 +02:00

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title teaser tag source
Vocab A storage class for vocabulary and other data shared across a language class spacy/vocab.pyx

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__

Create the vocabulary.

Example

from spacy.vocab import Vocab
vocab = Vocab(strings=["hello", "world"])
Name Type Description
lex_attr_getters dict A dictionary mapping attribute IDs to functions to compute them. Defaults to None.
strings StringStore / list A StringStore that maps strings to hash values, and vice versa, or a list of strings.
lookups Lookups A Lookups that stores the lemma_\*, lexeme_norm and other large lookup tables. Defaults to None.
lookups_extra 2.3 Lookups A Lookups that stores the optional lexeme_cluster/lexeme_prob/lexeme_sentiment/lexeme_settings lookup tables. Defaults to None.
oov_prob float The default OOV probability. Defaults to -20.0.
vectors_name 2.2 str A name to identify the vectors table.

Vocab.__len__

Get the current number of lexemes in the vocabulary.

Example

doc = nlp("This is a sentence.")
assert len(nlp.vocab) > 0
Name Type Description
RETURNS int The number of lexemes in the vocabulary.

Vocab.__getitem__

Retrieve a lexeme, given an int ID or a string. If a previously unseen string is given, a new lexeme is created and stored.

Example

apple = nlp.vocab.strings["apple"]
assert nlp.vocab[apple] == nlp.vocab["apple"]
Name Type Description
id_or_string int / str The hash value of a word, or its string.
RETURNS Lexeme The lexeme indicated by the given ID.

Vocab.__iter__

Iterate over the lexemes in the vocabulary.

Example

stop_words = (lex for lex in nlp.vocab if lex.is_stop)
Name Type Description
YIELDS Lexeme An entry in the vocabulary.

Vocab.__contains__

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.

Example

apple = nlp.vocab.strings["apple"]
oov = nlp.vocab.strings["dskfodkfos"]
assert apple in nlp.vocab
assert oov not in nlp.vocab
Name Type Description
string str The ID string.
RETURNS bool Whether the string has an entry in the vocabulary.

Vocab.add_flag

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).

Example

def is_my_product(text):
    products = ["spaCy", "Thinc", "displaCy"]
    return text in products

MY_PRODUCT = nlp.vocab.add_flag(is_my_product)
doc = nlp("I like spaCy")
assert doc[2].check_flag(MY_PRODUCT) == True
Name Type Description
flag_getter dict A function f(str) -> bool, to get the flag value.
flag_id int 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.
RETURNS int The integer ID by which the flag value can be checked.

Vocab.reset_vectors

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.

Example

nlp.vocab.reset_vectors(width=300)
Name Type Description
keyword-only
width int The new width (keyword argument only).
shape int The new shape (keyword argument only).

Vocab.prune_vectors

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.

Example

nlp.vocab.prune_vectors(10000)
assert len(nlp.vocab.vectors) <= 1000
Name Type Description
nr_row int The number of rows to keep in the vector table.
batch_size int Batch of vectors for calculating the similarities. Larger batch sizes might be faster, while temporarily requiring more memory.
RETURNS dict 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.

Vocab.get_vector

Retrieve a vector for a word in the vocabulary. Words can be looked up by string or hash value. If no vectors data is loaded, a ValueError is raised. If minn is defined, then the resulting vector uses FastText's subword features by average over ngrams of orth (introduced in spaCy v2.1).

Example

nlp.vocab.get_vector("apple")
nlp.vocab.get_vector("apple", minn=1, maxn=5)
Name Type Description
orth int / str The hash value of a word, or its unicode string.
minn 2.1 int Minimum n-gram length used for FastText's ngram computation. Defaults to the length of orth.
maxn 2.1 int Maximum n-gram length used for FastText's ngram computation. Defaults to the length of orth.
RETURNS numpy.ndarray[ndim=1, dtype='float32'] A word vector. Size and shape are determined by the Vocab.vectors instance.

Vocab.set_vector

Set a vector for a word in the vocabulary. Words can be referenced by by string or hash value.

Example

nlp.vocab.set_vector("apple", array([...]))
Name Type Description
orth int / str The hash value of a word, or its unicode string.
vector numpy.ndarray[ndim=1, dtype='float32'] The vector to set.

Vocab.has_vector

Check whether a word has a vector. Returns False if no vectors are loaded. Words can be looked up by string or hash value.

Example

if nlp.vocab.has_vector("apple"):
    vector = nlp.vocab.get_vector("apple")
Name Type Description
orth int / str The hash value of a word, or its unicode string.
RETURNS bool Whether the word has a vector.

Vocab.to_disk

Save the current state to a directory.

Example

nlp.vocab.to_disk("/path/to/vocab")
Name Type Description
path str / Path A path to a directory, which will be created if it doesn't exist. Paths may be either strings or Path-like objects.
keyword-only
exclude Iterable[str] String names of serialization fields to exclude.

Vocab.from_disk

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

Example

from spacy.vocab import Vocab
vocab = Vocab().from_disk("/path/to/vocab")
Name Type Description
path str / Path A path to a directory. Paths may be either strings or Path-like objects.
keyword-only
exclude Iterable[str] String names of serialization fields to exclude.
RETURNS Vocab The modified Vocab object.

Vocab.to_bytes

Serialize the current state to a binary string.

Example

vocab_bytes = nlp.vocab.to_bytes()
Name Type Description
keyword-only
exclude Iterable[str] String names of serialization fields to exclude.
RETURNS bytes The serialized form of the Vocab object.

Vocab.from_bytes

Load state from a binary string.

Example

from spacy.vocab import Vocab
vocab_bytes = nlp.vocab.to_bytes()
vocab = Vocab()
vocab.from_bytes(vocab_bytes)
Name Type Description
bytes_data bytes The data to load from.
keyword-only
exclude Iterable[str] String names of serialization fields to exclude.
RETURNS Vocab The Vocab object.

Attributes

Example

apple_id = nlp.vocab.strings["apple"]
assert type(apple_id) == int
PERSON = nlp.vocab.strings["PERSON"]
assert type(PERSON) == int
Name Type Description
strings StringStore A table managing the string-to-int mapping.
vectors 2 Vectors A table associating word IDs to word vectors.
vectors_length int Number of dimensions for each word vector.
lookups Lookups The available lookup tables in this vocab.
writing_system 2.1 dict A dict with information about the language's writing system.

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.

Example

data = vocab.to_bytes(exclude=["strings", "vectors"])
vocab.from_disk("./vocab", exclude=["strings"])
Name Description
strings The strings in the StringStore.
lexemes The lexeme data.
vectors The word vectors, if available.
lookups The lookup tables, if available.