scikit

Vocab
class
A storage class for vocabulary and other data shared across a language.

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.

NameTypeDescription
lex_attr_gettersdict A dictionary mapping attribute IDs to functions to compute them. Defaults to None.
tag_mapdict A dictionary mapping fine-grained tags to coarse-grained parts-of-speech, and optionally morphological attributes.
lemmatizerobjectA lemmatizer. Defaults to None.
stringsStringStore or list A StringStore that maps strings to hash values, and vice versa, or a list of strings.
returnsVocabThe newly constructed object.

Vocab.__len__
method

Get the current number of lexemes in the vocabulary.

NameTypeDescription
returnsintThe number of lexems in the vocabulary.

Vocab.__getitem__
method

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

NameTypeDescription
id_or_stringint / unicodeThe hash value of a word, or its unicode string.
returnsLexemeThe lexeme indicated by the given ID.

Vocab.__iter__
method

Iterate over the lexemes in the vocabulary.

NameTypeDescription
yieldsLexemeAn entry in the vocabulary.

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 .

NameTypeDescription
stringunicodeThe ID string.
returnsboolWhether the string has an entry in the vocabulary.

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).

NameTypeDescription
flag_getterdictA function f(unicode) -> bool, to get the flag value.
flag_idint 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.
returnsintThe integer ID by which the flag value can be checked.

Vocab.reset_vectors
method
v2.0 This feature is new and was introduced in spaCy v2.0

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.

NameTypeDescription
widthintThe new width (keyword argument only).
shapeintThe new shape (keyword argument only).

Vocab.prune_vectors
method
v2.0 This feature is new and was introduced in spaCy v2.0

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.

NameTypeDescription
nr_rowintThe number of rows to keep in the vector table.
batch_sizeint Batch of vectors for calculating the similarities. Larger batch sizes might be faster, while temporarily requiring more memory.
returnsdict 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
method
v2.0 This feature is new and was introduced in spaCy v2.0

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.

NameTypeDescription
orthint / unicodeThe hash value of a word, or its unicode string.
returnsnumpy.ndarray[ndim=1, dtype='float32'] A word vector. Size and shape are determined by the Vocab.vectors instance.

Vocab.set_vector
method
v2.0 This feature is new and was introduced in spaCy v2.0

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

NameTypeDescription
orthint / unicodeThe hash value of a word, or its unicode string.
vectornumpy.ndarray[ndim=1, dtype='float32']The vector to set.

Vocab.has_vector
method
v2.0 This feature is new and was introduced in spaCy v2.0

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

NameTypeDescription
orthint / unicodeThe hash value of a word, or its unicode string.
returnsboolWhether the word has a vector.

Vocab.to_disk
method
v2.0 This feature is new and was introduced in spaCy v2.0

Save the current state to a directory.

NameTypeDescription
pathunicode or Path A path to a directory, which will be created if it doesn't exist. Paths may be either strings or Path-like objects.

Vocab.from_disk
method
v2.0 This feature is new and was introduced in spaCy v2.0

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

NameTypeDescription
pathunicode or Path A path to a directory. Paths may be either strings or Path-like objects.
returnsVocabThe modified Vocab object.

Vocab.to_bytes
method

Serialize the current state to a binary string.

NameTypeDescription
**exclude-Named attributes to prevent from being serialized.
returnsbytesThe serialized form of the Vocab object.

Vocab.from_bytes
method

Load state from a binary string.

NameTypeDescription
bytes_databytesThe data to load from.
**exclude-Named attributes to prevent from being loaded.
returnsVocabThe Vocab object.

Attributes

NameTypeDescription
stringsStringStoreA table managing the string-to-int mapping.
vectors
v2.0 This feature is new and was introduced in spaCy v2.0
VectorsA table associating word IDs to word vectors.
vectors_lengthintNumber of dimensions for each word vector.